Ijebea vol1 print by iasir journals

ISSN (ONLINE): 2279-0039 ISSN (PRINT): 2279-0020

Issue 7, Volume 1 December-2013 to February-2014

International Journal of Engineering, Business and Enterprises Applications

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: India, Australia, Germany, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, ijebea@gmail.com

PREFACE We are delighted to welcome you to the seventh issue of the International Journal of Engineering, Business and Enterprises Applications (IJEBEA). In recent years, advances in science, engineering, and business processes have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. IJEBEA is publishing high-quality, peer-reviewed papers covering a number of topics in the areas of business process models, engineering and enterprise applications, knowledge engineering science, modeling and designing, control and deployment techniques, e-Commerce applications, B2B and B2C applications, Protocol management and channel management, Mobility, process, engineering, security and technology management, Semantic Web and interfaces, Enterprise applications for software and web engineering,

open-source

platforms,

Human

resource

management,

Operations

management, Organizational and management issues, Supply chain management, Strategic decision support systems, Cloud computing, Risk management, Information technology, Information retrieval systems, Aspect-oriented programming, e-Libraries and e-Publishing, Data mining and warehousing, Distributed AI systems and architectures, Bioinformatics and scientific computing, Knowledge and information management techniques, and other relevant fields available in the vicinity of engineering, business and enterprise applications. The editorial board of IJEBEA is composed of members of the Teachers & Researchers community who have expertise in a variety of disciplines, including business process models, software and technology deployments, ICT solutions, and other related disciplines of engineering, business and enterprise applications. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to engineering, business and enterprises applications. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic

Search,

INSPEC,

ProQuest,

ArnetMiner,

Base,

ChemXSeer,

citebase,

OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley,

ResearchGate,

cogprints,

OCLC,

iSEEK,

Scribd,

LOCKSS,

CASSI,

E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the IJEBEA for entrusting us with the important job. We are thankful to the members of the IJEBEA editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this seventh issue, we received 92 research papers and out of which only 21 research papers are published in one volume as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the fields of engineering, business and enterprises applications.

This issue of the IJEBEA has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in engineering, business and enterprise applications and may open new area for research and development. We hope you will enjoy this seventh issue of the IJEBEA and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The International Journal of Engineering, Business and Enterprises Applications (IJEBEA), ISSN (Online): 2279-0039, ISSN (Print): 2279-0020 (December-2013 to February-2014, Issue 7, Volume 1). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA. Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman. Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India. Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune -411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106, India. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg., R.K.University, Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana. India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar,Punjab (India). Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, 313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia-81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women’s College, Gardanibagh, Patna, Bihar. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia. Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009,India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University,Coimbatore-641003,TamilNadu,India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066. Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India. Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057, India. Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India. Prof. (Dr.) B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India.

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Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Eng.,Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty,Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT, Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India. Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale , Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka, India.

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Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman. Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India. Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India. Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia. Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India. Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal. Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi- 835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS- 38655, USA. Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India. Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal, India. Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu, India. Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India.

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Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics, Bhilai Institute Of Technology, Durg (C.G.) 491001, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan. Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel. Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India. Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’s SCHOOL, ATHANI Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India. Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering, Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India. Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology,Jalandhar,Punjab, India. Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology , Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand, India. Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India. Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India. Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura, India. Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India. Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai-400103, India. Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, India. Prof. (Dr.) Har Mohan Rai , Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand. Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India. Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India. Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036,India. Prof. (Dr.) Aparna Sarkar,PH.D. Physiology, AIPT,Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP ,India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. . Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, INDIA. . Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University,Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV), Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar,PhD(CS), M.Phil(CS),MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana) Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College ,Govind Nagar,Kanpur208006, India Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura Prof. (Dr.) T Venkat Narayana Rao, C.S.E,Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Satya Rishi Takyar , Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh, Punjab, India. Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:                                                    

e-Commerce applications using web services B2B and B2C applications Advanced web service technologies including security, process management and QoS Surveillance technologies and security policies Security for protocol management Resource and channel management Mobility management Network Security management Technology management Information security management Semantic web for e-Business and e-Learning e-Learning design and methodologies Instructional design methodologies Content management and development Knowledge and information management techniques Enterprise Applications for software and web engineering Open-source e-Learning platforms Internet payment systems Techniques for B2B e-Commerce e-Business models and architectures Service-oriented e-Commerce Human resource management Business-oriented and consumer-oriented e-Commerce Development of e-Business and applications Supply chain management Strategic decision support systems Enterprise resource planning and e-Business Intranet and extranet business applications Enterprise-wide client-server architectures Information systems analysis and specification Strategic issues in distributed development Semantic web technologies and cloud computing Legal aspects of e-Government Risk management Methods and tools for e-Government e-Democracy and e-Voting Operations management Information technology Information retrieval systems Aspect-oriented programming e-Libraries and e-Publishing Intelligent tutoring systems Digital libraries for e-learning Web-based learning, wikis and blogs Social networks and intelligence Social science simulation Information retrieval systems Wired and wireless data communication networks Data mining and warehousing Distributed AI systems and architectures Bioinformatics and scientific computing Knowledge and information management techniques

TABLE OF CONTENTS (December-2013 to February-2014, Issue 7, Volume 1) Issue 7, Volume 1 Paper Code

Paper Title

Page No.

IJEBEA 14-105

Ethical Orientation in Management Education – Evidence from the Indian National Capital Region Pallab Sikdar, Amresh Kumar, Munish Makkad

01-10

IJEBEA 14-109

SPATIAL INTEGRATION OF MAIZE MARKETING IN NIGERIA Jonah Ikoku John, Marcus Samuel Nnamdi, Folarin Kehinde Samuel, Adewumi Samson Adekunle

11-21

IJEBEA 14-113

The Determinant Factor of Dividend Policy at Non Finance Listed Companies Farah Margaretha Leon & Pradana Maulana Putra

22-26

IJEBEA 14-114

Does Long Memory Matter in Oil Price Volatility Forecasting? Majid Delavari, Nadiya Gandali Alikhani, Esmaeil Naderi

27-34

IJEBEA 14-116

A CRITICAL REVIEW ON DIFFERENT COIL CONFIGURATIONS USED FOR INDUCTION HEATING SYSTEM Tejas G. Patil, Atul A. Patil, Vijay H. Patil

35-39

IJEBEA 14-127

Timing Jitter and Quantization Error Effects on the Performance of Sigma Delta ADC used in SDR Receivers Preeti Trivedi, Dr. Ajay Verma

40-48

IJEBEA 14-134

Evaluation of Impact of Safety Training Programme in Indian Construction Industry – Analytic Hierarchy Process Approach S.V.S. Raja Prasad, P.Venkata Chalapathi

49-54

IJEBEA 14-141

A Survey on Data Mining Techniques for Customer Relationship Management S. Janakiraman, K. Umamaheswari

55-61

IJEBEA 14-145

A Study on Marketing Effectiveness of Sales Promotion Strategies on The Dairy Industry (With reference to Sangam Dairy Vadlamudi of Guntur (D.T), (A.P), India) S Ayyappa Naik Nenavath

62-66

IJEBEA 14-146

IMPLEMENTATION OF HUMAN RESOURCE IN TOTAL QUALITY MANAGEMENT Surender Kumar

67-72

IJEBEA 14-147

Studying the Degree of Readiness of Small and Medium-Sized Enterprises to Enter the International Markets (Case Study: Food companies) Mohammadreza Hamidizadeh, Maryam Zargaranyazd

73-77

IJEBEA 14-149

PERCEPTIONS THAT MOTIVATE PURCHASE Shruti V. Joshi

78-82

IJEBEA 14-157

THERMO STRUCTURAL ANALYSIS ON A MARINE GAS TURBINE FLAME TUBE L.S.V.Prasad, K.Rajesh Chandra

83-88

IJEBEA 14-163

Implementation of Coherent Optical Digital Communication Systems Using Digital Signal Processor & FPGA Mr. A.H.Karode Miss. Preeti V. Murkute

89-94

IJEBEA 14-164

Theoretical Investigation of Refrigeration System for Rapid Cooling Applications Nilesh Pawar, Dnyaneshwar Pawar, Dayanand Gorabe

95-98

IJEBEA 14-165

Design, Analysis of Flow Characteristics of Exhaust System and Effect of Back Pressure on Engine Performance Atul A. Patil, L.G. Navale, V.S. Patil

99-103

IJEBEA 14-166

Emotional Intelligence and Conflict Management: An Empirical Study in Indian Context Tanu Sharma & Anil Sehrawat

104-108

IJEBEA 14-173

Survey Paper on Image Retrieval Algorithms Khushdeep Kaur, Hardeep Singh

109-112

IJEBEA 14-178

SEISMIC ANALYSIS OF TALL TV TOWER COSIDERING DIFFERENT BRACING SYSTEMS Hemal J shah, Dr. Atul K Desai

113-119

IJEBEA 14-183

EFFECT OF ON PARENTING STYLES ON ACADEMIC ACHIEVEMENT AND ADJUSTMENT PROBLEM OF TEENAGE Sunil Kumar

120-129

IJEBEA 14-187

HIGH RATED INTEGRATED SOLAR DRYER AND COOKER Kiran kumar Rathod, H.S.Ashoka

130-137

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Ethical Orientation in Management Education – Evidence from the Indian National Capital Region Pallab Sikdar1, Amresh Kumar2, Munish Makkad3 Research Scholar (Dept. of Management)1, 2, Professor (Dept. of Management) & Director3 Birla Institute of Technology (BIT), Noida, A-7 Sector-1, Noida-201301 (UTTAR PRADESH), INDIA _______________________________________________________________________________________ Abstract: Our research is an attempt to identify the ethical dimension of management students across Indian national capital region of Delhi. It deciphers the underlying drives which lead the B-school students to adopt unethical conduct within academic settings. A structured questionnaire was developed towards identifying three dimensions in the context of B-school students - their awareness of what constitutes unethical behavior, the reasons prompting the students to resort to unethical practices in academic settings and probability of their future corporate behavior reflecting unethical conduct. Ethics is majorly viewed as one or other form of moral objectives perceived by respondent group. Inspite of general agreement that presence of ethical conduct in academic settings is important, diverse unethical conducts exists in varying degrees within academic settings. The awareness as to what constitutes Plagiarism is found to be alarmingly low among students. The B-schools may look towards employing suggested means of evaluation and plagiarism detection for thwarting academic cheating practices. Future researches can identify linkage between current ethical orientation of B-school students and their future conduct at respective workplaces. Future researchers may consider frequency of resorting to individual cheating methods towards deriving a classification of academic cheaters. Keywords: Ethics, Academic Cheating, B-School, Magic Broom Acquired Syndrome __________________________________________________________________________________________ I. Introduction According to Oxford Advanced Learner’s Dictionary ‘Ethics’ refers to moral principles that control or influence a person’s behavior. In other words, the inherent principles within an individual guiding his action in response to a situation constitute the ethics. Ethical orientation remains of paramount importance at various levels of individual dealings and hence is a factor to be emphasized upon by all the elements within a societal setup. Over the past decade ethical issues in business have gained considerable significance in the developed countries and more recently in the emerging nations. With the advent of free-market, deregulation and privatization of the Indian economy, the ethical issues in business perspective have been accentuated. It is evident from inclusion of specific corporate governance code towards executing listing agreement with stock exchanges in India (Kumar Mangalam Birla Committee). During the initial phase when management education in India was gaining ground, there existed few institutes of repute, both public and private, who ensured imparting holistic and cutting edge education majorly culminating into attractive employment opportunities. The corporate houses were competing with a view to attracting talents by offering higher pay packages. Such a belief was termed as ‘Magic Broom Acquired Syndrome’. Over a period of time this led to a notion among aspiring students that an MBA degree is an instant gateway to hallowed corporate organizations. But as all good trends come to an eventual halt, it was not much different here as well. In the present times, an MBA degree no longer commands the status of panacea of all ills unlike the traditional belief. This has been the result of rapid influx of private education institutes, resulting from implementation of AICTE report recommending thrust on setting up of additional B-schools, offering numerous conventional and hybrid management programs with scant regard towards quality of such programs and their capacity to conduct such programs in terms of faculty competency and infrastructure. In many instances faculties neither having sound industry nor adequate teaching experience are recruited to teach courses which require sound industry experience on the part of faculties. The intense competition amongst existing and mushrooming institutes has led them to focus all their energies in maximizing intakes, many a times by hook or crook. While this may give short term revenue benefits to the institute, but it hurts the long term standing of its students and its own image in the society as well as in the eyes of prospective recruiters. Thus, when the management institutes are themselves not ethical in terms modus-operandi, they can’t be expected to either inculcate or enforce ethical standards in their enrolled students. The total absence of ‘Magic Broom Acquired Syndrome’ with respect to a management degree has led to rise in unethical malpractices within the B-School setup. Due to constant influx of management institutes, the students graduating from these institutions are not able to differentiate themselves

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purely on the strength of their degrees. This gives a strong impulse to students towards resorting to various unethical means with the intent of excelling and differentiating themselves both during the course of the program and post completion as well. The current irrelevance of Magic Broom Acquired Syndrome in terms of management education has been a major contributor towards promoting unethical practices amongst students. Some of the rampant forms of unethical practices are plagiarism, falsification of survey findings, and deliberate delay in project submission inspite of knowing the deadline. Prevalence of such practices has reduced the value of an MBA degree to ‘Managing Bogus Activities’ a far departure from it is true potential. Thus, it is an appropriate time given the current scenario, that ethical issues in management education must be explored with a view to determine the underlying reasons towards adoption of unethical approaches at all levels of management education. This will aid towards ensuring true potential of management education which ideally is ‘Managing Beyond Academics’, but which sadly in the present times owing to degradation of quality and associated unethical practices have become Managing Bogus Activities. II. Objectives The present research has a three-fold objective.  To begin with, the research aims to identify student level awareness of opportunities to indulge in common unethical actions.  Moving ahead, the research attempts to unearth proportion of management students portraying a tendency to resort to unethical practices in an academic set up and the underlying reasons prompting such conduct as reported by the student community themselves.  Finally, the study attempts to identify predictors of probable future unethical corporate behavior at workplace. An attempt has also been made to decipher possible influence of gender on unethical conduct of B-school students. III. Review of Literature From the family to society at large, from government to the private workplace, ethical violations have become a conspicuous feature of the contemporary landscape globally. Tellingly, most of the Indian colleges and universities have been plagued by ethical misconduct ranging from widespread cheating by students to faculty who have falsified data and manipulated research findings. The concept of ethics is not new to the domain of management education, but on having a synoptic view of existing literature, very few researches come to fore with a focused assessment of a particular dimension indicating ethical orientation of B-school students. In the past, researches have majorly focused on effect of business ethics courses towards ethical orientation of B-school students (Glenn 1992, Carlson & Burke 1998, Stead & Miller 1988). In addition, certain other studies have concluded the fact that most of the B-school students view their course curriculum as having value neutral impact (McCabe 1994). The underlying focus of the present study i.e. identifying the inherent drives which lead management students to resort towards unethical means at different points of the program have remained fairly untouched till date in research circles. Past studies have found that there exists significant correlation between cheating in schools and unethical behavior at work place (Sim 1993). This is particularly worrisome as it suggests the fact that inability to control the unethical student conduct at school will have a propagating effect on future workplace behavior. There exists other set of studies which have attempted to establish relation between Age and Marital Status and inclination to cheat. Age and marital status have a moderate linear relationship with cheating; younger and unmarried students cheat more (Whitley 1998). Bushweller found that an astonishing 50 percent of the students surveyed did not perceive cheating as necessarily wrong and 95 percent of those who had cheated sated that they have never been caught (Bushweller 1999). Koch offered the most staggering statistic of 20 – 30% of college undergrads cheat on a regular basis (Koch 2002). Based on the literature, McCabe’s erstwhile conclusion is reaffirmed “…that these results indicate that dishonesty appear to not carry the stigma that it used to”. A major reason students continue to cheat is that they rarely get caught. In 1999, McCabe interviewed 1,000 faculty members from 21 campuses and nearly a third admitted to observing cheating in their classes yet doing nothing about it. Fear of lawsuits, time required to handle cheating incidents, and lack of institutional rewards for catching cheating are all cited as rationalizations for this behavior (Koch 2002). From the undergraduate to the master’s and doctoral levels, business schools must encourage students to develop a deep understanding of the myriad challenges surrounding corporate responsibility and corporate governance; provide them with tools for recognizing and responding to ethical issues, both personally and organizationally; and engage them at an individual level through analyses of both positive and negative examples of everyday conduct in business.

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IV. Methodology The present study is based on an exploratory cum descriptive research design. To begin with an initial pilot study across categories of students was carried out wherein focused group discussions were conducted to determine common reasons which drive the management students to adopt unethical approaches towards excelling in written examinations, in preparing dissertations and research papers, and for meeting project submission deadlines. As a part of present research, a structured questionnaire was developed on the basis of focused group interactions with the aim of identifying three dimensions with respect to B-School students i.e. their awareness of what constitutes unethical behavior, the reasons prompting the students to resort to unethical practices in academic settings and probability of their future corporate behavior reflecting unethical conduct. Over 600 students pursuing management programs at undergraduate and postgraduate levels at two major management institutes within National Capital Region (NCR) were contacted as part of the survey to gather relevant inputs for the study using the structured questionnaire. The final sample comprised of 336 valid respondents distributed across five distinct academic programs (Table 1). The sample had fair representation of students from both the genders, comprising of 192 male respondents and 144 female respondents. The study includes determination of propensity of students to adopt unethical cheating habits in the context of written examinations and academic dissertations/project report submissions. Towards this, the respondent sample have been divided into multiple categories comprises of students who have resorted to only one unethical action from the given list, those who have adopted only two actions, those with only two actions and those who reported to have resorted to four or more actions. The categorized data was further classified on the basis of respondent gender to detect inherent inclination of a specific gender towards higher propensity to indulge in academic cheating. Further, cross-tabulation analysis between respondent gender and various measurement dimensions of academic ethics forming the part of survey questionnaire was undertaken. In addition, chi-square test of association was utilized to decipher possible underlying associations between gender and individual measurement parameter for academic ethics. As part of the study an effort has been made to predict future workplace behavior of respondent group when they step out to the corporate world. Towards this 14 distinct statements highlighting probable future corporate behavior were included within the survey questionnaire (Reddy & Krishnan, 2002). The respondent group comprising of management students were asked to rate the statements on a 4 point likert scale towards specifying their extent of agreement with respect to each individual statement. By undertaking Exploratory Factor Analysis (EFA) using SPSS the rated statements were categorized under two extracted factors based on the item-wise factor loadings specified by ‘Rotated Component Matrix’ generated as a part of EFA. V. Analysis and Findings The research was broadly undertaken with the intent of exploring three major dimensions, viz. ‘Perception of the concept of Ethics in general’, ‘Academic Integrity and Inclination towards Cheating Habits’ and ‘Probable Future Corporate Conduct’ with respect to current management students.  Ethics – Perceptual Revelation As part of the survey questionnaire respondents were asked to reveal their perception of the concept of ‘Ethics’. Varied responses were received ranging from Rules and Regulations to be followed, Moral objectives/values of an individual or a group to Choice between Right and Wrong. On a detailed review of the entire response set, it was revealed that majority of the respondent group (55.3%) perceive ethics as moral values either in context of an individual, a community or within a business setting. In addition, some of the other major response categories include choice between right and wrong, rules and regulations to be followed, code of conduct in the context of a profession or a specific activity (Table 2).  Academic Integrity – Adoption of Unfair Means in Written Examinations The survey conducted as a part of the study attempted to identify the degree significance attached to ethics in academic settings by the student group. From the respondent sample, (65%) considered ethics as ‘Significant’ in the academic context, while another respondents (26%) considered it as ‘Highly Significant’. Further, from a list of commonly cited unethical academic practices which were included as a part of survey questionnaire 49% of the respondents ranked ‘Lying/giving false excuses to faculties’ as least unethical, while 60% of the respondents ranked ‘Submitting fake certificates towards gaining admission and other benefits’ as most unethical conduct within an academic setting. Towards determining the extent of integrity being maintained by students pursuing management programs, some common Unfair Means (UFMs) observed by the authors as well as highlighted within past researches were included as part of survey instrument. The respondents were asked to opt from the given list the UFMs which they had resorted to during written exams appeared until now. In addition, an option was also provided wherein the respondents were asked to express any other UFM they have adopted while appearing for a written

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examination. Further, the respondents were also quizzed towards underlying reasons which prompt or many a times compel them to adopt UFM in written examinations. Analysis of the responses sought in the above specified context highlighted significant facts. ‘Copying from other candidates’ emerged as the most extensively resorted UFM with 86% students assenting to it. ‘Usage of crib notes/chits’ garnered favor of 69% respondents, while ‘Exchanging answer-sheets’ emerged as third most popular UFM with 56%. The respondents were subjected to classification towards determining the proportion of ‘Novice Cheaters’ (those having resorted to only one form of listed UFMs), ‘Amateur Cheaters’ (those having resorted to exactly two forms of listed UFMs), ‘Pro Cheaters’ (those reporting adoption of exactly three forms of listed UFMs) and ‘Expert Cheaters’ (those consenting to have resorted to 4 or more forms of listed UFMs). The categorized data on basis of propensity to cheat was further classified on basis of respondent gender which highlighted major trends. When it comes to low (Novice) to medium (Pro) category of cheaters are Females students are having a clear lead over their Male counterparts in two out of the three categories. But when it comes to category representing highest risk and cheating propensity (Expert), the Male students trump the Females by a significant margin. Our findings reveal that both Male and Female students resort to academic cheating practices in the context of written examinations. But, the propensity to cheat in the context of Female students reduces significantly as we gradually move up the ladder of cheaters classification (Table 4). Past studies on this by McCabe & Trevino too classified students pursuing undergraduate programs according to number of academic cheatings methods adopted. His study found that 67% of the students admitted to resorting to one or more instances of cheating, while 38% students admitting were in the ‘Active Cheaters’ category having admitted to at least three incidents of cheating. Thus, it is evident that inspite of being conscious of the importance of ethics in academics, majority of the student group still resort to unfair means during written examinations. When our survey probed the underlying reasons as to why students engage in such behavior, the majority students (71%) considered ‘Constant pressure to excel from family and society’ one of the factors prompting them to adopt such behavior (Table 5). In addition, Chi-Square significance value of 0.014 indicates an association between gender and ascertaining the contents of a question paper beforehand (Table 6).  Academic Integrity – Adoption of Cheating Habits in Academic Submissions The study found a deep rooted presence of unethical cheating practices in the context of academic submissions such as thesis, dissertations and project reports too. From the initial pilot interview and past studies some of the commonly cited cheating methods were included as a part of final questionnaire. The respondents were asked to opt for the cheating methods resorted to in the context of academic submissions made till now. Findings revealed that as many as 60% of the students agreed to having copied an existing published material and turned it as their own work, while 53% of the respondents agree to having copied few points from a published work without required footnoting. The results of the findings have been depicted within Table 7. On the basis of categorization of cheaters as discussed in the preceding section, the student respondents resorting to cheating practices in academic submissions were classified. It was revealed that significant proportion of respondents is falling under Novice cheater and Amateur cheater categories. On reviewing the gender wise status of respondents across various cheater categories, it is evident that in the context of academic submissions Male students are on the forefront in three out of four categories. In the Expert cheaters category, Female students take a slight lead over the Male counterparts (Table 8). As a part of the study, underlying reasons which lead the students to resort to cheat in the context of academic submissions were explored. Majority of the students (46%) attribute ‘No appreciation for genuine hard work from the subject instructor leading to dissatisfaction’ as the chief reason behind adoption of in-genuine means towards submissions. Closely followed reason was ‘Readily available secondary reports on all themes at affordable prices’ which had been cited by 43% of students (Table 9). Further, chi-square test indicates an association between respondent gender and copying few points from published notes without footnoting as means of cheating in academic submissions (chi-square Sig. value = 0.025) Table 10.  Quality of Academic Submissions The modern day academicians, in general, are united on the view that the average quality of academic submissions have plummeted over time. Such a trend is particularly worrisome given the fact that current crop of students will be the future corporate executives and inferior reporting or documentation skills is bound to significantly hamper their goodwill and performance in future workplace environment as well. Our study attempted to relate the falling quality of academic submissions with the average time devoted to the assigned projects by students and their awareness of the precise concept of ‘Plagiarism’ which is hurting the quality of academic submissions big time. Results of our survey indicates that 39% of the students reported that they would start working on a academic project/assignment (assigned on 1st day of the month and due to be submitted on 30 th day) between 15th and 25th day of the month, while another 28% asserted willingness to work only after 25 th day of the month. Thus, a whopping 67% of the reporting students are themselves reducing the allocated timeline by 50 percent (Table 11), and with ultimately inadequate time remaining at their disposal such students are left with no alternative but

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to indulge in cheating/unethical tactics to meet the fast approaching submission deadline. In addition, the survey results revealed that only 37% of the students are aware of the correct meaning of ‘Plagiarism’, while another 24% of the respondents have explicitly indicated that they ‘Don’t Know’ the concept of ‘Plagiarism’. Thus, go slow approach by the students coupled with unawareness regarding the Plagiarism can be attributed as the major contributors towards gradual fall in quality of academic submissions.  Academic Submissions - Management Perception vs. Student Perception The importance attributed to projects and assignments by the students and the institute management authorities are many times at a variance. The primary objective of the institutes towards assigning projects is to enable students to learn and imbibe the practical aspects of specific academic concepts with a view to ensure their smooth transition into corporate work environment. But when as a part of our survey we explored the perception of students towards project/assignment submissions, 61% of the respondents indicated the fact they find an academic project interesting only if the specific theme is of own preference. In addition, another 27% students view projects/assignments as a challenge to overcome. Further, when the students were quizzed regarding their awareness of academic projects being a part of evaluation component, 61% reported of being aware. Thus, it is evident that inspite of knowing the fact that submitting genuine and quality projects on time will fetch them good marks, they appear to be lacking adequate sincerity towards such submissions.  Influence of External Environment towards Academic Submissions Further, certain external environmental factors are lowering the quality of academic submissions. The authors have come across existence of retailers blatantly selling second-hand project reports to students over the counter. Their modus-operandi is to offer printing and photocopying facilities to students as a peripheral service. When a student who had worked hard and in good faith towards preparing an academic project approaches such a service provider for printing his submission report, the retailer retains a soft copy of the project file without the knowledge of the student. Such a practice over a period of time leads to large collection of project reports with the retailers which in turn are offered to the desiring students over the counter for a price. As part of our survey, we determined the extent of student awareness towards prevalence of such practices. From the students surveyed as many as 88% asserted to knowing the fact that there are such retailers in existence. It has been observed that such suppliers of second-hand project reports mushroom in dedicated institutional areas which are hub of major educational institutions. Some of the identified market areas within the NCR wherein such illegal retailers are active include Ber Sarai market catering to students of adjoining Qutab Institutional Area and Atta market in Noida catering to students from Noida Institutional Area as well as Knowledge Parks of Greater Noida region. This ensures availability of ready market for such illegal retailers. All the institutional areas specified above houses a host of management and engineering institutions and represent dedicated educational nerve centers of the respective regions. The authors have specific evidences and personal observations in support of their claims.  Future Corporate Behavior – Predictors The present generation of management students will be the torchbearers of the future corporate landscape. Hence the ethical orientation of the student population currently enrolled across various management institutes assumes considerable significance. With the aim of gaining an outlook to probable future conduct of students within a workplace set up 14 statements reflecting behavioral conducts were included within the survey instrument and respondents were asked to rate them on a 4 point likert scale indicating their degree of agreement towards the given statements. On the basis of the response ratings received, EFA was performed using SPSS predictive analysis software with a view to extract relevant factors. Two major factors were extracted on the basis of factor loadings indicated by the Rotated Component Matrix (Table 12) generated as part of EFA. The two factors identified were named as ‘Personal and Situational Stimuli’ and ‘Performance Drives and Bribery’ (Table 13). KMO measure for the sampling adequacy equals 0.895 (Table 14) and Cronbach’s Alpha reliability statistic equals 0.801 (Table 15). VI. Conclusion and Recommendations Our research reveals trends towards definite existence of deep-rooted unethical cheating practices in academic settings. Though our study is limited to B-School students within the NCR region, but such unethical practices are not restricted to a particular stream of education or geographical area. In fact, in present times similar cheating practices in one form or the other had been reported from various academic institutions spread across the length and breadth of the country, and such practices are found to be neutral in terms of academic programs. Restoring the academic integrity is the need of the hour and academic institutions are required to take concrete steps towards realizing this need. As an initiating step in this direction, B-schools and other academic institutions within India may look towards implementing a system of ‘Honor Codes’ which has effectively used by institutions in UK and USA, but remains fairly unexplored in the Indian academic settings. ‘Peer Integrity’ i.e. impact of peers holding high integrity and ethical dimension towards reduction in an individual’s propensity to indulge in cheating, and ‘Peer Review’ i.e. entrusting the responsibility of academic vigilance to members of student group itself, forms major components of Honor Codes system.

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Findings of our survey reveal that only 11% of the respondent students have expressed explicit willingness to indulge in unethical means to obtain certain benefit within a peer-reviewed and peer-integrity based environment (Table 16). Thus, well implemented system of ‘Honor Codes’ can act as a strong deterrent to rising menace of academic cheating across institutions, leading to prevalence of honesty and integrity, in the multiple context of academic examinations and academic submissions. In addition, academic instructors dealing with specific courses may look to innovate and break-free from conventional methods of academic testing and evaluation of students. Various tests and assignments administered round the academic semester should be drafted with an aim to assess a student’s ability to apply the conceptual knowledge derived in class, instead of mere ability to demonstrate memorization. As part of academic evaluation components conventional examination pattern may be replaced with open book essay based tests, viva-voce based sessions pertaining to the concepts covered in class, conducting presentations cum vivavoce session individually or on a group basis. Such evaluation techniques will aid in rendering academic cheating practices majorly difficult, if not impossible. Academic institutions in the present times have been severely impacted by falling quality of theses, dissertations and project reports. A major factor resulting in such a trend has been the concept of ‘Plagiarism’ i.e. using the published/in-progress work of others and misrepresenting it as one’s own work. As discussed in the preceding sections, although our survey found that as high as 63% of the responding students have explicitly indicated unawareness of the precise meaning ‘Plagiarism’, but still the same is being practiced extensively as evident from frequently cited academic concerns. Thus, it is high time that B-Schools and other academic institutions in India take steps towards both detecting plagiarized submissions and use such detection systems as deterrent towards future instances of plagiarism in the context of academic submissions. Academicians should assign a list of highly specific assignment topics instead of giving a single topic to entire class. Assignment of extremely narrowed down and specific topics will make it very difficult for the students looking to plagiarize from online databases or illegal retailers selling readymade theme specific reports as discussed in the preceding sections. An extreme form of such illegal practice was detected by the authors when they observed the retailers enquiring with approaching students as to which academician/faculty from a particular institute have assigned this topic. The retailers claimed to supply readymade reports tailored to the preference or liking of the assigning academician/faculty. This was quite a startling revelation for the authors and a reality check as to what heights the practice of plagiarism has gained. Our study revealed the fact that students deliberately adopt a go-slow approach when a faced with a relatively long submission deadline which leaves inadequate time at their disposal at the end. Towards negating such behavior academicians can break the assignments into various distinct phases and review the extent of work completed in individual phases periodically leading up to the final submission deadline. Such a practice is likely to keep a tab on willful delaying habit of students by leaving the entire work for the last minute just before the final deadline. The academic institutes may look to organize seminars and workshops towards making their students aware of what precisely constitutes ‘Plagiarism’ and how to avoid same towards enhancing the quality of academic submissions. As part of such workshops student community may also be briefed on the importance of live projects, operational workouts, internship projects etc., and how genuine work in respect such academic components would ensure a smooth and successful transition in to the future corporate environment. While it is recommended to take adequate steps towards ensuring that the menace of plagiarism is completely thwarted, but B-schools and other academic institutions must not lose sight of the fact that detection efforts towards existence of plagiarism in the submitted contents is equally critical to ensuring quality. In the present times, academic and research institutes globally are increasingly subscribing to web based plagiarism detection services such as TurnItIn, Ithenticate, My Drop Box, Easy Verification Engine (EVE2) etc. In India, the academic institutes are yet to embrace and fully realize the benefits of such detection systems, financial considerations being a hindrance in this regard. But, it is prudent to undertake an investment towards raising the quality of institutional research bringing laurels and recognition to the institution in the global arena. The findings of study in terms of predictors of future workplace behavior of students classified under two factors on the basis of EFA can be used by the recruiters towards screening and selecting the applicants who are ethically upright. Thus, reinstating academic integrity and ethical conduct among students is the need of the hour and B-schools and other academic institutions in India will be well served to consider the same as one of their topmost priority to keep pace with their global counterparts. It is a situation meriting now or never approach. VII. [1]. [2].

References

AACSB International (2004), Ethics Education in Business School, Report of the Association to Advance Collegiate Schools of Business Bushweller K. (1999), Generation of Cheaters, The American School Board Journal

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Pallab Sikdar et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 01-10 Carlson P. J. & Burke F. (1998), Lessons learned from Ethics in the Classroom – Exploring Student Growth in Flexibility, Complexity and Comprehension, Journal of Business Ethics, Vol. 17. Glenn J. J. (1992), Can a Business and Society Course affect the Ethical Judgement of the Future Business Managers?, Journal of Business Ethics, Vol. 11. Hongyan J. M. et al. (2008), Digital Cheating and Plagiarism in Schools, The College of Education and Human Ecology Journal Koch K. (2000), Cheating in Schools, The CQ Researcher Vol. 10, No. 32, Sept 2000 McCabe D. L. & Trevino L. K. (1995), Cheating Among Business Students: A Challenge for Business Leaders and Educators, Journal of Management Education Vol. 19 No. 2 McCabe D. L. & Trevino L. K. (1993), Academic Dishonesty: Honor Codes and Other Contextual Influences, Journal of Higher Education Vol. 64 Reddy C. M. & Krishnan R. T. (2002), Measuring the Ethical Orientation of MBA Students – A Scale Development, WP 183 Working Paper Series, Indian Institute of Management (IIM) Bangalore Sims R. L. (1993), The Relationship between Academic Dishonesty and Unethical Business Practices, Journal of Education for Business Vol. 68, Issue 4 Stead B. A. & Miller J. J. (1988), Can Social Awareness be increased through Business School Curriculum, Journal of Business Ethics, Vol. 7 Whitley B. E. (1998), Factors Associated with Cheating among College Students, Research in Higher Education, Vol. 39 Issue 3

[3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12].

Tables and Exhibits Table 1: Student Response Rates Academic Programs Bachelor of Business Administration (BBA) Bachelor of Business Economics (BBE) Integrated Masters in Business Administration (IMBA) Integrated Masters in Business Economics (IMBE) Masters in Business Administration (MBA) Totals

Questionnaires Disseminated

Returned & Valid

Percent

150

46.6

51.4

200

121

60.5

150

620

336

54.2

Table 2: Perception of Ethics Perception of Ethics Moral Values/Objectives Right vs. Wrong Code of Conduct Others Total

Number of Responses 186 39 32 79 336

Percentage 55.4 11.6 9.5 23.5 100

Table 3: Common Cheating Methods – Written Examinations Cheating Method Using crib notes (short chits with indicative answers) Copying from other candidates Using unfair methods to derive the contents of the question paper beforehand Exchanging answersheets Assisting someone else to cheat Others

Opting Respondents 230 288

Percentage 69 86

189 139 37

56 41 11

Table 4: Classification of Academic Cheaters – Written Examinations Novice Cheaters Gender M

Count 39

F Total

Amateur Cheaters % 59

Gender M

Count 35

100

Pro Cheaters % 45

Gender M

Count 43

100

Expert Cheaters % 47

Gender M

100

Count 68

% 68

100

*M: Male, F: Female Table 5: Cheating in Written Examinations – Reasons Reasons

Opting Respondents

Percentage

Constant pressure to excel from family and society

238

Rising level of competition

192

Demonstrating risk taking ability to peer group

202

Chances of getting caught are negligible and punishment is quite lenient

161 10

48 3

Others

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Table 6: Chi-Square Tests (Gender – Using unfair methods to derive the contents of Question paper) Value 6.061a 5.435 6.217

Pearson Chi-Square Continuity Correctionb Likelihood Ratio Fisher's Exact Test Linear-by-Linear Association N of Valid Cases

Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) .014 .020 .013 .018 .009 1 .014 1 1 1

6.043 336

0 cells (.0%) have expected count less than 5. The minimum expected count is 33.43. b. Computed only for a 2x2 table

Table 7: Common Cheating Methods in Academic Submissions Cheating Method Copying material and turn it in as your own work Falsifying a bibliography Receiving unpermitted help on an assignment Collaborating on an assignment when the instructor asked for individual work

Opting Respondents 201 72 87

Percentage 60 21 26

Copy few points from published notes without footnoting

177

Table 8: Classification of Academic Cheaters – Academic Submissions Novice Cheaters

Amateur Cheaters

Pro Cheaters

Expert Cheaters

Gender

Count

Gender

Count

Gender

Count

Gender

Count

Total

129

100

129

100

M: Male, F: Female

Table 9: Cheating in Academic Submissions – Reasons Reasons Readily available secondary reports on all themes at affordable prices. Lack of concern for project quality/originality from the subject instructor No appreciation for the genuine hard work from the subject instructor leading to dissatisfaction It is just another component of curriculum to be undertaken somehow or the other Work assigned appears to be boring and meaningless Others

Opting Respondents 144 110

Percentage 43 33

155

109 101

32 30

Table 10: Chi-Square Tests (Gender – Copy few points from published notes without footnoting) Value 5.015a 4.533 5.036

Asymp. Sig. (2sided) Exact Sig. (2-sided) .025 .033 .025 .028 .025

Exact Sig. (1-sided)

Pearson Chi-Square 1 Continuity Correctionb 1 Likelihood Ratio 1 Fisher's Exact Test Linear-by-Linear Association 5.000 1 N of Valid Cases 336 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 68.14. b. Computed only for a 2x2 table

.016

Table 11: Student Activation Timeline towards Academic Submissions Actively Start Working nd

From 2

Opting Respondents

Percentage

On 15th

Between 15th and 25th

131

Between 2nd and 15th

After 25

Never

0.9

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Pallab Sikdar et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 01-10

Table 12: Rotated Component Matrixa Component 1

If bribe is a must even to get what is legitimate, as happens in some government offices, we may have no choice but to pay up. It is impossible to do business in India without paying bribes.

.737

.048

.671

.214

It is a dog-eat-dog world. Each person has to take care of his own interest first, before being concerned about other’s interests.

.660

.200

Companies have a responsibility to ensure well being of the society.

.621

.125

The ethics of business are different from the ethics of personal & family life.

.604

.258

To achieve success in business dealings one may have to occasionally indulge in a certain degree of dishonesty and telling half-truths.

.503

.264

In a situation where my performance is being measured relative to that of others, I might not mind doing things which can hinder the performance of others. In order to achieve high performance, it is OK if an organization is somewhat unethical in its business practices.

.158

.655

.031

.616

I have personal experience of having given a bribe. It is OK to sift through a competitor’s garbage to obtain their documents.

.312 .164

.613 .583

It is alright to exaggerate the performance of a product in order to achieve higher sales. In a business situation, if the need arises I would not hesitate to pay a bribe to get business. I would rather get a lower grade than copy project reports in order to meet deadlines or get false attendance to meet the stipulated norm.

.204

.557

.330

.547

.080

.423

If a person manages to do well in his/her career and life by networking and politicking even without doing his/her work properly, it is OK. It for each person to decide on what he/she wants to accomplish his/her goals.

.330

.422

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 3 iterations. Table 13: Factors Extracted based on Exploratory Factor Analysis S. No.

Personal and Situational Stimuli To achieve success in business dealings one may have to occasionally indulge in a certain degree of dishonesty and telling half-truths. The ethics of business are different from the ethics of personal & family life. It is a dog-eat-dog world. Each person has to take care of his own interest first, before being concerned about other’s interests. It is impossible to do business in India without paying bribes. If bribe is a must even to get what is legitimate, as happens in some government offices, we may have no choice but to pay up.

Factor Loading 0.503

0.604

0.660

0.671

0.737

Performance Drives and Bribery In a business situation, if the need arises I would not hesitate to pay a bribe to get business. It is alright to exaggerate the performance of a product in order to achieve higher sales. It is OK to sift through a competitor’s garbage to obtain their documents. I have personal experience of having given a bribe. In order to achieve high performance, it is OK if an organization is somewhat unethical in its business practices. In a situation where my performance is being measured relative to that of others, I might not mind doing things which can hinder the performance of others.

Factor Loading

0.547

0.557

0.583

0.613

0.616

0.655

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Pallab Sikdar et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 01-10

Table 14: KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity

.895 934.970

Approx. Chi-Square df Sig.

91 .000

Table 15: Reliability Statistics

Cronbach's Alpha .801

Cronbach's Alpha Based on Standardized Items .793

N of Items 17

Table 16: If your peers are maintaining ethical integrity will you look to adopt unethical means to obtain certain benefit? Proposed Action Yes Depends on the nature of realizable benefit No

Opting Respondents

Percentage

167

49.7

132

39.3

Page 10

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net SPATIAL INTEGRATION OF MAIZE MARKETING IN NIGERIA 1

Jonah Ikoku John; 2Marcus Samuel Nnamdi., 3 Folarin Kehinde Samuel;4Adewumi Samson Adekunle 1,2 Department of Economics3,4Department of Business Administration. Achievers University,Owo. Km1,Idasen/Ute Rd,P.M.B.1030,Owo. Nigeria. ______________________________________________________________________________________ Abstract: This study investigates market integration for local maize market in Oyo State, South Western part of Nigeria. Monthly retail prices per kilogram of milled local maize between January 1998 and December 2007 was sourced from the Agricultural Development Programme of the State. Descriptive statistics which included mean, coefficient of variation and graphical analysis, correlation analysis, co- integration analysis and the Granger causality test were carried out. The descriptive analysis showed that urban market recorded the highest average monthly prices of local maize in Oyo State than their rural counterparts. Graphical trend of the variables showed that price series of local maize is more stationary in the rural areas than the urban areas. Correlation analysis of the variables showed that there is co-movement of prices within state. The cointegration results also showed that there is a long run relationship between the paired markets (that is rural and urban markets) of the state studied. It is recommended that Government should invest hugely on research involving pricing policy and marketing which will help in understanding price behaviour both in the short and long-run respectively which will enhance agricultural development and food security. Keywords â&#x20AC;&#x201C;co-integration, granger causality test, integration, spatial, trend analysis. _____________________________________________________________________________________ I. Introduction Maize is one of the staples widely grown in Nigeria. However, its production is more in the Northern part of the country than in the South, which is largely forest in nature with heavy rainfall. The maize grown in the southern part of the country is rain fed while that grown in the Northern part of the country is an admixture of rainfed and irrigation. Because of the scanty rainfall in the Northern part of the country, maize growers support their production with fertilizer and water from irrigation. Maize as a cereal crop is high yielding, easy to process and readily digested. It is a versatile crop that grows across a range of agro ecological zones. That might explain why almost all farmers in Nigeria grow maize. Maize is one of the most important cereals in the world followed by rice, wheat and millet. Maize has its significance as a source of large number of industrial products besides its uses as human food and animal feed. Over 50% of the population in the Sub-Saharan Africa has maize as a staple food. It is an important source of carbohydrate, protein, iron, vitamin and minerals. Africans consume maize as a starchy base in a wide variety of porridges, pastes, grits and beer. Green maize (fresh on the cob) is eaten parched, baked, roasted or boiled and plays an important role in serving as a stable menu for the downtrodden masses. There are principally two types of maize that are produced in Nigeria-yellow and white maize. The two varieties of maize are grown in all parts of the country in almost equal proportions. Grain of these varieties of maize contains between 5-6 grains. Nigeria produces annually over 6million tonnes out of 624 million tonnes of maize produced worldwide. The world major producer of maize is USA followed by China and Brazil. The world trade in maize in year 2002-2003 was estimated at75.85 MT. The major exporters of maize are USA, China, Argentina, Brazil, Hungary and South Africa. The major importers of the same product during the same period were Japan, South Korea, Mexico, Egypt and Taiwan. Nigeria does not participate much in the world trade in maize due to its high domestic demand pressure on the maize for industrial uses, animal feeds and consumption. Maize is used as food in form of porridges of different types, boiled or roasted, corn flakes and popped grains. More than 60% of the Nigeriaâ&#x20AC;&#x2122;s production of maize is consumed by the industrial sector for production of flour, beer, malt drink, corn flakes, starch, syrup, dextrose and animal feeds. Maize plant can be used as dry fodders and green fodders [1] Nigerian maize is traded both locally and internationally with a considerable percentage filtering into Niger, Chad, Mali, Benin Republic and some other countries in the West African Sub-region. Locally, white and yellow maize are sold almost in all markets in Nigeria with the commanding markets for the commodity being Dawanau market in Kano, Dandume and Jibia market in Katshina, Giwa Market in Kaduna, Shinkafi and Talata Mafara in Zamfara, Bodija in Ibadan, Osi market in Onitsha and Mile12 market in Lagos. A market system in which there is synchronous movement of prices in different markets over time is said to be integrated[2]. Market integration is a concept with application in spatial, temporal and product from market inter-relatedness. Without

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market integration, price signals will not be transmitted from food deficit to food surplus areas, agricultural producers will fail to specialize according to comparative advantage and the gains from trade will not be realized[3].Market integration analysis attempts to make statements on the functioning of markets, it tries to find information on the speed with which arbitrage takes place between markets, it also gives indication on commodity price interrelatedness[4].Market integration is often used as proxy for the â&#x20AC;&#x153;efficiencyâ&#x20AC;? with which a market system operates and the level of competitiveness in the market[5]. It is thus believed that the perfectly competitive market condition is the ideal market structure for market integration as this will force prices to adjust instantaneously to any new price of information so that all available information is reflected in prices. A.

Statement of research problem. Past policies have arrived at increasing agricultural productivity through increased investment in agronomic and production systems research. Efforts have also been made to restructure the agricultural sector, by removing every element of control in the supply and pricing of agricultural produce through the abolition of marketing boards, and reducing level of subsidies on agricultural inputs. This latter policy has contributed enormously to the erratic price variations in agricultural produce observed in recent years. Not much emphasis has been placed on the study, evaluation and development of marketing system. Policy formulation has failed to take cognizance of the fact that production and marketing constitute a continuum and the absence of development in one retards progress in the other. [6] Previous studies in the marketing and pricing of staple food stuffs in different parts of Nigeria have concluded that the marketing and price information transmission mechanism are inefficient although there are many buyers and sellers in the market[7],[5],[8].The paucity of physical infrastructure such as storage facilities, transportation systems, access roads, communication channels and inadequacy of economic data for planning and research are some of the factors identified as source of inefficiency. Others include, a high number of intermediaries in the marketing chain, high and erratic prices which will further depress the level of agricultural production. Thus, Oligopolistic competition is present in food stuff marketing in Nigeria. B. Justification of the study The integration of food grain market is central to the direction and success of agricultural policies in the West African Semi-Arid Tropics (WASAT).A well-integrated market system is essential to household food security in both food deficit rural areas and those witnessing a rise in the relative importance of non-food cash cropping. It is also key to the sustained success of extensions of new food production technologies, which might otherwise flood stagnant local markets with unstable surpluses. It can also be shown that the degree of food grain market integration determines whether equity oriented production policies in less favoured food-producing areas should be oriented toward food or some alternative activity[9] If price transmission does not occur, the localized scarcities and abundances may result in excessive strain on the population[10]. Market integration analysis attempts to make statements on the functioning of markets, it tries to find information on the speed with which arbitrage takes place between markets, it also gives indication on commodity price interrelatedness[4]. Market integration is often used as proxy for the efficiency with which a market operates[8] and the level of competitiveness in the market[5] .It is thus believed that the perfectly competitive market condition is the ideal market structure for market integration as this will force prices to adjust instantaneously to any new piece of information so that all available information is reflected in prices. In view of the importance of maize which is believed will help increased production. Since an inefficient marketing can cause decline in production, because it would lead to high price spread between the farm gate and the retail end. It could fail to transmit the correct price signals to the farmers. C. Objectives of the study The general objective of the study is to examine how integrated the local maize market in urban and rural market of Oyo State of Nigeria is. The specific objectives are: 1 To present the trend analysis of the market studied. 2 To compare the market integration between urban and rural markets in the state. 3 To make policy recommendations. II. Literature review Studies on marketing of staple food stuffs in various regions of Nigeria abound, while those on maize are few. Of recent, the use of time series econometric techniques has improved the understanding of the performance of food markets and impact of reforms using price data. [5] studied food market integration in Northern Nigeria using correlation analysis, compares market performance of staple (garri, rice, yams, and maize) and supplementary (cowpea, sorghum, millet) food. The study finds that out of 3,527 coefficients of pairs of markets for seven food crops only,19 have a correlation

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above 0.9 and more than 2000 coefficients are less than 0 .50.The correlations among garri prices are the highest and described as a fairly well-integrated system with more than one third having correlation above 0.80. This is followed by cowpea with 11.8 percent of the coefficient above 0.80, 1.0 percent for millet prices, 0.7 percent for maize prices, 0.6 percent for sorghum prices, 0.3 percent for rice prices and none for yam prices. [11] studied the extent of market integration of maize to understand how liberalisation affects markets in Malaysia. The study concentrated on movement of prices and price adjustment process over time using correlation and co-integrated analysis. The study concluded that market liberalization enhances the degree of market integration. In addition, almost all the markets studied exhibit a long run stable relationship shown by the existence of a stationary linear combination of price series and the number of markets that are co-integrated increased after liberalization. Also, the study finds that three major cities, Lilongwe, Zomba, and Blantyre are pivoted in the transmission of price signals to other markets. [ 12] applied the concept of co-integration in the study of market integration in seven spatial markets in Indonesia. The result of the study indicates high co-integration of markets, but only 17 relationships out of 56 are strongly exogenous. They conclude that supply sources are more important than demand sources in the driving process. [13] applied co-integration techniques in the study of market integration in ten spatial markets in Ethiopia. The study applied the analysis of shocks such as war and market liberalization. The results showed that liberalization reduces the margins between some of the main producing areas and the main consuming area. In addition, more markets become integrated with Addis-Ababa in the period after the policy change. On the whole, liberalization has important effects on the long-run and short-run integration of food markets. [14] investigated the structural determinants of market integration using 64 rice markets in Bangladesh. Correlation and co- integration analyses were used to obtain measures of market integration on which were in turn regressed on structural characteristics such as marketing infrastructure, vitality of policy and production levels. The study concluded that the degree of market integration in Bangladesh is moderate as segmented markets and only 10 percent of a network of 2016 market links. In addition, the determinants of market integration were sensitive to the measures of market integration but results suggested that market integration is negatively affected by distance and the number of strikes and possibly affected by the number of production shocks. [15] worked on pricing dynamics and market integration of cassava roots and products in Edo and Delta states using correlation and Ravallion-type model. Out of 56 market pairs of garri, 40 had correlation coefficients of above 0.80 for cassava roots,16 market pairs had coefficients above 0.80.The result of the Ravallion-type model revealed that changes in cassava and garri prices in each of the markets studied could be explained by changes in their own historical prices as well as contemporaneous and historical price variation in other markets. Another important finding was that cassava root prices in spatial markets were more integrated than garri prices in the short-run. She also reported a generally low level of price association between cassava roots and garri prices and absence of instantaneous price adjustments between markets. [16] in a co- integration study of cocoa supply in Nigeria found that weather effect is stationary while producer price and hectrage planted on cocoa have a long run equilibrium relationship with its determinants. The result suggested that cocoa export supply responds to relationship with its determinants. Their findings unequivocally support the deregulation of cocoa pricing as done under the structural adjustment programme of the late 1980s.The study recommended that government should intensify positive price policy measures that will greatly enhance cocoa production and hence its export supply to the world market. This is due to the fact that Nigeriaâ&#x20AC;&#x2122;s supply is not significant enough to decisively upset world market. [17] worked on structure and performance of cotton marketing in Northern Nigeria. Using Ravallion models, he found only 10 pairs of cotton markets to be integrated out of 56 pairs. Thus the degree of integration of cotton markets is very low in Northern Nigeria. He also found that parity prices were either above or below producer prices thus giving risk to either positive or negative price spread. For the three marketing years studies 1996/97, 1997/98 and 1998/99, the highest marketing margin of N18,000 was recorded in 1998/99 and this is equivalent to 32.7 percent of the producer price. This marketing margin was seen as high in his opinion. He concluded that the structure of cotton markets is not free of defects, the conduct is not flawless and expectedly the performance of the markets exhibits pricing inefficiency and high degree of segmentation or independence. [18] used co-integration and Granger-Causality to determine whether or not there is a long-run relationship between the domestic price and foreign markets. The results of 1970-1998 period covered by the study the world price of cocoa powder and cocoa butter. Granger causes the price level of their respective domestic prices and causes them to change at 1% and 5% levels of significance respectively. No causalities were found from world price to producer price of cocoa bean. The results of the exogeneity test for all the market pairs revealed that all the relationship were exogenous.

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The measure of contemporaneous price effect was unity and this indicates that the transmission of new information about price changes is within one year. In a study by [19], it was found that despite the low status of garri, its price seasonally remains high for 8months and low for 4months of the year. Evidence from the spatial price movements indicate existence of a high degree of integration amongst markets for garri in different geographical areas. III. Methodology. A. The study area. The area of study is Oyo State. Oyo State is bordered by Benin Republic in the West, in the North and East by Kwara and Osun States respectively and by Ogun State in the South. The state as it is presently constituted came into being in August 31, 1991 when the State creation excised the present Osun State from the old Oyo State. It covers a land area of 27,000sq km and made up of 33 local governments in four Agricultural Zones of Ibadan/Ibarapa, Oyo, Ogbomosho and Oyo Central, Oyo South and Oyo North Senatorial districts. Oyo state is located between 7 031 and 90121 North of the equator and longitudes 20471 and 40231 East of the Meridian. This location confers on the state the equatorial climatic conditions. There are two district seasons namely wet and dry seasons. B. Method of data collection. Time series data (monthly prices) of retail prices of local maize in the state between 1998-2007 inclusive (ten years) was sourced from each stateâ&#x20AC;&#x2122;s agricultural development programme (ADP). Interview was conducted with relevant bodies such as Oyo State Ministry of Agriculture, Agricultural Development (OYSADEP), Project Coordinating Unit (PCU) and sellers and buyers of local maize in the state. C.

Method of data analysis. Market integration refers to co-movement of prices and more generally in smooth transmission of price signals and information across spatially separated markets. Time series analysis of price data is conducted (from the secondary source). Correlation coefficient of prices and co- integration analysis will be used to measure the market integration in this study. Granger causality test will be used to measure the market integration in this study. Granger causality test will be used to evaluate which is the leading market in each of the market in the state. ARCH â&#x20AC;&#x201C; model will also be used if appropriate. C.1 Correlation analysis One simple way to study market integration is to consider the correlation of price series for different markets.Correlation( ) is a measure of the extent to which variables ' move together' and it is defined as. ij=

it-P)(Pjt-Pi 2

) )2 Where Pit=denotes the prices for local maize urban market (i measured at time t) Pjt=denotes the prices for local maize in rural (j measured at time t). Pit and Pjt denotes the means of each prices series. n=number of observation. If two prices co-vary perfectly in the same direction, it will equal one and the price series will move in parallel. Testing for market integration then reduces to assessing whether or not the sample correlation ( is significantly different from one. C.2 Co-integration analysis. Co-integration analysis is concerned with the existence of a stable relation among prices in different localities. When a long-run linear relation exists among different series, these are said to be co integrated. The presence of co- integration between two series is indicative of interdependence; its absence indicates market segmentation. In particular, a segmented link is one where there is no integration, whereas an integrated link is one where we have co integration. Basically, the idea of co -integration rests on the thesis that even though two time series may not themselves be stationary, a linear combination of the two non-stationary time series are said to be co integrated. Usually, for co- integration, the two time series have to be of the same 'order' i.e. they should be stationary after the same number of differencing. If a time series is said to be at its level i.e. without differencing such time series is said to be integrated of order zero I(0).If a non-stationary time series becomes stationary after first differencing, it is said to be integrated of order one I(1) etc.

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In co integration analysis, the concern is usually with a co integration of order I(1) between a pair of nominal price series. This type of co integration requires that two price series Pit and Pjt are each non-stationary in levels but stationary in first differences. It is accordingly necessary to test each of the univariate series for stationary individually and then, if they are both shown to be 1(I), proceed to test for co-integration. The most commonly used method is to run an OLS regression of one I(1) price series on another 1(1) price series and a time trend[20]. Pit=α +βPjt +Yt+Ei Where Pit and Pjt=Price series of a commodity. α= Constant β and Y=Parameter estimators. Ei=error term. The above equation is known as 'Co integration regression’. The next step is to test the residual Et from the co integration regression for stationary using the DE or ADF test on the β of the equation below. ∆Et=βEt-1+∑Yj∆Et-j+λ1 Where Et=Error term of co integrating regression. Y= Parameter estimator of lagged error term. The constant and time trend are now committed from the test because the residuals from the co integration will have zero mean and be detrended. The null hypothesis β=0 will be tested (Note that this is a test of the non-stationary of the residuals rather than the original time series). If the 't-statistics' on the β coefficient is less(i.e. more negative) than the relevant critical value, the null hypothesis may be rejected and the two series are said to be co integrated of order1(1). A stationary residuals implies that the two variables are co integrated (Thomas, 1997). When two series are stationary of the same order and co integrated, one can proceed to investigate causality. This is because at least, one Granger-Causal relationship must exist in a group of co integrated series . The causality test is represented as follows. ∆Pjt=βo+β1Pi(t-1)+β2J(t-1)+ + λt Where m and n are the numbers of lags determined by a suitable information criterion. C.3 Arch models ARCH stands for Auto regressive conditional Heteroskedasticity. It is a technique used in finance to model asset price votality over time. It is observed in much time series data in asset prices and these are periods when variance is high and periods where variance is low. The ARCH econometric model for this( introduced by[20] is that the variance of the series itself is an AR (Autoregressive) time series, often a linear one. Formally,[20] states that an ARCH model is a discrete time stochastic process {et} of the form. Et=ZtSt Where the Zt's are id overtime,E(Zt)=0,Var(Zt)=1 and St is positive and time varying. Usually St is further modelled to be an autoregressive process. ARCH models are usually estimated by maximum likelihood techniques. IV Data analysis and interpretation Trend analysis Trend analysis was carried out by the use of descriptive statistics which include mean, coefficient of variation and graphical analysis. Urban markets in Oyo State recorded the highest average monthly price for local maize than their rural markets (see table 4.1).The reason for this may be because consumption is higher in the urban areas than the rural areas of the state. Table 4.1: Summaries of Monthly Local Maize Prices from 1998-2007. A.

YEAR 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Average

URBAN PRICES(N/kg) 22.01 30.50 19.78 33.06 35.57 27.56 35.77 59.69 40.52 35.48 28.32

RURAL PRICES(N/kg) 20.78 30.58 17.36 31.37 34.84 25.89 33.77 52.22 34.41 30.31 25.96

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Source: Computed Price Series Data 2008. The trend in market prices of maize in the various markets is represented graphically in figures 4.1. Figure 4.1: The Graphically Trend of Oyo State's Urban and Rural Market Price series of Local Maize (1998-2007). 120 100 80 60

RURAL PRICES(N/kg)

URBAN PRICES(N/kg)

20 0 1

(months͢͢ Figure 4.1 contains the graphical representation of urban and rural market’s of maize price series of Oyo State respectively. The figure revealed that monthly price of maize increased at an increasing rate between 19981999,but there was a sharp decrease in 2000,it later increased between 2001-2002 and decreased in 2003.20042005 experienced an increase in the price of maize at about N33.77-N60.00/kg, while there was a fall in 2006 and 2007.The decrease in 2000,2003,2006 and 2007 may be as a result of surplus of harvest in those years while the rise in prices were as a result of shortage in supply of the commodity in such periods. B. Correlation analysis The correlation analysis carried out showed that the paired markets were correlated (that is there is comovement of prices between the paired market). Table 4.2: Summaries of the Correlation Results. Market Pairs OYR-OYU

Pearson Correlation Coefficient 0.966

Spearman’s Rho Correlation Coefficient 0.914

Source: Computed from the correlation analysis result,2008. From table 4.2, it could be seen that a correlation coefficient of 0.966 was obtained with Pearson Correlation which was significant at the 0.01 level (2-tailed).Since the coefficient is closer to unity and since correlation coefficients directly measure how closely prices of a commodity (maize) move together in spatially dispersed market, the market for maize is said to be integrated and efficient in urban and rural market respectively. This is also supported by Spearman’s rho coefficient which happens to be a non-parametric statistical method which gave a correlation coefficient of 0.914 which was significant at 0.01 level (2tailed).This result also shows that the two markets move together. However, the overall result showed that there is a co-movement of maize price in the maize market in Western Nigeria since all the paired markets have correlation of above 0.60.The result of the growth rate as shown in Table 4.3 shows that it is Exponential. C. Co-integration analysis.\ C.1 Unit root test. Unit root tests were carried out to examine the stationarity of the variables in the model to be estimated. A series is said to be stationary if the means and variances remain constant over time. It is denotedI(0), denoting 'Integrated of order zero'. A series is non-stationary if the means and variances vary over time and the variances are infinite. In general, a variable is said to be integrated of order d , written I (d),if it must be differenced “d” times to be made stationary. This study employs the Augmented Dickey Fuller (ADF) unit root to test for stationarity. This involves running a regression; ∆Z=αZtj-1+ . t-test is performed to find out whether α is significantly different from zero. The price series is adjudged to be stationary if the absolute value of the ADF test statistic is greater than the absolute value of the critical value, significant at p (0.01,0.05 and 0.10). From our result shown in Table 4.4, since the value of the ADF test statistic is greater than the absolute value of the critical value at p(0.05 and 0.10) for the rural market we concluded that there is stationarity at 5% and 10% while there is none at 1% at level I(0). We then proceed to first differencing i.e.I(1). However, at first differencing I(ɸ), all the rural market price series were stationary at all levels of significant i.e. 1%, 5%, and 10% respectively( See table 4.5).

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Table 4.4: Results of the Stationarity Tests for the level of the Variables. Variable(Market price series) OYR-OYU

ADF Test Statistics at level I(0) -3.140103

1% test critical value for the ADF statistics=-3.486551. 5% test critical value for the ADF statistics=-2.886074. 10% test critical value for the ADF statistics=-2.579931. *Indicates that Null hypothesis is rejected at 95% and 90% level of significance. H0=Price series are not stationary. H1=Price series are stationary. The conclusion is that there is stationarity at 95% and 90% respectively. Source: Computed from Dickey Fuller regression result. Table 4.5: Results of the Stationarity Tests for the First Difference of the Variables. First differencing of the market price series OYR-OYU 1% 5% 10%

ADF Test Statistics at first difference of the price series level I(1) -8.557351 -3.486551 -2.886074 -2.579931

The result shows that there is stationarity at all levels. *Significant at all levels (1%, 5%, and 10%). Source: Computer Output 2008. In the urban market as shown in Table 4.6 at Integration of order 0 i.e. I(0), only at 10% is the market stationary that is absolute value of ADF statistics is greater than absolute critical value. However at first difference I(1), the urban market for maize was stationary at all levels of significance 1%,5% and 10% respectively. Having observed that the urban and rural markets for maize are stationary at the first difference, we then proceed to test for the market integration using co integration analysis and Granger causality test. D. Co-integration tests. The co-integration tests carried out was based on Eigen value and Trace statistic. The result from Table 4.8 showed that there was co-integration of the order (1,1) since the absolute ADF test statistic was greater than the absolute value of the critical values at 1%,5% and 10% level respectively. Also the Durbin-Watson statistics shows that there is positive first-order autocorrelation since the value of the coefficient lies between zero and 2(i.e. 0 0.792795 2. The result also shows that the R-squared was high with the value of 0.932214.This implies that 93% of the variation in the rural market prices of maize in the rural areas are accounted by the urban maize prices. The Unrestricted Co integration Rank Test result as shown in Table 4.9 shows that max-eigen value test indicates no integration at both 5% and 1% levels. E. Granger causality test. This test was carried out to identify which markets occupy position of price leadership and to measure the speed of price adjustment. The variables were lagged by 2 months. Results from Table 5.0 showed that when lagged by two month,bi-directional causality exists between rural and urban markets of Oyo State. This shows that any of the markets could take the lead in each market. This result also showed that there is perfect relationship between rural and markets for maize. V Summaries and conclusion This chapter presents the summary and conclusion of the findings from the study upon which recommendation is made for policy and further study on similar topic. A.

Summary The study was carried out to investigate how integrated the local maize market in south western part of Nigeria choosing Oyo State as the study area. Monthly retail price per kilogram of milled local maize between January 1998 and December 2007 was sourced from the Agricultural Development Programme of the State. Trend analysis was carried out by the use of descriptive statistics which include mean, coefficient of variation and graphical analysis. Employing the correlation analysis, co-integration analysis and the Granger causality test, market integration test based on econometric model was carried out. The descriptive analysis showed that urban market recorded highest average monthly prices of local maize in Oyo State than their rural counterparts. Graphical trend of the variables showed that price series of local maize is more stationary in the rural areas than the urban areas.

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Jonah I. John et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 11-21

The unit root tests proved that Oyo State s price series are stationary at first difference at all levels1%,5% and 10% for both markets. Correlation analysis of the variables showed that there is co-movement of prices within state. The cointegration results also showed that there is long run relationship between the paired markets (that is rural and urban markets) of the state. When lagged by 2months, the Granger causality tests revealed that bi-directional causality exists between the paired markets of the state. This also confirmed that there is perfect movement of prices between paired market of the state. B.

Conclusion Results of the findings showed that Oyo States rural and urban markets are integrated of order I( I(1).It implies that there is co-movement of prices between urban and rural markets of the state studied. C. 1.

2. 3.

Recommendation Government should invest hugely on research involving pricing policy and marketing which will help in understanding price behaviour both in the short and long-run respectively which will enhance agricultural development and food security. Government should help in the development and maintenance of infrastructures such as good roads, storage facilities, processing facilities etc which will reduce market inefficiencies. There is the need to intensify efforts on the area of data collection, information dissemination through mass media agents such as television and radio stations, newspapers and magazines to broadcast or publish price information about maize and other major staple food stuffs in different market locations at regular intervals. Policy makers need to develop new schemes that would make credit available to traders particularly those in the rural areas to enhance their ability to supply more maize and hence increase in the level of competition which will reduce perfect collusion or price co-operation. References.

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]

Abuja Securities and Commodity Exchange Plc(2006) Market-price- www.abujacomex.com2006. ,C.B.(1996): Market Analysis Methods: Are Our Enriched Toolkits Well-Suited to Enlivened Markets? American Journal of Agricultural Economics.78(3),825-829. Baulch,B.(1997): Testing Food Market Integration Revisted.Journal of Development Studies,33(4):512-534. Dahlgran,K.A.,and Blank,S.C.(1992).Evaluating the integration of Continuous Discontinous Market.In American Journal of Agricultural Economics.74(2);469-479. Jones,W.W.(1969).Marketing of Staple Food Crops in Tropical Africa.Overall Analysis and Report.Standford,California,Pp.60100. Olayemi, J. K(1972). Rice Marketing and Prices: A case Study of Kwara State. Nigeria Bulletin of Rural Economics and Society Vol.8 No.2 pp211-220. Anthonio,Q.B.O;(1968).The Marketing of Staple Food Stuffs in Nigeria: A Study in Printing Efficiency.Ph.D Dissertation,University of London.Pp.98-167. Dittoh.S.(1994).Market Integration:The Case of Dry Season Vegetables in Nigeria,Issues in African Rural Development.2:89-101. Delgado S. (1995).A Variance components approach to food grain market integration in Northern Nigeria. American Journal of Agricultural Economics 68:970-979. Ravallion M;(1986).Testing Market Integration, American Journal of Agricultural Economics 68:102-109. Golleti,F.and Babu,S.(1994): Market Liberalisation and Integration of Maize Markets in Malawi.Journal of Agricultural Economics,11:311-324. Alexander.C.and Wyeth,J.(1994): Cointegration and Market Integration: An Application to the Indonesian Rice Market.Journal of Farm Economics,Vol.49,No.3 pp721. Decron,S.(1995): On Market Integration and Liberalisation:Method and Application to Ethiopia: Journal of Development Studies,32(1):112-143. Gollete,F,Ahmed,R. and Farid,N.(1995).Structural Determinant of Maize Integration: The case of Rice Market in Bangladesh.Developing Economics,23(2),185-202. Okoh, R. N. (1999), An analysis of Oligopolistic Pricing and Market Integration of cassava Roots and Products in Delta and Edo State of Nigeria .Unpublished Ph.D. Thesis Department of Agricultural Economics, University of Ibadan. Tijani.A.A;J.O.Ajetomobi and O.Ajobo(1999): A Cointegration Analysis of Nigeria Cocoa Export Supply.Journal of Rural Economics Development.Vol.13 No1.45-53. Onu,J.I.(2000): An analysis of the structure and performance of cotton marketing in Northern N igeria.An Unpublished Ph.d Thesis Department of Agricultural Economics,University of Ibadan. Salami,O.A.(2000): Economic Analysis of cocoa bean and cocoa products Marketing under the regulated and free market regimes in Nigeria. A Ph.d Dissertation,Department of Agricultural Economics,University of Ibadan. Oludimu,O.(1982).The Marketing of Processed Cassava in Nigeria: An Insight.Nigerian Journal of Development Studies 2(1);93102. Engle,R.F. and Granger C.W.J.(1987): Cointegration and Error Correction, Representation,Estimation and Testing.Econometrica.55:251-276.

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Jonah I. John et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 11-21

Mnth JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC

1998 Urb 20.35 18.61 20.96 19.22 21.57 26.92 29.18 19.04 16.69 21.64 23.85 26.06

Rur 19.39 17.68 20.00 18.26 20.61 26.01 27.68 17.54 15.19 20.14 22.35 24.56

1999 Urb 26.22 30.14 27.34 31.96 34.84 29.37 29.14 30.30 30.12 34.70 29.00 32.97

Rur 22.25 26.17 23.37 27.99 30.87 33.34 33.11 34.27 34.09 30.73 32.97 37.85

2004 Rur 26.0 28.32 28.80 35.25 41.43 41.44 41.60 38.43 29.25 29.40 30.55 34.95

Variable TREND C R-squared Adjusted R-squared S.E.of regression Sum squared resid Log likelihood Durbin-Watson stat

2000 Urb 17.65 18.12 18.59 19.06 19.48 19.90 20.37 20.84 18.51 19.91 22.34 22.64

Urb 40.26 45.41 50.48 71.29 79.99 74.75 76.70 70.54 59.19 52.94 50.11 44.70

Rur 15.40 15.68 16.15 16.62 17.04 17.46 17.93 18.40 16.07 17.47 19.90 20.20

2001 Urb 23.39 26.11 32.21 34.69 42.65 44.68 40.27 30.75 30.40 29.32 30.21 32.10

2002 Urb 30.06 28.47 34.06 39.65 39.86 45.53 43.66 38.07 32.48 30.07 31.66 33.25

Rur 21.06 27.99 33.85 28.84 36.60 39.80 38.53 28.68 27.28 30.06 31.30 32.52

2005 2006 Rur Urb Rur Urb 38.04 45.65 39.35 32.37 43.20 45.90 37.97 32.21 48.59 45.12 40.32 31.48 60.15 44.39 40.35 34.37 71.90 46.02 41.97 37.88 68.26 49.64 42.75 43.19 62.69 43.86 36.68 33.33 60.65 38.09 31.26 34.01 48.98 34.37 27.75 34.01 44.55 31.95 25.73 38.57 42.82 30.03 23.54 37.22 36.82 31.23 25.36 37.22 Method: Least Squares. Date: 07/21/08 Time: 15:36. Sample: 1998: 01 2007:12. Included observations: 120.

Coefficient 0.004771 3.096631 0.256365 0.250063 0.283821 9.505406 -18.13476 0.266484

Std. Error 0.000748 0.052144 Mean dependent var S. D. dependent var. Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)

2003 Urb 29.69 29.71 29.09 30.93 30.50 35.68 29.06 26.78 21.32 21.48 22.71 23.77

Rur 27.86 27.76 27.02 28.46 29.78 32.07 28.21 24.51 22.30 20.42 20.01 22.34

2004 Urb 26.10 29.49 31.90 35.76 42.99 41.90 44.02 39.86 31.74 34.80 35.35 35.39

2007 Rur 27.68 27.68 27.72 30.56 34.88 37.19 31.11 26.91 26.91 31.00 31.00 31.09

t-Statistic 6.378079 59.38623

Dependent Variable: LOG (URBAN). Method: Least Squares. Date: 07/21/08 Time: 15:37. Sample: 1998:01 2007:12. Included observations: 120. Coefficient Std. Error t-Statistic 0.005856 0.000687 8.525482 3.115952 0.047887 65.06902 0.381175 Mean dependent var 0.375930 S.D. depentent var 0.260650 Akaike info criterion 8.016709 Schwarz criterion -7.914799 F-statistic 0.253405 Prob(F-statistic)

Stationarity TABLE 4.4. Null Hypothesis: RURAL has a unit root. Exogenous: Constant. Lag Lenght:1(Automatic based on SIC,MAXLAG=12) t-Statistic Augmented Dickey-Fuller test statistic -3.140103 Test critical values: 1% level -3.486551 5% level -2.886074 10% level -2.579931 MacKinnon(1996) one-sided p-values.

Rur 31.82 33.41 35.00 36.52 38.07 43.66 45.25 39.66 34.07 28.48 26.89 25.3

Prob. 0.0000 0.0000 3.385249 0.327742 0.335579 0.382037 40.67989 0.000000

Prob. 0.0000 0.0000 3.470247 0.329944 0.165247 0.211705 72.68385 0.000000

Prob.* 0.0263.

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Jonah I. John et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 11-21 TABLE 4.5. Null Hypothesis: D(RURAL) has a unit root. Exogenous: Constant. Lag Lenght: 0(Automatic based on SIC, MAXLAG=12). Augmented Dickey-Fuller test statistic t-Statistic *Prob. -8.557351 0.0000 Test critical values: 1% level -3.486551 5% level -2.886074 10% level -2,579931 *MacKinnon(1996) 0ne-sided p-values. Table 4.6. Null Hypothesis: URBAN has a unit root. Exogenous: Constant. Lag Length: 1(Automatic based on SIC, MAXLAG=12). Augmented Dickey-Fuller test statistic t-Statistic Prob* -8.652448 0.0000 Test critical values: 1% level -3.486551. 5% level -2.886074. 10% level -2.579931. MacKinnon (1996) one-sided p-values. Stationary at 1st difference. Co-integration test TABLE 4.8. Dependent Variable: RURAL. Method: Least Squares. Date:07/21/08 Time: 15:41. Sample: 1998:01 2007:12. Included observations: 120. Variable Coefficient Std. Error t-Statistic Prob. URBAN C 0.842632 0.020917 40.28368 0.0000 2.510594 0.755453 3.323295 0.0012 R-squared 0.932214 Mean dependent var. 31.15792 Adjusted R-squared 0.931640 S. D. dependent var. 10.68100 S.E.of regression 2.792635 Akaike info criterion. 4.908374 Sum squared resid. 920.2593 Schwarz criterion. 4.954832 Log likelihood. -292.5024 F-statistic 1622.775 Durbin-Watson stat 0.792795 Prob (F-statistic) 0.000000

Null Hypothesis: RESIDUAL has a unit root. Exogenous: Constant. Lag Lenght: 0(Automatic based on SIC, MAXLAG=12) t-Statistic Prob* Augmented Dickey-Fuller test statistic -5.334914 0.0000 Test critical values: 1% level -3.486064 5% level -2.885863 10% level -2.579818 MacKinnon (1996) one-sided p-values. Date: 07/21/08 Time: 15:40. Sample (adjusted): 1998:06 2007:12 Included observations: 115 after adjusting endpoints. Trend assumption: Linear deterministic trend. Series: RURAL URBAN. Lags interval (in first differences):1 to 4. Unrestricted Co-integration Rank Test TABLE 4.9. Hypothesized No. of CE(s) Eigenvalue Trace statistic 5 Percent Critical Value 1 Percent Critical Value. None* 0.110415 18.57290 15.41 20.04 At most 1* 0.043528 5.117903 3.76 6.65 *(**) denotes rejection of the hypothesis at the 5%(1%) level. Trace test indicates 2 co-integration equation(s) at the 5% level. Trace test indicates no integration at the 1% level. Hypothesized No. Of CE(s) Eigenvalue Max-Eigen Statistic 5 Percent Critical Value None at most 1* 0.110415 13.45500 14.07 0.043525 5.117903 3.76

1 Percent Critical Value 18.63 6.65

*(**) denotes rejection of the hypothesis at the 5%(1%) level Max-eigenvalue test indicates no integration at both 5% and 1% levels. Granger causalty TABLE 5.0 Pairwise Granger Causality Tests. Date:07/21/08 Time: 15:44 Sample: 1998:01 2007:12 Lags:2

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Jonah I. John et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 11-21 Null Hypothesis URBAN does not Granger Cause RURAL RURAL does not Granger cause URBAN

Obs 118

F-statistic 4.54002

Probability 0.01269

0.42157

0.65704

TABLE 4.2 Correlations Urban Rural Urban Pearson Corr. 1 .966(**) Sig.(2-tailed) .000 N 120 120 Rural Pearson Corr. Sig.(2-tailed) .966(**) 1 .000 N 120 120

**Correlation is significant at the 0.01 level(2-tailed). Correlations Urban Rural Spearman's Rho Urban Correlation coeff. 1.000 .914(**) Sig.(2-tailed) .000 N 120 120 Rural Correlation Coeff .914(**) 1.000 Sig.(2-tailed) .000 N 120 120 **Correlation is significant at the 0.01 level(2-tailed)

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net The Determinant Factor of Dividend Policy at Non Finance Listed Companies Farah Margaretha Leon1 & Pradana Maulana Putra Faculty of Economics, Trisakti University, Jakarta – Indonesia Abstract: This paper aims to investigate the determinant factor of dividend policy. Sample that use in this paper are non-finance company which listed in Indonesian Stock Exchange on 2006-2009 period. This paper use profitability, cash flow, sales growth, tax, debt equity ratio, dan market to book ratio as independent variable. The variables dependent of this study is dividend payout ratio, and it is measured by standard dividend payout ratio and adjusted dividend payout ratio. The purposive sampling was used to determine which acceptable company to support this paper. Method that used is ordinary least square regression. According to value of significance of the regression, profitability is the factor that affect dividend payout ratio on all measurement, then sales growth is the factor that affect dividend payout ratio only measured by adjusted dividend. The other variables are found that they are not the determinant factors of dividend payment. This result indicate that management should pay attention to profitability and sales growth when makes dividend policy. Keywords: adjusted dividend payout ratio, cash flow, debt equity ratio, market to book ratio, profitability, sales growth, standard dividend payout ratio, tax.

I. Introduction Dividend policy is really important for investors and company. Dividend payment is one of the investors’s purpose when investing their money for the stock. Firm uses dividend to attract investor and to maximize shareholder’s wealth. Dividend payout has been a subject of debate in financial literature. Many researches revealed factors that company should consider while make dividend policy. In this study, dividend policy is policy that manager made for determine amount of distributable earning for shareholder. Modgliani and Miller (1958, 1961) conclude that in perfect market, firm’s value is not affected by payment of dividend. Based on their researches, firm’s value is determined by ability of firm to obtain earning. Their researches assume no taxes, no floatation cost, and investor has same information as manager has. But at recent, many researches find contradictive evidence and conclude that dividend policy could affect firm’s value. Firm’s earning can be invested into operating assets, to acquire securities, to retire debt, or distributed to shareholder. There are many reasons firm should pay or no pay dividend. For example, dividend is important for investor, because it provide information of company’s income. Dividend can be used to manage stock price, but when company retained its earning, that money can be reinvested. Since as many researches find that firm’s value can be affected by dividend policy, many factors came up to be considered while make dividend policy. Research studied by Gill et al.(2010) used standard dividend payout ratio and adjusted dividend payout ratio as proxy of dividend payout. Different between two is depreciation is used on formula of adjusted dividend payout ratio. Based from the background, the purpose of this study is to analysis the impact of profitability cash flow, corporate tax, sales growth, market to book ratio and debt equity ratio with dividend payout ratio. II. Literature and Hypothesis Profitabilty has always become mayor factor when determines dividend policy (Gill et al., 2010). Amidu and Abor (2006) found that firm with high profitability tend to pay high dividend. Then profitability has positive influence to dividend payout ratio. Same result founded by Pruit and Gitman (1991), that profitability affect dividen positively. They conclude firm’s earning at current year and last year can affect dividend payment. Al-

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Farah Margaretha Leon et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 22-26

Nazzar also found positive relation between profitability and dividend payout ratio, higher profitability makes higher dividend payout ratio. Good cash flow position means good liquidity of firm. Alli et al.(1993) found positive relation between cash flow and dividend payout ratio. According to their study, cash flow is better than profitability to describe dividend payout ratio . The reason is because profitabilty is affected by accounting practices. Amidu and Abor (2006) found cash flow has positive relation to dividend payout ratio. They conclude firm will raise it dividend when has a good liquidity position, also firm with stable cash flow tend to pay higher dividend. Same result also founded by Anil and Kapoor (2008), that cash flow affected dividend payout ratio positively and cash flow is an important factor to determine dividend. Modigliani and Miller (1961) argues that high tax liabilities will raise payment of dividend. They found positive relation between tax and dividend payout ratio. Their study assumes high tax liabilities are effect of high profitability. While high profitability has positive relation to dividend payout ratio. Amidu and Abor (2006) found positive relation between tax and dividend payout ratio. This result also found by Gill et al.(2010), that tax affected dividend payout ratio positively. Sales growth can affect dividend payout ratio. Firm with high growth will retained their earning to reinvest it rather than to ditribute as dividend. Then high growth means high needs of funding or money, so it can reduce the payment of dividend (Myers, 1984). Amidu and Abor (2006) found negative relation between sales growth and dividend payout ratio. Same result also founded by Gill et al. (2010), which conclude sales growth affect dividend payout ratio negatively. Good market assestment means firm has better future growth. Firm that has good growth will reduce their payment of dividend, because its need of fund (D’Souza and Saxon, 1999). They found negative relation between market to book ratio and dividend payout ratio. Amidu and Abor (2006) also found negative relation between market to book ratio and dividend payout ratio. They conclude when firm has high growth, it will retained more earning thus reduce their dividend. According to Gill et al.(2010) debt to equity ratio can be refer as gearing or leverage or risk. Pruit and Gitman (1991) conclude that debt can affect dividend payout ratio. Firm with higher debt will reduce its dividend paymetnt. This can happen because firm tend to pay its liabilities (debt) than to pay dividend, thus there is a negative relation between debt equity ratio and dividend payout ratio. D’Souza and Saxon (1999) found leverage affect dividend payout ratio negatively. Same result also founded by Al-Nazzar (2009), firm with higher debt tend to reduce their dividend. III. Hypothesis Based from the literature review the hypothesis at this study are: H1 : Profitability has influence relation to dividend payout ratio H2 : Cash flow has positive influence between cash flow and dividend payout ratio H3 : Tax has positive influence to dividend payout ratio H4 : Sales growth has negative influence to dividend payout ratio H5 : Market to book ratio has negative influence to dividend payout ratio H6 : Debt to equity ratio has negative influence to dividend payout ratio IV. Data and Methodology Samples in this study are non finance company which listed in INDONESIAN STOCK EXCHANGE, from 2006 to 2009. The sample should have complete financial report which require in this study. Based from this criteria there are twenty six companies as a sample. This study is using ordinary least square regression as data analysis method. STANDARD PAYOUTi = b0 + b1 PROFi + b2CASHi + b3TAXi + b4GROWi + b5MTBVi + b6D/Ei + μi,t ADJUSTED PAYOUTi = b0 + b1 PROFi + b2CASHi + b3TAXi + b4GROWi + b5MTBVi + b6D/Ei + μi,t STANDARD PAYOUT = standard deviation of dividend payout ratio ADJSUTED PAYOUT = adjusted dividend payout ratio PROF = profitability CASH = cash flow TAX = tax rate GROW = sales growth MTBV = market to book ratio D/E = debt to equity ratio μ = error

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Farah Margaretha Leon et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 22-26

V. Result and Discussion Companies in Indonesia pay their dividend on second quartile of the year. This is occurred because the time lag from making annual reports of the company. The annual reports must meet all the terms, conditions, and other administration requirements from IFRS. That is why the annual reports is done a year after the current fiscal year. Meanwhile, dividend policy is based on the annual reports of the firms. Therefore the dividend policy must be postponed by the making of annual reports. The detail data can be seen at table 2. Profitability has positive influence to dividend payout ratio-which measured by standard dividend payout ratio and adjusted dividend payout ratio. This result also found by Gill et al. (2010), Pruit and Gitman (1991), and AlNazzar (2009). Higher profitability may make firm has more money. More money makes firm able to pay dividend and also to retain their earning. Therefore higher profitability tend firm pay more dividend. There is no influence of cash flow to dividend payout ratio. This result is contradict with Amidu and Abor’s (2006). They found a positive influence for cash flow to dividend payout ratio. However, this result is similar with Gill et al. (2010). No influence of cash flow might happen because the firm do not hang dividend policy in cash flow, but in profitability. This research shows there is no influence from tax to dividend payout ratio (table 3). The result is contradict with Amidu and Abor (2006) and Gill et al. (2010). The different between tax policy in Indonesia with another country may be the reason behind this. Sales growth has no influence to standard dividend payout ratio (table 3). This is contrast with Amidu and Abor (2006) and Gill et al. (2010). However sales growth has negative influence to adjusted dividend payout ratio (table 4), which is in line with result from Gill et al. (2010). Gill et al. (2010) said that this might happen because different measurement used by standard dividend payout ratio and adjusted dividend payout ratio, which is use of depreciation. Adjusted dividend payout ratio does not use depreciation when calculate net income, but standard dividend payout ratio use it. Sales growth has no influence to dividend is supported by dividend residual theory. The theory says firm will pay its dividend until all earning has been funded all acceptable investment. So as long as company still needs earning, dividend may not be paid. Based on this research there is no influence from market to book ratio to standard dividend payout ratio which contradict to result from Amidu and Abor (2006) but same with Gill et al. (2010). Moreover, market to book ratio also has no influence to adjusted dividend payout ratio (table 4) which contrast with result from Amidu and Abor (2006) but same as D’Souza and Saxena (1999). These results might happen because there is no relation between share price to dividend, as described in dividend irrelevance. Meanwhile measurement of market to book ratio uses share price. Debt to equity ratio has no influence to dividend payout ratio in this research. This result similar to Gill et al. (2010) and Afza and Amirza (2010), but contrast to Al-Nazzar (2009). The reason is may because firm does not use debt to pay their dividend and to settle their dividend policy. This research shows that profitability is used to pay and settle dividend of the firm instead of debt. VI. Conclusion Purpose of this study is to find any influence between dividend payout ratio with profitability, cash flow, tax, sales growth, market to book ratio, and debt to equity ratio in non-financial firms which listed in Indonesian Stock Exchange year 2006-2009. Based on results and analysis, profitability has positive influence to dividend payout ratio. Sales growth has negative influence to dividend payout ratio which measured by adjusted dividend payout ratio. Variables cash flow, tax, market to book ratio, and debt equity ratio has no influence to dividend payout ratio. Based on this research, firms can use profitability and sales growth when make dividend policy. Higher profitability makes firm has more money, so firm can pay more dividend. Higher sales growth makes firm needs more money, so it can lower payment of dividend. Profitability and sales growth can be used for investor, in their effort for dividend purposes. VII. References 1. 2. 3. 4.

Afza, Talat and Mirza, Hammad Hassan. (2010). Ownership Strucuture and Cash Flow as Determinant of Dividend Policy in Pakistan, International Business Research, 3(3) :210-223. Al-Najjar, Basil. (2009). Dividend Behaviour and Smothing New Evidence from Jordanian Panel Data, Studies in Economics and Finance, 26(3): 182-197. Alli, Kasim L.; Khan, A.Qayyum and Ramirez, Gabriel G. (1993). Determinants of Corporate Dividend Policy: a Factorial Analysis, The Financial Review, 28(4): 523-547. Aivazian, Varouj; Booth, Lawrence. and Cleary, Sean. (2003). Do emerging market firms follow different dividend policies from US firms?, Journal of Financial Research, 26(3): 371-387.

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5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

Amidu,Mohammed and Abor,Joshua. (2006). Determinants of Dividen Payout Ratios in Ghana, The Journal of Risk Finance, 7(2): 136-145. Anil,K. and Kapoor, S. (2008). Determinants of Dividend Payout Ratios-A Study of Indian Information Technology Sector, International Research Journal of Finance and Economics, 15: 64-71. Baker,H.Kent.; Farrelly,Gail E. and Edelman,Richard,B.(1986). A Survey of Management Views on Dividend Policy, Financial Mangement,14(3): 78-84. Brigham, Eugene F. and Erhardt, Michael C. (2005). Financial Management: Theory and Practice(11th ed.), USA, Thomson: Southwestern D’Souza, Juliet and Saxena,Atul T. (1999). Agency cost, market risk, investment opportunities and dividend policy: an International Perspective, Manajerial Finance, 25(6): 35-43. Fairfield, Patricia M. and Harris, Trevor S. (1993). Price-earnings and price-to-book anomalies: Tests of an intrinsic value explanation, Contemporary Accounting Research, 9(2) : 590-610. Gill, Amarjit; Biger, Nahum. and Tiberwala, Rajendra. (2010). Determinant of Dividend Payout Ratios : Evidence from United States, The Open Business Journal,3: 8-14. Gitman, Lawrence J. (2009), Principle of Managerial Finance, (11 th edition). Pearson Education .Inc. Jensen, Michael C. and Meckling, William. (1976). Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure, Journal of Financial Economics, 3: 305–360. Masulis, Ronald W. and Trueman,Brett. (1988). Corporate Investment and Dividend Decisions Under Differential Personal Taxation, Journal of Financial and Quantitative Analysis, 23(4): 369-384 Miller, Merton H. and Rock, Kevin. (1985). Dividend Policy under Asymmetric Information, The Journal of Finance, 40(4): 10311051. Modigliani, Franco. and Miller, Merton H. (1958). The Cost of Capital, Corporate Finance, and The Theory of Investment, The American Economic Review, 48(3): 261-280. Modigliani, Franco. and Miller, Merton H. 1961). Dividend Policy, Growth, and the Valuation of Shares. The Journal of Business, 34(4): 411-433. Myers, Stewart C.(1984). The Capital Structure Puzzle, The Journal of Finance. 39(3): 575-592. Myers, Stewart C. and Majluf, Nicholas S.(1984). Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have, Journal of Financial Economics, 13: 187-221. Pruitt, Stephen W. and Gitman, Lawrence J.(1991). The Interactions Between the Investment, Financing, and Dividend Decisions of Major U.S. Firms, 26(3): 409-430. Shahjahanpour,A. ; Ghalambor,H. and Aflatooni,A.(2010). The Determinant of Capital Structure Choice in the Iranian Companies, International Research Journal of Finance and Economics,56: 167-175. Zeng, Tao. (2003). What Determines Dividend Policy : a Comperhensive test, Journal of American Academy of Business, 2 (2): 304310.

Table 1: Variable and Measurement Variable

Measurement

Dependent 1. Standard deviation of dividend payout ratio

S= xi = payout ratio = mean of payout ratio

Adjusted dividend payout ratio

Adj DPR =

Independent 1. Profitability

Profitability =

2. 3.

Cash flow (CF) Tax

Cash flow = Log cash flow from operation activities

Sales growth

Sales growth =

5. 6.

Market to book ratio Debt to equity ratio

Taken from summary report of Indonesian Stock Exchange Taken from summary report of Indonesian Stock Exchange

Tax rate =

Table 2 : Statistic Descriptive Variables

Minimum Maximum Mean Standard Deviation

Standard deviation of dividend payout ratio

0,01

1,57

0,4650

0,30759

Adjusted dividend payout ratio

0,01

1,05

0,3240

0,24541

Profitability

0,02

0,56

0,1928

0,13006

Cash Flow

3,37

7,02

5,4966

0,80213

Tax

-1,32

3,53

0,3127

0,40888

Sales Growth

-0,201

1,133

0,0505

0,1686

Debt equity ratio

0,10

8,44

0,9177

1,13243

Market to book ratio

0,22

22,79

3,4232

4,22817

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Farah Margaretha Leon et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 22-26

Table 3 : Regression of Standard Dividend Payout Ratio Independent Variables Profitability Cash Flow Tax Sales growth Debt to equity ratio Market to book ratio R square = 0,187

Coeffecient of Regression 0,414 -0,102 -0,121 -0,167 0,112 0,081 F-value = 3,727

Significance 0,015 0,358 0,218 0,101 0,429 0,601 sig = 0,002

Table 4 : Regression of Adjusted Dividend Payout Ratio Variable Independence

Coeffecient of Regression

Significance

Profitability

0,687

0,000

Cash Flow

-0,077

0,392

Tax

-0,121

0,131

Sales growth

-0,234

0,005

Debt to equity ratio

-0,008

0,924

Market to book ratio R square = 0.465

0,098 F-value

= 14.050

0,433 sig = 0.000

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Does Long Memory Matter in Oil Price Volatility Forecasting? Majid Delavari Assistant Professor of Department of Economics Science and Research Branch, Islamic Azad University, khouzestan-Iran. Nadiya Gandali Alikhani MA in Economics, Department of Economics Science and Research Branch, Islamic Azad University Khuzestan, Iran. Esmaeil Naderi Corresponding Author, MA in Economics, Faculty of Economic, University of Tehran, Tehran, Iran. _____________________________________________________________________________________ Abstract: This study attempts to introduce an appropriate model for modeling and forecasting Iran’s crude oil price volatility. Specifically, we will test whether long memory matters in forecasting the price of this commodity. For this purpose, using Iran’s weekly crude oil price data, the long memory feature will be considered in both return and volatilities series, and furthermore, the fractal markets hypothesis will be examined with respect to Iran’s oil market. In addition, the best model for forecasting oil price volatilities will be selected from the different conditional heteroscedasticity models based on the forecasting error criterion. The main hypothesis of this study was tested using the Clark-West test (2006). The results of our study confirmed the existence of a long memory feature in both the mean and variance equations of these series. However, among the conditional heteroscedasticity models, the ARFIMA-FIGARCH model was selected as the best model based on the Akaike and Schwarz information criteria (for modeling) and the MSE criterion (for forecasting). Lastly, the Clark-West test showed that the long memory feature is important in forecasting oil price volatilities. JEL Classification: E37, C58, C12, Q47. Key Words: Oil Price, Volatility, Long Memory, FIGARCH, Clark-West. _________________________________________________________________________________________ I. Introduction The oil market is one of the world’s most important financial markets, and it affects the structure of the economy of oil exporting and importing countries, the process of managing the financial risk of the portfolios of companies, and overall investment in the manufacturing sectors (Wei et al. 2011). Recent studies on the worldwide oil price (Mostafaei, Sakhabakhsh 2011; Wang et al. 2011; Prado 2011; Zhou, Kang 2011; Wei et al. 2010; Choi, Hammoudeh 2009; Ayadi et al. 2009; Cheong 2009) are indicative of the high importance and the special position of this market in the world economy; the reason may be the high sensitivity of the oil price to political, economic and cultural issues in the world, and consequently, the oil price’s volatility and the considerable influence of this volatility on macroeconomic variables (Kang et al. 2011). Due to the influential role of the oil price in the world economy, consumers, producers, governments, and macroeconomic decision makers have always paid special attention to this commodity in modern times (Wang et al. 2011). Oil exporting countries pay more attention to the oil price and the evolutions in the oil market than the prices and evolutions in other markets because of the special position of petroleum products in their economy. Indeed, the importance of this issue is twofold for Iran, which is one of the principal oil exporting countries, because a high proportion of its GDP is from oil income; for this reason, oil shocks have an influential role in Iran’s GDP movements (Mehrara, Mohaghegh 2011). In Iran, oil constitutes 90 percent of the export value, and crude oil and gas exports constitute approximately 60 percent of the government's income (Farzanegan, Mrakwart 2011). This fact makes price movements of oil an important factor that may potentially cause significant, durable macroeconomic consequences (Mehrara, Oskoui 2007). After reviewing the history of oil exporting economies, one realizes that several economic (whether positive or negative) shocks in these countries have been due to oil price variations (Komijani et al. 2013). Therefore, examining the volatilities of the oil price and forecasting its changes are very vital and significant for Iran. Furthermore, due to the high importance of forecasting economic variables, different models have been proposed for modeling the relationship between the variables and forecasting them. These models can be divided in different ways as either time series and structural models or linear and non-linear models. The growing importance of forecasting economic factors and the small number of structural models in forecasting has led to the emergence of time series (including linear and nonlinear) models for modeling and forecasting. However, one of the basic points that has been ignored in econometric analyses, which affects the accuracy of forecasts, is the behavior and the type of time series data; this issue is vital because in some cases, a dynamic nonlinear process is estimated using a linear model. Therefore, the forecasts made by linear models that are

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used to explain nonlinear processes have doubtful validity. Recently, many economists have used nonlinear tests and methods to forecast the process of movements and the volatilities of the variables to eliminate these problems and increase the accuracy of the models for forecasting the variables. One of the models used for explaining the behavior of the mean equation is the Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model, which was first introduced by Granger and Joyeoux (1980) in econometrics; another such model is the FIGARCH model (Baillie 1996), which is used in forecasting the economic variables’ volatilities (Zhou, Kang 2011). Overall, it is well known that the prices in the world’s financial markets are dynamic and highly volatile. For this reason, in the literature on the econometrics of these markets is primarily modeled and forecasted using GARCH-type models. This model has solved the problem of volatility clustering and fat tails in the time series; it also pays special attention to the factors that highly influence the assets, such as sudden shocks and structural movements (Vo 2011). The oil market is one of the financial markets that has consistently (especially during recent years) experienced high volatilities such that forecasting its price is hardly possible. This market frequently undergoes sudden structural movements that lead to economic and political shocks. Due to the special position of oil in the world market, even a small decrease in the price of this commodity will lead to an increase in the volatility of financial markets (Erbil 2011). Therefore, due to the high volatility of the oil price (which constitutes one of the world’s financial markets), this price can be modeled and forecasted using different GARCH-type models (Kang et al. 2011). Thus, we examine whether the crude oil price has the long memory property. On this basis, the main purpose of this study is to compare the performance of models based on long memory and short memory in modeling and forecasting the volatilities of Iran’s crude oil price. That is, we attempt to examine these hypotheses: first, whether the volatilities of the oil price have the long memory feature; second, whether the model based on long memory (the FIGARCH model) has better performance in forecasting the volatilities of the oil price compared to a short-memory model (GARCH); finally, whether long memory affects oil price forecasting. For these purposes, the GARCH and FIGARCH models (with both the ARIMA and ARFIMA models in the mean equations) are used to explain the existing volatilities in Iran’s crude oil price. II. Methodology After many important studies were conducted on the existence of Unit Roots and Cointegration in time series starting in 1980, econometrics experts examined other types and subtypes of non-stationary and approximate persistence that explain the processes that exist in many financial and economic time series. Today, different studies have been and are being conducted on these processes, including "Fractional Brownian Motion," "Fractional Integrated Processes," and "processes with long memory" (Lento 2009). Hurst (1951) first discovered processes with long memory in the field of hydrology. Then, in the early 1980s econometricians such as Granger and Joyex (1980) and Hosking (1981) developed econometric models with long memory and specified the statistical properties of these models. During the last three decades, numerous theoretical and empirical studies have been conducted in this area. For example, the studies (Mandelbrot 1999; Lee et al. 2006; Kang et al. 2009; Aloui, Mabrouk 2010; Tonn et al. 2010; Belkhouja, Boutahary 2011; Wei 2012; Li, Fei, 2013; Kang, Yoon 2013) are among the most influential in this regard. The concept of long memory includes a strong dependency between outlier observations in time series, which means that if a shock hits the market, the effect of this shock remains in the memory of the market and influences market activists’ decisions; however, this effect will ultimately disappear after several periods of time. Thus, by considering the nature and the structure of financial markets that are easily and rapidly influenced by different shocks (economic, financial and political), such as the oil market, it is possible to analyze the effects of these shocks and determine the time of their disappearance by observing the behavior of these markets (Los, Yalamova 2004). In addition, long memory can be used to show the memory of a market. By examining this long memory, the ground will be prepared for the improvement of financial data modeling. A. Different Types of ARCH Models Auto-Regressive Conditional Heteroscedasticity (ARCH) models were first proposed by Engel (1982) and were later expanded by Borlerslev (1986); these models include the types of models that are used to explain the volatilities of a time series. Subsequently, different types of ARCH models were introduced, and they are divided into two groups: linear (IGARCH and GARCH) and nonlinear models (EGARCH, TGARCH, PGARCH, FIGARCH, etc.). A.1. Linear GARCH Models Borlerslev (1986) first introduced the generalized model of ARCH, i.e., the GARCH model based on Engel’s ARCH model. The distinguishing factor between these two models is the existence of variance lags in the conditional variance equation. In fact, the GARCH model has a similar structure to ARMA. Stipulated forms of this model include the following:

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M t  t   t

(1)

 t  zt ht , zt ~ N (0,1) ht     t21  ht 1 ht   t2 Equation (1) is a mean equation that includes two sections: explaining the mean equation, and

t ,

(2)

 t , which should be an appropriate structure for

which is indicative of residuals in the above model, which has

heteroscedasticity and consists of two normal elements ( zt and the conditional standard deviation ( ht )). In fact, ht is a conditional variance equation that is estimated along with the mean equation to eliminate the

 t . In equation (2),  is the average of the values of  t2 , the  t21 coefficient indicates the effects of ARCH and the ht 1 coefficient represents the effects of GARCH. One of the problems related to the heteroscedasticity

most important features of this model is the existence of temporary shocks imposed on the time series under investigation (Kittiakarasakun, Tse 2011). Furthermore, the results of Engel and Borlerslev’s (1986) studies show that in some cases, the GARCH equation that is mentioned above has a unit root. The existence of this root means that, for example, in GARCH(1,1) the value of 1  1 is very close to one. In this case, the GARCH model is cointegrated, and the result is referred to as IGARCH. In these models, if there is a shock to the time series under investigation, this shock will have lasting effects and become noticeable in the long term (Poon and Granger, 2003). A.2. Nonlinear GARCH Models or the FIGARCH Model The FIGARCH model was first proposed by Baillie (1996). In this model, a variable has been defined as fraction differencing, which is between zero and one. A general form of the FIGARCH(p,d,q) model is as follows: (3) (1  L) d ( L) t2    B( L)t In equation (3), (L) is a function of the appropriate lag (q), B(L) is a function of the appropriate lag (p), L is the lag operator, and d represents the fraction differencing parameter. If d=0, the FIGARCH model will turn into GARCH, and if d=1, this model will turn into IGARCH. It should be noted that in these models, the effects of the shocks are neither lasting as in IGARCH models nor temporary as in GARCH models. Instead, the shocks’ effects are between these two extremes, and thus, these effects will decrease at a hyperbolic rate. B. Criteria for Comparing Forecasting Performance After estimating the model that is intended to evaluate the performance of competing models, the models’ forecasting ability should be examined. Overall, the MSE and RMSE criteria are among the most frequently used criteria for comparing the forecasting accuracy of the models, but there are other criteria for estimating the accuracy of predictions. In this study, we used the MSE criterion to compare the forecasting accuracy of the models because this criterion has important features, e.g., it takes into account the outlying data in comparing the forecasting accuracy of the models. In addition, this criterion has a higher accuracy than RMSE, indicating lower error differences (Swanson et al. 2011).

MSE 

 ( yˆ

 yt ) 2



SSR n

(4)

Basically, after modeling, estimating, and forecasting time series data, there is a question about to what extent the resulting forecasts are appropriate and reliable. Usually, some models can be found that have high-quality fitting onto the sample data such that forecasting is possible using all of them; it should not be simplistically assumed that any model that has a better fitting onto the data will yield a better forecast. Many researchers use Mean Square Prediction Error (MSPE) as the criterion for selecting the best model. Using this method is dependent upon the fulfillment of two assumptions: the forecasting errors are either normally distributed or have zero mean and these errors do not have any correlation to each other. Two criticisms have been raised against these assumptions: firstly, although it is usually assumed that forecasting errors are normally distributed, these normally distributed errors do not necessarily have zero mean. The second criticism is that the probability that there is a high correlation between the forecasting errors from two competing models is very high, and this probability is especially high when forecasts have been made using multi-period-ahead forecasting. To eliminate these problems, tests such as the Granger and Newbold Test and the Diebold and Mariano Test can be used; however, each test has its own unique shortcoming. To compare the Mean Square Prediction Error in different models, the formula introduced by Clark and West (2006) was used in this study. The formula of this

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



test is Z i  e1i  e2i   f1i  f 2i  , where 2

first model and

f1i represents the forecasted values that were obtained using the

f 2i are the values that were obtained from forecasts made by the second model. In addition,

e1i are the forecasting errors that resulted from applying the first model and e2 i are the forecasting errors of the second model. III. Empirical Results A. Descriptive Statistics In this study, the data are from the period between the first week in 2000 and the last week in 2012 and they include 676 observations, of which approximately 90% were used for the estimation of models and the rest (60 observations) were used for out-of-sample forecasting. Table 1 reports the main descriptive statistics for the series of the natural logarithm of the oil price (LOIL) as well as the oil-price-return series (DLOIL). Table 1: Descriptive Statistics Stat.

Return Of Oil Prices Series

Tests

Return Of Oil Prices Series

Observations

676

ADF

-47.481(0.000)

Mean

0.000653

-47.719(0.000)

S.D

0.021420

ERS

0.0345(3. 26)

Skewness

-0.291589

Box- Ljung Q(10)

23.107(0.010)

Kurtosis

6.186527

McLeod-Li Q2(10)

477.64(0.000)

Jarque- Bra

1109.814(0.000)

ARCH (10)=F(10,666)

25.312(0.000)

* All of the numbers in parenthesis are probabilities of the related test, except the ERS test, where these numbers indicate the critical value of the test. Source: The Findings of the Study As Table 1 shows, the return series of the crude oil price has the mean of 0.000653 and the standard deviation of 0.0214 in the sample period, suggesting that it has been highly volatile. In addition, Jarque-Bera and kurtosis statistics show that the series is not normally distributed and has wide tails. Based on the Ljung-Box statistics (10 lags), the null hypothesis of “No serial correlation” is rejected. Similarly, the McLeod-Lee statistics reject the null hypothesis of “No serial correlation in the squared series” and confirm heteroscedasticity in the return series, suggesting that there is a nonlinear relationship in the squared series. This conclusion is also approved by the ARCH test. Lastly, according to the unit root tests known as the ADF 1 and PP2 tests, the return series is stationary; however, the ERS3 unit root test indicates that this series is non-stationary. These conditions might have been caused by the long memory feature in this series. For this reason, tests that check for the existence of this feature will be utilized in the next part. B. The Predictability of the Oil Price i. The Variance Ratio Based on Lo and MacKinlay (1988), the variance ratio test investigates the Martingale hypothesis. As shown in Table 2, the martingale hypothesis in the return series and its lag series is strongly rejected. Hence, it can be concluded that the generating process of the data is not random walk, i.e., the series is predictable. Thus, this series can be modeled and forecasted by different models. Table 2: The Variance Ratio Test Value d.f Prob. Criterion 14.74 675 0.000 Variance ratio test Source: The Findings of the Study The most salient point about this test is that it cannot determine the linearity or nonlinearity of the behavior of the time series under investigation. However, this determination can be made using other tests (e.g., the BDS test). ii. The BDS Test The BDS test was developed by Brock, Dechert and Scheinkman (1987). The main concept behind the BDS test is the correlation integral, which is a measure of the frequency with which temporal patterns are repeated in the data. The BDS test makes it possible to distinguish between a nonlinear process and a chaotic process. The result of the BDS test is presented in Table 3, and this result indicates that the null hypothesis that “the residual series is not random” is rejected. This conclusion implies the existence of a nonlinear (and possibly a chaotic) process in the data. It should be noted that when the BDS test in two (or more) dimensions rejects the hypothesis that the series is random, the existence of a nonlinear process is quite probable. This result leads us 1

ehT Augmented Dicky-fuller Test ehTPhillips-Perron Test 3 ehTElliott-Rothenberg-Stock Test 2

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to the conclusion that the BDS test also implies that the data-generating process in this study is nonlinear. Therefore, the validity of applying the conditional heteroscedasticity models as a set of nonlinear models is confirmed; this process was also confirmed by the McLeod-Lee, ARCH and BDS tests. Table 3: The BDS Test Dimension 2 3 4 5

BDS Stat. 0.011549 0.028762 0.038347 0.041352

Std. Error 0.001635 0.002591 0.003077 0.003198

z-Stat. 7.667829 10.53126 12.32419 13.69544

Prob. 0.0000 0.0000 0.0000 0.0000

Source: The Findings of the Study C. Quantitative Analysis of the Long Memory Process Estimating the long memory parameter (d) is the critical part of modeling the long memory property; ACF and GPH are two commonly used methods for this purpose. Graph 1 depicts the ACF of the logarithm of the time series of the crude oil price. Fig. 1: The ACF of the LOIL

ACF

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

1.2 1 0.8 0.6 0.4 0.2 0 Lags

Source: The Findings of the Study As clearly shown, the graph follows an exponential trend and decreases very smoothly, and furthermore, it has a typical shape for time series that are non-stationary and have the long memory property. If such a series does not have the long memory property, it is expected that after the first differencing, the series will become stationary. Table 4: The Unit Root Tests Tests ADF Phillips-Perron ERS KPSS

Accounting Value -47.572 -47.659 0.0355 2.159

Critical Value -1.9409 -1.9409 3. 26 0.463

Result Stationary Stationary Non-Stationary Non-Stationary

Source: The Findings of the Study According to Table 4, although the ADF and PP tests recognize that the oil price series are stationary after the first differencing, the ERS and KPSS1 tests show a type of non-stationarity in the data. This result provides more evidence for the existence of the long memory property. Models that consider the long memory property are very sensitive to the estimation of long-memory parameters as well as the pattern of damping of autocorrelation functions. In this study, the long-memory parameter was estimated using the GPH approach. This method, which was invented by Gewek, Porter-Hudak (1987), is based on frequency domain analysis. The GPH method applies a special regression technique called Log-Period Gram that allows for us to distinguish between long-term trends and short-term trends. The slope of the regression line calculated by this technique is exactly equal to the long-memory parameter. Table 5 reports the estimated longmemory parameters for both the logarithmic series and the return series. To compute these parameters, we have used the OX-Metrics software. Table 5: The Estimated Long Memory Parameters Variable

d-Parameter

t-stat.

Prob.

LOIL

1.11249

46.3

0.000

dLOIL

0.10937

2.88

0.003

Source: The Findings of the Study

The Kwiatkowski-Phillips-Schmidt-Shin Test

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As table 5 shows, the estimated long-memory parameter is statistically significant, i.e., it is not equal to zero, suggesting that the series of (the logarithm of) the crude oil price’s level has the long memory property. However, the return series should be modeled after another differencing (namely, fractional differencing). D. Modeling the Return Series of the Crude Oil Price Because the crude oil price’s level has the long memory property, in this step we fit an econometric model to our data. In this study, the most famous and flexible long memory model, i.e., ARFIMA, was applied to specify the mean equation, which is as follows: (5)  ( L)(1  L) d ( yt  t )   ( L) t t  1,2,3,...,T

 (L) and  (L) indicate Auto Regressive (AR) and Moving Average (MA) polynomials, respectively. L is the lag

operator,

t

represents the mean of the series, d is the differencing parameter and

(1  L) d stands for the

fractional differencing operator. If d=1, this model is reduced to the ARIMA model. However, if d  0.5 , the covariance is fixed, and if d  0 , the long memory property exists (Husking, 1981). When 0  d  0.5 , the ACF has a hyperbolic decreasing pattern, and when  0.5  d  0 , medium-term (or short-term) memory exists; this property suggests that too many differencings have been made. In such cases, the inverse of the ACF has a hyperbolic decreasing pattern. To estimate the ARFIMA model (and the d parameter), three methods were implemented: Exact Maximum Likelihood (EML), Modified Profile Likelihood (MPL), and Non-Linear Least Square (NLS). Table 6 compares various estimated models on the basis of the Akaike Information Criterion (AIC). Table 6: The Estimated ARFIMA models AIC

Model

ARCH-TEST

ARFIMA(1,0.11,1)

MPL -5.69612541

NLS -5.73642397

EML -5.72786302

ARFIMA(1,0.11,2)

-5.68547234

-5.72397862

-5.71882163

F(1,659)=27.659(0.000) F(1,658)= 29.438(0.000)

ARFIMA(2,0.11,1)

-5.68630893

-5.72531429

-5.71939564

F(1,658)= 28.019(0.000)

ARFIMA(2,0.11,2)

-5.68001954

-5.72197485

-5.71432768

F(1,659)=27.736(0.000)

Source: The Findings of the Study As shown in this table, ARFIMA (1,0.11,1) has the best performance compared to other models. Moreover, with respect to the volatility equation, the diagnostic ARCH tests signified the existence of ARCH effects in the residual series; to model this conditional heteroscedasticity, both fractional (to track the long memory property) and non-fractional GARCH models were estimated. Table 7 compares these models on the basis of the AIC and the Schwarz-Bayesian Criterion (SBC). This Table has three different parts: part 1 includes non-fractional heteroscedasticity models, part 2 is dedicated to an integrated non-fractional heteroscedasticity (IGARCH) model, and part 3 includes various fractional heteroscedasticity (FIGARCH) models. Each of these three categories has been estimated separately by two mean equations of fractals (ARFIMA) and non-fractals (ARIMA). Among the non-fractal models, ARIMAEGARCH had the best performance. Furthermore, among the models based on long memory (in both the mean and the variance equation), the ARFIMA-FIGARCH (BBM) model had the best specification. On this basis, in the process of examining the performance of these two types of models, the out-of-sample forecasting will be focused upon to answer the main question of the study, i.e., whether long memory matters in oil price volatility forecasting. Table 7: The estimation results for different volatility models Part

1 2 3

ARIMA(1,1)

Models GARCH EGARCH GJR-GARCH APGARCH IGARCH FIGARCH (BBM) FIGARCH (Chang)

SBC -5.2367 -5.2745 -5.2593 -5.2511 -5.2428 -5.2981 -5.2923

AIC -5.3319 -5.3627 -5.3431 -5.3428 -5.3309 -5.3851 -5.3864

ARFIMA(1,1) SBC -5.6437 -5.6546 -5.6498 -5.6342 -5.6271 -5.9724 -5.9512

AIC -5.9231 -5.9294 -5.9240 -5.9138 -5.9017 -6.4873 -6.2091

Source: The Findings of the Study E. Comparing Different Models Considering that the main purpose of this study is to investigate the importance or unimportance of using the long memory feature in forecasting oil price volatilities, in this subsection, the forecasting ability of the best models that were mentioned above (namely, EGARCH and FIGARCH) will be compared. Then, the significance of the differences between these models in out-of-sample forecasting performance will be assessed.

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Table 8: A Comparison of the accuracy of the research models Rows

Models

MSE

RMSE

1 2

EGARCH (as a non-fractal model) FIGARCH (as a fractal model)

0.0000364 0.0000047

0.00603 0.00216

Source: The Findings of the Study As shown in Table 8, the performance of the types of models in out-of-sample forecasting confirms the superiority of the model based on the long memory feature over the competing model. Thus, we must find out if the differences between these two models are significant or if they are small and can be ignored. Clark and West’s (2006) test will be used for this purpose. Hence, after calculating the value of Z i , this value was regressed on a fixed value and the significance of this fixed value was tested. If the null hypothesis of the study is accepted (i.e., if there is no significant difference between the fixed value and zero), the two models (model 1 and model 2) have the same forecasting ability (i.e., the differences between their forecasts are negligible). Otherwise, depending on the positivity or negativity of the estimated fixed value, the superiority of each model in giving more accurate forecasts will be proved. Table 9: The Clark-West test results Models First Model Second Model GARCH FIGARCH

Constant Coefficient

t-Stat.

Prob.

0.27

3.46

0.008

Source: The Findings of the Study The results presented in Table 9 show that the out-of-sample forecasting of the fractal and non-fractal models are significantly different, leading us to the conclusion that using the long memory feature does matter in forecasting oil price volatilities and can help obtain better results. IV. Conclusions Generally, oil has a basic role in the world economy, and especially in oil exporting countries such as Iran. The importance and the special position of this commodity have attracted the attention of many researchers, and for this reason, many studies have been conducted in recent years on the oil market and its volatility. The results of our study confirmed the existence of the long memory feature in the mean and variance equations of Iran’s crude oil price. The existence of the long memory feature in this series indicates that if there is a shock to the oil market, the effects of this shock will last a long time and finally disappear after several periods of time. Indeed, among all of the models that were examined with respect to estimating the volatilities of the oil price, the best model is based on the information criteria (Akaike and Schwarz) and forecasting error criteria (MSE and RMSE) used in this study; this model is ARFIMA-FIGARCH. It should also be mentioned that in this model, the value of the fraction-differencing parameter (d) equals 0.11, which implies that the return series of the oil price is not completely stationary (even with a one-order differencing of the logarithm of the crude oil price), and there is a need for another differencing (which must be fractional). Furthermore, we modeled the volatilities of the crude oil price and selected two sets of the best models (including fractal and non-fractal models) to answer more correctly the main question of the study. We then evaluated the accuracy of these models with respect to out-of-sample forecasting of the volatilities of the oil price based on the MSE criterion. The results were indicative of the superiority of ARFIMA-FIGARCH (BBM) relative to the other models. Furthermore, the significance of the difference between these models’ out-of-sample forecasting was confirmed based on the Clark-West (2006) test. Lastly, it is worth mentioning that the results from the modeling and forecasting were consistent. Therefore, the main question of the study can be answered as follows: using the long memory feature can help one obtain (significantly) more accurate forecasts of the volatilities of the price of Iran’s crude oil compared to when this inherent feature of the market is ignored. Two suggestions can be offered based on the findings of this study. First, the nature of the long memory feature can be analyzed such that current shocks will have their effects in part during the same period or after some time lags, and furthermore, a considerable part of the effects of these shocks can influence the future behavior of a time series with this feature. Naturally, being aware of this issue and ignoring it indicates unconcern and indifference. Therefore, investors and macroeconomic decision makers can be advised to use models based on the long memory property to forecast the oil price. Our second suggestion is that confirming the existence of the long memory feature highlights the fact that, although using other complicated methods can yield better results, combining these methods and the long memory feature can help one obtain better results. This practice could be focused upon in future studies with a hybrid approach to forecasting models. V. References [1] [2] [3] [4]

Ayadi, O.F., Williams, J., Hyman, L.M., (2009), "Fractional dynamic behavior in Forcados Oil Price Series: An application of detrended fluctuation analysis", Energy for Sustainable Development, Vol. 13, PP. 11–17. Baillie, R.T., King, M.L., (1996), "Fractional differencing and long memory processes", Journal of Econometrics, Vol. 73, Issue 1, PP. 1-3. Baillie, R.T., Bollerslev, T., Mikkelsen, H.O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 74, PP. 3–30. Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31(3), pp. 307-327.

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Brock, W.A., W.D. Dechert, J.A. Scheinkman and B. LeBaron, 1996, A test for independence based on the correlation dimension, Econometric Reviews, Vol. 15, PP. 197-235. Cheong, C.W., (2009), "Modeling and Forecasting Crude Oil Markets Using ARCH-Type Models", Energy Policy, No. 37, PP. 2346–2355. Choi, K. & S. Hammoudeh, (2009). "Long memory in oil and refined products markets", The Energy Journal, Vol. 30, No. 2, PP. 97-116. Clark, T.E., West, K.D. (2006), “Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis”, Journal of Econometrics, Vol. 135, No. 1-2, PP. 155-186. Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK Inflation. Econometrica, 50, PP. 987–1008. Engle, R. F., Bollerslev, T. (1986). Modeling the Persistence of Conditional Variances (with discussion). Econometric Reviews, 5, PP. 1–50. Engle, R. F. , Lilien, D. M. , Robins, R. P. (1987). Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M Model. Econometrica, 55, PP. 391-407. Erbil, N., (2011), "Is Fiscal Policy Procyclical in Developing Oil-Producing Countries?", IMF Working Paper, Vol. 171, PP. 1-32. Farzanegan, M., Markwardt, G. (2009). The Effect of Oil Price Shocks on Iranian Economy. Energy Economics, Vol. 31, PP. 134151. Geweke, J., Porter-Hudak, S. (1983). The Estimation and Application of Long-Memory Time Series Models. Journal of Time Series Analysis, 4, PP. 221–238. Granger, C. W. J., Joyeux, R., (1980), "An introduction to long memory time series models and fractional differencing", Journal of Time Series Analysis, No. 1, PP. 15-29. Hosking, J.R.M., (1981). Fractional differencing, Biometrika. 68, PP.165–176. Hurst, H.R. (1951). Long-Term Storage in Reservoirs. Transactions of the American Society of Civil Engineers, Vol. 116, PP. 770799. Kang, S.H., Cheong, C., Yoon, S.M., (2011), "Structural Changes and Volatility Transmission in Crude Oil Markets", Physica A, Vol. 390, PP. 4317–4324. Kang, S.H., Kang,S.M., Yoon, S.M., (2009), "Forecasting Volatility of Crude Oil Markets", Energy Economics, Vol. 31, PP. 119– 125. Kittiakarasakun, J., Tse, Y. (2011). Modeling the fat tails in Asian stock markets. International Review of Economics and Finance, 20, PP. 430–440. Komijani, A., Gandali Alikhani, N., Naderi, E., (2013), "The Long-run and Short-run Effects of Crude Oil Price on Methanol Market in Iran", International Journal of Energy Economics and Policy, Vol. 3, No. 1, PP. 43-50. Lee, J.W., Lee, K.E., Rikvold, P.A. (2006). Multifractal Behavior of the Korean Stock Market Index KOSPI. Physica A: Statistical Mechanics and its Applications, 364, PP. 355-361. Lento, C. (2009). Long-term Dependencies and the Profitability of Technical Analysis. International Research Journal of Finance and Economics, Vol. 269, PP. 126-133. Lo, A. W. and MacKinlay, A. C. (1988), "Stock market prices do not follow random walks: evidence from a simple specification test", Review of Financial Studies, Vol. 1, No. 1, 41-66. Los, C.A., Yalamova, R. (2004). Multi-fractal Spectral Analysis of the 1987 Stock Market Crash, Working Paper Kent State University, Department of Finance. Mehrara, M., Mohaghegh, M., (2011), "Macroeconomic Dynamics in the Oil Exporting Countries: A Panel VAR study", International Journal of Business and Social Science, Vol. 2 No. 21, PP. 288-295. Mehrara, M., Oskoui, N. K. (2007). The sources of macroeconomic fluctuations in oil exporting countries: A comparative study. Economic Modeling, Vol. 24, PP. 365–379. Mostafaei. H & Sakhabakhsh. L, (2011), Modeling and Forecasting Of OPEC Oil Prices with ARFIMA Model, International Journal of Academic Research, Vol. 3. No.1, Part Iii. Mandelbrot, B.B. (1999). A Multi-fractal Walk Down Wall Street. Scientific American, 280(2), PP. 70-73. Swanson, D.A., Tayman, J., Bryan, T.M. (2011). MAPE-R: a Rescaled Measure of Accuracy for Cross-Sectional Subnational Population Forecasts. J Pop Research, 28, PP. 225–243. Poon, H., Granger, C. W. J. (2003). Forecasting Volatility in Financial Markets: A Review. Journal of Economic Literature, XLI, PP. 478–539. Prado. S, (2011), Free lunch in the oil market: a note on Long Memory, Working Paper, No.23. Vo, M., (2011), "Oil and Stock Market Volatility: A Multivariate Stochastic Volatility Perspective", Energy Economics, Vol. 33, PP. 956–965. Wang, y., Wu, c., Wei, y., (2011), "Can GARCH-class models capture long memory in WTI crude oil markets?", Economic Modeling, Vol. 28, PP. 921–927. Wei, Y., Wang, Y., Huang, D., (2010), "Forecasting crude oil market volatility: Further evidence using GARCH-class models", Energy Economics, Vol. 32, PP. 1477–1484. Zhou. J, Kang. Z, (2011), “A Comparison of Alternative Forecast Models of REIT Volatility”, J Real Estate Finance Econ, No 42, PP 275–294.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net A CRITICAL REVIEW ON DIFFERENT COIL CONFIGURATIONS USED FOR INDUCTION HEATING SYSTEM Tejas G. Patil1, Atul A. Patil2, Vijay H. Patil3 Department of Mechanical Engineering GFâ&#x20AC;&#x2122;s Godavari college of Engineering, Jalgaon, INDIA Abstract: Induction heating system has a number of inherent benefits compared to traditional heating systems due to a non-contact heating process. The main interesting area of the induction heating process is the efficiency of the usage of energy, choice of the plate material and different coil configurations based on application. Correctly designed, manufactured and maintained induction coils are critical to the overall efficiency of induction heating solutions. The important investment in induction heating process is in highly qualified coil technicians and advanced coil design equipment and got benefit by having coils that are customized to their specific needs and conditions, The objective of the present work reviews different coil configurations used for the induction heating Process which is based on application such as surface hardening, Casting, Rolling, welding, Brazing, Bonding, Post Heating and Water Heating.

Keywords: Induction Heating, Induction Coil, Helical, Pancake, Skin Effect I.

Introduction

Induction method of heating is wide spread used in numerous technological processes for hot forming, surface hardening, annealing, etc. It is extremely effective because of its contactless energy transfer, unlimited power densities and controlled temperature field in the work piece. However, high potential of induction heating can be fully realized on the basis of numerical simulation only. All technological processes used induction method of heating is multi physical. Heating by induction it includes electromagnetic and thermal physics, which are strongly coupled because of temperature dependent properties of the work piece material. Induction heating is a process which is used to bond, harden or soften metals or other conductive materials [5]. For many modern manufacturing processes, induction heating offers an attractive combination of speed, consistency and control. Many industries have benefited from this new breakthrough by implementing induction heating for furnacing, quenching and welding.

Figure 1 Basic setup of induction heating

The basic principles of induction heating have been understood and applied to manufacturing since the 1920s. During World War II, the technology developed rapidly to meet urgent wartime requirements for a fast, reliable process to harden metal engine parts. Induction heating has made it easier to set the heating parameters without the need of an additional external power source. More recently, the focus on lean manufacturing techniques and emphasis on improved quality control have led to further research in induction technology specially in precise coil design and equipment required for manufacturing of the coil which is key factor in process, along with the development of precisely controlled. In addition, it can be used special application like welding operations joining, brazing, bonding etc. Some industrial applications of the induction heating Process is shown on the Table 1.1 Induction Welding Induction Melting - Induction Brazing Special Applications - Induction Soldering - Shrink Fitting Induction Heating Heat Treatment - Cap Sealing - Billet and Bar heating - Hardening - Banding - Strip and plate heating - Tempering - Heat staking metal to plastic - Wire and cable heating - Annealing - Hot material testing - Tube and pipe heating - Normalizing - Sintering Table 1.1 list of industrial application of induction heating

II.

Mechanism of Induction Heating System

Induction heating is contactless electrical heating. It is a costly but extremely versatile process with innumerable applications. It can be used for partial and through heating, melting, and brazing of all metallic materials. In this section, we will review the basic principles of Induction Heating System. The principle of induction heating is mainly based on two well-known physical phenomena:

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1) Electromagnetic Induction:The energy transfer to the object to be heated occurs by means of electromagnetic induction. It is known that in a loop of conductive material an alternating current is induced, when this loop is placed in an alternating magnetic field (see Figure 2).

Figure 2 Induction law of faradays

Figure 3 Induction of eddy currents

When the loop is short-circuited, the induced voltage E will cause a current to flow that opposes its cause the alternating magnetic field. This is Faraday - Lenz’s law. If a ‘massive’ conductor (e.g. a cylinder) is placed in the alternating magnetic field instead of the short-circuited loop, than eddy currents (Foucault currents) will be induced in here (see Figure 3). The eddy Currents heat up the conductor according to the Joule effect. 2) The Joule effect:When a current I [A] flows through a conductor with resistance R [Ω], the power is dissipated in the Conductor. Induction heating is based on the principles of an electric transformer. The job or work-piece is the secondary while a surrounding copper coil is the primary. The two are linked or coupled by air. Thus, this is an air core transformer with a single turn secondary.

Figure 4 Actual scenario of induction working [7]

High frequency (HF) current (1,000–100,000 Hz) is Passed through the primary (coil) by connecting it to a suitable HF generator A similar HF current is induced in the job, i.e., secondary. This current circulates and produces heat. These are induced eddy currents and circulate circumferentially as shown in Figure 4. Eddy currents, and therefore the heating, are concentrated in a thin outer layer or skin of the work. The primary coil gets heated due to the I2R losses in it. Some heat is also absorbed from job radiation. The coil is therefore made of copper tubing through which cooling water is circulated. The job thus gets heated by the induced current and there is no contact between the primary and secondary. Iron and other soft magnetic materials are used in ordinary transformers to act as subsectors, i.e., to concentrate the magnetic linking flux. They cannot be used in high frequency fields as they heat up excessively. Hence, air is used as the coupling medium. A few large scale induction heating processes (melting, heavy billet heating) that operate at mains or low frequencies (50–150 Hz) do use iron or alloy cores. The concentration of induced currents in the surface layer is called the “skin effect.” Skin effect plays an important role in the design and operation of the induction heating process [8]. Induction heating systems works with the alternating current, as a result the induced current inside the work piece is concentrated at the outer surface with a thickness called penetration thickness (depth). This effect is also named as skin effect. The heat generation take place in this penetration part therefore material heated from surface (skin) to core. The most interesting side of the method is that heating occurs without contact and by this way locally and precise heating can be applied. The amount of heat created by induction heating is depend on the several independent parameters such as power supplied, induction heating time, work piece geometry, material properties, work piece positioning in the coil, coil structure geometry, number of turning in the coil and also induction power supply frequency . III.

Different Coil Configuration Used in Induction Heating System

As we have discussed above heat generation rate in induction heating is depend on the several independent parameters such as power supplied, induction heating time, work piece geometry, material properties, work piece

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positioning in the coil, coil structure geometry, number of turning in the coil and also induction power supply frequency; we have to review the different coil structure used for induction heating. Coil design is one of the most important aspects of an induction system. A welldesigned coil provides easy part handling, maintains the proper heating pattern and maximizes the efficiency of the induction heating power supply. Inductor coil for high frequency induction heating usually referred to as heating coils and it can be made in a large variety of types and styles which depends on the shape of the metal surface to be heated. Heating of metal parts is the result of internal energy losses within the material being treated, which causes the temperature to rise. Their design must follow certain principles to achieve maximum efficiency. Some of them which are mostly used in induction heating process are shown as follows: 1) Round Coil:The most common is a cylindrical or Round coil which is suited for short length of work piece. The copper conductor is wound or formed either symmetrical in contour or shaped to suit the outline of the part to be heated. This Round coil is especially use for surface heating of shafts and round parts

Figure 5 Round coil

2) Helical and Spiral Helical coil:A helical coil is generally used for the part or area to be heated located within the coil and, thus, in the area of greatest magnetic flux. In general, helical coils used to heat round work pieces have the highest values of coil efficiency and internal coils have lowest values. The copper conductor is wound or formed either symmetrical in contour or shaped to suit the outline of the part to be heated [3]. This type is suited to surface heating of shafts and bars. The shape of helical coil mainly depends on the shape of work piece [1]. So, the coil may be in the shape of round, rectangular, formed, spiral helical and others. Spiral coils are generally used for heating bevel gears or tapered punches.

Figure 6 Spiral helical coil

3) Pancake Coil:A pancake induction coil usually multi-turn. It heats a ring on surface; central zone may be heated by heat conduction or coil part movement such as eccentric part rotation. Concentrator is strongly desirable to improve coil parameters and reduce under heated central zone. Concentrator is strongly recommended for this coil. A pancake coil is used for heating flat surfaces and so flux from only one surface intersects the work piece. Pancake coils are generally utilized when it is necessary to heat from one side only or when it is not possible to surround the part.

Figure 7 Pancake coil

4) Irregular Shape coil:The use of the coil is solely depends upon the size of the work piece and most of cost spending in induction heating is design of precise coil and manufacturing of the same. This irregular shape coil is used for work piece with irregular shape. One of the irregular shape coils is shown in figure 8 which is used for Induction Hardening.

Figure 8 Irregular coil

5) Single Turn Coil:The use of single-turn coil and the use of multi-turn coil are based on usually the area of the zone to be heated; Single-turn coils are preferred when the heated area is narrow or restricted. Single-turn coils are more practical

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where the height does not exceed the diameter. Single-turn coils are also effective for heating bands that are narrow with respect to the part diameter. Depending upon the application, it is a single-turn, single-place coil or a multi-turn, single-place coil. Both coils are used for heating a single part at a time. And it is a single-turn, multiplace coil or a multi-turn, multi-place coil for heating the multi parts at a time [2].

Figure 9 Single turn coil

6) Internal coil:Tubing for internal coils should be made as thin as possible, and the bore should be located as close to the surface of the coil as is feasible. Because the current in the coil travels on the in-side of the inductor, the true coupling of the maximum flux is from the ID of the coil to the bore of the part. Thus, the conductor cross section should be minimal, and the distance from the coil OD to the part (at 450 kHz) should approach 0.062-inch (0.16-cm).The coil tubing has been flattened to reduce the coupling distance, and the coil OD has been increased to reduce the spacing from coil to work [4]. An internal coil is used for heating inner surfaces of holes.

Figure 10 Internal coil

7) Split Coil:Split coils are generally utilized as a last resort for applications in which it is difficult to provide a high enough power density to the area to be heated without very close coupling, and where part insertion or removal would then become impossible. One such situation is the hardening of journals and shoulders in crank-shafts. In this case, the split-coil design would also include the ability to quench through the face of the inductor. It should be noted that with a split inductor, good surface-to-surface contact must be made between the faces of the hinged and fixed portions of the coil. Generally, these surfaces are faced with silver or special alloy contacts that are matched to provide good surface contact. Clamps are used to ensure closure during heating [2].

Figure 11 Split coil

IV.

Factor consider for different coil geometries required for induction heating system

According to the geometry of work piece surface the shape of work coil is varied and the size of work coil is governed by the length of work piece. Basic points to consider for efficient working of Induction heating process [6]: 1. Work coil for induction hardening is made in a wide variety of styles, shapes and sizes. Depending upon the natures of work piece the styles of work coil are changed 2. The inductor coil itself is only a part of the generator output system and Success is directly dependent on proper design of the Inductors (work coils). 3. The same principles of design must be applied to the leads which connect the coil to the output terminals of the generator or remote heating station [8]. Following factors have been considered for construction of heating coil required for induction heating system [8]:  Type of wire: It may be solid wire or multi stranded litz wire. At high frequency, the skin effect loss will be more in case of solid wire. Thus for energy efficient induction cooker, the heating coil may be made by multi stranded litz wire.  Work piece Shape: It governs the important factor for design of induction coil because shape of induction coil design according to shape of the work piece  Shape of wire: It can be round or rectangular cross sectional wire or it may be foil coil. At round cross-sectional wire the current flows uniformly through the whole cross section. But in case of rectangular or foil coil current density is more at the corner or edge section.  Size of strand: From the litz wire manufacturer’s manual at the frequency range of 20 to 50 kHz, the strand size lies between 30 to 36 AWG. Three different strand sizes, such as 30, 33 and 36 AWG have been use for special applications.

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       

Number of turns of spiral coil or Pitch: It depends on the size of the heating coil. It is varied from 30 to60 in the present study and optimal value is chosen. Heating Time: The time values necessary to reach the prefixed final temperature affect the parameters of coil design. Number of twist per feet or lead: The stranded litz wire is twisted so that each strand can possess both azimuthal and radial transposition. For thick wire number of twist can be 12 twists per feet and for thinner wire it may be up to 200 twists per feet. Lead: It is desirable to keep them as close together as possible in order to avoid inductance losses between the leads Operating frequency: Induction cooker operates at the frequency range of 4 kHz to 50 kHz. For different strand size and number of strands suitable operating frequency will be different and it is to be determined in an optimal sense. Coil efficiency: Coil efficiency is that part of the energy delivered to the coil that is transferred to the work piece which is mainly depends upon frequency and geometry shape of induction coil. Heating pattern: It depends upon the shape of the work piece to be heated and the coil is to be design for specific work Part motion relative to the coil: The motion of the work piece decides the turns of the coil and increase in geometry [8]. V.

Conclusion

The review indicates that Induction heating (IH) is a contactless energy-efficient method of heating. In this process for the efficient working key factor is induction coil design for particular application because it governs other factor of induction heating system; so we have studied the different type of induction coils used in industry especially based on geometry like Helical, Round, Pancake coil etc.. The Heat generated in the conductors is depends upon the shape and distance of the coil from work piece placed inside the induction coil. It was observed that the multi-turns are preferred to be effective as compared single turn induction coils due to large variety of irregular shapes of work piece. Also design of induction coil some of factors consider plays important role like Frequency, Size of wire, Pitch and Lead etc. it is observed that Lead will affect magnetic field and increase inductance loss. Finally this paper reviewed the understanding the basic concepts of induction heating and overall mechanism of the Induction Heating process, Different coil geometries used and common factors required for proper design of induction coil for specific application. References [1] [2] [3] [4] [5]

[6] [7] [8]

Mohamed Abo Elazm, Ahmed Ragheb, Ahmed Elsafty, Mohamed Teamah (2012). “Computational Analysis for the Effect of the Taper Angle and Helical Pitch on the Heat Transfer Characteristics ofthe Helical Cone Coils”,VOL. LIX10.2478/v10180-012-0019-9 Stanley Zinn and S. L. Semiatin (1988), “Coil design and fabrication: basic design and modifications”, Heat Treating/June-October 1988. Han Phyo Wai, Soe Sandar Aung, Jr., and Thidar Win (2008), “Work Coil Design used in Induction Hardening Machine”, Work Academy of Science, Engineering and Technology 2008. Sunderarajan S. Mohan, Maria del Mar Hershenson, Stephen P. Boyd, and Thomas H. Lee (1999), “Simple Accurate Expressions for Planar Spiral Inductances. IEEE Journal of Solid-State Circuits”, Vol. 34, No. 10, October 1999. Goce Stefanov, RistoDambov (2009), “Fundamental Principles of Working in Resonant Converter For Induction Heating”, Annual of the University of Mining and Geology “St. Ivan Rilski”, Vol. 52, Part III, Mechanization, electrification and automation in mines, 2009. Artuso I, Dughiero F, Lupi S, Partisani S, facchinelli P, “Installation of the continuous induction heat treatment of Wires”, Taylor & Francis Group, LLC (2005), “A Handbook on Industrial Heating : Principles, Techniques, Materials, Applications and Design” D. Sinha, A. Bandyopadhyay, P. K. Sadhu and N. Pal, “Computation of Inductance and AC Resistance of a Twisted Litz-Wire for High Frequency Induction Cooker”, in the proceeding of IEEE sponsored International Conference on “Industrial Electronics, Control & Robotics”(IECR 2010) at NIT, Rourkela, India, 2010

Acknowledgement Firstly the author would like to thank her parents for their best wishes and Special thanks to Assistant Professor Atul A. Patil of Mechanical engineering Department, NMU Jalgaon, India for his guidance about the selected topic. The author greatly expresses her thanks to all people whom will concern to support in preparing this paper.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Timing Jitter and Quantization Error Effects on the performance of Sigma Delta ADC used in SDR Receivers Preeti Trivedi1, Dr. Ajay Verma2 Assistant Professor, SGSITS, Indore, Madhya Pradesh, INDIA 2 Prof. and Head, IET, DAVV, Indore, Madhya Pradesh, INDIA __________________________________________________________________________________________ Abstract: This paper presents the effect of timing jitters on the performance of sigma delta ADC for SDR mobile receivers. The time-varying behavior is caused by the non-stationary nature of the clock jitter process. Jitter is the limiting effect for high speed analog to digital converter with high resolution and wide digitization bandwidth, which are required in receivers in order to support high data rates. Mathematical modeling has been done for the same to show the effect of clock jitter as well as aperture jitter on the performance of Sigma delta ADC for SDR mobile receivers in terms SNR. The present work shows that there is degradation in the system performance due to timing jitters. It is also shown that when clock jitter becomes more dominant, increasing the OSR does not improve the performance of Sigma Delta ADC. SNR with quantization error (SQNR) has been evaluated using second order sigma delta ADC. It is shown that at 256 OSR the SQNR is 118.9dB which is very close to the calculated theoretical value. Simulation has been done using SD toolbox of MATLAB Simulink. Key Words: Sigma delta ADC, Clock jitter, Aperture jitter, Software Defined radio, Signal To noise ratio (SNR). ______________________________________________________________________________________ 1

I. Introduction A radio that defines in software its modulation, error correction and encryption processes, exhibits some control over the RF hardware, and can be reprogrammed is clearly Software radio [2]. The wideband ADC is one of the most challenging tasks in software radio design. The input analog signal is sampled at a frequency , which converts it into a discrete time signal. The rapid development of digital wireless system has led to a need for high resolution and high speed band pass analog to digital converters. Continuous time band pass Sigma delta modulator are very suitable for such high frequency application. To achieve high-resolution without requiring high precision analog components, over sampling techniques are often used. This relies on three techniques: oversampling of the input signal, quantization error shaping and digital filtering. The core circuit of the ADC is a sigma-delta modulator, which acts as a high pass filter to filter the quantization error in the signal bandwidth. Because of the over sampling that makes the signal bandwidth much smaller than half of the sampling frequency, a digital decimation filter can be used to down sample and filter the modulated signal to the Nyquist bandwidth. In Sigmaâ&#x20AC;&#x201C;Delta Modulators, the over sampling is used along with noise shaping to get rid of the quantization error as much as possible. By introducing a Digital to analog filter in the feedback loop, thereby feeding back the quantized signal back to the input as depicted in Figure 1, the shape the quantization error can be out of band of interest[1-3]. The paper is organized as follows: The timing jitters are introduced in section III. An. Jitter model is discussed in sectionIV. SNR calculation is given in section V, Results are discussed in section VI and the work is concluded in section VII. The present work presents a technique called oversampling technique to reduce the timing jitter effects using oversampling method. II. Timing Jitters The rapid development of digital wireless system has led to a need of high resolution and high speed analog to digital converter. The performance of a data converter is dependent upon the accuracy and stability of the clock supplied to the circuits. When data converter employs a high sampling rate, clocking issues become magnified and significant distortion can be result[13]. Analysis of Signal to noise ratio For a sinusoidal signal which does not exceed the FSR of the ADC, the SNR due to the quantization error is given by (1) Here is the sampling rate and and also given by

is the maximum frequency of analog signals and N stands for bit resolution (2)

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Where . For sampling rate equal to the Nyquist rate (k=1) then (3)

Figure 1. Signal Flow of the Sigma-Delta Modulator It is seen that the signal transfer function is simply a delay, while the noise transfer function is a discrete-time differentiator. Analysis of the first-order sigma-delta modulator reveals that the quantization noise power is given by (4). Where frequency

amplitude of signal and OSR is is oversampling ratio defined as the ratio of . So the SNR of the sigma-delta ADC with first order noise shaping is

(4) to nyquist

(5) For improving the SNR, increase the OSR. In second-order modulator, the improvement is 15dB with doubling the OSR while for the first-order modulator the improvement is only 9 dB. SNR for modulator is given by (6) Here m is the order of filter, OSR is the oversampling ratio. The SNR can be calculated using various loop orders (m= 2, 6, 8) and 1-bit quantizer (n=1). With varying the OSR, different gains in SNR can be achieved. III. Jitter Model in High Speed ADC In circuit design that involves the use of a high-performance, high-speed analog-to digital converter (ADC), one of the main care is the clocking scheme. The conversion process starts when a clock signal tells the Sample and Hold (S&H) to take the samples. Up to that instant, the internal switch on the S&H circuit is closed, allowing the voltage across the capacitor to track the input signal. One of the edges of the input clock then indicates when to open this switch, and the capacitor holds the voltage at that instant in time.A mathematical estimation of the best-case signal-to -noise ratio without other noise sources is (7)

2  2 A cos  1 T = Jitter 2   T T 0  T

  2 da 2 ,   d  a  T d T   (8) (9)

The theoretical limitation of the SNR due to jitter is given by

Assuming all these sources of noise are uncorrelated, the total noise is the addition of a noise term independent of input frequency and a noise term dependent on input frequency [6-7]. The theoretical limitation of the SNR due to jitter and other sources of noise are considered given by (11) Two of the most significant timing issues are Aperture Jitter and Clock Jitter, which are produced from timing errors in the sample and hold circuit of an ADC and the decoder circuit of the DAC [1]. -Aperture jitter-Due to uncertainty in sampled pulse -Clock jitter –Due to time variation in clock time period

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In [10] Walden discovered the aperture jitter as dominating error effect that limits the achievable SNR. In the last few years different authors derived formulas to quantify the SNR limiting effect of jitter in ADCs. Koyabashi et al. presented a formula which allows to calculate the SNR in the presence of an aperture jitter [13]. The effect of clock jitter was investigated by Awad[7]. A. Aperture Jitter In communication system, aperture jitter causes uncertainty in phase of the sampled signal, degradation of the noise floor of a data converter, Aperture jitter is random variation in time of the exact sampling instant that causes phase modulation. It results in an additional noise component in the sampled signal. Aperture jitter is caused both by the sampling circuit and sampling clock, the latter source being closely related to the phase noise of the sampling clock oscillator. B. Clock Jitter Clock jitter is what engineers would readily call time-domain distortion. Clock Jitter is caused by phase noise. The resolution of ADCs with a digitization Bandwidth between 1MHz and 1 GHz is limited by jitter. Clock jitter does not actually change the physical content of the information being transmitted. Depending on circumstance, this may or may not affect the ultimate decoded output [2].

Aperture error

Signal to be sampled

Aperture

jitter

Figure 2: Aperture Uncertainty and Aperture Jitter [2] C. Improving SNR by Over Sampling Method Over sampling is a popular method used for improving SNR in ADC. The input is sampled at a rate higher than the minimum required Nyquist sampling rate, . When over sampling with a factor of k = 16, the same 100-Hz input signal is sampled at 3200Hz. The samples obtained by over sampling are low-pass filtered and decimated using a digital filter to achieve a reduction of the quantization noise. The signal at the frequency band of interest is not affected by the filter, and the result is an improved SNR [13-14]. D. Aperture Jitter Effect in Sigma Delta ADC Aperture jitter is random variation in time of the exact sampling instant that causes phase modulation and results in an additional noise component in the sampled signal. Aperture jitter is caused both by the sampling circuit and sampling clock, the later source being closely related to the phase noise of the sampling clock oscillator. In communication system, aperture jitter causes uncertainty in phase of the sampled signal and degrades the noise floor of a data converter. Aperture jitter stands for the random sampling time variations in ADCs which are caused by broadband noise in the sample and hold circuit. Here it is found that the aperture jitter affects the sampled signal.

Figure 3: Amplitude error due to aperture jitter

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Consider a signal g(t) that is to be sampled, as shown in Figure (3). This signal would ideally be sampled at points that are multiples of Ts, the sampling period. However, due to jitter, sampling does not occur at exactly multiples of Ts, and hence the sampling period varies between samples. This in turn results in an amplitude error. Therefore the uncertainty in sampling position results in an amplitude error of the sampled signal. The effects of aperture on ∑∆ ADC can be predicted by the following simple analysis. Assume an input signal is sine wave and is given by the equation The rate of change of this signal is given by (12) Now let = the rms voltage error and ∆t = the rms value of aperture jitter and substitute these values in equation (15). (13) The rms value of the full-scale input sine wave is / 2 , the noise produced by aperture jitter is usually modeled as white noise, therefore the signal to noise ratio (expressed in dB) is given by

(14) Where is the aperture jitter. This amplitude error and hence the signal-to-noise ratio due to jitter becomes worse, if the signal to be sampled varies fast i.e. such as a high frequency carrier. If the deviation of sampling points from the ideal position is large, as deduced from equations above, SNR is poor. Conclusion is that the uncertainty is proportional to the frequency of the input analog signal and it is independent of sampling rate. The equation (14) assumes an infinite resolution ADC where aperture jitter is the only factor determining SNR. It can be proved that the SNR of an ADC is also affected by aperture jitter.

E. Clock Jitter Effect on Sigma Delta ADC Clock jitter is defined as the uncertainty of the sampled signal in time domain due to the uncertainty of the sampling clock [13][17]. It is mainly caused by the instability of the oscillator resulting in sampling time errors in the ADC, therefore degrading the converter’s achievable SNR and resolution.

Figure 4: Discrete time ∑∆ modulator in the presence of clock jitter Examination of sigma delta modulator in the condition of clock jitter, the output yj(n) now contains error from both the quantization noise and clock jitter, given by: Yj(n) = (x (nT + dn) − yj(n))* h(n) + qj(n) (15) Additional error due to jitter

 1   H ( z)  Ey ( z)   Eq ( z )     X ( z) 1  H ( z )  1  H ( z ) 

(16)

IV. SNR Calculation In analytical calculations, the clock jitter error and the quantization noise are assumed to be independent of each other. The achievable SNR in the clock jitter condition can be numerically approached (in dB) by (17) Where represent the power of input signal, quantization noise power respectively.

and

indicate the jitter- triggered error and

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The average noise power due to the clock jitter can then be formulated as follows [17]: =A*A/2 (18) = (A*A)/ (12*L) (19) Consider a real Analog input signal x(t). In the ADC, x(t) is sampled at the time instant tn=nT+Jn with the nominal sampling period T. Jn are the random sampling time variations due to aperture jitter and clock jitter. For a block of N sampling point ,the mean error power caused by random jitter process can be calculated as (20) where ) The corresponding error powers due to error : (21) The above formula is in time domain, By Fourier transform, the resultant formulae of in frequency domain can be obtained and written as: (22) Here (f) is the power spectral density(PSD) of x(t). The Jitter dependent SNR (in Decibels) is given by (23) Where Therefore the

is (24)

Where (f) represents the PSD of input signal, Table 1 shows that performance for different standard with different value of OSR as well as it shows jitter tolerance [19]. V. Simulation Results In order to confirm the above analysis, some simulation results are presented. Calculation of the SNR at the ADCâ&#x20AC;&#x2122;s output for an maximum input frequency and a sampling frequency = has been done while varying the bit resolution. Assumed that the error is due to only the quantization and not due to aperture jitter. For high frequencies above the Nyquist rate, equation 3 is valid. An increase in the sampling rate leads to an increase in the SQNR. For a sampling rate equal to the Nyquist rate (k=1), decrease the quantization noise: the term 10log (k) indicates that it is preferable to use sampling rates as greater than the Nyquist rate as possible (over sampling) because the SQNR has been increased with increasing OSR as shown in fig.(5). 100

N=14

N=12

80 N=10 70 N=8 60 SQNR in dB 50 40

N=4

N=2

20 N=0 10 0

Figure 5: SQNR versus k(oversampling ratio), for N= 2, 8, 10, 12, 14 bits Figure (6) shows the SNR degradation due to jitter, varying the input frequency (in MHz) with different value of jitter in pico second. In time domain jitter calculation using equation (10), to kept constant number of sample N is 1000 and vary jitter variance 0 to 5*10^-13.,SNR degrades as we increase jitter variance as shown in figure (6). Similarly in figure (7) keep constant jitter variance 0.5*10^-13 and vary the number of samples N, SNR decreases. We improve SNR with jitter by using over sampled ADC.

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110 o.1ps 0.2ps

100

0.4ps 0.6ps 0.8ps 3.2ps

SNR(dBc

150

200

250

300 fin(MHz)

350

400

450

500

Figure 6: Limitation of SNR due to jitter as a function of input frequency By using SNR equation (6) of sigma delta ADC, figure (5) shows that as we increase over sampling ratio with order of sigma delta, SNR increases. Table.1 is also given in which effect of SNR and OSR with increase in order of Sigma-Delta ADC and bit resolutions. We calculate effect of clock jitter on Sigma Delta ADC . Greater value of vco leads to the greater SNR reduction. Clock jitter degrades the SNR of the sigma delta ADC. 48 47 46 45 SNR in DB

44 43 42 41 40 39 38 20

80 100 N number o f sample

120

140

160

Figure 7: SNR verses N for constant clock jitter variance of 0.5*10^-13 The parameter used as centre frequency fc is 70 MHz sampling frequency is 280 MHz and sampling point N=9*10^6. The values of vco for integrated Oscillator range from 10^19 s to 10^21. In Figure (8), oversampling is expected to improve the system performance by spreading out the quantization noise to a much larger bandwidth. However, this improvement can only be perceived for the low value of OSR. For high value of OSR, the clock jitter noise becomes more dominant compared to the quantization error and leads to the inevitable noise floor. Table 2: Effect of SNR and OSR with increase in bit resolution of Sigma-Delta ADC (Theoretical Values) Oversampling factor k 2 4 8 16 32 64 128 256

SNR Improvement in dB 3 6 9 12 16 18 21 24

Bit resolution 0.5 1 1.5 2 2.5 3 3.5 4

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Using equation(14),Taking input frequency fc=100MHz aperture jitter is equal to 2ps and sampling rate fs vary from 200MHz to 300MHz Figure(8) shows that as the sampling frequency increases, SNRaj is constant. We conclude that aperture jitter SNR (SNRaj) is independent of sampling rate. 100 90 80 70 SNRaj(dB)

60 50 40 30 20 10 0 200

210

220

230

240

250 260 fs(MHz)

270

280

290

300

Figure 8: SNRaj versus sampling rate (fs) Figure 9 shows the aperture jitter power spectrum. The signal was sampled with a sampling frequency of 400 MHz and white Gaussian aperture jitter with a standard deviation (rms aperture jitter) taj = 0.25 ps for fi <<taj^1 The error power spectrum is white and distributed over whole digitized band. Therefore we increase aperture jitter SNR ( ) by using over sampling technique. But in case of clock jitter, over sampling does not improve the clock jitter SNR ( ). Figure 10 shows the clock jitter‘s power spectrum which has narrow peaks at ±20MHz and ±90MHz surrounded by Lorentzian shaped spectra which are spread across the spectrum of input signal. The chosen phase noise constant fi<< (vconT) ^0.5. In traditional ADCs, jitter with SNR in between 25-5dB whereas the Sigma Delta ADC has SNR in between 48-38.5db in case of clock jitter and 12060dBin case of aperture jitter. Jitter tolerance is much better in sigma Delta ADC which is -1*10ps.Sampling technique for improving SNR in case of jitter does not apply on traditional ADC because these ADCs use sample and hold circuit which is an active device and bandwidth of active device is limited. It does not employ on RF or intermediate frequency. -30 -35 -40

Power Spectral in dB

-45 -50 -55 -60 -65 -70 -75 -80

5 6 Frequency (Hz)

Figure 9: Mean error PSD caused by aperture jitter 0 -10

Power spectral in dB

-20 -30 -40 -50 -60 -70 -80

0.2

0.4

0.6

0.8 1 1.2 Frequency (Hz)

1.4

1.6

1.8

Figure 10: Mean error PSD caused by clock jitter

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Simulation of second order sigma delta ADC has been done using MATLAB Simulink. Simulation diagram of second order sigma delta ADC is shown in figure 12. The simulated result for SNR and SQNR is shown in fig. (13). It shows that SNR is 94.8 dB with 256 OSR. Table 4 Calculated values of SNR and SNDR with respect to OSR OSR

SNR in dB

54.8

SQNR in dB 69.8

RESOLUTION(ENOB) (bits) 8.8

69.8

87.8

11.3

128

83.9

105

13.6

256

95.7

119.8

15.6

OSR vs SQNR 140

SQNR -->

120 100 80 60 40 20 0 0

100

150

200

250

OSR -->

Figure 11: Theoretical graph of SQNR Vs. OSR

Figure 12: Simulated diagram of second order Sigma Delta ADC

Figure 13: Simulated result for SNR and SQNR Vs frequency

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VI. Conclusion This presents that how degradation is there in the system performance due to clock jitter and aperture jitter and concluded that aperture jitter process is stationary as shown in fig (8). Its characteristic functions do not depend on the absolute sampling time instant nT, i.e. they are time-invariant. In the case of clock jitter the characteristic functions strongly depend on the absolute sampling time.. Error power spectra of clock jitter and aperture jitter are significantly different. In the case of aperture jitter the mean error power is uniformly distributed over the whole digitization band, so that the jitter dependent SNR in a given frequency band can be increased by over sampling techniques. In the case of clock jitter the error power is concentrated around the frequencies of the input signal components. Analysis shows that Clock jitter is dominating error and severely degrades the system performance in terms of achievable SNR. The SQNR is 118.9dB. 15.5 bits is the Bits of resolution for the second order sigma delta ADC using simulation. Practically it can be considered as 15 bits. Here it can be seen that simulated results are very close to the theoretical results. [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12].

[13]. [14]. [15]. [16]. [17]. [18]. [19]. [20].

References J Paul Burns, “Software Defined Radio for 3G”, First Edition, Artech House, Boston ,London, 2002, pp. 51-70. J.H. Reed , “Software Radio –A modern approach to radio engineering” Peason Education , 2002. S. Haykin, Communication Systems, John Wiley & Sons Inc, ch. 6, pp. 374-378. J.C. Candy and G.C. Temes, Oversampling Delta-Sigma Data Converters. IEEE Press, New York, 1992 . R.T.Baird and T.S.Fiez, “Stability Analysis of High-Order Delta-Sigma Modulation for ADC’s,” IEEE Trans Circuit Syst.II.vol. 41, pp. 59-62, Jan 1999. S. S. Awad, “The effects of accumulated timing jitter on some sine wave Measurements,” IEEE Trans.Instrum. Meas.,vol. 44, pp. 945–951, Oct. 1995. Selim saad Awad “Analysis of Accumulated Timing-jitter in Time Domain,” IEEE transaction on Instrumentation and measurement, Vol. 47, No. 1, Feb 1998. H. Tao, L. T´oth, and John M. Khoury, “Analysis of Timing Jitter in Band pass Sigma-Delta Modulators,”IEEE Transactions on Circuits and Systems—Part II: Analog and Digital Signal Processing, vol.46, no. 8, pp. 991–1001, 1999. R. Meyer, "Software-Defined-Radio Technology Targets 3G Designs ",Wireless Systems Design,vol. 5, no. 2, pp. 16-20, 2002. Joseph Mitola and Zoran Zvonar,”Software and DSP in radio,”IEEE Communication Magazine, Feb 2000 . R. H. Walden, “Analog-to-Digital Converter Survey and Analysis,” IEEE Journal on Selected Areas in Communications, vol. JSAC-17, no. 4, pp. 539– 550, Apr. 1999. Alper Demir, Amit Mehrotra, and Jaijeet Roychowdhury, “ Phase Noise in Oscillators:A Unifying Theory and Numerical Methods for Characterization”IEEE transaction on Circuit and System-I; Fundamenta Theory and Application Vol, 47, No.5, May2000. H. Kobayashi, M.Morimura, K. Kobayashi, and Y. Onaya, “Aperture Jitter Effects in Wideband ADC Systems,” in Proc. of the 6th IEEE International Conference on Electronics, Circuits and Systems (ICECS ’99), Pafos, Cyprus, Sept. 1999, pp. 1705-1708. A Hairong Chang, Hua Tang, “ Simple Technique to Reduce Clock Jitter Effects in Continuous-Time Delta- Sigma Modulators”, IEEE International symposium on Circuits and Systems (ISCAS) 2007. Neil Roberts, “Understanding phase noise and jitter,” High-performance Analog Designer Zarlink Semiconductor Inc. Planet Analog, “Understanding Analog/digital Converter clock jitter-and why you should care”2007. Eduardo Bartolome, Vineet Mishra, Goutam Dutta and David Smith, “clocking high-speed data converters”, Texas Instruments Incorporated 2008. U Derek Redmayne, Eric Trelewicz and Alison Smith, “understanding the effect of clock jitter on high-speed ADCs Linear Technology” published by Linear Technology. M.Lohning and G. Fettweis, “The effects of aperture jitter and clock jitter in Wideband ADCs,” International Journal Computer Standard and Interfaces (CS&I), vol. 29(1), pp. 11–18, January 2007. Bakti Darma Putra and Gerhard Fettweis, “Effect of Clock Jitter on the Performance of Band pass sigma delta ADCs,” ISCCSP08 proceedings, pp. 67-74, 2008.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Evaluation of Impact of Safety Training Programme in Indian Construction Industry – Analytic Hierarchy Process Approach S.V.S. Raja Prasad1, P.Venkata Chalapathi2 Research Scholar, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation (KLUniversity) Vijayawada, AP, India /Associate Professor, NICMAR, CISC, Hyderabad 2 Professor, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation (K L University), Vijayawada, AP, India. __________________________________________________________________________________________ Abstract: Safety in the construction industry has always been a critical issue and large numbers of workers in industry are susceptible to the various workplace accidents and occupational health problems. Construction safety management indeed is a challenging task due to the dynamic nature of construction activity coupled with involvement of unskilled, illiterate and mobile work force. Construction hazards are more risky than other industries and the employees must have knowledge about hazards/safe operating procedures to mitigate the risk involved. Safety training is assumed to be an ongoing process to educate employees in safety matters, owing to the fact that it would enhance positive changes in safety procedure and legislation within organizations. Safety training helps employees to acquire the skills, knowledge and attitudes to make them competent in the safety and health aspects of their work. Nevertheless, to date far too few attempts have been made to empirically study the impacts and influence of safety training on safety performance, especially in developing country like India. The study attempts to identify safety training’s impact with regard to improved safety outcomes over a period of time in an Indian construction industry and adopts Analytic Hierarchy Process (AHP) as a multi-criteria decision making technique for evaluating training programs. The factors characterizing impact of training programs are first identified using interviews with experts along with questionnaires. Once the factors were identified, the hierarchy was constructed and the factors were ranked according to their importance with respect to achieving the overall goal set for training. Keywords: Analytic Hierarchy Process (AHP), Factor analysis, Impacts, safety training 1

I. Introduction Workplace conditions in construction industry are more hazardous compared to other industries, although the construction industry has become the most important sector contributing to economic development of the country. Workplace safety is a crucial component to organization competitiveness and in a more global context it has been noted that the most competitive countries are the safest countries[1].Implementation of safety practices in construction industry programme is always isolated and not integrated with other organizational functions in India. The safety management system has been widely recognized as the most effective way to improve working conditions, influences positive employees’ safety attitudes [2].Now days, managements of construction organizations are emphasizing safety training as an important safety intervention within organization and training will help individuals in attaining knowledge, changing attitudes, and performing safe work behaviours . Safety training is a tool to change people’s safety behaviour and attitudes in the workplace [3]. Attempts to examine safety training outcomes on the significant improvements in safety knowledge, safety attitude and safety behaviour, as well as safely performed work activities, have been identified in the literature ([4],[5],[6],[7],[8]). It is believed that it could help to reduce accidents, injuries, compensation costs and increase employees’ safety awareness in the workplace ([9],[10]). By means of safety training, employees are expected to possess adequate knowledge and skill to promote safety in an effective way ([11],[12],[13]) as the ultimate goal of workplace safety training is injury prevention and control .Very limited research related to safety training and safety outcomes have been conducted in developing country like India. Safety training is one of the most important techniques for developing human resources and it is concerned with improving employees’ skills and enhancing their capacity to cope with ever-changing workplace hazards in construction industry. All employees in a workplace irrespective of cadre are to be trained on general aspects and on specific job contexts. Safety training outcomes helps employees to gain new knowledge and skills to perform their job effectively, but is also prepared to meet foreseeable changes that take place in their jobs. The efforts of safety trainings are to be transferred to the job context to achieve desire results and effective when it has translated to and enhanced job performance, as this gives more benefits to the organization ([14],[15],[16],[17]). Transferring the results of safety training is crucial in evaluating the effectiveness of training. Results and effectiveness of training outcomes are evaluated by training models and number of training valuation models that exist in training literature. The purpose safety training evaluation is to ascertain learning outcomes by trainees and also whether achieving predetermined objectives results in better performance on the

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job. Kirkpatrick (1998) noted that no final results could be expected from the training program unless a positive change in behaviour occurs. Therefore, it is important to observe if the knowledge, skills and/or attitudes learned in the programme transfer to the job. This framework is used for determining the success of the training programme using the four key items are reaction, knowledge, behaviour and results[18].Safety training is believed, to make a significant difference to both employee and organizational performance However, transfer of training and training evaluation to facilitate organizational effectiveness. Safety training has been used as an effort to change people’s safety behaviour / attitudes in a workplace and also argues that safety training plays a role as a lower-order measure for controlling risk and it also should not be a substitute for proper risk control [3].Safety training is a planned activity related to safety and health with specific goals and application that is undertaken by an employee primarily so that they can apply new skills and knowledge. II Material and methods The study was conducted in Indian construction industry to evaluate the outcome of safety trainings employing analytic hierarchy process. The study was conducted in two stages; in first stage, identifying the factors characterizing impact of training programs through interviews with experts and framing a questionnaire. Factors were identified by conducting factor analysis and the hierarchy was constructed 1 Questionnaire Survey From extensive literature review and subsequent discussions with corporate safety heads, safety managers of the construction organizations, a questionnaire was prepared comprising of 25elements. The questions were framed to fulfill the requirements of impact of elements of safety trainings in construction industry in India and shown in Table 1 Table 1 Questionnaire S.No

Parameter

My role towards OHS is clear

I have understanding about site safety rules

I got opportunity to learn about safety issues associated with job.

I apply knowledge gained through safety training in my job.[19]

I share knowledge gained with co employees

I gained knowledge through mode of training

I feel safety trainings must be continuous

I am in a position to give suggestions after safety trainings.

I trust safety is everyone’s responsibility

I motivated through safety trainings towards safety

Safety trainings have influence on my behavior.[20]

I feel everyone must exchange safety issues with others

I correlate learning’s of safety trainings at workplace

I practice and implement good housekeeping procedures.

I apply &follow safe operating procedures [21]

I follow work permit system wherever required. [21]

I follow safe material handling procedures.

I correct unsafe conditions at work place.

I follow safety issues relating to occupational hazards/diseases.

20 21

I report unsafe acts/conditions/near misses for rectification [21] I take shortcuts that involve little or no risk

My work behavior was influenced after safety trainings.

I always follow safety rules irrespective of risk involved.

I give importance to safety than work if risk is associated

I will not carry out work activities that are forbidden

SDA

2 Data Collection The suitability of the instrument to meet the purpose of the research is tested by conducting pilot study. The pilot study is useful tool to avoid mistakes in the real research and no one can write a perfect instrument, even though researcher has years of experience in developing instruments [22]. The developed questionnaire was circulated to safety professionals to ascertain reliability and internal consistency.

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To conduct factor analysis, the questionnaire after examining the internal consistency was circulated to employees of all levels who have undergone safety trainings during the period Jan 2012 to Dec2012 are requested to exercise their options on 1-5 likert scale (1- strongly disagree and 5- strongly agree). A total of 500 questionnaires were sent equally to the respondents working in construction industry through e-mail and covering both infrastructure/ real estate segments. The process of sending and collecting responses from respondents took four months (Jan2013 to April 2013).The responses were verified for completeness and 426(85%) are found suitable for further analysis. 3 Analytic Hierarchy Process (AHP) In order to evaluate the impact of elements of safety trainings in construction industry in India, AHP methodology was employed. The Analytic Hierarchy Process (AHP) is a theory of measurement through pair wise comparisons and relies on the judgments’ of experts to derive priority scales. It is these scales that measure intangibles in relative terms. The comparisons are made using a scale of absolute judgments that represents how much more; one element dominates another with respect to a given attribute. The judgments may be inconsistent, and how to measure inconsistency and improve the judgments’, when possible to obtain better consistency is a concern of the AHP [23].AHP involves the decomposition of a complex problem into a multilevel hierarchical structure of characteristics and criteria with the last hierarchical level constituting the decision alternatives [24]. AHP is a systematic procedure that organizes the basic rational of the decision problem by breaking it down into smaller parts, then calling for a simple comparison with respect to pairs of judgments to develop priorities within each level of hierarchy. Finally, results are synthesized to obtain overall weights of the alternatives. 3.1Steps involved in AHP The following paragraphs briefly describe the steps involved in AHP[25], Step 1: Breaking down the decision problem into a hierarchy of interrelated decision elements .This hierarchy consists of at least three levels, the goal of the decision problem is placed at the top, the second level includes the criteria affecting the decision, and the last level contains the alternatives, which are to be evaluated and compared. Additional sub- criteria levels may be added where needed. Step 2: Comparing the elements in each level in pairs using Saaty's scale, which is shown in Table 2. These comparisons are made using judgments based on knowledge and experience in accordance with their contribution to the main element in the level immediately above.Due to reciprocity, the number of needed comparisons for (n) criteria is given by n*(n-1)/2. Table 2 Intensities of Relative Importance for Pair wise Comparison Intensity 1 3 5 7 9 2,4,6,8

Definition Equal importance Moderate importance of one over another Essential or strong importance Demonstrated importance Extreme importance Intensities values between the two adjacent judgments

Step 3: Calculating the average relative weight vector. Step 4: Calculating the relative weights of the alternatives with respect to each criterion. For (n) criteria and (m) alternatives, the relative weights of the alternatives with respect to all criteria will form an m x n matrix. Step 5: Evaluating the consistency of the resulting weights Consistency is evaluated using the principal eigen values (λmax) which is calculated through multiplying the pair-wise comparison matrix by the corresponding weights vector, then dividing the resultant matrix by the weights vector. Finally, the average value of the resultant vector λmax is calculated. Once the value of λmax is obtained, it is compared with the pair-wise comparison matrix size (n). If λmax = n, a perfect consistency is said to exist, otherwise, there is an inconsistency with respect to the pair comparisons. Inconsistency is calculated using the consistency ratio (CR), CR = CI/RI (1) Where RI is a random number index, the values of which are shown in Table 3.CI is a random index of a randomly generated reciprocal matrix and it is calculated as, CI = (λmax – 1) / (n – 1) (2) While, CI is a random index of a randomly generated reciprocal matrix and it is calculated as, Table 3: Reference values of RI N

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.51

If CR < 0.1, then with respect to the pair comparisons are said to be consistent, otherwise, reasons contributing to lack of inconsistency are investigated, and logic is used to revise the comparisons until CR is acceptable. Step 6: Calculating the overall weights of alternatives. The overall weights are determined by multiplying the relative weights of an alternative with respect to criteria by the relative weights of the corresponding criteria and summed over all criteria.

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III Results Firstly,the factor analysis has been carried out to develop a construct for options of impact of elements of safety trainings in construction industry in India. 1Pilot study Preliminary study was conducted to ascertain the reliability and internal consistency of the questionnaire. A total of 100 safety professionals working in construction industry are requested offer their remarks on 1-5 scale (1strongly disagree and 5- strongly agree) to measure internal consistency of the questionnaire. The respondents are safety professionals response rate returned questionnaires is 90%.The data was inputted into MINITAB statistical software and conducted reliability analysis that is Cronbach’s Coefficient α and the value for all options of the questionnaire is more than 0.75. If the Coefficient α value exceeds .7, it shows that the questionnaire has high reliability [26].The questionnaire was utilized without any changes to conduct factor analysis. 2 Factor analysis The correct responses 426 were utilized to conduct factor analysis by principal component method &varimax rotation,using MINITAB.Twelve options failed to load more than 0.65 and were not considered and remaining 14 options which are loaded more than 0.65 are considered for furthur analysis. The options failed are 1,4,6,7,11,13,15,17,18,20 and 21 . The fourteen options are grouped in four criteria as safety knowledge,safe behaviour, safety attitude & safe practices and the details are shown in Table 4. Table 4 Results of Factor Analysis Criteria safety knowledge

safe behaviour

safety attitude

safe practices

Item No 3 8 2 5 22 24 23 25 12 10 9 16 14 19

F1 0.950 0.932 0.929 0.923

0.981 0.966 0.964 0.956

α 0.824

0.786

0.969 0.961 0.893

0.886

0.969 0.957 0.858

0.904

3 Hierarchical structure of impacts of safety training Decision making hierarchical structure of impacts of safety trainings in construction industry is shown in Fig1.The impact of safety trainings consists of three levels that is objectives, criteria and alternatives. First level (Objective) is to identify the impacts of safety trainings and ranking them is the objective of the research. Second level (Criteria) is based on factor analysis results that are safety knowledge,safe behaviour, safety attitude and safe practices.Third level is to examine the validity and reliability of the alternatives related to every criterion. Figure 1 Impacts of safety trainings

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A group of six evaluators were interviewed for evaluating impacts of safety trainings .The evaluators are safety trainers, safety consultants and academicians in the field of safety engineering and all the evaluators are having more that 15 years of experience in their respective field of occupational health and safety. The objectives of the study was explained to the evaluators and requested to perform the rating. A matrix was obtained as results of pair wise comparisons.Inconsistency ratios were calculated to verify the consistency of the comparison process. The computations and analysis of interview findings were made using Analytic Hierarchy Process (Expert Choice, 2002). Table 5 shows the weights of the second level criteria of impacts of safety trainings. It should be mentioned that the consistency rate of this model is 0.035. Table 5: Weights of safety training evaluation criteria Criteria

Weight(percentage)

Priority

Safety knowledge Safe behaviour

0.146 0.489

3 1

Safety attitude

0.319

Safe practices

0.046

The weight of the third level sub criteria, which shows the prioritization of the elements of safety training impacts based on weight percentage, are presented in Table 6. Table 6:Weights of elements of safety training impacts(third level) S.No 3

Critical Factors Safety knowledge I got opportunity to learn about safety issues associated with job.

Abbreviation

Weight %

Rank

Opportunity

0.656

I am in a position to give suggestions after safety trainings.

Suggestions

0.170

I have understanding about site safety rules

Understanding

0.114

I share knowledge gained with co employees

Knowledge sharing

0.060

Safe behaviour 22

My work behavior was influenced after safety trainings

Work behaviour

0.137

I give importance to safety than work if risk is associated

Importance to safety

0.509

I always follow safety rules irrespective of risk involved

Follow safety rules

0.045

I will not carry out work activities that are forbidden

Work execution

0.309

Safety attitude 12

I feel everyone must exchange safety issues with others

Exchange safety issues

0.089

I motivated through safety trainings towards safety

Motivation

0.559

I trust safety is everyoneâ&#x20AC;&#x2122;s responsibility

Trust

0.352

I follow work permit system wherever required.

Work permit system

0.249

I practice and implement good housekeeping procedures

House keeping procedures

0.157

I follow safety issues relating to occupational hazards/diseases.

Occupational hazards&diseases

0.594

Safe practices

IV Conclusion Criteria for successful training program are identified. They are safety knowledge, safe behavior, safety attitude and safe practices. The most important criteria as per the results of impacts of safety training are safe behavior and safety attitudes which represent 49% and 32% respectively. As per Heinrich theory of accident prevention, the accidents are mainly due to unsafe acts (88%) and unsafe conditions (10%).The results of the study shows that safe behavior and safety attitudes contribute to 81% due to outcome of safe trainings and these two criteria are the major factors for unsafe actions which results in accidents. Focusing safety training programmes on safety behavior and attitudes will have positive effect in reducing accidents. Elements of each sub criteria was identified and ranked. Basing on the rankings, it is concluded that the employees have got opportunity to learn about safety issues associated with work, to follow issues relating occupational hazards& diseases which will have long term effect on the health of the employees and motivating employees towards safety is a tool to develop safety culture in the industry. Conducting similar studies at the organization level is useful to prioritize the criteria and sub criteria of outcome of safety training programmes and to initiate measures to overcome deficiencies.

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References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26]

P.Hamalainen,K.L.Saarela and J.Takala, “Global trend according to estimated number of occupational accidents and fatal workrelated diseases at region and country level”. Journal of safety Research, vol.40, 2009, pp.125-139. B.Fernandez-Muniz,J.M.Montes-Peon and C.J.Vazquez-Ordas,”Safety management system: Development and validation of a multidimensional scale”,Journal of Loss Prevention in the Process Industries,vol.20,2007,pp. 52-68. M.Cooper, Health and Safety Training,2nd ed., London: Financial Times Management Briefing,1998. M.J.Burke, “Relative effectiveness of worker safety and health training methods”, American Journal of Public Health, vol.96 (2), 2006, pp. 315-324. D.L.Goetsch, Occupational Safety and Health for Technologists, Engineers and Managers. New Jersey, Prentice Hall,2005. R.C.Jensen, “Safety training Flowchart model facilitates development of effective courses”, Professional Safety ,vol.4,2005,pp.26-37. H.Lingard, “The effect of first aid training on Australian construction workers' occupational health and safety motivation and risk control behavior”, Journal of Safety Research ,vol.33, 2002,pp. 209-230. R.C.Sinclair, “Evaluation of safety training program in the three food service companies”, Journal of Safety Research,vol.34,2003,pp.547-558. S.C.Gillings and B.H.Kleiner, “New development in health and safety programmes”. Work Study, vol.42(5),1993,pp. 9-12. T.Marsh, “Improving safety behaviour using goal setting and feedback”, Leadership &Organization Development Journal, vol.16(1),1995,pp. 5-12. D.L.Fender, “Student and Faculty Issues in Distance Education Occupational Safety and Health Graduate Programs”, Journal of Safety Research,vol.33,2002,pp. 175-193. S.C.K.Yu and B. Hunt, “A Fresh Approach to Safety Management System in Hong Kong”. The TQM Magazine,vol.16(3),2004,pp.210-215. J.J.Johnston,G.T.T. Catteledge and J.W.Collins, “The efficacy of training for occupational injury control”, Occupational Medicine: State of the Arts Reviews, vol.9(2),1994,pp.147-158. E.F.I.Holton,”The flawed four-level evaluation model”, Human Resource Development Quarterly, vol.7(1),1996. R.A.Noe, Employee training & development,4th ed., New York: McGraw-Hill,2008. H.Aguinis andK.Kraiger,”Benefits of training and development for individuals and teams, organizations and society” Annual Review of Psychology,vol.60,2009,pp. 451-474. S.Yamnill and G.N.McLean “Theories supporting transfer of training”, Human Resource Development Quarterly, vol.12(2),2001. D.L.Kirkpatrick, Evaluating Training Programs The Four Levels,2nd ed.,SanFrancisco: Berret Koehler,1998. K.C.Cheung and J. Spicket, “Laboratory safety training: influence on knowledge and attitudes of undergraduate students in Hong Kong”, Journal Occupational Health Safety, vol.23(2), 2007,pp.187-194. S.Mukherjee, “ Evaluating of worker safety and health training”, American Journal of Industrial Medicine,vol. 38,2000,pp. 155163. M.J.Burke, “General safety performance: a test of a grounded theoretical model”, Personnel Psychology,vol.55,2002,pp. 429457. M.A.Pett, N.R.Lackey and J.J.Sullivan, Making sense of factor analysis Thousand Oaks: Sage Publications,2003. Thomas L. Saaty, “Decision making with the analytic hierarchy process”, International. Journal of Services Sciences, Vol. 1, 2008 ,pp.83-98. J.Sevdel,“Data envelopment analysis for decision support”, Industrial Management &Data Systems, Vol. 106 ,2006, pp. 81-95. Salah R. Agha, “Evaluating and Benchmarking Non-Governmental Training Programs: An Analytic Hierarchy Approach”, Jordan Journal of Mechanical and Industrial Engineering, Vol. 2,2008,pp. 77 – 84. R.F.DeVellis, Scale development: Theory and applications,2nd ed., Thousand Oaks: Sage Publications, 2003..

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net A Survey on Data Mining Techniques for Customer Relationship Management S. Janakiraman1, K. Umamaheswari2 Pondicherry University, Puducherry, India 2 Research Scholar, Bharathiar University, Coimbatore, India ______________________________________________________________________________________ Abstract: CRM is basic need for any organizations for retaining and attracting the most valuable customers. In Corporate world, “Customer Retention” strategy in CRM is an increasingly pressed issue. For better CRM, Data mining techniques play a vital role by extracting the information of customer from the database. Data mining can help service sector like banking, insurance, and telecommunication to make crucial business decisions. The aim of the paper is to give a summary on the applications of data mining in the customer relationship management domain. This review paper explores how data mining techniques such as K-means, SVM, Decision tree, Neural Network etc which has been developed to support for customer relationship management process. Keyword: Data Mining, Data Mining Applications, Customer Relationship Management, Review, CRM. ________________________________________________________________________________________ 1

I. Introduction In the present era marketing strategy changed from product oriented to customer oriented concept. Customer expectations are always increasingly and business services must increase along with their expectations. CRM used as a business tool to identify, select, acquire and develop its profitable customers. Today organizations are faced with the challenge of how to collect, analyze and manage such large volume of data. We need new technologies to manage this complication Customer relationship management is a process of managing interactions between a company and its customers and its help us to increase the business revenues.. Data mining is a suitable tool of one such technology which can help the business to make better decisions. By the use of data mining techniques, organizations can extract the hidden information of the customers. So easily can determine the values of customers and predict the future behaviors and requirements . With the significance in mind, this focuses on surveying how data mining techniques applied in customer relationship management through a literature review. For article filtering we search the keyword “data mining” and “CRM”. The article is carefully reviewed to eliminate those articles that are not related to application of data mining in customer relationship management. The purpose of the paper is to review literature related to the application of data mining techniques for customer relationship management in various academic journals. We organize this paper as follows, first briefly describes about the data mining, CRM and its tasks. Secondly, how data mining techniques used for CRM are summarized, Third, the literature survey are discussed, fourth about the benefits of CRM and finally the conclusion of the study are described . A. Data Mining Data mining refers to extracting or “mining” knowledge from large amount of data. Data mining as a synonym for another popularly used term, knowledge discovery from data or “KDD” The goal of this technique is to find pattern that was previously unknown data [3]. Data mining is an essential step in the knowledge discovery in databases (KDD) process. The process of discovering useful knowledge from a huge data is called Knowledge discovery in database. Selection: Selecting data relevant to the analysis task from the database. Preprocessing: Removing noise and inconsistent data, combining multiple data sources. Transformation: Transforming data into appropriate forms to perform data mining Data mining: Choosing a data mining algorithm which is appropriate to pattern in the data; extracting data patterns. Interpretation/ Evaluation: Interpreting the patterns into knowledge by removing redundant or irrelevant patterns. Translating the useful patterns into terms that human understandable. A.1. Challenges of Data Mining There are many challenges faced by data mining as follows and these challenges of data mining are pointed as follows [27] Scalability, Complex & heterogeneous data, Data Quality, Data ownership and Distribution, Dimensionality, Privacy presentation, Streaming Data. A.2. Trends in Data mining Some of the trends in data mining that reflect the pursuit of these challenges [28] Application exploration, Scalable and interactive data mining methods, Integration of data mining with database systems,

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Standardization of data mining language, Visual data mining, New methods for mining complex types of data, Biological data mining, Data mining and software engineering, Web mining, Distributed Data mining, Real time or time-critical data mining, Graph mining, link analysis and social network analysis, Multi-relational and multidatabase data mining, and Privacy protection and information security in data mining A.3. Pros and cons of Data Mining Data mining has a lot of pros and cons depend on which specific industry used. The below are listed pros and cons of data mining. [38] Pros of Data Mining: Finance/Banking: Data mining supports for financial activities in banking such as maintain the loan information of customers and credit card reporting. Marketing/ Retail: It helps marketing companies to build the model based on the historical data such as online marketing, direct mail etc. Same as marketing ,data mining bring lot of benefits in retail industry. Manufacturing: By applying data mining techniques, manufacturers detect faulty equipments and determine optimal control parameters. Government: Data mining helps government agency by digging and analyzing records of financial transactions to build patterns that detect money laundering or criminal activity. Cons of Data Mining Privacy Issues: Because of the privacy issues, people are afraid of their personal information is collected and used in unethical way. Security Issue: Security is a big issue. In Business industry, how the information is taken care is a big question. Misuse of information: The information may be exploited by other persons and used for decision making. It will cause serious problems. B. Customer Relationship Management In the era of cut throat competition, the customer is considered as the King. Data mining can be helpful in all the phases of Customer relationship cycle such as Customer Acquisition, Increasing Value of the Customer and Customer Retention.[23]. CRM consists of three words Customer, Relationship, and Management [32] Customer: Customers are those engines without which business cannot run. So, customer’s become very important. Relationship: That attachment which organizations have to make with their customers is called relationship. Management: It involves all those activities which requires to plan organize motivate and control relations with customers. B.1. Definition of CRM CRM defined “the process of acquiring, retaining and growing profitable customer which requires a clear focus on service attribute that represent value to the customer and creates loyalty” [1]. CRM is relationship marketing, which aims are improving the long-term profitability of customers through moving away from product-centric marketing to customer-centric [6]. CRM is defined as an opportunity to increase profit, attracting and retaining economically valuable customers through removing “economically invaluable” ones.[4] B.2. CRM Dimensions According [1] CRM consists of four dimensions: Customer Identification, Customer Attraction, Customer Retention and Customer Development. These four steps can be considered as closed loops. Customer Identification: CRM begins with customer identification which is referred to as customer acquisition. This phase involves targeting the population who are most likely to be a customer or profitable to the company. Elements of customer identification include target customer analysis and customer segmentation. Customer Attraction: This is the following phase after the customer identification. It attracts the target customers. After identifying the segments of potential of customers, organizations can direct effort and resources into attracting the target customers. Elements of customer attraction are Direct Marketing. Customer Retention: This is the central concern for CRM. Customer satisfaction which refers to the comparison of customer expectations, it is the essential condition for retaining customer’s .Elements of customer retention includes one-to-one marketing, loyalty programs and complaint management. Customer development: This involves consistent expansion of transaction value and individual customer profitability. Elements of customer development include customer lifetime value analysis, up/cross selling and market basket analysis. Market analysis goal is to maximizing customer transactions and reveal trends in customer’s behaviors. II. Data mining techniques for CRM Each of the CRM elements can be supported by different data mining models, which generally include classification, association, clustering, regression, sequence discovery and visualization. [1] Classification: Classification is a supervised learning approach. It not only enables the study and examination of the existing sample but also enable to predict future customer behavior through classifying database records into a number of predefined classes based on criteria. Classification builds up and utilizes a model to predict the

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categorical label of unknown objects. Common classification technique is neural network, the naïve Bayes technique, decision tree and support vector machines. Association: Association aims to establishing relationships between items which exist together in a given record .Market basket analysis and cross selling programs are typical examples for which association modeling is usually adopted. For analyzing the customer data the association rules are used. Clustering: Clustering techniques identify meaningful natural groupings of records and group customers into distinct segments with internal cohesion. Simply it is grouping similar objects. Clustering is an essential task in data mining. Regression: Regression is a kind of statistical estimation technique used to map each data object to real value to provide prediction value. The regression technique is typically undertaken mathematical models [35]. Regression is a powerful tool for summering the nature of the relationship between variables and for making prediction of likely values of the dependent variables. Common tools for regression include linear regression and logistic regression common tools for sequence discovery are statistics and set theory. Forecasting: Forecasting estimates the future value based on a record patterns. Prediction estimates numeric and ordered future values based on the future values base on patterns of data set.[36]. Time series data can be used for business gain if the data is converted into information and then into knowledge. Sequence discovery: Sequence discovery is the identification of associations over times or pattern over time. Sequential pattern mining has become the challenging task in data mining due to complexity. Most common tools are statistics and set theory. Visualization: Visualization refers the presentation of data so that users can view complex patterns. Visualization involves mapping of the data into some types of drawing or graphical objects. The visualization also helps in acquiring knowledge more comprehensively and most important, very quickly. [37] Data can be presented in visual form ,such as curves, surfaces like graphs. Data in a database can be viewed at different combinations of attributes. A. Applications of data mining for CRM The applications of data mining are divided into following various categories [ 3 ]. Some of the few applications domains and discussed how data mining tools should be developed for the such applications. Financial data analysis: Financial data collected in the banking and financial industry are often relatively complete, reliable and of high quality which facilities systematic data analysis and data mining. Some examples cases such as Design and construction of data warehouses for multidimensional data analysis and data mining, Loan payment prediction and customer credit policy analysis, Classification and clustering of customers of targeting marketing, Detection of money laundering and other financial crimes. Retail Industry: It is the major application area of data mining because to identify customer buying behavior, achieve good customer retention. Retails data mining can help identify customer buying behaviors and achieve better customer retention and satisfaction. Some examples of retail industry are Design and construction of data warehouses based on the benefits of data mining, , Multidimensional analysis of sales, customers , products , time and region, Analysis of the effectiveness of sales campaigns, Customer retention . Health Care and Insurance Industry: The growth of insurance industry entirely depends on the ability of converting data into the knowledge information or intelligence about customer’s competitors and its markets. Data mining also applied in claims analysis such as identify which medical procedures are claimed together. The techniques which are used for fraud detection in insurance. In insurance, four categories such as home insurance, life insurance, motor insurance, medical insurance. Among the four, the motor and medical insurance have much more fraud problems. The data mining techniques which are more helpful for detecting the fraud in the insurance sector. Telecommunication industry: Telecommunication market is rapidly expanding and highly competitive. It creates a demand for data mining in order to understand the business, identify the telecommunications patterns and improve the quality of service. The scenarios for which data mining may improve telecommunications services as such as Multidimensional analysis of telecommunications data, Fraudulent pattern analysis and the identification of unusual patterns, Mobile telecommunications, use of visualization tools in telecommunications data analysis. III. Literature Survey Keshav Dahal et al., [5] proposed a new set of features for enhanced the classification data mining models such as Naïve bayes. Naïve Bayesian classifier is a statistical classifier based on Bayes Theorem and maximum posteriori hypothesis .This classifier is so common and easy to implement and fast. The experiment were conducted using the various classification algorithms and compare the performance among then classifiers such as decision tree, neural network etc. and they found that Naïve Bayes classifiers had helped in large deal to solve many complex problems. He improved the lot of changes to enhance the performance and accuracy better. It considers for making classification useable is to identify a similar group of data from the whole training set of data and then training each group of similar data. For better results, K-means clustering used to split the training data and then train each group with Naïve Bayes Classification algorithm. The results proved that the proposed

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models (ECNBDMM-I and ECNBDMM-II) provide better result in terms of classification accuracy than conventional Naïve Bayes classifier. It has shown better results than the other classifier (Decision tree. Neural network etc). The experiment analysis was tested with the help of thyroid benchmark dataset.. It also tested proposed model with other benchmark dataset and attained higher classification accuracy than the naïve bayes classifier. This achieves better results for a data mixing up with supervised and unsupervised learning Narender Kumar et al., [6] used K-means method to develop a model to find the relationship in a customer database. Cluster analysis (K-means) find the group of persons belongs which criteria. The customer data of LIC have taken for the experiment. Only the age and three premium policy are used for analysis. Cluster analysis using K-means to find the distance between the three customers. K-means is suitable technique for cluster analysis. It may set a path and make a good relationship between the customer and insurance policy organization. This method is to find the cluster (C1) have the three customers (S1,S2,S10) which satisfied with all the benefits terms and conditions of cluster same as the S1,S2,S10 then allocated the cluster C1. Cluster C2, C3 allocated as the cluster C1.It will increase the profit of the organization. Clustering optimization method is used to find the appropriate or local optimal solution. Indranil Bose et al., [7] projected on two-stage hybrid models to combine unsupervised learning technique with supervised learning technique. It developed a model for the prediction of customer churn. The important decision in customer churn management is the separation of churners from non-churners. Decision tree model are very popular in prediction of churn. It used multiple variables for clustering and examines different approaches of hybridization for utilizing the results of clustering in order to build supervised learning models for prediction of churn. In the hybrid method, clustering used as a first stage and decision tree used as a second stage. C5.0 decision tree model is found be efficient compared to other C4.5 algorithm. C5.0 decision tree models with boosting improved the performance of models in term of top decile lift. Three customers churn dataset used in this paper. Yaya Xie et al., [8] developed the standard random forests approach in effectively for predicting the customer churn. In this study, proposed a improved balanced random forests method (IBRF) . The experimented were conducted with the help of real bank customer churn dataset. The dataset extracted from the bank’s data warehouse. IBRF proved that better prediction results among the random forests such as balanced random forests and weighted random forests. The proposed method combines the two random forests such as balanced random forest and weighted random forest. IBRF is better than that of ANN, DT and SVM. This method to be proven that better accuracy, faster training speeds. S. Balaji et al., [11] focused on Naïve Bayesian Classification algorithm for customer classification and prediction on Life Insurance of customers and used Naïve Bayes classification for classifying the customers from the huge dataset. It also examines the challenges of using data mining technology for predicting the customer behavior. In this analysis, they have experimented on 10.000 sample of Life Insurance of customers the raw data can be converted into meaningful information and then into knowledge for which predictive data mining techniques are used. They experimented with classification technique namely Naïve Bayes Classification and Data collected from IRDA Dataset of Life Insurance Corporation of India. For experimental analysis, we eliminate some attributes because too many attributes used it is difficult to interpret. In depth discussion, finally 7 attributes only taken, predicting the class label using naïve bayes classification. In this paper, posteriori classification process applied for the data. It clearly proved that the naïve bayes classifier is more better than other classifier to conduct the policy preferences towards the customers. This technique helps us to increase the revenue of the organization Prabha Dhandayudam et al,. [12] attempted to improve clustering algorithm for segmenting the customer using RFM (Recency, Frequency, Monetary) values. Then the performance of the algorithm compared with other traditional techniques such as K-means, single link and complete link.RFM is very effective method for customer segmentation. For segmenting the customers, the attribute R, F and M are used as three in clustering techniques. For finding the distance between from each object to all other object, here Manhattan distance used and store it in distance matrix. It experimented with real data set of the customer transaction details are used for clustering. In each iteration the pair of each cluster of same distance is merged in parallel instead of merging only one pair of cluster at a time. The parallel merging of clusters pairs improves the quality of clustering algorithm. It will improve the performance the clustering algorithm better than the other traditional clustering algorithm. The performance of the clustering algorithms were measured in term of four criteria (MSE, Intra cluster distance, Inter Cluster distance, Intra cluster distance divided by inter cluster distance .In this paper, the cluster technique used for customer segmentation. P. Isakki alias Devi et al., [13] developed a method to design retail promotions, informed by product associations observed in the same groups of customers. It used the Clustering and association rule find to identify customer behavior. It can easily predict the sales. The customer with similar purchasing behavior are first grouped by means of clustering techniques such as K-means method and for each cluster an association rule (Apriori algorithm) to identify the products that are brought together by the customers. Association rules are adopted to discover the relationship and knowledge of the database. It proved that apriori algorithm is the

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most well known association rule mining algorithm because it is easily found the frequent item datasets from the database. Data analysis done by the open source data mining tool such as WEKA. Analysis of customer behavior aims to improve the overall performance of the enterprise. This paper focused on getting more customer satisfaction. Bart Baesens et al., [15] focused on introducing a measure of a customer future spending evolution . It improves marketing decision making. Bayesian network classifier used for customer life cycle slope estimation problem. They concluded that Bayesian network classifiers are performed well in predicting the future customer evolution. It augmented that loyal customers be always a regarded as a homogeneous group of profitable customers of a company, In this study,. Bayesian network classifiers have a good performance. This major focused the predictability of the sign of the slope and compare the performance of Bayesian network with other artificial intelligence technique .In this study , they tried to acknowledge the heterogeneity in the long-life customer and it is proved that possible to predict the slope of customer life cycle of long life customers. (TAN) Tree Augumented Naïve Bayes Classifiers were presented extension of naïve bayes classifier.To measure the performance of classifier , the (PCC) performance of correctly classified used .In this paper, clearly stated that bayesian network classifier is suitable for the customer lifecycle estimation problem and Markov blanket concept effective for attribute selection. Table 1: list of articles according to data mining task, techniques and limitations S.No

References

Data mining task

Data mining Techniques

Limitations

[5]

Classification, Clustering

Naïve Bayes classification algorithm-means

Assumes independence of features.

[6]

Clustering

K-Means

problems occurs when empty clusters, or cluster differing in size and densities

[7]

Clustering, Prediction

[8]

Classification

5 .

[11]

Classification, Prediction

[12]

Clustering

K-means, Single link, Complete link

[13]

Clustering. Association, Prediction

K-means Apriori algorithm

[15]

Classification

[18]

Clustering

10.

[19]

Classification, Clustering

Decision tree, C5.0,SOM, FCM BIRCH ,Boosting Improved balanced random forests method Naïve Bayes classification algorithm

Bayesian Network classifier Adaptive resonance Theory 1 algorithm Semi supervised learning algorithm

Each node in CF tree can hold only a limited no of entries It most robust to noise The values of attributes are assumed to conditionally independent of another Traditional k-means takes long time compared with improved clustering algorithm Low comprehensibility used algorithms have too many parameters means obtained rules far too many. Sometimes, it computationally infeasible It is effective only handle the large amount of data. Cannot say too much in terms of convergence

Manjari Anand et al., [18] analyzed the performance of ART (Adaptive Resonance Theory ) algorithm for the classification of the customer on the basis of their choices. The experimented were conducted using the database of the customers of the company dealing with selling of the vehicles. For classification of customers using ART algorithm. The performance of this algorithm compared with back propagation algorithm. It proved ART is more efficient algorithm to use for the customer classification in CRM. This algorithm taken only less time to provide the customer classification The time complexity of this algorithm is less than the backpropagation algorithm The algorithm was implemented in MATLAB 7.0. Siavash Emtiyaz et al., [19] investigated the use of technique such as semi-supervised learning. It is used to predict the category of an unknown customer. Semi- supervised learning (SSL) is a halfway between supervised and unsupervised learning. Self-training is a commonly used technique for semi-supervised learning. For customer behavior modeling, it used self-training algorithm It proposed a model by means of feed forward neural network trained by back propagation algorithm in order to predict the unknown customer. Multi-layer perceptron neural network algorithm in which a back propagation algorithm is used to classifier. Semisupervised machine learning techniques are used to construct customer behavior modeling to improve accuracy. The performance of the semi-supervised algorithms is compared with other algorithms ( SVM, KNN, Naïve Bayes) on the bank data set. This technique can be used with data mining tool Rapid Miner for both labeled and unlabeled data.

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IV. Data mining Challenges & opportunities for CRM Following are the key data mining challenges and opportunities for better customer relationship management.[25]  Non-trivial results almost always need a combination DM technique  There is a strong requirement for data integration before Data mining  Diverse data types are often encountered, which requires the integrated mining of diverse and heterogeneous data  Highly and unavoidably noisy data must be dealt with  Real world validation of results is essential for acceptance.  Developing deeper models for customer behavior.  Acquiring data for deeper understanding in an non-intrusive, low cost, high accuracy manner. A. Benefits of CRM for Customers According [ 4] , we summarize benefits of CRM for customers into the following cases i. Improve customer services, ii. Increased personalized service or one to one service, iii. Responsive to customer’s needs, iv. Customer segmentation, v. Improve customization of marketing, vi. Multichannel integration, vii. Time saving, viii. Improve customer knowledge B. Advantages of using Data mining in CRM Some of the advantages of using data mining in CRM are [41] Fast and accurate access to information for easy answer to customer questions, Increase customer satisfaction and loyalty, Integrated data and advanced tools to data analysis for reporting, Response to rapidly changing business environments and customer needs, and Attract new customers and increase market share. V. Conclusion In any organization, CRM is an important issue and managing the good relations customer has become a strong demand for development of any CRM. It was further more beneficial. .This survey paper categories and summaries from all published technical and review articles in CRM.. From the literature review it is clearly understood that the data mining techniques is very important for the customer relationship management. And its techniques become mandatory for all the kind of service industry. This paper has some assumptions as follows  Publication rates (research on applications of data mining for customer relationship management) will be increase in future.  The hybridization techniques of classification and clustering in order to solve different CRM problems. So this trend also rises in the future.  Data mining is an interdisciplinary research area. Thus in the future data mining development may need integration with various technologies. In this survey paper, we have shown that data mining can be integrated into customer relationship management and enhanced the process of CRM with betterment. In marketing, to retain their customer, CRM is one of the best leading approach. This study shows that data mining techniques in CRM which improve the efficiency of CRM and provide a better prediction ability to the organizations. Data mining will have major impact of customer relationship management and will present challenges for future research. References [1]. [2].

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Rajagopalan, “Analysis of Customer Behavior using Clustering and Association Rules”, International Journal of Computer Applications, Vol. 43, No.23, April 2012, pp.19-27 Priyanka L.T, Neethu Baby, “ Classification approach based Customer Prediction analysis for Loan Preferences of Customers”, International Journal of Computer Applications, Vol. 67, No. 8, April 2013, pp.27-31 Bart Baesens, Geert Verstraeten, Dirk Van den Poel, Michael Egmont – Petersen, Patrick Van Kenhove, Jan Vanthienen, “ Bayesian network classifiers for identifying the slope of the customer lifecyele of long life customers”, European Journal of Operational Research, Vol. 156, 2004, pp. 508-523 Dr. Illango Krihnamurthi and Prabha Dhandayudam,” Enhanced Rule induction algorithm for Customer Relationship management”, An International Journal of Applied Mathematics and Information Sciences, Vol. 7, No. 4, 2013, pp. 1471-1478. I Ketut Gede Darma Putra, A.A.Kt. Agung Cahyawan, Dian Shavitri H, Combination of Adaptive Resonance Theory 22 and RFM Model for Customer Segmentation in Retail Company”, International Journal of Computer applications, Vol. 48, No.2, June 2012, pp. 18-23. Manjari Anand, Zubair Khan, Ravi S. Shukla, “Customer Relationship Management using Adaptive Resonance theory”, International journal of Computer Applications, Vol. 76, No. 6, August 2013, pp. 43-47. Siavash Emtiyaz, Mohammad Reza Keyvanpour, “Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship management “, Advances in information sciences and Service Science, Vol.3, No. 9, 2011, pp. 229-236. Reza Allahyari Soeini, Keyvan Vahidy Rodpysh , “Applying Data Mining to Insurance Customer Churn Management”, IPCSIT, Vol. 30, 2012 S. Balaji, Dr. S.K. Srivatsa, “ Decision tree induction based classifiers for mining Life Insurance databases”, International journal of Computer Science and Information technology & Security, Vol. 2, No. 3, June 2012, pp. 699-703. Vivek Bhambri,”Application of Data Mining in Banking Sector”, International journal of Computer Science and Technology, Vol. 2, No. 2, June 2011, pp.191-201 Kazi Imran, Dr. Qazi Baseer Ahmed, “ Use of Data Mining in Banking”, International Journal of Engineering Research and Applications,Vol.2, No.2, April 2012, pp. 738-742. Harvinder Singh, “ Implementation Benefit Business Intelligence using Data Mining Techniques” , International journal of Computing & Business Research . Md. Rashid Farooqi, Khalid Raza, “ A Comprehensive Study of CRM through data mining Techniques”, Proceedings of the National Conference , Sep 09,2011, New Delhi. Jayanthi Ranjan, “ A Review of Data mining Tools in Customer Relationship Management”, Journal of Knowledge management Practice, Vol. 9, No.1, March 2008. S. Hameeta Begum, “Data mining Tools and Trends- An Overview”, International journal of Emergning Research in Management & Technology ,pp. 6-12 Feb 2013. Olof Wahlberg, Christer Strandberg, Hakan Sundberg, Karl W. Sandberg, “Trends, Topics and under Researched Areas in CRM Research”, Internalnational Journal of Public Information System. Ris Rygieliski, Jyun-Cheng Wang, David C. Yen, “Data mining techniques for Customer Relationship management”, Technology in society, Vol. 24, 2002, pp. 483-502. Dr. U. Devi Prasad, S. Madhavi, “Prediction of churn behavior of Bank Customers”, Business Intelligence journal, Vol 5, No. 1, January 2012. Adela Tudor, Adela Bara, Iuliana Botha, “Data Mining Algorithm and Techniques in CRM systems”, Recent Researches in Computational techniques, Non Linear Systems and Control, pp.265-269. Rajni Arora, “Customer Relationship Management”, International Journal of Research in IT & Management, Vol. 3, Issue 8, August 2013, pp. 48-57. Bhoj Raj Sharma, Daljeet Kaur and Manju, “ A Review on Data Mining: Its Challenges , Issues and Applications,”, International Journal of Current Engineering and Technology, Vol. 3, No. 2, June 2013. Hemlata Sahu, Shalini Shma, Seema Gondhalakar, “ A Brief overview of Data mining Survey”,International Journal of Computer Technology and Electronics Engineering , Vol . 1, No. 3, pp. 114-121. H. Lookman Sithic, T. Balasubramanian, “Survey of Insurance Fraud Detection using Data mining technique”, International journal of Innovative and Exploring Engineering , Vol. 2, No. 3, Feb 2013. Ahmed .S.R.”Application of data mining in retail business”, International conference on Information technology: coding and computing, vol.2, No.2, 2004, pp. 455-459. C.M. Velu , Kishana R. 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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net A Study on Marketing Effectiveness of Sales Promotion Strategies on The Dairy Industry (With reference to Sangam Dairy Vadlamudi of Guntur (D.T), (A.P), India) S Ayyappa Naik Nenavath, Guest faculty, Dept of Commerce and Business Administration, Acharya Nagarjuna University, Guntur, A.P,INDIA ______________________________________________________________________________________ Abstract: Dairying a historical and mythological focus. India is probably the only cow worshipping country in the world. Milk and milk products are the integral part of our rituals and we have the largest liquid milk consuming population of the world. The first mention of milk trading occurred during Mahabharata time (nearly 2500 BC) when butter (milk fat) is taken out of milk to ease movement from Gokul to Mathura. Lord Sri Krishna has been considered as a true cow savior. In the mythological India in Krishna Era if ‘Dwepar’ it is said that rivers of milk were flowing in India and it was free of cost. Sales promotion has been deified as “a direct inducement that offers on extra value or incentive for the product to the sales force, distributors or the ultimate customer with the primary objective of creating a immediate sales”. Keywords: FAO, NDDB, AMUL, APDDCE, MT, Dairy and India. ______________________________________________________________________________________ I. Introduction: Dairying a historical and mythological focus. India is probably the only cow worshipping country in the world. Milk and milk products are the integral part of our rituals and we have the largest liquid milk consuming population of the world. The first mention of milk trading occurred during Mahabharata time (nearly 2500 BC) when butter (milk fat) is taken out of milk to ease movement from Gokul to Mathura. Lord Sri Krishna has been considered as a true cow savior. In the mythological India in Krishna Era if ‘Dwepar’ it is said that rivers of milk were flowing in India and it was free of cost. Ever house hold has cows and surplus milk production. Indian dairy industry has shown an unprecedented growth in milk production from about 51.4 million tons in 1990 to about 115 million tones in 2010 -11. India has emerged as the largest milk producer in the world, but could only expect to reach about 160 million tons by 2020. The overall growth rate of the dairy industry in India is around 4 per cent, which is almost 3 times the average growth rate of the dairy in the world. The milk production grew by 3 million MT per annum from 1992 to 2007 and now we need it to grow at 5 Million Mt per annum in next fifteen years so as to meet the ambitious target of around 183 million MT in 2022. Believe it or not, it looks impossible as on now until the whole system and policies are geared for some radical changes in the areas of breeding, fodder, health and CMP. Dairy till date has not been considered as a full time business by the farmers. Around 70 per cent of milk production is carried out by small and marginal farmers and organized dairy farms with more than 500 animals can still be counted on fingers. Dairying in India is as old as the India civilization itself. Milch animals like cattle and buffalo are a symbol of purity and motherhood in the Hindu religion and domesticated as an integral part of the Indian social system. If the genesis of organized dairying in India is traced, it was rooted only in the early part of 20 th Century, during the pre-independent era, with the establishment of military farms, by the British government. They established these farms to ensure supply of milk for their arm. Consequent to the establishment of organized dairying, in India , milk procurement from rural areas and its processing and marketing in urban areas continued to be a major problem for the growth of the sector. There was no integration between milk procurement, processing and marketing. II. Review of Literature: A review of literature is made relating to the identified research problem to know what has been so far found. Then the relevance and significance of the proposed study are outlined. The study of Graf (1986) articulates that the dairy industry is conformed with a myriad of problems including stagnant prices, increasing costs, surplus, lagging per capita consumption, subsidized imports, an unfavorable international market and imitation dairy products United States consumers are eating more fat than ever, but they are eating less animal fat. The shift to imitation dairy products is reflected in dairy product surpluses and resulting downwards pressure on dairy product prices. The imitation product that threatens that dairy industry most is imitation cheese. Average retail prices for various types of imitation chess are 16 to 32 % lower than natural cheese prices.The following strategies are recommended to counter the imitation problem 1. Reduce price advantage of imported casein through negotiations, to reduce export subsidies.2. Work for clearer, more

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prominent labeling of imitation dairy products. 3. Put more emphasis on better margin blend products, which compete more directly with margarine than butter. 4. Use more of the annual promotion funds from farmer check offs for promoting cheese. Acharya. R. M (2009) felt that to ensure that buffalo production should be economically sound and socially satisfying. There is a need for improvement of production, improvement in methods of handling milk at the source of production and improvement in economic returns from disposal of milk of live animals for breeding, draught or meat. Ezhil Raj. R., Safiullah. A. Md., Selvam. S., Annal Villi. R., Senthilkumar. V (2009) says that Production of dairy tends to be region specific. In the production and marketing of indigenous dairy products, there exist various cost components. The present study done at Uthukuli block in Erode district of Tamil Nadu, famous for butter and curd making, aims to analyze the production of butter in economic term. Suresh, R (2009) assumed that Milk and milk products undeniably have great potential both as a source of precious nutrients and as the basis for functional foods that will contribute substantial health benefits to the consumers. Functional foods have vast potential and are going to be the mega-trends of the future in dairy products beginning with metro-markets and gradually spreading to other big cities and towns. The Indian Dairy Association 2010 (east zone) in association with the Bihar State Co-operative Milk Producers Federation (Compfed) organized a seminar on dairy development in eastern India focusing on the impact of natural calamities on milk production. The seminar also reviewed the giant leap taken by the federation in producing a record quantity of milk and milk products against all odds. III. Objectives and Methodology Need for the study: Today dairying industry has become permanent phenomenon along with the manufacturing industry. Revolutionary changes have taken place in the dairy Industry right from the procurement at the gross root level from the farmer, processing and preservation and distribution of the same unto the ultimate customers, taking the unprecedented experiment of Anand, Gujarat as a model. In the process, lot of problems have to be faced by the farmer, collection centers, issues involved at the factory regarding strategies related to sales promotion. Against this back ground, an attempt is made in this study to make an in depth enquiry in to all the identified issues of Dairy marketing in a scientific manner. Hypotheses: ď&#x201A;ˇ It is also further hypothesized that the present strategy for sales promotion for milk and milk products has been found to be competitive and yielding results. Objectives of the Study: The broader objectives of the study are to evaluate the effectiveness of the strategies of the Sangam Dairy regarding sales promotion of the products. ď&#x201A;ˇ to assess the impact of sales promotion of the Sangam Dairy for its products based on the opinions of the customers; Methodology: Survey method is adopted for the study and both secondary and primary sources of data are used. The secondary data sources include Dairy India, Indian Dairyman, Yozana, kurukshera, National Journal of Rural Development, Indian journal Agricultural Economics, Kisan world etc., Primary data are collected from respondents on preferences and satisfaction about the Sangam dairy sales promotion for the product in selected municipalities of Guntur and Ponnuru Towns. Tools for data collection: The schedules are constructed to collect the primary information from the sample respondents in Guntur district. Data are collected by employing stratified random sampling method. Scope of the study: Guntur district of Andhra Pradesh state has been purposively selected for the present study. Moreover, Guntur district has been found to be on par with the other leading district of the state, so far as the coverage of dairy products used by the respondents of Sangam dairy. The Guntur district has one municipal corporation at Guntur and ten other municipalities which include Tenali, Vinukonda, Sathenapally, Macherla, Narsaraopet, Ponnuru, Repalle, Mangalagiri, Bapatla and Chilakaluripet. Size of the Sample: The present study has employed stratified Random sampling technique for the selection of the sample towns respondents. The study has selected two municipalities at random accounting for 10 per cent of the total municipalities. One is Guntur Municipal Corporation and other is Ponnuru municipality. Among the municipalities, required numbers of 2 wards each have been selected at random. From these wards sample customers to the extent of 10 per cent have been selected. The sample wards and respondents selected are distributed as follow: Sample size of wards and respondents

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Guntur Municipal Corporation Selected No of wards(52 No of Respondents wards) 11 40 21

Total

Ponnuru Municipality Selected No of wards(31 wards) No of Respondents 4

40 80

Sources: Guntur & Ponnuru municipality office

Thus, the present study covers two municipalities of the district and covers 160 respondents or customers both the Guntur Municipal corporation (80 respondents) and the Ponnuru Municipality (80 respondents) representing two sample municipalities. Sales promotion: Marketers have come to recognize that advertising alone is not always enough to move their products in to the hands of customers. Companies also use sales promotion methods targeted at the both customer and wholesalers and retailers and that distribute their products to stimulate the demand. Advertising tells what products to buy and sales promotion tells when to buy. Sale promotion programmes oriented towards both customers as well as trade. Sales promotion has been deified as “a direct inducement that offers on extra value or incentive for the product to the sales force, distributors or the ultimate customer with the primary objective of creating a immediate sales”. The promotion mix: When the firm considers its promotion as a whole, it faces two major decisions. The first is how much total effort to invest in promotion; the second how much relative usage should be made of the different promotional tools. Since promotion is only one of several ways to stimulate company not be spent better in new product development, lower prices, more customer services, or in some other way. In fact, these latter alternatives tend mind. Buyers, if asked, would probably want the company to cut down on promotion (that is, persuasive communication) and use the funds to make the offering itself more attractive. Yet some promotion is essential in order to create customer awareness of the product’s existence and characteristics. Furthermore, promotion can create positive psychological associations that can enhance the buyer’s satisfaction. In this last sense promotion may be considered to add to the real value of the company’s offering. The problem of how much for promotion is not difficult in principal. The total promotional budget should be established at a level where the marginal profit from the marginal promotional dollar just equals the marginal profit from the marginal promotional dollar just equals the marginal profit from using the dollar in the best no promotional alternative. The problem is difficult chiefly because of the lack of data on the effects of investments in promotion versus other sales stimulants or cost-reduction activities. The ratio of the firm’s promotional budget to the total marketing budget is certainly no basis for judging whether the promotional effort is adequate. A relatively high ratio may signify that the firm is trying to compensate for inadequacies in the product, its price or the service. A relatively low ratio may signify that the product’s quality acts in a quasipromotional capacity. The decision on how much for promotion requires a careful analysis of the individual circumstances surrounding each case. A few generalizations might help indicate whether promotion will be a relatively important or unimportant of the marketing mix. In general, promotion will be more important in market where: 1. Products are alike, thus leading manufactures to try to differentiate then psychologically. 2. Products are in the introductory stage of the life cycle, where awareness and interest must be built, or in the mature stage, where defensive expenditures are required to maintain market shares. 3. Products are sold on mail order basis. 4. Products are sold on a self-service basis . 1. Table indicating response regarding source of information regarding the sangam dairy products. Source information of products

Guntur Town No of respondents

Ponnuru Town

Television

Percentage to total 11.25

Movies

Newspapers/magazines

6.25

26.25

Displays the out lets

63.75

Transmit media

2.5

Total

100

Percentage to total

No of respondents

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S Ayyappa Naik Nenavath, International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 62-66 Source: primary data

Source of information: Table No 1 demonstrates the response of the customers regarding source of information of the sangam dairy products. It satisfying to observe from the table that about 51 out of 80 accounting for 63.75 per cent from Guntur town as compared to 40 out of 80 accounting for 50 per cent from Ponnuru town have stated that they received information regarding the sangam dairy products from displays outlets. Closely followed is the news papers or magazines source as 21 out of 80 respondents accounting for 26.25 per cent from Ponnuru town in relation to 12 out of 80 respondents accounting for 15 per cent from Guntur town got the information. Contrast to the above situation, the least no of respondents accounting for 26.25 per cent from ponnuru town got the information from News paper/Magazines against 15 per cent from Guntur town got the information from the transmit media. 2. Table indicating to the response regarding the element which attracts most. Advertisements to products Favorite celebrity Ad given colorfully Product information Kind of serves offered by the product Any other, please specify Total Source: primary data

Guntur Town No of respondents Percentage to total 12 15 16 20 39 48.75 6 7.5

Ponnuru Town No of respondents Percentage to total 14 17.5 12 15 40 50 6 7.5

7 80

8 80

8.75 100

10 100

Element which attracts most: Table No. 2 demonstrates the response of the respondents regarding the elements which attracts most in advertisements. The table provides an opinion mixed in nature. It is satisfying to observe from the table that about 40 out of 80 respondents accounting for 50 per cent from Ponnuru town compared to 39 out of 80 accounting for 48.75 per cent from Guntur town have stated they were the attracted mostly in information in advertisement. Closely followed is the advertisement given colorfully as 16 out of 80 accounting for 20 per cent from Guntur town in relation to 14 out of 80 respondents accounting for 17.5 per cent from Ponnuru town, who stated they were attracted to favorite celebrity. Contrast to above situation, the least number of respondents accounting for 7.5 per cent from Guntur town and Ponnuru town respectively stated that they were attracted to the kind of offer serves offer products. 3. Table indicating to the response regarding remembrance of the celebrity in the recent dairy products advertisement. Celebrity in recent dairy Milk Curd Ghee Milk powder Butter milk Total Source: primary data

Guntur Town No of respondents Percentage to total 42 52.5 8 10 7 8.75 8 10 15 18.75 80 100

Ponnuru Town No of respondents Percentage to total 45 56.25 6 7.5 12 15 7 8.75 10 12.5 80 100

Remembrance of celebrity: Table No. 3 demonstrates the response of the customers regarding remembrance of the celebrity in recent dairy products advertisement. It can be observed from the table that majority of customers of about 45 out of 80 accounting for 56.25 per cent from ponnuru town as compared to 42 out of 80 accounting for 52.5 per cent from Guntur town stated that the milk is major product that the customers remember the celebrities found in advertisement. Closely followed is that 15 out of 80 accounting for 18.75 per cent from Guntur town have celebrity advertisements products of butter milk as against about 15 per cent from Ponnuru town stated the celebrity of the Ghee. From the above analysis, it is pertinent to note that for milk product the celebrity in advertisements used as stated by the majority of customers in both the towns. Contrast to the above the situation, the least no of customers 7.5 per cent from the Ponnuru town stated celebrity to the product is curd and 8.75 per cent from Guntur town as celebrity to the product is Ghee. Suggestions: ď&#x201A;ˇ Packaging technology for extended and longer shelf life of products need to be developed in our own country. It is still very difficult to procure there layer carbon blackened extended milk as well as square or rectangular transport report able trays for long shelf life pannier in India.

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 Members and non members can be informed about the usefulness of dairy cooperative organization and its philosophy through effective and efficient promotional programmes and advertisement methods.  The study reveals that in two towns of Guntur and Ponnuru, the majority of respondents stated that the leakage problems are higher in packaging. The management of Sangam dairy organization has to identify well in advance areas where leakage is occurring and has to take corrective steps at the production level, quality control level, transportation side and lastly, at the time of delivery of products to the customer.  The analysis brings out a fact that there’s mixed opinion among the respondents as to the type of quality required. By taking the facts into account the Sangam dairy has to produce and distribute the milk products as per the requirement of respondents such as thickness as well as freshness and even the element of fat content also.  As the majority of the customers are lured by the advertisement to Sangam dairy Products, it is better, if the advertisement contains the price in addition to the regular product features.  majority of the customers have been delivered with products immediately after placing an order however, it appears that a segment of customer could not get the product on in time. In order to satisfy the needs of all customers to get the products timely, additional arrangements have to be made by the Sangam dairy such as keeping the outlets open for additional time. References: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12 13. 14. 15. 16. 17. 18. 19.

Truman. Graf (1986), “Effect of limitation or filled dairy products”, Journal of dairy science vol 69, no.5, 1986. R.M. Acharya (2009), “Buffalo-India’s Black Gold Need for Better Appreciation, Prioritization of Future Research and Development Programmes,” Indian Dairyman, Vol. 61, No. 5, May, pp. 25 – 30,2009. Animesh Banerjee (2009), “Global Financial Crisis: An Opportunity to Reinvent Indian Dairying,” Indian Dairyman, Vol. 61, No. 2, February 2009, pp. 25 - 28. R. Ezhil Raj, A. Md. Safiulah, S. Selvam, R. Annal Villi, V. Senthilkumar (2009), “Economics of Butter Production in Uthukuli Block of Tamil Nadu”, Indian Dairyman, Vol.61, No.1, January 2009, pp.70-72. R. Suresh (2009), “Milk and Milk Products: Basis for functional Foods,” Indian Dairyman, Vol. 61, No. 4, April, pp. 59 – 64, 2009. Madhuri Kumar(2010), TNN, Meet on dairy development Jan 24, 2010 Abdul Samd, “Dairy Heard Health and Production Management Programme”, P.R. Gupta (ed.), op. cit., pp. 297 – 315, 2007. Benerjee. M & Yadav. S. R, “Production & Marketing of Milk in Central Uttar Pradesh”, Productivity, Vol.44, No. 3, October – December,2003. Capper, J.L., Cady, R.A. & Bauman, D.E, “The environmental impact of dairy production: 1944 compared with 2007”. Journal of Anim. Sci., 87:2160–2167, 2009. Economic Survey, “Economic Division”, Ministry of Finance”, Government of India, New Delhi, 2005-2006. FAO on Global Live Market “Managing Growth is the Challenge”, Dairy India Year book, 6 th Edition, New Delhi, p. 40,2007. Gupta, S.C, “Dairy Education in India,” Indian Dairyman, Vol. 60, No. 6, June, pp. 30 – 35,2008. Khanna, A. S., “Setting up and Management of Viable Dairy Farms”, P.R. Gupta (ed.), op. cit., pp. 173 – 180, 2007. Rao,V.M., “Women dairy Co-operatives”, Rural Credit & Co-operative Development, Deep and Deep Publishing PVT.Ltd., New Delhi 1st Edition, pp. 124 – 131,2006. Sanjib Kumar Hota, “Co-operative in Rural Economy”, Kurukshetra, pp.2-7,August,2000. T. R. Rajarajan “Trade Liberalization and Terms of Trade in Dairy Products in India”, ICFAI Journal of Agricultural Economics, Vol. 3, No. 1, pp. 22 – 26, January-2006. V.Kurien , “Managing Growth is the challenge” Dairy India, year book, p 18,2007. White Paper Draft Committee, “IAI VISION 2020, December 2011, National Dairy plan , New Delhi, April 24,2012. Z.Bouamra Mechemache, Jean-Paul Chavas, “Partial Market Liberalization and the Efficiency of Policy Reform: The Case of the European dairy Sector”, American Journal of Agricultural Economics, Vol. 84, pp. 1003-1020, 2002.

Websites: www.indairyasso.org www.inidadairy.com www.fao.org www.milkmagic.com

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net IMPLEMENTATION OF HUMAN RESOURCE IN TOTAL QUALITY MANAGEMENT Surender Kumar Assistant Professor (Guest Faculty), Department of Commerce Indira Gandhi University, Meerpur (Rewari), Haryana, India __________________________________________________________________________________________ Abstract: As for the importance of the human resource tasks in the implementation of total quality management, it is relevant to ascertain which tasks human resource employees would be assigned in this context. They are expected to be involved in the design and in the implementation of the new human resource policies as a matter of necessity to support the change. Such policies seem like critical symbols of the change so the human resources department contribution is one of the significant areas of total quality management. The human resource department has; however, for a long time been recognized as an ambiguous function the contribution of which to organization aims could be difficult to estimate. Human resource employees may also find it difficult to clearly define their particular expertise for generally all managers are concerned about the management of the staff. However, in recent years a stronger profile of the human resource tasks in the organization has emerged but it does not necessarily lead to a stronger profile of the human resource department, merely because the staff and the management show a higher degree of attention and liability to the tasks of the human resource department. An appropriate documented Quality Management System will help an organization not only achieve the objectives set out in its policy and strategy, but also, and equally importantly, sustain and build upon them. It is imperative that the leaders take responsibility for the adoption and documentation of an appropriate management system in their organisation if they are serious about the quality journey. The Systems section discusses the benefits of having such a system, how to set one up and successfully implement it. Once the strategic direction for the organizationâ&#x20AC;&#x2122;s quality journey has been set, it needs Performance Measures to monitor and control the journey, and to ensure the desired level of performance is being achieved and sustained. They can, and should be, established at all levels in the organisation, ideally being cascaded down and most effectively undertaken as team activities and this is discussed in the section on Performance. _________________________________________________________________________________________ I. INTRODUCTION Total Quality Management is an approach to the art of management that originated in Japanese industry in the 1950's and has become steadily more popular in the West since the early 1980's. Total Quality is a description of the culture, attitude and organization of a company that aims to provide, and continue to provide, its customers with products and services that satisfy their needs. The culture requires quality in all aspects of the company's operations, with things being done right first time, and defects and waste eradicated from operations. Many companies have difficulties in implementing TQM. Surveys by consulting firms have found that only 2036% of companies that have undertaken TQM have achieved either significant or even tangible improvements in quality, productivity, competitiveness or financial return. As a result many people are sceptical about TQM. However, when you look at successful companies you find a much higher percentage of successful TQM implementation. Origins Although W. Edwards Deming is largely credited with igniting the quality revolution in Japan starting in 1946 and trying to bring it to the United States in the 1980s, Armand V. Feigenbaum was developing a similar set of principles at General Electric in the United States at around the same time. "Total Quality Control" was the key concept of Feigenbaum's 1951 book, Quality Control: Principles, Practice, and Administration, a book that was subsequently released in 1961 under the title, Total Quality Control (ISBN 0070203539). Joseph Juran, Philip B. Crosby, and Kaoru Ishikawa also contributed to the body of knowledge now known as TQM. The American Society for Quality says that the term Total Quality Management was first used by the U.S. Naval Air Systems Command "to describe its Japanese-style management approach to quality improvement."[1] This is consistent with the story that the United States Department of the Navy Personnel Research and Development Center began researching the use of statistical process control (SPC); the work of Juran, Crosby, and Ishikawa; and the philosophy of Deming to make performance improvements in 1984. Quality:1. Quality means fit ness for use. 2. Quality means productivity, competitive cost, and timely delivery, total customer satisfaction. 3. Quality means conformance to specification and standard. 4. Conformance to requirements. 5. Quality is what the customer says

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6. Quality means getting everyone to do what they have agreed to do and to do it right the first time and every time. Total Quality:- It means all the people of the organization are committed to product quality by doing right things right, first time, every time by employing organization resource to provide value to customer. II. TOTAL QUALITY MANAGEMENT It is the process designed to focus external/ internal customer expectation preventing problems building, commitment to quality in the workforce and promoting to open decision making. Definition: As defined by ISO: "TQM is a management approach of an organization, centered on quality, based on the participation of all its members and aiming at long-term success through customer satisfaction, and benefits to all members of the organization and to society." In Japanese, TQM comprises four process steps, namely: 1. Kaizen – Focuses on Continuous Process Improvement, to make processes visible, repeatable and measureable. 2. Atarimae Hinshitsu – Focuses on intangible effects on processes and ways to optimize and reduce their effects. TQM requires that the company maintain this quality standard in all aspects of its business. This requires ensuring that things are done right the first time and that defects and waste are eliminated from operations. Total Quality Management (TQM) is a management strategy aimed at embedding awareness of quality in all organizational processes. TQM has been widely used in manufacturing, education, government, and service industries, as well as NASA space and science programs. III. CHARACTERISTICS OF TQM The characteristics of TQM, as revealed from above definition are as follows 1. TQM is customer oriented. 2. TQM requires a long term commitment for continuous improvement of all processes. 3. The success of TQM demands the leadership of top management and continuous involvement. 4. Responsibility for establishments and improvement of systems lies with the management of an organization. 5. TQM is a strategy for continuously improving performance at all levels and in all areas of responsibility. Potential Benefits of TQM:- The advantages of adopting TQM system compared to conventional quality system are numerous and are outlined below. 1. TQM helps to focus clearly on the needs of the market. The traditional approach of quality control focusses on the technical details of a product so as to satisfy the customer. However, the customer longs for different satisfaction perspectives which are generally overlooked in the traditional approach.The needs change from person to person and also from place to place. As TQM focuses on the concept of university, it tries to abstract the satisfaction perceptions of the market and thus helps the organisation to identify and meet the requirements of the market in a better way. 2. TQM facilitates to aspire for a top quality performer in every sphere of activity. It is a well accepted fact that the negative attitudes of employees and no participative culture of the organization pose the greatest hurdle to organizations’ success, growth and prosperity. TQM emphasis, on bringing about attitudinal and cultural change through promotion of participative work culture and effective team-work. This serves to satisfy the higher human needs of recognition and self-development and enhances employee’s interest in the job. The employee’s performance, thus, is not restricted to the product or service areas but reflects in other spheres as well. 3. It channelises the procedures necessary to achieve quality performance. Quality in its true sense cannot be achieved instantly. It requires a systematic and a long-term planning and strategic approach. By focusing on defining the quality policies, goals and objectives, and communicating these properly to one and all in the organization, adopting SQC and SPC techniques and developing and using a system of evaluation , the organization can channelize their efforts to achieve the desired and objectivities quality performance. 4. It helps examine critically and continuously all processes to remove nonproductive activities and waste. The organizations always aim at improving productivity as it leads to reduction in cost resulting in increase in profitability. The efforts in this direction are contributed because of the formation of quality improvement teams which meet regularly and through a systematic approach which tries to remove nonproductive activity. A continuous effort to identify the problems and resolve them helps to reduce the waste. The culture of well being thus improves housekeeping, cost-effectiveness and safety. 5. It gears organizations to fully understand the competition and develop an effective combating strategy. The dynamic changes in the global market and the open market policies adopted by a large number of organizations has resulted in increased competition and for many organizations the survival has become a key issue.

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For this cause it is essential for the organizations to understand the competition and develop and adopt suitable strategies to meet the challenges. As TQM helps to understand the pulse of customer and thus the market, it gives an edge to the organizations of variable nature to meet the competition. 6. It helps to develop good procedures for communication and acknowledging good work. Improper procedures and inadequate communication are yet another bane of many organizations, which result in misunderstanding, confusion, and low productivity, duplication of efforts, poor quality, and low morale and so on. TQM brings together members of various related sections, departments and different levels of management thereby providing an effective vehicle of communication and interaction. 7. It helps to review the process needed to develop the strategy of never ending improvement. Quality improvement efforts cannot be restricted to any time period. They need to be continuous to meet the dynamic challenges. TQM emphasizes on a continuous and periodic review so as to make the required changes. The benefits derived by the organizations, therefore, are many and multifaceted. Many of these can be measured in quantitative terms. However, the intangible benefits, which include enrichment of the quality of the work life and many more, are not quantifiable. At the same time, it has to be established whether they do occur or not in order to prove or disapprove the efficacy of the concept. This can be assessed by a well-planned research project or by carrying out an opinion survey periodically. The tangible and intangible benefits of TQM are variable in nature. IV. SOME USEFUL SUGGESTIONS FROM RESULTS OF TQM IMPLEMENTATIONS If you want to be a first-rate company, don't focus on the second-rate companies who can't handle TQM, look at the world-class companies that have adopted it the most effective way to spend TQM introduction funds is by training top management, people involved in new product development, and people involved with customers. Itâ&#x20AC;&#x2122;s much easier to introduce EDM/PDM in a company with a TQM culture than in one without TQM. People in companies that have implemented TQM are more likely to have the basic understanding necessary for implementing EDM/PDM. For example, they are more likely to view EDM/PDM as an information and workflow management system supporting the entire product life cycle then as a departmental solution for the management of CAD data. Important Aspects of TQM include customer-driven quality, top management leadership and commitment, continuous improvement, fast response, actions based on facts, employee participation, and a TQM culture. Customer-driven quality: TQM has a customer-first orientation. The customer, not internal activities and constraints, comes first. Customer satisfaction is seen as the company's highest priority. The company believes it will only be successful if customers are satisfied. The TQM Company is sensitive to customer requirements and responds rapidly to them. In the TQM context, `being sensitive to customer requirements' goes beyond defect and error reduction, and merely meeting specifications or reducing customer complaints. The concept of requirements is expanded to take in not only product and service attributes that meet basic requirements, but also those that enhance and differentiate them for competitive advantage. Each part of the company is involved in Total Quality, operating as a customer to some functions and as a supplier to others. The Engineering Department is a supplier to downstream functions such as Manufacturing and Field Service, and has to treat these internal customers with the same sensitivity and responsiveness as it would external customers. TQM leadership from top management: TQM is a way of life for a company. It has to be introduced and led by top management. This is a key point. Attempts to implement TQM often fail because top management doesn't lead and get committed - instead it delegates and pays lip service. Commitment and personal involvement is required from top management in creating and deploying clear quality values and goals consistent with the objectives of the company, and in creating and deploying well defined systems, methods and performance measures for achieving those goals. These systems and methods guide all quality activities and encourage participation by all employees. The development and use of performance indicators is linked, directly or indirectly, to customer requirements and satisfaction, and to management and employee remuneration. Continuous improvement: Continuous improvement of all operations and activities is at the heart of TQM. Once it is recognized that customer satisfaction can only be obtained by providing a high-quality product, continuous improvement of the quality of the product is seen as the only way to maintain a high level of customer satisfaction. As well as recognizing the link between product quality and customer satisfaction, TQM also recognizes that product quality is the result of process quality. As a result, there is a focus on continuous improvement of the company's processes. This will lead to an improvement in process quality. In turn this will lead to an improvement in product quality, and to an increase in customer satisfaction. Improvement cycles are encouraged for all the company's activities such as product development, use of EDM/PDM, and the way customer relationships are managed. This implies that all activities include measurement and monitoring of cycle time and responsiveness as a basis for seeking opportunities for improvement. Elimination of waste is a major component of the continuous improvement approach. There is also a strong emphasis on prevention rather than detection, and an emphasis on quality at the design stage. The customer-driven approach helps to prevent

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errors and achieve defect-free production. When problems do occur within the product development process, they are generally discovered and resolved before they can get to the next internal customer. Fast response: To achieve customer satisfaction, the company has to respond rapidly to customer needs. This implies short product and service introduction cycles. These can be achieved with customer-driven and processoriented product development because the resulting simplicity and efficiency greatly reduce the time involved. Simplicity is gained through concurrent product and process development. Efficiencies are realized from the elimination of non-value-adding effort such as re-design. The result is a dramatic improvement in the elapsed time from product concept to first shipment. Actions based on facts : The statistical analysis of engineering and manufacturing facts is an important part of TQM. Facts and analysis provide the basis for planning, review and performance tracking, improvement of operations, and comparison of performance with competitors. The TQM approach is based on the use of objective data, and provides a rational rather than an emotional basis for decision making. The statistical approach to process management in both engineering and manufacturing recognizes that most problems are system-related, and are not caused by particular employees. In practice, data is collected and put in the hands of the people who are in the best position to analyze it and then take the appropriate action to reduce costs and prevent non-conformance. Usually these people are not managers but workers in the process. If the right information is not available, then the analysis, whether it be of shop floor data, or engineering test results, can't take place, errors can't be identified, and so errors can't be corrected. Employee participation: A successful TQM environment requires a committed and well-trained work force that participates fully in quality improvement activities. Such participation is reinforced by reward and recognition systems which emphasize the achievement of quality objectives. On-going education and training of all employees supports the drive for quality. Employees are encouraged to take more responsibility, communicate more effectively, act creatively, and innovate. As people behave the way they are measured and remunerated, TQM links remuneration to customer satisfaction metrics. A TQM culture: It's not easy to introduce TQM. An open, cooperative culture has to be created by management. Employees have to be made to feel that they are responsible for customer satisfaction. They are not going to feel this if they are excluded from the development of visions, strategies, and plans. It's important they participate in these activities. They are unlikely to behave in a responsible way if they see management behaving irresponsibly - saying one thing and doing the opposite. Product development in a TQM environment: Product development in a TQM environment is very different to product development in a non-TQM environment. Without a TQM approach, product development is usually carried on in a conflictual atmosphere where each department acts independently. Short-term results drive behavior so scrap, changes, work-around, waste, and rework are normal practice. Management focuses on supervising individuals, and fire-fighting is necessary and rewarded. Product development in a TQM environment is customer-driven and focused on quality. Teams are process-oriented, and interact with their internal customers to deliver the required results. Management's focus is on controlling the overall process, and rewarding teamwork Awards for Quality achievement: The Deming Prize has been awarded annually since 1951 by the Japanese Union of Scientists and Engineers in recognition of outstanding achievement in quality Strategy, management and execution. Since 1988 a similar award (the Malcom Baldrige National Quality Award) has been awarded in the US. Early winners of the Baldrige Award include AT&T (1992), IBM (1990), Milliken (1989), Motorola (1988), Texas Instruments (1992) and Xerox (1989). OBJECTIVES: Quality is the key to competitive advantage in today's business environment. As more organizations opt for Total Quality Management (TQM), the choices open to those wanting to set up a quality system are becoming increasingly varied. The value of TQM rich experience outlined within the boundaries of the existing quality theory which can easily convince the reader of its applicability in the real world. Principles of TQM: The key principles of TQM are as following: Management Commitment 1. Plan (drive, direct) 2. Do (deploy, support, participate) 3. Check (review) 4. Act (recognize, communicate, revise) Employee Empowerment 1. Training 2. Suggestion scheme 3. Measurement and recognition 4. Excellence teams Fact Based Decision Making 1. SPC (statistical process control) 2. DOE, FMEA 3. The 7 statistical tools 4. TOPS (FORD 8D - Team Oriented Problem Solving)

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Continuous Improvement 1. Systematic measurement and focus on CONQ 2. Excellence teams 3. Cross-functional process management 4. Attain, maintain, improve standard Customer Focus 1. Supplier partnership 2. Service relationship with internal customers 3. Never compromise quality 4. Customer driven standards in its run, and ultimately identify trends, shifts and patterns.        

V. FINDINGS Maximum no. of staff members agreed that the org. Is quality conscious towards employees? All of the staff members says that the org. Have the certification of ISO 9000. Only 58% workers think that org. Providing the quality assurance system and operation. 90% of the staff members agreed that the org. have the quality circle. Approximate half of the workers think that org. have the biweekly meeting of quality circle. All of the staff members agreed that the org. have the quality information system. Most of the staff members having the good relationship with the superiors. Only half of the workers said that the org. provides the right environment to apply knowledge from new programs to job.

VI. RECOMENDATIONS The suggestions I have given for the betterment are explained below:  It is very important to provide the opportunity to the employees of the organization to express their ideas or whatever they want to express.  Management should clear their vision mission and goals towards the employees in the organization.  Management should involve the workers representatives in managerial activities so that the transparency could be maintained and through this they can win the confidence of the employees.  Management should give due importance to mental relaxation &social cultural development of an employees who strives hard for the company.  Reward or Praise/appreciation works as magic for an individual and motivates them for work.  Role clarity of each position should be defined and based on that individuals can plan their work accordingly.  Self-potential system should be encouraged.  There are regular review and comparison of current & past performance to detect gradual deterioration in the strategy.  Proper cooperation should be necessary in the company. VII. CONCLUSION As we Know, Quality is an all pervasive concept and is an ongoing process. This project for TQM in Training Institutions is to be seen as a spearhead for introducing quality concepts into government. The project itself will first focus on the quality aspects in training institutions, covering the faculty, staff, resources, facilities and the programmes. The project would also require all the institutions to develop a long term vision for themselves, a strategy for achieving that vision and a sequence of activities and efforts to implement the strategy This project concludes that Total Quality Management (TQM) has many benefits but implementing TQM is not a bed of roses. It cannot be left to its own fate after the launch and requires constant nurturing and follow-up by the management. Management must keep its fingers on the pulse of TQM efforts as bringing a change in culture, attitudes, and beliefs in a sensitive and delicate matter. Problems in implementation are, therefore, to be expected and are universal in nature. However patience and loyal efforts are required to solve these problems. TQM can lead to a drastic change in the productivity of an org. if implemented properly. In recent years, TQM has been the most focused area of research as compared to other disciplines both in the industrial and academic world. Since the benefits of TQM are many therefore it doesn’t pervade only to all the sectors of the business but also to the society. REFERENCES Ivanceivich, John M., Human Resource Management, Tata McGraw Hill, New Delhi, Dessler, Gary, Human Resource Management, Pearson Education, Shell, Scott and George Bohlander, Human Resource Management, Thomson Learning Inc., Pattanayak, Biswajert, Human Resource Management, PHI, New Delhi

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Surender Kumar, International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 6772 Zikmund, Millian G., Business Research Methods , Thomson Learning , Bombay, Shekhran, Uma, Business Research Method, Miley Education, Singapore Kothari, C.R., Research Methodology T .N. CHABBRA, “HUMAN RESOURCE MANAGEMENT”, NEW DELHI, Dhanpat Rai Publication Ltd. ARTICLES: TQM Tools And Tool Kits”, J.Revelle(Hesan A. Quazi, Samuel R. Padibjo) ; The TQM Magazine (Hesan A. Quazi, Samuel R. Padibjo) ; The TQM Magazine; 09: 5 1997, (R. Jagadeesh) ; The TQM Magazine; 11: 5 1999, (Samuel K. Ho and Svetlana Cicmil) ;The TQM Magazine; 08: 1 1996, WEBSITES http://www.apmforum.com http://www.papermasters.com http://www.allprojectreports.com http://www.google.com www.qaproject.org/images/scatterdiagram.gif www.Wikepedia.Com

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Studying the Degree of Readiness of Small and Medium-Sized Enterprises to Enter the International Markets (Case Study: Food companies) 1

Mohammadreza Hamidizadeh, 2Maryam Zargaranyazd Full Professor, Shahid Beheshti University, Tehran, Iran, 2 Associate, Shahid Beheshti University, Tehran, Iran Abstract: In the present-day world, the concept of internationalization plays a significant role in organizational achievements. Therefore, it is crucial to identify the factors affecting the readiness of small and medium-sized enterprises to enter the international markets. Based on the conceptual model, environmental factors have been divided into external (micro and macro environment) and internal (managerial and organizational factors). This research is descriptive, co-relational survey in terms of data collection and analysis. The statistical samples in this study include: managers, professional and expert supervisors from 33 small and medium-sized food enterprises in the City of Tehran. Participants were members of Tehran Chamber of Commerce, Industry, Mines and Agriculture engaged in international activities. The data were collected by using questionnaires which were validated by a panel of experts and the reliability was obtained through a pilot study with Cronbach’s Alpha Coefficient of 0.879 and 0.816 for environmental factors and international markets’ readiness, respectively. The findings indicate that there are positive and significant relationships between independent variables (environmental, external, micro environmental, macro environmental, internal, organizational, and managerial factors) and a dependent variable (international markets’ readiness). In general, the results of this study present a new classification of factors affecting the readiness of enterprises to entry into international markets which could be used as a guideline for firms’ managers and decision-makers. Keywords: Environmental factors, International readiness, food SMEs, International Business. I. Introduction From Tokyo to New York, from London to Japan, it is obvious that what happens in one corner of the world totally influences us all. International commerce no longer identifies national frontiers, international buyers, global supply chains and joint ventures are disfiguring the business environments. Internationalization has lessened barriers to enter global markets, and foreign market entry no longer claims the essential resources and organization of large firms. Entrepreneurs and small to medium size enterprises (SMEs) are inspired to “go international”, and firms are increasingly influenced into pursuing foreign markets. There are many motives and the obstacles of entering into foreign markets have led to many questions regarding about the “readiness” of these firms to guarantee international activities and to successfully conduct the internationalization process [5].Iran, the second largest OPEC1 oil producer, embraces 100 billion barrels of proven oil reserves, about 10% of the world’s total, after Saudi Arabia and Iraq [23]. Generally speaking, oil exports account approximately half of Iran’s government expenses, and Iran’s oil exports have decreased to about 1.1 million barrels—less than half of the 2.5 million barrels per day Iran exported during 2011. Due to loss of revenues from oil, coupled with the cut-off of Iran from the international banking system, Iran’s economy declined partially from 2012 to 2013 and will plausibly do so again during 2013 [4]. Studies have reported few firms have entered foreign markets in Iran, especially in some potential industries like food industry. This study significantly focuses on Internationalization and export, inasmuch as business has become progressively more globalized, companies encounter rising competition, and in point of fact that non-petroleum export in Iran is not adequately in comparison with other countries in neighbor . II. Literature Review Internationalization readiness: From the early work of Cavusgil (1990) that clearly examined the internationalization readiness of firms, which indentified software (CORE: Company Readiness to Export) that assesses a firm’s export preparedness. Indeed, Cavusgil (1990) distinguished readiness to export into two key dimensions: organizational (which includes top management commitment, the availability of human and financial resources, and the wholeness of the organizational structure) and product (e.g. product adaptation, design, and positioning). Furthermore, Liesch and Knight (1999) considered the topic of readiness, highlighting the function of information internalization in the internationalization process of SMEs. They defined internationalization readiness as “being a function of its state of awareness on foreign market(s) and the means for entering them” [15]. According 1

Oil Producer Exporter Countries(OPEC)

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to the definition, the accessibility of information and knowledge about foreign markets is essential for a firm’s preparedness to internationalization. External environmental factors are uncontrollable forces which are external factors upon that the management or decision maker has no control [24]. Therefore, this study proposes the following hypotheses: H1. Environmental factors have significant positive effect on Iranian SME's readiness to enter international markets. H2. External factors have significant positive effect on Iranian SME's readiness to enter international markets. Based on conceptual model, environmental factors divided into external micro and macro environmental factors. Macro environmental factors include political, economical and cultural factors. It is determining for firms whether in domestic or foreign, large or small that considers the political environment of the target country before starting internationalization activities [11]. Indeed, any firm engaging in international marketing has to also be aware of the essence of national governments and its consequences for global operations [14]. It is argued that a firm needs to consider economical development and performance of a country in international process. Economic growth influences the request for goods and the distribution system found within the country. Indeed, consideration of economical environment in target country is essential not only to gain understanding the developing countries, but also according to market potential and market growth. Another micro environmental factor is culture, which is considered as the most challenging measure for opting targeted country. When marketers are designing a product, service, promotion, packaging and other connected marketing activities, they have to be satisfactory to the connected cultural market [11]. As Zekiri and Angelova (2011) explained marketers need to distinguish cultural delicateness and concepts that are related to one’s culture, and to comprehend the cultures’ differences by evaluating in a purpose. Drawing on the above discussion, the following hypothesis will be tested: H3. Macro environmental factors have significant positive effect on Iranian SME's readiness to enter international markets. In this paper, micro environmental factors include market potentials, government policies and competitive circumstances. Evaluation of foreign markets size and estimation of international market potentials comprise important challenges that many enterprises have failed to meet. Furthermore, competitive pressure is another micro environmental factor. Enterprises may fear losing domestic market share to competing enterprises that get benefited from economies of scale acquire by international marketing operations. Competitors are a significant micro environmental factor encouraging internationalization [24]. H4. Micro environmental factors have significant positive effect on Iranian SME's readiness to enter international markets. Internal environmental forces are controllable factors upon which the decision makers manage to conform to changes in the uncontrollable forces [24]. In this study, internal environmental factors divided to Managerial and organizational ones. This leads to the following hypothesis to be tested: H5. Internal factors have significant positive effect on Iranian SME's readiness to enter international markets. It has been identified in many international SMEs and entrepreneurship empirical and theoretical studies that management characteristics (both objective and subjective) play an important role in the international development of SMEs [7-9-1-12-3-16-13]. Hutchinson et al. (2006) explain the management or decision-makers’ objective (International experience) and subjective (attitudes, perceptions, and personality) characteristics within smaller firms, and assesses the impact of such factors upon the international progression of the firm. The international experience as an objective characteristic includes the extent to which management or decision maker has involved in foreign travel, the number of languages spoken by management, whether the decision maker was born, lived, or worked abroad, and their access to networks [22-6-19]. Drawing on the above discussion, the hypothesis for testing is: H6. Managerial factors have significant positive effect on Iranian SME's readiness to enter international markets. Hence in this paper, the literature pertinent to firm size and firm age considered as firm competencies. Firm size considered as the determinant of firms competence. It is assumed to be a representative of firm resources and capabilities [2] defined as a number of employee. Firm age as another determinant of firm competence is not basically the age of the firm based upon its founding date. Drawing on this brief review the following hypothesis on organizational factors and Internationalization readiness was tested: H7. Organizational factors have significant positive effect on Iranian SME's readiness to enter international markets.

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This framework also incorporates marketing/operational readiness, functional readiness and managerial commitment Readiness to evaluate internationalization preparedness of Iranian SMEs as follow: Marketing/operational readiness: obtaining reliable foreign representation abroad; absence of tough competition internationally; low transportation costs; ability to adapt to customer preferences overseas; ability to offer competitive prices overseas; short distance from overseas market; appropriate export strategy. Functional Readiness: familiarity with export documentation; no fear of the unknown export venture; availability of a unique and differentiated product; availability of qualified export personnel; availability of financial resources. Managerial Commitment Readiness: appreciation of the importance of the export; market to the firmâ&#x20AC;&#x2122;s overall success; devotion of managerial time to exporting; international outlook among export decision- makers; incorporation of exporting into the overall firm strategy [25]. III. Research Methodology Research framework and variables: As it is shown in figure 1, the influential factors in attainment to international preparation include environmental factors which have been divided into external (micro and macro environmental factors) and internal (managerial and organizational factors) categories. External Environmental Factors

International Readiness

Factors

Indexes

Macro Environmental Factors

Marketing/Operational

Micro Environmental

Readiness

Factors

Functional Readiness Internal Factors

Managerial

Managerial Factors

Commitment Readiness

Organizational Factors Figure1. Conceptual model of the study

Population and Sample Size: This paper is an applied, descriptive one and used survey to collect data. This is a correlation analysis which evaluates significant relations between environmental factors (internal/external) which influence on SME's readiness to enter international markets. Literature review and research community consisted of managers, professionals and expert supervisors from food SMEs that operated in Tehran area. These firms were all members of Tehran Chamber of Commerce, Industry, Mines and Agriculture with experience in international activities. At last, a well-designed questionnaire was distributed among managers, professionals and expert supervisors. Finally, 135 returned questionnaires were used in this study. The initial version of the questionnaire was submitted to supervisor and consultant professors and it was modified based on their recommendations. We calculated Cronbach's alpha in SPSS 18 to assess the reliability of questionnaire. Cronbach's alpha score for environmental factors and SME's preparation index to enter international markets were 0.879 and 0.816, respectively. It shows that the questionnaire reliability is fairly good and acceptable. Hypothesis Testing: Likertâ&#x20AC;&#x2122;s scale used for view-polling. Kolmogorov-Smirnov was used to test data for normality. Also linear regression and correlation test were used for data analysis. IV. The Results of Hypothesis Testing Environmental factors influencing SMEs' readiness to enter international markets were evaluated with regard to external (the outside environment of the firms) and internal (organizational and managerial) factors. Majority of environmental factors had mean value more than 5 which implies that these factors have moderate influence on

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SME's readiness to enter international markets. We found that macro environmental factors (5.89), micro environmental factors (5.82), management factors (5.62), and organizational factors (5.52) had most significance, respectively. With regard to table 1, we calculated determination coefficient between environmental factors and SME's preparation to enter the international markets which was equal to 0.702. Because its significance level is 0.0000 (<0.05) the H0 was rejected. Therefore, it means that environmental factors have significant and positive impact on SME's readiness to enter international markets. Table1. Research hypothesis test based on conceptual model factor regression Dependent R Independent Variables R Standard β T test Sig Variables Square Environmental Factors International 8.0.0 8.780 8.0.0 0.100 8 Readiness External Factors International 8.0.0 8.7.0 8.0.0 18.11 8 Readiness Macro Environmental International 8..01 8.010 8..01 18.8. 8 Factors Readiness Micro Environmental International 8...1 8.070 8...1 18.11 8 Factors Readiness Internal Factors International 8.007 8.7.1 8.007 ..88. 0 Readiness Managerial Factors International 8..1. 8.0.. 8..1. 7.8.0 0 Readiness Organizational Factors International 8..71 8..10 8..71 ...0 8.881 Readiness

Results H1:Accepted H2:Accepted H3:Accepted H4:Accepted H5:Accepted H6:Accepted H7:Accepted

Results for regression test in table 1 show that determination coefficient between environmental factors and SME's readiness to enter international markets is equal to 0.702. Because its significance level is 0.0000 (<0.05), H 0 was rejected. It means that the linear relationship between environmental factors and SME's readiness to enter international markets is confirmed. Regression test results imply that following linear relation is held between the variables. Table2. Demographic characteristics Frequency Distribution Education

Frequency Distribution Percent

Men

Women

Total

Diploma

%1....

Associates Degree

Bachelor

Master's Degree

PhD Total

Work Experience

Percent Men

Women

Total

None

%0.08

%1...0

Less than 5 years

%0..08

%.....

Between 5-10 years

%00.18

%1....

Between 11-15 years

%1...8

%1.11

Between 16-20 years

%1...8

180

1..

%188

More than21 years

%11..8

V. Conclusion, implication, limitations and future research Comparatively, few studies have investigated SMEs’ internationalization readiness. This study addressed a knowledge gap in respect of the firm internationalization readiness in the context of Iran. Furthermore, this research provides an integrative conceptual framework and practical implication on how international growth can be inspired, given its significance to economic growth prospects within the Iran. As the reduction in the obstacles for international trade continues and the country economy becomes more integrated, there is a raised consideration being placed on the internationalization of small and medium-sized enterprises in Iran. As indicated earlier, the role of organizational and managerial factors on the internationalization’s readiness of an enterprise cannot be denied. The empirical findings of this research provide evidences that manager’s attitude and perception toward international markets play a significant role in firm’s internationalization process. The higher a degree of knowledge about the foreign market and the international marketing process, the more managers and decision-makers handle and overcome potential obstacles and progressively obtain a positive perception of the foreign market environment [21]. Rasmussen et al. (2011) emphasizes the findings by Nummela et al. (2005) that INVs are knowledge intensive firms, and the making knowledge depends on the relationship between research and educational infrastructure in the area. The entrepreneurs and managers need to continuously assess different factors related to internationalization. Particularly critical are the skills, competencies, and management know-how the entrepreneur requires to enhance due to be successful in the process of internationalization [20].

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In this paper, the literature pertinent to firm size and firm age considered as firm competencies. Firm age positively influences higher survival rates in international markets due to the more advanced organizational capabilities and resources of more established firms. In contrast to the firms that survive in international market(s), younger firms showed higher short-term growth rates than their more established partners [8]. Iranian enterprises can use their potentials and capabilities to select right number of markets and then by getting informed about rivals conditions, marketing mix and regulations they will be able to plan to enter the international markets with a well-established marketing program. On the other hand, these corporations can improve their international activities through quality improvement measures based on consumers' taste and interests in target markets. Those companies which haven't had international activities mentioned following causes for absence from international markets: (1) difficulty in finding new costumers or establishing channels in abroad; (2) lack of enough experience in international markets; (3) cross-cultural differences or inability an another languages; (4) risks in conducting foreign operations; (5) achieving to the expected profit in the foreign markets is time consuming; (6) satisfaction with local/domestic market. In present paper, we simply evaluated the influence of environmental factors (micro/macro environmental, management and organizational factors) on SME's preparation to enter international markets. It is suggested that future studies can focus on international networks and information factors and their effect on SMEs' preparation. VI. References [1] [2] [3] [4] [5]

[6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]

Aaby, N. and Slater, S.F. (1989), “Management influences on export performance: a review of empirical literature 1978-88”, International Marketing Review, Vol.6 No.4, pp. 7-26. Ali, Y. (2004), “Impact of firm and management related factors on firm export performance”, Journal of Asia pacific marketing, Vol.3 No.2, pp. 5-20. Apfelthaler, G. (2000),” Why small enterprises invest abroad: the case of four Austrian firms with US operations”, Journal of Small Business Management, Vol.38 No.3, pp. 92-8. Ataev, N. (2013), “Economic sanctions and nuclear proliferation: the case of Iran”, Doctoral dissertation, Central European University. Béliveau,D. and Haskell,N. (2007), “The International Readiness Diagnosis :A Modular Framework to Diagnose and Track, the International Readiness of International New Ventures”,Faculté dessciences de l’administration, Université Laval, Québec Canada, Vol.1 No.7, P.4. Bijmolt, T. H. and Zwart, P. S.(1994), “The impact of internal factors on the export success of Dutch small and medium-sized firms”, Journal of Small Business Management, Vol. 32, p.69. Brooks, M. R., & Rosson, P. J. (1982), “A study of export behavior of small and medium-sized manufacturing firms in three Canadian provinces”, Export management: An international context, pp.39-54. Carr, J. C., Haggard, K. S., Hmieleski, K. M., & Zahra, S. A. (2010), “A study of the moderating effects of firm age at internationalization on firm survival and short‐term growth”, Strategic Entrepreneurship Journal, Vol.4 No.2, pp.183-192. Cavusgil, S. T. (1984), “Differences among exporting firms based on their degree of internationalization”, Journal of Business Research, Vol.12 No.2, pp.195-208. Cavusgil, S. T. (1990), “Assessment of company readiness to export. In H. B. Thorelli, & S. T. Cavusgil (eds.)”, International Marketing Strategy, Oxford: Pergamon Press. Cateora, P. R., Graham, J. L., Cateora, P. R., & Cateora, P. R. (2005), “International marketing”, McGraw-Hill/Irwin. Chetty, S. K. (1999), “Dimensions of internationalization of manufacturing firms in the apparel industry”, European Journal of Marketing, Vol.33 No.1, pp.121-142. Hutchinson, K., Quinn, B., & Alexander, N. (2006), “The internationalization of small to medium-sized retail companies: towards a conceptual framework”, Journal of Marketing Management, Vol.21 No.1, pp.149-179. Keegan, W. J. and Schlegelmilch, B.B. (2001), Global Marketing management: A European perspective, Harlow: Financial Times. Liesch, P. and Knight, G. A. (1999), “Information internalization and hurdle rates in small and medium enterprises internationalization”, Journal of International Business Studies, Vol.30 No.1, pp.383-394. Lloyd-Reason, L., & Mughan, T. (2002), “Strategies for internationalization within SMEs: the key role of the owner-manager” Journal of Small Business and Enterprise Development, Vol.9 No.2, pp.120-129. Nummela, N., Puumalainen, K. and Saarenketo, S. (2005), “International growth orientation of knowledge-intensive SMEs”, Journal of International Entrepreneurship, Vol. 3 No. 1, pp. 5-18. Rasmussen, E., Jensen, J. M. and Servais, P. (2011), “The impact of internationalization on small firms’ choice of location and propensity for relocation”, Journal of Small Business and Enterprise Development, Vol. 18 No. 3, pp. 457-474. Reuber, A. R. and Fischer, E. (1997), “The influence of the management team's international experience on the internationalization behaviors of SMEs”, Journal of International Business Studies, pp.807-825. Ruzzier, M., Hisrich, R. D. and Antoncic, B. (2006), “SME internationalization research: past, present, and future”, Journal of Small Business and Enterprise Development, Vol. 13 No. 4, pp. 476-497. Shamsuddoha, A.K. , Yunus Ali, M. and Oly Ndubisi, N. (2009), “Impact of government export assistance on internationalization of SMEs from developing nations”, Journal of Enterprise Information Management, Vol. 22 No. 4, pp. 408-422. Weidersheim-Paul, F., Olson, H. C., and Welch, L. S. (1978), “Pre-export activity: the first step in internationalization”, Journal of International Business Studies, Vol.9 No.1, pp.47-58. World Bank Report (2009), “Global economic prospects 2009: Commodities at the crossroad analyzes”, World Bank, Washington, DC. Zekiri, J., & Angelova, B. (2011), “Factors that Influence Entry Mode Choice in Foreign Markets”, European Journal of Social Sciences, Vol.22 No.4, pp. 57 Gerschewski, S., Rose, E. L., & Scott-Kennel, J. (2007). Understanding Pre-export Behavior of Small and Medium-sized Firms in New Zealand: Towards a Model of Export Readiness. Unpublished masters thesis, Victoria University of Wellington, Wellington.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net PERCEPTIONS THAT MOTIVATE PURCHASE Shruti V. Joshi Associate Professor Sinhgad Institute of Management and Computer Application, Pune, Maharashtra, India Abstract: Direct selling(Multilevel marketing) as a alternate channel of distribution has grown in India over the past decade .Its usefulness as a viable channel of distribution however remains a matter of debate. Understanding the perceptions of consumers towards direct selling companies is the key to the growth of this industry. This study carried out in Pune city with a respondent size of 223 attempts to investigate the perceptions that are likely to impact and influence the buying behavior of consumers towards direct selling and more so towards multilevel marketing as a channel of distribution. Factor analysis is used to identify the variables that are likely to drive consumer behavior and multiple regression to identify the predictors of purchase intention. Product and Brand, Network Credibility, Distribution, Communication and Performance Delivery, are the factors which influence the perceptions of consumers towards direct selling. Distributors with good knowledge and information on products, pricing at par with stores, and good products that help to associate brands with the company are likely predictors of intention to purchase from MLM companies. Keywords: Multilevel marketing, perceptions, purchase intention, Factor analysis, regression, I. Introduction The concept of direct selling is here to stay in India and has caught the fancy of both the distributors and public alike. Direct selling began in India in the 1990s with only a few companies like Avon, Time Life Asia, Amway and Oriflame in the business. The idea has turned into a deluge with over 50 companies selling home and personal care products, cosmetics, nutrition, weight management products and premium books.(IDSA Report 2008-09) . Retail customers nowadays can find every type of consumer product and service offered by MLM and direct selling companies in India. Even major corporations are rethinking their future marketing strategies in an effort to get closer to their customers and prospects using tactics that are very familiar to us in Multi-Level Marketing. Though the still nascent domestic market seems to be taking off, and direct selling companies posting a growth of 18%,(Ernest & Young 2010-11)MLM companies need to adapt and change to suit the India mind set. Despite the potential, issues like awareness among people and changing consumers' perceptions and attitude towards direct selling are required to be addressed for the industry to grow. Perception is the method or way of thinking or point of view. Some people called it paradigm. Consumer perception and its role in purchase intention and decision making has always been an area of interest for researchers of consumer behavior decisions. However a literature review revealed that there was not much research investigating consumers’ perceptions of multilevel marketing as a type of direct selling. Direct selling and more so multilevel marketing as a type of non store retailing continues to increase in popularity both internationally and on the domestic front. Yet it seems to have incurred a degree of consumer suspicion and negative perception. A study was developed in New South Wales and Victoria to investigate consumer perceptions and to determine its relationship to consumer purchase decision( Kustin, Richard A, Jones, Robert A,1995 ).Results indicated that consumers had negative perceptions towards multilevel marketing while having a low positive view of direct selling and there was no influence on consumer purchase. Two related research studies concerned with direct selling were carried out by Peterson et al.1989 and Wotruba 1990. The first study by Peterson was an investigation of direct selling in USA to determine consumer perception of this form of non-store retail purchase behavior. They researched how consumers used direct selling to make purchases; they identified consumer characteristics, and consumers’ perceptions of the advantages, disadvantages and risk in purchasing products from direct sellers. 988 consumers responded to this survey and they reported that convenience of shopping at home was an advantage, although sales pressure by the salesperson was seen as a disadvantage. Peterson et al. (1989) also found that risk of purchase was perceived to be greater through direct selling than at a conventional retail store. The second study, by Wotruba (1990), researched the effect public image of the selling job has on the sales activity or inactivity of direct selling salespersons. He found a direct relationship between a salesperson’s low self-image, activity on the job and job satisfaction. However, this varied between high and low performers. Overall, the salesperson’s job image and job satisfaction and performance were positively related. Differences also occurred based on reaction to image, length of time on the job and general successful selling performance.

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Public perceptions and experiences on direct selling as a channel of distribution of goods and services and also as a business opportunity were studied by Stewart Brodie et al 2004. Non customers irrespective of gender ,age based on indirect information, opinion and hearsay showed a slightly negative perception of direct sellers. Customers based on experience had a considerable higher positive perception associated with direct selling. Value for money and convenience were identified as positive indicators and pressure to buy was the strongest negative perception associated with direct selling. Alexrod Joel and Wybenga Hans 1985 studied what perceptions motivate or demotivate purchase behavior. Motivators / demotivators to purchase were uncovered by cross tabulating responses to level of purchase interest. The study revealed that sometimes positive attributes can also act as demotivators. Based on these studies an attempt is made to understand perceptions that could possibly give an understanding of the perceptions most associated with direct selling and multilevel marketing. India, with its large population and increasing per capita income, growing awareness and changes in the consumption basket, has attracted a large number of Indian and foreign direct selling companies. Despite the potential, the direct selling industry is not growing to its expected potential. Besides the need to address regulatory issues, understanding and adapting to consumer perceptions, is essential to drive growth in the direct selling industry. Changing the approach to suit the Indian mind-set is necessary for the sustained growth of this industry. II. Research Objectives: The objective of this study is to identify the predictor perceptions that significantly influence the intention to purchase from direct selling companies more specifically Multilevel marketing companies and to gain insight into the important perceptions associated with direct selling ( multilevel marketing) III. Methodology and Design of Study A survey was carried out using a well structured questionnaire. The first part was designed to obtain demographic information related to the respondent’s age, gender, educational qualification, income and purchase from a direct selling company. The second part consisted of a set of 24 statements reflecting various attributes of direct selling. The respondents were asked to rank the statements on a 5–point scale basis (1=completely agree; 5=completely disagree). The sample was chosen as per convenience from different areas in Pune city. The questionnaire was distributed to 250 consumers and 232 responded out of which some were discarded for incomplete or incorrect responses bringing the total sample size to 223. The data collected was coded and tabulated keeping in view the objective of the study. It was further analyzed by calculating frequencies, percentages, means and Factor analysis (a data reduction method) was used to identify consumer’s perceptions towards direct selling and MLM and multiple regression was used to show the association between perception variables and purchase intention. The data was analyzed using Statistical Package for Social Sciences (SPSS) version 11.0 . Reliability analysis of scale was performed using Cronbach’s Alpha (Cronbach 1951) IV. Brief Socio Economic Profile Of Respondents The sample selected tended to be lean towards males(71%) rather than females.89 % of the respondents were between the age group of 16 to 35. 48.87 % were employed in government or private jobs and 23.28% were in the income group of Rs.20,000 to Rs. 30,000.More than 50% of the respondents were graduates Table 1:Cronbach’s Alpha Test of Reliability R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A) Reliability Coefficients 19 items Alpha = .8986 Standardized item alpha = .8988 N of Cases = Statistics for Scale

Mean

223.0 Variance

97.7848 497.7102

Std Dev 22.3094

Variables 19

The Alpha value for nineteen variables is 0.8986 and scale reliability is .8988 indicating that the scale constructed is a reliable scale and can be used for further analysis. .(Nunnally,1978). The adequacy of the data is evaluated on the basis of the results of the Kaiser-Meyer-Olkin (KMO) measures of sampling adequacy and Bartlett’s test of sphericity (homogeneity of variance). Table 2: KMO and Bartlett's Test Kaiser Meyer OlkinMeasure of Sampling Adequacy Bartlett's Test of Sphericity

.898 df Sig.

Approx. ChiSquare

1700.637 276 .000

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The KMO measure of sampling adequacy is 0.898,indicating that the present data are suitable for Factor Analysis. Similarly Bartlett’s test of sphericity is significant (p<0.001) indicating sufficient correlation exists between the variables to proceed with the analysis. The Bartlett’s test statistic is approximately distributed and it is accepted as it is significant. V. Analysis Factor analysis is a widely used technique for reducing data complexity by reducing the number of variables being studied. Factor Analysis is a good way of identifying latent factors from an array of seemingly important variables. (Nargundkar,2005) Factor analysis was carried out on 19 items to investigate factors influencing customers’ perceptions towards direct selling. A factor loading and a cumulative proportion of variance were calculated with Varimax rotation to raise discrimination. Here the factor loading and the communality for each variable should be greater than 0.5.Factors having Eigen value > 1 were extracted. A total of five factors were extracted accounting for 53.58% of the total variance which is fairly reasonable and establishes the validity of the study. Further in order to assess the appropriateness of data for factor analysis, the communalities derived from factor analysis were reviewed. These are all greater than 0.5 ,suggesting that the data is appropriate. Table 3: Total Variance Explained Initial Eigen values

Extraction Sums of Squared loading

Total

% of variance

Total

% of variance

7.25

30.21

Cumulative % 30.21

7.25

2 3 4 5

2.06 1.32 1.16 1.05

8.61 5.50 4.86 4.38

2.06 1.32 1.16 1.05

Component

Rotation sums of squared loading Total

% of variance

Cumulative %

30.21

Cumulative % 30.21

3.30

13.78

8.61 5.50 4.86 4.38

38.82 44.33 49.19 53.58

3.03 2.74 2.11 1.66

12.65 11.41 8.80 6.92

26.43 37.85 46.65 53.58

Extraction Method: Principal Component Analysis Rotation method:Varimax with Kaiser Normalization a. Rotation converged in 10 iterations Table4 :Rotated Component Matrix Component V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19

Sufficient choice and variety of products No scope for comparison Information provided by distributors useful Products exclusive and good quality Distributors aggressive and pushy Distributors have good knowledge and information on products More genuine than non genuine MLM’s Products from direct selling companies good value for money No retail presence, hence grievance handling and returns difficult Ads by direct selling companies’ help associate brand with company Products not suited to Indian conditions MLM companies unfamiliar names MLM co’s trustworthy though they do not advertise Brand image of direct selling companies good Products expensive compared to similar products of other companies Direct selling companies good only for fast moving consumer products not consumer durables Distributors interested only in increasing network not sales Products of some MLM companies good Direct selling companies that don’t advertise lack credibility

1 .579

5 .694

.653 .720 .564 .535 .624 .508 .523 .632 .589 .633 .588 .670 .660 .540 .641 .574 .667

The variables under each of the five derived factors are explained below: 1.The first factor termed as “Product and Brand ” refers to all the features that help in association with a good brand and it is measured by items 1,3,4,8,13,14. Variable 4(products exclusive and good quality) is the strongest and has a total factor load of 0.720. The other emerging elements are ‘Sufficient choice and variety of products’, ’Information provided by distributors useful’, ’Products from direct selling companies, good value for money’, Mlm companies trustworthy though no advertisements’, Brand image of direct selling companies good’. 2.The second factor “Networking Credibility’ includes items15,16,17. Variable 15 (”Products expensive compared to similar products of other companies)is the strongest and has a factor load of 0.660.’Distributors more interested in increasing Network than sales”, ”Direct selling companies good for fmcg products and not consumer durables”,.” are the other elements that measure this factor. 3.The third factor extracted can be termed “Distribution”. This includes variables 5,7,9,10,18.The emerging elements are ‘Distributors aggressive and pushy”. More genuine than non genuine MLM’s ”,”No retail presence,

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hence grievance handling and returns difficult”. Variable 10(advertisements by direct selling companies help associate brand with company) is the strongest and has a factor load of 0.632.. 4.Factor 4 refers to “Communication” and includes variables11,12,19.Variable 19 is the strongest and has a factor load of 0.667.”, “MLM companies, unfamiliar names”, and “Direct selling companies that don’t advertise lack credibility” “Products non suitability to Indian conditions” are the other emerging elements. 5.The fifth factor is “Performance delivery” and has emerging elements 2,8.Variable 2 is the strongest, with a factor load of 0.694 .”No scope for comparison” and ”Distributors have good knowledge and information on products” are the emerging elements. Relative Importance of Product and Brand Dimensions The results of regression analysis are summarized below and it is observed that overall regression model is significant at 1% significance level with18.7% of variance explained in purchase intention by the independent variables. F value is significant(0.000) which is less than p(0.05),hence a linear relationship exists between dependant and independent variables . Using the enter method ,a significant model emerged(F2,220)=25.080,P<0.05 Adjusted R square=0.178,Significant variables are shown below: Predictor variable Beta p MLM companies are trustworthy though .335 p<0.005 they don’t advertise For the dimension “Product and Brand”, Information provided by distributors useful, Products exclusive and good quality, Products from direct selling companies good value for money’, and “Brand image of direct selling companies good’ are not significant predictors of Purchase intention. Relative Importance of Network Credibility Dimensions The regression model is significant at 1% significance level with18.7% of variance explained in purchase intention by the independent variables. F value is significant(0.001) which is less than p(0.05),hence a linear relationship exists between dependant and independent variables . Using the enter method ,a significant model emerged(F3,219)=6.139,p<0.05 Adjusted R square=0.65,Significant variables are shown below: Predictor variable Beta p Direct selling companies good only for fmcg products not consumer durables .211 p<0.05 Products expensive as compared to similar products of other companies and Distributors interested only in networking not sales are not significant predictors in this model Relative Importance of Distribution Dimensions The regression model is significant at 1% significance level with6.5% of variance explained in purchase intention by the independent variables. F value is significant(0.000) which is less than p(0.05),hence a linear relationship exists between dependant and independent variables Using the enter method ,a significant model emerged(F5,217)=6.110,p<0.05 Adjusted R square=10.3%,Significant variables are shown below: Predictor variable Beta p Products of some MLM’s are good .213 p=0.004 Ads by direct selling companies help associate brand .148 p= 0.049 with company Distributors aggressive and pushy, More genuine than non genuine companies, No retail presence hence grievance handling and returns difficult are not significant predictors in Distribution dimensions. Relative Importance of Communication Dimensions The regression model is significant at 1% significance level with6.5% of variance explained in purchase intention by the independent variables. F value is significant(0.001) which is less than p(0.05),hence a linear relationship exists between dependant and independent variables Using the enter method ,a significant model emerged(F4,218)=4.880,p<0.05 Adjusted R square=0.065,Significant variables are shown below: Predictor variable Beta p Prices reasonable and at par with stores .220 p=0.001 MLM companies unfamiliar names and Direct selling companies that don’t advertise lack credibility are not significant to the Communications dimension. Relative Importance of Performance Delivery Dimensions The regression model is significant at 1% significance level with6.9% of variance explained in purchase intention by the independent variables. F value is significant(0.000) which is less than p(0.05),hence a linear

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relationship exists between dependant and independent variables Using the enter method ,a significant model emerged(F2,220)=9.266,p<0.05 Adjusted R square=0.069,Significant variables are shown below: Predictor variable Beta p Distributors have good .232 p=0.001 Knowledge and information on products No scope for comparison is not a significant predictor in this model VI. Conclusion The market in India is turning competitive due to large number of players and alternative channel options available to the consumer. Changing lifestyles are also making consumers’ look for alternate and convenient shopping channels. The growth drivers of such channels is dependent on an understanding of the consumers’ perceptions towards their products and also their service delivery .A focus on eliminating the negative perceptions and consolidating on the positive perceptions will ensure a steady growth pattern for direct selling mlm companies. Knowledgeable distributors, value for money on products, trustworthiness are all favorable perceptions and all contribute to developing a good brand image. Sufficient variety, useful information on products, reasonable pricing at par with other retail formats are positive motivators for purchase and they influence perceptions positively. Distributors more interested in Networking than sales, Time gap in delivery and lack of retail presence and a visible system for grievance handling are negative motivators impacting the perception of consumers unfavorably. Lack of advertising and Unfamiliarity with direct selling companies names is the shortfall in communication which negatively impacts perception towards direct selling companies. However “ Suitability of products for Indian conditions” is a factor that seems to be important in influencing the consumers’ perceptions adversely. The presence of several global companies who were the pioneers in the Indian markets for direct selling could have affected the perception of consumers making them think that the products are not suitable for Indian conditions. Companies that use MLM should focus on selling Fast moving consumer goods as consumers feel that this channel is more suitable for consumables than durables. Consumers do not feel that companies that advertise are necessarily trustworthy. MLM’s that have distributors with good knowledge and information on products, pricing at par with stores, and good products are more likely to create trustworthiness and favorably affect intention to purchase from MLM companies. Advertisements however do help in brand association. MLM companies need to eliminate the negative perceptions while consolidating on the positive in order to sustain growth of this channel of distribution.

References 1.Alexrod Joel N.& Hans Wybanga, Perceptions That Motivate Purchase ,Journal of Advertising Research, June 1985 2. Brodie Stewart and Albaum, Gerald and Chen, Der-Fa Robert and Garcia, Leonardo and Kennedy, Rowan and Msweli- Mbanga, Pumela and Oksanen-Ylikoski, Elina and Wotruba, Thomas R. (2004) Public perceptions of direct selling: an international perspective. University of Westminster Press, London, U.K. 3.Ernst and Young 2010-11 4.IDSA Report 2008-2009 5.Kustin .Richard A, Jones Robert A:. Research note: A study of direct selling perceptions in Australia , International Marketing Review. London:1995.Vol.12, Issue. 6; pg. 60. 6.Peterson.R.A.;Albaum,G.;Ridgway,N.M.(1989):Consumers Who Buy from Direct Selling Companies, in: Journal of Retailing,Vol.65,No.2,pp.273--286 7.Wotruba Thomas, Brodie Stewart & Stanworth John (2005), Differences in Turnover Predictors between Multilevel and Single Level Direct Selling Organizations, Int. Rev. of Retail, Distribution and Consumer Research, Vol. 15, No. 1, 91–110, January 2005

Bibliography 1.Nargundkar R.(2005) Marketing Research, McGraw 12 th ed. 2.NunnallyJ.C. Psychometric theory(2nded)New York McGraw Hill

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Thermo Structural Analysis on a Marine Gas Turbine Flame Tube L.S.V.Prasad1*, K.Rajesh Chandra2 Associate Professor, Mechanical Engineering, College of Engineering, Andhra University, Visakhapatnam 2 Scholar, Mechanical Engineering, College of Engineering, Andhra University, Visakhapatnam. _________________________________________________________________________________________ Abstract: Gas turbine flame tube modeling has become essential to predict the flame tube temperature distribution and thermal stress as combustor components operate under high temperature due to combustion instabilities. Thermal analysis is performed on a marine gas turbine flame tube using FEM approach. A commercial software package relying on finite element method is used for simulation and mesh convergence. The main objective of this paper is to analyze the thermal stress on the structural elements of a gas turbine flame tube made up of oxidation resistant alloy steel (GH3039). The present model is divided into nine sections, operating temperatures with heat transfer coefficients varying along the axial direction of the flame tube. Key words: Thermal structural analysis, Gas turbine, flame tube. _________________________________________________________________________________________ 1*

I. INTRODUCTION Gas turbine engines, an engineering development that has served mankind for over six decades. Both industrial sector and government research institutions have invested and still are investing millions to improve gas turbine performance. As technology advanced throughout the years, designers have been able to push the combustion process further and closer to the stoichiometric limit with the help of high temperature materials and advanced analytical and fabrication tools. However, this positive progress has also created more challenges to engineers and scientists, there are still many issues requiring greater improvement and better solutions. Immediately following the big north east blackout of 1965, air cooling and improved material allowed firing temperatures to increase above 845°C with efficiency approximately 25%. Because the fuel use act of 1978 seemed to require phasing out natural gas as a fuel, the gas turbine market again became sluggish. Nevertheless, material technology continued to develop quickly and the marked returned in the late 1980’s manufacturers were ready with more compressor refinements, high temperature material and even better cooling techniques. Due to the uncertainty in today’s electric energy supply market, both the utilities and non utility generators have put a premium on generation technologies that can be built with a short lead time, at reasonable cost and in affordable increments. Gas turbines in simple cycle, combined cycle and cogeneration modes fit these requirements well in many applications and thus they have come a long way in the past 100 years. The objective of the present study is to determine the temperature distribution in the axial direction of the flame tube of combustor and to determine the stresses developed due to high temperatures in the flame tube. Thermal analysis is carried out with live trial data from an onboard marine gas turbine. Simulation results can help to identify the areas of concentrated thermal gradients for possible cooling studies to extend the expected life of the gas turbine combustor. Simulation studies enable us to forecast cooling strategies and significantly improve the design of the combustor. Dattaet et al., (1998) studied the influence of combustor pressure on combustion characteristics and found that an increase in combustor pressure for a fixed inlet temperature results in reduction in combustion efficiency. Min- Ki Kim et al., (2004) studied the effect of fuel-air mixture velocity on combustion instability in a model gas turbine combustor and predicted shape changing of the flame is mainly related to mixture fluctuation and the instability caused by flame-vortex interaction which can cause local flow vibration around the swirler. Gordon et al., (2005) performed a 3D numerical simulation on a small annular, reversal flow type combustor and predicted liner wall temperatures based on solutions from numerical simulations. Tinga et al., (2006) performed a life assessment on a fighter jet engine annular combustor liner, using a combined fluid/structural approach and developed models and tools that can be applied to perform comparative life assessment for different mission types. Yap-Sheng Goh (2006) studied thermo-fluid dynamic effects inside a combustor equipped with a swirler nozzle and predicted that the heat transfer peak location on a gas turbine combustor liner strongly depends on the peak location of turbulent intensity of the swirling flow. Siaw Kiang Chou et al., (2008) developed a simple ﬂame model to analyze the heat transport occurring in the cylindrical micro combustors and investigated the effects of various parameters. Kyung Min Kim et al., (2009) discussed the failure analysis in after shell section of gas turbine combustor liner under base-load operation and proposed the discrepancy in thermal expansion between hot and coolant side walls. Khaled Zbeeb et al., (2010) performed numerical simulations to test the combustion performance and emissions from the vortex trapped combustor when natural gas fuel (methane) is replaced with syngas, methane/hydrogen mixtures, and pure hydrogen fuels. Li Jibao et al., (2011) studied NOx emissions in a model commercial aircraft engine combustor and predicted using CFD under the condition of engine takeoff. The low emission stirred swirl (LESS)

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combustor has large potential to reduce NOx emission. S. Matarazzoet al., (2011) performed fluid analysis on gas turbine combustor and investigated the effects of changes in operational parameters on the temperature profile and heat flux distribution at the liner inner and outer interfaces. II. OPERATING CONDITIONS OF THE FLAME TUBE Combustors play a crucial role on performance characteristics, including thermal efficiency and the level of emissions. These are two well defined philosophies by which air is added to the chamber the first being small and frequent additions, the second as large and infrequent additions. The operating conditions in the flame tube are presented in Table.1 Table 1 OPERATING CONDITIONS IN GAS TURBINE COMBUSTOR Combustor inlet temperature Combustor outlet temperature Air mass flow rate Fuel consumption Exhaust gas flow rate Exhaust gas temperature Combustor inside max temp Fuel

140°C 600°C 86 kg/s for ten flame tubes 0.236 kg per HP per hour 98.5 kg/s 360°C 865°C Low Sulphur High Speed Diesel

III. MODELLING OF THE COMBUSTOR Structural and thermal analysis is performed using a finite element package. The geometry is modeled with CATIA V5 and the analysis is carried out using ANSYS 12.0. The structural, thermal modules of ANSYS 12 are used for the analysis of the flame tube. The flame tube is analyzed for temperature distribution, combined mechanical and thermal stresses with elongations. The combustor is mounted on the marine gas turbine engine between the compressor outlet and the turbine inlet. Fuel burning takes place inside the flame tube. It comprises of swirled spacer, three conical ferrules and a mixing chamber. The end of the mixing chamber is made into a segment shaped at tail end in cross section. Its forward side rests on the burner and retainer. All the burners are inter-connected with short tubes of elbow shaped. The air supply to the flame tube is 25% for complete burning, 55% for mixing in the mixing chamber to reduce temperature, 20% for cooling of walls and ferrules of HP turbine. The flame tube wall is kept cooled by the stream of air inside and outside through corrugated holes on the ferrules. The flame tube is made up of oxidation resistant alloy steel (GH3039). The properties and composition of oxidation resistant alloy steel (GH3039) is presented in Table. 2 and Table. 3 respectively. Table 2 PROPERTIES OF OXIDATION RESISTANT ALLOY STEEL GH 3039 Density of the material Thermal conductivity Specific heat capacity Coefficient of linear expansion Yield strength

8300 kg/m3 13.8 W/m°C 544 J/kg°C 11.5 x 10-6 /°C 735 Mpa

Table 3 COMPOSITION OF OXIDATION RESISTANT ALLOY STEEL GH 3039 Carbon Silicon Manganese Chromium Nickel Titanium Aluminum Molybdenum Niobium Sulphur Phosphorous Iron

0.10% Max 0.80% Max 0.40% Max 19-22% Base 0.35-0.75% 0.35-0.75% 1.8-2.3% 0.9-1.3% 0.012% Max 0.020% Max 3.0% Max

IV. GENERATION AND MESHING OF MODEL The present model of the gas turbine flame tube is generated using CATIA V5 and the present model is divided into nine sections, heat transfer coefficients and temperatures vary along the axial direction the combustor. The analysis is carried out using ANSYS 12.0 software. The geometry is modeled using CATIA V5 and imported into HYPERMESH 11.0 for meshing. It is meshed using tetramesh with element size 5 as shown in Fig. 1. The quality of the mesh is checked in terms of skewness, minimum angle, minimum length, maximum angle, maximum length, aspect ratio. It is later imported from Hypermesh to ANSYS 12.0 for thermal analysis. In the element type table, two element types are used for analysis. Element for thermal analysis is solid quadrilateral four noded 55 and element in structural analysis used is plane 182.

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Fig. 1 MESHING OF THE MODEL USING TETRAHEDRAL MESH

Heat Transfer Coefficient (W/m2K)

V. RESULTS AND DISCUSSION The convective heat transfer coefficient along the axial direction of the flame tube is shown in the Fig. 2 and Fig. 3. Maximum value of the convective heat transfer coefficient is found to be 590.2 W/m2K, which occurred at a section of the combustor between 0.208m to 0.268m from the inlet of the flame tube. Minimum value of convective heat transfer coefficient is 504.63W/m2K which occurred near the inlet of the flame tube. 600 580 560 540 Heat Transfer 520 Coefficient 500 0 0.5 1 Dimensionless length

Fig. 2 HEAT TRANSFER COEFFICIENT ALONG DIMENSIONLESS LENGTH OF FLAME TUBE

Fig. 3 CONVECTIVE HEAT TRANSFER COEFFICIENT INSIDE FLAME TUBE, W/mm2 K

Fig. 4 TEMPERATURE DISTRIBUTION ON THE OUTER SURFACE OF THE FLAME TUBE IN °C Fig. 4 reveals the temperature distribution on the outer surface of the flame tube. Temperature profile along the length is graphically represented in Fig. 5. The outer surface of the flame tube lies in the temperature range of 97°C to 428°C. Minimum temperature of 97°C occurs at the inlet of the flame tube where the working fluid

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Temperature (0C)

enters at a temperature of 140°C and the maximum temperature of 428°C occurs near the igniter from where the combustion phenomenon begins. The temperature on the wall decreases along the length and it reaches a minimum value towards the tailend of the flame tube. 1000 900 800 700 600 500 400 300 200 100 0

Temperature Profile

0.5

Dimensionless length

Fig. 5 TEMPERATURE PROFILE ALONG DIMENSIONLESS LENGTH OF FLAME TUBE

Fig. 6 TEMPERATURE DISTRIBUTION INSIDE THE FLAME TUBE IN °C Fig. 6 shows the temperature distribution inside the flame tube. The temperature inside the flame tube reaches to a maximum of 842°C in the combustion zone where the combustion phenomenon begins. The temperature of the fluid at the entry of the flame tube is 140°C and leaves the flame tube at 600°C. The temperature initially is low, reach to a maximum after the combustion starts and then decreases slowly along the downstream of the flame tube. The dilution air mixes with the working fluid in the dilution zone bringing down to a temperature of approximately 600°C, which is acceptable by the first stage of turbine blade.

Fig. 7 THERMAL GRADIENT VECTOR Fig. 7 shows the thermal gradient vector. The thermal gradient is minimum at the entry of the flame tube. As the combustion process begins it increases near the combustion zone and decreases towards the downstream of the flame tube. The maximum value of thermal gradient is 181.616°C /m and the minimum value of thermal gradient is 0.904°C /m.

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Fig. 8 STRESS IN RADIAL DIRECTION IN N/mm2

Fig. 9 STRESS IN LONGITUDINAL DIRECTION IN N/mm2 Figs. 8 and 9 shows the stresses developed along the radial and longitudinal directions. The maximum stress in radial direction is found to be 546.36 Mpa which is less than the yield strength value of 735 Mpa. The minimum stress in radial direction is found to be 21.14 Mpa. Maximum stress in longitudinal direction is found to be 516.44 Mpa which is less than the yield strength. The minimum stress in the longitudinal direction is estimated as 9.24 Mpa.

Fig. 10 VON MISES STRESS IN N/mm2 Fig. 10 reveals the von Mises stress in the flame tube. The maximum stress is 633.56 Mpa which is developed near the regenerative burner of the flame tube. The minimum von Mises stress in the flame tube is 7.35 Mpa. The estimated maximum stress developed due to thermal loading in the selected material (Oxidation resistant alloy steel) is well below the yield strength. VI. CONCLUSIONS The thermal energy released during combustion inside the flame tube is transferred to the structure causing temperature gradients and heat flux oscillations in the flame tube. The following are the conclusions from the above analysis: ď&#x201A;ˇ The thermal analysis performed on the flame tube reveals the areas of thermal concentrations inside and outside the flame tube. It is found that temperature concentration is high in the pre combustion and combustion zones inside the flame tube.

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  

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [15]

The temperature distribution on outer surface of the flame tube is found to be around 420°C near the combustion zone and 180°C at the tail end of the flame tube. The maximum stress developed is found to be 633 Mpa near the regenerative burner in the flame tube and by virtue of primary, intermediate and dilution holes on the liner, stresses is found to decrease along the downstream of the flame tube. The maximum stress obtained from the analysis is found to be within the yield strength of the selected material. REFERENCES Datta, A., Som, S.K., 1998, “Combustion and emission characteristics in a gas turbine combustor at different pressure and swirl conditions”, Applied Thermal Engineering, Vol.19, pp. 949-967. Gordon, R., Levy, Y., 2005, “Optimization of wall cooling in gas turbine combustor through three-dimensional numerical simulation”, Journal of Engineering for Gas Turbines and Power, Vol.127, pp. 704-723. Ko, T.H., 2006, “A numerical study on the effects of side-inlet angle on the mixing phenomena in a three-dimensional side-dump combustor”, International Communications in Heat and Mass Transfer, Vol.33, pp. 853-862. Jun Li., Siaw Kiang Chou., Zhiwang Li.,Wenming Yang., 2008, “Development of 1D model for the analysis of heat transport in cylindrical micro combustors”, Applied Thermal Engineering, Vol.29, pp.1854-1863. Zhang Man., FU Zhenbo., Lin Yuzhen., Li Jibao., 2011, “CFD study of NOx emissions in a model commercial aircraft engine combustor”, Chinese Journal of Aeronautics, Vol.25, pp. 854-863. Chaouki Ghenai., Khaled Zbeeb., Isam Janajreh., 2012, “Combustion of alternative fuels in vortex trapped combustor”, Energy Conversion and Management, Vol.65, pp. 819-828. Kyung Min Kim., Yun Heung Jeon., Namgeon Yun., Dong Hyun Lee., Hyung Hee Cho., 2010, “Thermo-mechanical life prediction for material lifetime improvement of an internal cooling system in a combustion liner”, Energy, Vol.36, pp. 942-949. Lei-Yong Jiang., Ian Campbell., 2007, “Reynolds analogy in combustor modeling”, International Journal of Heat and Mass Transfer, Vol.51, pp. 1251-1263. Paolo Gobbato., Massimo Masi., Andrea Toffolo., Andrea Lazzaretto., 2010, “Numerical simulation of a hydrogen fuelled gas turbine combustor”, International Journal of Hydrogen Energy, Vol.36, pp. 7993-8002. T. Tinga, J.F., Van Kampen, B., De Jager, J.B., Kok, W., 2007,“Gas turbine combustor life assessment using a fluid/structural approach”, Journal of Engineering for Gas Turbine and Power, Vol.109, No.69, pp. 632-644. Matarazzo, S., Laget, H., 2011, “Modelling of heat transfer in a gas turbine combustor liner”, Heat Transfer in Industrial Combustion, Vol.6, pp. 117-129. Kyung Min Kim., Namgeon Yun., Yun Heung Jeon., Dong Hyun Lee., Hyung Hee Cho., 2009, “Failure analysis in after shell section of gas turbine combustion liner under base-load operation”, Engineering Failure Analysis, Vol.17, pp. 848-856. Danis, A. M., Burrus, D. L., and Mongia, H. C., 1997, ‘‘Anchored CCD for gas turbine combustor design and data correlation”, ASME J. Eng. Gas Turbines Power, Vol.119, pp. 535-545. Mongia, H. C., 2001, ‘‘Gas turbine combustor liner wall temperature calculation methodology”, AIAA Paper 2001-3267, 36th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 16–19 July, 2000, Huntsville, Alabama.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Implementation of Coherent Optical Digital Communication Systems Using Digital Signal Processor & FPGA Miss. Preeti V. Murkute1, Mr. A.H.Karode2 ME 1st year Digital Electronic, 2Assistant Professor, Electronics and Telecommunication department S.S.B.T’s C.O.E.T. Bambhori, North Maharashtra University Jalgaon, Maharashtra, India

Abstract: The emergence of capable semiconductor processes has allowed digital signal processing to extend

the application range of high-capacity optical systems. Coherent communication systems have dominated the world of wireless communication almost since its beginnings. The practical optical coherent communication systems became feasible only recently. Digital signal-processing-based coherent optical communication systems are widely viewed as the most promising next generation long-haul transport systems. One of the biggest challenges in building these systems is the implementation of signal processors that are able to deal with signalling rates of a few tens of giga-samples per second. This paper covers implementation options and design considerations with respect to hardware realization and DSP implementation. Keywords: Digital signal processors, coherent communication, quadrature phase-shift keying, FPGA, QPSK I. Introduction We have been studying about the coherent systems, communication systems, digital processing and implementation of these techniques. Coherent detection offers the advantage of access to the amplitude and phase of the optical electric field in the electronic domain at the receiver. This allows digital linear filters to compensate the linear channel transfer functions. Today’s motivation to revive coherent concepts in optical communication is twofold. First, coherent receivers enable reliable data transmission with much higher spectral efficiency than conventional direct-detection systems, and second, coherent receivers can compensate for linear impairments, most notably, polarization-mode dispersion (PMD) to a degree that is out of reach for conventional systems. Also, the technical difficulties that the first generation of coherent systems in optical communications faced have been lessened. This is caused by two developments. First of all, the symbol rate to carrier frequency ratio of modern optical communication systems approaches the ratio that is commonly used in wireless systems. For a system that transmits at data rate of 100 Gb/s in two polarization orientations utilizing QPSK (Quadrature Phase Shift Keying) signaling, the symbol rate is 25 GBd. With a carrier frequency of roughly 200 THz, the symbol rate to carrier frequency ratio is 1.25e-3. This indicates that it is possible for optical systems to achieve similar phase noise to symbol rate ratios, as in wireless systems. Second, the performance of digital signal processing (DSP) equipment has been improved dramatically over the past two decades, which makes it feasible to implement the complex signal processing steps required to synchronize to the received signal in digital domain. Implementations of optical coherent receivers have been demonstrated in CMOS-based application specific ICs (ASICs). Albeit a coherent optical communication system can utilize single or multiple carrier [e.g., orthogonal frequency-division multiplexing (OFDM)] transmitter and any modulation format, with QPSK being the most popular and higher order quadrature amplitude modulation (QAM) and phase-shift keying (PSK) systems under investigation, this paper will concentrate on single-carrier frequency-domain-equalized systems, which has become more popular in the wireless domain as well. The modulation format discussed here will be QPSK. Phase coherence between a data signal and the reference is typically established at the receiver side. II. Implementation Considerations Today’s FPGAs offer processing speeds in the order of a few 100 MHz. The achievable processing speed for an ASIC using the same CMOS generation is typically higher by a factor of about two to three. Nevertheless, the processing speeds available in today’s technologies are about two to three orders of magnitude smaller than the data rates in optical communication systems. The maximum achievable processing speed of a

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digital circuit is given by the longest time a signal needs to travel between two clocked storage elements (e.g., flip–flops). The path between these two storage elements is called the “critical path.” There are two commonly used techniques to reduce the length of the critical path, and therefore, increase processing: pipelining and parallel processing. Pipelining reduces the critical path by inserting additional retiming elements along the signal path in a manner that does not alter the result of the processing but of additional latency. This only allows an increase of processing speed up to the maximum speed of a single element (gate in an ASIC or lookup table (LUT) for an FPGA). For being able to process data at multiple gigabit per second up to 100 Gb/s and beyond, a parallel processing structure has to be implemented. Unfortunately, not all algorithms can be parallelized without modifications and loss of performance. In general, all structures that can be pipelined can also be processed in parallel. Algorithms that are time invariant can simply be parallelized without loss of performance by instantiating the circuitry that implements the algorithm multiple times. Often, the complexity can be reduced by sharing resources between multiple instances. An example of a structure that can easily be parallelized is a finite-impulse response (FIR) filter with constant coefficients. If the filter coefficients are not constant, e.g., within an adaptive filter structure, there might not be an equivalent parallel structure, e.g., when the update of the filter coefficients is performed might not be an equivalent parallel structure, e.g., when the update of the filter coefficients is performed once per sampling period. If one is willing to compromise on update speed by accepting an update rate once every clock cycle, with the clock cycle being 1/n times the sampling period, with n the parallelization factor, an equivalent structure can be implemented.

Figure1: Digital signal processing steps III.

Quadrature-Imbalance Compensation

In an initial step, impairments of the optical and electrical frontend are compensated. One example is the correction of quadrature imbalance stemming from imperfect phase control in the optical hybrid. Quadratureimbalance compensation is well known in wireless communications and has been proposed to be used in optical communications as well. If there is no amplitude and offset error of the in-phase and quadrature component of the signal, quadrature-imbalance compensation can be performed by first measuring the cross correlation between the in-phase (I) and quadrature (Q) components of the received signal, which is proportional to the sine of the phase error ψ of the optical hybrid where I and Q are assumed to be normalized. The components can then be transformed in corrected orthogonal components I and

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Equations (1) and (2) can be implemented in a feed-forward structure. This has two major drawbacks. First, the trigonometric functions have to be implemented in an LUT, and second, the normalization of the two components has to be performed accurately; as any error in the normalization will lead to quadrature imbalance.

Figure 2: Circuit for quadrature-imbalance compensation The circuit shown avoids these drawbacks by employing a feedback structure. The cross correlation is measured after the actual compensation, weighted with a convergence factor μ and integrated. After convergence is achieved, the cross correlation is zero in average and the output of the integrator is constant and, according to (1), it is proportional to the sine of the phase error. This result is then multiplied with the I-tributary and added to the Q-tributary to yield the corrected output. Note that, according to (2), the result should also be divided by the cosine of the phase error to yield the correct amplitude. This step is omitted in Fig. 2, as it would require an LUT and another multiplier. If a gain control circuit is placed after the quadrature-imbalance compensation, this would be automatically compensated for. IV.

CD Compensation

It is advisable to split the equalization of the received signal in two steps. First, perform a static or slowly adaptive equalization on each polarization tributary separately, and second, perform a fast adaptive joint equalization on both polarization tributaries. The first equalizer is typically chosen to have a much longer impulse response and can be used to compensate for quasistatic effects as CD or frequency response of the optical frontend. The second one having a shorter impulse response but a faster adaptation speed is typically used for polarization tracking, equalization of PMD, as well as residual CD not compensated for by the static equalizer. Typically, equalizers for data rates considered here employ digital block filters. Block filtering involves the calculation of a finite set, or block of output values based on a finite set of input values. This can be performed in time domain or equivalently in frequency domain [11]. Algorithms have been developed for block filtering to achieve identical outputs as sequential filtering, most notably, the “overlap-and-save” method and the closely related “overlap-and-add” method. Let us assume that the input data sequence is partitioned in blocks of length n and that k is the impulse response length of the desired filter function. In case of overlap-and-save, n input samples are concatenated with k symbols from the next block, and then, convoluted with the impulse response. The first k samples of the output of the convolution are not used, while the remaining n samples constitute the correct filter output. Therefore, this method is often also referred to as “overlap-and dump” or “overlap-and-scrap.” In case of overlap-and-add, n input samples are padded with k zeros before being convoluted with the impulse response of the desired filter. After convolution, k trailing samples are stored to be added to the k leading samples of the following result of the convolution. Overlap-and-add is typically slightly more efficient in terms of implementation complexity and is often chosen when the filter response is static or changes only slowly with time. For fast adaptive filters, the overlap-and-save method is preferred, because for the overlap-and-save method, each output block is the filtering result of exactly one impulse response function, while in case of the overlap-and-add method, the portion that is saved from the previous result to be added to the current result might have been calculated with a different impulse response function if the filter function changed in-between two clock cycle. Block filtering can be efficiently implemented in frequency domain, especially if the impulse response length is comparable to the block length.Frequency-domain filtering requires the implementation of discrete Fourier and discrete-inverse Fourier transforms. The most commonly used algorithm to implement Fourier transforms in hardware is the Cooley–Tukey fast Fourier transform (FFT) algorithm [4].Basic idea of the Cooley–Tukey algorithm is to break up a transformation of length N in two transformations, each of length N/2 (Danielson– Lanczos Lemma). This can be done recursively, until one reaches a transform of trivial size (two, four, or eight, for instance). It is possible not only to divide up the FFT in two parts, as described earlier (radix-2). Very common are also radix-4 implementations, where in each step, the FFT is split in four sub-FFTs, or mixture of radix-2 and radix-4 (split radix), which are most suitable for hardware implementation.

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V. Timing Recovery The received data and the sample timing need to be synchronized so that a fixed ratio (typically two samples per symbol) is established. Timing recovery comprises two components, a timing-error detector and an interpolator. Interpolation can easily be implemented utilizing an FIR filter. One of the most commonly used timing-error detectors in digital communication is the Gardner timing-error detector. Unfortunately, if PMD causes differential group delays approaching half symbol duration, the Gardner timing-error detector will fail. Extensions of the Gardner timing-error detector have been proposed to overcome this limitation. VI.

Polarization Tracking

Polarization tracking and PMD equalization is typically performed using a two-in two-out adaptive filter. An adaptive filter can be partitioned in three parts: the actual filter bank, an error estimator, and a device for updating the filter coefficients. The filter itself has typically a rather short impulse response. Because it needs to follow arbitrary polarization rotation, a rather fast update of the coefficients is required. Therefore, as discussed earlier, an overlap-and-save implementation is preferable. In a second step, the error of the signal coming from the filter bank needs to be estimated. There are a number of techniques available for error estimation, namely insertion of training symbols, decision feedback, or measuring a known property of the signal. The former have good tracking properties but require the inclusion of carrier synchronization in the feedback loop. The latter one has advantages with respect to loop delay, and therefore, potentially offers faster tracking speed. A very popular measure is the constant modulus criterion. The constant modulus criterion penalizes deviation of the amplitude of the equalized signal from a desired fixed value. It is obvious that this criterion is optimally suited for PSK-modulated signals. Actually, this criterion can also be utilized in QAM-modulated systems, albeit with a penalty with respect to noise and convergence speed. In a third step, from the estimated error, updated filter coefficients have to be calculated. Several algorithms for this are known in literature, for instance, the Wienerâ&#x20AC;&#x201C;Hoff solution, the method of steepest descent. Most practical from an implementation standpoint of view is the LMS algorithm. The idea of the LMS algorithm is to estimate the gradient of the error by partial derivatives of the mean-squared error with respect to the filter coefficients. The gradient estimates are calculated from instantaneous measures of the error, i.e., the difference between the desired amplitude and the instantaneous signal amplitude after the adaptive filter. In each step, the filter coefficients are updated by adding a small portion proportional to the negative gradient estimate. A weighting factor Îź is again utilized for controlling the adaptation speed and residual error of the adapted filter coefficients. Exact formulation of this algorithm and comparison with a decision-feedback structure can. VII.

Carrier Synchronization

Carrier synchronization (i.e., frequency offset and phase-error estimation and correction) is probably the most comprehensively treated topic in literature, as this is the minimum processing that needs to be performed in any DSP-based coherent receiver. The first published work therefore concentrated on this topic. Frequency offset correction and phase-error correction is conceptually very similar, as both involve the estimation of an error, filtering of that estimate, and correction of the data utilizing the filtered estimate. Phase synchronization typically comprises two steps. First, removal of the modulated information to obtain an instantaneous phase estimate, and second, filtering of the phase estimate to minimize the influence of noise. In most communication systems, information removal is performed by employing a decision-directed scheme, where the difference between a symbol before and after decision is taken as instantaneous estimate for the phase error. Decision-directed schemes typically utilize feedback, which poses a challenge for pipelining and block processing. In a direct parallelization of the decision-directed feedback structure, the feedback delay is multiplied by the parallelization factor, which leads to an equivalent reduction in phase noise tolerance. Lookahead techniques can be utilized to improve performance. In case of PSK-modulated signaling, information removal can also be performed by applying power-law nonlinearity. This is a feed-forward technique that can be easily implemented in a block-processing scheme. The same hold for the post-estimation filtering, as a simple FIR filter can be utilized. The most important problem encountered in broadcasting via terrestrial transmitters is the interference from other broadcasters. In principle, each broadcaster has a different radio frequency (planned by the public authority) in a common reception area to avoid interference from each other. However still there are two problems: spurious radiation of adjacent channels and fringe reception. Fringe reception is unintended reception under certain weather conditions. The exceptionally long-range reception means that the receiver may be tuned to more than one transmitter (transmitting at same frequency) at the same time. These transmitters may transmit programs of different broadcasters as well as the programs of the same broadcaster. In analogue transmission, even the transmitters transmitting the very same program

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interfere each other because of phase differences of the incoming signal, but in digital transmission the transmitters transmitting the same program in the same channel may reinforce each other. VIII.

FPGA Implementation of Signal Processing Algorithms in Coherent Optical Systems [5]

Coherent optical communications carry several advantages over intensity modulated direct detection systems, namely the ability to use phase modulated (M-QPSK) and multi-level constellations (M-QAM), due to the preservation of the electric field from the optical domain to the electrical domain, provided the sampling is at least at Nyquist rate. Additionally, it enables quasi-exact compensation of linear transmission impairments, such as chromatic dispersion (CD) and polarization mode dispersion (PMD) by a linear filter, which can operate adaptively to overcome time-varying impairments. These systems have gained renewed interest due to the availability of high speed digital signal processing, which allows for complex operations to be carried out in the digital domain, enabling high potential for reconfigurable software defined optical receiver. AD converters will be able to satisfy the required high sampling rates in the near future for optical long-haul high speed transmission systems. Moreover, as soon as sufficiently high speed data converters are available, field programmable gate arrays (FPGA) are a very flexible implementation platform. Considering that the Local Oscillator (LO) phase needs to be locked to the signal phase, to avoid the difficulties associated with the Optical Phase Locked Loop (OPLL), the synchronization can be done in the DSP, by digital phase estimation techniques, allowing for a free running LO. Algorithms suitable for phase estimation and dispersion compensation have been studied. IX.

Adaptive Equalization

A. Constant Modulus Algorithm – CMA The CMA is the most used algorithm for adaptive equalizers, essentially because of its robustness and ability to converge prior to phase recovery. The signal at the equalizer output is obtained through convolution of the equalizer coefficients with the digitized signal. Then the error signal is calculated as follows: Where R2 is a constant dependent on the selected constellation. The coefficient update is then given by:

where µ is the algorithm step size. It is important to consider the initialization of the coefficients for successful convergence. A centre spike initialization is required, where the central coefficient is equal to the unity, while all other coefficients are set to zero. This algorithm provides good performance, but disregards the signal phase, causing the constellation to twist. On the other hand, the next algorithm takes the phase into account. B. Least Mean Squares - LMS The LMS is very similar to the CMA, except for the signal error calculation, where an extra module is used to calculate symbol decisions at the equalizer output. The error can be calculated as: where d (n) is the decided symbol. The LMS initialization might be done in two ways. A training sequence is used for initial convergence, the LMS being switched to Decision Directed (DD) mode thereafter. However as training sequences are difficult to provide, a more elegant approach can be used, where the LMS coefficients are initialized to the coefficients obtained after CMA convergence. X. Conclusion and Future Scope The implementation of adaptive equalization algorithms was carried out in a FPGA platform. We conclude these algorithms might be implemented with good performance given that a sufficient number of bits is used. We have successfully initialized the LMS algorithm with the coefficients resulting from CMA convergence. Furthermore, for high fiber distances an equalization module with fixed coefficients would be required. References 1.

2. 3.

“Real-Time Implementation of Digital Signal Processing for Coherent Optical Digital Communication Systems”, Andreas Leven, Senior Member, IEEE, Noriaki Kaneda, Senior Member, IEEE, and Stephen Corteselli, ieee journal of selected topics in quantum electronics, vol. 16, no. 5, september/october 2010 Book title- “SIGNALS AND SYSTEMS”, Edition 2, by Alan V. OPPENHEIM, Alan S. Willsky Massachusetts Institute Technology with S. Hamid Nawab, Boston University, Prentice-Hall International,Inc “Digital Signal Processing”, A Practical Guide for Engineers and Scientists by Steven W. Smith

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9. 10. 11. 12.

“Digital Signal Processing Principles, Algorithms and Applications”, Third Edition by John G. Proakis Northeastern University, Dimitris G. Melonakos ,Boilon College, Prentice-Hall International,Inc “FPGA Implementation of Signal Processing Algorithms in Coherent Optical Systems”, N. M. Pinto, L. M. Pessoa, J. C. Ferreira and H. M. Salgado “Digital Signal Processing for Optical Coherent Communication Systems”, by Xu Zhang Delivery Date: April 27th 2012 ,DTU Fotonik, Department of Photonics Engineering, Technical University of Denmark, Building 343 2800 Kgs. Lyngby,DENMARK JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 26, NO. 10, MAY 15, 2008 1309,Phase Noise Effects on High Spectral Efficiency, Coherent Optical OFDM Transmission ,Xingwen Yi, William Shieh, Member, IEEE, and Yiran Ma “Coherent Receivers for Practical Optical Communication Systems”, Andreas Leven, Noriaki Kaneda1, Ut-Va Koc, Young-Kai Chen Bell Laboratories, Lucent Technologies, 600 Mountain Ave., Murray Hill, NJ 07974, aleven@lucent.com 1: Optical Networking Group, Lucent Technologies, 101 Crawfords Corner Rd., Holmdel NJ 07733 “SM320C6455-EP”,FIXED-POINT DIGITAL SIGNAL PROCESSOR Manual, Literature Number: SPRS462B,SEPTEMBER 2007–Revised JANUARY 2008,PRODUCTION DATA information is current as of publication date, Products conform to specifications per the terms of the Texas Instruments standard warranty. “Optical OFDM Basics” , Qi Yang, Abdullah Al Amin, and William Shieh IEEE. Coherent Digital Polarization Diversity Receiver for Real-Time Polarization-Multiplexed QPSK Transmission at 2.8 Gb/s Timo Pfau, Ralf Peveling, Jérôme Hauden, Nicolas Grossard, Henri Porte, Member, IEEE, Yaakov Achiam, Sebastian Hoffmann, Selwan K. Ibrahim, Member, IEEE, Olaf Adamczyk, Suhas Bhandare, Member, IEEE, David Sandel, Mario Porrmann, and Reinhold Noé, Member, IEEE .

XI.

Acknowledgments

I am very thankful to Mr. Atul Karode Sir who has been guiding me throughout my research paper work. My parents and friends for their support and coordination.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Theoretical Investigation of Refrigeration System for Rapid Cooling Applications Nilesh Pawar1, Dnyaneshwar Pawar2, Dayanand Gorabe3 Students (Department of Mechanical Engineering), Sinhgad Institute of Technology, Lonavala, Maharashtra, INDIA. __________________________________________________________________________________________ Abstract: Nearly half of the vaccines in developing countries go to waste every year due to temperature spoilage, according to the World Health Organization. Current transportation and storage methods in remote regions depend on ice packs that last just a few days. In order to maintain the optimal temperature range for vaccine preservation, this has become restriction to people to use refrigerator for medical purpose which is to deliver special medicine to village. Because of the above stated problem, this paper introduces a design of a meso-scale refrigerator. The system of refrigerator used is the ideal vapor-compression refrigeration cycle and the components of the system are a condenser, a compressor, a capillary tube and an evaporator. The design of prototype refrigerator more focus on designing length of condenser, evaporator and capillary tube. From the ph diagram, the data will be calculated using ideal gas equation and energy balance equation to find mass flow rate and the length. Keywords: Vaccines, meso-scale refrigerator, vapor-compression refrigeration cycle, component design, p-h diagram. __________________________________________________________________________________________ 1, 2, 3

I. Introduction A refrigerator is a cooling appliance comprising a thermally insulated compartment and a mechanism to transfer heat from it to the external environment, cooling the contents to a temperature below ambient. Refrigerators are extensively used to store foods which deteriorate at ambient temperatures; spoilage from bacterial growth and other processes is much slower at low temperatures. Before the invention of the refrigerator, icehouses were used to provide cold storage for most of the year. After that, the first known artificial refrigeration was demonstrated by William Cullen at the University of Glasgow, Scotland in 1748. In 1805, Oliver Evans designed refrigerator based on a closed cycle of compressed ether, represented the first effort to use simple vapor instead of vaporizing a liquid. After that, in 1857, James Harrison introduced vapor-compression refrigeration to the brewing and meat packing industries [1]. II. Basic Theory The design for contemporary refrigerator is based on two basic laws of physics: first, that heat flows from warmer material to cooler materials and never the reverse; second, when decreasing the pressure of a gas also decreases its temperature. The refrigeration systems typically include a compressor, a condenser, an expansion valve (capillary tube), and an evaporator. All components are interconnected to form a fluid circuit. Cooling is accomplished through evaporation of a liquid refrigerant under reduced temperature and pressure [1]. The raw material in refrigerators today consists of several basic components: the exterior cabinet and door, the inner cabinet or liner, the insulation inserted between the two, the cooling system, the refrigerant, and the fixtures. The cabinet and door are made of aluminum or steel sheet metal that is sometimes prepainted. The inner cabinet is made of sheet metal, like the outer cabinet, or of plastic. The insulation that fills the gap between the inner and outer cabinets consists of fiberglass or polyfoam. The components of the cooling system (compressor, condenser, coils, and fins) are made of aluminum, copper, or an alloy [2]. Refrigerators available in market have been designed in various sizes and different applications but its limited for indoor usage since it is large and powered by electricity. So, this paper focuses on designing of a small refrigerator. Refrigerator can be used to deliver the special medicines to villages by doctors. Beside that it can be an useful application in outdoor activities such as picnic and sport. III. Design The design of the vapour compression system shall consist of a system having 200 W cooling capacity. The other major components include an air cooled condenser, a water cooled evaporator and a capillary tube as an expansion device. Also the refrigerant considered for the design purpose is R-134a. A. Design of Evaporator In an evaporator, the refrigerant boils or evaporates and in doing so absorbs heat from the substance being refrigerated. The name evaporator refers to the evaporation process occurring in the heat exchanger. The shell-

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and-coil type evaporators are very efficient and require minimum floor space and headspace. As the name implies, a shell-and-coil evaporator consists of a shell and a coil arranged spirally in the shell. The shell diameters range from 150 mm to 1.5 m. The coil length may be between 1.5 m to 6 m [2]. Heat transfer rate at evaporator or refrigeration capacity, Qe is given by: QE = m (h1− h4) (1) Where, m is the refrigerant mass flow rate in kg/s, h1 and h4 are the specific enthalpies (kJ/kg) at the exit and inlet to the evaporator, respectively. (h1−h4) is known as specific refrigeration effect or simply refrigeration effect, which is equal to the heat transferred at the evaporator per kilogram of refrigerant [2]. B. Design of Compressor A compressor is the most important and often the costliest component of any vapour compression refrigeration system (VCRS). The function of a compressor in a VCRS is to continuously draw the refrigerant vapour from the evaporator, so that a low pressure and low temperature can be maintained in the evaporator at which the refrigerant can boil extracting heat from the refrigerated space. The compressor then has to raise the pressure of the refrigerant to a level at which it can condense by rejecting heat to the cooling medium in the condenser. Rolling piston or fixed vane type compressors are used in small refrigeration systems (up to 2 kW capacity) such as domestic refrigerators [2]. In this type of compressors, the rotating shaft of the roller has its axis of rotation that matches with the centerline of the cylinder, however, it is eccentric with respect to the roller. This eccentricity of the shaft with respect to the roller creates suction and compression of the refrigerant. A single vane or blade is positioned in the non-rotating cylindrical block. The rotating motion of the roller causes a reciprocating motion of the single vane [2]. The leakage is controlled through hydrodynamic sealing and matching between the mating components. The effectiveness of the sealing depends on the clearance, compressor speed, surface finish and oil viscosity. Close tolerances and good surface finishing is required to minimize internal leakage. Unlike in reciprocating compressors, the small clearance volume filled with high-pressure refrigerant does not expand, but simply mixes with the suction refrigerant in the suction space. As a result, the volumetric efficiency does not reduce drastically with increasing pressure ratio, indicating small re-expansion losses [1]. The compressor runs smoothly and is relatively quiet as the refrigerant flow is continuous. Power input to the compressor or work of compression WC is given by: WC = m (h2 − h1) (2) Where, h2 and h1 are the specific enthalpies (kJ/kg) at the exit and inlet to the compressor, respectively. (h 2 − h1) is known as specific work of compression, which is equal to the work input to the compressor per kilogram of refrigerant [2]. C. Design of Condenser Condenser is an important component of any refrigeration system. In a typical refrigerant condenser, the refrigerant enters the condenser in a superheated state. It is first de-superheated and then condensed by rejecting heat to an external medium [1]. The refrigerant may leave the condenser as a saturated or a sub-cooled liquid, depending upon the temperature of the external medium and design of the condenser. At present for simplicity, it is assumed that the refrigerant used is a pure refrigerant and the condenser pressure remains constant during the condensation process. An air cooled condenser has been considered in the present design. As the name implies, in air-cooled condensers air is the external fluid, i.e., the refrigerant rejects heat to air flowing over the condenser [1]. Aircooled condensers can be further classified into natural convection type or forced convection type. In natural convection type, heat transfer from the condenser is by buoyancy induced natural convection and radiation. Since the flow rate of air is small and the radiation heat transfer is also not very high, the combined heat transfer coefficient in these condensers is small [1]. In forced convection type condensers, the circulation of air over the condenser surface is maintained by using a fan or a blower. Heat transfer rate at condenser, QC is given by: QC = m (h2 − h3) (3) Where, h3 and h2 are the specific enthalpies (kJ/kg) at the exit and inlet to the condenser, respectively [1]. D. Design of Capillary Tube A capillary tube is a long, narrow tube of constant diameter. The word “capillary” is a misnomer since surface tension is not important in refrigeration application of capillary tubes. Typical tube diameters of refrigerant capillary tubes range from 0.5 mm to 3 mm and the length ranges from 1.0 m to 6 m [1]. The pressure reduction in a capillary tube occurs due to the following two factors: • The refrigerant has to overcome the frictional resistance offered by tube walls. This leads to some pressure drop, and • The liquid refrigerant flashes (evaporates) into mixture of liquid and vapour as its pressure reduces. The density of vapour is less than that of the liquid. Hence, the average density of refrigerant decreases as it flows in the tube. The mass flow rate and tube diameter (hence area) being constant, the velocity of refrigerant increases since, m = ρVA. The increase in velocity or acceleration of the refrigerant also requires pressure drop [1].

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For the isenthalpic expansion process, the kinetic energy change a cross the expansion device could be considerable, however, if we take the control volume, well downstream of the expansion device, then the kinetic energy gets dissipated due to viscous effects, and h3 = h4 (4) The exit condition of the expansion device lies in the two-phase region, hence applying the definition of quality (or dryness fraction), we can write: h4 = (1 â&#x2C6;&#x2019; x4) hf,e + (x4*hg,e) = hf +(x4*hfg ) (5) IV. Results The results for the original evaporator, the overall heat transfer coefficient is 281.98 W/m2K. Table I Water Cooled Evaporator Results Sr. No.

Parameter

Inlet State

Exit State

Temperature(degree C)

Pressure(bar)

2.928

11.597

Enthalpy(kJ/kg)

263.712

Capacity(W)

Tube Diameter(mm)

10.37

Tube Thickness(mm)

0.514

Length(m)

1.95

200

Table II Rotary Compressor Results Sr. No. 1 2 3 4 5 6 7 8 9

Parameter Temperature(degree C) Pressure(bar) Enthalpy(kJ/kg) Capacity(W) Displacement(cc) Compressor Speed(rpm) Height(mm) L/D Volumetric Efficiency

Inlet State 0 2.928 397.203

Exit State 49.597 11.597 425.635

43 1.83 4500 2.456 100 75.144 %

Table III Plate-Fin-And-Tube Type Air Cooled Condenser Results Sr. No.

Parameter

Inlet State

Exit State

Temperature(degree C)

49.597

Pressure(bar)

11.597

Enthalpy(kJ/kg)

425.635

263.712

Capacity(W)

Tube Diameter(mm)

11.26

Tube Thickness(mm)

0.254

Length(m)

243

1.4

Table IV Capillary Tube Result Sr. No.

Parameter

Inlet State

Exit State

Temperature(degree C)

Pressure(bar)

11.597

2.928

Enthalpy(kJ/kg)

263.712

Tube Diameter(mm)

Length of tube(m)

1 1.36

V. Conclusion The development of compact refrigeration systems presents innumerous challenges and demands technological competence in various fields of engineering. In this article, a complete refrigeration system was designed for maintaining vaccines at low temperature which can benefit this characteristic in order to increase its life. With this objective, a new compressor was built, whose main feature is the fact of being extremely slim, enabling the development of a refrigeration system which is also slim. In addition to the compressor, the compact heat

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exchangers present elevated energy dissipation power for the small space used. Different refrigeration techniques can be applied for the same purpose, nevertheless the search for a compact system, lightweight and of high energy efficiency generates a highly valuable attribute in many applications which is portability. This same refrigeration system can be a part of portable applications for a broad range of purposes, which today vapor compression refrigeration usage is limited. It is seen that the study of Phelan [5] may be compared with the present study in terms of the sizes of the cycle. However, it should be noted that the study of Phelan [5] is an experimental study. Therefore, the present study may be thought as a milestone in the literature where a meso-scale refrigerator with all of the components is designed theoretically. In conclusion, a meso refrigerator has been successfully designed within the scope of this paper. The results actually indicate that it is possible to construct and test such a cycle. Table V Comparison of Present Study with Literature Sr. No.

Parameter

Chow [3]

Heydari [4]

Phelan [5]

Heat Load (W)

2 3 4

Te (0C) Tc (0C) Flow Rate (g/s) Refrigerant COP Compressor Type

12 16.3

20 60 -

R-134a 3.34 Centrifugal

R-134a 3.0 Piston

100300 5 55 0.8272.47 R-134a 3.0 Scroll

5 6 7

Chriac and Chriac [6] 100

Present Study 200

10 55 -

0 45 1.498

R-134a 4.5 Scroll

R-134a 4.7 Rotary

VI. Future Work For different application areas, the cycle may be analyzed for different temperatures, pressures and for different refrigerants. Different types of compressors may be designed for the cycle, such as, screw, centrifugal or scroll compressors. In the present study, a steady-state analysis of the refrigeration cycle has been performed. The transient response of the cycle may also be investigated. The designed cycle would be constructed and tested during the operation to verify the design procedure adopted from the literature. Finally, the design procedure may be improved by a variable speed compressor to cope with the variation of the refrigeration load due to different modes of operation. References [1] [2] [3]

[4] [5] [6]

C. P. Arora, Refrigeration and Air Conditioning, Tata-McGraw-Hill, 2006, pp. 252-384. Manohar Prasad, Refrigeration and Air Conditioning, New Age International, 2005, pp. 438-483.. Chow L.C., Ashraf N.S., Carter III H.C., Casey K., Corban S., Drost M.K., Gumm A.J., Hao Z., Hasan A.Q., Kapat J.S., Kramer L., Newton M., Sundaram K.B., Vaidya J., Wong C.C., Yerkes K., "Design and analysis of a meso-scale refrigerator," Proceedings of the ASME International Mech. Eng. Congr. and Expos., ASME, 1999, pp. 1-8. Heydari A., "Miniature vapor compression refrigeration systems for active cooling of high performance computers," Proceedings of the Inter Society Conference on Thermal Phenomena, IEEE, 2002, pp. 371-378. Phelan P.E., Swanson J., Chiriac F., Chiriac V., "Designing a meso-scale vapor-compression refrigerator for cooling high-power microelectronics," Proceedings of the Inter Society Conference on Thermal Phenomena, IEEE, 2004, pp. 218-23. Chiriac, F., Chiriac, V., “An alternative method for the cooling of power microelectronics using classical refrigeration”, 2007.

Acknowledgments It gives us great pleasure to present a paper on ‘Theoretical Investigation of Refrigeration System for Rapid Cooling Applications’. In preparing this seminar number of hands helped us directly and indirectly. Therefore it becomes our duty to express gratitude towards them.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Design, Analysis of Flow Characteristics of Exhaust System and Effect of Back Pressure on Engine Performance Atul A. Patil1, L.G. Navale2, V.S. Patil3 Mechanical Department North Maharashtra University, Umavi Nagar, Jalgaon, Maharashtra, INDIA Abstract: Now a days the global warming and air pollution are big issue in the world. The more amount of air pollution is due to emissions from an internal combustion engine. Exhaust system plays a vital role in reducing harmful gases, but the presence of after treatment systems increases the exhaust back pressure. This paper deals with the exhaust system designed and through CFD (Fluent) analysis, a compromise between two parameters namely, more maximization of brake thermal efficiency with limited back pressure was aimed at. In CFD analysis, two exhaust diffuser system (EDS) models with different angels are simulated using the appropriate boundary conditions and fluid properties specified to the system with suitable assumptions. The back pressure variations in two models and the flow of the gas in the substrate are discussed in. Finally, the model with limited backpressure was fabricated and Experiments are carried out on single cylinder four stroke diesel engine test rig with rope brake dynamometer. The performance of the engine and the exhaust diffuser systems are discussed. Keywords: Exhaust Diffuser system (EDS), Computational Fluid Dynamics (CFD), Backpressure, Fuel Consumption. I.

Introduction

Energy efficient exhaust system development requires minimum fuel consumption and maximum utilization of exhaust energy for reduction of the exhaust emissions and also for effective waste energy recovery system such as in turbocharger, heat pipe etc. from C.I. engine. To analyses the exhaust energies available at different engine operating conditions and to develop an exhaust system for maximum utilization of available energy at the exhaust of engine cylinder is studied. Design of each device should offer minimum pressure drop across the device, so that it should not adversely affect the engine performance. During the exhaust stroke when the piston moves from BDC to TDC, pressure rises and gases are pushed into exhaust pipe. Thus the power required to drive exhaust gases is called exhaust stroke loss and increase in speed increases the exhaust stroke loss. The network output per cycle from the engine is dependent on the pumping work consumed, which is directly proportional to the backpressure. To minimize the pumping work, backpressure must be low as possible. The backpressure is directly proportional to the exhaust diffuser system design. The shape of the inlet cone of exhaust diffuser system contributes the backpressure. This increase in backpressure causes increase in fuel consumption. Indeed, an increased pressure drop is a very important challenge to overcome. [1], [2] II.

Diesel Exhaust Systems

Backpressure on engine cylinder is completely dependent on exhaust system design, its operating condition and atmospheric pressure (i.e. almost constant). The exhaust system routes exhaust gas from the engine and then exhaust it into the environment, while providing noise attenuation, after treatment of the exhaust gas to reduce emissions and energy recovery. One of the most important sources of vehicle noise, the noise associated with exhausting combustion gases from the engine, is controlled using mufflers. A number of sound reduction techniques are employed in mufflers, including reactive silencing, resistive silencing, absorptive silencing, and shell damping. Exhaust gas properties which are important for the exhaust system design include its physical properties; exhaust gas temperature, which depends on the vehicle duty and/or test cycle and the exhaust gas flow rate. Exhaust system materials are exposed to a variety of harsh conditions, and must be resistant to such degradation mechanisms as high temperature oxidation, condensate and salt corrosion, elevated temperature mechanical failure, stress corrosion cracking, and inter granular corrosion. Engine performance improvement by developing energy efficient exhaust diffuser system requires understanding of integrated component performance aspects to achieve overall system improvement to increase fuel efficiency and to reduce the engine exhaust emissions. The exhaust system design with minimum back pressure requirements is the key factor for upgrading engine performance [3]

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III.

Methodology

The analysis has been carried out on two designs an existing one that is EDS – I with 0° inlet cone angle and a modified one that is EDS – I with 90° inlet cone angle, results are subsequently compared. It was observed that the brake thermal efficiency improved drastically upon modification in exhaust geometry. Physical models of the same these two systems are subsequently manufactured and exhaustive experiments are carried out on them. The results obtained through CFD analysis are experimentally confirmed. In CFD analysis two major flow characteristics (back pressure and engine performance) are studied. Study I: In study I, the change in pressure of structure was studied. This study offered to find the change in pressure difference in inlet and outlet of exhaust diffuser system. The models which produce the higher pressure difference are selected for further studies. Study II: In study II, the models which had the higher pressure difference are studied for the flow pattern. The backpressure characteristics of the models are modeled and the model having the lesser backpressure was taken for experimental study of engine performance. [4] IV. THREE DIMENSIONAL CFD STUDY A three- dimensional model of exhaust diffuser system is generated in CFD Fluent for the analysis. A. Modelling and Meshing: The geometry of the element is made as tetrahedral mesh, with a refined mesh near the wall. The K-E turbulence model is used, with standard wall functions for near-wall treatment for analysis of Exhaust system. B. Boundary Conditions: Boundary conditions used at inlets mass flow rates and Temperatures of Fluid are applied and at outlets pressure outlet is applied. Domain surface is used as a wall with ‘No Slip condition’ and heat transfer coefficient of 45w/m2 ºk and wall surface roughness as 0.00508 mm is used [5]. V. CFD RESULTS & DISCUSSION The primary aim of this CFD analysis is to find out the right shape of catalytic converter for the exhaust manifold which can offer minimum back pressure.[6]

Figure 1: This depicts the Pressure Contour which indicates the change on Pressure along the X- Axis for EDS – I at Constant Load 5 Kg.

Figure 2: This depicts that variation in backpressure on engine during the flow through EDS – I along its length at Constant Load 5 Kg It is observed that the back pressure at inlet of EDS- I is found to be 1659 Pa, as shown in Figure 1and 2. The back pressure is found to be increase with the increase in length of EDS for the same inlet pressure.

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Figure 3: This depicts the Pressure Contour which indicates the change on Pressure along the X- Axis for EDS – II at Constant Load 5 Kg.

Figure 4: This depicts that variation in backpressure on engine during the flow through EDS – II along its length at Constant Load 5 Kg. Similarly the back pressure analysis is carried out for other EDS – II is found to be 1585 Pa, as shown in Figure 3 and 4. The back pressure is found to be decrease with the increase in inlet cone angle of EDS for the same inlet pressure. [7] [10] VI. EXPERIMENTAL RESULT & DISCUSSION The experimentation was conducted with the EDS - I and EDS – II in single cylinder four stroke diesel engines. The exhaust system was fitted on the engine exhaust flange. Then the performance study was conducted and plotted against the brake thermal efficiency. [8], [9].

Figure 5 Schematic view of experimental set up 1 Fuel Flow Measurement

5 C.I. Engine

2 U- Tube Manometers

6 Exhaust Gas Calorimeter

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Atul A. Patil et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 99-103 3 Dynamometer

7 Exhaust System

4 Air Flow Meter X-X: Inlet to Exhaust Diffuser System

Y-Y: Inlet to Exhaust Diffuser System

All dimensions are in CM

Figure 6: Schematic view of exhaust diffuser system

Figure 7: Variation in brake thermal efficiencies vs. backpressure on engine for different load conditions using Exhaust diffuser systems. The figure 7 shows that the variations in the brake thermal efficiency. Considerable increase in brake thermal efficiency is observed while using the EDS – II. There is 9 to 14% of brake thermal efficiency increased.

Figure 8: Variation in heat carried away by exhaust gases in % vs. backpressure on engine for different load conditions using exhaust diffuser systems. The figure 8 shows that the variations in heat carried away by exhaust gases Vs. backpressure on engine for different load conditions using exhaust diffuser systems depicts that when the load is kept constant load at different level viz. 0.5 to 5 kg the backpressure on engine decreases and heat carried away by exhaust gases decreases. Value for heat carried away by exhaust gases for EDS – I is decreasing as load increasing. It is also

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found that for EDS – II backpressure on engine decreases and heat carried away by exhaust gases decreases. Heat carried away by exhaust gases decreases approximately 4% for EDS – II system as compared to EDS – I.

Figure 9: Variation in backpressure on engine using experimentation vs. different load conditions for exhaust diffuser systems. The figure 9 shows that the variations back pressure on engine using values observed during experimentation Vs. different load conditions with exhaust diffuser systems; when the load is kept constant load at different level viz. 0.5 to 5 kg the backpressure on engine is decreases. Value for backpressure on engine for EDS – I is increasing as load increasing. It is also found that for EDS – II backpressure on engine decreases. Backpressure on engine decreases which results increase in brake power of engine. VII. CONCLUSION The following conclusions may be drawn from the present study. The Exhaust system is successfully designed. Through CFD analysis, the backpressures of various Exhaust diffuser systems are studied. The increase in inlet cone angle increases the pressure of the flow which leads to reduce the recirculation zones. Installation of the EDS – II increases the brake thermal efficiency and decreases the backpressure. ACKNOWLEDGMENTS Authors are thankful to the Godavari College of engineering, Jalgaon for providing laboratory facility and financial support. The authors gratefully acknowledge the support of the Ph.D. research Centre SSBTE’S College of engineering Jalgaon, without which experimentation could not have been done. REFERENCES [1] J. B. Heywood; “Internal Combustion Engine Fundamentals”; McGraw –Hill, ISBN 0-07-100499-8, 1988. [2] R.G. Silver, J. C. Summers and W. B. Williamson , “Design and Performance Evaluation of Automotive Emission Control Catalysts”, Evier Science Publishers B. V. Amsterdam, 1991. [3] Desmond E Winter Bone & Richard J Pearson, “Theory of Engine Manifold Design", Professional Engineering Publishing, 2000. [4] G.Venkatesh, “Power Production Technique Using Exhaust Gas From Present Automobiles via Convergent-Divergent Nozzle”, 0-7803-9794-0/06 IEEE, 2006. [5] John D. Anderson, Jr., “Computational Fluid Dynamics - The Basic with Applications”, McGraw - Hill International Editions, 1995. [6] Meda Lakshmikantha & Mathias Keck, “Optimization of Exhaust Systems”, SAE Paper No. 2002-01-0059, 2002. [7] Atul A. Patil, Dr. L. G. Navale, Dr. V. S. Patil, “Simulative Analysis of Single Cylinder Four Stroke C.I. Engine Exhaust System”, Pratibha International Journal of science, Spirituality, Business and Technology (IJSSBT), Vol. 3, ISSN (print) 22777261, November 2013, pp 79-84. [8] D.S. Deshmukh, J.P. Modak and K.M. Nayak, "Experimental Analysis of Backpressure Phenomenon Consideration for C.I. Engine Performance Improvement" SAE Paper No. 2010-01-1575, International Powertrains, Fuels & Lubricants Meeting, Rio De Janeiro, Brazil, on dated- 5 May 2010. [9] A.A. Patil, D.S. Deshmukh, L.G. Navale and V.S. Patil, “Experimental Investigation of a C.I. Engine Operating Parameters For Energy Efficient Exhaust System Development" International Journal of Innovations In Mechanical & Automobile Engineering (IJIMAE), ISSN 2249-2968 (Print), Sept.2011, Issue – I, Vol. -II, Pp. 60-64 [10] Atul A. Patil, L. G. Navale, V. S. Patil, “Experimental Investigation and Analysis of Single Cylinder Four Stroke C. I. Engine Exhaust System”, International Journal of Energy and Power (IJEP) Volume 3 Issue 1, February 2014, pp. 1-6.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Emotional Intelligence and Conflict Management: An Empirical Study in Indian Context Tanu Sharma & Anil Sehrawat Jaypee University of Information technology, Waknaghat, Solan, Himachal Pradesh- 173234, INDIA

_________________________________________________________________________ Abstract: The role of Emotional intelligence in the workplace has attracted the attention of researchers in the last two decades. Researchers have made many claims about the relationship between emotional intelligence and performance, job satisfaction, work motivation. The present study aims at extending the existing empirical base of emotional intelligence in Indian Context. The objective of the present study are to explore relationship between emotional intelligence and different styles of conflict management and to find out whether people differing in levels of emotional intelligence show a preference for a particular style of conflict management. Keywords: Emotional intelligence, conflict management, job satisfaction, performance, diversity. __________________________________________________________________________________ I. Introduction Salovey and Mayer [1] coined the term “ Emotional Intelligence” to explain a different type of Intelligence but it became popular with the publication of Goleman’s book Emotional Intelligence [2].Thereafter it became an important topic of research in the field of management.Mayer & Salovey (1997), defined emotional intelligence as the ability to perceive accurately, appraise, and express emotion; the ability to understand emotion and emotional knowledge; and the ability to regulate emotions to promote emotional and intellectual growth. Gardner explained emotional intelligence as a deep awareness of one's own emotions and the ability to label and draw upon those emotions as a resource to guide behavior [3]. Goleman defines EI as “the capacity for organizing our own feelings and those of others, for motivating ourselves and for managing emotions well in ourselves and in our relationships” [2, p.317]. Mayer and Salovey classified emotional intelligence in five domains- self-awareness, managing emotions, motivating oneself, empathy, and handling relationships[1]. Goleman later developed his four dimension model of emotional intelligence to include knowing and managing one’s emotions, self-motivation, empathy toward others, and social deftness [2]. According to Goleman there are five components of EQ at work. Self-Awareness refers to the ability to recognize and understand one’s own moods, emotions, and drives, as well as their effect on others; Self-Regulation refers to the ability to control or redirect one’s disruptive impulses and moods; Motivation refers to a passion to work for reasons that go beyond money or status; Empathy refers to the ability to understand the emotional framework of others, and Social Skill refers to proficiency in managing relationships and building networks [6]. Scholars like Neisser noted the distinction between academic intelligence and social intelligence [7]. Marlowe defined social intelligence as the ability to understand other people and social interactions and using this knowledge to lead and guide others to mutually satisfying outcomes [8]. Goleman has acclaimed EI as the best predictor of work and life success [2, 5]. Many scholars have made claim about the potential of EI to predict work outcomes, such as job satisfaction, turnover [5], and performance [9]. Researchers agreed that emotional intelligence is important for academic and career achievement [10, 11]. The use of EI tests for personnel selection purposes has been advocated, claiming a strong correlation between EI and job performance. Goleman also claimed that employees with high EI are “star performers.” [5] These claims, however, have been strongly criticized as being implausible and lacking empirical support [12] 2000a). Scholars have criticized these claims argue that these claims are based on unpublished studies, anecdotal accounts, and misinterpreted data [13, 14]. The interest in EQ is growing with increasing organizational change and organizational contextual volatility [15]. Moreover, global mergers and acquisitions, growing number of MNC’s have increased the pace of organizational change and cultural diversity. Organizational change is frequently associated with emotional conflict or interpretative conflict [15]. In such a situation awareness of the style employees use to handle conflicts would be helpful [16, 17, 18]. The term conflict has been employed in different ways reflecting the different levels at which conflicts exist [19, 20]. Thomas has given two broad uses of the term conflict. The first use refers to incompatible response tendencies within an individual, e.g., behavioral conflicts where one must choose whether or not to pursue a particular course of action or a goal, or role conflict where one must choose between several competing sets of role demands. The second use refers to conflicts that occur between different individuals, groups, organizations, or other social units [20]. Hence, the terms interpersonal, inter-group, and

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inter-organizational conflict are used. Putnam and Poole and Thomas on the basis of their analysis of numerous conceptualizations and definitions of conflict identified three characteristics: interdependence, disagreement, and interference [22, 20, 21]. Interdependence exists when each party involved depends, at least in part on the actions of the other for the attainment of their goals. Without Interdependence, the actions of each party have no impact on the outcomes of the other party. Therefore, interdependence is an essential pre-condition of any conflict situation, providing an interpersonal context in which conflicts may arise. Disagreement exists when parties involved think that different values, needs, interests, opinions, goals, or objectives exist. IN interpersonal conflict , disagreement is a key component . However, disagreeing parties will not experience conflict when the point of disagreement is irrelevant or unimportant (for e.g., when there is no interdependence, or when the issue of disagreement is minor). Interference exists when one party interferes with or opposes the other party’s attainment of its, objectives, goals or interests. Many researchers believe that the core process of interpersonal conflict is the behavior where one or more disputants oppose their counterpart’s interests or goals[23]. The role of negative emotion (jealousy, anger, anxiety, or frustration)has been emphasized into into conceptualizations of conflict by many researchers [24, 25, 26, 27, 20, 21. These emotions are thought to emerge when there are major disagreements, or when parties interfere with the attainment of each others’ important goals. Therefore, a fourth property, negative emotion, can also be added. In this study, we focus on interpersonal conflict which has been defined in many different ways [21, 23]. Researchers Within the conflict domain have identified a number of conflict management styles and their role in satisfactory management and resolution of conflicts have been identified [28, 29, 22, 30, 21, 23]. Several measures assessing styles of conflict management have also been developed [31, 32, 33]. Traditionally, five different styles of conflict management: asserting, accommodating, compromising, problem-solving, and avoiding are classified. These styles are seen as general strategies or behavioral orientations that individuals adopt for managing and resolving conflicts. Asserting style occurs when individuals strive to win. In this style one party gains at the cost of the gains of other party. Conflict, therefore, is considered a win- lose situation. Like asserting, accommodating style occurs when individuals sacrifice their own needs and desires in order to satisfy the needs of other parties. This occurs as individuals oblige or yield to others’ positions, or cooperate in an attempt to resolve conflicts. Compromising style frequently splits the difference or involves give and take behaviors where each party wins some and loses some. Problem-solving style occurs when individuals involved in conflict try to fully satisfy the concerns of all parties. In this style, actions are aimed at the achievement of goals and objectives of all parties. Hence, it results as a win-win solution. At last, avoiding style occurs when individuals are indifferent to the concerns of other party and refuse to act or participate in conflict. Here, one party withdraws, physically or psychologically, abdicating all responsibility for the solution. Literature in this field reflects that cooperative styles (problem solving, accommodating and compromising) are positively associated with constructive conflict management and with individual and organizational outcomes [34] and show substantial concern for the other party. Among these three, problem solving style is generally perceived as the most appropriate, most effective, and highly competent style in managing conflicts [35, 36]. Weider-Hatfield and Hatfield found problem-solving positively related to interpersonal outcomes [37]. Burke suggested that, in general, problem solving style was related to the effective management of conflict, while asserting and avoiding were related to the infective management of conflict [38]. Lawrence and Lorsch suggested that a confrontation style dealing with intergroup conflict was used to a significantly greater degree in higher than lower performing organizations [39]. Scholars believe that an individual’s EI influences one’s way of handling interpersonal conflict. Individuals’ with high EI may be more effective in resolving conflict than those with low EI [5, 40]. Jordon and Troth emphasized that individuals with high EI prefer to seek cooperative solutions when confronted with conflict [13]. Goleman suggested that emotionally intelligent employees are better at negotiation and effectively handling of their conflicts with organizational members [5]. A growing number of scholars suggest that emotional intelligence (EI) plays an important role in managing interpersonal conflicts [16]. However, there is little or no empirical data on relationships between EI and handling interpersonal conflicts conducted in an Indian organizational context. To bridge the gap, the present study seeks to explore the relationship between Emotional Intelligence and conflict management styles. II. Method and Objectives of the study The objective of the study was to explore the relationship between emotional intelligence and conflict management. Sample A sample of 100 working mid-level managers from different organizations of north India was selected. The subjects thus covered in the study were the willing participants drawn from a mix of socio-economic backgrounds in the 28-45 years age range. Data Collection

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The study was limited to organizations established in North India. The managers were contacted personally with each organization and requested to complete the survey questionnaire in 30 minutes. All participants completed the surveys in their scheduled time. Instrumentation Emotional Competence Inventory (ECI) by Boyatzis et al. was used in this study to measure emotional intelligence [41]. It consists of 80 items that reflect adaptive tendency toward emotional intelligence. Each item in the questionnaire described a work-related behavior. Respondents used a 7-point Likert scale. Higher the score, greater the tendency an individual possessed to exhibit emotionally intelligent behavior. ECI is divided into the following four sub-skills, as defined in Goleman's (2001) emotional intelligence model: Self-awareness, Self-management, Social awareness, and Relationship management. An average for each cluster was found by summing responses (1-7) to the corresponding questions that pertain to a cluster. A questionnaire measuring Interpersonal Conflict and Conflict Management Styles adapted by Barki and Hartwick was used In addition to the ECI [42]. In this questionnaire, twenty items, adapted from previous measures were used to assess the extent to which students employed five styles (problem-solving, asserting, avoiding, compromising, and accommodating) [31, 33]. For each style, two items inquired respondent’s own behaviors, and two items asked about the behaviors of the other party(ies). Conceptually, these indices measure the overall usage of each style by everyone involved, and not only the respondent’s own usage of the style. The style items assessed these behaviors on 7-point scales ranging from 1 (never) to 7 (always). III. Results First of all, the reliability of the data was tested by computing Cronbach’s Alpha Model. The variable wise reliability coefficients are emotional intelligence α = .823 and conflict management α = .673.The descriptive statistics of the data are given in table 1. Table 1: Descriptive Statistics of the Data N=100 Variable

Range of scores

Min score

Max score

Mean score

Std. Dev.

Self-awareness

0 - 105

73.11

10.38

Self-management

0 - 182

169

127.24

18.37

Social-awareness

0 - 105

116

80.65

11.27

Social-skill

0 - 168

165

125.37

21.02

Emotional Intelligence

0 - 560

180

514

406.36

51.46

Problem Solving

0 -28

20.43

4.16

Asserting

0 - 28

18.93

4.43

Avoiding

0 - 28

14.04

5.43

Compromising

0 - 28

18.61

4.26

Accommodating

0 - 28

17.48

4.07

The Pearson correlation coefficients were used to examine any relationship that may exist between emotional intelligence measure and different styles of conflict management. Table 2: Correlation between the Emotional Intelligence sub-scales and Conflict Management styles Variables ↓→

Problem Solving

Asserting

Avoiding

Compromising

Accommodating

Self Awareness

0.095

0.228*

0.069

0.273**

0.044

Self Management

0.394**

0.405**

-0.041

0.333**

0.350**

Social Awareness

0.475**

0.507**

-0.060

0.357**

0.363**

Social Skills

0.332**

0.441**

-0.068

0.331**

0.290**

Emotional Intelligence

0.409**

0.474**

-0.037

0.383**

0.238**

** Correlation is significant at the 0.01 level, * Correlation is significant at the 0.05 level (2-tailed) Table 2 presents the correlations between EI skills and different styles of conflict management. Self awareness has a significant positive correlation with asserting and compromising styles of conflict management, and there is no relationship between problem solving, avoiding and accommodating styles of conflict management. Self management has significant and positive correlation with problem solving, asserting, compromising and accommodating styles of conflict management. It has significant relationship with avoiding styles of conflict management. Social awareness is positively and significantly correlated with problem solving, asserting, compromising and accommodating styles of conflict management. It has no relationship with avoiding style of conflict management. Social skills are positively correlated with problem solving, asserting, compromising and accommodating styles of conflict management. Emotional intelligence as a single variable has a positive and

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significant correlation with problem solving, asserting, compromising and accommodating styles of conflict management. However, there is no correlation between EI and avoiding style of conflict management. The results show that EI of an individual affects his handling of a conflict situation. It means that knowledge of self and of others helps in the resolution of conflicts and through collaborative and cooperative methods. IV. Discussion In the present study, presents the correlation between sub-scales- self awareness, self management, Social awareness and social skills of emotional intelligence and five styles- problem-solving, asserting, avoiding, compromising and accommodating of handling conflict. The literature suggests that EI has been associated with cooperative solutions, which results in more effective conflict management. The results showed significant influence of EI on problem solving, asserting, compromising and accommodating styles. EI has no significant relationship with avoiding style. Social awareness or empathy refers to the awareness of others’ feelings, needs, and concerns. According to Goleman empathy involves understanding others, developing others, and having a service orientation [2]. It implies that the more an individual understands others/colleagues, the more likely he or she will use the problem solving style to handle conflict. EI is positively correlated asserting dominating style of handling conflict. It is surprising because asserting style maximizes one’s own needs at the expense of the other individuals’ needs [43]. This result matches with the study of Yu, Sardessai, Lu and Zhao [44]. Further exploration found that integrating, dominating, and compromising share one characteristic: concern for self. Concern for self is one of the two basic dimensions that differentiate the five styles of handling conflict. Concern for self explains the degree to which a person attempts to satisfy his or her own interests [45]. However, no specific reason can be given for this outcome but we can say that in South Asia people use self-awareness in deciding their self-interests. V. Managerial implications The globalization and privatization of the workplace has lead to increasing organizational change and organizational contextual volatility, which, in turn, produce increasing differences and conflicts [46] as cited in [44]. Furthermore, Indian organizations are involved in margers and acquisitions at international level that result in workforce diversity and cultural differences that is another major reason of conflicts among employees. Therefore, the findings of this study have importance to Indian organizations in managing conflicts. The problem-solving style is generally perceived to be a more appropriate, effective, and competent style in managing conflict. Individuals who have batter self-awareness also recognize their strengths, weaknesses, needs, and drives. Additionally, these people know how their feelings affect themselves, other people, and their job performance [6]. Self-regulation is an important component of social development and it contributes to the quality of interpersonal relationships [47]. Empathy involves understanding others, developing others, and having a service orientation [2]. Self-regulation and empathy can be developed [48’ 49]. To manage a conflict effectively it becomes necessary for a manager to adopt and develop an integrative style. Salopek noted in an interview with Goleman that emotional intelligence abilities are learned and tend to improve as one ages and matures. Therefore, organizations have to consciously and continuously strive to inculcate self-regulation and empathy among their managers through an effective programme of training and development [50]. VI. Limitations and future directions The limitations of the study are first, it is an exploratory study that examines the relationship between EI and conflict management styles in the Indian context. As in any cross-sectional studies, data collected at a single moment in time may limit the accuracy of this research. As such, a longitudinal study could be considered in order to get convincing evidence of the relationship between EI and conflict management styles. Second in this study, the data is self reported, therefore, subject to limitation of the process. In addition, the scales used to evaluate EI and conflict management styles were developed by Western scholars and tested in a Western setting. Thus, the investigators’ indigenous culture is likely to bias the design of the research instrument (Hofstede, 1991; Adler et al., 1989). Therefore, it would be desirable to develop a scale to measure EI and conflict management styles based on the Indian context. References [1] [2] [3] [4] [5] [6] [7] [8]

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Survey Paper on Image Retrieval Algorithms Ms.Khushdeep Kaur, Mr.Hardeep Singh Department of Computer Science Lovely Professional University, Jalandhar, Punjab, INDIA _________________________________________________________________________________ Abstract: As we all know that popularity of images is increasing briskly from day to day because of improving technologies and suitable availability promoted by the internet. Therefore how to retrieve an image is a difficult task. In this paper I define various techniques for retrieving an image from database. Data mining is the technology that combines historic data analysis practices with practical algorithms for transforming enormous amount of data [3]. Image mining in simpler words is image processing in data mining. __________________________________________________________________________________________ I. Introduction Image mining is an evolution for data mining which comprehend various disciplines. The rapid evolution of multimedia and imaging technology tends to the growth of image data. Image mining is defined as the technology which helps in searching profitable information from huge image data [2]. The basic difference between image mining and image processing is that image mining focuses on huge data image set. The main aim of image data mining is to generate patterns [1]. The image database comprises of massive volume of information and it is becoming progressively complicated when its size continues to rise at a momentous rate. It becomes very difficult to retrieve an image from a large database. Huge number of images have acquired on internet and various other applications. Therefore how to mine different images from database is gaining more attention these days. As we use advanced technology, the storing, acquiring and sharing images becomes easy and it helps in increasing the available images and their wide varieties. Therefore the significance of image retrieving algorithms is increasing. There are various techniques that are used for retrieving an image from database. There are different techniques and algorithm already defined for retrieving an image. These defined algorithms are being used in various fields like medical, diagnostics, space research, weather forecasting etc for retrieving the images. Various image retrieval algorithms and techniques used are text based image retrieval, content based image retrieval, region based image retrieval, hierarchical clustering, fuzzy shape clustering etc [3]. In this paper, I defined different techniques for retrieving an image from the database. This paper is basically a review on various techniques that already have been defined for retrieving an image from database. There are many techniques used these days for retrieving an image. In this paper I have explained five techniques out of many already defined techniques which are normally and widely used for retrieving images. In the next sections I am going to explain about the methods, algorithms being used for image retrieval. II. Methods Images should be retrieved according to the specifications of the requirement analysis. The requirement analysis can be categorized into various levels. These levels are based on the complexity [3]. Level-1: Image Retrieval by Primitive Feature: Primitive features are defined as the color, shape, texture or the location of an image element. Exampleretrieving an image with brown object in the left hand corner. Level-2: Image Retrieval by Derived or Logical Feature: These are defined as the person or object. Example- retrieving images of red car. Level-3: Image Retrieval by Abstract Attribute: These are defined as the scene or purpose of the object. Example- retrieving images of a badminton match. III. Image Retrieval Algorithms There are various algorithms that have already been defined. In this paper, I defined five usually used algorithms or techniques for retrieving images. A. Text Based Image Retrieval It was started with Boolean search of words with combination of AND, OR, NOT. There are different techniques used for text based retrieval: ď&#x201A;ˇ Bag of words approach â&#x20AC;&#x201C; This approach is used for representing a sentence, document or text in the form of bag of words. Example- Rohan likes to watch football matches. Sohan likes too. Rohan also likes to watch cricket matches.

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Representation“Rohan” 1 “matches” 6 “likes” 2 “also” 7 “to” 3 “cricket” 8 “watch” 4 “sohan” 9 “Football” 5 “too” 10  Stop words approach- This approach is used for the words which needs to be filtered out. There is not specific list of these words. In text based retrieval, stop words need to be eliminated.  Stemming- It is the process of deriving the words from the root, stem or base form. Example- if we enter the word “stem” all the words associated with stem like “stemming”, “stemmed”, “stemmer” etc will be shown. Many research engines use this technique for searching the words. Limitations of text based retrieval: 1. Difficult in case of large databases. 2. It is subjected to human perception. 3. B. CONTENT BASED IMAGE RETRIEVAL Recently used image retrieval technique is CBIR. CBIR techniques are more accurate that text based image retrieval. Content based image retrieval is defined as the process of retrieving the features and searching the database for the similar images [7]. Content based image retrieval extracts the images depending upon the visual features like shape, color, texture. The initial phase of CBIR is to evaluate the features and producing it in the form of numeric values. For better representation, more number of features should be evaluated [6]. The second phase is numeric values which are evaluated should be compared with images present in the database. The distance vector is computed. Different aspects of the image are:  Color- Color is very essential level of any image. Color feature acquires more human attention. This feature is categorized into local and global descriptors. Local descriptors are defined as the descriptors which represent the color with reference to the spatial location. Local descriptors are more useful than global descriptors.  Texture- Texture feature is basically defined as the merging of pixels that has occurred many times in an image. The importance of texture feature is that it helps in differentiating the objects with the backgrounds.  Shape- Shape is an important aspect. It defines the relative frequency of the occurrence.

Figure 1: Architecture of Content based Image Retrieval Advantages of Content based image retrieval: The main advantage is the feasibility of an automatic retrieval process, instead of the text based approach, which usually requires very difficult and time-consuming. C. REGION BASED IMAGE RETRIEVAL For retrieving an image the most ordinary approach used is to work on low level aspects like color, texture and shape but still there is a scope of improvement as there is a difference between the low level and high level feature approaches. Researchers defined new methods which uses objects and regions to process the content of

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images [8]. Then the region based methods come into consideration. The main aim is to embellish the ability to store and represent the image content. Region based image retrieval works as follows:  Images are divided into assorted regions  Features are drawn out from each region  The combination of all the features represents the image content. Features are retrieved first then compared with the sketch or region of an image provided by the user. RBIR provides high premium in segmentation quality which is difficult to achieve in practice. D. HIERARCHICAL CLUSTERING This approach is basically used for retrieving images from databases. The images in database are collectively associated into clusters of images having same features or properties. In case of searching the sample image is not compared with all the images but is compared with the subset [9]. Hierarchical clustering approach provides high response time and better accuracy in retrieval. Searching and retrieving for a particular image in large databases is a challenging task. Search engines usually compute the likenesses between sample image and the database images and rank them accordingly. The retrieval time for clustered images is T(cluster) = KT1(sim) + LT1(sim) + O(llogl) Where T(cluster) = time for clustered images K= number of clusters T1(sim) = time to calculate the similarity between two images L = number of images in the clusters nearest to the query O(nlogn) = time to sort n elements. Let n= number of images present in the database. The hierarchical clustering is calculated as follows:  The n images are located in n distinct clusters. These are grouped as (C1, C2, C3,….,Cn). For any Rth cluster,  E(R) = all images contained in the cluster  N(R) = number of images contained in the cluster  C(p) and C(r) are the two clusters that are to be merged such that S(p,r) is the similarity measure which is largest and denoted as C(p+r). The set of all unmerged clusters are also calculated.  The above steps are repeated till number of clusters has decreased to the defined number or the largest similarity measure between clusters has dropped to lesser threshold. E. FUZZY SHAPE CLUSTERING In order to retrieve an image from a database based on the shape, a sample shape is taken from the user and accordingly it is matched with the set of images present in the database [10]. Usually when user enters an input shape, the Fourier descriptors of that sampled shape (sp) is matched with the Fourier descriptors of all the images present in the database (sq). To perform matching process M(pq)= 1-||sp – sq|| After the matching process, results obtained denoted as prototypes are sorted in the order of degree of matching. The prototypes obtained by the higher matching degree are provided as retrieval results.

Figure 2: Different shapes of images of a Guitar

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IV. Conclusion In this paper, we have mentioned various algorithms or techniques which are being used these days in image mining for retrieving an image from a database. Image mining is the current topic in research. Image mining is a vital technique which is used to mine knowledge straightforwardly from image. Image mining is simply an expansion of data mining in the field of image processing. Image mining deals with extracting hidden knowledge, image data association and add on patterns which are not clearly gathered in the images. This paper is basically a review of some of the techniques being used to retrieve images from the database. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

Image mining: A new approach for data mining based on texture. A Survey on Image Mining Techniques: Theory and Applications. Image mining: Issues, frameworks and techniques. Image Mining by Data Compactness and Manifold Learning. Image Mining Using Association Rule. Content-Based Image Retrieval: Theory and Applications. Efficient Content Based Image Retrieval by Ruba A. A. Salamah. Region based image retrieval using probabilistic feature relevance learning by Byoungchul ko, jing peng, hyeran byun. Hierarchical clustering algorithm for fast image retrieval by Santhana Krishnamachari , Mohamed Abdel-Mottaleb Philips Research. Fuzzy Shape Clustering for Image Retrieval by G. Castellano, A.M. Fanelli, F. Paparella, M.A. Torsello. http://digital.cs.usu.edu/~xqi/Teaching/REU07/Notes/CBIR.pdf http://pages.csam.montclair.edu/~peng/publication/PAA.pdf

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net SEISMIC ANALYSIS OF TALL TV TOWER COSIDERING DIFFERENT BRACING SYSTEMS Hemal J shah 1 Dr. Atul K Desai 2 Assistant Professor, Govt. Engineering College, Surat, INDIA. b Professor, Sardar Vallabhbhai National Institute of Technology, Surat, INDIA. a

Abstract: Television towers are constructed to transmit the television signals on the wider areas and this television towers are also used for the purpose of transmitting the radio as well as telecommunication signals. These towers must be properly designed so that they will not fail during the natural disasters such as earthquakes. In past researchers had studied the effect of different earthquakes on 3 legged tall telecommunication towers. In the present study earthquake response of 4 towers of different height are studied considering different bracing system of the tower. The towers of different height are modeled in SAP 2000 software and static and dynamic analysis of the tower has been carried out. In addition to this time history of the bhuj earthquake is applied on all tower and the response of the tower is studied Keywords: Television tower, seismic analysis, time history analysis, response of tower I. Introduction The television and telecommunication industry plays a great role in human societies and thus much more attention is now being paid to telecommunication/tv towers than it was in the past. During the natural disasters such as the earthquakes telecommunication and TV towers have the crucial task of instant transmission of information from the affected areas to the rescue centres. So that relief works and evacuation of the people can be done as early as possible, In addition, performance of infrastructure such as dams, electric, gas, and fuel transmission stations, depends extensively on the information being transmitted via these towers. These tall towers are also used by Military and defence industries so it create the necessity for further research on telecommunication towers. There are three types of steel telecommunication towers mainly known to engineers as guyed towers, self-supporting towers, and monopoles. Guyed towers normally provide an economical and efficient solution for tall towers of 150 m and above, compared to self-supporting towers. Self-supporting towers are categorized into two groups of 4-legged and 3-legged lattice towers. The researchers have studied static and dynamic response of the 3 legged tv towers.as well as some tall tv towers such as milad, cannon tv tower are also studied. In my work the actual drawings of the towers are collected from the Indian government authorities. The various dimensions that is the width of the tower at base, height of the tower and different bracing system used are studied. The bracing system provided in the tower must be such that it has minimum weight so it gives less earthquake forces as well as higher stiffness so it can resist the earthquake forces more efficiently. For the analysis purpose the 3 bracing system as shown in the figure.1 are considered to study the earthquake response of the structure. To study the effect of different system on television towers different height of the tower such as 80 meter, 110 meter, 150 meter and 175 meter is considered. The towers of this 4 different height considering cross type, M type and K type bracing are modeled in SAP 2000 software. Figure 2 shows the model of 80 meter and 110 meter high tower modeled in SAP 2000 software. Fig. 1: Different bracing system of the towers

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Fig. 2: Model of tower in SAP software (a) 80 meter high tower (b) 110 meter high tower

Fig. 3 model of tower in SAP software (a) 150 meter high tower

(b) 175 meter high tower

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After modeling all 12 towers in the sap software the towers were analyzed for the dead loads and result are obtained. The static and dynamic seismic analysis of all the 12 towers considering following data as per Indian standard I.S.- 1893-2002 has been carried out. Data for static analysis: 1) Zone : Zone -II , Zone -III , Zone -IV and Zone - V 2) Type of soil : Type 窶的I medium soil 3) Importance factor : 1.5 4) Response Reduction factor : 5 In addition to static analysis of the towers the dynamic analysis of the tower considering time history method is also carried out. For time history method, the data of BHUJ earthquake occurred in Gujarat, India on 26 January 2001 is used and it is applied on the tower. The linear time history analysis of the tower has been carried out. The details of bhuj earthquake are as under. Fig. 4 shows the acceleration time history of bhuj earthquake. 1) Name of time history : Bhuj 2) Magnitude : 7.7 3) Duration of earthquake: 133.53 second 4) Peak ground acceleration : 1.0382 m/sec2 5) Time for PGA : 46.940 second 6) Duration: long 7) Total no of acceleration records : 26706 8) Time step :0.005 second Fig. 4 Acceleration time history of Bhuj earthquake

The static and dynamic analysis of all 12 towers has been carried out in sap software. The no of mode shapes considered in dynamics analysis are such that dynamic participation factor is more than 90 percentage as per the recommendation of the I.S. -1893-2002. To study the seismic response of all 12 towers the base shear due to static and time history method has been compared. As the leg member near the ground level in each tower is severely loaded by dead and seismic forces, the axial forces in the main leg member due to the dead+static load case and dead + time history analysis has been considered. Fig. 5 Base shear for 80 meter tower BASE SHEAR FOR 80 MT TOWER

140 120 100 80 60 K 40 N 20 0

123 106

121 111

S H E A R

80 34 18

CROSS BRACING

65 70

54 29

M BRACING

47 29

K BRACING

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Fig. 6 Axial Forces for 80 meter tower AXIAL FORCE IN BOTTOM MEMBER FOR 80 MT TOWER F O R C E

600 500

397 396 339 296 283 I 300 224 N 200

K N

400

242259263 188211 149

541523 445 384 338 262

100 0

CROSS BRACING

M BRACING

K BRACING

Fig. 7 Base shear for 110 meter tower BASE SHEAR FOR 110 MT TOWER

S H E A R K N

250

210 177 160 118

200 138

150 92

100 50

79 49

26 39 39 11 17

CROSS BRACING

M BRACING

K BRACING

Fig. 8 Axial Forces for 110 meter tower AXIAL FORCE IN BOTTOM MEMBER FOR 110 MT TOWER

F O R C E K N

800 692709 700 597 533 600 486 500 407 400 300 200 100 0

CROSS BRACING

695 571 488 427 323

M BRACING

543

276275 223244 206 180

K BRACING

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Fig. 9 Base shear for 150 meter tower

BASE SHEAR FOR 150 MT 350360

400 350 300 250 200 150 K 100 N 50 0 S H E A R

331

220 98

210

138 40 63

CROSS CROSS BRACING BRACING

M BRACINGM

60 25 40

115

BRACINGK BRACING

Fig. 10 Axial Forces for 150 meter tower

AXIAL FORCE IN BOTTOM MEMBER FOR 150 MT 2500 F O R C E K N

2021 2000

1653 1408 1406 1500 1224 918 1000

1170 1107 986 906 745845

500

647728672 487554594

CROSS BRACING

M BRACING

K BRACING

Fig. 11 Base shear for 175 meter tower BASE SHEAR FOR 175 MT TOWER

450 400 350 300 250 200 150 K N 100 50 0

383 384

S H E A R

380

255

253

170 106

293 198

169 105 55

CROSS CROSS BRACING BRACING

M BRACINGM

132

164

BRACINGK BRACING

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Fig. 12 Axial Forces for 175 meter tower AXIAL FORCE IN BOTTOM MEMBER FOR 175 MT TOWER F O R C E

2500

1925 1607 1399 1395 1500 1236 972 K 1000 2000

1774 1466 1260 1200 1107 850

1091 910 832 794 704 554

500 0

CROSS BRACING

M BRACING

K BRACING

II. Conclusions Figure 5 to 12 shows the details of base shear for zone-2 to zone-5 as per I.S.-1893-2002 and Bhuj time history considering different bracing system. The graph also shows the axial forces in bottom member of the tower for different seismic zones. The following conclusions were drawn from the study. [1] As the height of tower increase the weight of the tower increase, and hence the earthquake forces at the base i.e. base shear increases. We are getting maximum base shear for cross type bracing system in all 4 towers, hence the cross bracing is most uneconomical system for tall towers. [2] For the 80 meter high tower we are getting minimum base shear in the M bracing system so it is economical for the medium height tower up to 80 meter height. [3] As the seismic zone changes the earthquake forces increases and the increase of forces are linear. [4] We are getting minimum value of base shear in K type bracing for 110 meter, 150 meter and 175 meter high towers. So K type bracing is most economical system to resist seismic forces for tall towers. [5] The axial forces in bottom member due to the dead+ seismic forces are also less in m type bracing for 80 meter tower so it is economical bracing system for medium height towers. [6] The axial forces in bottom member due to the dead+ seismic forces are less in K type bracing for 110 to 175 meter tower so K type system is economical bracing system tall tv towers. [7] The base shear for 175 meter tower by static method is 31 percentages higher than dynamic method so for seismic analysis of tall structures dynamic methods such as response spectrum or time history method must be used. III.

References

Amiri G. G., Boostan A. “ Dynamic Response Of Antenna-Supporting Structures”, 4th Structural Specialty Conference of the Canadian Society for Civil Engineering, 2002 [2] Amiri G. G., Barkhordari M.A., Massah S. R., “Seismic Behavior Of 4-Legged Self-Supporting Telecommunication Towers”, 13th World Conference on Earthquake Engineering., Canada August 2004 Paper No. 215 [3] Chen W.H., LuZ. R., Lin W , Chen S.H., Ni Y.Q., Xia Y. , Liao W.Y., “Theoretical and Experimental modal analysis of the Guangzhou New TV Tower” Elsevier engineering structure. Aug-2011 [4] Glanville M.J., Kwok K.C.S. “Dynamic Characteristics And Wind Induced Response Of A Steel Frame Tower” Journal of wind Engineering And Industrial Aerodynamics 1995 Paper No: 54 [5] Gholamreza G. A. “Seismic Sensitivity Of Tall Guyed Telecommunication Towers” Ph.D. Thesis, February 1997, Mcgill University, Canada. [6] Guo Y.L., Kareem A., Ni Y.Q., Liao W.Y. ,“Performance Evaluation Of Canton Tower Under Winds Based On Full-Scale Data” Journal of Wind Engineering And Industrial Aerodynamics April 2012 [7] H. Zafarani, Ghorbani-Tanha A.K., Rahimian M. And Noorzad A., “Seismic Response Analysis Of Milad Tower In Tehran, Iran,Under Site-Specific Simulated Ground Motions”, The 14Th World Conference on Earthquake Engineering October 12-17, 2008, Beijing, China [8] He M.J. Li X., Ma R.L. and Chen J.L., “Seismic Resistant Performance Analysis On An Unsymmetrical Super-High Steel Tv Tower” Tongji University, Shanghai 200092, China. [9] IS: 1893(Part 1):2002,”Criteria for Earthquake Resistant Design of Structures (General Provisions & Buildings)”, Bureau of Indian Standards [10] Mohamed A. H. “Seismic Analysis of Lattice Towers” Ph.D. Thesis October 1998, McGill University, Canada [11] Minjuan H., Renle M., And Zhao L., “Design Of Structural Vibration Control Of A Tall Steel TV Tower under Wind Load” Steel Structures 7 (2007) 85-92 [1]

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Hemal J shah et al., International Journal of Engineering, Business and Enterprise Applications, 7(1), December 2013- February 2014, pp. 113- 120 [12] OliveiraI M I R., Jose G.S.S d., Vellasco P. C. G. S., AndradeIV S.A. L. d., Lima L.R.O.D., “Structural analysis of guyed steel telecommunication towers for radio antennas” Society of Mechanical Science & Engineering. vol.29 paper no.2 [13] Satishkumar S.R. and santhakumar A.R.”Lecture notes on design of tower foundations” Indian institute of technology , madras. [14] Siddesha H., “Wind Analysis of Microwave Antenna Towers” , International Journal Of Applied Engineering Research, Dindigul Volume 1, No 3, 2010 [15] Sullins E.J. “Analysis of Radio Communication Towers Subjected To Wind, Ice And Seismic Loadings” Ph.D. Thesis, May 2006, University of Missouri – Columbia. [16] Seyed A. G. O.“Earthquake-Resistant Design Procedures For Tall Guyed Telecommunication Masts” Ph.D. Thesis, August 2010, Mcgill University,Canada. [17] Tabeshpour M. R., Bakhshi A., And Golafshani A. A. “Seismic vulnerability, performance andDamage analyseis of special structures”,13th World conference on Earthquake Engineering., Canada August 2004 Paper No.1431 [18] Xing M., Zhaomin W., “Design Of Chinese Steel TV Tower” 2006 NZSEE Conference, paper no 50. Yan A. Z., Teng J. and LU Z.X.,“ Analysis For Seismic Response Of Wutong Tv-Tower With Variable Stiffness Tuned Mass Dampers” ,4th International Conference on Earthquake Engineering Taiwan Paper No. 186.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net EFFECT OF ON PARENTING STYLES ON ACADEMIC ACHIEVEMENT AND ADJUSTMENT PROBLEM OF TEENAGE Sunil Kumar ____________________________________________________________________________________________ Abstract: A home is a place of acceptance where psychological and physical needs are cared for. Unfavorable home atmosphere imposes a barrier to acceptance and causes a strain in interpersonal relations. On the basis of parentchild relation the children may be broadly classified into two groups i.e. accepted and rejected children. Accepted children are those who are loved either physically or verbally. Physical attention may be shown by hugging, kissing or caring a child. Verbal affection is shown by saying words of encouragements, appreciation and complementing them. All these behavioral patterns are likely to instill in a child, a feeling that he is wanted and accepted. Rejected children are those who are disliked or disapproved of without any valid reason by their parents. Rejection is manifested in two principal ways i.e. parental hostility / aggression and parental indifference. Hostility / aggression are expressed by hitting, kicking, pushing, beating and pinching a child. Indifference is the lack of concern or interest in the child. These behavioral patterns are likely to make a child feel unloved and rejected. Thus parenting style has a great influence on the personality development and their academic achievement. It is also said that parental acceptance is positively related with self-concept, curiosity, cognitive competence and achievement of adolescents. Normally, we see that the adolescents of affectionate parents were found socially and emotionally stable and energetic. Thus the family provides an atmosphere for the children to grow and shape their personalities, improve potentialities to the optimum level. The adolescent’s achievements and success in formal education system depends on the social learning and the parenting style he has received in the family. The parenting style may be held responsible for various behavioral profiles among the children like self-esteem, self-confidence, energetic, friendly disposition, self-reliance, curiosity, ability to coping in stress, cooperation with adult, purposiveness, achievement oriented and negative ones are irritable, fearful, unhappy, hostile, vulnerable to stress, easily annoyed, unfriendly, aggressive, rebellious, domineering, impulsive, low in achievement etc. The different types of parenting styles are certainly bound to have either favorable or adverse influence on the student’s habits of studying, interest, motivation and aptitude for the subjects learning and selections of subjects and vocations and their achievement. Moreover a different unhealthy parenting style may create different problems related to physical health and fitness, self and selfimage, friendship, marriage and sex, family, social, education, vocational, economic and material facilities, morality, customs and religion for the students. ___________________________________________________________________________________________ I. Introduction The concept of parenting has been described as the most important tool of the socialization process. Veda illustrates it as MATR-DEVO-BHAVA, PITR-DEVO-BHAVA; ACHARYA-DEVO BEHAVES (Let the mother, the father and the teacher be revered as Gods). The first two are as important as the creator of the world namely God as these two create the existence of the child in this creation. Whereas the third, the teacher, creates intellect and SANSKARA in the child. It may however be added that environment also acts as a basic factor, as the three mentioned above are also subject to the vagaries or excellence of it. Parenting thus, has been described as the style of child upbringing in relation to a privilege or responsibility of both mother and father together or independently to prepare a child for the society and culture, where the child uses his parents as models for making his social adjustment. Parents at this stage are supposed to play a key role in preparing for them a more congenial, happy, lucid, and warm atmosphere along with careful nurturing for their children (Erickson, 1974). And this relationship between parents and child happens to be a central factor in the social upliftment of the individual. Furthermore, the relationship of the parent with child and of the child with the parents always remain in a constant state of flux and requires adequate adjustment on the part of both of them. This results in a continuous delicate interplay of psychological forces essential for maintaining a state of well-being in the parent child relationship (Hurlock, 1956). A few studies clearly indicate that the performance of the child depend on the various parenting styles by which they are being brought up. It was found that good relations with parents tend to show better adjustment, emotional

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adjustment and development of self-esteem. Whereas deprivation of opportunities to be loved and belonged retards the development and affects the patterns of personalities. A healthy parent child relationship leads to the feelings of being loved and accepted with a high degree of self-confidence and non-dependency. Parental acceptance was found to be positively related to achievement and educational competence. Whereas parental rejection makes children fearful, insecure, attention seeking, jealous, aggressive, hostile and emotionally less stable with poor educational achievement and more aggressive tendencies. The carelessness on the part of parents may grow the feeling of unwantedness and may also result in chemical dependence (Bhardwaj, 1995, 1996). Since large population of parents are either illiterate or have undergone inadequate schooling, such parents fail to provide good schooling to their children. Their children's aptitude, interest for learning and the achievements in different subjects are rarely paid attention and therefore students have to face many such problems related to their educational and vocational interest and needs. Though various significant recommendations have been made for providing vocational education in the school in the new education policy, yet large population of students fail to get admissions in vocational courses suiting to their interest. Increasing needs of parents to lead an ostentatious life leaves them with little income for any other pressing need such as the education of children. Furthermore during the preceding years a trend of commercialization and privatization in education have been encouraged by the government which demand high expenses from parents on the education of their children. Thus students sometimes have to cope up with many economic problems like lack of adequate funds for gaining meaningful entry into high fee charging institutions and joining competitive classes or hiring personal tutorial classes. They also suffer from the fear of not getting admission to the desired courses, lack of adequate space for studies at home, improper environment and lack of adequate facilities for entertainment. Due to increasing emphasis on imparting education of science subjects in schools right from early primary classes, a scientific reasoning, temperament and educational awareness has been developed among the people. Consequently answers to questions pertaining to religious practices, customs and traditions are explored scientifically by the new generations. But the unquestionable beliefs and unflinching faith of parents in their religious practices and customs prove to be more enduring than their scientific explanations. Thus the modern generations face many such problems and fail to accommodate themselves to the changing perspectives. II. Family – The First School for Learning Family life is the first school for learning. In this intimate cauldron we learn to feel about ourselves and how others will react to our feelings. Some parents are gifted emotional teachers, others atrocious. A strong foundation of parental love during childhood gives the youngsters an invaluable resource as they embark on an adolescent phase and career and continued assurance of parental love is an invaluable asset during growing up years. The period of adolescence has been described as the period of social construction or as a period of awakening and self-discovery with regard to a few essentials viz. physical, social, intellectual and emotional competencies. Adult fail to understand the behavior of adolescents, even though they themselves were once adolescents. The word adolescence comes from the Latin word 'adolescence', which means to grow or to mature. Adolescence is a period of transition where the individual changes physically and psychologically from a child to an adult. Adolescence in human life is the stage where rapid changes take place. The individual’s physical, mental, social, moral and spiritual outlooks undergo revolutionary changes. Such changes, during adolescence, are more rapid than during infancy or childhood. Due to this growth, human personality develops new dimensions. During adolescence, the individual wants to take independent decisions in various situations of his experiences. This is regarded by the elders as an act of indiscipline or of misconduct. Gradually the adolescent starts to control his desires according to the standards set by the society. He also begins to realize his social responsibilities. A. Parenting – The Concept Parenting is a complex, dynamic process that affects both parent and child. A parent develops and uses the knowledge and skills required to plan for children, give birth to them, and/or rear and care for them (Morrison, 1978). In general, one or more adults are primarily responsible for a child’s basic care, direction, support, protection, and guidance. Most people play a parenting role, either directly or indirectly, in their lifetime. A parent may be a child’s biological parent, foster parent, stepparent, aunt, uncle, older siblings, relative, or parent surrogate. Parents are of many ages, single or married, male or female. Fromm’s statement about loving applies also to parenting if love is a capacity of the mature, productive character, it follows that the capacity to love an individual living in any given culture depends on the influence this culture has on the character of the average person. Many parents raise their children following the social conventions of their generations. They remain out of touch with their own parents. Effective parents do not view the society as it is, not as it was when they were children, nor as the ideal society do they wish it could be.

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B. The Art of Parenting Parenting is a dynamic process. Parents perpetually adapt their parenting to fit their children’s emerging needs and interests, to meet their own needs and reflect new learning, and to respond to the ever-changing influence of society. Parents guide their children’s efforts to maintain the often precarious balance between love and hate, acceptance and rejection, work and idleness, kindness and cruelty, constructiveness and destructiveness, honesty and dishonesty, learning and ignorance, openness and prejudice, and self-discipline and self-indulgence. Research has shown certain components of parenting to be responsive to scientific analysis, but helping a child develop and maintain a positive balance is ultimately an art. It requires knowledge, skill, understanding and dedication equal to or exceeding that required of sculptors, composers, painters, poets and other artists. Like artists, parents are self-disciplined in their personal lives and support their children’s efforts to attain self-discipline. They concentrate on the art of parenting, devoting time and energy to analyze their functions and responsibilities. C. Adolescence in Indian Context It is aptly said that a child during babyhood leans to adjust to himself and also discovers his own personality. In the pre-school period the child simply learns to adjust to his family. When he reaches middle childhood he learns to adjust to the society. This adjustment is considered to be the most difficult one and is usually dependent on the earlier adjustment to self, family and school. From a theoretical standpoint, adolescence is best regarded as recapitulation of the period of life. It is a second turn on the spiral of development. In this period, the child again becomes unstable. His physical and mental adjustment is shaken and he at times behaves like an infant to adopt himself to his environment. In an individual’s life adolescence is that period which begins at the end of childhood. Among girls the beginning of adolescence is generally marked by the appearance of menses. Among boys the beginning of adolescence not clearly marked. Usually, the criterion employed for the onset puberty is the appearance of pubic hair. We have already referred that there are individual differences in the age at which adolescence begins in different individuals. This age also varies with the changes in culture. In our country even now in some village communities a child of fourteen may be considered a fully grown-up man, who may be married and in all probability be father of a child. Till very recently, it was considered a sin if a girl was not married before her first menses. The result of this was that a child usually entered the stage of manhood or womanhood just after childhood. The period of adolescence of individual was totally cut short in a large number of communities in our country. The girls and boys, as soon as they were mature enough produce an offspring and were supposed to behave as grown up women or men. But this situation resulted in degenerations of the Indian society and an enlightened public opinion was formed to oppose this system. At present, an Indian child is supposed to have passed into the period of adolescence after his childhood is over even though in many cases it may still be quite short. Adolescence in human life is the stage when rapid change takes place. The individual’s physical, mental, social, moral and spiritual outlooks undergo revolutionary changes. Such changes during adolescence are more rapid than during infancy or childhood. Due to this growth, human personality develops new dimensions. During adolescence, the individual wants to take independent decisions in various situations of his experiences. This is regarded by the elder as an act of indiscipline or of misconduct. Gradually, the adolescent starts to control his desires according to the standards set by the society. He also begins to realise his social responsibilities. D. Relevancy The family provides an atmosphere for the children to grow their and increase their potentialities to the optimum level. The students’ achievements and success informal education system depends on the social learning and the parenting style he has received in the family. The parenting style may be held responsible for various behavioral profile among the children like self-esteem, self-confidence, energetic, friendly level, self-reliance, curiosity, ability to coping with stress, cooperation with adult, purposiveness, achievement oriented, irritable, fearful, unhappy, hostile, vulnerable to stress, easily annoyed unfriendly aggressive, rebellion, domineering, impulsive, low in achievement etc. The different types of parenting styles are certainly bound to have either favorable or adverse influence on the student’s habits of studying, interest, motivations and aptitude for the subjects’ learning and selection of subjects and vocation and their achievement. Moreover, a different unhealthy parenting style or overprotective parenting style may create different problems related to physical health and fitness, self and selfimage, economic material and facilities, custom, morality and religion, friendship, marriage and sex, family, social, vocational and educational for the students. A perennial emphasis in both theoretical and applied research in child development has been extrinsic, modifiable factors of which parental influence comprises of a large component outlining the influence of parenting style on many aspects of children’s functioning, including their cognitive development. The present study intends to maintain home-school continuity. The findings of this study are expected to be helpful in providing guidelines to the teachers and parents. It emphasizes the importance of parenting in the development of adolescent children, exerting

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an influence on the academic achievements or performance of the children. While exercising authority, parents tend to either neglect or suppress their children’s reaction. They frequently confuse permissiveness and democracy and continually bounce from extreme authoritarian strictness to guilt feeling about over control. On the other hand, overprotective parents do not allow children to develop and take decisions independently. This study will enable parents to know the response to children with regard to their involvement and participation in their academic life. There are rare studies which examine the relationship of parenting style and the adolescents’ problem and their academic achievement. Thus with this motive the investigator has decided to take up the problem entitled, “Impact of parenting style on adolescents’ problems and their academic achievement.” III. Objectives To study the impact of parenting styles (caring, non-caring and moderately caring) on adolescents’ physical and health fitness problems and their academic achievement. (b) To study the impact of parenting styles (caring, non-caring and moderately caring) on adolescents’ self and self image and their academic achievement. (c) To study the impact of parenting styles (caring, non-caring and moderately caring) on adolescents’ problems pertaining to economic and material facilities and their academic achievement. IV. Hypotheses (i) The adolescents with parents whose parenting style is caring towards them would score higher on physical health and fitness problems and low in academic achievement as compared to those whose style is moderately caring. (ii) The adolescents with parents whose parenting style is caring towards them would score higher on self and self image problems and low in academic achievement as compared to those whose style is moderately caring. (iii) The adolescents with parents whose parenting style is caring towards them would score higher on economic and material facilities problems and low in academic achievement as compared to those whose style is moderately caring. V. Delimitations It is not easy to maintain a balance between realism and hope. But to accept the world in which we live as the inevitably given, may be to deny the possibility that educational development and social reforms can change the world for the better. The present researcher believes that the wide gap between realism and hope can be minimized by making one-self aware of one’s limitation and possibilities in achieving a goal and thereby taking necessary action. The present study is oriented in the same directions with its delimitations and scope. The delimitations in respect of the present study are as follows: a) It has been delimited to three parenting styles (Moderately caring, caring, non-caring) b) The study has been delimited to compare the impact of parenting styles on adolescents’ academic achievement. c) The study has been delimited to class XI students studying in Haryana Board Schools and CBSE schools of Rohtak city. d) 120 adolescents (40+40+40) of each parenting styles was selected for the study. e) The study is conducted by survey method of research. f) The present study has been delimited to schools located in urban area of Rohtak city. (a)

VI. Sample After finalizing the variables, consideration was given to whether the entire population is to be made the subject for the data collection or a particular group is to be selected as representative of the whole population. The entire population refers to the XI class students studying in the schools recognized by the Board of School Education, Haryana and Central Board of Secondary Education Delhi of Rohtak city (males and females in the age group of 15+) studying in Sr. Sec. Schools (CBSE and Haryana Board). In the present study, multi-stage the random sampling Technique was used to select the subjects from the entire population. Table 1: Shows the random sampling used for the study Subjects

Caring

Non-Caring

Males

Moderately Caring 40

Females

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VII. Tools Research tools are the sole factors in determining the round data and in drawing accurate conclusions about the problem on hand. The conclusions ultimately help is providing suitable remedial measures to the problem concerned. (i) Family Relationship Inventory (FRI) by Dr. G.P. Sherry (ii) The Students Problem Inventory (SPI) by Dr. Herkant D. Badami (iii) Academic Achievement VIII. Procedure of Data Collection Data collected at the first and second phases was analysed and interpreted with the help of appropriate statistical tools. First the FRI (Family Relationship Inventory) made by Dr. G.P. Sherry was administered on the 120 students selected from different schools as per scheduled prepared. The objective of administering the Inventory was to find out the students out of 400 populations, whose parents are classified into different parenting styles. The FRI was filled in by each student of the population was scored. The FRI’s were classified keeping in the scores in descending order. Out of 400 students’ whoever got highest score in either kind of parenting styles in FRI. Then 120 students were taken out of 400 which constituted 80 boys and 80 girls. Thus, 120 students became the subject for the study. This process took 15 to 16 days alternately. Then selected students were given SPI (Students Problems Inventory) made by Dr. Herkant D. Badami, was scored and administered which gave the scores for the study and to find out mean, standard deviation. Thus, to determine the relationship of parenting styles and adolescents problems, consequently, the statistical technique of analysis of variance (ANOVA) was adopted to measure the relationship between these variables. To fulfil the objectives of study and to test the hypotheses, mean, S.D 2×3 ANOVA. and DRT test were used as statistical measures. A. Mean It is commonly taken as arithmetic average. It is computed by dividing the sum of all the scores by number of scores. M=

x N

Where: M = mean; x = Scores in distribution;  = Summation; N = Number of Scores B.

Standard Deviation It is used as a measure of the spread of scores in a distribution. S.D. =

C. C1. A1 B1 C1 C2. H1 H 1.

x 2 N

Data Analysis and Interpretation Objectives To study the impact of parenting styles (Caring, Non-Caring and Moderately Caring) on adolescents’ Physical health and fitness problems and their academic achievement. To study the effect of gender on adolescents’ problems and their academic achievement due to difference in parenting styles. To study the effect of interaction between parenting styles and gender on adolescents’ academic achievement and their physical health and fitness. Hypotheses The adolescents with parents whose parenting style is caring towards them would score higher on physical health and fitness problems and low in academic achievement as compared to those whose style is moderately caring. The adolescents with parents whose parenting style is non-caring towards them would score higher on physical health and fitness problems and low in academic achievement as compared to those whose style is moderately caring. To test the validity of hypothesis 1and 10 mean of the score on PHF was calculated.

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Table 2: Shows the distribution of respondents on the dimension of PHF problem Subjects Male Female Total

Moderately Caring 3.7

Caring

Non-caring

Total

3.95

4.4

11.67

6.7

6.18

7.35

19.55

10.4

10.13

11.75

32.28

The above table indicates that the total mean of adolescents of moderately caring on PHF is 10.4 whereas for caring and non-caring are 10.13 and 11.75 respectively .It is clear that as compared to moderately caring subjects, caring and non-caring subject scored high scores on PHF. Lower score on PHF indicate a better health, so these result reveals that adolescents with the parents whose style was moderately caring had less PHF problems compared to other parenting style (Non-caring and caring) thus hypothesis 1 and hypothesis 10 stands accepted. Likewise table 3 indicates the mean scores on academic achievement. Table 3: Means scores of academic achievement Subjects

Caring

Male

66.59

Female

63.54

Total

130.13

Moderately Caring 61.61

Non-caring

Total

50.94

184.14

59.62

54.93

178.09

126.23

105.87

362.23

Table 3 indicates that the total mean of moderately caring subject is 130.13, which is higher than caring subject 126.23 and non-caring 105.87. Which means that different parenting styles effect the academic achievement of the adolescents? And moderately caring style is the best. However, statistical test must be applied to test the significance of difference between the obtained means. Since two ways Anova was employed to the obtained mean which has been shown in the table 4. Table 4: Summary of ANOVA on PHF Source of variance Between pa.att. Between sex

Sums of sqs.

Means

F – value

30.26

15.13

1.32

445.53

39.11*

Interaction

7.53

3.77

0.33

Within group

2665.17

234

11.39

C3. Significant at 0.05 level The above table 4 clearly indicates for pa.att. F-value is 1.32 only, which is not significant at any level. It indicates that the means difference on PHF regarding the effect of pa.att. was not found to be significant, in other words the PHF amongst the children of either kind of parenting does not affect the PHF in any way. The summary table 4 also indicates that the f-value between sexes was 39.11 which is significant at .05 level. Since it is a matter of only two groups, no post-hoc analysis was required. A look at the table 2 shows that the mean on PHF for moderately caring was 10.4 and for non-caring 11.75. As higher score indicates a poorer health. These results are congruent with many earlier studies, which has clearly established that moderately caring type of parenting to be the best. Non-caring type of parents lead to problematic adolescents. (Baumrind 1966, 1971 and Bhardwaj, 1995). C3.1. A2 B C2

Objectives To study the impact of parenting styles (caring, non-caring and moderately caring) on adolescents’ self and self image and their academic achievement. To study the effect of gender on adolescents’ problems and their academic achievement due to difference in parenting styles. To study the effect of interaction between parenting styles and gender on adolescents’ academic achievement and their self and self image problems.

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C3.2. H 2.

Hypotheses The adolescents with parents whose parenting style is caring towards them would score higher on self and self image problems and low in academic achievement as compared to those whose style is moderately caring. H 3. The adolescents with parents whose parenting style is non-caring towards them would score higher on self and self image problems and low in academic achievement as compared to those whose style is moderately caring. To test the validity of hypotheses 2 and 11 mean of the score on self and self image was calculated. The following table 5 shows the distribution of respondents on the dimension of SSI problem. Table 5: Shows the distribution of respondents on the dimension of SSI problem Subject

Moderately Caring

Caring

Non-caring

Total

Male

17.3

20.07

26.1

63.47

Female

22.17

22.37

28.77

73.31

Total Mean

39.47

42.44

54.87

136.78

The above table 5 indicates that the total mean of adolescents with moderately caring is 39.47 whereas total mean of caring subject and non-caring subject are 42.44 and 54.87 respectively. It is clear that as compared to moderately caring, caring subject and non-caring subject scored higher score on SSI. Higher score on SSI indicate a poorer image whereas lower score indicate better self and self image. So these result reveals that adolescents with the parents whose style was caring or non-caring had more SSI problem as compared to moderately caring style. Thus hypothesis 2 and 11 stands accepted. Table 6: Indicates the mean scores on academic achievement Subject

Caring

Non-caring

Total

Males

Moderately Caring 66.59

66.61

50.94

184.14

Female

63.54

59.62

54.93

178.09

Total

130.13

126.23

105.87

362.23

The above table 6 indicates that the total means on academic achievement of adolescents with moderately caring subject was 130.13, which was higher than other two parenting style (caring and non-caring) i.e. 126.23 and 105.87 respectively. Which means that parents affect the adolescents’ academic achievement? And moderately caring is the best Parenting style. However, statistical test must be applied to test the significance of difference between the obtained means. Since two way Anova was employed to the obtained mean, which has been shown in the table 4. Table 7: Summary of ANOVA for SSI Source of variance Between pa.att.

Sums of sqs.

Means

F – value

2669.27

1334.6

12.43*

Between sex

646.8

6.02*

Interaction

77.41

38.70

0.16

Within group

25118.9

234

107.34

C4. Significant at 0.05 level From table 7 it is apparent that overall difference between parental attitudes was 12.43 and between sex 6.02 which was significant at 0.05 level, confirming to the earlier discussion. Edwards (1954) has recommended the use of DRT

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to find out the significance of the between group, difference, after overall F-test. It was hypothesized that the adolescents with the parents whose style is caring and non-caring would score higher on SSI problem as compared to those whose style is moderately caring. To verify this DRT amongst the scores of subjects representing three parenting style on SSI was employed in table 5. Table 8 represents the summary of DRT employed to test the significance of mean differences of different parental attitude groups on the basis of DRT. Table 8: Represents the summary of DRT employed to test the significance Groups

G1 Moderately Caring

G2 Caring

G3 Non-Caring

Means

39.47

42.44

54.87

All means comparisons are significant at 0.05 level in other words the SSI problem was found to be minimum in moderately caring subject and maximum amongst non-caring subjects. Caring subjects rated themselves between moderately caring and non-caring. These results indicate that differences in parental attitude have significant effect upon the self and self image of the child. These results are also in accord with earlier finding of (Baumrind 1971 and Garrison 1959). In which they have said that accepted parents are the best as compared to concentrated and avoidance parents. Thus parents shouldn’t make hard restrictions for a child or let them too loose. Due to which a child faces and lack in SSI. Whereas moderately caring style look after and teach their child time to time by giving them equal value means they never let them very free or make very hard restrictions. Main finding and conclusions: From the analysis of data, the following conclusions are drawn and these are followed by necessary discussion: 1. The adolescents with the parents whose style is caring or non-caring towards them would score higher on physical health and fitness problems and low in academic achievement as compared to those whose style is moderately caring. Studies of Baumrind (1971, 1991), Nagaich (1987) supports this finding that the adolescent with the parents whose style is moderately caring faces less problems related to physical health and fitness as compared to those whose style is caring or non-caring. Parents play a significant role in the lives of their children and they normally take care of their children’s health by providing them balance nutritious diets and keep an eye on their physical health and fitness. In a nutshell we can say that present study is accepted. 2. The adolescents with the parents whose style is ca`ring or non-caring towards them would score higher on self and self-image problems and low in academic achievement as compared to those whose style is moderately caring. The study conducted by Chauhan and Khokar (1985), Symond L.E. (1974) also supports the finding of the present study that the adolescents with the parents whose style is caring or noncaring would have more problems on self and self-image as compared to those whose style is moderately caring. Thus parents should see that they keep friendly relations with their children. They should keep their style accepted (moderately caring) and should teach them in a very positive manner into all activities so that they can have better self and self-image. 3. The adolescents with the parents whose style is caring or non-caring would score higher on economic material and facilities and low in academic achievement as compared to those whose style is moderately caring. This hypothesis is accepted and supported by the studied conducted by Steinberg (1966), Baumrind (1991), Erickeson, K. (1974) that the adolescents with the parents whose style is caring or non-caring would score higher on economic material and facilities problems as compared to those whose style is moderately caring. Thus parents should keep in mind that too much showering or bestowing of material facilities can boomerang and result into spoiling the children. Hence, a moderate and a balanced path is the need of the hour. 4. The adolescents with the parents whose style is caring or non-caring would score higher on friendship, sex and marriage as compared to those whose style is moderately caring. This study is also accepted. It is supported by the studies done by Mishra (1993), Srivastava (1990). They reported that the adolescents brought up by avoided and concentrated parents are unable to have good relations with their friends as they are more aggressive or they are quiet and like to sit idle. They have problems like heterosexual and homosexual. Thus parents’ friendly relationship with the child gives birth to stable and well balanced

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adolescents, whereas too much discipline or lack of discipline by the parents gives birth to the problems related to friendship, sex and marriage. The adolescents with the parents whose style is caring or non-caring would score higher on family problems and low in academic achievement as compared to those whose style is moderately caring. Studies done by Martin (1983), Gottman (1996) also supports the finding of the hypothesis that the adolescents with the parents whose style is caring or non-caring would score higher on family problems and low in academic achievement as compared to those whose style is moderately caring. They reported that different parental attitude has effect on child’s emotional, social, family and intellectual development. They proved that the combination of warm nurturing parenting (accepted) with clear behavioural standards result in the formation of qualities instilled in children who are component, independent and do not have problems related to family. The adolescents with the whose style is caring or non-caring would score higher on social problems and low in academic achievement as compared to those whose style is moderately caring. The result of the present study proves the hypotheses accepted. This is supported by the studies done by Baumrind (1971), Lewis (1945), Jain (1986) that the child brought up by accepted parents are well set and able to cope up with different social changes. As they are treated equally in rewarding and positive manner. They show better social adjustment as compared to those who are brought up under the parents whose style is avoided or concentrated. The adolescents with the parents whose style is caring or non-caring score higher on educational problems as compared to those whose style is moderately caring. The findings of the present study is supported by the studies conducted by Mammen (2000), Sengar (1990), Srivastava (1990) and Baumrind (1991) that the adolescents with the parents whose style is avoided or concentrated faces more problems related to education as compared to those whose style is accepted. Accepted children show better academic achievement. Thus parents who follow reward, punishment and positive guidance shows better result in education of the child. The adolescents with the parents whose style is caring or non-caring would score higher on vocational problems and low in academic achievement as compared to those whose style is moderately caring. This hypothesis is supported by the studies conducted by Roe A. (1957), Westbrook (1973), Zytowski (1968). They reported that the children of accepted parent’s faces less problems related vocational. As these parents show healthy and emotionally stable feelings, they are sensible due to which child gets good start and opt for the best subject choice. Thus parents should concern about the feelings of the child and provide stability, so that the child achieves self-satisfaction in life. The adolescents with the parents whose style is caring or non-caring would score higher on customs, morality and religion problems and low in academic achievement as compared to those whose style is moderately caring. The result of present study is supported by the studies conducted by Dinkmeyer (1967), Zytowski (1968). They reported that the customs and beliefs of our society are first handed down within family, they child first observes then internalizes the values related to social, morality, customs and religious beliefs through parents. Thus accepted parents take care of the problems which are related to customs, morality and religious as compared to those whose style is concentrated and avoided.

IX. Educational implications There is no doubt that adolescents’ problems are increasing day by day. The results of present study indicate that both non-caring and caring i.e. pampering or overprotection are harmful for the children and lead to various problems (i.e. physical and health fitness, self and self-image, family, educational, vocational , economic and material facilities, friendship and sex problems etc.). Rejection of child from parents proved to be the worst as it produces an extremely high level of mental strain. So, the adolescents’ problems could be overcome by moderately caring parents and understanding the needs and attitudes of children by their parents and teachers. We know that family provides an atmosphere for the children to grow and increase their potentialities to the optimum level. The students’ achievements and success in formal education system depends on the social learning and the parenting style he has received in the family. The parenting style may be held responsible for various behavioural profile among the children like self-esteem, self-confidence, energetic, ability to coping with stress, cooperation with others or may be fearful, unhappy, hostile, vulnerable to stress, easily annoyed unfriendly aggressive, low in achievement etc. The different types of parenting styles are bound to have either favourable or adverse influence on the students’ habits of studying and setting up in the social environment.

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Therefore, keeping in mind the effect of parents and their styles on adolescents, this investigation has immense practical utility and social implications. Thus, this study would be of great use to educational planners, institutional heads and teachers along with parents and society. X.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net HIGH RATED INTEGRATED SOLAR DRYER AND COOKER 1 ,2,3

Kirankumar Rathod1, H.S.Ashoka2, S.S.Inamdar3 Assistant Professors, Department of Chemical Engineering SDM College of Engineering and Technology, Dharwad- 580 002. Karnataka, INDIA.

Abstract: Energy being a necessary element in the fields of industry, agriculture, communication, transport and other sectors, the demand for it is growing manifold and the energy sources are becoming scarce and costlier. Solar energy is one of the most ancient sources which is easily available and the root for almost all fossil and renewable types. Special devices have been used for benefiting from the solar and other renewable energy types. In this paper, an effort is made to design a high rated integrated solar drying and cooking system. This system is composed of solar Flat Plate Collector (FPC), and chamber housing both drying and cooking facility, and positive displacement blower. The air present in the collector is subjected to forced circulation with the help of positive displacement blower and sent to the chamber having solar dryer and cooker. The forced air helps in increasing the cooking rates during the solar cooking process. Simple passive and active designs were followed to enable to save energy requirement for fast operation. Potato moisture reduction rate, drying system efficiency and time taken for cooking were determined. Thus proving solar energy is one of the best remedies for fuel crisis. Keywords: Flat Plate collector (FPC), High rated solar cooker and dryer, Integrated solar cooking and drying 1. INTRODUCTION Sufficient, reliable sources of energy are the essential commodities for industrialized nations. Energy can be generally classified as renewable (can be regenerated) and non-renewable (cannot be regenerated quickly enough to keep pace with their use). According to a new report from the United States Energy Information Agency, world marketed energy consumption is projected to increase by 50 percent from 2005 to 2030 [1] and around 85% of energy used in the world is from non-renewable supplies [2]. But since the non-renewable energy sources are depleting quickly and getting exhausted eventually, this worldwide increase in energy demand has put ever-increasing pressure on identifying and implementing new and noble ways to save energy. The first best way to save non renewable energy sources is to use the alternative forms of energy that is Renewable sources of energy. The decision as to which type of renewable energy source suits best for utilization has to be made on the basis of economic, environmental and safety considerations. In this context, solar energy has a greater relevance. Solar energy is utilized instead of any other forms of alternative energies because the desirable environmental and safety aspects are satisfied by it even though the costs involved are slightly higher. It is estimated that the solar energy equivalent to over 15,000 times the world’s annual commercial energy consumption reaches the earth every year. It is the most ancient source and available in abundance. The greatest advantage of solar energy as compared to other forms of energy is that it is clean and can be supplied without any environmental pollution. The other benefits arising from the installation and operation of the solar energy systems are energy saving and generation of new working posts. The sun is a continuous fusion reactor in which hydrogen is turned into helium. The sun’s total energy output, 3.8 ×1020 MW (which is equal to 63 MW/m2) is produced by nuclear fusion reaction. Only a small fraction, 1.7×1014 kW, of the total radiation emitted is intercepted by the earth. However, even with this small fraction it is estimated that 30 min of solar radiation falling on earth is equal to the world energy demand for one year. In this project, emphasis is given to a solar thermal system. Solar thermal system is non-polluting and ensures significant protection of the environment and public health. Reduction of greenhouse gases is the main advantage of utilizing solar energy. Therefore, solar thermal system should be employed whenever possible in order to achieve a sustainable future. The objective of this project is to present a solar thermal system in which drying and cooking can be done at a faster rate. The high rated integrated solar dryer and cooker consists of a chamber which is facilitated with cooking and drying and a blower to provide forced convection. The undertaken study evaluates the performance of high rated integrated solar dryer and cooker by designing and fabricating the same. It is tested for the solar drying and cooking. II. INTEGRATED SOLAR DRYER AND COOKER Integrated solar dryer and cooker is an indirect solar system in which solar cooking and drying takes place in a single chamber with the help of a flat plate collector (FPC) which is used to harness the solar energy. The modes by which the heat transferred are Conduction and convection. A. Components of Integrated Solar Dryer and Cooker

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The main components of Integrated solar cooker or solar dryer are: 1. Solar cooker, 2. Solar dryer and, 3. Solar collector. A1. Solar Cooker or Solar Oven Solar cooker (Fig. 1) is a device which has an insulated box with a glazed cover that cooks food through the "greenhouse effect." The main principle used in solar cooker is conversion of sunlight into heat energy which is retained for cooking.

Fig. 1: Different Types of Solar Cooker (Box Type, Parabolic Cooker) Sunlight enters the oven through the glazing and heats the dark inside walls and cooking vessels. Since the heat cannot escape through the glass, the oven gets very hot. Mirrors around the window send even more sunlight into the oven [3]. This increases the intensity of sun light falling on the cooker and helps in increasing the rate of cooking. It is a form of outdoor cooking, and is often used in the situations where minimum fuel consumption or fire risks are considered highly important  Concentrators: some device, usually mirror or some type of reflective metal, is used to concentrate light and heat from the sun into a small cooking area, making the energy more concentrated and therefore more potent.  Absorbers (Converting light to heat): Any black on the inside of the solar cooker, as well as certain materials for pot, will improve the effectiveness of turning light into heat. A black pan will absorb almost all of the sun’s light and turn it into heat, substantially improving the effectiveness of the cooker. Also, the better the pan conduct heat, the better the oven will work.  Glazing material (Trapping heat): Isolating the air inside the cooker from the air outside the cooker makes an important difference. Using a clear solid, like a plastic bag or a glass cover, allow light to enter, but once the light is absorbed and converted to heat, a plastic bag or a glass cover trap the heat inside. This makes it possible to reach similar temperatures on cold and windy days as on hot days.  Plastic sheet: Uses plastic sheet to assure that the liquids do not seep through the oven. Also to prevent staining of the underlying sheet in the oven. There are many different types of Solar cooker, however they all will follow three basic designs namely Box Solar cooker, Panel solar cooker and Parabolic solar cooker [4]. The type of cookers used in the Integrated solar dryer and cooker is Box type solar cooker. Despite the name “box cooker”, they are made in both circular and rectangular shapes. They consist of an enclosed inner box covered with clear glass or plastic, a reflector, and insulation. There is a wide variety of patterns and plans for the box cooker. While they do not heat quickly, they do provide slow, even cooking and are extremely cheap to make. Box cookers are very easy and safe to use, and fairly easy to construct [4]. A2. Solar dryer Solar dryers (Fig. 2) are the specialized devices that control the drying process and protect produce from damage by insects, dust and rain. The basic principle of a solar dryer is that air is heated by the sun in the collector by green house effect and then, passed over the produce. The hot air then dries the produce in the drying chamber. Depending on the construction, both collector and drying chamber can be combined or separated [5].

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Solar dryer use sun’s heat to evaporate the moisture. Water content of properly dried food varies from 5 to 25% depending on the food. Successful drying depends on:  Enough heat to draw out moisture, without the cooking food.  Dry sir to absorb released moisture  Adequate air circulation to carry off the moisture. [6] There are mainly three types of solar dryers: traditional open air dryer, Direct solar dryer (Cabinet dryer), Indirect solar dryer (thermosyphon dryer). The dryer which is used in Integrated solar dryer and cooker is Thermosyphon dryer type [5]. Thermosyphon dryer have a collector and a separate drying chamber. They operate more efficiently and allow more control over the drying. The collector heats up air, which then rises up by natural convection, forcing its way through the racks of drying produce in the drying chamber. These dryers may be with or without flow enhancement. Such dryers are best suited for high value commercial scale drying [5].

Fig. 2: Solar Dryer A3. Solar Collectors It is a device that transforms solar radiation into heat and transfers that heat to a medium (air, water or any fluid). Solar collectors are of two types namely: Non-concentrating collectors (Flat plate collectors) and Concentrating collectors (Focussing collectors). The Flat plate collector (FPC) is used in the Integrated solar dryer and cooker. Flat plate collector is basically a flat box and is composed of three main parts, a transparent cover (glazing), tubes which carry a coolant and an insulated back plate (absorber). The solar collector works on the green house effect principle; solar radiation incident upon the transparent surface of the solar collector is transmitted through the surface. The inside of the solar collector is usually evacuated, the energy contained within the solar collect is basically trapped and thus heats the coolant contained within the tubes. The tubes are usually made from copper, and the back plate is painted black to help absorb solar radiation. The solar collector is usually insulated to avoid heat losses. These collectors heat liquid or air at temperatures less than 180°F [7]. Flat-plate collectors (Fig. 3) are used for residential water heating and hydronic space-heating installations.  Liquid flat-plate collectors heat liquid as it flows through tubes in or adjacent to the absorber plate. The simplest liquid systems use potable household water, which is heated as it passes directly through the collector and then flows to the house. Solar pool heating also uses liquid flat-plate collector technology, but the collectors are typically unglazed.  Air flat-plate collectors are used primarily for solar space heating. The absorber plates in air collectors can be metal sheets, layers of screen, or non-metallic materials. The air flows past the absorber by using natural convection or a fan. Because air conducts heat much less readily than liquid does, less heat is transferred from an air collector's absorber than from a liquid collector's absorber, and air collectors are typically less efficient than liquid collectors. Air flat-plate collectors are used for space heating.

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Fig. 3: Flat Plate Collector B. Mechanism of Integrated solar dryer and cooker Integrated solar dryer and cooker is a single chambered solar system. In this system, the chamber is sub divided into the upper part, solar cooker and lower part, solar dryer. The air inlet is provided at the lower part whereas outlet is at the upper part of the chamber. The system contains Flat Plate Collector as well, which is also having inlet and outlet. The inlets and outlets are produced for the air circulation. In this system, the air is circulated naturally. The atmospheric air enters the tubes of Flat plate collector and start flowing through it. The solar radiations received by the absorber of FPC are transferred into heat form. The converted form of solar energy heats up the air which is present in the tubes of FPC. The hot air is sent to the chamberâ&#x20AC;&#x2122;s lower part by connecting the outlet of FPC to inlet of chamber. The hot air is circulated over the produce which needs to be dried and it takes out the moisture content. Later the hot air moves to cooking chamber. The left out heat of air helps in cooking process. The solar cooker gets the heat from diffused solar radiations as well. Solar radiations fall directly on the glazing material and enter the cooking chamber. A mirror is attached to the chamber such that the angle of it with the glazing material of the cooker can be varied. The mirror reflects the solar radiations which fall on it there by increasing the intensity of radiation. Therefore, cooking is done with the help of direct solar radiation and also with the heat rejected by the air after drying. The cooled air moves out from the outlet of the chamber by natural convection. III. BLOWER Blowers are the equipments used for ventilation and for industrial processes that need an air flow. These systems are essential to keep manufacturing processes working and consist of a fan, an electric motor, a drive system, ducts or piping, flow control devices and air conditioning equipment (filters, cooling coils, heat exchangers, etc.). Blowers can achieve higher pressures of 1.20 kg/cm2. They are also used to produce negative pressures for industrial vacuum systems. A. Variations in Blower There are mainly two types of blowers: The centrifugal blower and the positive displacement blower. 1. Centrifugal blower: Centrifugal blowers look more like centrifugal pumps than fans. The impeller is typically gear-driven and rotates as fast as 15,000 rpm. In multi-stage blowers, air is accelerated as it passes through each impeller. In single-stage blower, air does not take many turns, and hence it is more efficient. Centrifugal blowers typically operate against pressures of 0.35 to 0.70 kg/cm2, but can achieve higher pressures. One characteristic is that airflow tends to drop drastically as system pressure increases, which can be a disadvantage in material conveying systems that depend on a steady air volume. Because of this, they are most often used in applications that are not prone to clogging. 2. Positive displacement Blowers: Positive displacement blowers have rotors, which "trap" air and push it through housing. These blowers provide a constant volume of air even if the system pressure varies. They are especially suitable for applications prone to clogging, since they can produce enough pressure (typically up to 1.25 kg/cm2) to blow clogged materials free. They turn much slower than centrifugal blowers (e.g. 3,600 rpm) and are often belt driven to facilitate speed changes. IV. HIGH RATED INTEGRATED SOLAR DRYER AND COOKER: As seen above, positive displacement blower provides constant air from the atmosphere to the tubes of FPC which is a favourable condition for the solar drying and cooking. Hence, the positive displacement blower is connected to the inlet of FPC. A. Proposed system

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The High rated integrated solar dryer and cooker provides higher rate of drying and cooking than the solar cooking and solar drying which is conducted in separate chambers. But to further increase the rate of solar cooking and drying, positive displacement blower is used. This creates the forced circulation of air throughout the system. The main parts of proposed system are: Chamber (Solar cooking and drying), FPC and blower. 1. Chamber: A space where solar drying and cooking takes place. 2. Solar collector: Solar collectors are the key component of active solar-heating systems [7]. It harness the solar radiations 3. Blower: Increases the flow rate of air by forced convection.

Fig. 4: Experimental Setup of Integrated Solar Dryer and Cooker B.       

Material used Aluminium vessels with lid :2 Weighing machine Rice :100 gm , water :200 ml Dhal: 100gm , water :200 ml Potato slices: 200gm Hygrometer Fan

C. 

Methodology The food of required quantity is taken in the vessel with proper closing and placed in solar cooking chamber. The produce is placed on the trays of drying chamber. The air is heated due to the solar heat and stored in the collector. The blower is put on for forced circulation of air. The heated air is passed to the chamber and evaporates the moisture from products. The constant circulation of heated air accelerates the drying of the product. The solar radiations fall on the mirror and get reflected to the transparent glass of cooking chamber. Due to the black body of the cooking chamber, heat will be absorbed quickly and heats up the chamber and the vessel. The food items kept in the vessels will be cooked.

      D. 



Experimental Procedure For cooking: At first, about 100 gram of rice was taken in an aluminium box with black coating and about 200 ml of water was added. Then the initial temperature of the vessel and the cooking chamber was noted down. Soon after which box was kept inside the cooking chamber and top cover of glass was closed. Then the mirror was adjusted such that maximum reflection falls on the cooking box. Initial time was noted down and system was allowed to cook. Then final temperature was noted. The above procedure is carried for other products. For drying of Potato: First the potato was weighed by removing its peel. It was sliced into round pieces of equal size (about 3mm), and it was placed uniformly in the trays. Air flow rate and temperature inside the chamber was taken down. Then sliced potato was kept in the drying chamber. Forced circulation was provided at the inlet of the collector and hot air was blown to the drying chamber through the outlet of the flat plate collector. During the drying experiments, air temperature was recorded at inlet and outlet of drying cabinet. Dry bulb temperature was measured using thermometer

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and wet bulb temperature was measured using hygrometer. Temperature measurements were performed at the regular intervals of 1 hour. Air temperature was recorded continuously at ambient, air inlet and outlet of collector and the weight was noted till the product was dried. After that, final weight and time was noted down. V. A.

RESULTS AND DISCUSSIONS

Temperature plot Table 1. Flat Plate Collector vs Time Time (Hrs)

Ti(oC)

To(oC)

Ta (oC)

9.30

10.00

10.30

11.00

11.30

101

12.00

103

13.00

103

14.00

15.00

16.00

As the time was increased from 9.30 AM to 12 PM the ambient temperature of the flat plate collector was increased from 30°C to 36°C, after 12 PM, the temperature decreased due to tilt angle of sun. The inlet temperature of flat plate collector increased from 66°C to 97°C from 9.30 AM to 12.00 PM then decreased from 97°C to 62°C so as the outlet temperature from 68°C to 103°C and decreased from 103°C to 62°C. The maximum temperature of the model was about 376 K. The absorber plate had the maximum temperature. It was obvious from the fact that the thermal conductivity of the absorber (aluminum surface) plate is very large compared to the other materials. B.

Drying unit observation Table 2 Product Weight of trays

Potato Tray 1 = 740 g Tray 2 = 785 g 200 g 30 0C 32 0C 1 m3/min 30 0C

Weight of product Initial temperature of potato slice Initial temperature of drying cabinet Air flow rate entering heat unit Air temperature before passing through heating unit Air temperature before passing through 65 0C drying cabinet Air vent temperature 36 0C Time required to dry 200 g Mw*hfg= 500.4 kJ of potato slices Ma* (W2-W1) =33 min Hear required to dry 200 g of potato chips Q = Mw*hfg/ Ma*(W2-W1)= 421.2 W

Table 3: Drying Unit Observation Time

Outlet of collector

Outlet of dryer

Total weight of potato chips

(hrs)

DBT(°C)

WBT(°C)

DBT(°C)

WBT(°C)

(g)

9.00

24.4

24.6

200

10.00

28.6

26.2

150

11.00

36.1

31.1

28.4

12.00

34.5

Time taken for drying potato is 3 hrs by forced convection only on clear sunny days.

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Fig. 6: Dried Potato Chips C. Drying rate calculations Quantity of potato: 200g Time taken for drying: Moisture percentage on wet basis: M% = {(Mi-Mf)/Mi} × 100 M% = {(200-150)/200} × 100 M = 25%

Fig. 7: Graph of Drying Rate vs Time Raw potato slices with initial moisture contents in the range of 445.41–599.3% (DBT) or 81.67– 85.7% (WBT) were dried until reaching their equilibrium moisture contents to 25 %. Fig 7 shows the drying curve of potato chips undergoing hot air drying. The drying rate increases from 9.00 AM to 11.00 AM and then decreases due to solar insolation. It was found that drying at higher temperature took shorter time to reach the desired moisture content because of a larger driving force for heat transfer. Table 4 Product

Rice

Dhal

Weight

100

Water quantity

200

Initial temperature

Final temperature

Start time

10.00

End time

12.00

Test showed that the total time taken for cooking rice and thur dhal (Fig. 8) was exactly 2 hours on clear sunny days.

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Fig. 8: Cooked Rice and Dhal VI. BENEFITS OF HIGH RATED INTEGRATED SOLAR DRYING AND COOKING SYSTEM  The positive displacement blower helps in providing constant air flow causing uniform drying of the produce.  Low cost system.  Low maintenance cost and easy in operation.  Doesn’t demand frequent attention.  Fast drying takes place by adopting forced convection method. The higher temperature, movement of the air and lower humidity increases the rate of drying.  Food to be cooked or to be dried was enclosed in the chamber and therefore protected from dust, insects, birds and animals.  The higher temperature deters insects and the faster drying rate reduces the risk of spoilage by microorganisms.  Saving the fuel cost as no fuel is required.  Hygiene and quality of the food is maintained.  There are neither fire hazards nor damage to the vessel. VII. CONCLUSION Since day-by-day the demand for energy is increasing and there is exhausting of non-renewable sources like coal and other fuels, renewable sources preferably solar energy need to be utilized. Non-renewable energies also contribute to the warming of the planet, Global warming. It causes pollution resulting in acid rain, which harms the flora and fauna on the earth [8]. To overcome all the disadvantages of non-renewable resources, renewable resources are necessary.  Low cost system can be designed for integrated solar cooking and drying.  Flat plate collectors can be constructed from locally available materials and are relatively low cost.  The application areas described show that solar energy collectors can be used in a wide variety of systems, which include water heating, space heating and cooling, refrigeration, industrial process heat, desalination, thermal power systems, solar furnaces which could provide significant environmental and financial benefits, and should be used whenever possible. VIII. NOMENCLATURE  Ta = Ambient temperature of FPC  Ti = Inlet temperature of FPC  To = Outlet temperature  DBT = Dry Bulb temperature  WBT = Wet Bulb temperature  Mi = Initial weight of potato slices  Mf = Weight of potato slices after drying IX. [1] [2] [3] [4] [5] [6] [7] [8]

REFERENCES

Source: Red green and blue organisation. Website address: http://redgreenandblue.org/2008/06/30/eia-predicts-energy-50increase-in-world-energy-consumption-by-2030/ UCCP, A study on non-renewable energy resources, University of California. Website address: http://www.yoursolarpowerhome.com/Solar_Water_Heating.pdf Bowman, Thomas, 1982, Understanding solar cookers and ovens. (ISBN: 0-86619-247-6) March 04, 2008, Types of solar cooker. Website address: http://solreka.com/blog/solar-cooking/types-of-solar-cooker/ Solar dryers. Website address: http://igadrhep.energyprojects.net/Links/Profiles/SolarDryers/SolarDryers.htm Whitefield, David, Solar Dryer Systems and the Internet: important resources to improve food preparation. 26th-29th November, 2000, International conference on solar cooking. Kimberly-South Africa. U.S. Department of Energy - Energy Efficiency and Renewable Energy. Solar Energy Technologies Program Jones, Jed, Platinum author, Advantages and disadvantages of non renewable resources.

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