International Journal of Engineering, Business and Enterprises Applications issue 11 vol.2

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

Issue 11, Volume 1 & 2 December-2014-February-2015

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: Germany, Australia, India, 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 eleventh 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 eleventh issue, we received 98 research papers and out of which only 39 research papers are published in two volumes as per the reviewers’ 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 eleventh 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-2014 to February-2015, Issue 11, Volume 1 & 2).

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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.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati517502, 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.

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.) Shriram K V, Faculty Computer Science and Engineering, Amrita Vishwa Vidhyapeetham University, Coimbatore, India.

Prof. (Dr.) Sohail Ayub, Department of Civil Engineering, Z.H College of Engineering & Technology, Aligarh Muslim University, Aligarh. 202002 UP-India Prof. (Dr.) Santosh Kumar Behera, Department of Education, Sidho-Kanho-Birsha University, Purulia, West Bengal, India.

Prof. (Dr.) Urmila Shrawankar, Department of Computer Science & Engineering, G H Raisoni College of Engineering, Nagpur (MS), India.

Prof. Anbu Kumar. S, Deptt. of Civil Engg., Delhi Technological University (Formerly Delhi College of Engineering) Delhi, India.

Prof. (Dr.) Meenakshi Sood, Vegetable Science, College of Horticulture, Mysore, University of Horticultural Sciences, Bagalkot, Karnataka (India) Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur, India.

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, India.

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, India.

Prof. (Dr.) Sandhya Mehrotra, Department of Biological Sciences, Birla Institute of Technology and Sciences, Pilani, Rajasthan, India.

Prof. (Dr.) Dr. Ravindra Jilte, Head of the Department, Department of Mechanical Engineering,VCET, Thane-401202, India.

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.) ABHIJIT MITRA , Associate Professor and former Head, Department of Marine Science, University of Calcutta , India.

Prof. (Dr.) N.Ramu , Associate Professor , Department of Commerce, Annamalai University, AnnamalaiNadar-608 002, Chidambaram, Tamil Nadu , India.

Prof. (Dr.) Saber Mohamed Abd-Allah, Assistant Professor of Theriogenology , Faculty of Veterinary Medicine , Beni-Suef University , Egypt.

Prof. (Dr.) Ramel D. Tomaquin, Dean, College of Arts and Sciences Surigao Del Sur State University (SDSSU), Tandag City Surigao Del Sur, Philippines.

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, India.

Prof. (Dr.) Sandeep Gupta, Department of Computer Science & Engineering, Noida Institute of Engineering and Technology, Gr.Noida, India.

Prof. (Dr.) Mohammad Akram, Jazan University, Kingdom of Saudi Arabia.

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Prof. (Dr.) Sanjay Sharma, Dept. of Mathematics, BIT, Durg(C.G.), India.

Prof. (Dr.) Manas R. Panigrahi, Department of Physics, School of Applied Sciences, KIIT University, Bhubaneswar, India.

Prof. (Dr.) P.Kiran Sree, Dept of CSE, Jawaharlal Nehru Technological University, India Prof. (Dr.) Suvroma Gupta, Department of Biotechnology in Haldia Institute of Technology, Haldia, West Bengal, India.

Prof. (Dr.) SREEKANTH. K. J., Department of Mechanical Engineering at Mar Baselios College of Engineering & Technology, University of Kerala, Trivandrum, Kerala, India Prof. Bhubneshwar Sharma, Department of Electronics and Communication Engineering, Eternal University (H.P), India.

Prof. Love Kumar, Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), India.

Prof. S.KANNAN, Department of History, Annamalai University, Annamalainagar- 608002, Tamil Nadu, India.

Prof. (Dr.) Hasrinah Hasbullah, Faculty of Petroleum & Renewable Energy Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.

Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India.

Prof. (Dr.) Bhargavi H. Goswami, Department of MCA, Sunshine Group of Institutes, Nr. Rangoli Park, Kalawad Road, Rajkot, Gujarat, India.

Prof. (Dr.) Essam H. Houssein, Computer Science Department, Faculty of Computers & Informatics, Benha University, Benha 13518, Qalyubia Governorate, Egypt.

Arash Shaghaghi, University College London, University of London, Great Britain.

Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India.

Prof. (Dr.) Anand Kumar, Head, Department of MCA, M.S. Engineering College, Navarathna Agrahara, Sadahalli Post, Bangalore, PIN 562110, Karnataka, INDIA. Prof. (Dr.) Venkata Raghavendra Miriampally, Electrical and Computer Engineering Dept, Adama Science & Technology University, Adama, Ethiopia.

Prof. (Dr.) Jatinderkumar R. Saini, Director (I.T.), GTU's Ankleshwar-Bharuch Innovation Sankul &Director I/C & Associate Professor, Narmada College of Computer Application, Zadeshwar, Bharuch, Gujarat, India.

Prof. Jaswinder Singh, Mechanical Engineering Department, University Institute Of Engineering & Technology, Panjab University SSG Regional Centre, Hoshiarpur, Punjab, India- 146001.

Prof. (Dr.) S.Kadhiravan, Head i/c, Department of Psychology, Periyar University, Salem- 636 011,Tamil Nadu, India.

Prof. (Dr.) Mohammad Israr, Principal, Balaji Engineering College,Junagadh, Gujarat-362014, India.

Prof. (Dr.) VENKATESWARLU B., Director of MCA in Sreenivasa Institute of Technology and Management Studies (SITAMS), Chittoor.

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,Tamil Nadu, 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 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.

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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.

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 Engg., 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.

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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 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.

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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.) K. Ramesh, Department of Chemistry, C .B . I. T, Gandipet, Hyderabad-500075 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 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 Prof. (Dr.) Y.P.Singh, (Director), Somany (PG) Institute of Technology and Management, Garhi Bolni Road, Delhi-Jaipur Highway No. 8, Beside 3 km from City Rewari, Rewari-123401, India.

Prof. (Dr.) MIR IQBAL FAHEEM, VICE PRINCIPAL &HEAD- Department of Civil Engineering & Professor of Civil Engineering, Deccan College of Engineering & Technology, Dar-us-Salam, Aghapura, Hyderabad (AP) 500 036.

Prof. (Dr.) Jitendra Gupta, Regional Head, Co-ordinator(U.P. State Representative)& Asstt. Prof., (Pharmaceutics), Institute of Pharmaceutical Research, GLA University, Mathura.

Prof. (Dr.) N. Sakthivel, Scientist - C,Research Extension Center,Central Silk Board, Government of India, Inam Karisal Kulam (Post), Srivilliputtur - 626 125,Tamil Nadu, India.

Prof. (Dr.) Omprakash Srivastav, Centre of Advanced Study, Department of History, Aligarh Muslim University, Aligarh-202 001, INDIA. Prof. (Dr.) K.V.L.N.Acharyulu, Associate Professor, Department of Mathematics, Bapatla Engineering college, Bapatla-522101, INDIA. Prof. (Dr.) Fateh Mebarek-Oudina, Assoc. Prof., Sciences Faculty,20 aout 1955-Skikda University, B.P 26 Route El-Hadaiek, 21000,Skikda, Algeria.

NagaLaxmi M. Raman, Project Support Officer, Amity International Centre for Postharvest, Technology & Cold Chain Management, Amity University Campus, Sector-125, Expressway, Noida Prof. (Dr.) V.SIVASANKAR, Associate Professor, Department Of Chemistry, Thiagarajar College Of Engineering (Autonomous), Madurai 625015, Tamil Nadu, India (Dr.) Ramkrishna Singh Solanki, School of Studies in Statistics, Vikram University, Ujjain, India Prof. (Dr.) M.A.Rabbani, Professor/Computer Applications, School of Computer, Information and Mathematical Sciences, B.S.Abdur Rahman University, Chennai, India Prof. (Dr.) P.P.Satya Paul Kumar, Associate Professor, Physical Education & Sports Sciences, University College of Physical Education & Sports, Sciences, Acharya Nagarjuna University, Guntur.

Prof. (Dr.) Fazal Shirazi, PostDoctoral Fellow, Infectious Disease, MD Anderson Cancer Center, Houston, Texas, USA Prof. (Dr.) Omprakash Srivastav, Department of Museology, Aligarh Muslim University, Aligarh202 001, INDIA. Prof. (Dr.) Mandeep Singh walia, A.P. E.C.E., Panjab University SSG Regional Centre Hoshiarpur, Una Road, V.P.O. Allahabad, Bajwara, Hoshiarpur Prof. (Dr.) Ho Soon Min, Senior Lecturer, Faculty of Applied Sciences, INTI International University, Persiaran Perdana BBN, Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia Prof. (Dr.) L.Ganesamoorthy, Assistant Professor in Commerce, Annamalai University, Annamalai Nagar-608002, Chidambaram, Tamilnadu, India.

Prof. (Dr.) Vuda Sreenivasarao, Professor, School of Computing and Electrical Engineering, Bahir Dar University, Bahirdar,Ethiopia Prof. (Dr.) Umesh Sharma, Professor & HOD Applied Sciences & Humanities, Eshan college of Engineering, Mathura, India.

Prof. (Dr.) K. John Singh, School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India.

Prof. (Dr.) Sita Ram Pal (Asst.Prof.), Dept. of Special Education, Dr.BAOU, Ahmedabad, India.

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Prof. Vishal S.Rana, H.O.D, Department of Business Administration, S.S.B.T'S College of Engineering & Technology, Bambhori,Jalgaon (M.S), India.

Prof. (Dr.) Chandrakant Badgaiyan, Department of Mechatronics and Engineering, Chhattisgarh.

Dr. (Mrs.) Shubhrata Gupta, Prof. (Electrical), NIT Raipur, India.

Prof. (Dr.) Usha Rani. Nelakuditi, Assoc. Prof., ECE Deptt., Vignan’s Engineering College, Vignan University, India.

Prof. (Dr.) S. Swathi, Asst. Professor, Department of Information Technology, Vardhaman college of Engineering(Autonomous) , Shamshabad, R.R District, India.

Prof. (Dr.) Raja Chakraverty, M Pharm (Pharmacology), BCPSR, Durgapur, West Bengal, India Prof. (Dr.) P. Sanjeevi Kumar, Electrical & Electronics Engineering, National Institute of Technology (NIT-Puducherry), An Institute of National Importance under MHRD (Govt. of India), Karaikal- 609 605, India.

Prof. (Dr.) Amitava Ghosh, Professor & Principal, Bengal College of Pharmaceutical Sciences and Research, B.R.B. Sarani, Bidhannagar, Durgapur, West Bengal- 713212.

Prof. (Dr.) Om Kumar Harsh, Group Director, Amritsar College of Engineering and Technology, Amritsar 143001 (Punjab), India.

Prof. (Dr.) Mansoor Maitah, Department of International Relations, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 21 Praha 6 Suchdol, Czech Republic.

Prof. (Dr.) Zahid Mahmood, Department of Management Sciences (Graduate Studies), Bahria University, Naval Complex, Sector, E-9, Islamabad, Pakistan.

Prof. (Dr.) N. Sandeep, Faculty Division of Fluid Dynamics, VIT University, Vellore-632 014.

Mr. Jiban Shrestha, Scientist (Plant Breeding and Genetics), Nepal Agricultural Research Council, National Maize Research Program, Rampur, Chitwan, Nepal.

Prof. (Dr.) Rakhi Garg, Banaras Hindu University, Varanasi, Uttar Pradesh, India.

Prof. (Dr.) Ramakant Pandey. Dept. of Biochemistry. Patna University Patna (Bihar)-India.

Prof. (Dr.) Nalah Augustine Bala, Behavioural Health Unit, Psychology Department, Nasarawa State University, Keffi, P.M.B. 1022 Keffi, Nasarawa State, Nigeria.

Prof. (Dr.) Mehdi Babaei, Department of Engineering, Faculty of Civil Engineering, University of Zanjan, Iran.

Prof. (Dr.) A. SENTHIL KUMAR., Professor/EEE, VELAMMAL ENGINEERING COLLEGE, CHENNAI Prof. (Dr.) Gudikandhula Narasimha Rao, Dept. of Computer Sc. & Engg., KKR & KSR Inst Of Tech & Sciences, Guntur, Andhra Pradesh, India.

Prof. (Dr.) Dhanesh singh, Department of Chemistry, K.G. Arts & Science College, Raigarh (C.G.) India.

Prof. (Dr.) Syed Umar , Dept. of Electronics and Computer Engineering, KL University, Guntur, A.P., India.

Prof. (Dr.) Rachna Goswami, Faculty in Bio-Science Department, IIIT Nuzvid (RGUKT), DistrictKrishna , Andhra Pradesh - 521201 Prof. (Dr.) Ahsas Goyal, FSRHCP, Founder & Vice president of Society of Researchers and Health Care Professionals Prof. (Dr.) Gagan Singh, School of Management Studies and Commerce, Department of Commerce, Uttarakhand Open University, Haldwani-Nainital, Uttarakhand (UK)-263139 (India) Prof. (Dr.) Solomon A. O. Iyekekpolor, Mathematics and Statistics, Federal University, WukariNigeria.

Prof. (Dr.) S. Saiganesh, Faculty of Marketing, Dayananda Sagar Business School, Bangalore, 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, India 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

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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 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 Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, TamilNadu, 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. .

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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), India.

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, India.

Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura, India.

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.

Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India.

Prof. (Dr.) Chitranjan Agrawal, Department of Mechanical Engineering, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur- 313001, Rajasthan, India.

Prof. (Dr.) Rangnath Aher, Principal, New Arts, Commerce and Science College, Parner, DistAhmednagar, M.S. India.

Prof. (Dr.) Chandan Kumar Panda, Department of Agricultural Extension, College of Agriculture, Tripura, Lembucherra-799210 Prof. (Dr.) Latika Kharb, IP Faculty (MCA Deptt), Jagan Institute of Management Studies (JIMS), Sector-5, Rohini, Delhi, India.

Raj Mohan Raja Muthiah, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts.

Prof. (Dr.) Chhanda Chatterjee, Dept of Philosophy, Balurghat College, West Bengal, India.

Prof. (Dr.) Mihir Kumar Shome , H.O.D of Mathematics, Management and Humanities, National Institute of Technology, Arunachal Pradesh, India Prof. (Dr.) Muthukumar .Subramanyam, Registrar (I/C), Faculty, Computer Science and Engineering, National Institute of Technology, Puducherry, India.

Prof. (Dr.) Vinay Saxena, Department of Mathematics, Kisan Postgraduate College, Bahraich – 271801 UP, 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 (PB) 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.

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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 International Journal of Engineering, Business and Enterprises Applications (IJEBEA) ISSN (Print): 2279-0020, ISSN (Online): 2279-0039 (December-2014 to February-2015, Issue 11, Volume 1 &2) Issue 11, Volume 1 Paper Code

Paper Title

Page No.

IJEBEA 15-104

Algorithm for Content Adaptation of Multimedia Information Grigor Mihailov, Teodor Iliev, Elena Ivanova

01-07

IJEBEA 15-106

Physical and Mechanical Property Evaluation of Some Clay Deposits in Mubi for Production of Glazed Roofing Tiles S.A. AKANJI, C. NATHAN, J. WADAI

08-14

IJEBEA 15-107

PRICE LIMITS AND INFORMATIONAL EFFICIENCY Tamir Levy and Joseph Yagil

15-26

IJEBEA 15-109

Evaluation of Effect of Policy Making Models on Organizational innovation Indices Alireza Booshehri, Iraj Masoomi Baran

27-32

IJEBEA 15-110

Student Acceptance of Web-based Learning for Universities in Thailand Rungsan Suwannahong, Terawat Piboongungon and Werayuth Charoenruengkit

33-35

IJEBEA 15-112

INTEGRATING E-SERVIVE WITH A OIL REFINERY E-COMMERCE USING DATA MINING T.Saranya, J.K. Anu Shakthi Priya, K.Poornima, K. Kumar

36-39

IJEBEA 15-113

Business Correspondent (BC) Model – A Bridge between Banks and Unbanked Dr. N. Sundaram, Mr. M. Sriram

40-44

IJEBEA 15-115

Fault Detection, Protection and Monitoring of Induction Motor Using Zigbee N.SOLAIYAMMAL, N.KANAGAPRIYA

45-47

IJEBEA 15-117

LITERATURE REVIEW ON FACTORS INFLUENCING DIVIDEND DECISIONS P.G.Thirumagal, Dr. S. Vasantha

48-52

IJEBEA 15-118

Metrics and Performance Measurement of Banks J. Priyankha, Dr .S .Vasantha

53-57

IJEBEA 15-119

E-Marketing Types, Practices, Emerging Trends and Technologies: State-of-the-Art Anuja Bokhare and Pravin Metkewar

58-63

IJEBEA 15-127

Automatic Video Annotation using ECGM S. Divya Meena, S. Vidhya meena

64-67

IJEBEA 15-132

Harnessing the power of Viral Marketing through Social Media–A study on IT industry Dr.Anita Venaik

68-72

IJEBEA 15-134

A Knowledge Management approach, for Developing Research Community among Universities Dhananjay S. Deshpande, P. R. Kulkarni, Pravin S Metkewar

73-77

IJEBEA 15-135

Brain Actuated Wheelchair Using Brain Wave Sensor Aswathy M

78-82

IJEBEA 15-138

Review on E-Learning Effectiveness Models A.Bindhu, Dr. Hansa Lysander Manohar

83-88

IJEBEA 15-143

Protocol Based On Round Trip Delay and Paths for Sensor Node Failure Detection S.Sam Perinba Nayagan

89-93

IJEBEA 15-147

AN EMBEDDED REAL TIME FINGER VEIN RECOGNITION SYSTEM FOR ATM Sonu.P.Sam

94-98

Issue 11, Volume 2 Paper Code IJEBEA 15-148

Paper Title Study & Performance of Coated Cutting Tool Suraj R. Jadhav, Aamir M. Shaikh

Page No. 99-104

IJEBEA 15-149

EFFICIENT IMPLEMENTATION OF RSA ON FPGA USING VERILOG A.Santham Bharathy ME (VLSI)

105-109

IJEBEA 15-150

A Hybrid Model for Stock Market Trend Analysis Ch. Vanipriya,Thammi Reddy.K

110-115

IJEBEA 15-161

Construct of Iran’s National Innovation System Based on Innovation and Foresight (Usage of Benchmarking and Delphi Method) Saeid Ghorbani Iraj, Iraj Masoomi Baran, Mahdi Jafari Nadooshan

116-122

IJEBEA 15-162

Architectural Structure of Geographic Information System Anoop Singh, Ramanjyot Kaur

123-127

IJEBEA 15-164

Managing the Mandate: The Emerging Tool in the Indian Political Scenario Amit Kumar, Prof. Somesh Dhamija, Dr. Aruna Dhamija

128-131

IJEBEA 15-165

Industrial Democracy: an essential part of a Business Seema Rani

132-135

IJEBEA 15-166

Socioeconomic influence on farmers in seri-business from Tamil Nadu Sunitha Rani. D and Jayaraju. M, Kannan. M and Ashok Kumar. K

136-140

IJEBEA 15-167

Design and Development of an Electric Vulcanizing Machine C. Nathan, N. Jones and J. Wadai

141-146

IJEBEA 15-172

Logistics Support for Agro business in context of the Supply Chain of Perishables Md. Kamruzzaman, Md. Amirul Islam

147-152

IJEBEA 15-177

A STUDY OF ONLINE SHOPPING CONSUMER BEHAVIOUR IN CHENNAI G.R.Shalini, K.S.HemaMalini

153-157

IJEBEA 15-178

Capital Budgeting in Practice: An Explorative Study on Bangladeshi Companies Shakila Yasmin

158-163

IJEBEA 15-180

Impact of Television Advertisements on Buying Patterns of Consumer Durables in Chennai -A Retailers’ Perspective K.S.HemaMalini, Dr.R.Venkatesh

164-167

IJEBEA 15-181

INTRODUCTION TO MOBILE AD-HOC NETWORK, ITS APPLICATIONS AND STACK Vijayendra Kushwaha, Imran Khan, Neelham Singh Parihar

168-170

IJEBEA 15-185

Human Machine Interface (HMI) For DC Motor Drives with Self Generator S. Natarajan, Dr. M. AntoBennet, M. Manimaraboopathy, S. Sankararnarayan, N. Srinivasan

171-178

IJEBEA 15-187

Analysis of the consumer benefits and factors of life insurance to rural region of Odisha Bidyadhar Padhi

179-184

IJEBEA 15-189

Brand Preference in Water Purifiers G.N. Prasaath

185-188

IJEBEA 15-191

An analysis on different experiential value sought by travel website users Mrs.Veto Datta, Dr.S.Vasantha

189-192

IJEBEA 15-192

A Study of the Software Development Using Agile Divya Prakash Shrivastava

193-197

IJEBEA 15-193

VIRTUAL ENTERPRISE AND THE FAST FOOD INDUSTRY: A CASE STUDY OF FAST FOOD OPERATORS IN EDO STATE ADANNA.E. ONONIWU

198-200

IJEBEA 15-195

Study the effects of job stress on resistance of employees against changes in the Gymnastics Federation of Tehran Fatemeh Kiani Nejad , Dr. Ahmadreza Kasraee

201-205

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 Study & Performance of Coated Cutting Tool Suraj R. Jadhav1, Aamir M. Shaikh2 P.G. Student, Department of Mechanical Engineering, 2 Assistant Professor in Production Engineering Department, Karmaveer Bhaurao Patil College of Engineering, Sadar Bazar, Satara, Maharashtra, India.

1

Abstract: The use of coated cutting tools in the machining of various materials now represents state of the art technology. Developments in coating equipment and processes now enable us to produce a wide range of different hard nitride, carbide and oxide films and to deposit them on various tool substrates as monolayer, multilayer, or composite coatings. A total coating thickness between 3-10 µm is generally appropriate. The challenge of modern machining industries is mainly focused on the achievement of high quality, in terms of work piece dimensional accuracy, surface finish, high production rate, less wear on the cutting tools, economy of machining in terms of cost saving and increase the performance of the product with reduced environmental impact. This paper reports a case study on the AISI 4340 alloy steel cylindrical work pieces of 50 mm diameter hardened steel of 45HRC with coated tungsten carbide tool and gives the detail about the performance of coated cutting tool and their parameters at different cutting conditions.

Keywords: Coatings; Cutting tools; Tool wear; Cutting parameters; Wear resistance.

I. Introduction Cutting tools are subjected to high stresses in modern machining practice like dry, high-speed or highperformance machining. The development of new processes demands adapted cutting tools. An ideal cutting material combines high hardness with good toughness and chemical stability. In particular, hardness and toughness represent opposing properties and there is no single cutting material, which achieves all three conditions simultaneously. In order to merge the mentioned characteristics, wear resistant coatings with tough substrate materials are combined. The research of a correlation between the results of the most common laboratory tests and the cutting performances of the coatings is of great interest not only for the cutting tool makers, but also for the end users, since this could lead to the formulation of a test protocol to forecast cutting tool life. Furthermore, in the case of the nanocomposite coatings, for which the cutting tests can be very expensive due to their high cutting life, a set of laboratory tests can be cheaper than an extensive experimental plan of cutting tests. Surface engineering recently became a major way to improve cutting tools wear resistance and productivity. There are several ways of cutting tool surface engineering evolution. First one (and most commonly used) is a development of advanced PVD coating compositions. But application of an advanced coating for HSS tools cannot guarantee the optimal result without the special substrate surface treatment prior to the hard coating deposition [1] [2].

II. Theory of Metal Cutting Metal cutting process forms the basis of the engineering industry and is involved either directly or indirectly in the manufacture of nearly every product of our modern civilization. The cutting tool is one of the important elements in realizing the full potential out of any metal cutting operation. Over the years the demands of economic competition have motivated a lot of research in the field of metal cutting leading to the evolution of new tool materials of remarkable performance and vast potential for an impressive increase in productivity. As manufacturers continually seek and apply new materials for products that are lighter and stronger and therefore more efficient employing that cutting tools must be so developed that can machine new materials at the highest possible productivity. The main properties which any cutting material must possess in order to carry out its function are: Hardness, Hot strength, sufficient toughness etc. In general, increasing hardness brings with it a reduction in toughness and so those materials in the higher hardness region of the list will fail by breakage if used for heavy cuts, particularly with work pieces which have holes or slots in them which give rise to interruption in the cut [3].

III. Tool Wear Cutting tools are mostly assessed in terms of wear studies during and after the manufacturing processes. The prediction and control of wear is one of the most essential problems emerging in the design of cutting operations.

A useful definition for a worn out tool is: “A tool is considered to be worn out when the replacement cost is less than the cost for not replacing the tool”. Tool failure is said to occur when the tool no longer performs the desired function whereas total failure (ultimate failure) is defined as the complete removal of the cutting edge, a condition

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obtaining when catastrophic failure occurs. Therefore, in machining operations, tools are considered to be worn out and are changed long before total failures to avoid incurring high costs associated with such catastrophic failures. The tool may experience repeated impact loads during interrupted cuts, and the work piece chips may chemically interact with the tool materials. The useful life of a cutting tool may be limited by a variety of wear processes such as crater wear, flank wear or abrasive wear, built up edge, notching and nose wear [4].

Fig. 1: Machining with uncoated tool.

IV. Coatings Machining efficiency is improved by reducing the machining time with high speed machining. But the softening temperature and the chemical stability of the tool material limits the cutting speed. When cutting ferrous and hard to machine materials such as steels, cast iron and super alloys, softening temperature and the chemical stability of the tool material limits the cutting speed. Therefore, it is necessary for tool materials to possess good high-temperature mechanical properties and sufficient inertness. While many ceramic materials such as TiC, Al2O3 and TiN possess high temperature strength, they have lower fracture toughness than that of conventional tool materials such as high- speed steels and cemented tungsten carbides. The machining of hard and chemically reactive materials at higher speeds is improved by depositing single and multi-layer coatings on conventional tool materials to combine the beneficial properties of ceramics and traditional tool materials [5][6].

Carbide substrates are used because of their - Higher Productivity by high speed machining - Improved toughness and crack resistance by optimal dispersion of hard particles.

- Improved plastic deformation resistance and welding resistance for high cutting speed operations.

The effects of coatings are 1. Reduction in friction 2. Reduction in generated heat 3. Reduction in cutting forces.

4. Reduction in the diffusion between the chip and the surface of the tool, especially at higher speeds (the coating acts as a diffusion barrier).

5. Prevention of galling, especially at lower cutting speeds.

Methods of Coating: A. Chemical Vapour Deposition(CVD) Chemical Vapor Deposition (CVD) is an atmosphere controlled process conducted at elevated temperatures (~1925° F) in a CVD reactor. During this process, thin-film coatings are formed as the result of reactions between various gaseous phases and the heated surface of substrates within the CVD reactor. As different gases are transported through the reactor, distinct coating layers are formed on the tooling substrate. CVD method deposits thin films on the cutting tools through various chemical reactions. CVD coated cemented carbides have been a huge success since their introduction in the late 1960’s. Since then, chemical vapour deposition technologies have advanced from single layer to multi-layer versions combining TiN, TiCN, TiC and Al2O3. Modern CVD coatings combine high temperature and medium temperature processes in complex cycles that produce excellent wear resistant coatings with a total thickness of 4-20 μm. However, the high deposition temperature (950-1059°C) during CVD results in diffusion of chemical elements from the carbide substrate to the coating during growth.

The main effect is to make brittle coating edge. In addition, the chemistry of the CVD process results in more rapid growth at the cutting edge resulting in an even coating thickness. Therefore, there was a strong driving force to find coatings that could be deposited at lower temperatures in order to allow tools with sharper edges to be coated without any embrittlement effect. The solution is PVD where deposition temperature can be kept at around 500°C [3].

B. Physical Vapour Deposition (PVD) Physical vapor deposition (PVD) describes a variety of vacuum deposition methods used to deposit thin films by the condensation of a vaporized form of the desired film material onto various workpiece surfaces. PVD method deposits thin films on the cutting tools through physical techniques, mainly sputtering and evaporation. PVD coatings, with deposition temperatures of 400-600°C, are gaining greater acceptance in the market place. Over the last decade, they have been successfully applied to carbide metal cutting inserts. They offer performance advantage in applications involving interrupted cuts, those requiring sharp edges, as well as finishing and other applications. Depending on the intended application, different PVD technologies such as electron beam

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evaporation, sputtering and arc evaporation are used. The metal cutting performance of PVD coated tools depend strongly on the composition, microstructure, internal stresses and adhesion of the coating to the substrate as well as the substrate composition and tool geometry. PVD process chain includes pre-PVD processes and post-PVD processes. Pre-treatment processes such as plasma etching and chemical etching influence adhesion, grain growth, stress at substrate surface and coating structure, whereas post-PVD processes influence smoothness of coating surface and better chip flow. PVD coatings attribute excellent cutting performance to cemented carbide inserts. The reason that PVD has more and more taken over with regards to deposition of many coatings is the advantages that lower coating temperatures give with regard to micro-toughness [3]. It is more environmentally friendly than traditional coating processes such as electroplating and painting.

V. Types of Coatings A. Single layer coating The first coating was a single layer of TiC.10 to 12 micrometers thick, which was deposited by a process known as chemical vapour deposition (CVD) onto a substrate of hard metal. During the deposition process some carbon was taken up from the surface of the hard metal as part of coating and this changed the carbon balance at the junction of the coating and the hard metal substrate. This lowering of the carbon balance caused the formation of a brittle compound at the interface between the coating and the substrate and made early coated indexable inserts sensitive to chipping of cutting edge. The next development was to put down a coating of TiN which prevented any decarburizing of the hard metal substrate but the coating which is gold in colour, did not adhere well to the hard metal base. TiN is an even better diffusion barrier than TiC but TiC has better abrasion resistance [3].

B. Multi-layer coating Although single-layer coatings are finding a range of applications in many sectors of engineering, there are an increasing number of applications where the properties of a single material are not sufficient. One way to surmount this problem is to use a multilayer coating that combines the attractive properties of several materials, each chosen to solve a problem in the application. Multi-layer coatings can consist of as many as eight layers within a total thickness of 10 micrometers or less. Simple examples of this include the use of interfacial bonding layers to promote adhesion, or thin inert coatings on top of wear- resistant layers to reduce the corrosion of cutting tools. There is, however, mounting evidence that the multilayer structure produced when many alternating layers of two materials are deposited can lead to improvements in performance over a mixed coating even if the two materials do not have specific functional requirements in the intended application [3].

VI. Common Coatings Titanium Nitride (TiN): General purpose PVD coating that increases hardness and has a high oxidation temperature. This coating works great while cutting or forming with HSS tooling.

Titanium Carbo-Nitride (TiCN): The addition of carbon adds more hardness and better surface lubricity.

Titanium Aluminum Nitride (TiAlN or AlTiN): A formed layer of aluminum oxide gives this tool better life in high heat applications. This coating is primarily selected for carbide tooling where little to no coolant is being used. AlTiN offers a higher surface hardness than that of TiAlN, along with different percentages of aluminum and titanium. It is another viable option in the world of HSM. Diamond: A CVD process that offers the highest performance available in non-ferrous materials. Ideal for cutting graphite, MMC (Metal Matrix Composites), high silicon aluminum and many other abrasive materials. Coatings for hard milling, tapping and drilling all vary and are application-specific. Also available are multi-layer coatings that chip to the next layer instead of the tooling substrate, providing a further increase in tool life.

Chromium Nitride (CrN): The anti-seizure properties of this coating make it preferred in situations where BUE is common. HSS or carbide cutting and forming tools will be seen with this almost invisible coating [3].

VII. Parameters Used in Cutting Tool Table 1: Various Parameters/Properties of Coated & Uncoated Tool Sr. No.

Properties/Parameters

1 2 3 4

Surface roughness Chemical stability Anti-welding & anti-diffusivity Thermal conductivity

Satisfactory Poor Satisfactory Poor

Uncoated tool

Good with same cutting conditions as of uncoated tool Improved results with same cutting conditions as of uncoated tool Improved results with same cutting conditions as of uncoated tool Good with same cutting conditions as of uncoated tool

5

Thermal expansion coefficient

Medium

Low with same cutting conditions as of uncoated tool

6

High strength and wear resistant

Satisfactory

Improved results with same cutting conditions as of uncoated tool

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Coated tool

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Sr. No. 7 8 9 10 11 12 13

Properties/Parameters Resistance to thermal and mechanical shock Resistance to diffusion High resistance to the brittle fracture Cost Low Cost and ease of fabrication Surface lubricity Anti-seizure

Uncoated tool

Coated tool

Poor

Improved results with same cutting conditions as of uncoated tool

Satisfactory

Improved results with same cutting conditions as of uncoated tool

Poor

Improved results with same cutting conditions as of uncoated tool

High

Low

Satisfactory

Improved results with same cutting conditions as of uncoated tool

Less Low in this case

High & scope of improvement High in this case

VIII. Performance of Coated Cutting Tool in Machining Hardened Steel In hard turning the material is harder, specific cutting forces are larger than in conventional turning, and thus the engagement between cutting tools and the work piece must be limited. In hard turning small cutting depths required, cutting takes place on the nose radius of cutting tools, and the tools are typically prepared with chamfered or honed edges to provide a stronger edge geometry that is less prone to premature fracture. The large negative rake angles yield increased cutting forces compared to machining with positive rake tools, and also induce larger compressive loads on the machined surface. Higher temperatures are also generated in the cutting zone, and because cutting is typically done without coolant, hard turned surfaces can exhibit thermal damage in the form of micro structural changes and tensile residual stresses [7].

Experimentation: This case deals with the study of the performance of coated tools in machining hardened steel under dry conditions. This involve machining of AISI 4340 hardened steel using coated tungsten carbide tool by considering full factorial experiments. In the present case the experiments were conducted on AISI 4340 alloy steel cylindrical work pieces of 50 mm diameter hardened steel of 45HRC with coated tungsten carbide tool at different cutting conditions. The objective of this study is on the effect of the cutting conditions such as cutting velocity, feed, and depth of cut on the surface finish in machining AISI 4340 hardened steel. Machining of hardened steels has become an important manufacturing process, particularly in the automotive and bearing industries [7].

Table 2: Machining parameters & their levels Machining Parameters Cutting Velocity (m/min) Feed Rate (mm/rev.)

Depth of Cut (mm)

Symbol v f d

Level 1 60 0.2 0.25

Levels Level 2 80 0.4 0.5

Level 3 100 0.63 0.75

The cutting parameters selected for the present investigation is cutting velocity (v), feed (f) and depth of cut (d) as shown in Table 2.In machining of parts, surface quality is one of the most specified customer requirements where major indication of surface quality on machined parts is surface roughness. Surface roughness is mainly a result of process parameters such as tool geometry and cutting conditions (feed rate, cutting speed, depth of cut, etc.). The surface roughness was measured by using Mitutoyo surface roughness tester (SJ-201 P) stylus type [7].

Table 3: The measured surface roughness at different cutting conditions Sr. No.

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

Cutting Velocity v (m/min) 60 60 60 60 60 60 60 60 60 80 80 80 80 80 80 80 80 80 100 100 100 100

Machining Parameters Depth of Cut d (mm) 0.25 0.25 0.25 0.5 0.5 0.5 0.75 0.75 0.75 0.25 0.25 0.25 0.5 0.5 0.5 0.75 0.75 0.75 0.25 0.25 0.25 0.5

IJEBEA 15-148; Š 2015, IJEBEA All Rights Reserved

Feed Rate F (mm/rev.)

0.2 0.4 0.63 0.2 0.4 0.63 0.2 0.4 0.63 0.2 0.4 0.63 0.2 0.4 0.63 0.2 0.4 0.63 0.2 0.4 0.63 0.2

Surface Roughness Ra (Âľm) 0.46 2.27 2.34 0.48 2.17 3.53 1.93 2.53 3.62 0.54 2.56 2.03 1.86 3.02 3.54 2.63 4.01 5.22 0.18 1.39 2.74 0.46

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Sr. No.

23.

24.

25.

26.

27.

Cutting Velocity v (m/min) 100 100 100 100 100

Machining Parameters Depth of Cut d (mm) 0.5 0.5 0.75 0.75 0.75

Feed Rate F (mm/rev.)

0.4 0.63 0.2 0.4 0.63

Surface Roughness Ra (Âľm) 2.33 3.74 1.59 3.53 4.44

Fig. 2: The variation is surface roughness with feed rate at constanti) Cutting velocity (60m/min) at different depth of cuts.(Fig.a) ii) Cutting velocity (100m/min) at different depth of cuts.(Fig.b) iii) Depth of cut (0.25mm) at different feed (d=0.25).(Fig.c)

Fig. (a)

Fig. (b)

Fig. (c) IX. Discussion & Result The variation is surface roughness with feed rate at constant cutting velocity (60 m/min) at different depth of cuts is shown in Fig.(a). Fig.(b) shows the variation is surface roughness with feed rate at constant cutting velocity of 100 m/min at different depth of cuts. The variation is surface roughness with feed rate at constant depth of cut (0.25 mm) at different feed is shown in Fig.(c) The experimental results show at some selected condition the surface roughness is observed to be poor. The experimental result shows with increasing the feed rate the surface roughness is increasing. From the selected conditions for feed rate of 0.63mm/rev the surface roughness is high when compared to other selected feeds. At constant feed the effect of cutting velocity on surface roughness is less as shown Fig.(c). In the present work the performance of coated tools in machining hardened steel under dry conditions is studied. The experimental results showed with increase in feed the surface roughness is observed is very poor. The effect of cutting velocity on surface roughness is relatively low when compared to feed rate. With increase in depth of cut the surface roughness is increased. Here experimental results shows by selecting the proper cutting parameters the coated tools are suitable to produce fine surface finished components.

X. Conclusion This work considered some ways to improving cutting tool life by means of coating method. The performance of coated cutting tool is better than the conventional cutting tool. Tool coating improves properties of cutting tool such as surface roughness, Chemical stability, anti-welding and anti-diffusivity, thermal conductivity, Surface lubricity and anti-seizure. Coated tool also suitable for various cutting conditions such as cutting velocity, feed rate and depth of cut. Tungsten Carbide coated cutting tool cuts about 3 to 5 times faster than conventional cutting tools.

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References [1] [2] [3] [4] [5] [6] [7]

G.S. Fox-Rabinovich, N.A. Bushe, A.I. Kovalev, S.N. Korshunov. “Impact of ion modification of HSS surfaces on the wear resistance of cutting tools with surface engineered coatings”, Elsevier, 2001, 1051-1058.

Luca Settineri, Maria Giwai Faga. “Laboratory test for performance evaluation of nanocomposite coatings for cutting tools”, International journal of engineering science & technology, 2006, 326-332.

Supriya Sahu, “Performance Evaluation of Uncoated and Multi Layer Tin Coated Carbide Tool in Hard Turning”, NIT Rourkela,May 2012.

Pinar Demircioglu. “Surface topographical evaluation of coated cutting tools with different coating technologies”,Measurement 47 (2014) 893-902.

Satish Chinchanikar, S.K. Choudhury. “Evaluation of Chip-Tool Interface Temperature: Effect of Tool Coating and Cutting Parameters during Turning Hardened AISI 4340 Steel”, Procedia Materials Science 6 ( 2014 ) 996 – 1005.

Akira Hosokawa, Koji Shimamura, Takashi Ueda. “Cutting characteristics of PVD-coated tools deposited by unbalanced Magnetron sputtering method”, CIRP Annals – Manufacturing Technology, 2012, 95-98.

Subramanyam, Dr.CH R. Vikram Kumar, Dr. C. Eswar Reddy. “Performance of coated cutting tools in machining hardened steel”, International journal of engineering Science & technology, Vol.2 (10), 2010, 5732-5735.

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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 EFFICIENT IMPLEMENTATION OF RSA ON FPGA USING VERILOG A.Santham Bharathy ME (VLSI) Department of Electronics & Communication Engineering, Anna University-Chennai/ Nehru Institute of Technology, Jawagar Garden, Kaliyapuram, Coimbatore, Tamil Nadu, INDIA Abstract: This research paper is aimed to implement the RSA algorithm 1024-bit in the FPGA with the help of Verilog HDL. The RSA algorithm using FPGA can be used as a standard device in the secured communication system. A simple nested loop addition and subtraction have been used in order to implement the RSA operation. The modification of RSA algorithm includes pipeline and data dependence computational block. This results in very low frequency, high speed, low power consumption and low cost compared to other algorithm methods. The beneficial of a pipelining approach is provide a clock input to each modules and enabling them each time the output from the previous module is available. The data dependence computational block is used to provide low power dissipation by reducing unnecessary switching takes place in the architecture. The information to RSA encryption side is in the form of statement and the same will appear in the decryption side and its real time input/output also achieved effectively. Each sub-component and module of RSA was simulated in Xilinx Spartan 3E software tools and provide functionally correct.

Keywords: Cryptography, FPGA, Verilog, Security, Communication.

I. Introduction Public-key cryptography uses asymmetric key algorithms (such as RSA), and can also be referred to by the more generic term "asymmetric key cryptography." The algorithms used for public key cryptography are based on mathematical relationships that presumably have no efficient solution. Although it is computationally easy for the intended recipient to generates the public and private keys, to decrypt the message using the private key, and easy for the sender to encrypt the message using the public key, it is extremely difficult (or effectively impossible) for anyone to derive the private key, based only on their knowledge of the public key. Advantages of asymmetric key algorithm are It solves the problem of distributing the key for encryption. Everyone publishes their public keys and private keys are kept secret.

Public key encryption allows the use of digital signatures which enables the recipient of a message to verify that the message is truly from a particular sender. The use of digital signatures in public key encryption allows the receiver to detect if the message was altered in transit. A digitally signed message cannot be modified without invalidating the signature. This is why, unlike symmetric key algorithms, a public key algorithm does not require a secure initial exchange of one (or more) secret keys between the sender and receiver. The use of these algorithms also allows the authenticity of a message to be checked by creating a digital signature of the message using the private key, which can then be verified by using the public key. In practice, only a hash of the message is typically encrypted for signature verification purposes.

Fig. 1: Asymmetric-key cryptography This work approaches hardware implementation of RSA algorithm scheme using the modular exponentiation operation. Simple nested loop addition and subtraction have been used to implement the modular exponentiation

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operation. And to implement this, only shift registers, XORs and LUTs are used. The usage of NAND gate is avoided to reduce the complexity of the circuit by employing with reusability characteristics of XORs. Here, it supports multiple key sizes for RSA according to the application requirement. This new approach helps to reduce the system processing time, gate counts, frequency requirement and power consumption. And also the system could take the information in the form of statement say word format (real time input/output) not in binary or hex format as the earlier approaches handled.

II. Design Overview A distinct feature that can be found in the RSA algorithm is that it concedes most of the integral part used in encryption to be re-used in the decryption process, which can decrease the resulting hardware area. In RSA, a plaintext block M is encrypted to a cipher text block C by: e C = M mod n (1) The plaintext block is recovered by: d M = C mod n (2) RSA encryption and decryption are mutual inverses and commutative as shown in equation (1) and (2), due to symmetry in modular arithmetic. One of the potential applications for which this design of RSA has been targeted is the secured data communication. In this application, the data input could be a statement which is fed into FPGA board directly via serial communication. The encryption module takes care of the security. The process at the receiving end is same as the process that has been followed at the sending end except that the sequence of the module is reverse. The RSA covers both the operation of encryption and decryption.

A. Fundamental RSA process The RSA algorithm needs estimation of the modular exponentiation, which is separated into a series of modular multiplications by the application of exponentiation inquiring. The RSA encryption process is the mathematical e operation, c= m mod n. This mathematical operation has involved a few modular operations like modularexponentiation, multiplication, addition and subtraction process on large integers. Detail algorithms of the above operations for hardware implementation have been discussed in the following sections B. Modulus Exponentiation Process The modular exponentiation operation is simply an exponentiation operation where multiplication and squaring e operations are modular. The exponentiation operation developed for computing M are applicable for computing e M (mod n). In the concern of hardware implementation, a clever algorithm is required in order to extent a superior efficiency. Hence, exponentiation is acquired by doing a number of squaring and multiplications.

C. Modular Multiplication Process The modular multiplication problem is defined as the computation of P =(A x B) (mod n), given the integers A, B, and n. It is usually assumed that A and B are positive integers with 0 ≤ A, B < n. The modulus multiplication operation is required after the separation of exponentiation into a number of squaring and multiplication. There are basically four general approaches for computing the product. Multiply and then divide, Interleaving multiplication and reduction, Brick ell‟s method and Montgomery‟s method. All the above approaches have a common disadvantage that it doubles up the number of bits for each multiplication. For example, when two 32-bit numbers are multiplied together will cost a 64-bit result and hence a large register is needed to store this result. A modified algorithm is used in this design which will be discussed later. The modified algorithm overcomes the problem by separating the multiplication operation into a number of modular addition operations.

D. Complete Algorithm 1. Generate two large random primes, p and q, of approximately equal size such that their product n = p q is of the required bit length, e.g. 1024 bits.

2. Compute n = p q and (phi) φ = (p-1)(q-1).

3. Choose an integer e, 1 < e < phi, such that gcd(e, phi) = 1.

4. Compute the secret exponent d, 1 < d < phi, such that ed ≡ 1 (mod phi).

5. The public key is (n, e) and the private key (d, p, q). Keep all the values d, p, q and secret. [We prefer sometimes to write the private key as (n, d) because you need the value of n when using d.]  n is known as the modulus.  e is known as the public exponent or encryption exponent or just the exponent.

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         

d is known as the secret exponent or decryption exponent Choose p = 7 and q = 17 Compute n = p * q = 7 * 17 = 119 Compute φ(n) = (p - 1) * (q - 1) = 6 * 16 = 96 Choose e such that 1 < e <φ(n) and e and n are coprime. Let e = 5 Compute a value for d such that (d * e) % φ(n) = 1.One solution is d =7 [77 * 5 =385=4*96+14] Public key is (e, n) => (5, 119) Private key is (d, n) => (77, 119) The encryption of m = 19 is c = 195 % 119 = 2476099 mod 119 = 66 The decryption of c = 66 is m = 6677 % 119 = 19

III. Modification A. Pipelining The encryption and decryption portion in the core consists of many ‘FOR’ loops which are again synthesized as a series of identical modules connected in series. Whenever new data appears at the input each module does a small portion of the encryption operation and passes it to the next module. Each module remains idle after before and after performing its operations. This path is connecting all the modules is also the critical path which decides the clock period and hence the throughput of the system. It is hence beneficial to follow a pipelining approach by providing a clock input to each of these modules and enabling them each time the output from the previous module is available.

This increases throughput, so that output appears at the end of each clock cycle. The pipelined architecture is shown below.

Fig. 2: Pipelining B. Data Dependent Computation Block The architecture shown in Fig is used for intra data dependencies analysis. The data correlation between the next input is analyzed and based on the dependencies certain arithmetic operation can be skipped up so as to reduce the computation and reduce the critical path of the computation. The correlation finder block detects the correlation and pass appropriate control signal to enable and disable the multiplier and adder. If the detector comes across the subsequent INPUT CORRELATION FINDER AND CONTROL

R E G

R E G

INPUT OUTPUT K

0

CLOCK

1 SEL

PREVIOUS RESULT

DATA DEPENDENT COMPUTATION BLOCK

Fig. 3: Data Dependent Computation Block IV. VHDL MODELING A. Device Utilization Summary From device utilization summary, it is clear that total equivalent gates used for the approach is 951 only. And apart from the number of slices, all other components utilized percentile is less than 10 which includes XORs, LUTs and shift registers. Because of total equivalent gate count is very low that results in very low frequency requirement to

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perform this operation, low power consumption and low cost compared to earlier methods.

V. SIMULATION, SYNTHESIS AND DISCUSSION VHDL is commonly used to write text models that describe a logic circuit. Such a model is processed by a synthesis program, only if it is part of the logic design. A simulation program is used to test the logic design using simulation models to represent the logic circuits that interface to the design. This collection of simulation models is commonly called a test bench. VHDL has file input and output capabilities, and can be used as a general-purpose language for text processing, but files are more commonly used by a simulation test bench for stimulus or verification of data.

There are some VHDL compilers which build executable binaries. Here, VHDL program is used for RSA, data control, UART and device drivers to write a test bench, to verify the functionality of the design using files on the host computer to define stimuli, to interact with the user, and to compare results with those expected. After the generation of codes that simulates successfully, it may not be synthesized into a real device or is too large to be practical. Once synthesis is over, the input message is in the form of data can be given as input to FPGA from computer via into a real device or is too large to be practical Hyper Terminal or Ethernet in serial communication media. This message is moved to FPGA in binary form with selected bit values one by one. Now, RSA algorithm which is implemented using VHDL program in FPGA processes this message and produces encrypted data as output in the hyper terminal screen. So, the output, which is not in readable format, can be saved in note pad. Now, the encrypted file can be sent for the decryption process to get the original message. All these operations have been carried out in the Spartan 3E FPGA electronic module and its real time output could be seen through hyper terminal screen and not in the form of binary or hex formats. Hence, this FPGA module can be used as a standard device in the secured closed network data communication system.

So, a step has been taken to design hardware in a VHDL IDE for FPGA implementation using Xilinx ISE to produce the RTL schematic of the desired circuit. After that, the generated schematic can be verified using simulation software which shows the waveforms of inputs and outputs of the circuit after generating the appropriate test bench.

Finally, the VHDL model is translated into the gates and wires that are mapped onto a programmable logic device FPGA. Hence it is the actual hardware which is configured as processor chip rather than the VHDL code which is used for implementing the RSA Algorithm. .

Fig. 4: Output Waveform for 1024 Encryption

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Fig. 5: Output Waveform for 1024 Decryption

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VI. CONCLUSION The detailed implementation techniques for 1024-bit RSA encryption and decryption algorithm were presented. The modular exponential for encryption and decryption process is performed by using pipeline and data computational block components in algorithm. The add and shift algorithm is used to perform the modular multiplier where the algorithm are implemented using Verilog code targeting Virtex-5 xc3S500e-4fg320 FPGA Xilinx. The whole design is tested and each sub-component of module of RSA are simulated using Xilinx Spartan 3E device and provide functionally correct. The future work that can be done in this regard includes, multiple key generation from 1 to 1024-bit encryption and decryption that enhance more secure for information securing platform.

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Adam J.Elbirt, W. Yip, B-Chetwynd, and C. Paar, ―An FPGA Based Performance Evaluation of the RSA Algorithm IEEE Finalist‖TRANSACTION VLSI SYSTEMS,VOL.9, NO.4, AGUGEST-2012.

Chen-Hsing Wang, Chieh-Lin Chuang and Chang-Wen Wu‖An efficient Multiplier Supporting RSA Public-Key Cryptosystems‖ IEEE TRANSACTION, VOL. 18, NO.4,April-2011.

Devendra N. FPGA Kayatanavar,―Implementation of RSA Encryption and Decryption ―International Conference on Control, Automation, Communication and EnergyConservation-2009, 4th-6th June 2009.

FPGA Implementation of RSA Cryptosystem‖, International Journal of Engineering and Applied Sciences 2:3 2010.

International Journal of Reconfigurable and Embedded Systems (IJRES), ―RSA Encryption Algorithm Hardware Implementation: Throughput and Area Comparison‖ 4864ISSN:Vol.1,No.20892,July-2012.

M. D. Shieh, J. H. Chen, H. S.Wu, and W. C. Lin, "A new modular exponentiation Architecture for efficient design of RSA cryptosystem," IEEE Trans. Very Large Scale Integration. (VLSI) Syst., vol. 16, no. 9, pp. 1151-1161,Sep. 2010.

M. D. Shieh, J. H. Chen, H. S.Wu, and W. C. Lin, "A newmodular exponentiation Architecture for efficient design of RSA cryptosystem," IEEE Trans. Very Large Scale Integration.(VLSI) Syst., vol. 16, no. 9, pp. 1151-1161,Sep. 2010.

S. Nimmagadda, Oformance.Elkeelany,evaluationof ―P different hardware models of RC5 of The IEEE 39th Southeastern Symposium on System Theory, 2010.

William Stallings, Cryptography and Network Security Principal and Practices, 4th Edition, Pearson Education, Inc., 2006, ISBN 817758-774-9.

Xinmiso Zhang , IEEE and Keshab K. Parhi Fellow, High IEEE-Speed VLSI‖Architectures for the RSA Algorithm‖ IEEE TRANSACTIONS, VOL.12, NO:9, SEP-2010.

W. Stallings, Cryptography and Network Security: Principles and Practice 3/e. Prentice Hall.

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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 Hybrid Model for Stock Market Trend Analysis Ch. Vanipriya1,Thammi Reddy.K 2 Department of Information Science and Engineering, Sir M Visvesvarya Institute of Technology, Yelahanka, Bangalore, Karnataka, INDIA 2 Department of Computer Science & Engineering, GIT, GITAM University, Vishakhapatnam, Andhra Pradesh, INDIA __________________________________________________________________________________________ Abstract: Stock Market is often viewed as a way to grow the wealth for individuals and companies. Which is the company to invest in, by analyzing the company performance needs a greater insight. Many researchers have attempted to analyze the stock market and to predict the stock price by using some statistical and non-statistical methods such as analyzing news articles using sentiment analysis. In this paper we propose another novel method which combines these two methods and analyze the performance of some of the selected companies listed in BSE .For Technical analysis the moving average indicator, and for sentiment analysis NBC classifiers have been chose for the proposed work.

Keywords: Sentiment analysis, Technical analysis, Moving Average, NBC. __________________________________________________________________________________________ 1

I. INTRODUCTION The stock market gives an impression of a legalized gambling for the first time investor. In India, Bombay Stock Exchange (BSE) is the stock exchange where most of the trading takes place. The BSE has been around since 1875. The exchange is home to about 5,300 listed companies as of October 2013. Internet was introduced to India in the early 90's and the use started to increase exponent. It has the world's third-largest Internet user-base with over 137 million as of June 2012.The stock markets introduced Internet trading (online-trading) in February 2002.

People want to invest their money in stock market and expect high returns in short period of time. They used to analyze the technical predictors to evaluate the performance of the company. Now the online media is playing an important role in the stock market. There are the evidences [1] that clearly show that the news articles published through the online media, regarding companies have greater impact on the stock price. A small issue may alarm a great impact on the people’s money. By tracking the news about a particular financial firm on various online media, the stock price of that may be evaluated.

Some of the new investors don’t have any idea of how the future of the stock market will be like. It’s like a show of hands; invest in, any one of the randomly chosen stock, if its price goes up, you are lucky enough or else, better luck next time! But the truth is, it doesn’t work so! The astrology of predicting the future stock prices is one of the most trending topics in many fields including trading, finance, statistics and computer science. The motivation for which is naturally to predict the direction of future prices such that stocks can be bought and sold at profitable positions. Stock price and Share price changes depend on many factors like market forces, supply and demand etc. If the demand for a particular stock is more than what is supplied, then the price of the stock goes up, or if it is otherwise, that is, supply is more than demand, the price of the stock goes down!

Most of the investors are great believers! Some believe that it’s impossible to predict the stock price while some others say it is completely predictable with a little bit of graphical analysis and a few calculations based on the past. While the latter kind of investors is right in their beliefs, but practically, it’s quite more than that. The sentiments, attitude and expectations of the investors also play a major role in predicting the stock price.

The field of Computer Science has the most effective solution to this. The stock prediction can be most efficiently undertaken by combining tools like sentiment analysis and other technical analysis! With its unbelievable popularity, the stock market trending will then become clear and understandable as it brings decent yields with little or no efforts. The stock market is all about dynamics and that is why it is important to accurately forecast further movements of stock bids.

In this paper as mentioned earlier the techniques of Sentiment analysis and Technical analysis were combined to analyze the performance of the chosen company ,so that they can take a decision to invest in that company or not II. RELATED WORK A pretty good number of authors have already attempted to apply the sentiment analysis technique to predict stock prices, while a few others have used technical analysis for the same. Hence, the combination of the both types of analysis is more likely to give an accurate result, on which the real time investors can rely on!

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Masoud Makrehchi et al. [2] proposed a novel approach to label the text messages in social media using the major stock market events. They aggregated the net sentiment of a day and proved its predictive power of stock market movement.

Johan Bollen et al. [3] published a paper “Twitter Mood Predicts the Stock Market “in the year 2010.They analyzed tweets in the social media website called twitter.

EnricJunque de Fortuny et al. [4] discussed about appropriate metrics to evaluate models and gave an insight into evaluating ,validating and refining the models for predicting the stock market.

In the work done by [5], they examined the stock message boards for cross-sectional and time-series determinants of message-postings. He showed that variation in message-posting volume is related to underlying firm characteristics and stock market activity. He used a sample of over 3,000 stocks listed on Yahoo! message boards. [6] Developed a system called E-Analyst which collects two types of data, the financial time series and time stamp news stories. It generates trend from time series and align them with relevant news stories and build language models for trend type. In their work they treated the news articles as bag of words.

III. TECHNICAL ANALYSIS The History of the stock prices that can be used to predict the future forms the basis of technical analysis.

Technical Analysis is predicting the future financial price movements by taking into account, its previous movements. With analogy to the weather forecasting, the technical analysis too does not give the absolute predictions about the future. Instead, it can help investors anticipate what is the probability of a certain thing to happen to the prices over time.

Basically, the technical analysis helps the investors to decide on the best time to invest and the best time to sell in order to get the maximum profit. It completely relies on the study of historical price fluctuations. People who do technical analysis before investing in stock market usually study the price charts for price patterns and use price data in different calculations to forecast future price movements.

The technical analysis makes use of the correlation between the price and the company and thus gives the investor a fair chance to decide on the best time to enter/exit the market. The company’s fundamentals are not at all a concern for the technical analysts. Technical analysis is variedly applicable to stocks, indices, commodities, futures or any tradable instrument.

A major drawback of technical analysis is that it just considers price forecasts, ignoring the fundamental factors of the company. However, technical analysis assumes that, at any given time, a stock's price reflects everything about that company which necessarily includes fundamental factors.

A. BASIS FOR TECHNICAL ANALYSIS The Dow Theory laid the foundation for the modern technical analysis. There are three main theorems of the Dow Theory. i)Price Discounts everything. The technical analysts believe that all the information can be obtained from the current price and it forms the basis for analysis. After all the market price reflects the sum knowledge of all the participants which includes traders, investors, portfolio managers, analysts and the others. Technical analysis utilizes the information captured from the price to analyze what the market is saying with a purpose of forming a view on the future ii)Price movements are not totally random. Most of the Technical analysts agree that prices trend. Some Technical analysts also acknowledge that there are times when the prices don’t trend, in which case it would be difficult to gain profit using Technical Analysis. iii)“What” is more important “Why”. There are two things that Technical analysts are concerned of. a) What is the current price? b) What is the history of the price movement?

The price is the end result of the battle between the forces of supply and demand for the company’s stock.

The technical analysts believe it is best to concentrate on “what” and never mind “why” since the price goes up when there are more buyers than sellers.

B. TECHNICAL ANALYSIS INDICATORS There are many Technical indicators, some of them are Trending Indicators, Momentum Indicators, Volatility Indicators, Strength and Sentiment Indicators, Stock Market Indicators, Moving average indicators. Among these “Moving average indicators” was chosen for the proposed work.

The moving average indicator gives the average value of a Stock’s price over a period of time. The moving average indicator can be calculated by considering mathematical analysis of a stock’s average value over a predetermined time period. It is a trend following or lagging indicator because it is based on the past prices.

The 200- day moving average is a popular technical indicator which the investors use to analyze the price trends. It is nothing but the stock’s average closing price over the last 200 days (i.e. (stock price of day 1 + stock price of day 2 up to stock price of day 200)/200).

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Where MA=Moving Average and SP=Stock Price It says about the market trend, by looking at the previous 200 trading days and taking the average of closing prices of 199 previous trading days.

Where YAP=Yesterday’s Average Price, CP=Closing Price and PTD=Previous Closing Days Mark that point on a chart and make another dot by considering the closing price of yesterday.Again calculate today’s average price by dropping a day off backend Where TAP=Today’s Average Price Again mark that point on a chart and make another dot by considering the closing price of today. This can be repeated every day and both the values can be plotted. It shows the direction of the market and measures the trend.Extremely high readings are a warning that the market may soon reverse to the down side whereas very low readings signify the reverse. The shorter the moving average, the sooner there will be a change in the market. It is used to determine whether the stock is technically healthy or not. The percentage of stocks above the 200- day moving average helps to determine the overall health of the market.

IV. SENTIMENT ANALYSIS Sentiment analysis also referred to as Opinion Mining is the use of NLP tools, text analysis, machine learning and computational linguistics to identify and extract the sentiment in the source materials which can further be used for various kind of analysis. It is a general binary opposition in opinions. It makes up a great tracker for all kinds of data; a few noteworthy are products, brands, people etc. It usually does the tracking process based on a few keywords and their frequencies in a particular dataset.

It’s understood from the previous work that the news items of a particular company have significant impact on stock indices and prices of that company. The people go through the news articles regarding the company before selling or buying a stock of that company. A lot of work was done to extract the sentiment from the static text using Text Mining and NLP techniques. We can analyze news items for sentiments using dynamic data sources such as online news stories and streaming data such as blogs.

Manually going through all the news items and analyzing them is a tedious task. Sentiment Analysis can be used to automatically identify the opinion or sentiments about a company in news article.The sentiments can positive, negative or neutral. The advantage of this automated tool is its ability to evaluate large quantities of news articles or posts without manual intervention. Earlier this tool was used for analyzing the product from various review sites or discussion forums. Most recently, this tool is being used to measure the sentiment of news articles in the stock market domain.

V.PROPOSED SYSTEM ARCHITECTURE Trading information on Stock Exchange

Financial news on the Internet

Sentiment Analysis

Data base

Technical Analysis Results

Technical Analysis

Sentiment Analysis Results Combine using 60-40 ratio yes

no

Fig. 1: Proposed System Architecture

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The architecture consists of two major modules, one for technical analysis and the other one for sentiment analysis.

Algorithm: Stock market analysis by using technical and sentiment analysis 1. Choose the company.

2. Extract the stock prices of a period of one year of that company.

3. Calculate moving average (m) for a day which is Average value of previous 200 days and compare with that day’s closing price (cp). if (m>cp) then “it is Uptrend” else “it is Downtrend.

4. Extract the news articles of that company for a period of time.

5. Manually tag some of these news articles as positive, Negative or neutral.

6. Train the classifier on these set of data 7. Predict the sentiment of news articles.

8. Extract the percentage of good vs bad.

9. Combine Technical and sentiment analysis to analyze the company trend STEPS INVOLVED IN TECHNICAL ANALYSIS A) Extract Stock Values The historical stock values are extracted for a period of one year from a website in.yahoo.finance.com. The data extracted is in a CSV format. This data is then placed into a database table which can then be queried and processed.

B) Processing the stock data The 200- day moving average is calculated using a query. The closing price value on the 200 th day is then compared with the moving average value.

If it is greater than the latter then it signifies an uptrend. The count of the number of times this occurs is taken.

This serves as a parameter to gauge the performance of the selected company. The percentage of time that this occurs is also calculated.

STEPS INVOLVED IN SENTIMENT ANALYSIS A) Website Reviews The initial step in sentiment analysis is the identification of the website containing the news articles of the chosen company. In our work we are using “moneycontrol.com” to extract only the news articles that will be analyzed. Once the required site is chosen, we then begin the process of extraction which includes the extraction of only the news articles over a period of 6 months while ignoring the various ads and other unnecessary contents from the link. Only the link to the news articles are taken into consideration and stored in a file.

B) Extract Reviews The essential data, i.e., the links to news articles obtained from the previous step is then subjected to extraction and the content of each of the links in the said file is extracted and stored in a separate file. This file containing the articles will serve as an input to the sentiment analysis code.

C) Analysis of each review In this step the sentiment of a review is predicted. The Naïve Bayes Classifier is used for this purpose. A training set is created which contains of manually tagged articles in a Comma Separated Value (CSV) format.

The classifier is trained with this set. After training, the file with the extracted reviews is passed as an input to the classifier which then predicts the sentiment of each article. The final output is a percentage of positive and negative articles which is represented graphically in pie chart.

D) Calculation and Result The result of the previous 2 analyses has to be combined. The 2 percentages, i.e., the percentage of time that the closing price is higher than the 200- day moving average and the percentage of the positive news articles are combined in a 60-40 ratio to give the final percentage. The final percentage that is obtained from the previous step is then checked to give the final outlook for the company. Depending on the percentage we can say if the company has been consistently seeing an uptrend and the news articles pertaining to the company are on the positive side. If so, then it is safe to invest in the company as there is a positive outlook for the company. On the other hand, if the percentage signifies that the company has been seeing a downtrend, then it can be concluded

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that there is a negative outlook and there is a risk in investing in the company. This helps the user make an informed decision while investing in a particular company.

VI. EXPERIMENTS AND RESULTS The stock prices for the selected company are extracted from the website in.yahoo.finance.com for a period of 1 year. This data is stored in a database and the 200-day moving average is calculated and compared with the closing price. A count of the number of times this occurs serves as a parameter to determine the performance of the selected company. A graph is also plotted using Google Charts to show the variation of 200-day moving average and closing price over the said period of time.

The graph shows the variation of 200-day moving average and the closing price over time. It shows the pattern of the price movement. If the closing price is greater than the 200-day moving average, it is regarded as an uptrend.

Fig 2: Results of Technical Analysis This screenshot shows the results of the Technical Analysis for the selected company, which in this case, is HCL Technologies. The graph shows the variation of the 200-day moving average and the closing price over a period of one year.

URLs for the news articles for the selected company were extracted from the website www.moneycontrol.com and stored in a file. This file is then used to open each URL separately and extract the news article which it contains. The URLs that do not contain news articles are filtered and only the relevant URLs are written into the file. The end result is file that contains all the articles specified by the collected URLs and this file serves as an input to the Sentiment Analysis module which does the Sentiment Analysis of the news articles pertaining to a particular company. A training set in the Comma Separated Value (CSV) form is also separately created with manually tagged articles which serve as an input to this module. The NaĂŻve Bayes Classifier (NBC) is first trained with this training set. Then the file with the extracted articles is passed to the NBC for test. The percentage of articles that are positive and those that are negative are calculated and also represented in the form of a pie chart.

Fig. 3: Result of Sentiment Analysis and Final Results This screenshot shows the pie chart which depicts the results of the Sentiment Analysis. It shows the percentage of articles that positive and the percentage of articles that are negative. It also shows the final percentage after combination of the Sentiment Analysis results with the results of the Technical Analysis. This page also gives

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the final result which specifies whether the outlook for the company is positive or negative. In this case the outlook for the company chosen, i.e., HCL Technologies is positive.

VII. PERFORMANCE ANALYSIS AND EVALUATION. The accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity’s actual (true) value. Precision can be defined as the percentage of correct items that are selected. These metrics are calculated for the Sentiment Analysis module. The accuracy calculated by this code is found to be 62%. Precision is found to be 78.5714285714% and recall is found to be 40.7407407407%. These metrics are for the Sentiment Analysis.

VIII. CONCLUSION The aim of this work is to be able to extract large amounts of data in the field of stock market through the Internet at runtime for the purpose of analysis. The final analysis is the combination of the technical analysis and sentiment analysis and it helps in giving the overall outlook of the company that is chosen so that the user can make an informed decision. In this implementation we have extracted the historical stock prices of a certain company from a list of companies and analyzed those values to find the patterns in the stock values. We have also explored the predictive power of news articles on stock market domain to help the user make an informed decision. The historical prices are analyzed by technical analysis and the news articles are analyzed using sentiment analysis. This work could be extended by adding more companies and the exploration of various other techniques for both sentiment analysis and technical analysis.

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10].

Indian Stock Market Predictor System, Vanipriya CH,Thammi Reddy K,pp 17-26 Springer International Publishing,2014, DOI 10.1007/978-3-319-03095-1_3 Stock Prediction Using Event-based Sentiment Anlysis, MasoudMakrehchi,Sameena Shah and Wenhui and Liao,IEEE Computer Society,2013,pp 337-342 Twitter mood predicts the stock market,Johan Bollen1,Huina Mao,Xiao-Jun Zeng., Journal of Computational Science, March 2011, pp.1-8.

Evaluating and understanding text based stock price prediction models, EnricJunque de Fortuny ,Tom De Smedt,DavidMartens,WalterDaelemans,Journal of Information Processing and Management,2014,pp. 426-441.

Cheap Talk on the Web: The eterminants of Postings on Stock Message Boards, Peter D. Wysocki.,Issue 98025 of Working paper (University of Michigan. Business School. Research Support, 1998.

Mining of Concurrent Text and Time Series, VictorLavrenko, Matt Schmill, Dawn Lawrie, Paul Ogilvie,David Jensen, and James Allan,Proceedings of International conference on KDD Text mining workshop,2001.

Stock Trend Forecasting Method Based on Sentiment Analysis and System Similarity Model”,Kaihui Zhang, Lei Li , Peng Li and Wenda ,IEEE,2011.

Sentiment revealed in social media and its effect on the stock, Hailiang Chen, Prabuddha De, Yu Hu, and Byoung-Hyoun Hwang ,IEEE Statistical Signal Processing Workshop,2011, pp. 25 – 28.

Financial Text Mining: Supporting Decision Making Using Web 2.0 Content, Hsin-Min Lu and Hsinchun Chen, Tsai-Jyh Chen, Mao-Wei Hung and Shu-HsingLi.IEEE Intelligent Systems, 2010, pp. 78-82.

Sentiment Analysis and Subjectivity”,Bing Liu, Handbook of Natural Language Processing, 2010.

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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 Construct of Iran’s National Innovation System Based on Innovation and Foresight (Usage of Benchmarking and Delphi Method) Saeid Ghorbani Iraj1 Ph.D. Future Study, Assistant Professor Malek Ashtar Industrial University, Tehran, IRAN. Iraj Masoomi Baran2 2 Ph.D.Candidate, Malek-e- Ashtar University, Tehran, IRAN. Mahdi Jafari Nadooshan3 3 MS Industrial Engineering, Amir Kabir University, Tehran, IRAN. _________________________________________________________________________________ Abstract: Conditions and the atmosphere for business have been changed such that under these circumstances, the topic of innovation can be an effective responder to the needs of present and future stakeholders of organizations. With this regard, innovating organizations-including private and public- research centers and universities are like actors that in the event of establishment of effective relations with each other can benefit better and more economically from their technological abilities and the latter realizes when a national innovation system based on Foresight and in Accordance with requirements is designed and implemented at the national level.

This manuscript was prepared based on a qualitative research and by the description method. The purpose is by developing the three key concepts of organizational innovation, Foresight and national innovation system to describe the effect of innovative and foresight genes on the construct of Iran’s national innovation system. At the end, using the methods of benchmarking and Delphi, it is attempted to present a six level national innovation system such that the themes of foresight and organizational innovation for effective role-playing is reflected at all the relevant levels.

1

Keywords: Innovation, Foresight, National Innovation System, Benchmarking __________________________________________________________________________________________ I. Introduction In the opinion of Lundvall, innovation is a consistent process of seeking, screening and achieving the result of which is new products, technology and new forms of organizations and creation of future organizations and markets (Lundvall, 2005). Basically, innovation occurs when ideas in the form of technological change are developed in the framework of products/services (innovation of product) or are developed in a non technological change in the framework of organizational structure (organizational innovation) (Armbruster, 2008). Innovation is characteristically creative and is performed by one individual. Yet, in today’s changing world which is managed by organizations, innovation also most commonly occurs in organizations. On the on hand, the speed of development of market needs is consistently more than the speed of technological innovation; as a result, the gap between the present technology and the market needs itself demonstrates that need for organizational innovation exists in a systematic way.

Due to specialization, innovation is rooted in science and technology and is among the major abilities of organizations and countries in creation of wealth and the result of preemptive preparation for facing the continuous changes of present and the future. As a result, innovative organizations are generally futuristic organizations that search for building their desired future from the heart of today’s changes so that they can coordinate themselves with the longer term needs of stakeholders; and this dual strategy means that foresighted attention to the essence of innovation and taking advantage of it for shaping future is preferred.

Since the gist of foresight and innovation is the topic of changes in various domains such as science and technology, economics and social, the main question is: what is the role and position of innovation and foresight in formation of Iran’s national innovation system? In general, the national system of innovation seeks to identify the actors in the realm of science and technology and create a meaningful relationship between them for achieving additional benefit from each other’s technological abilities for better response to the national market needs (Martin, Johnston, 1999). Therefore, to create and strengthen a national system of innovation there is need for more universities, private and public innovating organizations that can reach the necessary maturity and coevolution by futuristic interactions and strategic agreements.

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As a result, in this study initially the theoretical concepts of innovation, foresight and the national innovation system have been presented. Next, Japan’s national innovation system has been considered as a benchmarking system. Additionally and subsequently, by describing the Delphi method, a suggested model for Iran’s national innovation system –as a new processing model- is presented and the relevant analysis has been performed.

II. Innovation The first definition of innovation was presented by Joseph Schumpeter. According to this definition, innovation is reflected in outputs including the following items that lead to the occurrence of a different activity (Schumpeter, 1934).

1-A new product or new quality of a commodity, 2-Presentation of a new production method , 3-Creation of a new market, 4-A new method of provision, 5-A new organizational structure.

Trott has classified innovation into seven groups of innovation in product, innovation in process, organizational innovation, management innovation, production innovation, business innovation, services’ innovation (Trott, 2005). Armbruster and colleagues (2008) also, by considering the classification of innovation presented by Teed and colleagues (2005), identified four kinds of innovation which are: 1-Technological innovation in product 2Non technological services’ innovation 3-Innovation in the technological process 4-Innovation in non technological process (organizational innovation) (Armbruster, 2008).

On this basis, now a day, innovating organizations try to take advantage of all dimensions of innovation in an integrated fashion such that their abilities in using and developing needed technologies for the desired response to the stakeholders’ present and future needs consistent with national goals are strengthened.

III. Foresight In the year 2000, Ben Martin defined Foresight by implementing revisions as follows: “Foresight, is the process of systematic endeavor for longer-term outlook towards the future of science, technology, environment, economy and society whose goal is identification of general novel technologies and strengthening of strategic research domains and probably brings with it the most economic and social benefits.”

(Center for Future Study in Science and Technology of Defense, 2010) and (Martin, 2001).

In the Foresight program of the European Union (STRATA), Foresight is: an important tool in development and management of future based innovation systems that are built on the basis of a wide array of future based coordinated activities in a society (Miles & Keenan, 2002).

Considering the mentioned definitions, it becomes clear that technology and innovation are in very close relation with foresight. Such that, realization of any of these elements without the other lacks full meaning. As a result, when foresight is implemented in a real way, advantages such as: increase and advancement of communications, more concentration on a longer term future, creation of coordination, consensus on an ideal future in the next 10 to 20 years and creation of commitment for putting the futuristic ideas into action are presented as the 5 C’s.

IV. National Innovation System Scientists have presented various definitions of the national innovation system and some of the important definitions are brought below:  National innovation system is a system in which private and public organizations, universities and governmental agencies interact to help create science and technology within national boundaries.

Interaction between these units may be technological, trading, legal, social or financial and the purpose of these interactions is development, support, investment and law making for new science and technology (Niosi, 2002).  National innovation system is a network of organizations and collection of policies and institutions that influence the creation of new technology in an economy (Martin, 1999).

As a result, the national innovation system, by attention to a desired future, attempts to realize the grounds for advancement of capacity for innovation and development of technology in organizations and by strengthening the communication of innovating organizations, it provides for realization of priorities and national goals V. Japan’s National Innovation System: A Worldwide Benchmark In the year 1949, Japan’s scientific council was established for presenting opinions and suggestions to the government in areas such as advancement of the level of science and technology, training of researchers and making research applicable. This council showed that there it lack of movement in the direction of business and political interests after the second world war; As a result, part of its counseling role was delegated to the science and technology council. This council is under the auspices of the prime minister and scientists form its members.

Japan’s science and technology policy are implemented by the ministers of the cabinet (particularly minister of education, minister of industry and international business), and the science and technology agency.

Agency of science and technology also has various duties the most important of which is coordination of technological scientific activities of various ministries and specification of the source performing research which is not located in domain of individual ministry activities (such as nuclear energy, science of aviation and aerospace, marine and natural resources’ science). The national innovation system of Japan is among the most progressive and effective systems of national innovation at an international level and the council of science and technology as its major trustee, divides this country’s technological capital to counseling groups, council for

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science and technology policy making, Japan’s science council and ministries (particularly ministry of economy, business and industry and ministry of education, physical education and science and technology). Ministries have formulated the strategies for achievement and use of technologies and announced them to offices, agencies and science and technology parks. These offices have the role of supporting and facilitating performance of research projects and send them to major centers of knowledge creation (universities, centers for development research and …). Agencies also exist at the lower levels of the national innovation system that have the responsibility to expand new technologies. Agencies on the one hand have the responsibility of acquiring the needs of industry and announcing it to scientific policy makers and on another hand have the duty of presenting new technologies to industry and supporting researchers (Boushehri, 2009), (Sadeghi, 2011) and (Ghazinouri, 2008). In this paperbased on descriptions provided for the method of benchmarking1 in the methods section-using benchmarking Japan’s national innovation system’s structure, it is attempted to design an appropriate structure for Iran’s national innovation system while keeping in mind the two concepts of innovation and foresight.

VI. Study Method This study was qualitative and its purpose was descriptive. As a result, a general and schematic description of structure/diagram of Iran’s national innovation system has been provided. This research tries to demonstrate the construct of Iran’s national innovation system based on constituents of innovation (displayed as innovating organizations) and foresight and by using methods of procedural benchmarking and the classic Delphi.

VII. Method of Benchmarking Benchmarking includes systematic search for obtaining the best methods and organizational experiences for achievement of the highest level of functioning including the three kinds of strategic, process and product oriented approaches (Stapenhurst, 2009).

In this study, considering that the title is related to the structure and process of the national innovation system, the method of process benchmarking has been used, the stages of which have been designed and implemented as follows: 1. Determination of the purpose of benchmarking.

2. Formation of a steering committee including 5 experts.

3. Identification and extrusion of information about various countries’ national innovation system: Japan, Germany, England, South Korea and ….

4. Determination of indices of screening and prioritizing including: antiquity, success and system’s maturity.

5. Evaluation and prioritization of the relevant national innovation systems by the steering committee.

6. Performing the study and taking ideas from the structure of Japan’s national innovation system (first priority).

VIII. The Classic Delphi Method This is a method for collection of the view points of professionals with respect for anonymity and summarizing their votes with the approach of prediction and foresight regarding a particular issue (Keeney, Hasson, Mckenna, 2001).

For performing this method, the relevant steering committee, with attention to the data on process benchmarking from Japan’s national innovation system-which has had good success in creating a national innovation systemand the initial question of this paper, prepared a questionnaire. Subsequently by attention to the approach of foresight in construct of a national innovation system, the following four panels including 18 experts and professionals in innovation, technology and futures study participated: 1. Foresight panel 2. Science and technology policy panel 3. Organizational innovation panel 4. Technology and industry panel In the first stage of performing the Delphi method, a questionnaire based on open questions and with consideration for anonymity was provided to the panels and after response of the members, the questionnaires were collected by the steering committee and grouped. This work resulted in creation of a questionnaire with 20 key questions in the second stage. This questionnaire along with responses in the first step was distributed among the members of the panels. At this stage and after collection and analysis of the responses it was observed that based on a Likert scale, even though the viewpoint of the professionals had converged, yet, numerous differences based on the quaternary axis existed. Therefore, the steering committee after grouping the responses in the second stage redistributed the questionnaire and integrated responses for a third time. In this stage, members of the panels with attention to two prior responding experiences and familiarity with the professional view points of other experts presented their supplementary opinions. Such that after collection and analysis of the view points, it became evident that convergence of opinions and similarity of ideas have a good quality. Therefore, the results of the second and third viewpoints in addition to the findings from the benchmarking study were used for designing Iran’s national innovation system.

1

The diagrams of national innovation system for countries such as: Japan, Germany, England, South Korea, have been presented in fifth reference which was used for benchmarking in this study.

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IX. Results and Analysis Suggested National Innovation System for Iran Even initial attempts for creating a national innovation system has been made, yet, this issue still needs more precise designs and increasing support. Currently, some relevant organizations in science, technology and innovation policy making such as the ministry of science, research and technology, the higher council for cultural revolution, expediency council, and the presidential scientific deputy are considered among its most important care takers in the country (Hosnavi et al, 2013). In the suggested flow diagram, the parts that exist in a general fashion have been demonstrated and for the first time, this system is presented in a classified way. The structure of this flow diagram has been improved based on benchmarking Japan’s national innovation system which has been performed by implementing changes based on the results of the Delphi method. In this flow diagram, the place of foresight in Iran’s suggested national innovation system is clearly evident. The innovating organizations which are one of the major elements of the national innovation system also lead to the national innovation system’s gaining increased flexibility, speed and coordination with regards to transfer of technology in the industrial sector, services and agriculture. As shown in the figure 1 below, organizational innovation is placed in the location for facilitation of research and innovation and technology transfer. Off course, organizational innovation has indirect influence on performing R&D and applying technology. One of the important parts of the suggested diagram for Iran’s national innovation system is the foresight section which is present in all six levels and its position in the national innovation system is discussed below: Position of Foresight in the Suggested Model: First Level: Foresight and Preparation Public Policies By forming a compound specialty team from members of the major governmental organizations, foresight has been discussed and considering the results obtained from it, national priorities, national macro purposes and the general governmental policies are described. In other words, the pre requirement for describing general policies, is performing persistent foresighting in domains such as science and technology, sociology, economics, environment and ….

Second Level: Preparation and Coordination of Technology and Innovation Policies At this level also using technological foresight, the council for policy making and coordination of science and technology can specify the technological priorities in industry, services and agriculture. This council can also lead to positive role creation in the subdivisions by interaction and contact with institutes of scientific information and technology of Iran.

Third Level: Organizational Innovation (Facilitation of Research and Innovation) For organizations to free themselves from path dependency and prior processes and become close to newer processes, they need organizational innovation. Yet, organizational innovation finds meaning when the relevant organizations possess an appropriate level of future technology, organization and needed information. As a result, by using the capacity for anticipative foresight, one can reach effective organizational innovation for facilitation of research and innovation.

Fourth Level: Performing Research and Development Universities, industrial laboratories, innovating and progressive organizations, using the prepared document of foresight as well as strategic cooperation based on open innovation, can become familiar with future needed technologies and by performing research and development based on national necessities, can advance the ability for production or domestication of technologies and play an important role in decreasing the technological deficiencies and strengthening technological and non technological innovation.

Fifth Level: Expansion of Technology At the level of expansion of technology, agencies that commercialize research findings (universities and research centers) can by being informed of the future national needs, identify the group of technologies that are considered the national priorities and by applying them to business, they can reach a competitive advantage. In other words, by considering the organizational innovation approach, these agencies will increase the effectiveness and efficiency of their actions with regards to commercializing technologies and technological innovation to business.

Sixth Level: Application of Technology At the level of application of technology, organizations, companies and large and small industries with high, medium and/or low technologies can under the title of innovating organizations and by paying attention to the document of Iran’s national innovation system and open innovative approach, provide for increased industrial co-evolution, exchange and combine technologies at hand for presentation of future innovations, presentation of

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innovating products, obtain desired market equity, create and increase public welfare and finally produce national strength. Additionally, these organizations themselves can by systematic performance of foresight technological projects, while building their future, provide for their future technological needs and opportunities and think about their provision from before.

Applying technology Distribution of Technology

Performing R&D

Organizational Innovation

Inditement and Coordination of Foresight and Technology and Innovation Policies Overall Policy Inditement

Flow Diagram for Iran’s National Innovation System Using Organizational Innovation and Foresight Leadership Expediency Council The Parliament Judiciary System National Foresight Team

Other ministry

Educational and Teaching Policies

Center of Linking between industrial and university (in the university)

Universities

Government

Ministry of Education and Development

Ministry of Defense

Center of Linking between university and industrial (in the industrials)

Iran’s Organizations for Scientific Information and Technology

Ministry of Industry, Mining and Trade

Elite Foundation Agency for Exchange of Advancements is the Services Sector and Provision of the Needs of Services

Ministry of Science, Research and Technology

Agency for Exchange of Progress in the Industrial Sector and Provision of the Needs of the Industry

Private and Governmental R&D on Items needed by the Industry and Services

Science and Technology Parks

Other Innovating Organizations

Medical Research

Scientific Deputy of the President

Policymaking and Coordination of Science and Technology Council

Supreme Council of the Cultural Revolution

Document of Foresight

Various Offices of the Department of Industry, Mining and Trade

Industrial and Private Laboratories

Centers for National Strategic Research

Document of Foresight

Creation of Interactions between the scientific and industrial (ISRs) sectors and commercialization

Research Agreements

Industries with low or medium technology

Centers for Technology Transfer

Agencies for commercialization of research achievements in universities and research centers

Document of Foresight

Industries with high technology

Figure 1. Flow diagram for Iran’s National Innovation System X. Analysis and Suggestions Analyses are based on the suggested national innovation system for Iran and the results of the viewpoints expressed in the Delphi method which are presented as follows:  Presentation of the Innovating Comprehensive Evaluation Model For evaluating innovation, there is need for a comprehensive evaluation model at the three levels, macro (top level of national innovation system: policy makers of innovation), meso (middle of national innovation system: intermediate institutes of innovation) and micro (bottom of national innovation system: Innovating Organizations and companies) and the bases for precise evaluation is existence of appropriate

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measures/indicators so by this means while evaluating functioning, rate of convergence and maturity of the national innovation system and organizational innovation becomes clear.  Determination of the Effect of the National Innovation System on the comprehensive Scientific Plan of the Country and the Reverse One of the challenges of the Islamic Republic of Iran is realization of a comprehensive Scientific Plan for the country-that contains a futuristic approach- and the other challenge is stabilization of a national innovation system for realization of a desired future. As evidence shows, these two have close relationship with each other and each can be the other’s complement. Therefore, it is necessary that the synergy and co-evolution of the relation between these two architectures and their implementation be continuously evaluated.  Deepening of the Relationship between Organizational Innovation, Suggestion and encouragement Systems In most organizations, particularly public organizations, high value is placed on inventions and inventors.

Special incitements are considered for inventors (financial and spiritual). Yet, most of these organizations use less strategic actions for improvement in organizational processes or innovation. The problem is that for individuals who try to improve processes, less encouragement is used. As a result, organization personnel have low motivation for making suggestions. Therefore, since the system of suggestions is a bed for advancement of organizational innovation, it needs to include true and strategic support.  Vision of 2025 Iran and the Triple Helix In this study, referral has been made to the presence of industries, universities and government in the framework of the national innovation system. Yet, the important factor is the relations between these actors. For effective realization of the 2025 vision-that has a normative approach towards foresight -the relationship between actors needs to be stronger and more synergistic than before. In this regard, four strategic connections exist which need to be redesigned with an innovative insight. These relationships include: 1. Relationship of industry with university 2. Relationship of government with university 3. Relationship of government with industry 4. Relationship of industry, government and university  Foresight, Policymaking and Advancement of Innovation In general, realization of innovation at all levels (national level to the organizational level) requires technological and innovation policymaking and effective policymaking also needs performing extensive studies and having the advantage of the 5 C’s in foresight at related levels. Therefore, for achieving a national innovation system to stimulate technology and advancement of innovation, it is necessary that foresight studies be performed with an intelligent strategy and as the tool for policy making.

XI. Conclusion Foresight is a process for building the desired future based on increasing changes. As a result, precise analysis of changes and weak signals in domains such as science and technology, social, economics and environment will tremendously help shaping the future. On another hand, innovation also has roots in technological and non technological changes and organizations for effective response to the various present and future needs of their stakeholders to understand changes well and by innovation and re-architecting their methods and structures, attempt to create and present new value packages. These organizations who get to be known as innovating organizations are the main actors in strengthening economy and creating national power and who constantly seek development or attract their needed technologies. Yet, since no company alone has the ability to create all the innovative technologies, it is necessary that a bed be created for open communication between innovating organizations to facilitate their interaction and co-evolution. In general, this strategic desire shows itself in the framework of the national innovation system; because, the existence philosophy of this system is creation of an active network of innovative organizations, universities and governmental policymaking organizations for synergy of technologies and presentation of the needed innovation. As a result, in this study it was tried by consideration of the two genes of innovation and foresight on the one hand and benchmarking Japan’s national innovation system on the other hand, to suggest a model for Iran’s national system so that Iranian innovating organizations also by taking advantage from the capabilities of this system and in accord with national priorities and goals, can achieve more economic production and creation of increased national welfare.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Architectural Structure of Geographic Information System Anoop Singh1, Ramanjyot Kaur2 Department of Civil Engineering, SBBSIET, Jalandhar, Punjab, INDIA. 2 Department of Computer Science & Engineering, SBBSIET, Jalandhar, Punjab, INDIA. anoophayer@gmail.com1, er.ramanjyot@gmail.com2 _______________________________________________________________________________________ Abstract: Geographic information systems (GIS) have been an active research field for decades now, and they are becoming a very active commercial field due to the increasing demand for interactive manipulation and analysis of geographic information and the exponential improvement in the performance of computer-based technologies. In this paper the generic architecture if Geographic information system is discussed. It is considered that the functionality of a generic architecture for information systems must be divided into three separate tiers, namely: presentation tier, application logic tier, and data tier. This architecture is suitable for GIS applications, the special nature and exclusive characteristics of geographic information impose special requirements on the architecture in terms of conceptual and logical models, data structures, access methods, analysis techniques, and visualization procedures. Thus, in this paper the requirements for GIS and the internal architecture of the GIS have been discussed.

Keywords: Spatial data, mediator layer, geospatial, metadata, scalable _________________________________________________________________________________________ 1

I. Introduction A geographic information system (GIS) is a computer system designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data. The acronym GIS is sometimes used for geographical information science or geospatial information studies to refer to the academic discipline or career of working with geographic information systems and is a large domain within the broader academic discipline of Geoinformatics[1].

The term GIS describes any information system that integrates, stores, edits, analyzes, shares, and displays geographic information. GIS applications are tools that allow users to create interactive queries (user-created searches), analyze spatial information, edit data in maps, and present the results of all these operations[2][3].

Geographic information science is the science underlying geographic concepts, applications, and systems[4].

GIS is a broad term that can refer to a number of different technologies, processes, and methods. It is attached to many operations and has many applications related to engineering, planning, management, transport/logistics, insurance, telecommunications, and business.[3] For that reason, GIS and location intelligence applications can be the foundation for many location-enabled services that rely on analysis and visualization.

GIS can relate unrelated information by using location as the key index variable. Locations or extents in the Earth space–time may be recorded as dates/times of occurrence, and x, y, and z coordinates representing, longitude, latitude, and elevation, respectively. All Earth-based spatial–temporal location and extent references should, ideally, be relatable to one another and ultimately to a "real" physical location or extent. This key characteristic of GIS has begun to open new avenues of scientific inquiry.

Geographic Information Systems (GIS), a promising branch of Information Systems (IS), have achieved considerable success in recent years. This area of IS has concentrated on the construction of computer based information systems that enable capture, modeling, storage, retrieval, sharing, manipulation, analysis, and presentation of geographically referenced data [5].

II. Requirements for Geographic Information System A set of requirements for Geographic Information System have been produced after many years of research [6].

Some of the requirements are discussed in the following: A. Flexibility: The functional requirements and the runtime environment of an information system often change during its lifetime. It is important that the system can adapt easily to these changes. It’s also desirable that the information system can be used in different technological platforms with different functionality (e.g., personal computers, mobile devices). Hence, the architecture and the functionality of the information system must be easily adaptable to different platforms.

B. Extensibility: In addition to changes to the functionality or runtime environment, during the lifetime of an information system it is often necessary to support and incorporate new requirements and technological advances. The information system must provide a highly customizable framework that can be extended with new features by mean of a programming language or additional software components.

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C. Reusability: The development process of an information system is costly. Therefore, it is important that the components of the information system can be used again without significant modification as a building block in a different information system from the one that it was originally designed for. This requires the development of generic modules that can be configured for specific tasks by means of high-level languages.

D. Scalability: Even though it is possible to estimate the number of users that will use the information system or the computing power needed by the functionality in the information system, these estimations are likely to change during the lifetime of the system. Hence, the system must be designed in a way that it is possible to increase the number of users or expand the capabilities of a computing solution without making major changes to the system.

E. Reliability and security: Information systems need to be fault-tolerant and highly-available. Moreover, it has been repeatedly proven that security is a very important requirement for computer-based information systems.

F. A conceptual model for geographic information: It is necessary to define a conceptual model that supports the description of geographic information. The model must include abstractions to model real-world geographic phenomena, using both the object-based and the field-based views of space.

G. Primitive operations on the abstractions of the conceptual model: An exhaustive set of primitive operations on the data abstractions of the conceptual model must be defined in order to support data querying.

H. A query language for the conceptual model: This language is used to retrieve and manipulate the data abstractions of an application schema defined using the conceptual model. The language must be composed by the operations defined previously. The results of queries must be represented by an appropriate language that captures all the abstractions of the conceptual model.

I. Problem-solving techniques: The abstractions in the conceptual model and the query language must be used to provide generic solutions for common geographic problems. There must be a precise definition of the problems to be solved, the information needed, and the techniques used to solve them.

J. Metadata and data cataloguing: In order to define generic methods to find out and use the information contained in any given application schema, it is necessary to provide models for metadata and data cataloguing.

K. Logical models for specific technological platforms: Given that each technological platform has particular limitations for the representation of information, it is necessary to define a logical model for the platform that takes into account these limitations. This model defines data structures for the data abstractions and algorithms for the operations that can be implemented in the technological platform.

L. Efficient physical models: Even though the logical model takes into account the limitations of computer systems with respect to the representation of information, it is necessary to define new storage techniques and access methods in order to achieve computational efficiency.

M. Visualization and interaction metaphors: There is a need for appropriate metaphors to manipulate geographic information at the user interface of the system. These metaphors must be based on the well-known map metaphor, and must incorporate dynamic operations such as zoom, pan and the addition and removal of information grouped in cartographic layers.

N. Abstractions for information visualization: The data abstractions used for the storage and processing of geographic information are not suitable for its presentation to the user. Therefore, new data abstractions are needed that support displaying different views of a single geographic object at different resolutions or with different visual styles according to display parameters such as scale.

III. Architecture Overview A. Description of the Architecture This section discusses the generic architecture for geographic information systems based on the proposal by the ISO and the OGC[7][8]. The specifications defined by these organizations are used where possible.

Architectural Structure of Geographic Information System separates the functionality of the system in three independent tiers, namely the Data Tier, the Application Logic Tier and the Presentation Tier [9]. In order to enable reusability and flexibility of the system architecture, the functionality of these tiers must be implemented independently of any particular application. The architectural structure of GIS is shown in Figure 1. i) Data Tier: It provides data management functionality independently from the software technology.

Information retrieval and manipulation requests for the data tier are expressed using a query language. Queries are evaluated within the data tier and the result is a set of geographic features that are represented using an information exchange language that is suitable for the conceptual model. ii) Application Logic Tier: It implements the problem-solving and the application specific functionality of the system. The top-most interface of this tier consists of a collection of operations for data processing tasks. These operations represent high-level abstractions of problem-solving techniques (e.g., find a route between two nodes in a network) instead of the primitive operations from the data tier query language (e.g., find whether there is an direct edge between those two nodes). A processing request to this tier is expressed as an invocation to one of

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these operations, which is then executed by the application logic tier either by using its internal information or by building and issuing the appropriate queries to the data tier and manipulating the returned data [10].

Figure 1: Layered architecture of GIS iii) Presentation Tier: It implements the user interface of the system, which enables data visualization, data manipulation and data entry. The presentation tier receives the user interaction in the form of mouse gestures, keyboard inputs or inputs from other devices. These inputs are evaluated and the appropriate operations in the application logic tier are invoked. When the results are returned, they are displayed to the user using the appropriate user interface controls and visualization metaphors [11].

B. Internal Architecture of the Tiers Each of these three tiers is not a monolithic software module. Instead, each tier is composed by a set of software layers that divide the functionality of the tier into independent components. Figure 2 shows the internal layers of the tiers in the architecture.

Figure 2: Internal architecture of Tiers i) Data Tier: This tier must be internally divided into three independent layers: the data services layer, the mediator layer, and the data sources layer. Considering that there may be many different types of data sources,

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the internal architecture of the tier must be organized in a mediator wrapper pattern. The mediator layer must offer a single conceptual model geographic information including data types, a query language, metadata and catalogue information. There is a wrapper in the data sources layer for each data source type that deals with the peculiarities of the data source. The data services layer provides a collection of profiles of the conceptual model for specific applications that enable to hide the complexity of the underlying conceptual model (e.g., by providing a simplified query language or a specific exchange language). ii) Application Logic Tier: This tier is not divided into layers like the data tier. Instead, the functionality of the tier must be implemented by a set of multiple independent services. Each service performs a well-defined and simple task, and it is defined by giving its interface as a set of operations and a description of the results. The services existing in the architecture cannot be predefined because the functionality necessary for specific GIS applications cannot be known in advance. iii) Presentation Tier: This tier must be divided into three layers: the client functionality layer, the map display layer and the portrayal layer [12]. a) Portrayal layer: This layer is in charge of converting a collection of geographic features into a collection of cartographic objects that can be rendered on a display device. The portrayal process is controlled by a set of styles definitions, which must define precisely the way in which each geographic feature must be rendered. b) Map Display Layer: It is responsible for rendering the cartographic objects on the display device and enabling the end-user to manipulate and interact with the cartographic objects to perform geographic operations and to request processing operations from the application logic tier. The definition of the actions associated to each of the user-interface events is represented as a collection of activity rules. c) Client Functionality Layer: This layer allows for the implementation of the user interface functionality.

Additionally, some basic geographic functionality can be implemented in this layer using the cartographic objects for the computations (e.g., client-side map zooms, or measurements). This avoids long processing times involving server queries for simple operations.

IV. Characteristics of the Architecture The architecture of GIS is conceptual in the sense that it can be put into practice using many different implementations [13]. As an example, Figure 3 shows two different implementations of this architecture. Figure 3(a) shows traditional client/server architecture. The client contains the presentation tier whereas the server contains both the application logic tier and the data tier. Figure 3(b) shows an example of a highly-distributed architecture. The presentation tier is implemented by a client computer, and the data tier is implemented by a server computer. However, the services of the application logic tier are distributed among multiple computers [16]. The architecture is flexible to changes in the functional requirements because of the separation of data management functionality from the application logic, and the application logic functionality from the presentation logic. Moreover, using independent services oriented to simple and specific tasks in the application logic tier causes that changes in functional requirements do not require complex software changes. The architecture can be adapted easily to multiple runtime environments because it is highly-modular, as it was shown in figure 3.

The separation of the application functionality into multiple tiers and modules within each tier makes it easier to implement reusable components. Moreover, new functionality can be easily added to the system using new modules for all the tiers, and thus, the architecture provides for high extensibility) [14]. Regarding scalability, the high modularity of the three-tier architecture allows distribution of application components across multiple servers thus making the system much more scalable. Finally, this architecture makes it easier to increase reliability by implementing redundancy at each tier.

It is very difficult to decide which functionality belongs to the application logic tier and which belongs to the data tier. DBMS vendors include more functionality in their products with each release, and tasks that were performed by services in the application logic tier are now carried out by the data tier. However, in order to provide a clear separation of functionality in the architecture, we have determined that the functionality in the data tier must consist of primitive operations oriented to solve multiple problems, whereas the functionality in the application logic tier must be oriented to solve complex and specific problems. For instance, operations to perform basic point-set operations on geographic values, or predicates to check topological relationships between geographic values belong to the data tier. On the other hand, a service that determines the optimal route given a network and two points in the network, or a service that solves allocation problems belong to the application logic tier. The separation of the functionality of the application logic tier in multiple independent services may affect the overall efficiency of the system. A monolithic system can be optimized to perform some tasks efficiently, but the resulting system is barely flexible [15]. On the other hand, a service-based architecture is highly flexible and performs efficiently each individual task, but the overall performance of a complex chaining of services is very difficult to optimize because of the inherent independence of the services. However, we believe that the benefits of service-based architecture outweigh its drawbacks, and therefore, we have chosen this type of architecture.

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Figure 3: Two implementations of the architecture V. Conclusion The architectural design discussed in this paper is inspired by the OGC and the ISO/TC 211. This implies that the three tiers of the architecture are similar in purpose to those defined by the OGC, including the organization of the tasks and the strict separation of the modules that interact only using well-defined interfaces. The different tiers of the architecture have been discussed in the paper. A mediator wrapper architecture for the data tier, and a data services layer to define specific application-profiles of the mediator layer have been considered.

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C. Amil, N.R. Brisaboa, A. Fariña Martínez, M.R. Luaces, M.R. Penabad, A.S. Places, and J.R. Viqueira. Una Interfaz Web para un Sistema Geográfico de Información Turística. In Actas de la II Jornada de Sistemas de Información Geográfica (JSIG), El Escorial, Spain, 2002.

C. Amil, N.R. Brisaboa, A. Fariña Martínez, M.R. Luaces, M.R. Penabad, A.S. Places, and J.R. Viqueira. Using Geographical Information Systems to Browse Touristic Information. Information Technology & Tourism, 6(1), 2003.

D.W. Adler. IBM DB2 Spatial Extender - Spatial data within the RDBMS. In Proc. of the 27th International Conference on Very Large Data Bases(VLDB ’01), pp. 687–692, Orlando, 2001.

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N.R. Brisaboa, J.A. Cotelo Lema, A. Fariña Martinez, M.R. Luaces, and J.R. Viqueira. E.I.E.L.: Una Experiencia de un Desarrollo SIG. In Actas de la I Jornada de Sistemas de Información Geográfica (JSIG), Almagro, Spain, 2001.

N.R. Brisaboa, J.A. Cotelo Lema, A. Fariña Martinez, M.R. Luaces, and J.R. Viqueira. S.I.T.P.A.C.: A Territorial Information System for A Coruña Province. In Proc. of the 8th International Congress on Computer Science Research (CIICC), Colima, Mexico, 2001.

N.R. Brisaboa, J.A. Cotelo Lema, A. Fariña Martínez, M.R. Luaces, and J.R. Viqueira. The E.I.E.L. Project: An Experience of GIS Development. In Proc. of the 9th EC-GI & GIS Workshop (ECGIS), A Coruña, Spain, 2003.

N.R. Brisaboa, J.A. Cotelo Lema, M.R. Luaces, and J.R. Viqueira. State of the Art and Requirements in GIS. In Proc. of the 3rd Mexican International Conference on Computer Science (ENC), Aguascalientes, Mexico, 2001.

N.R. Brisaboa, J.A. Cotelo Lema, M.R. Luaces, and J.R. Viqueira. S.I.T.P.A.C.: A Territorial Information System for A Coruña Province. I+D Computación, 1(2), 2002.

N.R. Brisaboa, J.A. Cotelo Lema, M.R. Luaces, and J.R. Viqueira. Sistemas de Información Geográfica: Revisión de su Estado Actual. In N.R. Brisaboa, ed., Ingeniería del Software en la Década del 2000, pp.77–94. Tórculo, A Coruña, Spain, 2003.

P. Burrough and R. McDonnell. Principles of Geographical Information Systems. Oxford University Press, 1998. ISBN: 0-19823365-5.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Managing the Mandate: The Emerging Tool in the Indian Political Scenario 1

Amit Kumar, 2Prof. Somesh Dhamija, 3Dr. Aruna Dhamija Assistant Professor, 2Head-Management (U.G. Programmes), 3Associate Professor Institute of Business Management, GLA University Mathura, Uttar Pradesh, INDIA. ____________________________________________________________________________________ Abstract: The general election for electing the sixteenth Lok Sabha would be remembered for many reasons in the history of Indian politics, with the mandate definitely being the top-most of them. For the first time in three decades, a single-party majority government was formed at the centre. Also, it was for the first time in the history of Indian politics that a non-Congress party formed a government and that too without the help of any other party. It is indeed something which would go down in the history as phenomenal and deserves attention.

This research paper would throw light on the mandate as received by the various political parties of the country, the trends which formed during the election, the vote share as garnered by the major parties, the voting patterns and other information of similar ilk. After reading this research paper, the reader would be in a position to gauge the way people voted in the nine-phase 36-day political spectacle, the largest and longest run election in the history of mankind.

Throughout this research paper, the authors would keep on highlighting the numerous aspects related to the mandate thrown in this election and how to read the same. It would be brought to the fore that getting a requisite share of the vote percentage doesn’t necessarily translate into number of seats. Bahujan Samajwadi Party is a prime example of the same. Despite being the third-largest party in the country in terms of vote-share, it ended up with no seats. Such interesting facts and the implications of the same would be discussed. ______________________________________________________________________________________ 1

I. Introduction The general election of 2014 in the republic of India to elect the 16th Lok Sabha delivered a landmark mandate which was historic in more ways than one. The magnitude of victory, as achieved by BJP, was nothing short of being stupendous, even beyond imagination by their own standards. Even the die-hard supporters of the party could not have imagined such a momentous mandate in the favour of the party. The mandate gave wings to the expectations of the people of the country. They voted for the BJP riding high on the hope of change and promise of development and growth.

A country of around 1.3 billion, out of whom 814 million people were eligible to vote. A record turnout of close to 66.6% meant approximately 55 crore people exercised their franchise to vote in the election, collectively making them the largest voting population anywhere in the planet. These are mind-boggling numbers which merit attention thus making this election unlike any other either in Indian history or for that matter anywhere else in the world.

The result of the election threw many interesting results. For the first time, Bharatiya Janata Party (henceforth BJP) spread its wings beyond the Northern part of the country and came up triumphant in large swathes of land across the nation. It became a national party in true sense and trounced a party whose legacy ran 125 years. Its mission of ‘Congress-mukt Bharat’ was very much realized in majority of the states.

Such was the pounding received by INC that it drew a blank in 7 of the states of the country not to mention its worst-ever showing of securing only 44 seats. To gauge the extent of drubbing faced by it, a regional party AIADMK which won seats only from the state of Tamilnadu secured 37 seats, just 7 shy of the national tally of INC. likewise, TMC secured 34, BJD 20. These were regional parties having limited appeal which didn’t go beyond their home states and they were within such a close distance from Congress, a national party which ruled India for the better part since general election first took place in 1952.

II. The Verdict Many people draw parallels of this election with that of 1977. The 1977 election was seen as one which resulted in a referendum which was given in favour of political freedom. The referendum given to the Janata Party was seen as a befitting reply by the Indian electorate towards the excesses of Late Indira Gandhi as committed by her during the Emergency. They were exasperated by the high-handedness of her and wanted to teach her party a lesson. It was this factor, more than the charm of Janata Party, which propelled it to power.

However, the mandate of 2014 was given to Bharatiya Janata Party, more so to Mr Narendra Modi, on the plank of development rather than anything else. People might say that being in power for the past one decade antiincumbency was a major factor for the defeat of INC and political pundits would not disagree with the same to

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an extent but it was the charisma of Mr Modi, above all else, which proved to be the decisive element in the lok sabha election of 2014. Many people hailed this mandate as akin to economic freedom as they were fed-up with the coalition compulsions, rampant corruptions, scandals, earth-shattering scams, unimaginative and weak leadership, graft, nepotism, red-tapeism, and other negative attributes as associated with the incumbent government.

Another interesting trend which came to the fore in the lok sabha election of 2014 was related to the median margin of victory. If one looks across the past four elections to elect the lok sabha, the victory margin was in the range of seven to nine percent which highlighted that the difference between winning and losing seats were relatively smaller. However, the result of 2014 indicaed a bigger margin between the winners and the losers. It shot up to a record high of thirteen percent. It is quite an achievement for the winners as it goes on to show that the electorate rejected candidates more vehemently this time than it has in the past two decades or so.

III. The BJP Juggernaut To give an idea about the magnitude of victory of BJP, it won 282 seats, 166 more than it won in the 2009 general election. The question of taking support of allies became redundant though it honoured all pre-poll alliances. The BJP-led NDA won 336 seats, an astronomical rise of more than twice from its 2009 showing of 159 seats. This was the highest tally put up by a party/coalition since the 414 seats garnered by Late Rajiv Gandhi-led Congress. However, the scenarios are as disparate as chalk and cheese. Congress won that historic mandate riding on the wave of sympathy which formed in the aftermath of assassination of Late Indira Gandhi.

Whereas the 2014 mandate, which BJP won with a landslide margin, was given on the agenda of growth and development more than anything else, even anti-incumbency. People voted for change, for pro-development policies. They believed in the charisma of the talismanic Mr Modi as they were fed up with the despondency and gloom as prevailing for the past decade, more so during UPA II. On the contrary, INC won 44, a huge let down from its show of 206 in the 2009 general election. Same was the case of INC-led UPA. It too suffered a major rout. It just managed to eke out a total of 60 seats, a far cry from the 262 seats it got in 2009, a huge loss of 200+ seats. Such was the battering received that even the most loyal followers found it hard to explain the debacle suffered by the party. They made up excuses but were found lacking the conviction for the same. Such was the scale of the defeat, that out of the 80 seats in Uttar Pradesh, the most in any state, only Mrs Sonia Gandhi and Mr Rahul Gandhi won seats for Congress and that too from their strongholds. The margin of victory for Mr Rahul Gandhi was less than one-third of what he got in the 2009 election from Amethi. On the contrary, BJP gained by more than seven times. The party won a paltry 10 seats in 2009 and this time it won 72! BSP scored a blank and SP came triumph only at 5 seats, all to the extended family of Mr Mulayam Singh Yadav.

Congress faced similar fate in another state and that was Maharashtra. From 17 MPs in the state, it went down to 2. It lost all the seven seats in Delhi.

BJP fulfilled its Mission 26 in Gujarat and won all of the seats there. It was equally stupendous success in Chattisgarh where it came triumphant in 10 out of the 11 seats. In Madhya Pradesh, barring two, it won all the 29 seats. A complete dominance in Delhi only sweetened the deal for the party where its candidates scored home in all the seven seats. Equally impressive were the victories in Bihar and Jharkhand where the party collectively won 40 out of the 54 seats. Along with its ally, it won on 41 seats out of 48 in Maharashtra.

These are some impressive numbers indeed. The incumbent government in J&K suffered whitewash and the six seats were shared between BJP and PDP three each. The juggernaut didn’t stop there. It rolled home in 7 out of the possible 10 seats in Haryana. In effect, it was a one-party show in almost entire North India. There was no answer to the Modi wave which took along with it all the prospects of its opposition.

Such was the dominance of BJP that it won an astonishingly 147 seats out of 168 the three states having a cumulative strength of almost one-third of the lok sabha seats, almost ninety percent of the available seats. No wonder, majority of the ministers, be it finance, defense, railways, home, HRD, have, in the past, came from these three states. Each one of these states has been crucial for any party to form government at the center in the past and BJP went on to stamp its authority in a big way in all of them.

IV. The Regional Influence On one hand BJP did phenomenally well in most of the states. Then there were those states were regional parties called the shots. They were unchallenged and despite the charisma of Mr Modi, held on their own to come out triumphant. There was AIADMK under the leadership of yet another talismanic leader, J Jayalalitha, the incumbent chief minister, who won a massive 37 seats out of the possible 39 seats in Tamil Nadu, a feat which has no parallels in the history of the state. It led to the abysmal performance of DMK, another prominent force (or used to be) in the state which failed to win even a single seat. BJP had to satisfy itself with just 1 seat.

Next state which proved the dominance of its regional leader was West Bengal where Mamata Banerjee’s Trinamool Congress stole the show big time. The incumbent chief minister’s party won 34 seats out of the 42 seats up for grabs in the state.

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Likewise, Odisha gave a huge mandate in favour of the incumbent chief minister’s party. Biju Janata Dal won 20 seats out of the 21 seats and BJP got just one.

Shiv Sena, an ally of BJP and an in important member of NDA, romped home in 18 of the seats in Maharashtra, the state where it had a 25-year old alliance with BJP. One interesting similarity among majority of the above-mentioned states is the fact that the incumbent government held sway over its competitors, including BJP. This goes on to prove that amidst the hype surrounding the brand appeal of Mr Modi, the regional leaders, specifically the incumbent chief ministers, held on their own.

At the same time, there were states like Uttar Pradesh, Bihar, J & K where the tide was against the incumbent government and they had to face the ire of the electorate.

V. Death of the King-Maker The parties in these states have, more often than not, played the king-makers in the past governments. Hence they were rewarded with plum posts out of coalition compulsion. They literally dictated their terms to the incumbent government who had no other option than to oblige them. This particularly held true for both the UPA regimes, more so during UPA II when allegations of rampant corruption, nepotism and graft were made against INC, and many of the members of its allies were implicated in such charges. It brought bad name on the party by heaps. The weak and unimaginative leadership didn’t help the cause either.

But this time around, the absolute dominance of BJP, at least in the states mentioned above and otherwise as well, made sure that no coalition compulsions deterred it from forming a stable government and that too without compromising on who will get what ministry. Mr Modi was known as a no-nonsensical leader since he took up the post of the CM in Gujarat in 2001 and stood by the same prior to and after the general election. He never let anyone dictate his/her terms on him, rather it worked the other way around. It was he who called the shots whether it be inside his party or among his allies. The senior members of BJP were relegated to the background under his leadership. One can very well imagine the traction which he would have given to his allies.

The king maker really lost its position of calling the shots in the government formation. Prior to the election, many psephologists predicted that Jayalalitha, Mamata Banerjee, Nitish Kumar, Mulayam Singh Yadav, Sharad Pawar might play the king makers like on previous instances when they were instrumental in the formation of the government. They spoke on the exit poll predictions majority of which forecasted that BJP might fall of the half-way mark in case of which these parties can play the role of king makers like they used to do so and benefit from the same. There were leaders like Mr Pawar who has been known for being on the most opportunistic ones and siding with the party at the center. Why just him? Majority of the regional leaders are like him who want to benefit from the current equation at play.

VI. Vote Share : A Different Ball Game Altogether ‘69% of the voting population doesn’t want Narendra Modi to become the prime minister of India’, was the highlight of one of the leading dailies in the aftermath of the election. BJP got the lowest vote share, 31%, of all the parties which have got past the majority mark of 272 in the history of Indian politics. The detractors of Mr Modi highlighted this aspect on every possible occasion. They told that less than 20 crore people in the country want him to be the next PM. The supporters thought otherwise. They highlighted the point that the vote share of BJP has gone up by almost 12 percent, that too at the expense of parties like INC. Congress’ fortune dipped both seat-wise and vote share-wise. However, the decline in its vote share was not as drastic as that in seats. It secured 19 percent of the popular votes, much on the same lines as BJP in 2009 but seat-wise they were poles apart. While BJP won 116 in 2009 election having the same vote share, INC could muster only 44.

The biggest surprise was the abysmal performance of BSP despite having the third-largest vote share nationwide. Despite having a vote share of 4.1 percent, it didn’t get even a single seat. On the contrary, TMC and AIADMK both got more than 30 seats each despite having lesser vote share than BSP. So much for the numbers! Then there is BJD which having won 20 seats was the fifth-highest party in terms of the number of seats won but it stood at a lowly 17th position in terms of votes polled in its favour. It garnered a measly 1.7%.

Same was the case with Shiv Sena which polled 1.9% votes and won 18 seats, eerily similar to BJD. But then even DMK polled 1.7% of the votes and still remained seat-less!

What is of interest here is to note that in total, 18 parties managed to secure one percent or more in terms of vote share in this election. That’s a huge number of parties given the huge impact which BJP had on this election. It also implies that the verdict was more fractured than shown by the seats won by parties.

VII. Conclusion One can safely say that this win by BJP was the biggest mandate given by the voters of the country against incumbency. Congress slumped to its worst-ever show and was reduced to two digits for the first time since the first general election was conducted in 1952. The massive verdict was won by BJP on the plank of development

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and its pro-growth policies rather than anything else, a far cry from the gain made on the ground of the sympathy wave and subsequent performance as registered by INC in the aftermath of assassination of Late Indira Gandhi in 1984. The first non-Congress majority government took shape for the first time in independent India.

What was indeed commendable on the part of BJP for achieving such a decisive mandate was the fact that geographically it was limited to mostly northern and western parts of the nation. Barring Karnataka, it hardly made any inroads in southern India. Even in Uttar Pradesh, it scored in those areas of the state which are dominated by the backward people, a traditional vote bank for the two parties – SP & BSP. The caste equations failed and the barriers were broken by the party successfully.

However, the vote share of the various parties presented a somewhat different picture. The starkness in terms of difference between BJP and INC was not to the same extent as reflected in the seats. The third-largest party in the country in terms of vote share failed to win even a single seat. Parties having less vote percentages won more seats than those having high percentages. It reflected the fractured nature of voting patterns as exhibited by the electorate of the country.

Overall, it was mandate which was historic in more ways than one and would go down in history for many reasons and remembered for numerous years to come.

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Henneberg, S.C. (2002) ‘Understanding Political Marketing’, in N. O’Shaughnessy and S. C. Henneberg (eds) The Idea of Political Marketing, pp. 93–171. Westport, CT: Praeger.

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Smith, G., & French, A. (2011). The political brand: A consumer perspective. In P.R. Baines (Ed.), Political Marketing (Vols. 13) (pp. 1-18). London: SAGE. Steger, W.P., Kelly, S.Q. and Wrighton, J.M. (2006) ‘Campaigns and Political Marketing in Political Science Context’, Journal of Political Marketing 5(1/2): 1–10.

Stromback, J. (2007) ‘Political Marketing and Professionalized Campaigning’, Journal of Political Marketing 6(2/3): 49–68.

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Decoding Rahul Gandhi by Aarthi Ramachandran. Published by Westland Books The Election that Changed India by Rajdeep Sardesai. Published by Penguin Books India Pvt. Ltd. The Man of the Moment - Narendra Modi by Kamath M.V. and Kalindi Randeri. Published by Times Group Books.

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Narendra Modi the Gamechanger by Sudesh K. Verma. Published by Vitasta Publishing Pvt. Ltd. Narendra Modi A Political Biography by Andy Marinoa. Published by Harper Collins India.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Industrial Democracy: an essential part of a Business Seema Rani Kurukshetra University, Kurukshetra, Haryana, INDIA _________________________________________________________________________________ Abstract: This paper discusses about the democracy effects in industrial area. It discusses about how the democracy is essential for business enterprises? It improves the relationship between management and employees, which is very important for a successful business. Later in this paper different levels and forms of industrial democracy are also explained. _______________________________________________________________________________________ I. Introduction Democracy means government which is elected by the people, of the people and for the people. This term is also used in the Industry and it is become “Industrial Democracy”. The term industrial democracy is a supplement of political democracy in which all citizens are treated as equal and are allowed to participate freely in the affairs of the state directly. In the same way, in industrial democracy, workers are treated as responsible partners of the concern and are permitted to participate in the decision making process through various methods. Industrial Democracy means that the management in industrial units is by the people, of the people and for the people.

Here people include all those persons who are engaged with the industrial unit. Industrial democracy involves workers making decisions, sharing responsibility and authority in the workplace. According to Indian company law, Industrial Democracy is generally used as co-determination, following the German word Mitbestimmung.

In Germany half of the supervisory board of directors is elected by the shareholders, and the other half by the workers.

II. Definitions According to H.A. Clegg, “Just as political democracy is based on the existence of an opposition, industrial democracy is contingent upon the existence of an opposition within the industry to the prevailing power of management ownership.”

In the words of Elliott, “Industrial democracy is a process of both industrial and political dimensions involves workers through trade union, claiming rights to have greater role in managerial decisions.  Essentials Features for Industrial Democracy: Following are the essentials Features for industrial democracy: (a) Involvement of both Employers and Employees: Both employers and employees should express their ideas and views. There should be no personal benefit for both. The industrial democracy scheme cannot be successful unless the employer and employee should have positive attitude towards each other. (b) Wide Publicity of its benefits: Employee should know the benefits of their participation with the management in decision making. If they don’t know the importance of industrial democracy, they will not express their views freely and will not be able to provide fruitful suggestions. (c) Responsible Trade Union: Not only the trade unions should be stronger but they should be responsible also. So that they contribute in the success of industrial democracy. Responsible trade union will look for the interest of both the employees and the management to facilitate effective decision making. (d) Free Flow of Communication: For making the industrial democracy effective, there should be a free flow of information. Because through communication employee’s can express their views freely and don’t hesitate. This two way communication is necessary for both employees and management. (e) Mutual Trust should be there: For making industrial democracy a success, there is a need that management and employees should trust each other and also cooperate with each other. If they don’t have faith in each other, their relations will be disturbed and industrial democracy will loose its importance. (f) Idea comes from employee’s heart: In industrial democracy, the employees should give the ideas, which come straight from their heart. They should not have to fulfill any legal formality while giving their views. So they should suggest what they feel. (g) Arrangement of Training: Employees should be properly trained and while their training they should be made clear about the benefits of industrial democracy. Training will also improve their skills and they will provide good suggestions to the management. (h) Decision should be implemented: For making the industrial democracy a success there is a need to implement the decisions which are suggested by the employees. By this employees will be motivated and also suggest good ideas in future. The delay in implementation of the decisions can adversely affect the morale.

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III. Levels of Employee Participation under Industrial Democracy The participation of the employees can be done at different levels. It can start from the lowest level and move up to the highest level. The following are the different levels of the employee participation: 1) Informative Level: This is the lowest level of the employee participation. In this the employees are allowed to obtain the required information from the management and can also present their views to the superior. It is the first level of participation of the employees with the management.

2) Consultative Level: This is the second step in the employee participation with the management. In this suggestions relating to the welfare of the employees are invited from the employees. But this is not necessary that these suggestions will be implemented or not.

3) Decision Making Level: This is the highest level of participation of the employees in the management.

In this employees are involved in decision-making process relating to different matters in the organisation. So we can say that at this level both the employees and the management take decision with the cooperation with each other.

IV. Objectives of industrial democracy The objectives of the industrial democracy are: 1.

To make Worker’s Role Important: The basic objective of industrial democracy is to make employees’ role important in an organisation. For successfully attaining the objectives and goals of the organisation, it is essential to make employees’ involved in the achievement of goal because without them it is not possible to achieve the goals.

2.

To Increase Productivity: When employees are involved in the decision making with the management, this motivates them and their morale increases. This leads to increase in their efficiency which brings increase in the level of productivity.

3.

To Satisfy the Needs of the Employees: Every employee wants to be recognized for his capabilities, so participation in management makes them feel recognized and they will be motivated to perform hard work. And moreover, employees social and esteem needs will also be satisfied.

4.

To Develop Human Personality: The industrial democracy gives the employees opportunity to express themselves. They express their views freely at various levels. Their hidden talent comes out. Thus they get an opportunity to develop their personality.

5.

To Strengthen the Employee Management Cooperation: Coordination and cooperation between the employees and management improves the relationship between them. Employees don’t feel neglected and when they participate in decision making they feel recognized. And their relations with their superiors also improve.

V. Importance of Industrial Democracy: (a) Increased Commitment: When employees are consulted for taking important decisions and also to formulate policies. They express their views and ideas. The employees feel that they are important to the organisation and their commitment also increases. (b) Organisational Peace: Organisational conflicts occur between the employees and the management and they become opponent of each other. But industrial democracy brings the cooperation between them and they come closer to each other. (c) Growth and Development of Employees: When employees are allowed to express their views freely, this makes them creative. They also design new methods for performing the work. Therefore, industrial democracy leads to their growth and development. (d) Increase in Mutual Understanding: In the present scenario the employees and the employers both think each other as their rivals. But industrial democracy bring both the parties on the common platform, they come close to each other and understand each other’s problem. This increases their mutual understanding. (e) Acceptance of Change: Whenever change is introduced, employees are first to resist the change and when the employees are the part of the decision, which introduces the change, they will accept it easily.

So in this way resistance to change is reduced through industrial democracy. (f) Increased Production: As discussed above the industrial democracy will increase the morale and the efficiency of the employees, which will lead to the increase in the production. Further, the organisational peace will also lead to increase in the contribution made by each employee.

VI. Forms of Industrial Democracy Following are the forms of industrial democracy: 1) Work Councils: Work councils are the representatives from both the parties i.e. employees and the employers. They meet regularly and discuss the different problems related to their work. Both the parties present their suggestions and take decisions by jointly participation. The matters which are normally discussed between these representatives are education, accidents, safety, welfare facilities etc. Main objective of the work council is

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to develop a spirit of cooperation and partnership among the employee and employer. This concept of work council has not been effective. The reasons behind its failure are due to vagueness about their exact role, power etc. The success of work council is depend upon some factors like responsible management attitude, support from union, exactness regarding the work and increase the scope of work council.

2) Collective Bargaining: An individual employee does not have the full knowledge about the appropriate pay rates for his services. So he joins the employee union and the representatives of employee union equally participate with the management in decision making process. So this process is known as collective bargaining.

Collective Bargaining is a process in which representatives of employer and employees meet and discuss the agreement that specifies the nature of failure relationships between the two, one of them is the trade unions who representatives of workers and the management which is the representatives of employers.

3) Co-Ownership: In this method the ownership rights are provided to the employees. Employees are motivates to purchase the shares, they will get the voting right to elect their directors and will be able to send their representations in the board of directors. Under this scheme workers are made partners in ownership. The main advantage of this scheme is that like shareholders workers become the owners in the organisation and this develop the sense of belongingness in them.

4) Representation in the Board of Directors: In this method the representative from the employee is taken in the Board of Directors. He is also named as employee director. But this method is not very much effective. It is because the workers representative would be in minority and his suggestion will have little weight. This creates inferiority feeling among the complex in him and he may be completely suppressed or frustrated.

5) Joint Management Council: In this method the joint committee represented by the employees and the management is set up to discuss and give suggestion for improvement with regard to matters of mutual interest.

The decision of such committee will be binding on both the parties. The main functions of these councils are divided in three categories: Advisory Functions Organisation’s management seeks advice of this council on the following matters:  Administration and standing orders  Amendment of standing orders  Retirement of the employees  Retrenchment of the employees  Rationalization  Stopping the work, reducing the hours etc.

Informative Functions: Joint Management Council will obtain the information on the following matters with the management:  General situation of the industrial organisation  Sales programme and market situation  Long term expansion and reconstruction of the company  Balance sheet and income statements  Production technique and activities.

Administrative Functions:  Award distribution to the workers for their valuable suggestions  Determination of hours of work, leave and holidays  Professional and apprenticeship training  Supervision of safety measures  Administration of welfare activities 6) Discussions: In this system managers call the meeting of the employees and share the information with them. He explains the problem to the employees gives information to them and invites suggestions. The employees give their opinion to their problem and decision making authority is not transferred to them.

7) Suggestions System: Under this system the employees are encouraged to make suggestions for the improvement. The employees may suggest some new methods of production or new schemes for the particular work. The employee who provides innovative suggestions are provided with monetary and other rewards.

8) Profit Sharing: Profit sharing is regarded as the stepping stone to industrial democracy. It is observed, “Profit sharing is an arrangement by which employees receives a share, fixed in advance of the profits”. Profit sharing is an agreement freely entered into by which an employee receives a share fixed in advance of the profits. Profit sharing usually involves the determination of an organisations profit at the end of the fiscal year and the distribution of a percentage of the profits to the workers qualified to share in the earnings. The percentage to be shared by the workers is often pre determined at the beginning of the work period and is communicated to the workers so that they have some knowledge of potential gains. To enable the workers to

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participate in the profit sharing they are required to work for certain number of years and develop some seniority. The main features of profit sharing scheme are:  Voluntary Agreement: The agreement is voluntary and is based on joint consultation made freely between the employers and the employees.  Form of Payment: The payment may be in the form of cash, stock, of future credits of some amount over and above the normal remuneration that would otherwise be paid to employees in a given situation.  Minimum Qualifications: The employees should have some minimum qualifications such as tenure or satisfy some amount over and above the normal remuneration that would otherwise be paid to employees in a given situation.  Employer Discretion: The agreement on profit sharing having been mutually accepted is binding and there is no room on the part of the employer to exercise discretion in a matter, which is vital to the employees.  Computation: The amount to be distributed among the participants is computed on the basis of some agreed formula, which is to be applied in all circumstances.  Dependability: The amount to be distributed depends upon the profits earned by an enterprise.  Advance Determination: The proportion of the profits to be distributed among the employees is determined in advance.

It should be noted that profit sharing is not a system of wage payment as such it is something else. Profit sharing and bonus are two different things for the former sharing implies sharing on an equal footing rather than yielding on the part of the management to a persistent demand. Profit sharing bonus on the other hand refers to the distribution of profits on the basis of a certain percentage on one monthly wages. Moreover, it is not voluntary and is based on agreement.

VII. Conclusion Following are the conclusions for the successful implementation of the industrial democracy: 1.

There should be proper communication at all levels of the management and there should not be blockage in the communication between the employers and employees.

2.

The employer should adapt a broad, progressive, and democratic attitude. They should be willing to associate with the employees and discuss the problems freely and frankly with them.

3.

The employers should be conscious of their obligation towards the employees and the benefits of employee participation. Employers and employees should agree on the objectives of the industry and their mutual rights and obligations.

4.

Managers should not treat participation as an imposed liability and employees should not use if for expressing and demands only.

5.

There should be mutual trust and faith among all the parties. Mere legislation cannot make participation successful. Existence of an atmosphere of trust, faith and confidence and recognition is a must on the part of the employers and the employees.

6.

Proper training should be provided to both employers and the employees. Employees and their representatives should be provided training and education in the philosophy and the process of employee participation. They should be taught what is expected from them and how they are expected to perform.

7.

Employees should be provided proper knowledge of their participation in the decision making.

8.

Participation should be done at all levels. Participation should be a continuous process. To begin with it must start at the operating level.

9.

Employer employee relations should be cordial or at least there should not be any tension in their relations. The objectives of participation should be decided mutually.

References [1] [2] [3] [4] [5] [6] [7] [8] [9]

Arun Monnappa, (1984), “Industrial Relations in New Delhi”: Tata Mc Graw.

Bhabotosh, Sahu, (1985), “Dynamics of Participative Management”, Himalaya, Mumbai David E. Bowen and Edward E. Lawler III, (1995), “Empowering Service Employees” Sloan Management Review, Summer, pp 73-83.

Employee Federation of India (1971), “Workers Participation in Management”.

John W. Newstorm and Keith Davis, (1998), “Organisational Behaviour: Human Behaviour at Work”, New Delhi: Tata Mc graw Hill, p.227 K.W. Thomas and B.A. Velthouse, (1990), “Cognitive Elements of Empowerment-An Interpretative Model of Intrinsic Task – Motivation”, Academy of Management Review, October.

Lawrence R. Rothstein, (1995), “The Empowerment Effort that Came Undone”, Harvard Business Review, January – February, pp20-31 N.N. Chatterjee, (1984), “Industrial Relations in a Developing Economy”, Allied, New Delhi.

V. Ravi, (2003), “Delivering Results by Empowering Employees”, Indian Management, April, pp. 72-75

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Socioeconomic influence on farmers in seri-business from Tamil Nadu Sunitha Rani. D1 and Jayaraju. M*, 1 Department of Economics, Sri Krishnadevaraya University, Ananthapur - 515055,Andhra Pradesh, INDIA. Kannan. M2 and Ashok Kumar. K2 2 Department of Environmental Biotechnology, Bharathidasan University, Tiruchirapalli - 620 024, Tamil Nadu, INDIA. 1

_______________________________________________________________________________ Abstract: Sericulture is one of the potential agro- based industry in India and worldwide. In the present study, the questionnaire based data’s has been collected by the interview method from randomly selected 58 sericulture farmers in and around krishnagiri district of Tamil Nadu to resolve and explore socioeconomic conditions of sericulture farmers. The primary data has been collected about the sericulture farmers as follows: education qualification, religions and community, family size, occupation, economic level, land coverage and Investment by the method of the questionnaire. Most of the farmers are Hindu (98.3%), involved seri-business has been observed from small size family land holders (89.7%). We found a positive correlation towards seri business with the farmers of OBC due to the less percentage of opportunities in government jobs other than SC whereas more opportunity is existing for SC farmers. Other information about socioeconomic conditions includes land utilization and investments towards sericulture are discussed in details. In conclusion, the sericulture is playing a vital role in the life of a small family holder of farmers to get more benefit out of agriculture. Due to water scarcity resulted the investment amount is increased at initial stage. However, sericulture farmers benefited additional income which generated from agriculture. The results of the present study show that > 8±10th standards studied sericulture farmers, approached from small size family holders were showing more interest on seri-business.

Index terms: Seri-business, education, Socio Economic, Development _________________________________________________________________________________________ I. Introduction India is one of the largest agrarian economies in the world. About 70% of the total population lives in the belts depending on agriculture and its allied activities for their live hood. Although commercialization in agriculture has been picking up well in recent years, the problems like land fragmentation predominance of marginal and small land holders ownership whose share constitutes to the tune of about 90% of total land ownership in the country; diversified social, economical and cultural factors prevailing in the rural segments and demand for higher investment in agricultural, are forcing the peasants and uneconomical landholders to change over minimum investment oriented cropping system in the country (1-3).

Sericulture being an agro-based business plays a predominant role in shaping the economic destiny of the rural people and fits very well in India’s rural structure, where agriculture continues to be the main occupation (4-10).

The process of sericulture involves rearing of food-plant to feed silkworms for the production of raw silk. In addition, silk cocoons reeling for unwinding the silk filament for value added benefits such as processing and weaving. Sericulture activities are considered to be well suited for marginal and small land holder to bring about economic transformations in the rural areas. It prevents not only rural migration but also has added advantages of low capital requirement of assured remunerative return within a short period. Attempts were made during different periods to estimate the cost and return profile from sericulture in different regions in harkhand, Chhattisgarh, Orissa, Madhya Pradesh, Uttar Pradesh, West Bengal, Maharashtra and Andhra Pradesh, Rajasthan, Karnataka and Tamil Nadu (10-13).

The periodical income from sericulture is one of the chief features for its existence However, many a time; it is observed that cost of production over run the benefits from cocoon production (14-17). In the present competitive environment, sericulture has to be competed with several agricultural cash crops. Therefore, the studies of economics of mulberry sericulture is not only essential to understand the cost of production and profit from a unit area but also it warrants to convince the farmers of its profitability as compared with other crops in the short period of time. Hence the present study aimed to in-depth analysis of small farm households economic system in mulberry sericulture, besides identified the factors responsible for declining the silk productivity and also the future prospects.

II. Materials and methods The district of Krishnagiri in Tamil Nadu was purposively selected for the study as it has more agricultural farmers who were more trained by sericulture technique due to more awareness about the seri-business offered

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by Central Sericultural Germplasm Resources Centre (CSGRC), Hosur. The data’s were collected from 2010 to 2013. This study is mainly based on secondary sources of information which are collected from various sources like Directorate of sericulture, research articles, journals and newspapers. The data’s were collected include namely education qualification, religions and community, family size, occupation, economic level, land coverage and Investment.

III. Data analysis All values were expressed as Percentage. The statistical analyses were performed by using Graphpad prism-6 software (Graphpad Software, Inc).

IV. Results and Discussion In the present study, we surveyed the educational quality of farmers who were involved in agriculture as well as sericulture. The educational qualifications of farmers are as follows: 43.1, 31, 19, 1.7 and 3.4 % for SSLC, 5th, 8th, 9th, B.Sc and uneducated respectively (Fig. 1).

Figure 1. Educational qualifications of farmers both in sericulture and agriculture.

This data revealed that SSLC studied farmers has associated in Sericulture due to the knowledge about seri business. Religion and community were playing a critical role in society to selection of occupation from earlier days onwards. Particularly, 98.3% of farmers observed as Hindu’s and only 1.7 % of farmers were Muslims (Fig.2.A). Four major castes have been observed in the study out of two major religions. In this study, 63.8, .9, 8.6, and 1.7 % of farmers are under BC, OC, MBC and SC respectively (Fig. 2.B).

Figure 2. Religions of farmers associated with sericulture and agriculture.

Figure 2. B. different community of farmers associated with sericulture and agriculture.

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This study clearly shows that the major factor affecting the socio economic conditions of farmers. Particularly, government jobs opportunity for SC is more, itâ&#x20AC;&#x2122;s indicated that the decrease in the percentage of farmers in sericulture whereas OBC (BC, MBC and OC) farmers are highly involved in seri-business due to the non availability of government jobs. However most of the farmers are highly involved in 65.5% in agriculture and 34.5 % of farmers were involved in Sericulture (Fig. 3).

Figure 3. Major occupation of farmers.

In this study, we further observed that most of the small size family holders (89.7%) are involved in agriculture when compared with Big (6.9%) and single family holders (3.4%) (Fig.4.A). Small family holders are acting as economic group when compared with high (6.90%) and marginal (10.3%) economic groups (Fig.4.B).

Similarly, Singh and Vasishti (18) reported that the occurrence of more small size sericulture farmers in salem district of Tamilnadu.

Figure 4. A. Family size of farmers.

Figure 4. B. Economic level of farmers.

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Most of the small holder farmers has 2, 3 and 4 acres were utilized for sericulture by 17.2, 25.9 for both respectively and other are as follows 1, 1.5, 2.5, 3.5, 5 and 9 acres by 5.2, 1.7, 6.9, 3.4, 12.1 and 1.7 % percentage respectively (Fig.5).

Figure 5. Percentage of farmers in Land utilization for sericulture.

This results shows that the small size family of farmers whoever studied SSLC with predominant economic groups and investing 2-4 acres for the sericulture. Around 3.5 to 9 acres land holders are involved in sericulture in very less might be due to the income from the agriculture, big family size and finally scarcity of waters problem for the entire agriculture. A highest amount investment for sericulture has been noted as 2 and 5 lakhs by 29.31 and 27.59 % of farmers respectively. However, most of the farmers are investing more or less amount other than 2 and 5 lakhs by were shown in figure 6.

Figure 6. Percentage of farmers in investment for sericulture.

The results of the present study indicated that the profit earned from sericulture was comparatively higher than agriculture. This shows that sericulture has potential role in the development of farmers from Krishnagiri.

Similarly, Sakthivel et al., (12) also reported the same criteria for sericulture farmers in Thirunelveli and Virudunagar districts.

V. Conclusion The results of the present study conclude that sericulture industry is an outstanding opportunity for employment with various entrepreneurial developments to farmers. The proposed studies are much helpful to identify the factors which are responsible for the decline /increase in the percentage of farmers in sericulture.

The seri business is especially improved in between the small land holder with several government opportunities such as loan, insurance, high temperature tolerant and high nutrient mulberry varieties, awareness and training programme for silk productivity and protective of silkworms from pest management through

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advance technologies. The government should take important steps in easy transportation facility to rural people and educating the farmers in seri-business for reducing the migration of farmers towards attractive salary earn jobs.

Acknowledgement I thankful to the management of Sri Krishnadevaraya University, Andhra Pradesh for their kind supports throughout my research work.

References [1].

[2].

[3].

[4].

[5].

[6].

[7].

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M. Boehlje, “Business Challenges in Commercialization of Agricultural Technology”, International Food and Agribusiness Management Review. 7, 1: 91-104. (2004).

R.B. Singh, P. Kumar and T. Woodhead, “Smallholder farmers in india: food security and agricultural policy", Food and Agriculture Organisation of the United Nations. (2003).

R. Mattigatti and M.N.S. Iyengar, “Role of different agricultural enterprises in agri-business with special reference to sericulture”, Indian J. Seri. 33:163-165. (1995).

R.B. Thapa, and K.B. Shrestha., “Silkworm rearing technology. Paper presented at the workshop at Bhaktapur, Nepal, 21.

(1999).

G.savithri and P.Sujathamma. “Entrepreneurial Opportunities in Sericulture Industry”, International Journal of Engineering, Business and Enterprise Applications, 3, 1:52-56 (2012-2013).

G. Savithri and P. Sujathamma, “Sericulture and Silk in India”, Journal of Agricultural Economics and Sustainable Development. Photon. 103: 160-164. (2014).

K. Prakasam & Dr. G. Ravi “Sericulture – An Ideal Enterprise for Sustainable Income in Erode District of Tamil Nadu”, Language in India www.languageinindia.com. 14:9. (2014).

N. Ravindran, S. Anita, B. Parthipan, S. Elangovan, “Sericulture: A profitable farm venture”, Agricultural Situation in India 18, 3: 23-26. (1993).

Lakshmanan, S., B. Mallikarjuna, H. Jayaram, R. Ganapathy Rao, M. R. Subramanian, R.G. Geeta Devi and R. K. Dutta.

“Economic issues of production of mulberry sericulture in Tamilnadu-Micro-economics study”, Indian J. Seric. 35, 2: 128-131.

(1996).

P. Kumaresan, G. Srinivasa and N.B. Vijaya Prakash, “Productivity and Profitability in Rainfed Sericulture – A Study in the District of Chamaraja Nagar in Karnataka”, Agricultural Economics Research Review. 18: 91-102. (2005).

K.K. Shetty et. al. “Vanya silks of India – Exploring New Horizons”, 21. (2007).

Sakthivel N, Kumaresan P, Balakrishna R, Mohan B, “Economic viability of sericulture in southern Tamil Nadu - A case study.

Agricultural Science Digest”, A Research Journal. 32, 2: 98-104. (2012).

D. Siddappaji, Latha C. M, Ashoka S R, M. G .Basava Raja, Socio-economic Development through Sericulture in Karnataka” IOSR Journal Of Humanities And Social Science19, 10: 24-26. (2014).

S.K. Dewangan, K.R, Sahu, K.V. Achari, S. Soni, “Socio-Economic Empowerment of Tribal Women through Sericulture a Study of Lailunga Block of Raigarh District, Chhattisgarh, India”, International Journal of Business and Management. 6, 12.

(2011).

S.K. Dewangan, Sahu, K. R., & Soni, S. K. Breaking of poverty through sericulture among the tribe-A Socio-Economic study of Dharamjaigarh block of Raigarh Dist, CG, India. Research Journal of Recent Sciences.1, 371-374. (2012).

R. Shukla, “Economics of rainfed sericulture-a study in the district of udaipur in rajasthan, india”, Bangladesh j. Agril. Res. 37, 1: 49-54. (2012).

K. Eswarappa. “Developmental Initiatives and Sericulture in a South Indian Village”, South Asia Research, 31(3), 213-229.

(2012).

C. Singh, and Vasishti, “Resource allocative efficiency of various sizes of farms in Salem district (Tamil Nadu)” Agricultural Economics Review, 7(2):141-145. (1994).

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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 and Development of an Electric Vulcanizing Machine C. Nathan*, N. Jones* and J. Wadai** *Department of Mechanical Engineering, Federal Polytechnic Mubi, Adamawa State, Nigeria. **Department of Mechanical Engineering, Technical Skill Acquisition Center Mubi, Adamawa State, Nigeria. ______________________________________________________________________________________ Abstract: The present method used in vulcanizing tyre tube was studied, and the problems associated with it include the release of carbon monoxide which reacts with hemoglobin in blood, converting it to carboxyhemoglobin which reduces oxygen in the blood. An improved electric vulcanizing machine was designed and developed with temperature regulator (a thermostat), timer switch and heating element connected to the aluminum fixed plate to eliminate the effect of inhaled carbon monoxide. A performance test was carried out on four different vehicles with punctured tubes, which are trucks, cars, motorcycles, and bicycle. The test was performed three times on each vehicle tyre tube and the average time to patch each tube was found to be 12 – 20 minutes for bicycle tubes, 17 – 22 minutes for motor cycle tubes, 18 – 25 minutes for car tubes and 20 – 27 minutes for trucks according to various tube thicknesses. The machine has succeeded in reducing the time used for vulcanizing a tyre tube, improved the efficiency of a vulcanizing machine, and reduced the hazards associated with the present method of vulcanizing.

Key words: Vulcanizing, Timer, Tyre tube, Heating element and Patch. _____________________________________________________________________________________ I. INTRODUCTION Trucks, Cars, Motorcycles, and Bicycle owners sometimes face the problem of a punctured tyre and this is as a result of bad roads, the presences of foreign materials, (sharp object) on our road surfaces and sometimes the use of inferior quality tubes (Clifford, 2003). Vulcanizing is a method of treating crude rubber with sulphur or a sulphur compound so as to make it strong, elastic and resistance to action of solvents and abrasives as well as heat and cold (Derry and trevor, 1990).

If a tyre tube has been punctured, it can be repaired with a patch. The tube is removed from the tyre to find the leak by inflating the tube and submerging in water; bubble will appear when there is a leak, the spot is marked and the tube deflated and dried (Lindenmuth, 2006).

There are two ways to patch a tube that is either by the cold patch method or by the hot patch method. With the cold patch method, the tube is cleaned, dried free from oil and grease. The area around the leak is roughed and covered with vulcanizing cement, which the allowed to dry until it is tacky. The patch is pressed into the place and rolled from the center out with the edge patch –kit can. With the hot patch method, the tube is prepared into the same way as for the cold patch. The tools used by the vulcanizers in the hot patch method include a piston or a hollow cylindrical object (e.g., engine cylinder liner), which is place over the patch; a clamp is then used to hold the piston or cylinder firmly to the tube at point where the patch is placed.

If a hollow cylindrical object is used, a little sand is poured on the patched area and fuel is poured on the sand.

When the fuel is ignited, the head generate will vulcanize the patch. In a place where the piston is used crown of the piston is placed on the patched area and held firmly on the patch with a clamp. A combustible material is then placed inside the piston and ignited, as the material burns heat is generated and conducted to the patch through the piston there by vulcanizing the patch. It is obvious that hot patch method of vulcanizing described above cannot be done in an enclosed area, or inside a workshop because of the carbon monoxide and carbon dioxide gasses which are formed during combusting of fuel. The tools used in this method of vulcanizing are obsolete and it takes more time to vulcanize a tyre tube.

In automobile industry, vulcanizing is the process of cooking or curing the vulcanizing rubber by heating it to a temperature of 120oC to 150oC. A temperature of 128oC is considered best. A layer of 1.5mm for vulcanizing requires 15 to 20minutes with 5 additional minutes for each additional 1.5mm layer.

The vulcanizing tube can be heated by electric power, gas, gasoline, or steam (Francis, 1988 and Thompson, 1992). Most vulcanizing process carried out across the country (Nigeria) today is still using the obsolete tools and equipment and this involves the burning of fuel to generate heat required. This method, apart from being time consuming, it constitute a health hazard such as myocardial ischemia (a lungs disease), accompanied by angina to the person carrying out the vulcanizing process and people within the vicinity where the vulcanizing is being done; it can also lead to a fire hazard as the fuel is highly inflammable (Heinz, 1989).

In view of the above problems, the electric vulcanizing machine was designed and developed. The machine uses electricity as its primary source of power; this electric energy is then converted to heat energy which is required for the heating of patch on the tyre. The vulcanizing temperature produced by the heating element will be

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conducted by the plate (housing) of the heating element to the required level automatically with the aid of a thermostat.

II. DESIGN OF A VULCANIZING MACHINE The machine consists of a heating element that converts electrical energy into heat energy, a thermostat to control the power to the machine at a pre-determined period of time. The device is constructed as a heat consumer and is fitted with an automatic temperature regulator connected to a heating element. These are attached to the upper plate of the machine. A two pole level switch is fitted in the lower part of the machine together with the timer. The necessary pressure on the vulcanizing patch of the tyre tube will be assigned through a pressure plate fastened to a reciprocating motion of a rod by means of gear mechanism controlled by the lever.

A. Design Analysis of the Heating Element Normally, wires of circular cross section or rectangular conducting ribbons are used as heating elements. Under steady state conduction, a heating dissipates as much heat from its surface as it receives the power from the electric supply (Theraja, 2008).

B. Data used Diameter of the heating element d = 10mm; Length of heating element l = 300mm; Voltage v = 240V; Current I = 6.7A. ; Resistance R = 36Ω (Cartridge and Insertion Heaters) Now,

v2 P ............................................................1 , R and

RP Where

A

 4

l ........................................................................2 A

d 2  17.66m 2 . Substituting for A in equation 2

R

4 Pl ...............................................................3 d 2

Substituting for R in equation 3, into equation 2;

P Where P  1.6 KW

d 2 v 2 4l

................................................................4

l v 2  ..............................................................5 4 d2

Where R = resistance, d = diameter of wire, ℓ = resistivity, A = area, v = voltage required, l = length of wire.

The total surface area of the wire is given by (πd)xl. If H is the heat dissipated per second per unit surface of the wire, then heat radiated per second is given by (Theraja, 1999).

P  d lH ................................................................6

From equation 5 and 6

P

d 2 v 2 4l

 (d )lH . Hence,

d 4H  2 ......................................................................7 l2 v If `P´ is the power input it receives and it is the heat dissipated by Conduction then P = H under steady state condition. From Stefan’s law of radiation (Rogers, 1992), heat by a hot body is given by:

H  (T 4  TC ) .....................................................8 4

Where P  H  1.6 KW And T = Temperature of hot radiating material in kelvin, T 2 = Temperature of Surrounding in kelvin, ℓ = Emissivity and δ = Stefan’s radiating constant.

B. Selection of Heating Element Dry temperature up to 40oC has little effect on rubber, but at a temperature of 181 oC 240oC, rubber begins to melt and becomes sticky. It becomes completely carbonized at higher temperature. The minimum and maximum

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vulcanizing temperatures for tyre are 130oC and 150oC (Eugene, 1996). In selecting the heating element for electric vulcanizing machine, the above temperature limits were considered. From table 1, the element with the least operating temperature is constantan, which was selected and has an operating temperature of 400oC. But the maximum vulcanizing temperature required is 150 oC; and in order to regulate the temperature a thermostat was connected to the element and adjusted to 150oC maximum.

Table 1: Composition and Operating temperatures of elements Element Constantan

Composition 45% Nickel, and 55% Copper

Nichrome

50% Nickel, 20% Chromium and 30% Copper

1150oC

70% Iron, 25% Chromium, and 5% Alluminium

1200oC

Kautha

Operating temperature 400oC

1450oC

Silicon carbide

Source: Thompson (1992).

C. Upper Plate Design (Heating Element Housing) The upper plate is a rectangular block of aluminium which was casted to size of 150mm by 220mm. the upper plate houses the heating element and the thermostat. The plate conducts the temperature of the heating body to the tyre tube being patched. Current flowing through the conducting element is derived from the power spent in the conductor (Eugene, 2001)

P  IV (W )...............................................................9 V  IR

By Ohm´s law, and substituting for V in equation (9) P  I 2 R(W )...............................................................10

In the electric vulcanizing machine, heat is transformed from the heating element to the upper plate by conduction. The greater the material thickness, the less the heat transfer. Heat transfer is therefore inversely proportional to thickness.

Assume the upper plate has a thickness x, and heat transfer Area A let the temperature of its faces be T 1 and T2 respectively, and an elemental thin slice within the material of thickness δx, and the temperature full across the elemental slice = δt (Jagar et´al, 1986). Then

t ...............................................................11 x t Q  kA ..............................................................12 x Q A

Where k = thermal heat capacitance of Aluminium.

Assuming the temperature fall to be linear through the material thickness = x, equation 12 becomes

Q  kA

(T2  T1 ) ...............................................................13 x

Q  kA

(T1  T2 ) ...............................................................14 x

becomes Heat transfer Q

D. Thermostat The thermostat is used to control the electric vulcanizing machine so that a defined temperature is maintained. It uses the principle of bimetallic trip to bend when a defined temperature limit is exceeded by switching off itself and returns to normal shape when lower temperature has been reached. Time switch is also incorporated to cut out power supply to the machine after a specified period of time.

E. Base Plate The base plate of the machine is made up of a mild steel of 2mm thickness. The plate is welded into a rectangular shape of 360mm by 250mm. The base plate supports the entire weight of the vulcanizing machine.

F. The Post The post is part of the machine on which the heating element is mounted. It is made up of a rectangular pipe made up of mild steel which is welded to the base plate of the machine. The area of the rectangular pipe is given by:

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A= lxb.

III. GEAR DESIGN Gear tooth design involves primarily the determination of the proper pitch and face width for adequate strength, durability, and economy of manufacture.

Guidelines for selection of minimum number of teeth of pinion are given in table 2 below.

Guideline for selection of the number of teeth on pinion Types of Service

Minimum Number of Teeth

Heavy duty and high speed

16

Medium speed

12

Light duty and Low speed

10

Source: (Gitin, 1989) The number of teeth of the pinion and the gear are to be chosen that a minimum value of 1:1 for contact ratio is assumed. For fast moving sets, it should be greater than 1:5 transmission ration and should not be a whole number. For the sum of the number or difference of teeth of the pinion and gear the following rules hold.

Np+Ng >= 24 for external gearing; where Np = number of teeth of pinion, Ng = number of teeth of gear.

And Zg – Zp>=10 for internal gearing.

Proportion of Standard Gear Teeth.

Characteristics

14.5o composite

14.5o full depth involute

20o full depth involute

20o stub involute

Addendum

M

m

m

0.8m

Minimum dedendum

1.157m

1.157m

1.157m

m

Whole depth

2.157m

2.157m

2.157m

1.8m

Clearance

0.157m

0.157m

0.157m

0.2m

Source: Joseph (2007).

The type of gears for the electric vulcanizing machine is the involute rack and pinion gear system. The involute has the same proportions as gears but the pitch circle diameter of the rack is infinitely large. The pressure angle for the involute teeth has been standardized at 14.5o or 20o. Using and angle of 14.5o which can cause interference and gives the teeth wider root (Gitin, 1989). The number of teeth for the pinion is selected from table 3 since the gear is for light duty and low speed; a minimum of 10 teeth is required. To calculate for the module, the following formula is used.

OD  m(T  2)

Where OD = outside diameter, m = module and T = number of teeth.

The pitch circle diameter of the pinion is given by

Center dis tan ce  0.5( D  d ) Pitch circle dis tan ce Module  Number of teeth

c

D N ,

Sum of number of teeth of pinion and wheel =Ng+Np>=24 Angular velocity = T/t. where T = number of teeth on the rack, t = number of teeth on the pinion.

Circular pitch = πm, tooth thickness = (circular pitch)/2 Base circle = pitch circle diameter x pressure angle and the angle is 20 o Addendum = Module m Dedendum = 1.157m Whole depth =2.157m Clearance = 0.157m IV. PERFORMANCE TEST The electric vulcanizing machine was tested to ensure that its working principle and its efficiency are in conformity with the reason of designing and fabrication of the machine. A punctured tyre tube was prepared and placed on the moveable plate of the machine; this plate was then raised so that it presses the tyre tube to be patched against the heating element. The machine was switched on, the temperature regulator set to vulcanizing temperature of 128oC, and the temperature timer set for 15minutes (being the minimum vulcanizing temperature for tube), and then repeated for 20minutes (being the maximum temperature for tube vulcanizing). The patched tyre was removed and tested for leakage; which was done by inflating the repaired tube and immersed in a solution of detergent with water. No air bubbles were seen, indicating no leakage on the tube. Different thicknesses of tubes were further tested to determine the temperatures for patching each tube thicknesses. The thicknesses are: 0.75, 1.5, 2.0, 2.5, 3.0, and 3.5 respectively.

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V. Temperature (oC) 128 128 128 128 128 128

RESULT AND DISCUSSION Table 1: Vulcanizing tests results.

Tube thickness (mm) 0.75 1.5 2.0 2.5 3.0 3.5

Time range (min) 12 – 17 15 – 20 17 - 22 18 – 23 20 – 25 22 – 27

From the table 1 above, it is noticeable that the time for vulcanizing varies with tube thickness. Nevertheless, the result shows that the electric vulcanizing machine provides a faster means, cleaner and faster way of vulcanizing tubes. The test also revealed that the time taken to patch a punctured tube depends on the voltage supplied at the time of the repair. In the case of the test carried above, the available voltage was between 220V and 240V which is mostly the normal temperature of appliances in Nigeria. The time to patch 0.75 tube thickness was found to be 12-17minutes.

A. Thermal Efficiency To determine the thermal efficiency of the vulcanizing machine, the heat lost must first be determined. The is the quantity of quantity of heat used in heating a body is given by Q  m  c  t (Rayner, 1987); where Q heat in Joules, m is the mass in kilogram, and specific heat in Joules per kilogram and (t) is the temperature rise in degrees Celsius.

Let the temperature drop of the component be toC, now the temperature drop = (128- t)oC; and temperature rise = (t-25)oC; where 128oC and 25oC are the room temperature of the pressing plate (Wikipedia) Therefore Heat lost by heating element housing = Q = (mcθ)rise =(mcθ)drop =1.8x921(128 – t)oC = 1.9x460(t – 25)oC Now the thermal efficiency = Eff.

Eff 

Energy input  Heatlost  100% Energy input

B. Operational Principles of the Electric Vulcanizing Machine When the device is connected to electricity and switched on, a circuit is made immediately; and flow of electric current is allowed from the electric source to the heating element. The heating element then converts the electrical energy into heat energy. This is done when the electric current encountered resistance. A thermostat is employed in order to automatically control or maintain the temperature to a desired degree while the function of the timer is to stop the machine at a stipulated period of time once the element get heated, the prepared tube is placed on the pressing plate and adjusted linearly with the aid of bevel gear mechanism to pass the tube to the heating element (Aluminium Housing) when the tube is vulcanizing. The temperature required for the vulcanizing is ideally 128oC for a tube 1.5mm thickness in 14 – 20minutes and with each additional 5 minutes with 1.5mm thick addition.

VI. CONCLUSION Electric vulcanizing machine was designed and developed. It drastically reduces the time spent in vulcanizing tyre tubes thereby providing an alternative way of repairing tyre tubes. The machine produced gave a new technology in vulcanizing tyre tubes and reduces the various hazards associated with local methods of vulcanizing.

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

Cartridge / Insertion Heaters: https://www.watlow.com/downloads/en/catalogs/cartridge.pdf.

Clifford, M. (2003): automotive service Technology; 3rd Edition, McGraw-Hill Book Company, Australia.

Derry, T. and Trevor, T.W. (1990): A short history, Technology from Earlier times to A.D 1900, Oxford, Clarendon press, England.

Eugene, A.A. and Teodore, B (2001): Marks standard Handbook for mechanical Engineers, 10th Edition, McGraw-Hill Book Company, Singapore.

Francis, T. G. (1988): Electrical installation work, 6 th Edition, Addition wisley Longman limited, England.

Gitin, M.M (1989): Hand book on gear design. 2nd Edition, Fata, McGraw-Hill Book Company, India.

Heinz, H. (1989): Vehicle and Engine Technology. Edward and Arnold publications, England.

Hugers, M. (1992): Engineering Thermodynamics: Work and Heat transfer. 4 th Edition, Longman, Scientific and Technical limited, England.

Jargar, B., Bon Mardion, G., Claude, G., and Desmaris, M. (1986). Heat Transfer in He I for industrially manufactured aluminium plate heat exchangers. Cryogenics, Vol. 26, issue 4, pp 222 – 225. Elsevier publishers.

Joseph, S., Charles, R. M., and Charles M. (2007). Mechanical Engineering Design. 6th Edition, McGraw-Hill Company, New York.

Lindenmuth, B.E. (2006): An Overview of Tire Technology. National Highway Traffic Safety Administration, U.S. Department of Transport.

Rayner, J. (1987): Basic Engineering Thermodynamics; 4th Edition, Longman, Scientific and Technical limited, England.

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Theraja, B.L. and Theraja A. K. (2009): A test book of electrical technology. S. Chand and company limited, Ravn nagar, New Delhi.

Thompson F. G. (1992): Electrical Installation Technology, vol. 3; Longman Scientific and Technical limited, England.

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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 Logistics Support for Agro business in context of the Supply Chain of Perishables 1

Md. Kamruzzaman Assistant Professor, Department of Business Administration, Pabna University of Science and Technology, Pabna-6600, BANGLADESH 2 Md. Amirul Islam 2 Assistant Professor, Department of Business Administration, Pabna University of Science and Technology, Pabna-6600, BANGLADESH 1

_________________________________________________________________________________________________________

Abstract: Bangladesh economy is primarily dependent on agriculture. About 84% 0f the total population lives in the rural areas and is directly or indirectly engaged in a wide range of agricultural activities. Agriculture contributes about 20.29% to the country’s GDP (23%) About 43.6% of the labor force is employed in agriculture with about 57% being employed in the crop sector.

Bangladesh has resource endowments to develop agro-based industries. It has rich alluvial soil, a year-round frost-free environment, available water and an abundance of cheap labor. Increased cultivation of vegetables, spices and tropical fruits now grown in Bangladesh could supply raw materials to local agribusiness for both domestic and export markets. Progressive agricultural practices have improved marketing techniques. Modern processing facilities have raised the quality of agribusiness and expanded production levels significantly.

Priority agro products are canned juices, fruits, vegetables, and dairy and poultry products.

Keywords: Agro food product, Supply chain management, logistics of the supply chain, Inventory management. ______________________________________________________________________________________ I. Objectives of the Study Objective of this study is to develop a logistics activity mix which will result in the highest possible return on investment over time. Keeping the imperatives in view, the following proponents have been embedded in this paper:  Snap of the main supply chains for agro food products and discussed the important features in Context of consolidation and distribution ;  Status of harvest handling and cold storage logistics of the supply chain;  Logistics management of the supply chains;  Main causes of success of agro food products supply chains;  Salient issues and impediments in enhancing performance of consolidation and distribution system for agro food products;  To identify findings and observations of agro food products supply chains;  Suggestions.

The key feature attributes of the supply chains are: The Supply Chain for Agro-Food Products in Bangladesh

Inter-business Co-ordination

Figure 1: A Model of Supply Chain Management

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A.    B.       C.      D.   

Maintain customer service standards: Supply chains determine customer needs and wants for logistics customer service; Determine customer response to service; Set customer service levels.

Transportation: Supply chains select mode and transport service; Consolidate freight chart; Select carrier route; Prepare vehicle scheduling; Choice appropriate equipment; Process claims document Inventory management: Supply chains formulate raw materials and finished goods stocking policies; Prepare short-term sales forecasting; Determine product mix at stocking points; Determine number, size, and location of stocking points; Formulate just-in-time, push, and pull strategies; Information flows and order processing: Supply chains prepare sales order-inventory interface procedures; Use appropriate order information transmittal methods Follow ordering rules

II. Post Harvest Handling and Cold storage Logistics of the Supply Chain Post harvest handling and cold storage logistics are a collection of functional activities (transportation, inventory control, etc) which are repeated many times throughout the channel through which raw materials are converted into finished products and consumer value is added. Because raw material sources, plants, and selling points are not typically located at the same places and the channel represents a sequence of manufacturing steps, logistics activities recur many times before a product arrives in the marketplace. The following figure exhibits post harvest handling and cold storage logistics

Warehouse/Cold storage

Processing Unit

Warehouse/Cold storage

Growing field /Cluster

Figure 2: Graphic view of Post-harvest handling and Cold-Storage logistics A single firm doing business of agro food products in Bangladesh is not generally able to control its entire product flow channel from raw material source to points of the final consumption, although this is an emerging opportunity. For practical purposes, the business logistics for the individual firm has a narrower scope.

In Bangladesh physical supply channels agro food products are impeded by time and space gap between a firm’s immediate material sources and its processing points. Similarly, the physical distribution channels have also the time and space gap between the firms’ processing points and its customers. Due to the similarities in the activities between the two channels, physical supply (more commonly referred to as materials management) and

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physical distribution comprise those activities that are integrated into business logistics. Although it is easy to think of logistics as managing the flow of products from the points of raw material acquisition to end customers, for many firms there is a reverse logistics channel that are managed as well. The life of a product, from a logistics viewpoint, does not end with delivery to the customer. Products become damaged, or nonfunctioning and are returned to their source points for disposition. Packaging materials may be returned to the shipper due to environmental regulations or because it makes good economic sense to reuse them. The reverse logistics channels are utilized by the supply chains. The supply chains terminate with the final disposition of a product.

The reverse channels are considered to be within the scope of logistics planning and control.

III.

Successful Consolidation and Distribution System of Perishables

Customer

Figure 3: Logistics activities of Agro Products The main causes of success are:  Agribusiness firms set customer service standards  It determines customer needs, wants concerning the products and sets customer service levels.  For transportation agro firms select appropriate mode and transport service ;  It consolidates freight chart and select carrier route;  Prepare vehicle scheduling;  Choice appropriate equipment;  Process claims document For Inventory management  Firm formulates raw materials and finished goods stocking policies;  It prepares short-term sales forecasting;  Determines product mix at stocking points;  Determine number, size, and location of stocking points;  Formulate just-in-time, push, and pull strategies; For order processing  Business firms prepare sales order-inventory interface procedures;  Use appropriate order information transmittal methods;  Follow ordering rules; For storage  Agro firms determines space;  It prepare stock layout and dock design;  Do warehouse configuration and accordingly  Complete stock placemen.  For materials handling these firms appropriate equipments;  It follows equipment replacement policies, order-picking procedures and stock storage and retrieval.

For purchasing/ procuring raw materials  the Group selects supply source ;  It prepares purchase timing and quantities.

For its products protective packaging are designed for handling, storage and protection from loss and damage agro firms specifies aggregate quantities ,sequence and time of production ,schedule supplies for production/operations .For information maintenance Information collection, storage and manipulation, data analysis and control procedures are followed.

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IV. Market responses and adjustments- Lesson Attributes Major marketing themes of the agro products are:  To focus on transactions to building long-term, profitable customer relationships. Companies focus on their most profitable customers, products, and channels.  The company offer to deliver a constantly needed product on a regular basis at a lower price per unit to capture the customer’s business for a longer period.  For gaining market share the company emphasizes on building customer share by offering a large variety of goods to existing customers.  They train the employees in cross-selling and up-selling.  The company’s marketing is facilitated by the proliferation of special-interest magazines, TV channels, and Internet, news groups.  For creating customer database, the company collects sales data about individual customer’s purchases, preferences, and demographics and profitability.  Companies apply data mining techniques to discover new segments and trends hidden in the data.  The company heavy relies on communication tools such as advertising or sales force to blending several tools to deliver a consistent brand image to customers at every brand contact.  The company thinks intermediaries as customer, treating them as partners in delivering value to final customers.

V. Salient Issues and Impediments SME Agro food manufacturers observe that the customers increasingly expect higher quality and service and some customization. The customers perceive fewer real product differences and show less brand loyalty. They obtain extensive product information from the Internet and other sources, which permit them to shop more intelligently. They are showing greater price sensitivity in their search for value.

SME manufacturers in Bangladesh are facing intense competition from domestic and foreign brands, which is resulting in rising promotion costs and shirking profit margins. They are being further buffeted by powerful retailers who command limited shelf space and are putting out their own store brands in competition with national brands. For this store-based retailers are suffering. Small retailers are succumbing to the growing power of giant retailers and `category killers. Store-based from catalog houses, direct-mail firms, newspaper, magazine, and TV direct-to-customer ads home shopping TV and e-commerce on the Internet. As a result, they are experiencing shrinking margins. In response, entrepreneurial retailers are building entertainment into stores with coffee bars, lectures, demonstrations, and performances. They are marketing an experience rather than a product assortment.

VI. Major findings and observation A. Sector Performance Production of the food items in 2011-2012 and 2012-2013(up to February, 2013) exhibits the following: Agro products

Production 2011-2012

2012-2013(up to February ,2013)

Food grains

34.11 million metric ton

37.04 million metric ton

Milk

2.37 million ton

1.89 million ton

Meat

1.26 million ton

1.27million

Eggs

5742.40 million pieces

4211.00 million pieces

Fish

2.90 million metric ton

3.10 million metric ton

Source: Bangladesh Economic Review-2013.

Business logistics is a relativity new field of integrated management study in comparison with the traditional fields of finance, marketing, and production. It is that part of the supply chain process which plans, implements, and controls the efficient, effective flow and storage of goods, services, and related information from the point of origin to the point of consumption in order to meet customers’ requirement. In business, logistics support ensures to get the right goods or services to the right place at the right time and in desired condition while making the greatest contribution to the firm.

Govt. sources and Bangladesh Economic Review-2013 articulate that there are about 0.80 million cottages, small and medium enterprises in Bangladesh of which about 30 percent are agro food industries. In 2011-2012, cottage, small, medium and large enterprises produced goods worth Tk.6, 25,707 million while these enterprises in 2012-2013 (up to February, 2013) produced goods of Tk. 6, 85,218 million.

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The main supply chains for agro products in Bangladesh have been snapped in the following paradigm: Name of the of Industries

1.

2.

3.

4.

5.

Name of the Products

Mondal Agro Industries Limited

Yellow Potato, Used Pet Bottle, Molasses

Golden Fisheries & Agro Industries

Fresh Vegetable, Fresh Fish.

Intercon Agro Limited Chowdhury Agro Industries

Agro Based products, Organic Fertilizer & Organic Pesticide, Fresh Vegetable.

Food, Fertilizer.

Surovi Agro Industries Ltd

Hybrid Seed, Gypsum Powder.

Northern Agro Services Ltd (NASL)

Organic Fertilizer, Food

BEXIMCO Bangladesh

Fresh Garments, garments stock lot, Finish Leather & goods.

PARTEX Group( Food group)

Spices, Beverages.

BD Foods Ltd.

Spices, Food.

Shajib Corporation

Spices, Beverages.

ACI Group

Food, Beverage.

Ship N Shore Shipping & Trading

Handy Craft, Garments, Agro Products

BRAC

Food, Milk products.

VII. Suggestions We should follow the model of supply chain management in a systematic way; Physical supply and physical distribution should be well Managed; Customers demand should be considered; Transportation cost, Inventory cost, ordering cost and packaging cost should be controlled; Management should ensure logistic support for the supply chain of perishable item.

VIII. Conclusion Transportation in the supply chain system is essential because no modern firm can operate without providing for the movement of its raw materials or its finished products. This importance is underscored by the financial strains placed on many firms by such disasters as a national railroad strike or independent truckers’ refusal to move goods because of rate disputes. In these circumstances, markets cannot be served, and products back up in the logistics pipeline to deteriorate or become obsolete.  Inventories are also essential to logistics management because it is usually not possible or practical to provide instant production or ensure delivery times to customers. They serve as buffers between supply and demand so that needed product availability may be maintained for customers while providing flexibility for production and logistics in seeking efficient methods for manufacture and distribution of the product.  Order processing is the final key activity. Its costs usually are minor compared to transportation or inventory maintenance costs. Nevertheless, order processing is an important element in the total time that it takes for a customer to receive goods or services. It is the activity triggering product movement and service delivery. Although support activities may be as critical as the key activities in any particular circumstance, they are considered here as contributing to the logistics mission. In addition, one or more of the support activities may not be a part of the logistics activity mix for every firm. For example, products such as  Protective packaging is a support activity of transportation and inventory maintenance as well as of warehousing and materials handling because it contributes to the efficiency with which these other activities are carried out. Purchasing and product scheduling often may be considered more a concern of production than of logistics. However, they also affect the overall logistics effort, and specifically they affect the efficiency of transportation and inventory management. Finally, information maintenance supports all other logistics activities in that it provides the needed information for planning and control.

In group discussions with the agro food business firms the following benefit attributes were derived:  Error rates of less than one per 1,000 orders;  Logistics costs of well under 5 percent of sales;  Finished goods inventory turnover of 20 or more times per year  Total order cycle time is five working days;  Transportation cost is one percent of sales revenue or less. 

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References [1].

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[3].

[4].

[5].

[6].

[7].

[8].

[9].

[10].

[11].

[12].

[13].

[14].

[15].

[16].

[17].

Bangladesh Economic Review-2013; SME Report-2013; Profiles of the Agro food Industries Azim. Syed, Market Strategy and Agro business Development (2009); Ahmed R, Supply Chain Management and SMEs (2010).

Andersen, O. and A. Buvik (2001) Inter-firm co-ordination: international versus domestic buyer seller relationship. Omega, the International Journal of Management Science 29: 207-219.

Boehlje, M., L. Schrader and J. Akridgel (1998) Observations on formation of food supply chains. In: Proceedings of the third international conference on chain management in the agribusiness and the food industry edited by G. W. Ziggers, J. H. Trienekens and P. J. P. Zrbier. Wageningen Buurma, J. S. et al. (2001) Developing countries and products affected by setting new maximum residue limits (MRLs) of pesticides in the EU. The Hague: Agricultural Economics Research institute (LEI). Cok, M. L., T. Reardon, C. Barrett and Joyce Cacho (2001) Agro industrialization in emerging markets: overview and strategic context. International Food and Agribusiness Management Review 2 (3/4): 277-288.

Farina, E.M.M.Q., and T. Reardon (2000) Agrifood Grades and Standards in the ExtendedMercosur: Their Role in Changing Agrifood System, American Journal of Agricultural Economics, 82(5): 1170-1176.

Grievelink, J. W., L. Josten, and C. Valk (2001) State of the art in food: the changing face of the worldwide food industry.

Amsterdam: Elsevier.

Gaisford, J. D. and W. A. Kerr (2001) Economic analysis for international trade negotiations: theWTO and agricultural trade.

Northamptom: Edward Elgar.

Handfield, R. B. and E. L. Nichols (1999) Introduction to supply chain management. N.J.:Prentice Hall.

Itharattana, K. (1996) Market Prospects for Upland Crops in Thailand, Working Paper 21, CGPRT Center, Bogor.

Jaffee, S. M. (1994) Contract farming in the shadow of competitive markets: the experience of Kenyan horticulture. In: Living under contract, contract farming and agrarian transformation in sub-Sahara Africa edited by P. D. Little and M. J. Watts.

Wisconsin: University of Wisconsin Press.

Kurt Salmon Associates (1993) Efficient Consumer Response: enhancing consumer value in the grocery industry, Washington DC: Food Marketing Institute.

Lambert, D. M. and M. C. Cooper (2000) Issues in supply chain management. Industrial Marketing Management 29: 65-83.

Lazzarini, S. G., F. R. Chaddad and M. L. Cook (2001) Integrating supply chain and network analyses: the study of netchains.

Chain and Network Science, 1 (1): 7-22.

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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 STUDY OF ONLINE SHOPPING CONSUMER BEHAVIOUR IN CHENNAI G.R.Shalini, Research Scholar, Vels University, Pallavaram, Chennai, Tamil Nadu, India.

K.S.HemaMalini, Associate Professor, Vels University, Pallavaram, Chennai, Tamil Nadu, India

___________________________________________________________________________ ABSTRACT: The purpose of this study is to understand and evaluate the consumer behavior towards online shopping specially done in case of Chennai city. The source of data is collected is through primary data and also collected structured questionnaire. The study finally concludes that in Chennai city online shopping is increasing tremendously. The factors which affect the consumer buying behaviour are preference , risk and frequency of buying of online consumer buyers.

KEYWORDS: Online shopping, Consumer buying behaviour, Factors affecting buying behaviour. ________________________________________________________________________________________ I. INTRODUCTION Online shopping behaviour refers to the process of purchasing products or services via the Internet. The process consists of five steps similar to those associated with traditional shopping behaviour (Liang and Lai 2000). consumers use internet for different purposes like searching product features, prices or reviews, selecting the products through online, placing order and making payments and getting delivered that products by different means (Sinha, 2010). Studying the factors affecting online shopping behaviour of a consumer is one of the most important research in e-commerce during these decades ( Mohammadhossein Moshref javadi, 2012). The research or case studies of online consumer buying behaviour is important because it helps to know about consumers demands, it helps to understand and analyze that when consumers buy products online ?and who buy products online ? and how consumers mindset for purchasing the products online ? I think the whole concept of online shopping has altered in terms of consumer’s purchasing or buying behaviours and the success of E-tailers is depending upon its quality, its branding image, its uniqueness and its popularity etc( Prasanth Singh, 2014).

Over the past few years, online shopping has increased percentage of online buyer's in Chennai.

II. RESEARCH OBJECTIVE  To understand the behaviour of the consumer in online shopping.  To evaluate the factors affecting online shopping behaviour.  To create an awareness about online shopping and make people interested in internet shopping.  To give suggestion for the consumer about online shopping.  III. RESEARCH METHODOLOGY The nature of this study is exploratory and descriptive because both primary and secondary data have been used.

Primary data is collected from respondents and secondary data is collected from journals, books and websites.

IV. SAMPLING TECHNIQUE In this study convenience sampling method is used. The source of the sample is limited to Chennai city. A structured questionnaire was used as the research instrument for this study.This structured questionnaire was prepared on the basis of objectives of the study.

V. SAMPLE SIZE The sample size we take was 30 consumers of online shoppers and the study was conducted in Chennai city. In case of sample size 10 members were male and 20 members were female and the age group is between 18 above 45.

VI. LIMITATION OF THE STUDY This study is limited to Chennai city and sample size. A time constraint is also one of the limitations for this study.

VII. REVIEW OF LITERATURE Prasanthsingh (2012) stated that online shopping is the new trend. The new online shopping concept is great example of the revolution in India. He concluded that online shopping has altered in terms of consumers purchasing or buying behaviour and also success of online shopping is based on the popularity, branding image and unique policies. koufaris (2002) stated that there are different factors which come from information systems (technology acceptance model), marketing (consumer behaviour ) and psychology (flow and environmental psychology). Hermes (2000) states that 72 percent of online consumers revealed that customer service is a major

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factor in online shopping satisfaction. if the customer service is not there customer will think that the organization is trying to hide something from them and not intending to solve the customers problem.

Mohammad Hossein (2012) states that financial risks and non-delivery risk negatively affect attitude toward online shopping. He also states that domain specific innovativeness and subjective norms, attitude towards online shopping positively affect online shopping behaviour.

Data analysis and interpretation Table I: Websites already visited Particulars

Frequency

Percentage

Amazon Snap deal

4 6

13.33 20

Flip cart

9

30

EBay Others

7 4

23.33 13.33

Total

30

100

Source: Primary Data Interpretation: From the above table it is observed that out of 30 respondents 30% of respondents use flip cart for online shopping, 23.33% use EBay, 20% use snap deal, 13.33% use Amazon and 13.33% use other websites for online shopping.

Table II: Consumers belief in using websites Particulars Security

Frequency 20

Percentage (%) 67

Trust

7

23

Privacy

3

10

Total

30

100

Source: Primary Data Interpretation: From the above table it is observed that out of 30 respondents 67% of consumers believe security is most important for website user, 23% believe in trust and 10% believe in privacy for website user.

Table III: Preferred method for online shopping Particulars

Frequency

Percentage (%)

E-tailing

15

50

Re-tailing

15

50

Total

30

100

Source: Primary Data Interpretation: From the above table it is observed that out of 30 respondents 50% prefer e-tailing and 50% prefer re-tailing for online shopping.

Table 1V: Best payment method for online shopping Particulars

Frequency

Percentage (%)

Cheque

-

-

Debit or credit card

2

7

Cash(sent via mail)

-

-

Postal draft

-

-

Cash on delivery

28

93

Total

30

100

Source: Primary Data Interpretation: From the above table it is observed that out of 30 respondents most of the respondents (i.e) 93.33% pay cash on delivery for online purchasing and 6.66% use debit or credit card for online purchasing.

Table V: Frequently purchased item while online shopping Particulars Apparel Electronic appliances Home and kitchen appliances Accessories Books Toys Vouchers Magazines Others

Percentage (%) 50 23 37 27 63 13 3 3 7

Source: Primary Data

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Interpretation: From the above table it is observed that out of 30 respondents 63% purchase books through online shopping, 15% purchase apparel through online shopping, 11% purchase home and kitchen appliances , 27% purchase accessories, 23% purchase electronic appliances, 13% purchase toys,7% purchase others, 3% purchase magazines and 3% purchase vouchers through online shopping.

Table VI: Best marketing approach to advertise online shopping Particulars

Frequency

Percentage (%)

Bill boards

14

47

Magazines

2

7

News paper

2

7

Search engine

11

36

Others

1

3

Total

30

100

Source: Primary Data Interpretation: From the above table it is observed that out of 30 respondents 47% choose billboards to advertise online marketing, 36% choose search engines, 7% choose magazines, 7% choose newspaper and 3% choose other methods to advertise online shopping.

Table VII: Preference of online shopping Preference

Strongly agree (%)

Agree (%)

Disagree (%)

Strongly disagree (%)

67

Neither agree Nor disagree (%) 10

Attractive Prices

23

-

-

Reliability

3

43

54

-

-

33

40

10

17

-

Popularity

20

43

30

7

-

Convenience

30

47

13

10

-

Cost

30

46

17

7

-

Time Efficiency

33

47

13

7

-

Information

13

50

23

14

-

13

44

40

3

-

Mass variety Products

Availability product services

of

of and

25 20 15

Strongly agree Agree Neither agree

10 5

Disagree Strongly disagree

0

Source: Primary Data Interpretation : From the above table it is observed that out of 30 respondents, 67% agree its preference in attractive prices for online shopping, 54% neither agree nor disagree its preference for its reliability, 40% agree its preference in mass variety and products, 43% agree its preference in popularity, 47% agree its preference in convenience, 46% agree its preference in cost, 47% agree its preference in time efficiency, 50% agree its preference in information, 44% agree its preference in availability of product and services.

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Table VIII: Buyers online shopping frequency Frequency

Strongly agree (%)

Daily

-

Weekly

Agree (%)

Neither agree nor disagree (%)

Disagree (%)

Strongly disagree (%)

-

10

60

30

-

-

14

63

23

Monthly

10

33

20

37

-

Annually

57

30

13

-

-

20 Frequency

15

Daily

10

Weekly Monthly

5

Annually

0 Strongly agree

Agree

Neither agree nor disagree

Disagree

Strongly disagree

Source: Primary Data Interpretation: From the above table it is observed that out of 30 respondents 60% ,63% and 37% disagree in purchasing through online daily, weekly and monthly respectively, 57% strongly agree in purchasing through online annually.

TableIX: Risk of online shopping Risk Risk of credit cards transactions

Strongly agree(%) 33

33

Neitheragree disagree (%) 17

Disagree (%) 17

Strongly disagree(%) -

Risk of identity theft

20

30

Risk of monetary transactions

27

30

23

20

7

27

16

-

Risk of internet hackers

30

27

13

13

17

Wastage of money Money deducted without booking Personal information is not safe Correct product may not get

20

13

40

17

10

20 10 37

30 37 20

33 20 43

17 20 -

13 -

Quality of product may not good Malfunction of product may occur

20 17

43 40

27 40

3

10 -

14 12 10 8 6 4 2 0

Agree (%)

nor

Strongly agree Agree Neither agree nor disagree Disagree Strongly disagree

Source: Primary Data Interpretation: From the above tables it is observed that out of 30 respondents 33% strongly agree that there is risk in credit card transaction for online shopping, 30% agree there is risk in identity theft, 33% agree in there is risk of monetary transactions, 30% strongly agree there is risk in internet hackers, 40% neither agree nor

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disagree in risk in wastage of money, 33% neither agree nor disagree there is risk in money deduction without booking, 37% agree there is risk in safety of personal information , 43% neither agree nor disagree there is risk in correct product may not get, 43% agree there is risk in quality of goods,40% agree in risk of malfunction of products.

Table X: Consumers rating on online shopping Particulars

Frequency

Percentage

Good

14

47

Very good

12

40

Excellent

4

13

Bad

-

-

Total

30

100

Source: Primary Data Interpretation: From the above table it is observed that out of 30 respondents 47% rate online shopping as good, 40% rate as very good and 13% rate as excellent for online shopping.

VIII. Findings From the above study it is found that:  The number of users of online shopping is increasing day by day. Mostly the age group from 18-25 are the frequent user's of online shopping and most of the females are very much interested in online shopping.  50% of consumers prefer online shopping (i.e) e-tailing and the rest prefer re-tailing.  30% of the online shoppers prefer flip cart for online shopping and 23.33% followed by e-bay.  67% of online buyers think security is most important for website users and 93% use cash on delivery for online shopping.  63% of online shoppers buy books through online shopping and followed by apparels.  47% prefer billboards for advertising online shopping and 36% followed by newspaper.  Mainly 67% of consumers prefer online shopping for its preference followed by information provided about the products, convenience and time efficiency.  It is also found that 57% of consumers choose online shopping annually rather than monthly and weekly.  Consumers also think that there is risk in online shopping. 43% of consumers think that the quality of the product which they buy through online may not be good, 40% of consumers think that wastage of money and also malfunction of the product may occur in cases.

IX. Conclusion In Chennai city most of the people select flip cart for online shopping and also online shopping had become a trend in this generation. Convenience and time efficiency are the dominating factors influencing online shopping consumers. Though there is financial risk and non-delivery of product risk 73% of the respondents prefer online shopping. This risk may not affect the buying behavior of the online shoppers.

References [1] [2] [3] [4] [5]

Prashant Singh "Consumer’s Buying Behaviour Towards Online Shopping A Case Study Of Flipkart.Com User’s In Lucknow City "-Volume III, February’14.

Mohammad Hossein Moshref Javadi "An Analysis of Factors Affecting onOnline Shopping Behavior of Consumers"International Journal of Marketing Studies; Vol. 4, No. 5; 2012.

Svatosova Veronika "Motivation of Online Buyer Behaviour"- Jounal of Competitiveness, Vol.5, Issue 3, Sep 2013.

Na Li and Ping Zhang "Consumer Online Shopping Attitudes AndBehavior: An Assessment Of Research" - 2002, Eighth Americas Conference on Information Systems.

Chayapa Katawetawaraks "Online Shopper Behaviour: Influences of Online Shopping Decision" - Asian Journal Of Business Research, Vol I, Nov 2, 2011.

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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 Capital Budgeting in Practice: An Explorative Study on Bangladeshi Companies Shakila Yasmin Assistant Professor Institute of Business Administration, University of Dhaka, BANGLADESH. ________________________________________________________________________________________ Abstract: This descriptive research sheds light on capital budgeting practices of the companies in Bangladesh.

56 companies from different industry sectors were chosen based on convenience of data collection for survey.

This is the first comprehensive study on this topic to focus on companies in Bangladesh. Questionnaire was developed based on [7]’s survey instrument. Results show that NPV and IRR are dominated methods of capital budgeting, however payback period (PBP) is used by large percentage of companies. In general, companies in Bangladesh are more sophisticated in terms of capital budgeting techniques used and risk adjustment practices thereby than companies in other emerging economies but less sophisticated than companies in developed world in terms of risk adjustment practice. Influence of some factors i.e., CEO/CFO background and company size on capital budgeting practices have also been explored. _________________________________________________________________________________________ I. Introduction This research explores the extent of using sophisticated Capital Budgeting techniques in Bangladeshi companies. Capital budgeting is concerned about evaluation of long term investment opportunities and committing companies’ limited productive resources in viable opportunities to strengthen and renew their productive capabilities [15],[3]. Such commitments are kind of irreversible because reversals disturb firms’ economic and financial performances. Therefore, success and growth of a company largely depends on its ability to effectively allocate capital in productive use. Lacks in evaluation and decision tools to identify and exploit long term investment opportunities risk survival and hinder strategic growth of companies [6], [2]. Due to the importance of capital budgeting decisions, it has been a topic of research for many years. Empirical researches of the sixties exhibited primacy of payback period [11]. From early 1970s a shift happened in the capital budgeting techniques used by firms. More companies not only in developed world but also in other economies such as Africa started using sophisticated techniques such as NPV (net present value), IRR (internal rate of return) etc [23], [2], [16].

An extensive survey on companies in the USA depict that large firms heavily rely on present value (discounted cash flow) techniques and use risk adjusted discount rate (required rate of return or cost of capital), while small firms heavily rely on payback period method [7]. Australian firms too use CAPM and weighted average cost of capital as the discount rate to use in present value techniques [20]. Studies on UK corporations reveals that majority of those firms use sophisticated textbook prescribed methods of capital budgeting which include NPV, IRR and others and formal risk appraisal methods [2], [5]. But most of the companies use firm risk rather than project specific risks in appraising investment projects and besides sophisticated discounted cash flow techniques, managers still use simple rule-of-thumb [7], [2].

Although companies in emerging economies such as Rwanda, Jordan, Czech Republic, Finland, India, Pakistan and others are shifting toward more sophisticated techniques of capital budgeting, this is true for large companies. Small companies in these economies still use payback period as the primary tool for investment evaluation. Even larger companies in many cases use discounted methods as a secondary technique. In terms of risk adjustment and estimation of cost of capital companies in emerging economies lags behind US and UK firms [14], [18], [22], [12], [10], [15].

There has been almost no research on capital budgeting and/or investment evaluation techniques of the companies in Bangladesh. One study as in reference [4] determined the comparative value of different capital budgeting techniques on the cash flows of a particular bank branch. So this study fails to provide any general insight about the capital budgeting practices of Bangladeshi companies. The study in reference [19] conducted a survey on 50 companies from different industry sectors to assess management accounting development and practices in Bangladesh. They collected and presented data on capital budgeting techniques used by firms along with other management accounting measures. According to them majority firms (50% to 80%) in all industry sectors use discounted cash flow techniques and few firms use payback period for long-term investment decisions. But, this study did not have any discussion/section on risk adjustment, discount rate and other factors related to capital budgeting. Therefore, this exploratory research to review capital budgeting practices in

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Bangladeshi companies is likely to bring out insights from an untapped area of corporate finance in the context of a developing economy.

II. Objective Broad objective of this research is to elaborate on the capital budgeting practices of Bangladeshi companies.

For that purpose following specific objectives are pursued Identify to what extend different capital budgeting techniques such as NPV, IRR, Payback Period, Discounted Payback Period, Profitability Index, Sensitivity Analysis, Scenario Analysis, Real Options Approach, and Decision Tree Analysis are used.  Identify the extent of using different discount rates i.e. firm/company, divisional, project specific, country, industry, risk adjusted and other.  Identify whether companies estimate their cost of capital or use a predefined discount rate (as prescribed by corporate headquarters of multinationals or using some rule of thumb)  Identify the risk factors usually taken into consideration to adjust cost of capital and cash flows  Evaluate the influence (if any) of some factors i.e. CEO/CFO background, tenure & age and company size (measured by annual sales revenue) on the capital budgeting practices.

III. Research Method A. Survey Instrument This is an exploratory research based on survey data. Previously used questionnaire developed by Graham & Harvey (2001) has been used as the survey instrument with some modifications. The questionnaire was first pilot tested on 3 companies to make sure that respondents understand the information solicited in the questionnaire. Based on recommendations of the respondents of pilot survey some adjustments (rephrasing) were made on the questionnaire.

B. Sample The questionnaire has been handed in person to finance managers of 56 companies in different industry sectors 50 of them returned filled in questionnaires. Companies from major industry sectors of the country were chosen for survey on the basis on available contacts of the researcher and accessibility of information.

Table 1: Respondent profile Industry Sector Pharmaceuticals Cement Ceramic Textile & Garments Food & Beverages Others Total

No of companies contacted 15 7 5 12 8 9 56

No of companies responded 15 6 5 10 6 8 50

C. Survey method Data has been collected as a part of assignment by MBA students taking an introductory finance course in the Institute of Business Administration, University of Dhaka, Bangladesh under the researcher. At the beginning of the semester each student were asked to choose a company and look for a contact person in finance department of the company. Students were allowed to use the personal contacts of themselves, their friends/family and of the researcher as well as they were provided access to the alumni database of the institute. After each student identified a company and contact there in, the researcher sent a letter of request to the concern person to allow the student to oversee their financial management practices as this will help the students to relate theory into practice. Through those contacts students got access to the finance manager of those companies. Students met the finance managers once in a month to clarify and understand the practical aspect of the theory they learnt in class room. In their last meeting, at the end of the 4 month semester, students handed in the questionnaire provided by the instructor/ researcher to the respective finance manager. Due to the relationship developed throughout the semester the response rate was as high as 89%. After the students submitted the filled in questionnaire, the researcher randomly checked with the respondent manager over phone for authenticity of the responses.

IV. Findings A. Capital Budgeting Techniques Respondents were asked to choose how frequently they use a particular capital budgeting technique. Responses were sought about the broad categories of capital budgeting techniques i.e. discounted cash flow methods (DCF) which include NPV, IRR, Profitability Index(PI) and discounted payback period (DPBP); non discounted cash flow methods (non-DCF) like Payback period (PBP) and risk adjusted methods such as sensitivity analysis, scenario analysis, real options approach and decision tree analysis. Results are shown in the table belowA large majority of firms always or often use DCF methods which are recommended as sophisticated techniques in finance literature. Of the different DCF methods, NPV and IRR are most frequently (always or often) used methods followed by PI. Percentage of companies frequently using NPV and IRR are respectively 88% and

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79% whereas about 67% of the companies surveyed frequently use profitability index (PI). In fact, PI is a technique to be used to choose among competing investment opportunities, if there is limited financial resources (capital rationing) [3]. Capital rationing especially hard rationing is not a regular phenomena, it thrives only in case of weak financials and/or high risk of financial distress [21], [3]. Therefore, less frequent use of PI makes reasonable sense from theoretical perspective. However, other DCF method, discounted payback period (DPBP) is not widely used technique. Nearly half of the firms surveyed rarely or never uses DPBP. Table: Degree of application of different capital budgeting techniques Capital Budgeting Technique NPV IRR PI PBP DPBP Sensitivity Analysis Scenario Analysis Real option approach Decision tree approach

Always 68% 60% 42% 56% 28% 23% 14% 5% 5%

Often 20% 19% 25% 19% 14% 26% 30% 14% 7%

Frequency of use Sometimes Rarely 7% 3% 12% 2% 19% 9% 9% 9% 12% 16% 26% 9% 21% 12% 19% 16% 18% 21%

Never 2% 7% 5% 7% 30% 16% 23% 46% 49%

In contrast to the heavy use of common DCF methods, payback period (PBP) a rudimentary non-DCF technique is quite popular among the respondent firms. 75% of the companies surveyed always or often use PBP, which is very close to the usage of IRR technique.

Among the risk adjusted methods sensitivity analysis and scenario analysis are found to be almost equally applied techniques, being frequently used by 49% and 44% firms. But decision tree approach and real option approach are rarely or never used by majority (62% and 70% respectively) of the respondent firms.

In terms of broad category DCF and non-DCF methods are almost equally used methods. In comparison to the risk adjusted methods, DCF and non-DCF methods are more commonly used by the respondent firms. However, almost all firms use several techniques in parallel.

B. Discount Rate To use DCF methods companies need to discount the expected cash flows using a discount rate. Discount rate is the opportunity cost of capital or the required rate of return. Discount Rate for the entire company may differ from the discount rate of a particular division/Strategic Business Unit (SBU) or of a foreign subsidiary or of a particular investment project. Companies need to choose the appropriate discount rate considering the risk associated with the investment opportunity. To get an overview of the discount rates used by the companies, respondents were asked to rate different discount rates in terms of their frequency of use. Following table shows the summary result: Table: Use of different discount rates Discount rate for the entire company Country specific discount in case of an overseas project Divisional discount rate in case project is under a particular division or SBU Risk-adjusted discount rate (considering country and industry risk) Different discount rate for each component cash flow that has different risk

Frequency of use Sometimes Rarely 14% 5% 5% 23% 12% 16%

Always 56% 11% 7%

Often 14% 5% 16%

Never 11% 56% 49%

35%

19%

12%

9%

25%

7%

21%

23%

9%

40%

Discount rate for the entire company is the most commonly used discount rate. 70% respondents always or often use this to discount expected cash flows. Next commonly used discount rate is the risk adjusted discount rate.

54% of the respondent companies frequently use this discount rate. On the other hand, very few companies use country specific or divisional discount rate or use different discount rate for each component of cash flow that has different risk. Percentages of respondent companies rarely or never using these discount rates are respectively 79%, 65% and 49%.

C. Estimation of cost of capital/ discount rate 86% of the respondent companies estimate their cost of capital/ discount rate. The rest either use non-DCF methods of capital budgeting or use a predefined discount rate determined based on some rule of thumb or a rate recommended by their corporate head quarters (in case of multinationals). Most (84%) of the companies estimating cost of capital use weighted average cost of capital (WACC) as discount rate. Others directly use the cost of debt (usually prevailing bank interest rate) as discount rate.

D. Risk Adjustment Discount rates and expected cash flows are not static. According to finance literature, the discount rate to be used must reflect the current and expected risks associated with the investment opportunity. As capital budgeting techniques are used to evaluate long term investment opportunities, factors like, unexpected inflation,

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change in foreign exchange rate, general interest rate change, term structure change, risks of financial distress and others not only change the risks associated with the investment but also the expected cash flows from/for the investment. Therefore, for proper evaluation of investment projects discount rate and cash flows should be adjusted there for. To review the practice of Bangladeshi companies in this respect, responses regarding adjustment of discount rates and/or cash flows due to the change in above mentioned risk factors are sought.

Results are summarized below: Table: Risk factors taken into account a) b) c) d)

Risk of unexpected inflation Foreign exchange risk Unexpected change in general interest rate Term structure risk (change in long term vs. short term interest rates)

Discount rate 11% 13% 24% 13%

e)

Risk of financial distress

3%

Cashflow

Both

None

13% 19% 3% 14%

57% 38% 42% 27%

19% 30% 31% 46%

14%

24%

59%

As evident from the high percentage of companies adjusting both discount rate and cash flows for unexpected inflation, unexpected change in foreign exchange rate and general interest rate; these three are considered to be the most important risk factors to influence discount rate and cash flows. Almost half of the respondent companies don’t make any adjustments for term structure risk and about 60% companies don’t make any adjustments for risk of financial distress. That means majority of companies perceive these two factors to have little influence on discount rate and cash flows.

E. Taking decision in contrast of the recommendations derived using capital budgeting techniques For number of reasons companies may take decision in opposition to the recommendation derived from capital budgeting techniques used. Although 51% of the companies surveyed rarely or never take such contrasting decision, 28% of the respondents frequently do so. Most frequently cited three reasons for such decisions are inadequate fund, policy and/or regulation and strategic alignment (in order) as presented in the following table.

Least common reasons are top management’s lack of confidence on the capital budgeting technique used and on the forecasted cash flows respectively.

Table: Common reasons for taking decision in contradiction to recommendation derived by capital budgeting Reasons Frequency of citing these reasons a) Inadequate fund for investment b) Policy or regulatory reasons c) Strategic alignment d) Top management’s lack of confidence on the capital budgeting technique used e) Top management’s lack of confidence on the forecasted cash flows f) Others (please mention)

always 30% 16% 21% 15%

often 33% 40% 25% 7%

15% 0%

15% 0%

Percentage of firms sometimes rarely 4% 15% 20% 4% 16% 21% 15% 7% 22% 3%

11% 0%

never 18% 20% 17% 56% 37% 97%

F. Factors influencing Capital budgeting Techniques Used Descriptive Statistical Analysis (cross tabulation) results from SPSS shows that companies have CFO/CEO with MBA/Business Masters are most frequent users of DCF and risk adjusted methods; Companies having CFO/CEO with non-business masters are frequent users of DCF methods but moderate users of risk adjusted methods. These two categories of companies also use non-DCF methods but less frequently. Companies having CFO/CEO with business or non-business undergrad degrees usually use Non-DCF methods (table: selected cross tabulation results are presented in next page). Analysis between CFO/CEOs age and tenure with the company shows that companies with older and longer termed CFO/CEOs have a tendency to rely more on nonDCF methods compared to DCF and risk adjusted methods. Majority of large companies frequently use nonDCF methods, followed by DCF and risk adjusted methods respectively. Company discount rate is most frequently used as the rate for discounting cash flows of investment opportunities by companies with CEO/CFOs of age between 50-59; tenure above 9 years and having (business or non-business) masters degree.

Majority of the companies rarely use country specific or divisional discount rate irrespective of their CEO/CFO background. Companies with CEO/CFOs of age between 40-59; tenure between 4-9 years and having business masters degree frequently adjust discount rate for country, industry and other risks. Very few companies use different discount rates for cash flows having different risks. Company size, measured in terms of sales revenue does not have any impact on the choice of discount rate. CEO/CFO background or company size does not have any impact on the frequency to taking decision contrasting to that derived by capital budgeting techniques.

However none of the crosstab results are statistically significant.

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V. Conclusion This study has added value in finance literature by bringing out insights about the capital budgeting practices in Companies operating in Bangladesh. This is the first comprehensive study on this topic on Bangladeshi companies.

Table: Selected Cross-tab output How frequently does your company use company discount rate?

CEO/CFO Education

nonbusiness graduate

business graduate

business masters

nonbusiness masters

Total

Never Rarely Sometimes Frequently Always Total

0.0% 0.0% 4.7% 0.0% 2.3% 7.0%

0.0% 0.0% 2.3% 0.0% 2.3% 4.7%

7.0% 4.7% 7.0% 11.6% 39.5% 69.8%

4.7% 0.0% 0.0% 2.3% 11.6% 18.6%

11.6% 4.7% 14.0% 14.0% 55.8% 100.0%

How frequently does your company use discount rate adjusted to country, industry and other risks? nonbusiness business nonTotal CEO/CFO business graduate masters business Education graduate masters Never Rarely Sometimes Frequently Always Total

4.8% 2.4% 0.0% 0.0% 0.0% 7.1%

0.0% 2.4% 0.0% 2.4% 0.0% 4.8%

9.5% 4.8% 9.5% 14.3% 31.0% 69.0%

9.5% 0.0% 2.4% 2.4% 4.8% 19.0%

23.8% 9.5% 11.9% 19.0% 35.7% 100.0%

NPV is the most frequently used capital budgeting technique in the companies surveyed. IRR, PI and PBP are next most popular methods. Majority of the companies use multiple techniques. The results in this aspect are line with the recommendations of finance texts. However studies on other countries especially emerging economies like Rwanda, South Africa, Czech Republic reveal prevalence of PBP and DPBP techniques [18], [1], [9], [13]. Even studies on European countries like Sweden and Finland depicts dominance of PBP method [17], [14]. So it can be said that Bangladeshi companies follow practices of firms in developed world (the USA, Great Britain) where NPV and IRR are prevalent methods followed by PBP of capital budgeting [7], [18].

Majority of the firms in this study use company discount rate for evaluating projects, a good percentage use risk adjusted discount rate. A large percentage of Bangladeshi companies studied also adjust discount rates and cash flows for several risk factors such as inflation, exchange rate fluctuation and change in interest rates. Therefore, it can be concluded that capital budgeting practices of the companies in Bangladesh is more sophisticated than many other emerging economies. Firms in the USA and other developed economies can be considered as benchmark for the capital budgeting practices of Bangladeshi companies. In this respect Bangladeshi companies lack in the use of risk adjusted methods namely sensitivity, scenario and decision tree analysis and other probabilistic techniques.

Although researches in other countries revealed a correlation between company size measured in annual sales and the sophistication in capital budgeting techniques this research did not find any such relationship [1], [7].

Rather CEO/CFO background i.e. education, tenure with the company and age influence the capital budgeting practices as found in [7].

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Results show that majority of companies usually follow the recommendation derived by the capital budgeting techniques, only few (less than one third) of them frequently take investment decision in opposition to the recommendation of the capital budgeting tools. This is sensible at the high level of sophistication (revealed from the research) in capital budgeting practices. Moreover, the frequently cited reasons for such opposing decisions i.e. fund constraint, strategic alignment, policy & regulation and others strengthens the conclusion that majority of the companies surveyed are sophisticated in terms of their capital budgeting practices.

However, results of the study cannot be generalized because the sample taken was chosen based on convenience. Scientific sampling was not possible because companies usually are reluctant to disclose information. Perhaps it could be done if the information was sought by some regulatory authorities. Moreover, the results were not statistically significant to worth generalizing. This research was a comprehensive one, because there were companies from many different industries in the sample. But, due to convenient sampling some industry sectors dominated and might have biased the results. Research on specific industry sector is worth exploring, as practices may differ from industry to industry. Even firms’ life cycle stage might have influence on the practice. Practices of firms at matured or growth stage are likely to be more sophisticated than those of new firms. Most firms of this study were at growth or matured stage. This can be a reason for finding high level of sophistication in practice. Analysis of small, new firms might have different results.

Another limitation of the paper is that it did not focus in detail on the process of estimating cost of capital, which is a crucial input in capital budgeting methods. Future researches can focus on estimation of cost of capital. In terms of factors influencing the choice of capital budgeting technique this research used only two factors CEO/CFO background and company size measured by annual sales volume. Other factors like life stage of the company, industry, size measured by paid up capital, type of ownership and others were left out of the scope of this study, but have the merit to be explored. Future researches can focus on these factors. Also, the impact of capital budgeting techniques used on company performance is another area of research to explore.

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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 Impact of Television Advertisements on Buying Patterns of Consumer Durables in Chennai -A Retailers’ Perspective K.S.HemaMalini, Research Scholar,VIT Business School Chennai.Tamil Nadu, INDIA. Dr.R.Venkatesh, Professor, VIT Business School, Chennai, Tamil Nadu, INDIA.

__________________________________________________________________________________ Abstract: Television is still perceived to be the most influential and persuasive media for advertisement. Though it faces lot of challenges from internet and social networking sites still it prevails to be the “king’ of all the advertising media. The study presents the impact of television advertisements on buying patterns of consumer durables and also measures the impact on the demographic variables taken for the study..Many studies have been done on consumers and their behavior is analyzed from consumer point of view. This study focuses on the impact of television advertisements on buying patterns of consumer durables with a retailer’s perspective. The consumer durables taken for the study are washing machine, refrigerator, air conditioner, television, mixer, music system vacuum cleaner and water purifier. The study was conducted using a questionnaire and the data collected was analyzed with statistical tools like Duncan's multiple range tests and descriptive test. The study was conducted with a sample size of 75 retailers from Chennai. The sample method used to conduct the study was convenience sampling The study indicates that television advertisements have a strong effect on purchase of new brands.

Keywords; Television Advertisement, impact, buying patterns, consumer durables and retailers. __________________________________________________________________________________________ I. Introduction This study aims to analyze the impact of television advertisements on buying patterns of consumer durables and also measures the impact on the demographic variables taken for the study. The greatest advantage of television advertisement is the integration of sight, sound motion and colour which offers extraordinary flexibility to make dramatic and lifelike portrayals of products and services. Sparrow (2007) parents watch the television commercials along with their children and have a discussion with them. Parents encourage their children, teach the effects of consuming the advertised products and make them better consumers in the future.

T Ravikumar(2012)The attitude towards advertising has been changing with lot of diversifications and dynamism. Women consumers attitude towards television advertisements and their buying behavior are directly related with each other. .An analysis of Indian television durable goods was conducted to find out the dominant cultural values. Some of the dominant cultural values present were technology, family, enjoyment and economy and also had subsidiary values.

II. Literature Review Bhawaniprasad (1987)conducted a research on the impact of advertising on consumer market, the study was conducted in Hyderabad and Secundrabad with 200 respondents of refrigerator users, and the study revealed that a strong positive impact of advertising was created in the minds of consumers.

Rizwana Ahmed(1992)Durable goods are generally purchased in recognition of a need that has to be fulfilled of the durable goods, and also they offer some convenience by way of reducing labor or time or effort involved in process such as food processor, microwave cooking oven, refrigerator, washing machine etc.

Venkateshwar and Rao(2000) made a study on 200 urban working women who belong to different occupation, education and income groups. In the study it was found that television was a major source of information for 65.5% of consumers.

Nidhi Kotwal (2008) Television advertising is the easiest way to reach out to all kinds of customers. The results revealed that advertisements play a vital role in introducing a new product in the family list and making better choice during shopping.

A Pughazhendi (2011) the study focuses on consumers of durable products and they have their motivational sources from need and product utility. The study includes that the celebrity advertisements motivate them to materialize the purchase of durables. Dr.K..Sreeranga Nathan and Lakshmi Bhai.P.S(2012)The study provides responses of consumers with respect to consumer durables in Kerala. The study reveals the impact of television on purchasing behavior and most of the durable manufacturers use television advertising as one of the marketing strategies. The survival and growth is not viable without advertising strategies.

Dr.R.Khader Mohideen (2015) The study presents that companies try to increase their sales through advertisements particularly television advertisements. The theme, message and language used in advertisements

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are influencing the buying decisions of consumers. Through advertisement companies try to position the brand in the minds of target audience.         

III. Objectives To study the impact of television advertisements on buying patterns of consumer durables in Chennai from a retailers perspective.

To measure the impact of television advertisements on buying patterns of consumer durables in Chennai on the demographic variables taken for the study.

To identify whether television advertisements help consumers to make better choices.

IV. Hypothesis There is no significant difference between age group and the impact of TV advertisement on buying patterns of consumer durables.

There is no significant difference between educational qualification and the impact of TV commercial on buying patterns of consumer durables.

There is no significant difference between number of years in business and the impact of TV commercial on buying patterns of consumer durables.

There is no association between retailers age group and their opinion on TV advertisements help consumers to make better choice.

There is no association between retailers educational qualification and their opinion on TV advertisements help consumers to make better choice.

There is no association between retailers number of years in business and their opinion on TV advertisements help consumers to make better choice.

V. Research Methodology The nature of the present study is descriptive in nature. The sample method used to conduct the study was convenience sampling. The study was conducted with a sample size of 75 retailers from Chennai dealing in consumer durables like washing machines, refrigerator, air conditioners, televisions, mixers, music systems, vacuum cleanser and water purifiers. The study was conducted using a questionnaire. The questionnaire describes the retailers profile which includes their age, education, and number of years in business and is designed to get the responses from retailers regarding the impact of television advertisements on buying patterns of consumer durables. The data collected was analyzed with statistical tools like Duncan's multiple range tests and descriptive test.

Results Table 1: ANOVA for significant difference between age group and the impact of TV commercial on buying patterns of consumer durables.

Over all impact Impact of TV commercial on buying patterns of consumer durables

Age Group in years 31-40 41-50

20-30 59.65b (11.123)

50.42a (6.104)

55.18ab (8.145)

Above 50 57.29b (3.315)

F value 4.576

P value 0.005**

Note: 1.** Denotes significant at 1% level 2. Different alphabet between age groups denotes significant at 5% level Using Duncan Multiple Range Test (DMRT) 3. The value within brackets refer to SD. Table 2: ANOVA for significant difference between educational qualification and the impact of TV commercial on buying patterns of consumer durables.

Educational qualification

Over all impact PG Impact of TV commercial on buying patterns of consumer durables

50.25a (4.634)

HSC

UG

F value

P value

54.37ab (7.302)

58.22b (9.538)

4.731

0.012*

Note: 1.** Denotes significant at 1% level

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2. Different alphabet between educational qualifications denotes significant at 5% level using Duncan Multiple Range Test (DMRT) 3. The value within brackets refer to SD Table 3: ANOVA for significant difference between number of years in business and the impact of TV commercial on buying patterns of consumer durables.

Over all impact Up to 5 Impact of TV commercial on buying patterns of consumer durables

No of years in business Above 10 6-10

55.27 (11.243)

54.44 (7.784)

57.40 (3.378)

F value

P value

0.686

0.507

Note: 1.** Denotes significant at 1% level 2. The value within brackets refer to SD Table 4: Chi-square test for association between Retailers age, qualification, no of years in business and their opinion towards TV advertisements help consumers to make better choice.

Chi-Square Value

Associations TV Ads help consumers to make better choice and Age TV Ads help consumers to make better choice and educational qualification TV Ads help consumers to make better choice and no of years in business

df

P Value

43.997

12

<0.001**

18.567

8

0.017

26.135

8

0.001

Mean

Std. Deviation

Table 5: Descriptive Statistics Statements Television advertisements influence consumers to buy products Television advertisements have a effect on purchase of new brands Television advertisements have a effect on reinforcing familiarity of the product Television advertisements create awareness about the location Television advertisements get consumer attention Television advertisements create interest for purchasing Television advertisements create desire for purchasing Television advertisements convince consumers to purchase the product Television advertisements build brand while selling Television advertisements have the show and tell effect Television advertisements add personality to the product More frequency of television advts increase the products demand Television advertisements create awareness about availability of wider choice Celebrity in Television advertisements influence purchasing Attractive Slogans in advertisement influence purchasing Television advertisements help consumers to make better choice

N

Minimum

Maximum

75

2

5

3.99

.647

75

3

5

4.23

.559

75

2

5

3.91

.720

75

2

5

3.31

1.039

75

2

5

3.76

.732

75

1

5

3.44

1.142

75

2

5

3.31

1.000

75

1

5

2.99

1.109

75

2

5

3.25

.917

75

2

5

3.49

.844

75

1

5

3.37

1.112

75

1

5

3.12

.958

75

1

5

3.08

1.281

75

2

5

3.52

.891

75

2

5

3.56

.874

75

1

5

3.24

1.206

VI. Findings and Discussion H1;There is no significant difference between age group and the impact of TV advertisement on buying patterns of consumer durables.

According to Table 1 - P value is less than 0.01, the null hypothesis is rejected at 1% level of significance with respect to age group and the impact of TV commercial on buying patterns of consumer durables. Hence there is significant difference at 5% level between age group and the impact of TV commercial on buying patterns of consumer. Retailerâ&#x20AC;&#x2122;s Age groups 20-30 and Above 50 differ from retailers age groups 31-40 and also differ from age group 41-50.The opinion of the retailers age groups 20-30 and above 50 differ significantly from the other age groups with respect to impact of TV commercial on buying patterns of consumer durables.

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H2; There is no significant difference between educational qualification and the impact of TV commercial on buying patterns of consumer durables.

According to Table 2 - P value is less than 0.05, the null hypothesis is rejected at 5 percent level of significance with regard to educational qualification and the impact of TV commercial on buying patterns of consumer durables .Hence there is significant difference between educational qualification and the impact of TV commercial on buying patterns of consumer durables. Retailers educational qualification PG, HSC and UG differ from each other. The opinion of the impact of TV commercial on buying patterns of consumer durables significantly differs with retailer’s educational qualification.

H3;There is no significant difference between number of years in business and the impact of TV commercial on buying patterns of consumer durables.

According to Table 3 - There is no significant difference between number of years in business and the impact of TV commercial on buying patterns of consumer durables. Since P value is greater than 0.05.Hence null hypotheses is accepted at 5% level with regard to number of years in business and the impact of TV commercial on buying patterns of consumer durables.

H4;There is no association between retailers age group and their opinion on TV advertisements help consumers to make better choice.

H5;There is no association between retailers educational qualification and their opinion on TV advertisements help consumers to make better choice.

H6;There is no association between retailers number of years in business and their opinion on TV advertisements help consumers to make better choice.

According to Table 4- Association between retailers age group and their opinion on TV advertisements help consumers to make better choice.-- Since P value is less than 0.01,the null hypothesis is rejected at 1 percent level of significance. Hence concluded that there is a association between retailers age group and their opinion on TV advertisements help consumers to make better choice.

Association between retailers educational qualification and their opinion on TV advertisements help consumers to make better choice –Since P value is less than 0.05,the null hypothesis is rejected at 5 percent level of significance. Hence concluded that there is a association between retailers educational qualification and their opinion on TV advertisements help consumers to make better choice.

Association between retailers number of years in business and their opinion on TV advertisements help consumers to make better choice--- Since P value is less than 0.01,the null hypothesis is rejected at 1 percent level of significance. Hence concluded that there is a association between retailers number of years and their opinion on TV advertisements help consumers to make better choice.

According to Table 5 –The descriptive statistics shows a dominant mean score of 4.23 for the statement, Television advertisements have a effect on purchase of new brands, followed by a mean score of 3.99 for Television advertisements influence consumers to buy products, which is followed by a mean score of 3.91 for Television advertisements have a effect on reinforcing familiarity of the product. The descriptive statistics shows the least mean score of 2.99 for the statement Television advertisements convince consumer to purchase the product.

VI. Conclusion Television advertisements play a major role in the buying patterns of consumer durables. The opinion of the impact of TV commercial on buying patterns of consumer durables significantly differs with retailer’s educational qualification. Television advertisements have a effect on purchase of new brands, they influence consumers to buy and television advertisements have a effect on reinforcing familiarity of the product but they do not play a major role in convincing consumers to purchase the product. According to retailers television advertisements help consumers to make better choice.

References [1].

[2].

[3].

[4].

[5].

[6].

Dr.R. Khadar Mohideen “A Study Of Factors Determining Buying Decisions Through Television Advertisements For Consumer Durable Goods” – IJM Vol 6, Issue 1, Jan 2015.

A.Pughazhendi “A Study On Celebrity Based Advertisements On The Purchase Attitude Of Consumers Towards Durable Products In Coimbatore City, Tamilnadu, India” – Far east journal of marketing and management, Vol.1, No.1,Dec 2011.

Nidhi Kotwal “Impact of T.V Advertisements on Buying Pattern Of Adolescent Girls” – J.Soc.Sci., 16(1): 51-55(2008) Bhawani Prasad “Impact of Advertising on Consumer Durable Market” – Indian.J.Market. Vol.18 : P.21-28 (1987) Venkateswarlu, H and Roa,P.P “ Women as consumer” – Indian Management , Vol.39:P.61-68 (2000) Rizwana Ahmed “ consumer Buying Decision for consumer Durable goods”

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

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net INTRODUCTION TO MOBILE AD-HOC NETWORK, ITS APPLICATIONS AND STACK Vijayendra Kushwaha1, Imran Khan2, Neelham Singh Parihar3 Student (M.Tech. CSE), 2Head of Department, 3Assistant Professor & Guide 1,2,3 Department of Computer Science & Engineering, Jawahar Lal Nehru College of Technology Rewa, Madhya Pradesh, INDIA. _________________________________________________________________________________________ Abstract: In this paper we will discuss about basics of mobile ad-hoc network, applications of ad-hoc network, its usage, stack, and mobile ad-hoc network base. We are talking about a brief introduction to mobile ad-hoc network, and its layer architecture, topologies and security issues.

Also we will have a brief introduction of routing which used in mobile ad-hoc network for carrying information from one node to another node.

Keywords: routing, topology, AODV, node. __________________________________________________________________________________________ 1

I. Introduction Mobile Ad-hoc network is a type of wireless network there are currently two variations of mobile wireless networks infrastructure and Infrastructure fewer networks. The infrastructure networks, also known as Cellular network, have permanent and wired gateways. They have permanent base stations that are connected to other base stations in the course of wires. The transmission range of a foundation station constitutes a cell. All the mobile nodes untruthful within this cell connect to and communicate with the closest bridge (base station). A hand off occurs as mobile host travels out of series of one Base Station and into the range of an additional and thus, mobile host is able to carry on communication flawlessly throughout the network. Example of this kind includes office wireless local area networks.

The other type of network, Infrastructure fewer network, is recognized as Mobile Ad network (MANET). These networks have no permanent routers. All nodes are accomplished of movement and can be connected energetically in arbitrary manner. The tasks for organizing and controlling the network are distributed among the terminals themselves. The entire network is mobile, and the individual terminals are permissible to move at will relative to each other. In this type of network, some pairs of terminals might not be able to converse directly to with each other and relaying of some messages is required so that they are delivered to their destination. The nodes of these networks also utility as routers, which discover and sustain routes to other nodes in the networks.

The nodes may be located in or on airplanes, ships, trucks, cars, possibly even on people or very small devices as shown in Fig1.0.

Fig. 1.0 Mobile Ad-Hoc Network The interest in wireless ad hoc networks stems from of their well-known advantages for certain types of applications. Since, there is no fixed infrastructure; a wireless ad hoc network can be deployed quickly. Thus, such networks can be used in situations where either there is no other wireless communication infrastructure present or where such infrastructure cannot be used because of security, cost, or safety reasons.

Applications of MANET: There are some main Applications of MANET which include:

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Dynamic Topologies: Since nodes are free to move arbitrarily, the network topology may change randomly and speedily at unpredictable times. The associations may be unidirectional bidirectional.

Bandwidth constrained, variable capacity links: Wireless links have significantly lower capacity than their hardwired counterparts. Also, due to numerous access, fading, noise, and interference environment etc. the wireless relations have low throughput.

Energy constrained operation: Some or all of the nodes in a MANET may rely on batteries. In this state of affairs, the most significant system design criteria for optimization may be power conservation.

Limited physical security: Mobile wireless networks are commonly more prone to physical security threats than are permanent- cable nets. The increased opportunity of eavesdropping, spoofing, and denial-of-service attacks should be carefully considered. Existing link security techniques are often applied within wireless networks to decrease security threats. As a advantage, the decentralized nature of network control in MANET provides additional robustness against the single points of breakdown of more centralized approaches.

Security: Security is the major issue in wireless Ad Hoc Networks and actually ought to receive a complete analysis of it than being presented as a part of the study on Ad Hoc Networks. The use of wireless links renders an ad hoc network susceptible to link attacks ranging from denial of service, passive eavesdropping to active impersonation, message replay, and message distortion. Eavesdropping might give an adversary access to secret information, violating confidentiality. Active attacks might allow the adversary to delete messages, to inject erroneous messages, to modify messages, and to impersonate a node, thus violating availability, integrity, authentication, and non-repudiation.

Nodes, roaming in a hostile environment (e.g., a battlefield) with relatively poor physical protection, have nonnegligible probability of being compromised. Therefore, we should not only consider malicious attacks from outside a network, but also take into account the attacks launched from within the network by compromised nodes. Therefore, to achieve high survivability, ad hoc networks should have a distributed architecture with no central entities. Introducing any central entity into our security solution could lead to significant vulnerability; that is, if this centralized entity is compromised, then the entire network is subverted.

An ad hoc network is dynamic because of frequent changes in both its topology and its membership (i.e., nodes frequently join and leave the network). Trust relationship among nodes also changes, for example, when certain nodes are detected as being compromised. Unlike other wireless mobile networks, such as mobile IP, nodes in an ad hoc network may dynamically become affiliated with administrative domains. Any security solution with a static configuration would not suffice. It is desirable for our security mechanisms to adapt on-the-fly to these changes. Finally, an ad hoc network may consist of hundreds or even thousands of nodes. Security mechanisms should be scalable to handle such a large network.

The denial of a service can be caused by such legitimate ways as a radio jamming or battery exhaustion. An attacker can cause a radio jamming by jamming a wider frequency band and in that way using more power. The latter can be of real threat, because once a battery runs out the attacker can walk away and leave the victim disabled. This kind of technique is called the sleep deprivation torture attack. Symmetric key cryptography is used to provide authenticity and integrity. Integrity means that no node has been maliciously changed. The devices themselves should be able to detect security breaches and plug them.

Future scope: There are a few areas that need to be given particular focus for improvement in Ad Hoc Networks.

Scalability: Currently the size of Ad Hoc Networks are small and work needs to be done to identify to what size can these networks grow and further try to increase the size of these networks to what is that of the Internet today.

Quality of Service: A quality of service is defined for the network with no losses and attempts should be made achieve that. QoS parameters will involve bandwidth considerations and savings of bandwidth will be implemented. Also finding the shortest path so as to save power in the devices as the source of power is very limited. Care should be taken so as to have no collision losses.

Power Control: Reducing power to the communications interface and entering sleep state are ways of extending battery life of mobile units. But these techniques make communication difficult. Hence some efficient technique should be developed to make this viable. Research should also focus on getting battery technology growth on par with Ad Hoc Network Technology growth.

Security: Security needs to be very widely investigated as they are imperative. Wireless networks are as such insecure and particularly so with Ad Hoc Networks. Implementations of current cryptography techniques are not good enough and also difficult.

Location Access: User location could be incorporated into routing.

II. Conclusion in this paper we discussed about basics of mobile ad-hoc network, applications of ad-hoc network, its usage, stack, and mobile ad-hoc network base. We are talking about a brief introduction to mobile ad-hoc network, and its layer architecture, topologies and security issues. Also have discussed about a routing.

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.

References [1] [2] [3] [4] [5] [6] [7]

A Brief Overview of Ad Hoc Networks: Challenges and Directions: - Ram Ramanathan and Jason Redi, BBN Technologies.

IEEE Communications Magazine, 50th Anniversary Commemorative Issue, May 2002 Ad Hoc Networking:- Charles E. Perkins. Addison-Wesley, December 2000.

Mobile Ad Hoc Networking: Imperatives and challenges: - Imrich Chlamtac, Marco Conti & Jennifer Liu. Ad Hoc Networks Publication, Volume 1, Issue 1, July 2003.

On reducing the Broadcast Redundancy in Ad Hoc Wireless Networks: - Wei Lou & Jie Wu, IEEE transactions on mobile computing. April- June 2002.

Secure message transmission in mobile ad hoc networks: Yih-Chun Hu; David B. Johnson; Adrian Perrig. Proceedings of the 2003 ACM workshop on Wireless security Security in Ad-hoc Networks: - Anne Vanhala, University of Helsinki, Department of Computing Science, Research seminar on Security in Distributed Systems.

Securing Ad Hoc Networks: - Lidong Zhou & Zygmunt J. Haas, Cornell University, Ithaca, NY. IEEE Networks Special Issue on Network Security. November/December, 1999

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

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Human Machine Interface (HMI) For DC Motor Drives with Self Generator 1

S.Natarajan, 2Dr.M.AntoBennet, 3 M. Manimaraboopathy,4S.Sankararnarayan, 5N.Srinivasan 2.3,4 Department of ECE, VELTECH, Chennai-600062, Tamil Nadu, INDIA 1,5 Department of EEE, VELTECH, Chennai-600062, Tamil Nadu, INDIA ____________________________________________________________________________________ Abstract: Direct current (DC) motor has already become an important drive configuration for many applications across a wide range of powers and speeds. The ease of control and excellent performance of the DC motors will ensure that the number of applications using them will continue grow for the foreseeable future.

This paper is mainly concerned about reducing the total power consumed by the DC drives by 30% and also to ease the control over the control system with the help of an HMI. Here the microcontroller generates the PWM signals for the DC motor drive circuit based on the inputs given from the HMI. In HMI Visual Basic 8.0 is used to create the faceplate of the motor. A generator is couple with the motor and the load through the means of line shafting. This arrangement makes the motor to supply both the load and the generator simultaneously.

Whenever the motor supplies the load the generator in turn generates the power. This power generate by the generator is stored in the battery and then it is supplied to the motor and the source supply to the motor from the mains is cut off. Whenever the battery loses power the motor is supplied from the source supply. This concept reduces the period of time that the motor is supplied from the source supply which makes it to consume less power from the mains.

Keywords: Human Machine Interface, DC Motor drive, Liquid Crystal Display, PIC16F877A MCU. __________________________________________________________________________________ I. Introduction The core objective of this paper is to reduce about 30% of the total power consumed by the industrial drives and to ease the control over entire control system for the operators with the help of a HMI. Direct current (DC) motors have variable characteristics and are used extensively in variable-speed drives. DC motor can provide a high starting torque and it is also possible to obtain speed control over wide range. DC motor plays a significant role in modern industrial. These are several types of applications where the load on the DC motor varies over a speed range. These applications may demand high-speed control accuracy and good dynamic responses. In home appliances, washers, dryers and compressors are good examples. In automotive, fuel pump control, electronic steering control, engine control and electric vehicle control are good examples of these. In aerospace, there are a number of applications, like centrifuges, pumps, robotic arm controls, gyroscope controls and so on.

And also in industries involving multiple drives the control system should be handled in a smooth way i.e. the operators must not find any difficulties in controlling the overall drive operations. Another major factor for a multiple drive installed industry is the total consumption of the drives. The power consumed increases with increase in number of drives.

Hence for an industry involving multiple DC drives the following factors must be handled with care: Speed control, Easy interface between the operators and the drive control system and total power consumption. In order to achieve the objective of the paper, there are several scope had been outlined. A graph of speed versus time is obtained by using Visual Basic 6.0 at computer to observe the performance of the system II.

PROPOSED SYSTEM -Hardware Implementation

The Motor control parameters are fed by the process operator via ‘Human Machine Interface (HMI)’ console.

The Human Machine Interface (HMI) is developed with Visual Basic 8 . As Micro Control Unit (MCU) fetches the motor control parameter by the HMI via MCU’s ‘Universal Asynchronous Receiver Transmitter’ (UART) port, it produces a corresponding Pulse Width Modulation (PWM) Signal. The PWM signal is used by the DC Drive circuit to drive the DC motor to the set point RPM value fed by the operator.

A Generator is coupled directly or via gear box with the DC Motor, the generated power will charge a ‘DC battery’ through ‘Battery charger circuit’. The MCU triggers the ‘source selecting circuit’ when the motor achieve its 90% of its speed & battery is full, where the MCU measures DC motor running RPM by the Dynamo tacho generation method .Initially the ‘Source selecting circuit’ supplies the Regulated 12v dc ‘source supply’ to the ‘DC Drive Circuit’ and latter when it is triggered by the MCU, then 12v dc ‘Battery Supply’ is supplied to the ‘Drive Circuit’ cutting off the Regulated 12v dc ‘source supply’. ‘ULN driver circuit’ is for driving the

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Start, Stop, Trip lamps and for driving a relay in the ‘Source Selecting Circuit’. The ‘Protection Circuit’ plays as an interlock for the Control System under abnormal conditions for the Motor Control Circuits. The ‘Source Supply’ Circuits are for the power supply of the electronic hardware. And Liquid crystal Display (LCD) is used to display the motor status, tripe & warning alert information is shown in fig 1.

Fig. 1: Block diagram of Human Machine Interface PIC 16F 877A MCU: The micro-controller is used to monitor the overall operations and it controls the overall process. The selection of micro-controller is based on the application and the economy of the system. Here we use two microcontrollers to ease the programming and to get a faster response for the inputs. For both the microcontrollers we use PIC16F877A shown in fig 2&3. The first Micro-controller is used for HMI interfacing which is to convert serial inputs available from the HMI to Parallel commands to the second Micro-controller. The second MCU controls the motor drive operations and also gives appropriate signals to the protection circuit during abnormal conditions such as battery failure, Drive system failure, and loss of source supply.

Fig. 2: Pin diagram of PIC16F877A MCU

Fig.3: Architecture of PIC16F877A

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The function of LCD drive module is to assist the operator in knowing the current situation of the entire control system by displaying it in the LCD display. During warning conditions the LCD display is the only means through which the operator can identify the abnormality.The LCD display is used to display the input command which is given via HMI such as start, stop and also used to display any abnormal situations. Here a 16 x 2 LCD display is used. Here the LCD display is interfaced with the Motor drive Microcontroller unit shown in fig 4.

Fig.4: LCD display Fig. 5: 7805 pin diagram The 7805 IC is used in the power supply module to get a regulated 5V supply. The feature of this IC is that for any input voltage upto 15V the output of the IC will be exactly 5V. The output of this IC is used to supply for the MCU and the other ICs shown in fig 5.

Fig. 6: 7812 pin diagram The 7812 IC is used in the power supply module to get a regulated 12V supply is shown in fig 6. The feature of this IC is that for any input voltage up to 15V the output of the IC will be exactly 12V. The output of this IC is used to energize the relay coil.The communication standard used in the PCs is RS-232 standard. The RS-232 standard defines information being transferred between data processing equipment and peripherals are in the form of digital data which is transmitted in either a serial or parallel mode. Parallel communications are used mainly for connections between test instruments or computers and printers, while serial is often used between computers and other peripherals. In this system we use Max232 IC as a UART. It is used to provide serial asynchronous transmission between the HMI and the MicroController. Asynchronous transmission means serial transmission in which there is start bit followed by the data and followed by a stop bit is present. An example of asynchronous transmission is shown where the presence of a start bit and stop bit in addition to the data bit is shown in fig 7.

Fig.7: Data bit

Fig. 8: MAX 232 pin diagram

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Fig. 9: ULN drive circuit

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The function MAX232 is to adjust the level of the signals so communication can take place between the PC and the microcontroller. The signal level on a PC is -10V for logic zero, and +10V for logic one. Since the signal level on the microcontroller is +5V for logic one and 0V for logic zero. MAX232 is used as an intermediary stage that will convert the signal levels shown in fig 8.

Human Machine Interface: Human machine interface is the successor of Man machine interface (MMI). A MMI requires real time inputs which have to be physically given by the humans to change its state of operation. Whereas the HMI has the advantage of making the system to work on its own by giving instructions to it in beforehand without the need for a human every time. Here we use a computer as the HMI device. It is established with help of Visual basic8.0 which forms the faceplate for the motor control. The inputs from the HMI are fed serially to the MCU unit. The ULN is a relay driver IC. It triggers the relay circuit to switch between the Source supply and Battery supply. The ULN in turn is operated by the Micro-Controller Unit. Here we use ULN2803A IC shown in fig 9.

It is 18-PIN IC. The PIN diagram of ULN2803 is shown in fig 10.

Fig. 10: ULN 2803 pin diagram

Fig.11: Relay pin diagram

ULN 2803 function: The function of ULN 2803 is to trigger the 12V DC relay. The DC relay coil gets energized for 12V whereas the microcontroller unit’s output is 5V. This ULN 2803 supplies the relay coil with 12V supply whenever the microcontroller wants the source select circuit to switch between the supplies. In turn the ULN IC is controlled by the microcontroller. The ULN IC also supplies the lamps that indicate the abnormality conditions such as warning, trip.The source select switch is nothing but a relay which is used to switch the supply given to the drive circuit between source and battery. Here we use a 12v DC relay. The motor drive MCU gives control signal to the source select circuit to switch between the source and the battery supply which is based on the battery voltage. The battery voltage is fetched by the MCU in terms of the RPM of the generator which is calculated through dynamo tachogeneration. Here the relay coil gets energized at 12V whereas the MCU’s output voltage is 5V; therefore there is a need for relay driver.The relay is a switch that can switch between normally open and normally close terminals. The source supply is given to the normally close pin and the battery supply is given to the normally open circuit whenever the microcontroller senses the battery’s voltage reached the set value it triggers the relay to switch from source supply to battery supply shown in fig 11.The drive circuit is used to drive the DC motor depending on its gate signals. The gate signals to the drive circuit can be either given from a Pulse Width Modulator (PWM) or it can be directly from the Micro-controller with the help of appropriate coding. In this model we are using a separate MCU for drive circuit i.e the PWM signals are generated from the MCU itself. The drive circuit used in this system is a H-bridge network. An H-bridge network is shown below. The H-bridge network requires 4 diodes and a minimum of 2 MOSFET unidirectional operation of a DC motor is shown in fig 12.

Fig.12.Drive circuit .

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L298 pin diagram:

Fig.3.19 L298 pin diagram

Fig. 13: L298 pin diagram The function of L298 IC is to drive the DC motor. It is a driver IC. It is a 15- pin IC shown in fig 13. The MOSFETS that are used in the H-bridge network shown in fig 14 are realized through this IC which is shown in the fig. below. The figure shows that the entire H-bridge network is reduced in terms of size and components and hence reduces the complexity.

Fig. 14: High bridge network using L298 Fuse link/Diagram:

Fig. 15: Fuse link Fig.16: 5408 pin diagram Here in the prototype of the model we are going to use a permanent magnet DC motor since it does require an additional field circuit shown in fig 15&16. But this paper can be implemented to all the types of DC motor since we are employing armature voltage control for the speed control of the motor. Here we are going to implement line shafting which makes the motor to supply both the load and the generator Here a generator is coupled to the motor through line shafting .This generator which is coupled to the motor allows it to generate its own power supply. A stepper motor is used as generator in the prototype of the project. The use of stepper

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motor for generator allows the speed measurement of the motor to be obtained through DC dynamo tachometer.

A battery is used to store the power generated from the generator. The battery then supplies the drive circuit.

The prototype of this proposed system uses a 12 volt DC motor, Hence a 12V, 1.5A battery is used in this system.

III. Experimental Analysis Dead Time Analysis: Dead time is the time of values of the quantity being measured for which it gives no reading. The motor that does not respond at very low input voltage supply due to frictional forces exhibits nonlinearity. There is a dead time at each speed. Figure 17 shows a graph of dead time versus motor speed. From Figure 17, the dead time of the system is decreasing as the motor speed is increasing. This is because for higher speed, the error speed at starting up condition will be higher. So, more voltage will supply to motor to induce more T to overcome motor

frictional forces. Thus, the motor will start running earlier and the dead time become smaller.

Fig.17: Dead time analysis Power Consumption Analysis: As the power consumption of the motor drive and its control system is ``reduced, by the ‘regenerative power’ produced by the ‘generator’ which is directly or indirectly coupled with motor drive. The below table(1) and graph show the total amount of regenerative power produced utilized by the system.

Where Power = Voltage * Current, as we keep the current constant of 2A. Voltage (V)dc 5 7.5 12

Current (A)

Power(Watt)

Power consumption per hour(Wh)

2 2 2

10z 15 24

600 900 1440

Table 1: power consumption based on motor drive voltage supplied to DC motor Pr = Ps – Ploss, we take 20% of the power is loss due mechanical friction and magnetic induction loss by the generator, So we take generator of higher efficiency design 20% more than the driving motor Where, Ps  Source Power, Pr Regenerated Power Let the Drive Circuit Module utilized dc source supply of 70% and it uses 30% from the generator supply. The totally 70% of the power consumed by the motor drive module from DC Source supply, remaining 30% of the power is saved by the regeneration process.

To calculate the 70% of DC source power consumed in 1 hour: X = (70 * 60)/100 = 42 min To calculate the 30% of regenerative power consumed in 1 hour: X = (30 * 60)/100 = 18 min So we assume the motor drive runs on the source supply for 42 min/hour, and remaining 18min/hour is runs on the regenerated power shown in table 2.

Voltage (V) dc

Power (Watt)

Power consumption per 70% of an hour(Wh) 420

Power consumption per 30% of an hour(Wh) 180

5

10

7.5 12

15

630

24

1008

Total power Consumption per an hour (Wh) 600

Total power consumption saved per hour(Wh) 180

270

900

270

432

1440

432

Table 2: Total power Consumed and saved by the system per hour.

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Suppose the drive motor runs for 10000 hours annually on its max RPM, so power consumption the motor drive is 24,0000 unit. Where 15 INR per unit so for (15 * 24,0000) = 3,600000 INR , the 70 % is 2,520000 INR and 30% is 1,080000 INR shown in fig 18.

70% 2,520000 INR

30% 1,080000 INR.

Fig. 18: Shows the amount of power saved annually by the motor drive system DC Motor Speed Control Result: Microcontroller acts as proportional (P) controller in the DC motor speed control system. At each speed, the result was collected by applying normal load, overload and then suddenly for no load condition. The performance of the system at each speed is shown in Figure 19 to Figure 22 respectively.

Fig. 19: DC motor running at speed 190.74 rpm

Fig. 21: DC motor running at speed 762.95 rpm

Fig. 20: DC motor running at speed 572.21 rpm

Fig. 22: DC motor running at speed 1144.43 rpm

IV. Conclusion Recent developments in science and technology provide a wide range scope of applications of high performance DC motor drives in area such as rolling mills, chemical process, electric trains, robotic manipulators and the home electric appliances require speed controllers to perform tasks. DC motors have speed control capabilities, which means that speed, torque and even direction of rotation can be changed at anytime to meet new condition.

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The goal of this paper is to design a control system for the DC drive that would ease the operations at work and to reduce the total consumption of the drives by 30% which are successfully fulfilled.

References [1].

[2].

[3].

[4].

[5].

[6].

[7].

[8].

[9].

A.Emadi, Handbook of Automotive Power Electronics and Motor Drives.Boca Raton,FL:CRC,2005.

T.Kenjo and S. Nagamori, Permanent Magnet Brushless DC Motors.Oxford, U.K.: Clarendon, 1985.

I.Boldea, “Control of electric generators: A review,”in Proc. 29th IEEE IECON, Nov.2003,vol. 1,pp. 972-980.

A.Mirecki,X. Roboam , and F.Richardeau,”Architecture complexity and Energy efficiency of small wind turbines,”IEEE Trans.

Ind. Electron.Vol. 54, no. 1, pp. 660-670,Feb. 2007L. Parsa and H. Lei, “Interior permanent magnet motors with reduced Torque pulsation,”IEEE Trans. Ind. Electron., vol. 55, no. 2, pp.602-609, Feb, 2008.

Y.-S. Kung, “Design and implementation of a high performance PMLS Drives using DSP chip,”IEEE Trans. Ind. Electron., vol.

55, no. 3, pp. 1341-1351, Mar.2008.

L. Harnefors, “globally stable speed adaptive observers for sensorless Induction motor drivesm ,” IEEE Trans. Ind. Electron., vol. 54, no. 2, pp 1243-1245, Apr. 2007.

C. Xia, Z. Li, and T. Shi, “A control strategy for four-switch three-phase Brushless dc motor using single current sensor,” IEEE Trans. Ind. Electron., Vol. 56, no. 6, pp. 2058-2066,Jun. 2009.

J. T. Bialasiewicz, “Furling control for small wind turbine power regulation “in Proc. IEEE Int. Symp. Ind. Electron., 2003, vol. 2, pp, 804-809.

Norman S. Nise. Control Systems Engineering. 2nd Edition. Redwood City, California: The Benjamin/Cummings Publishing Company, Inc. 1995.

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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 Analysis of the consumer benefits and factors of life insurance to rural region of Odisha Bidyadhar Padhi Assistant Professor Silicon Institute of Technology, Patia, Bhubaneswar, Odisha, INDIA. __________________________________________________________________________________________ Abstract: Odisha is the 9th largest state by area in India, and the 11th largest by population. . There is availability of much untapped rural potential with regards to life insurance. It should be possible to reach them and persuade them to seek a suitable cover, in both life and non-life segments. This offers great market potential and will call for greater marketing effort. The present study is based on primary data collected from three hundred respondents covering three district of Odisha like Cuttack, Puri and Balasore. The study focuses the consumers’ benefit of life insurance to rural people. The study will also reflect the various factors responsible in insurance penetration in the rural market of the selected district of Odisha.

Keywords: Policy preference, satisfaction level, claim settlement, disclosure of all terms and conditions __________________________________________________________________________________________ I. INTRODUCTION A large population of India lives in the rural areas. The impact of risks associated with life and health are far more severe on this population as compared to the urban population with higher levels of income. In the wake of development plans of government and agriculture prosperity at least in some pockets, there is better disposable income in the hands of more affluent rural population of rural Odisha. A large number as compared to past can afford to buy insurance covers. There is availability of much untapped rural potential with regards to life insurance. It should be possible to reach them and persuade them to seek a suitable cover, in both life and non-life segments. This offers great market potential and will call for greater marketing effort. In fact, higher growth n the insurance business is likely to available in rural Odisha, but this hampered on account of the poor quality of simple infrastructure and means of communication. This puts a limit in the ease and ability of the insured to pay up their premium. As a consequence, the lapsation of polices in these areas is also large (P.S. Palande, R.S. Shah & M.L. Lunawat, 2003) Both the public sector and private sector insurance companies have taken many steps to increase the insurance penetration n the rural region of Odisha. The LIC of India has tackled rural insurance in three ways: 1. By selling individual policies to the most affluent rural section; 2. By selling group insurance policies to the not so well-off sections; and 3. By popularizing government subsidies group insurance policies to the weaker segment.

The LIC of India has successfully tapped this market and its business in rural Odisha is growing. The business of LIC is also growing in all rural parts of India and as around 30 per cent, of its business currently comes from rural areas (P.S. Palande, R.S. Shah & M.L. Lunawat, 2003). Since around 1998-1999, the number of insurance policies sold in villages has grown by an annual average of 18 per cent, compared to 3.86 per cent n the urban areas. Moreover, the policies being taken out in villages are not small.

The private companies have also appreciated the scope in this sector as stated by Anuroop Singh (2000), “We believe that the rural life insurance sector has tremendously potential. As part of our effort to the benefits of life insurance to larger cross section of the Indian population, Max New York Life will develop product for the rural sector. Other companies also indicated similar interest. In reality, however, no noteworthy development in this area is yet visible.”

II. Objectives The present paper attempts to study the different factors responsible for insurance in rural areas. It will reflect the types of policies preferred, degree of satisfaction, Satisfaction level with the policies of the claim settlement, Performance of branch staff with the customers, Satisfaction level with the disclosure of all terms and conditions, customers’ satisfaction level with the insurance company.

III. Data Collection The factor analysis is based on primary data collected from three districts like Cuttack, Puri and Balasore. A questionnaire was developed (given in annexure) and data are collected from 300 people. From each district 100 data are collected out of which 50 data are collected from urban areas and the rest 50 data collected from rural areas. The data are based on different questions and the result of each questions are given bellow.

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IV. Analysis and Discussion A. Preference of Policies By Customers The first question was among all the insurance company policies you have opted, with which policy are you satisfied the most? The result is given bellow: TABLE: 1 Company LIC of India ICICI PRU SBI LIFE BAJAJ MAX LIFE HDFC Total Per cent

Total 203 35 44 5 8 5 300 100

Traditional 168 24 34 4 5 4 239 80

Money back 33 10 9 1 3 1 57 19

unit link 2 1 1 0 0 0 4 1

Source: Compiled from primary data collected from consumers Graph: 1 300 250 200

Traditional

150

money back

100

unit link

50 0 Type of policy preferred

Source: Compiled from primary data collected from consumers From the above table and graph it is clear that most of the policy holders are satisfied with the traditional policy offered by the insurance companies. The rate is very high and around 80 per cent of the policy holders preferred traditional policy. Few policy holders also prefer the money back policy as they will get their money back in fixed time interval. 19 per cent of the policy holders preferred money back policy. The number of policy holder preferred for unit link policy is very less. Only one percent of the policy holder prefers this policy. This shows that all the insurance companies preferred to offer more traditional policies than other policies.

B. Degree of Satisfaction with the Insurance Company The second question was Degree of satisfaction with the above company. The result is given bellow: (Indicate your response with the help of 5 point Likert Scale) a.

Satisfaction level of the customers with the regulation policies and procedures of the insurance company.

TABLE: 2(Satisfaction Level of Customers) Company

Total

LIC of India ICICI PRU SBI LIFE BAJAJ MAX LIFE HDFC Total Per cent

203 35 44 5 8 5 300 100

1. Least important 10 2 3

13 4.3

2. Somewhat important 42 8 10 1 2 1 66 22

3. Important 105 15 17 3 2 1 143 47.7

4. Very important 25 6 9 1 4 3 48 16

5. Extremely important 21 4 5

30 10

Source: Compiled from primary data collected from consumers GRAPH: 2(Satisfaction Level of Customers) 200 1

150

2 100

3

50

4 5

0 Satisfaction level of customer

Source: Compiled from primary data collected from consumers

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From the above table and graph it is observed that most of the customers are very careful regarding the regulation policies and procedures of the insurance company. More than 75 percentages of the policy holders think that regulation and policies of the companies are very important. Few policy holders think that is less important. a.

Satisfaction level with the policies of the claim settlement.

TABLE: 3 Company

Total

1.Least important

2. Somewhat important

3. Important

4. Very important

5. Extremely important

LIC of India ICICI PRU SBI LIFE BAJAJ MAX LIFE HDFC Total Per cent

203 35 44 5 8 5 300 100

5 1 2

16 2 4 1 1 1 25 8.3

22 4 9 1 2 2 40 13.3

78 12 18 3 2 2 115 38.3

82 16 11

8 2.7

3 112 37.3

Source: Compiled from primary data collected from consumers GRAPH: 3 140 120 100

1

80

2

60

3

40

4

20

5

0 Satisfaction level with the policies of the claim settlement.

Source: Compiled from primary data collected from consumers Claim settlement is very important in life insurance. Every policy holder takes it as the extremely important factor while buying the policy. Form the diagram; it is clear that most of the respondents choose claim settlement is the priority factor in insurance. More than 97 per cent of the policy holders think claim settlement is important to extremely important. Only 2.7% of the share holders think that claim settlement is least important. It may be due to their ignorance about the insurance policy. b.

Satisfaction level with the disclosure of all terms and conditions of the company TABLE: 4 Company

Total

1. Least important

2. Somewhat important

3. Important

4. Very important

5. Extremely important

LIC of India ICICI PRU SBI LIFE BAJAJ MAX LIFE HDFC Total

203 35 44 5 8 5 300

35 3 12 1 1 1 53

26 8 2 1 2 2 41

85 14 12 1 2 1 115

28 6 10 2 2 1 49

29 4 8 1 42

Source: Compiled from primary data collected from consumers GRAPH: 4 140 120 100

1

80

2

60

3

40

4

20

5

0 Disclosure of all terms and conditions of the company

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The above table and diagram explained that most of the policy holders are not bother about the disclosure of all terms and conditions of the company. Most of them rely on the insurance agents and advisors. Policy holders think term and conditions are important but few of them read all the term and conditions in the proposal form and also in the bond paper. When they take the policy the insurance agents or advisors may mislead them and they may not get the appropriate policy as per their need. This is happened in case of the rural policy holders those have little knowledge about insurance. c.

Performance of branch staff with the customers TABLE: 5 Company

Total

1. Least important

2. Somewhat important

3. Important

4. Very important

5. Extremely important

LIC of India ICICI PRU SBI LIFE BAJAJ MAX LIFE HDFC Total

203 35 44 5 8 5 300

23 2 3 2 1 1 32

34 5 6 1 2 1 49

55 8 8 1 3 1 76

67 9 7 1 1 1 86

24 11 20 1 1 57

Source: Compiled from primary data collected from consumers GRAPH: 6 100 80

1

60

2 3

40

4 20

5

0 Performance of branch staff with the customers

Source: Compiled from primary data collected from consumers Performances of the branch staff play a vital role in creation of policy and also retaining the customers. When a customer satisfied with the branch staff, he may take another policy or may new customers for the company.

The table shows that most of the customers take the attitude of the branch staff very seriously. Only few people think it is least important. d.

Performance of the branch manager with the customers (relationship management) TABLE: 6 Company Total 1. Least 2. Somewhat 3.

4. Very 5. important important Important important Extremely important LIC of India 203 85 30 65 12 11 ICICI PRU 35 16 6 5 5 3 SBI LIFE 44 22 10 4 5 3 BAJAJ 5 2 1 1 1 MAX LIFE 8 3 2 2 1 HDFC 5 3 1 1 Total 300 131 50 78 24 17 Source: Compiled from primary data collected from consumers GRAPH: 6 140 120 1

100 80

2

60

3

40

4

20

5

0 Performance of the branch manager with the customers

Source: Compiled from primary data collected from consumers

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In insurance business customer relationship is very essential. But few policy holders meet the branch manager to discuss about the insurance before taking a policy. This work is done by the advisors or development officers. In case any problem faced by the policy holders, they try to meet the branch manager to solve the issue. In the table and graph it is showing that most of the policy holders take this factor as least important. But from insurance company point of view this factor is the most important factor to enhance their business.

C. Customers’ Satisfaction Level with the Insurance Company The third question was Give your independent opinion regarding the satisfaction level in the insurance company under the following sectors (Give tick mark for the higher satisfaction). The result is given bellow: TABLE: 7 SL No

Degree of Satisfaction

1

Satisfaction level with the regulation and procedures of the insurance company Satisfaction level with the policies of the claim Settlement.

80

Satisfaction level with the disclosure of all terms and conditions of the company Performance of branch staff with the customers Performance of the branch manager with the customers(relationship management) Higher maturity benefit of the policy Lower premium of the policy Other Benefits

2

3 4 5 6 7 8

Private Sector Insurance Companies

Per Cent

Per Cent

27%

Public Sector Insurance Companies (LIC) 220

56

19%

244

81%

95

32%

205

68%

165

55%

135

45%

190

63%

110

37%

112 144 105

37% 48% 35%

188 156 195

63% 52% 65%

73%

Source: Compiled from primary data collected from consumers GRAPH: 7 350 300 250 200 Private sector2 150

Public sector

100 50 0 Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

Factor 6

Factor 7

Factor 8

Source: Compiled from primary data collected from consumers From the above table and graph it is observed that in most of the factor the customers satisfaction is highest with the public sector insurance company i.e. LIC of India. In regulation and procedures of the insurance company 73% of policy holders are satisfied with LIC and only 37% are satisfied with the private sector. In claimed settlement 81% of the policy holders are satisfied with LIC and only 19% are satisfied with the private sector. In disclosure of all terms and conditions of the company, 68% of the policy holders are satisfied with LIC and only 32% are satisfied with the private sector. In Performance of branch staff with the customers, LIC of India is far behind the Private sector and most of the customers are satisfied with the private sector. In this factor 55% of the policy holders are satisfied with private sector whereas only 32% are satisfied with LIC of India. In customers relationship management the private sector companies are also ahead the LIC of India. In the factors like higher maturity benefit of the policy, lower premium of the policy and other benefits LIC of India is far ahead than the private Insurance companies.

V. CONCLUSION Before taking an insurance policy most customers consider many factors but in rural area most of the customers are rely on the insurance agent and advisors. The important factors are claim settlement, disclosure of all terms and conditions, performance of branch staff with the customers, and customers’ relationship management. Out of those factors most of the customers are satisfied with the LIC of India. But the private sector companies are also ahead than LIC of India, in the factors like, performance of branch staff with the customers, and customers’

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relationship management. Most of the insurance company offer three types of policies like traditional, money back and unit linked policies. Out those policies many of the customers are opt for traditional policies and then followed by money back policy. Few customers prefer unit link policies as the benefit of the policies are not certain and based on the stock market.

VI. REFERENCES [1] [2] [3] [4]

Anuroop Singh, (2000), Max New York Life, www.mawbupa.com Hazell Pbr. (1992), “The appropriate role of agricultural insurance in developing countries.” journal of international development 4: 567-581 Hazell and seek,( 2005), Insuring against bad weather recent thinking 1sharma sk. (ed.), dynamics of development: an international perspective (Delhi : concept publishing company, 1978), vol. i, p. 2. and food safety: 65-74.

P.S. Palande, R.S. Shah & M.L. Lunawat, (2003), “Insurance in India: Changing Policies and emerging Opportunities”, SAGE Publication India, 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 Brand Preference in Water Purifiers G.N. Prasaath Hr-Executive, IT Gateway Solutions Private Ltd. Erode, Tamil Nadu, INDIA. __________________________________________________________________________________ Abstract: Water purifier is necessary nowadays especially if underground water is used for drinking. Water purification is the process of removing undesirable chemicals, biological contaminants, suspended solids and gases from contaminated water. With the increasing demand for water purifiers from the health conscious consumers, companies like HUL, Tata, Eureka Forbes, Philips, Bajaj etc. have entered into the segment and have launched various brands of water purifiers. They vary in size, purification technology, prize, colour, patterns, usage etc. The present study aims at analyzing the brand preference of respondents towards various water purifiers available in the market. The study also focuses on the perception of consumer towards water purifiers, consumer awareness about different water purifier brands available in the market and their buying behavior of different water purifier brands.

Keywords: water purifier, reverse osmosis, sampling theorem. __________________________________________________________________________________________ I. INTRODUCTION Water purifier is necessary nowadays especially if underground water is used for drinking. Water purification is the process of removing undesirable chemicals, biological contaminants, suspended solids and gases from contaminated water. The goal is to produce water fit for a specific purpose. Most water is purified for human consumption (drinking water), but water purification may also be designed for a variety of other purposes, including meeting the requirements of medical, pharmacological, chemical and industrial applications. In general the methods used include physical processes such as filtration, sedimentation, and distillation, biological processes such as slow sand filters or biologically active carbon, chemical processes such as flocculation and chlorination and the use of electromagnetic radiation such as ultraviolet light.

The purification process of water may reduce the concentration of particulate matter including suspended particles, parasites, bacteria, algae, viruses, fungi; and a range of dissolved and particulate material derived from the surfaces that water may have made contact with after falling as rain.

The standards for drinking water quality are typically set by governments or by international standards. These standards will typically set minimum and maximum concentrations of contaminants for the use that is to be made of the water.

It is not possible to tell whether water is of an appropriate quality by visual examination. Simple procedures such as boiling or the use of a household activated carbon filter are not sufficient for treating all the possible contaminants that may be present in water from an unknown source. Even natural spring water – considered safe for all practical purposes in the 19th century – must now be tested before determining what kind of treatment, if any, is needed. Chemical and microbiological analysis, while expensive, are the only way to obtain the information necessary for deciding on the appropriate method of purification.

According to a 2007 World Health Organization (WHO) report, 1.1 billion people lack access to an improved drinking water supply, 88 percent of the 4 billion annual cases of diarrheal disease are attributed to unsafe water and inadequate sanitation and hygiene, and 1.8 million people die from diarrheal diseases each year. The WHO estimates that 94 percent of these diarrheal cases are preventable through modifications to the environment, including access to safe water. Simple techniques for treating water at home, such as chlorination, filters, and solar disinfection, and storing it in safe containers could save a huge number of lives each year. Reducing deaths from waterborne diseases is a major public health goal in developing countries.

Water Purification Methods Different water purifiers use different methods of purification, some have three purification stages and some five or more. A higher number of purification levels ensure cleaner drinking water. It’s necessary to know about what each stage does and then choose the purifier you think is the best for your home.

A. Pre Filter Purification A micron pre-filter is designed to remove silt, dust and dirt particles from the water before it enters the main filter of the unit.

B. Ultra-Violet (Uv) Filtration Ultra filtration removes dissolved solids of and above the size 0.005-0.1 microns. It is capable of removing solids, bacteria and viruses.

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C. Reverse Osmosis (RO) System These systems get rid of disease bearing bacteria and viruses, and pollutants such as insecticides and cancerous dyes. They make hard water drinkable and are best for those living in coastal areas.

II. LITERATURE REVIEW ASIAN J. EXP. BIOL. SCI. VOL 1 (4) 2010 Non electrical water purification system (WPS), comprised of a non woven sediment filter followed by activated carbon and disinfectant, was evaluated for chemical as well as microbial disinfection efficacy following the general guidelines of the United States Environmental Protection Agency Guide Standard and Protocol for Testing Microbiological Water Purifiers. The all selected water purifiers was challenged against bacteria i.e. waterborne pathogens including Escherichia coli 0157:H7. The WPS was tested for the removal/inactivation of challenge organism in separate 1500 litre of test periods under specific water quality, at the system's maximum recommended flow rate. For organism tested, microbial challenges were conducted over the course of the test period at 0, 50 and 100% of the system manufacturer's rated water treatment capacity. Microbial challenge consisted of 05 L of influent water containing approximately 10 bacterial cfu/ 100 mL. Emmanuel K. Boon and Luc Hens Tribes and Tribals, Special Volume No. 1: 111-120 (2007) This paper explores the link between water supply problems and traditional water purification knowledge and how this information could be applied to improve water supply situation and enhance sustainable development in rural communities. Questionnaires, focus group discussions and participatory approaches were used to capture information on the water supply situation in Singida rural district. The findings show that potable water supply coverage in the study area is less than 20% due to malfunctioning water schemes. About 95 to 100% of households in the study area obtain domestic water supply from rainwater reservoirs, 10–22% from boreholes and windmills and 20 to 60% use traditional wells. Up to 70% of women walk a distance of 6-7 kilometres to various water sources.

Alvaro E. Gil , Kevin M. Passino control for drinking water purification (2004) The disinfection of raw water plays an important role in environmental engineering. In this document we overview several feedback controllers proposed by different authors to purify the water contained in water distribution systems. Several techniques to purify the water and the sensors needed as part of the whole system are presented to provide an overview of the component and processes encountered in water treatment plants.

III. RESEARCH DESIGN In this study, a descriptive research design was used in to identify the factors influencing their brand preference of respondents towards water purifiers. Since most of data is qualitative in nature, the descriptive research method has been used.

Questionnaire Design: The study is descriptive and mainly relies on the primary data. So with the objective of studying the brand preference towards water purifiers, the respondents were contacted with the predefined questionnaire which consists of the questions sufficient enough to collect information for achieving the objectives.

Nature and Sources of Data: Primary data: The primary data is the data is derived from a new or original research study and collected the questionnaire. In order to achieve the above set of objectives the researcher for collecting the data uses survey method.

In this project primary data is collected from common people through questionnaire.

Secondary data: It refers to the statistical material which is not originated by the investigator himself but obtained from someone else’s records, or when primary data is utilized for any other purpose at some subsequent enquiry it is termed as secondary data.

In this project, the secondary data were the details about various water purifier brands available in the market. It was collected from many water purifier companies’ websites.

S.NO 1

VARIABLES Gender

2

Age Group

3

Educational Qualification

4

Occupation

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CATAGERY Male Female 20-30 YEARS 31-40 YEARS 41-50 YEARS Above 50 YEARS Up to higher secondary Graduate Post graduate Others Government Employee Private employee

RESPONDENTS 81 39 30 40 43 7 23 62 30 5 29 37

PERCENTAGE 67 33 25 33 36 6 19 52 25 4 24 31

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5

Annual income

6

Type of family

7

No of member

S.N0 1

VARIABLES Water Source

2

Quality of drinking water

3

Reason to buy

4

Brand of water purifier

5

Price of water purifier

6

Type of purifier

7

No. Of years usuage

8

Know about your brand

9

What made to choose particular brand.

10

Influenced to buy

11

Overall performance

12

Awareness about other brands

13

Perception about water purifier brands.

14

Income level influence buying behaviour.

15

Recommend your brands to others.

Business Professionals Others Less than 1,50,000 1,50,001-3,00,000 3,00,000-5,00,000 Above Nuclear family Joint family Upto 2 3-5 Above 5 CATEGORY Municipal Water Bore well/Submersible Canned Water Hand pump Pure Impure and unhygienic Clean,healthy drinking water Bacteria & virus free water Protection from water borne diseases Aqua Pure it Kent RO Whirlpool 0-5000 5001-10000 10001-15000 15001-20000 Direct Storage Less than one year Two years Three years More than three years Advertisement Marketing representatives Dealers others Brand image Price Design/elegance self Family members Friends/relatives Fully satisfied satisfied Less satisfied Fully aware Less aware unaware Prefer to buy Technology may be inferior.

Link price with quality Yes No yes No

35 9 10 12 21 59 28 89 31 15 71 34 RESPONDENTS 47 34 37 2 55 65 28 25 67 36 47 34 3 3 26 89 2 50 70 27 60 29 4 32 49 30 7 29 37 54 28 35 57 26 83 11 16 58 46 29 47 44 59 61 75 45

29 8 8 10 18 49 23 74 26 13 59 28 PERCENTAGE 39 28 31 2 46 54 23 21 56 30 39 28 3 2 22 74 2 42 58 23 50 24 3 28 41 25 6 24 31 45 23 29 48 22 69 9 14 48 38 24 39 37 49 51 63 37

Table1: Comparison of brands BRANDS

BRAND

PRICE

DESIGN

TOTAL

Observed frequency 11 11

Expected frequency 10 10

Observed frequency 13 21

Expected frequency 10 10

Observed frequency 12 15

Expected frequency 10 10

36 47

7

10

18

10

9

10

34

Whirlpool ro

0

10

2

10

1

10

TOTAL

29

Aqua guard Pure it Kent ro

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54

37

3 120

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Table 2: Analysis table on different purifiers BRANDS

Aqua guard

SELF/FRIENDS Observed frequency 31

FAMILY

Expected frequency 15

Pure it

32

Kent ro

21

15

Whirlpool ro

12

15

TOTAL

TOTAL

Observed frequency 5

Expected frequency 15

36

15

15

47

13

15

34

2

15

15

96

35

3 120

Table 3: Relational Comparison on different purifiers IV. CONCLUSIONS Through water purifier have their presence in the market. Most of the consumers are using these purifiers nowadays. Despite of that some customers are hesitating to adopt these purifiers because of the problems they are facing earlier while using some other brands. So after the analyzing overall summary of this research, I would like to give few recommendations to these purifiers company. I hope this recommendation certainly is a great help of them.  Companies should more focus on the after sale services and customer care facility.  Companies should also focus more on the advertisement by using celebrity as it boosts their brand image as well as the scale by water purifier.  Consumers are health conscious so companies must have to improve the purification quality of their purifiers.  Purifiers companies have to look after the operational feature, design, price and performance of their products.

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

Allen, C. T., Machleit, K. A., & Kleine, S. S. (1992). A comparison of attitudes and emotions as predictors of behavior at diverse levels of behavioral experience. Journal of Consumer Research, 18(4), 493-504.

Andaleeb, S. S., & Caskey, A. (2007). Satisfaction with food services: Insights from a college cafeteria. Journal of Foodservice Business Research, 10(2), 51-65.

Andaleeb, S. S., & Conway, C. (2006). Customer satisfaction in the restaurant industry: An examination of the transactionspecific model. Journal of Services Marketing, 20(1), 3-11.

Areni, C. S. (2003). Exploring managers‟ implicit theories of atmospheric music: Comparing academic analysis to industry insight. Journal of Service Marketing, 17(2), 161-184.

Baek, T. H., Kim, J., & Yu, J. H. (2010). The differential roles of brand credibility and brand prestige in consumer brand choice.

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Bolton, R. N., & Drew, J. H. (1991). A multistage model of customers' assessments of service quality and value. Journal of Consumer Research, 17(4), 375-384.

Chow, I. H.-s., Lau, V. P., Lo, T. W.-c., Sha, Z., & Yun, H. (2007). Service quality in restaurant operations in China: Decisionand experiential-oriented perspectives. International Journal of Hospitality Management, 26(3), 698-710.

Hellier, P. K., Geursen, G. M., Carr, R. A., & Rickard, J. A. (2003). Customer repurchase intention: A general structural equation model. European Journal of Marketing, 37(11/12), 1762-1800.

Kim, W., Ok, C., & Canter, D. (2010). Contingency variables for customer share of visits to full-service restaurant. International Journal of Hospitality Management, 29(1), 136-147.

Kim,W. G., Han, J. S., & Lee, E. (2001). Effects of relationship marketing on repeat purchase and word-of-mouth. Journal of Hospitality & Tourism Research, 25(3), 272-288.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net An analysis on different experiential value sought by travel website users 1

Mrs.Veto Datta, 2Dr.S.Vasantha 1 Research Scholar, 2Professor Vels University, P V Vaithiyalingam Road, Velan Nagar, Pallavaram, Chennai, 600117, Tamil Nadu, INDIA. __________________________________________________________________________________________ Abstract: Travel and Tourism have for a long time been one of the top categories of websites visited by internet users. Internet user choose to visit travel and tourism websites to search for information or to buy travel and tourism products such as airlines tickets, accommodation or their full travel packages. To increase the effectiveness of a business website the manager should consider both advertisement strategy and other strategy to enhance customer experience. It is important to understand the customer value concept and its perception.

The main purpose of the research is to measure customer’s perception about the customer value. The concept of consumer value is based on (Holbrook, 1994; 1999) is used. Anonymous data was collected from 200 online travel websites users. Factor analysis was used to examine the objective of the study. Finding showing that the different type of experiential values is not same in terms of influencing the customer’s perceptions.

Keywords: Customer perception, Experiential value, Self oriented value, Other oriented value, travel websites ________________________________________________________________________________________ I. Introduction Now a days people are using internet because it is convenient, time saving, accessible. They are using internet to communicate, get information, buy products, consume products, voice opinions etc By providing various services related to different field, Internet is very famous for satisfying people .Around the world we can get any type of information on any product by using internet facility. Along with the facility of finding various services over internet, travel related searches are one of the most frequently used facilities in internet. With the increasing popularity of internet business around the world now they are trying to enhance their marketing and competitive strategy by focusing on resources on the virtual business world. Websites plays influencing role of a vehicle for the companies to communicate with consumer.(Karson and korgaonkar,2001).

Travel and tourism websites is one of the top visited websites by internet users (Lexhagen, 2008).Travel and tourism website can be used to search information, or buy travel planet to book tickets etc. Internet has become an important travel service channels for the customers. The presence of numerous travel websites creates difficulties for the managers and increases their competition with the others The value is termed as the value that motivated consumption behavior has been attributed to functional, conditional, social, emotional and epistemic utility. (Lee and Overby 2004).The importance of offering experiential value leads to positive outcomes such as customer satisfaction (Brakus, Schmitt and Zarantonello 2009) ,loyalty (Keng et al. 2007,Datta 2013 ) purchase intentions (Datta 2014) and customer emotions( Tsaur,Yi-Ti and Wang 2006) .

Perceived value is characterized as the essential outcome of marketing activity and as a primary motivation for entering into marketing relationships (Holbrook, 1994).For creating and managing such relationships, online providers need to develop websites that are sensitive and cater to the full range of components that define experience-based value ( Sigala 2014).

II. Literature of Review A. Customer value Hoolbrook (1994,9.27) asserted “ Value is an interactive relativistic preference experience” Holbrook proposed typology of consumer value with two Dimensions:Extrinsic/Intrinsic- The consumer perceives value in using or owing a product or service as a mean to an end versus an end in itself.A shift from Traditional marketing to experiential marketing was mentioned by Schmitt (1999). In today’s service economy, many companies simply wrap experiences around their traditional offering to sell them better.(Pine and Gilmore 1998, p.98).According to these author experience occur “when a company intentionally uses services as the stage , and the goods as props, to engage individuals customers in a way that creates memorable event.” .(Pine and Gilmore 1998, p.98)Later in 1999 Holbrook added the third dimension of self-oriented vs other oriented to his categorization of values. Self/Other oriented-The consumer perceives value for the consumer`s own benefits versus for the benefits of others. Oriented value is considered to be more typical and complex and according to (Oliver 1999) it is less related to consumer behavior.So it be be interesting to study the custpmer perception about all the eight value mentioned by Holbrooks.

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Self-oriented

Active Reactive

Other- oriented

Active Reactive

Extrinsic Efficiency (Output/input, convenience) Excellence (Quality) Status (Success ,impression Management) Esteem (Reputation, materialism, possessions)

Intrinsic Play (Fun) Aesthetics (Beauty) Ethics (Virtue ,Justice, Morality) Spirituality (Faith, Ecstacy, Sacredness, magic)

Table 1: Typology of consumer value (Holbrook, 1999) (Lexhagen, 2008) have also used the same model of Holbrook to explain the customer perceived value form the travel websites. Mathiwick et al.(2001)develop d experiential value scale that focused on self-oriented Dimensions of experiential value based on subset of Holbrook’s (1999).Same EVS scale is used by same author (Mathwick et al., 2002).The customer perceived value dimensions and its expressions in tour and travel websites and how it is created is identified by author (Lexhagen 2013).In contaxt of tourism value is under the debate from long ago.The quality-value-sataifcation its relationships and its impact is established by (Gallarza et al 2013).

III. Research Objective To study the different experiential value sought by travel website users?

IV. Research Methodology The study aimed to examine the different experiential values sought by online tourists. To capture the full typology of Holbrook the appropriate models is used for measuring the research constructs. The quantitive approach is used for the study. Mathwick et al`s (2001) EVS is used for self-oriented value and the other oriented value is borrowed from Soltani (2013). The research design is descriptive in nature. The simple random sampling is used for the study. The sample consists of online users, both working adults and students. The 5 point likert scale is used ranging from strongly disagree to strongly agree. The factor analysis is used to find out the objective of the study. The Stastical Package for Social Science (SPSS) software was used for the analysis of the collected data.

V. Data Analysis and Interpretation Total 235 questionnaires were distributed. Overall 200 data was collected for study. Among the gender of the respondent for the received data 57% was male. Most of the respondents are using travel website (59%) for booking the travel plan are professionals (34%). Most of the respondent are aware about internet using it from for their booking.

Table 2: Analysis of different experiential value.

S no

1 2 3 4 5 6

7 8 9

10

11

Statements

Mean

SD

ᾳ

Aesthetics (Visual Appeal)

5.12

0.69

.804

The way this travel website displays products is attractive This travel website is aesthetically appealing I like the way this travel website looks Entertainment value I think this travel website is very entertaining.

The enthusiasm of travel website is catching/picks me up.

This travel website does not sell- it entertains me.

Playfulness

Factor Load

.712 .653 .695 .642 .771 .635 4.04

.71

.758

Shopping from this travel website “get me away from it all” Shopping from this travel website takes me in another world I get so involved when shopping from this travel website that I forget everything else Enjoyment

.678

I enjoy shopping from this travel website for its own shake, not just for items I may have purchased I shop from this travel website for the pure enjoyment of it CROI Efficiency

.731

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.639 .764

.650 6.21

1.06

.898

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13 14

15 16 17

18 19 20 21

22 23 24 25 26

27 28

29 31

32 33 34 35 36

37 38 39

Shopping from this travel website is an efficient way to manage my time Shopping from my preferred travel website makes my life easier Shopping from this travel website fits with my schedule Economic Value This travel website products are a good economic value Overall, I am happy with this travel websiteâ&#x20AC;&#x2122;s prices The prices of the product(s) I purchase from this travel website are too high, given their quality and features Service excellence When I think of this travel website, I think of excellence I consider this travel website as an expert in travel products This travel website puts customer satisfaction as the number one priorty.

This travel website provides best service to each customer Ethics (Privacy and Security) This travel website appears to offer secure payments methods This travel website protects my personal information during transaction This travel website is credible.

You get what you ordered from this travel website This travel website displays the conditions of the online transaction before the purchase has taken place I can confirm the details of the transaction before paying Only the personal information necessary for the transaction to be completed needs to be provided Spirituality I feel that i do something that has a sense when i purchase from this travel website Shopping from this travel website is scared activity Status This travel website benefits from a good image on behalf of individuals The volume of sales achieved on this travel web site is very important.

This travel website benefits from a good status(place/position This travel website is very successful The service provided by this travel website is of high mark Esteem This travel website is very helpful Products/ services sold on this travel website are very interesting to me I love this travel website

.867

.814 .965

.799 .876 .843

6.98

0.74

.732 .745 .689 .892 .865

6.32

1.85

.721 .791 .653 .698 .741 ..760

.698 .651

4.65

.091

.613 .611 .634

5.15

1.38

.713 .765 .612 .632 .691 .760

4.87

.59

.798 .768 .753 .683

VI. Findings and discussions The study shows that the experiential value sought by the respondents in their preferred travel websites greatly impact them cronbachâ&#x20AC;&#x2122;s alpha were computed for testing the reliability of the all experiential value factors. As the reliability ranged from .613-.898, i.e higher than the acceptable level of 0.60 (Nunnally, 1978).The reliability test was passed. Loading factor of each item confirmed the uni-dimensionality of all eight constructs of experiential value. The above table shows from the self-oriented value of Holbrooks theory the Customer ROI and service excellence have higher mean score ( 6.98-6.21 ) in comparison of playfulness and Aesthetics value. The Playfulness of the website has lowest mean score. The CROI and service excellence have influenced customer preference .the results are quite similar to (sigala,2013)where the author stated that utilitarian value

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influence the customer preference. The (Mathwick et al 2001) also shows that CROI potentially yield a return.

The consumers show favorable response towards the second dimension of experiential value.There are very few studies who have studied the other- oriented value (Gallarza,2013, Lexhagen 2008, Soltani 2013) The other oriented value consist of ethics, spirituality, status and esteem. The first value “involves dealing in a manner that respects the rights and dignity of others (consumers)” ( Guilla 2004).Consumer will automatically prefer the website if it protects their personal information, as per (soltani2013) this type of value is more searched by the consumers. The mean score of ethics (6.32) is highest among all the searched value. The second value spirituality refers to “one experience of meaning and purpose in life” (Skokan and Bader, 2000).As the mean score of this value is lowest among the others. This value has low impact on customers we had done survey.

The third value status is referred as “an evaluation or as an assessment of how good or bad the site is” (Richins, and Dawson 1992). The values studies by (Soltani2013) towards ethics customers shows the favorable response or customers are very much influenced by the ethics of the websites.

VII. Conclusion The study aimed to investigate how the different experiential value sought by consumers. The customer perceived value is complex theoretical concept (Lexhagen 2008) but it is considered to be the central theory of marketing as the perceived value impacts consumers behavior (Dodds et al., 1991; Grewal et al., 2003; Parasuraman and Zinkhan, 2002)) The main input of this study is probably its attempt to discover the concepts of customer-perceived value, using Holbrook hierarchy model and the typology of consumer value, in a context of tourism website. The CROI, service excellence and ethics seems to be perceived to great extent by the customer using travel websites.

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Holbrook, M. B. (1994d). Defining Service Quality. In Rust, R. T. & Oliver, R. L. (eds.) Service Quality – New Directions in Theory and Practice. Sage Publications, USA. Holbrook, M. B. (1996). Customer value – A framework for analysis and research. In Corfman, K. P. & Lynch, J.G. Jr. (eds), Advances in Consumer Research, 23, Provo, UT:Association for Consumer Research, pp. 138-42.

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A Study of the Software Development Using Agile Divya Prakash Shrivastava Computer and Information Science Higher Colleges of Technology Abudhabi, UAE

________________________________________________________________ Abstract: Software development methodologies are constantly evolving due to changing technologies and new demands from users. While agile methods are in use in industry, little research has been undertaken into what is meant by agility and how a supposed agile method can be evaluated with regard to its veracity to belong to this category of software development methodological approaches.Here, one study conducted on some well-known agile methods. This information is shown to be useful, for when constructing a methodology from method fragments (method engineering) and when comparing agile methods.

Index Terms: Behavior Driven Development, Test Driven Development, Software Engineering, Agile Method. ______________________________________________________________________________________ I. INTRODUCTION To develop software, companies have tried many different techniques. In the past, programming an application was thought of as a solving a problem, and the method for solving the problem was thought to only be as good as the software that was created. The object-oriented paradigm plays a prominent role in the development of many modern software systems. The different structure and behavior of object oriented software helps in solving or mitigating several problems of procedural software [1].

Fig. 1: Waterfall Development Model Agile Development is a term used to define a modular approach to development. These incremental approaches have been used since the late 50's. The Agile philosophy focuses on communication with the client and clean, iterative development. Agile development also descended into development strategies known as Extreme Programming. Extreme Programming and Agile Development use iterations for development iterated thorough a full development cycle, from conception to production. Before the iterative development strategy that Agile Development utilizes, development was normally called a waterfall model (Fig. 1).Agile methodology has become the main stream of software development due to its ability to generate higher customer satisfaction. It is fast and flexible methodology that recommends to incorporating last minute changes and requirements provided by the customer at any stage of software development phase [2] [3].

II. AGILE METHODOLOGIES Agile development provides opportunities to assess the direction throughout the development lifecycle. This is achieved through regular cadences of work, known as Sprints or iterations, at the end of which teams must present a potentially shippable product increment. By focusing on the repetition of abbreviated work cycles as well as the functional product they yield, agile methodology is described as “iterative” and “incremental.”

Agile development is supported by a bundle of concrete practices suggested by the agile methods, covering areas like requirements, design, modeling, coding, testing, project management, process, quality, etc. Some notable agile practices include:  Acceptance test-driven development (ATDD)  Agile Modeling  Backlogs (Product and Sprint)  Behavior-driven development (BDD)

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 Feature Driven Development (FDD)  Continuous integration (CI)  Domain-driven design (DDD)  Dynamic Systems Development Method (DSDM)  Iterative and incremental development (IID)  Pair programming  Autism spectrum disorder(ASD)  Refactoring  Scrum meetings  Test-driven development (TDD)  Agile testing  Use case  User story  Story-driven modeling  Velocity tracking Agile methods have been extensively used for development of software products and some of them use certain characteristics of software, such as object technologies.

A. Test Driven Development Test Driven Development (TDD) is the core part of the Agile code development approach derived from eXtreme Programming (XP) and the principles of the Agile manifesto.

Write a Test Run the Test

Test

Modify Code

Run the Test Remove Bug Test

Refactor

Run the Test

Test

Fig. 2: Test Driven Development The TDD is not a testing technique, rather a development and design technique in which tests are written prior to the production code. The tests are added its gradually during its implementation and when the test is passed, the

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code is refactored accordingly to improve the efficacy of internal structure of the code. The incremental cycle is repeated until all functionality is implemented to final [4].

This is it. This is the simplest way of explaining TDD in my opinion. Now, the main focus of TDD will be on testing the low-level functionality and units (for single classes and methods) that can lead to more flexible code and easier refactoring. In a simple language we can say, we write these tests to check if the code we wrote works fine.

B. Agile Specification Driven Development This is a technique that combines both- TDD and Design by Contract (DbC). DbC –this allows a language to declare invariants for the class and pre and post condition for a method. It was given by Bertrand and Meyer.

Microsoft has introduced their version of DbC into .NET framework, called code contracts. Meyer has also introduced an approach called Quality-first Model to software development. Another developer described Quality approach as:  Write code as soon as possible because then supporting tools immediately do syntax, type and consistency checking.  Current set of functionality should be working before moving to next. Abnormal cases must be dealt with. Interwine analysis, design and implementation  Always have a working system. Get cosmetic and style right.

The DbC approach is at core of Quality-first model. Similarities in DbC and TDD: In both the approaches one unit of functionality must be finished before moving to next. Differences: in TDD, it asks the developer to focus on the most common case, whereas DbC expects the developer to focus on abnormal cases first. Acceptance Test Driven Development. Now, let us consider another approach which is Acceptance Test Driven Development (ATDD) that adds ‘A’ before TDD which stands for Acceptance. Now, why was this even needed? Wasn’t the TDD good enough and better? I would say no and the reason was that TDD was more of telling to make sure the code works fine but it did not say that if the code that is written was even required at first place. In ATDD the acceptance criteria are defined in early in application development process and then those criteria can be used to guide the subsequent development work. ATDD helps developers to transform requirements into test cases and allows verifying the functionality of a system. A requirement is satisfied if all its associated tests or acceptance criteria are satisfied. In ATDD acceptance tests can be automated. TDD and ATDD are adopted widely by the industry because they improve software quality and productivity [5, 6]. ATDD is a collaborative exercise that involves product owners, business analysts, testers, and developers. ATDD helps to ensure that all project members understand precisely what needs to be done and implemented. However, many developers find themselves confused while using TDD and ATDD in their projects, “programmers wanted to know where to start, what to test and what not to test, how much to test in one go, what to call their tests, and how to understand why a test fails” [6]. Some of the problems of TDD and ATDD are that they are focused on verifying the state of the system rather than the desired behaviour of the system, and that test code is highly coupled with the actual systems’ implementation [8, 9]. In addition, in these approaches unstructured and unbounded natural language is used to describe test cases which are hard to understand [6].

C. Behavior Driven Development To develop software, companies have tried many different techniques. In the past, programming an application was thought of as a solving a problem, and the method for solving the problem was thought to only be as good as the software that was created. As higher level languages evolved, software development became more popular and software became increasingly mainstream. Therefore the method that a developers used to create software came into question. Behavior Driven Development (BDD) was born out of that evolution. BDD has a strong focus on collaboration during software development, much more than it's predecessor Test Driven Development (TDD). BDD's focus does not end with developer collaboration, but instead attempts to bridge the gap between the developer and client, where features of an application are defined by how a user would interact with them rather than a feature being defined in technical terms. BDD bridges the gap between the developers and the stakeholders in software by providing a clear concise methodology to develop software. The basic work flow for software developed using BDD techniques requires the developer to communicate with the shareholders, and it requires developers to create a well defined picture of the application. Then, through the development process, shareholders communicate with the developer to shape each individual feature to the specific goals of the shareholders. This approach creates a dialogue between the developer and shareholder to create an end application that meets the specifications much more closely than applications developed in any other way.

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Fig. 3: BDD Implementation Cycle III. . COMPARATIVE STUDY So let us discuss about some difference between these terms and find out how are they different from each other.  As discussed earlier, the immediate obvious difference between TDD and ATDD is the ‘A’. While that may sound sarcastic, the point is that TDD (as usually practiced) has an implied U on the front, standing for Unit, while the A stands for Acceptance. TDD focuses on the low level, ATDD on high level.

Table 1: Summary Of Software Development Of Life Cycle Of Popular Agile Methods 



So if ATDD leans towards the developer-focused side of things like [U]TDD does, the BDD is where the step of making it more customer-focused comes in. BDD is usually done in very English-like language, and often with further tools to make it easy for non-techies to understand. This allows much easier collaboration with non-techie stakeholders, than TDD. By contrast, TDD tools and techniques are usually much more techie in nature, requiring that you become familiar with the detailed object model (or in fact create the object model in the process, if doing true test-first canonical TDD). The typical non-programming executive stakeholder would be utterly lost trying to follow along with TDD, let alone participate, and frankly shouldn’t be involved with that level of detail.

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

BDD focuses on the behavioral aspect of the system rather than the implementation aspect of the system that TDD focuses on. BDD gives a clearer understanding as to what the system should do from the perspective of the developer and the customer. TDD only gives the developer an understanding of what the system should do.

IV. CONCLUSION TDD and BDD practice follows the same basic idea as Acceptance Test-Driven Development, where acceptance tests are elaborated for each user story and automated as the software is built. BDD is more than TDD because it focuses on collaborating with business people. Dan North, who originated the BDD moniker, noticed that business people switched off in conversations about "tests" as these seem to be too technical. He hoped that framing conversations about "behaviours" would be a way to engage the whole team. BDD is about figuring out what to build that helps flesh out behaviour. BDD isn't an Agile framework or project management approach.

Teams using Scrum or Kanban with BDD will need to figure out what to put in their backlogs and boards for planning purposes. You might want to measure the number of BDD scenarios delivered instead of velocity but often these scenarios are too fine-grain for release planning purposes.

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A. Cockburn, Agile Software Development, Pearson Education, Asia, Low Price Edition, 2007.

Divya Prakash Shrivastava, Rachapudi. V. Lakshminarayan, Sagaram V.S.L. Sujatha, (2010) New Engineering Technique of Software Development in IEEE eXplore, on Computer Technology and Development,671-676, ISBN 978- 1-4244-8845-2/ 10 D. Janzen and H. Saiedian. Does Test-Driven Development Really Improve Software Design Quality?. IEEE Software. vol. 25, no. 2, 2008..

A. Gupta and P. Jalote. An Experimental Evaluation of the Effectiveness and Efficiency of the Test Driven Development. In Proc. of Empirical Software Engineering and Measurement , 2007, pp.285-294.

D. North, Introducing BDD, 2006. Available at: http://dannorth.net/introducing-bdd.

D. Chelimsky, D. Astels, Z. Dennis, A. Hellesoy, D. North. The RSpec book: Behaviour Driven Development with RSpec, cucumber and friends, Pragmatic Bookshelf, 2010.

D. Astels, A new look at test driven development, http://techblog.daveastels.com/files/BDD_Intro.pdf .

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

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net VIRTUAL ENTERPRISE AND THE FAST FOOD INDUSTRY: A CASE STUDY OF FAST FOOD OPERATORS IN EDO STATE ADANNA.E. ONONIWU Project Manager Penthouse, Learning Resource Centre Edo State, Nigeria _______________________________________________________________________________________ Abstract: Being the most populous black nation in the world; with a population of about 166 million, largest market in sub-Saharan Africa and the 24th largest economy in the World, Nigeria’s Food, Beverage and Tobacco industry is set to grow. Currently, it accounts for 4.62 percent of the nation’s gross domestic product (GDP). The Association of Fast Food and Confectioners of Nigeria (AFFCON) has noted that the fast food industry is currently worth about 250 billion naira, with a growth potential that is next only to the petroleum industry. Virtual enterprise brings about efficiency, competition on a global scale and improved customer service. This paper seeks to address how virtual enterprise could help the fast food operators in Nigeria and the fast food industry in general Key words: Virtual Enterprise, Fast food industry, Fast food operators, Virtual Industry Cluster, Collaborative Network. _____________________________________________________________________________________ I. Introduction In recent times, there are lots of competitions among fast food operators in Nigeria. Food inflation in Nigeria has been on a downward trend from 14.1percent in October 2010 to 9.7 percent in October 2011. Nigerian fast food industry experienced spike in growth in 2013, grossing a total of 230 billion naira in turnover up from the 200 billion naira in 2012. The fast food target market accounts for about 30.5 per cent of the population, approximately 51.8 million persons. However, most fast food raw materials are imported with the exception of perishables such as vegetables. The supply of these food items in Nigeria is restricted due to the ban on importation on some of these items as well as high tariffs/duties on others in a bid to develop local markets. This has led to high prices for the Industry’s raw materials. In spite of some challenges, the fast food industry remains resilient with operational efficiency and service delivery as key to the fast food restaurants’ ability to generate revenue in a highly competitive business. In addition, menu innovation, food price and location (catchment area) are important to the success of a fast food industry. The shift to virtual enterprises or organizations is a response to unprecedented customer expectations and alternatives, global competition, time compression, complexity, rapid change, and increased use of technology [1]. A virtual enterprise network consists of heterogeneous components located in different places. The adjective "virtual" can be interpreted as "artificially educated, "or as" a sham that does not exist in real physical space "or as" extended through the joint resources. According to the National Industrial Information Infrastructure Protocols consortium (NIIIP), virtual enterprise (VE) is a temporary consortium or alliance of companies formed to share costs and skills and to exploit fast changing opportunities. The Virtual enterprise does not exist in the physical sense but only on an electronic network representing a partnership of businesses existing as a nebulous form of business organization that only exists to meet a market opportunity [2]. Virtual enterprise brings about competition on a global scale, efficiency and improved services. In turbulent conditions, strategic alliances could lead to better improved quality of service.

Figure 1: Strategic Alliance leads to quality product or service

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II. Benefits Collaboration among food operators, companies, organizations in the food industry will provide the following benefits: 1. Opportunities: Being in a network offers opportunities for exchange of ideas which breeds innovation 2. Resource Optimization: When small companies or organizations share business risks, knowledge and infrastructures, invariably resources are optimized.

3. Agility: being able to recognize, react and cope with unpredictable changes in the market 4. Increased Competition and Quality: Being in a network, the company or organization will be fully aware that quality is key thereby leading to increased competition.

III. Case Study Edo State is located in the South-South of Nigeria and is the cradle of arts, culture, bronze casting and wood works. Its capital is Benin City and it has a projected population of about 5 million people with a total of 18 Local Government Areas. It is bounded in the north and east by Kogi State, in the south by Delta State and in the west by Ondo State. There are currently several food operators in the state such as Mr Biggâ&#x20AC;&#x2122;s, Chicken republic etc Some of the challenges faced by fast food operators include: i. Power: About 25 per cent of profits made by most fast food operators in the country is spent on alternative energy generation. Most fast food operators in Edo State spend money buying fuel to power their generators. ii. Poor Information infrastructure: Infrastructure facilitates the production of goods and services, and also the distribution of finished products to markets. Information infrastructure encompasses people, processes, procedures, tools, facilities, and technology which support the creation, use, transport, storage including the destruction of information. iii. Multiple taxes imposed by government agencies: government agencies levy most fast food operators thereby making it difficult for them to maximize profits. iv. Lack of trainings for staff: Some fast food operators do not help their staff advance in their careers through trainings neither are the staff disciplined if required by professional associations.

A collaborative network (CN) among fast food operators in Edo State and the fast food industry in general will lead to increased productivity, quality and flexibility.

IV. Architecture of VE Various entities (organizations and people) are involved in a virtual enterprise. These entities are largely distributed, autonomous and heterogeneous. The partners involved in virtual enterprise share resources, information and responsibilities to achieve a common goal (which could be to create a product or provide service). Virtual enterprise (VE) requires strong technology infrastructure and designing effective VE depends on the effective deployment of advanced information technologies. There are various pillars of virtual enterprise such as virtual industry cluster (VIC), virtual breeding environment (VBE), virtual enterprise broker (VEB), virtual master organization (VMO).

V. Fast Food Operators and the Virtual Industry Cluster Virtual industry cluster (VIC) represents a group of companies, typically located in the same geographical region and operating in a common business sector, developing some sort of bonds among themselves in order to increase their general competitiveness. These bonds may include sharing some buyer-supplier relationships, common technologies and tools, common buyers, distribution channels or common labour pools, all contributing to some form of collaboration when business opportunities arise. This requires strong information and communication technology (ICT) infrastructure [3].

VI. Framework for VE for Fast Food operators A 4.

Marketing and Promotion

A 2.

Strategic Managemen t

Information and Communication A 1. Supply Technologies as the backbone Chain

A 3. Quality Assurance and Control

Virtual Enterprise

Figure 2: Framework for VE for Fast Food operators

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The various clusters of proposed VE are described here: 1. A 1-Supply Chain Cluster 2. A 2-Strategic Management (Master Company) 3. A 3-Quality Assurance and Control Cluster 4. A 4-Marketing and Promotion Cluster Supply Chain Cluster: This consists of raw material suppliers to the fast food operators. The benefit will be improved bargaining power for purchase of raw materials due to bulk purchasing.

Strategic Management (Master Company): This involves formulation and implementation of the major goals and initiatives taken by a company's top management on behalf of owners, based on consideration of resources and an assessment of the internal and external environments in which the organization competes [4]. The master company will be responsible for formulating and implementing major goals, management strategies (human and financial management) and initiatives and it is responsible for coordinating all activities in the VE. Quality Assurance and Control Cluster: This is a procedure or set of procedures intended to ensure that a product or service adheres to a defined set of quality criteria or meets the requirements of the client or customer.

This cluster consists of organizations & individuals who may not be geographically concentrated at a single locality. All are connected through Information and Communication Technology (ICT) network for faster and better sharing of information, resources, and responsibilities. To implement an effective quality assurance and control program, an enterprise must first decide which specific standards the product or service must meet.

Marketing and Promotion Cluster: This consists of organizations/individuals that spread word about a product or service to customers, stakeholders and the broader public. This cluster promotes the fast food company and also gets feedback from clients and customers.

VII. Seven Loops of Collaboration A paradigm known as the “Seven Loops of Collaboration” in managing virtual enterprises in the 21 st century was proposed by the author in 2011. The ‘Seven Loops of Collaboration’ seeks to proffer solution (s) to the various challenges affecting the virtual enterprise. In proposing the paradigm, the author took into consideration the following key management competencies: Decision Making, Risk Taking, Managing Change, Communication, Results, Team Development, Strategic Planning, Information Management, Customer Service and how it affects the success of the Virtual Enterprise. To achieve reliable information sharing, the Virtual Enterprise networks require boundaries that are protected from external penetration.

Strategy Control and Trust

Change in the Market

Information and Communication Technology

Authentication and Encryption

Information and Knowledge Management

Competency and Resource Structure

Figure 3: Author’s Paradigm on Managing Virtual Enterprise in the 21st Century VIII. Conclusion Virtual enterprise brings about competition on a global scale and provides a new approach for competition in the fast changing business environments. Virtual enterprise could be the answer to some challenges faced by fast food operators in Nigeria and the fast food industry in general but optimal communication and trust between member companies is essential.

References [1] [2] [3] [4]

Greiner, R., and G. Metes. Going Virtual: Moving Your Organization into the 21st Century. Upper Saddle River, New Jersey: Prentice Hall, Inc.[1] [1995].

NIIP, Guide to the NIIIP Reference Architecture Model. NIIIP Reference Architecture, Book [2], [1998].

Chinmay Das, International Journal of Engineering, Business and Enterprise Applications [3], [2013].

Nag,R; Hambrick,D.C; Chen,M.J. What is strategic management, really? Inductive derivation of a consensus definition of the field. Strategic Management Journal [4], [2007].

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

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Study the effects of job stress on resistance of employees against changes in the Gymnastics Federation of Tehran Fatemeh Kiani Nejad 1, Dr. Ahmadreza Kasraee2 Corresponding Author. MA in Business Management, Islamic Azad University, Central Tehran Branch, Tehran, IRAN. 2 Department of Management, Islamic Azad University, Central Tehran Branch, Tehran, IRAN. __________________________________________________________________________________________ Abstract: The aim of the present research is to investigate the job stress level and resistance against changes in Gymnastics Federation staffs and also the relationship of job stress with resistance against changes in Gymnastics Federation staffs. In general, this study is an applied one in view of research aim and is a survey one in a view of descriptive data collection and analysis. Statistical population of this study includes of all 140 Tehran Gymnastics Federation staffs that 103 of them were determined based on Cochran formula. The classified random sampling was used in this study. Data were collected using a questionnaire. The questionnaire has been formed from two sections. First section contains 7 questions related to demographic variables and section two includes of 43 questions that assess job stress and resistance against changes. The face validity was used to investigate the validity of the questionnaire. Reliability of the questionnaire was determined 0.83 using Cronbach alpha indicating acceptable reliability of the questionnaire. Data analysis was carried out using SPSS software. Correlation factor test, regression, and uni-variable t-test was applied to examine hypotheses. The results showed that job stress of staffs and resistance against changes among staffs are in a high level and there is a positive significant relationship between job stress and resistance against changes among Tehran Gymnastics Federation staffs. The results of this study will help planners in Tehran Gymnastics Federation to achieve practical approaches to overcome job stress and decrease resistance against changes and organizational evolutions of staffs.

Keywords: Job stress, Resistance against change, Staffs __________________________________________________________________________________________ 1

I. Introduction Today, job satisfaction, satisfaction with management and the governed relationships, a sense of job security and a sense of ownership are of the effective psychological needs on mental quality of staffs that cause productivity and improved quality and production. The studies have shown that job satisfaction decreases with enhancement of job stress (Daniali et al., 2014; Sabouteh et al., 2013). When stress is argued, its complications and negative aspects are greatly considered, but in generally in psychology, the psychological pressure or stress is mental and physiological reaction of our body that breaks our balance against any changes, threat, and internal or external pressure (Behnoudi, 2005). The job stress is as a predictor of mental health in people (Aqilinejad et al., 2009).

The stress resulted from a job is one that a given person involves it due to a certain job. It is created form an interaction between job conditions and individual characteristics of a professional. The signs of job stress are: mental, physical, and behavioral signs. Mental signs: these are emotional and cognitive problems such as dissatisfaction, seclusion and job abhorrence, depression, anxiety, and frustration sense (Kazemi et al., 2011).

The stress is an inseparable component of today life and if work workplace or objectives change in relation to workplace factors or changes in job activities such as a new technology, this indicates the stress. Job stress can accompany by many psychological problems (anxiety, depression, nervous exhaustion, excitability, aggression, sudden emotional drain, overeating, impulsiveness, disability in decision making, weak focus, low attention, sensitivity to criticism) or physical problems (migraine headaches, enhancement of heart beat and blood pressure, cardiovascular diseases, musculoskeletal pains, pulmonary and digestive disorders, and kidney diseases and rheumatoid Arthritis) or organizational problems (absence in job, displacement, low production, becoming estrangement with colleagues, job dissatisfaction, decreased obligation and loyalty to the organization, and job performance and work quality drop) (Molaie et al., 2011). On the other hand, the job stress in organizations is one of the global problems that is considered by management experts and psychologists with the beginning of industrial revolution, promoting manufacture, forming governmental organizations and wide organizations and concerns such as health, human values, security, calmness, anxiety, failure, lack of sense of success and so on were stated (Azad Marzabadi and Niknafs, 2014). In some cases, use a person in a job that is not consistent with her/his abilities and information or a change in his/her job activity can result in creating stress in him/her. In recent years, many researchers such as Boid, Levin, and Sager (2009) believed that the stress is one of main and comprehensive problems in workplaces. Overall, we can consider the stress as a

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response to pressures resulted from the workplace (Lambert, Hogan, and Grifin, 2007). Many variables in the workplace may lead to job stress. Among these variables, we can count the precarious nature of the work, role conflict, role obscurity, role heaviness, duties, and inconsistency between job demands with available resources.

These factors can result in job stress based on demand-control pattern of burnout (Babakas et al., 2009), imbalanced pattern of effort reward (Lvyg and Dvlard, 2003) and job demands of resources (Dymryvty et al., 2001) (Golparvar et al., 2010). Job stress decreases efficiency and productivity of staffs and one of its outcomes is creating resistance in staffs against organizational changes. The resistance of staffs against changes is a complicated issue that managers face with it in complicated organizations. The change process always exists and resistance of staffs is counted as one of important and sensitive factors of failure of many efforts, however well-understood.

Therefore, considering the importance of the emphasis on two factors of ((job stress)) and ((resistance of staffs against changes)) the researchers aims to conduct a study in the field of effect of job stress on resistance of staffs against changes in Gymnastics Federation of Tehran. In fact, the effect of job stress on resistance of Gymnastics Federation staffs against changes has been investigated. The aim of the present study in to investigate the job stress level in Gymnastics Federation staffs and their resistance against changes among Gymnastics Federation staffs and also the relationship of job stress with resistance against changes among Gymnastics Federation staffs. The results of this study will help Gymnastics Federation planners to achieve practical approaches for eliminating job stress and decreasing the resistance against organizational changes.

II. Conceptual model of the research Organizational changes that are done without any positive performance or method by management, conduct staffs toward negative thoughts such as fear feeling, job insecurity, lack of control and etc. the outcomes resulted from negative perceptions and fears cause stress reaction. Stress reaction can be in physical, perceptional, and behavioral forms (Luck and Arnetz, 1997). Based on following model, stress and resistance are two phenomena connected to each other. The resistance against changes is a part of behavioral reactions such as stress. According to Kahen and Biosir (1990 quoted by Tavakkoli, 2010) more focus has been done on signs of resistance and effort for decreasing resistance instead of attention and investigation of job stress reasons.

III. Method Generally, this study is an applied one from view point of research purpose and is a survey one from view point of descriptive data collection and analysis. The statistical population of this study includes all Tehran Gymnastics Federation staffs that are 140 persons. The sample volume was determined 103 based on Cochran formula. A classified-random sampling method was used in this study. A questionnaire was used to collect information. The questionnaire has been formed from two sections. First section included 7 questions related to demographic variables and section two contained 43 questions assessing job stress and resistance against changes. In this study, the face and content validity were used to investigate the validity of the questionnaire.

Trial run was applied to assess face validity so that the questionnaire was distributed among 30 persons from the study sample in a trial and pilot way. The questionnaire was completed and how the questionnaire was completed was assessed. In pilot stage, it was found out that study samples have perceived exactly what has been considered by purpose of the study. They have assessed what has been purpose of the study. Questions have been clear for respondents; therefore, the questionnaire had face validity. The basis for content validity in

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this research was judgment of experts and comprehensive literature review so that for content validity assessment purpose after designing the questionnaire, each of questions was investigated by experts and professors to assess proportion of items with the variable under consideration. Improper and obscure items were omitted or moderated after investigations. Reliability assessment or repeatability of questionnaire items was done using Cronbach alpha that was obtained 0.83 indicating acceptable reliability of the questionnaire. Data analysis was performed using SPSS software. Correlation factor test, regression, and univariate t-test were applied to test hypotheses.

IV. Findings In the present research, 103 staffs from Tehran Gymnastics Federation were studied. 4 (3.9 percent) of them were between 18 to 25 years old, 25 (24.3 percent) were between 26 to 35 years, 47 (46.5 percent) were between 36 to 45 years, and 27 (26.2 percent) were more than 46 years old. 34 (33 percent) were female and 69 (67 percent) were male. 41 (39.8 percent) were single and 62 (60.2 percent) were married. The education of 17 (16.5) of them was diploma, 37 (35.9 percent) were associate degree, 45 (43.7 percent) were Bachelor of Science and 4 (3.9 percent) were Master of Science and upper. The work experience of 5 (4.9 percent) was between 6 to 15 years, 40 (38.8 percent) had a work experience between 16 to 25 years and 3 (2.9 percent) had a work experience more than 26 years. 13 (12.6 percent) of them were a manager, 35 (34 percent) were administrative staff, 35 (34 percent) were instructor and 20 (19.4 percent) were service workers. 4 (3.9 percent) of them reported their salary lower than 5 million RLS, 62 (60.2 percent) between 5 to 10 million RLS, and 32 (31.1 percent) between 10 to 15 million RLS. V. Testing hypotheses The job stress is effective on the resistance of staffs against changes.

There is not any significant correlation. H0: ρ=0.

There is a significant correlation. H1: ρ≠0.

Univariate regression test was used to test this hypothesis. Firstly, the correlation of variables was calculated and the relationship among variables was obtained and then regression test was used to predict dependent variable according to the independent variable.

Table 1- Correlation test of relationship between job stress and resistance against changes Pearson correlation factor (r) Significance level Sig. (1-tailed) Number

Resistance changes 1 0.211 0 0.016 103 103

Resistance against changes job stress Resistance against changes job stress Resistance against changes job stress

against

Job stress 0.211 1 0.016 0 103 103

It is seen from Table 1 that there is a significant relationship between job stress and resistance of staffs against changes and r equal to 0.211 is significant in alpha level of 0.05. Also, the direction of the relationship is positive and direct. It means the higher job stress in staffs, the more resistance against changes.

Table 2-Regression determination coefficient between job stress and resistance against changes Model

Correlation factor

Determination factor

Adjustment factor

1

0.211

0.045

0.035

Deviation of estimation error 1.77720

the

Table 2 shows that the regression determination coefficient between job stress and the resistance of staffs against changes is equal to R2=0.045. It indicates that 4.5 percent of the resistance in staffs against changes is related to the job stress.

Table 3- Variance analysis between job stress and resistance against changes Model Regression Residual Total

Summation squares 14.920 319.002 333.922

of

Degree of freedom

Mean squares

F

Significance level

1 101 102

14.920 3.158

4.724

0.032

Variance analysis in Table 3 confirms the results of regression determination coefficient and these results show that observed F (F=4.72) in an alpha level of 5 percent is significant and zero hypothesis is rejected. Therefore, regression determination coefficient is confirmable.

Table 4-Regression line equation between job stress and resistance against changes Regression model

B

Non-standard beta Standard error of

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Standard beta Beta

t

Significance level

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Intercept (a) Job stress

measurement 2.441 0.027

40.970 0.060

16.783 2.173

0.211

0.000 0.032

Above beta table shows final results of the regression and we can determine the equation of regression line according to this table: The resistance against changes = 40.97 + 0.060*job stress The results in Table 4 show that job stress has a significant and predictable effect on the resistance against changes. Also, according to beta it can be said that for a unit increase in job stress, the resistance against changes increases 0.211 percent.

Job stress is in a high level in Gymnastics Federation staffs.

Univariate t-test was used to examine this hypothesis.

H0=M=µ H1=M≠µ Table 5-descriptive statistics related to the job stress of Federation staffs.

Deviation of the average error 0.22

Job stress of staffs

Standard deviation

Average

Number

0.229

3.17

103

Table 6-Univariate t-test related to job stress of Federation staffs Expected average = 3 t Job stress

7.64

Degree freedom 102

of

Significance level 0.000

Difference means 0.172

of

Confidence level of 95% Lower limit Upper limit 0.127 0.217

The results in Table 6 show that the observed t is equal to 7.64 and zero-hypothesis is rejected. In other words, a significant difference between observed average (3.17) and expected one (3) is observed. Observed average is higher than expected one and thus it can be said that job stress of staffs is higher than average.

The resistance in Gymnastics Federation staffs against changes is in a high level in.

Univariate t-test was used to examine this hypothesis.

H0=M=µ H1=M≠µ Table 7-descriptive statistics related to the resistance against changes in Federation staffs.

Resistance changes

against

Deviation of the average error 0.0118

Standard deviation

Average

Number

0.121

3.08

103

Table 8-Univariate t-test related to the resistance against changes in Federation staffs Expected average = 3 t resistance against changes

7.07

Degree freedom 102

of

Significance level 0.000

Difference means 0.084

of

Confidence level of 95% Lower limit Upper limit 0.060 0.107

The results of Table 8 show that the amount of the observed t is 7.07 and zero hypothesis is rejected. In other words, there is a significant difference between the observed average (3.08) and expected one (3) and the observed average is higher than expected one. In conclusion, it can be said that the resistance in Federation against changes is higher than the average.

VI. Conclusion The results of the present research showed that job stress is effective on the resistance of staffs against changes and the more job stress in staffs, the more resistance against changes. Also, the results of the present research showed that job stress and resistance against changes in Gymnastics Federation staffs are in a high level. A medium job stress has been reported in Marzabadi et al. study (2014). Glican and Higins (2006) in their study about job stress, searching reasons, and outcomes, counted effective factors on job stress including unhealthy workplace, job duty, seclusion, work hours, job insecurity, lack of work independence, difficulty in communicating with the manager and colleagues, strengthening management, and organizational atmosphere.

These factors also have been referred as effective factors in creating job stress and the resistance against changes. Mohammad Mansouri (2013) in his study entitled “investigating the effect of job stress on job security and organizational obligation among permanent and temporary staffs of presidential planning deputy and strategic monitoring, management development, and human capital found out that job stress affects the

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organizational obligation and creating job insecurity among permanent staffs and also this effect among temporary staffs is higher than that of the permanent staffs. In this field it is suggested that the managers should exactly familiarize staffs with their duties by holding training courses to fields of job insecurity be removed.

They can decrease staffs’ resistance against changes through reducing job stress. In this field for purpose of decreasing job stress among staffs, Gymnastics Federation managers must avoid discriminatory behaviors causing discouragement and lower spirit in staffs. Salary and enough benefits consistent with their work and activity result in decreasing the stress and their enhanced satisfaction. Creating a proper encouragement and awarding system result in increasing motivation and decreasing staffs’ stress. Emotional support of persons when they involve in stressful affairs, creating a positive emotional atmosphere in the organization, decreasing individual vulnerability, creating counseling and individual and group guiding systems in the organization, creating job security, removing anxious factors in relation to losing the job, and increasing awareness level of employers and managers in better planning for reducing stressful factors in workplace are some of considerable proposed cases to decrease job stress and the resistance against changes among staffs of mentioned organization.

Also, it is worth noting that knowledge and manager recognition toward staffs’ job through assessment process about skills and job competences s an important issue, thus he must know the job and its required abilities. The more knowledge and recognition, the more exact investigation of staffs’ performance; and this issue results in decrease in stress among staffs and the resistance against changes. Also, one of the effective programs on decreasing job stress is the participation of staffs in decisions making that it results in a good bidirectional communication between supervisors and subalterns. Totally, structures that give their staffs more decision making powers cause lower stress and strengthening the sense of autonomy, responsibility, and sense of control in staffs and this issue reduces the resistance of staffs against changes. Finally, creating the spirit of cooperation, convincing staffs about benefits of a change, removing fear, obscurity resulted from accepting the changes, and developing executive instructions, and targeted programs to decrease the stress came from obscurity in staffs’ duties can be proposed that result in improvement in performances and outcomes.

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Molaie B, Mohamadi M, Habibi A, Zamanzadeh V, Dadkhah B, Molavi P,etal. (2011). A Study of Job Stress and Its Related Causes among Employed Women in Ardabil City. Ardabil University Of Medical Sciences Journal ; 11(1):76-85.

Sabouteh, S., Shahnazi H., Sharifzad G., Hassanzadeh, A., (2013), “Investigating the relation between the job stress with job satisfaction among midwives employed in Isfahan maternities, Health System Research Journal, Vol. 9, No. 7, pp. 682-692.

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