Vol 8 no 1 2014

ISSN 1905-9159

Silpakorn University

Science and Technology Journal Volume 8 Number 1 (January-June) 2014

Clinical Outcomes and Risk Factors Affecting 30-day Mortality and Treatment Failure of Patients Infected with Carbapenem-Resistant Acinetobacter baumanii in a General Hospital Wichai Santimaleeworagun, Wandee Sumret, Kanokwan Limsubjaroen, Nattaporn Ruangnara, Parada Sujarittham, Ploypailin Mulmek and Weerayuth Saelim

Factors Influencing the Success of an ERP System: A Study in the Context of an Agricultural Enterprise in Thailand Somsit Duangekanong

Solving the Course - Classroom Assignment Problem for a University Kanjana Thongsanit -8-

The Development of Web-Oriented Decision Support System for

User Interface

Supporting a Single-Level Task Assignment Process Patravadee Vongsumedh

Target Users (Job Supervisors or Subordinates)

Agronomic Traits and Fruit Quality of Pineapple with Different Levels of Chicken Manure Application Auraiwan Isuwan

Data Component

Communicative Component

Model Component

Knowledge Component

Figure 2: Components of the DSS Prototype Component 1: “Data Component” The component deals with the data used for supporting the workflow of the task assignment process

(Figure 1). These data are classified and stored in four data repositories as follows:

Effects of Asparagus Trims By-Product Supplementation in Laying I.Hens onDetails Nutrient Digestibility The TaskDiets Operation and Progress: The data stored inand the first repository are composed of Productive Performance

the on-going task’s status, task’s progress, and procedural steps of any task. While the subordinates are being assigned tasks, they can report these data back to the job director. Therefore, the job supervisor can check the

progress of any task, and and keep tracks of events occurred during task’s working duration.

Manatsanun Nopparatmaitree, Anunya Panthong, Siwaporn Paengkoum Pornpan Saenphoom

II. The Employee Profiles: The second data repository stores characteristics and capabilities of all

subordinates in a specific business unit, such as, age, gender, job position, task skills occupied by subordinates, work-starting date, and so on.

III. The Task Profiles: The third data repository stores characteristics of all tasks in a specific

http://www.surdi.su.ac.th http://www.journal.su.ac.th http://www.tci-thaijo.org/index.php/sustj

business unit, such as, task skills required by any task, task’s working duration, task’s started date, task’s submitted date, task’s status, and so on.

IV. The Task in Responsibility: The last data repository stores all assigned task’s data. It plays an important role in identifying tasks undertaken by a particular subordinate at any given time. These data show the relationship between the task and the subordinate. Moreover, the given data are necessary for the task assignment process, since the job supervisor must take them into account in the next task assignment.

Component 2: “Model Component” The component provides the analysis capability for the DSS in order to compare the possible alternatives (or subordinates) in steps A to D. Based on the theory of job analysis and information gathered

SILPAKORN UNIVERSITY Science and Technology Journal SUSTJ

Copyright All rights reserved. Apart from citations for the purposes of research, private study, or criticism and review, no part of this publication may be reproduced, stored or transmitted in any other form without prior written permission by the publisher.

Published by Silpakorn University Printing House.

Silpakorn University, Sanamchandra Palace Campus, Nakhon Pathom 73000 Š Silpakorn University ISSN 1905-9159

SILPAKORN UNIVERSITY Science and Technology Journal Editorial Office Silpakorn University Research and Development Institute (SURDI), Silpakorn University, Sanamchandra Palace Campus, Nakhon Pathom, Thailand

Editorial Policy All articles submitted for publication will be evaluated by a group of distinguished reviewers.

The editorial board claims no responsibility for the contents or opinion expressed by the authors of individual article.

Editorial Advisory Board Assist. Prof. Alice Thienprasert, Ph.D Director, Silpakorn University Research and Development Institute, Thailand Prof. Amaret Bhumiratana, Ph.D Department of Biotechnology, Mahidol University, Thailand Prof. Geoffrey A. Cordell, Ph.D Professor Emeritus, University of Illinois at Chicago, USA Prof. Kanaya Shiginori, Ph.D Department of Material and Life Sciences, Osaka University, Japan Prof. Keiji Yamamoto, Ph.D Graduate School of Pharmaceutical Sciences, Chiba University, Japan Dr. Pawapol Kongchum, Ph.D. Faculty of Animal Sciences and Agricultural Technology, Silpakorn University, Thailand Assist. Prof. Lerkiat Vongsarnpigoon, Ph.D National Metal and Materials Technology Center (MTEC), Thailand Assoc. Prof. Nijsiri Ruangrungsri, Ph.D College of Public Health Sciences, Chulalongkorn University, Thailand Assoc. Prof. Petcharat Pongcharoensuk, Ph.D Department of Pharmacy, Mahidol University, Thailand Prof. Piyasan Praserthdam, Ph.D Department of Chemical Engineering, Chulalongkorn University, Thailand Assoc. Prof. Surachai Nimjirawath, Ph.D Department of Chemistry, Silpakorn University, Thailand Prof. Tharmmasak Sommartya, Ph.D Faculty of Agriculture, Bangkhen Campus, Kasetsart University, Thailand Prof. Virulh Sa-Yakanit, Ph.D Department of Physics, Silpakorn University, Thailand

Editor Assoc. Prof. Onoomar (Poobrasert) Toyama, Ph.D Faculty of Pharmacy, Silpakorn University

SILPAKORN UNIVERSITY Science and Technology Journal Editorial Board Assist. Prof. Bussarin Ksapabutr, Ph.D Faculty of Engineering and Industrial Technology, Silpakorn University Prof. Chawewan Ratanaprasert, Ph.D Faculty of Science, Silpakorn University Assist. Prof. Chockpisit Thepsithar, Ph.D Faculty of Science, Silpakorn University Assist. Prof. Jittat Fakcharoenphol, Ph.D Faculty of Engineering, Kasetsart University Assoc. Prof. Mana Kanjanamaneesathian, M.Appl.Sc. Faculty of Animal Sciences and Agricultural Technology, Silpakorn University Assist. Prof. Pramote Khuwijitjaru, Ph.D Faculty of Engineering and Industrial Technology, Silpakorn University Smith Tungkasmit, Ph.D College of Social Innovation, Rangsit University Assoc Prof. Soraya Ruamrungsri, Ph.D Faculty of Agriculture, Chiang Mai University Agnes Rimando, Ph.D U.S. Department of Agriculture, Agricultural Research Service, USA Prof. Juan Boo Liang, Ph.D Institute of Bioscience, Universiti Putra Malaysia, Malaysia Prof. Shuji Adachi, Ph.D Graduate School of Agriculture, Kyoto University, Japan Vincenzo Esposito, Ph.D Department of Energy Conversion and Storage, Technical University of Denmark, Denmark

Managing Editor Pranee Vichansvakul

Periodicity Twice yearly

All correspondence should be addressed to: Managing Editor, E-mail address: pranee_aon1@hotmail.com Telephone: 080-5996680

Information about the Journal An electronic journal is provided on the web sites (http://www.journal.su.ac.th, http://www.tci-thaijo.org/index.php/sustj/index, http://www.surdi.su.ac.th/journal_th.html).

The journal is available at Silpakorn University Book Center.

Telephone: 66-2223-7345, 66-2434-1792, 66-3424-4054.

Instructions to Authors Silpakorn University Science and Technology Journal (SUSTJ) is a peer review journal published twice a year in January and July by the Research and Development Institute of Silpakorn University, Thailand. SUSTJ puts together articles in Science and Technology and aims to promote and distribute peer reviewed articles in the areas of science, health science, animal science, agriculture, engineering, technology and related fields. Articles from local and foreign researchers, invited articles and review from experts are welcome.

Publication Ethics and Publication Malpractice Statement

The Editor must ensure that each manuscript is initially evaluated by the editor/co-editor for the originality of the contents before forwarding to two referees for blind peer review. The review period will be from two weeks to one month (Maximum two months in extra ordinary circumstances).

The Editorial Board evaluates manuscripts on their intellectual content without regard to authors’ race, gender, sexual orientation, or citizenship and must not disclose any information about a submitted manuscript to anyone other than the corresponding author, reviewers, potential reviewers, and the publisher.

Reviews should be conducted objectively without personal criticism.

Reviewers should express their views clearly with supporting arguments, identify relevant published work that has not been cited by the authors, and report any similarity or overlap between the manuscript under consideration and other published papers to the editor’s attention. Reviewers should not review manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships or connections with any of the authors, companies, or institutions connected to the papers.

The authors should ensure that they have written entirely original works, and has been appropriately cited or quoted the work of others.

Manuscripts submitted must not have been published as copyrighted material elsewhere.

Manuscripts under review must also not be submitted for consideration by other publication as copyrighted material. This should be declared in the Submission Form. By submitting a manuscript and a Submission Form, the author(s) agree that the copyright will be transferred to SUSTJ if the manuscript and associated multimedia are accepted for publication. The author(s) retain the rights to the fair use (e.g., teaching and non-profit uses) of the published material.

Types of contributions

Short communications, Research articles, Review articles

Preparation of manuscripts

1. The text should be double-spaced with line number on A4 and a font Times New Roman size 11 should be used. When using MS Word, insert all symbols by selecting “Insert-Symbol” from the menu and use the “Symbol” font.

2. Manuscripts should be organized in the following order:

Cover page with title and authors’ names and affiliations

Abstract (in English and Thai)

Key Words Introduction

Materials and Methods, Area Descriptions, Techniques Results Discussion Conclusion Acknowledgements References

Tables and Figures Authors’ names and affiliations

Full names and affiliations (marked with superscript number) should be provided for all authors on the cover page, separately from the content. The corresponding author (marked with superscript asterisk) should also provide a full postal address, telephone and fax number and an e-mail address as a footnote on the title page.

Abstract

First page of the content starts with Abstract, including title of the article on top of page. Provide a short abstract not more than 200 words, summarizing the question being addressed and the findings.

Key Words

Provide 3-5 key words or short phrases in alphabetical order, suitable for indexing.

References

In text references: Refer to the author’s name (without initials) and year of publication, e.g., Feldmann, 2004 (for 1 author), Feldmann and Langer, 2004 (for 2 authors), or Feldmann et al., 2004 (for more than 2 authors).

Article references: References should be listed in alphabetical order of author(s). For journal, list all names of authors.

Book Feldmann, H. (2004) Forty Years of FEBS, 2nd ed., Blackwell Publishing Ltd., Oxford, pp.1121-1129.

Chapter in a book Langer, T. and Neupert, W. (1994) Chaperoning mitochondrial biogenesis. In The Biology of Heat Shock Proteins and Molecular Chaperones (Morimoto, R. I., Tissieres, A. and Georgopoulos, C., eds.), 3rd ed., pp. 53-83. Cold Spring Harbor Laboratory Press, Plainview, New York.

Article in a journal Hammerschlag, F. A., Bauchan, G., and Scorza, R. (1985) Regeneration of peach plants from callus derived from immature embryos. Journal of Natural Products 70(3): 248-251.

Hammerschlag, F. A., Bauchan, G., and Scorza, R. Regeneration of peach plants from callus derived from immature embryos. Journal of Natural Products (in press).

Article on the web Lee, K. (1999) Appraising adaptaive management. Conservation Ecology 3(2). [Online URL:www. consecolo.org/Journal/vol3/iss2/index.html] accessed on April 13, 2001.

Proceedings MacKinnon, R. (2003) Modelling water uptake and soluble solids losses by puffed breakfast cereal immersed in water or milk. In Proceedings of the Seventh International Congress on Engineering and Food, Brighton, UK. Patent Yoshikawa, T. and Kawai, M. (2006) Security robot. U.S. Patent No. 2006079998.

Tables and Figures

Each Table and Figure must be on a separate page of the manuscript.

Tables: Number the tables according to their sequence in the text. The text should include references to all tables. Vertical lines should not be used to separate columns. Leave some extra space instead.

Figures: Figures should be of high quality (not less than 300 dpi JPEG or TIFF format), in black and white only, with the same size as the author would like them to appear in press. Choose the size of symbols and lettering so that the figures can be reduced to fit on a page or in a column.

Submission of Manuscripts

All information contained in a manuscript is a full responsibility of the authors, including the accuracy of the data and resulting conclusion.

The editorial office will acknowledge receipt of the manuscript within 2 weeks of submission. The ‘accepted date’ that appears in the published article will be the date when the managing editor receives the fully revised version of the manuscript. The manuscript may be returned to authors for revision.

Authors will be given 2 weeks after receipt of the reviewers’ comments to revise the manuscript.

Please submit the manuscript with a submission form to the following address: e-mail: pranee_ aon1@hotmail.com Proofs

Proofs will be sent to the corresponding author by e-mail (as PDF file) or regular mail. Author is requested to check the proofs and return any corrections within 2 weeks.

Silpakorn University Science and Technology Journal

Contents

Volume 8 Number 1 (January - June) 2014

Research Articles

Clinical Outcomes and Risk Factors Affecting 30-day Mortality and

Treatment Failure of Patients Infected with Carbapenem-Resistant

Acinetobacter baumanii in a General Hospital................................................................................. 9

Wichai Santimaleeworagun, Wandee Sumret, Kanokwan Limsubjaroen, Nattaporn Ruangnara, Parada Sujarittham, Ploypailin Mulmek and Weerayuth Saelim

Factors Influencing the Success of an ERP System:

A Study in the Context of an Agricultural Enterprise in Thailand..................................…….......

18

Somsit Duangekanong

Solving the Course - Classroom Assignment Problem for a University .........................................

46

Kanjana Thongsanit

The Development of Web-Oriented Decision Support System for

Supporting a Single-Level Task Assignment Process......................................................................... 53

Patravadee Vongsumedh

Agronomic Traits and Fruit Quality of Pineapple with Different Levels of

Chicken Manure Application................................................................................................……...... 67

Auraiwan Isuwan

Effects of Asparagus Trims By-Product Supplementation in Laying

Hens Diets on Nutrient Digestibility and Productive Performance................................................ 74

Manatsanun Nopparatmaitree, Anunya Panthong, Siwaporn Paengkoum and Pornpan Saenphoom

Silpakorn University Science and Technology Journal (SUSTJ) is now available on the following databases: Chemical Abstract Service (CAS) SciFinder Scholar (CAPLUS) International Information System for the Agricultural Sciences and Technology (AGRIS) (FAO) AGRICultural Online Access (AGRICOLA) Food Science and Technology Abstracts (FSTA) The National Science Digital Library (NSDL) CAB Abstract Directory of Open Access Journals (DOAJ) Google Scholar Thai Journal Citation Index (TCI) Centre

Research Article Clinical Outcomes and Risk Factors Affecting 30-day Mortality and Treatment Failure of Patients Infected with Carbapenem-Resistant Acinetobacter baumanii in a General Hospital Wichai Santimaleeworagun1*, Wandee Sumret2, Kanokwan Limsubjaroen1, Nattaporn Ruangnara1, Parada Sujarittham1, Ploypailin Mulmek1, and Weerayuth Saelim1 Department of Pharmacy, Silpakorn University, Nakhon Pathom, Thailand 2 Pharmacy Department, Hua Hin Hospital, Prachuap Khiri Khan, Thailand * Corresponding author. Email address: swichai1234@gmail.com 1

Received August 7, 2013; Accepted January 6, 2014 Abstract

This study aimed to determine 30-day mortality and treatment failure rates in patients infected with carbapenem-resistant Acinetobacter baumanii (CRAB) and to evaluate predictive factors associated with 30-day mortality and treatment failure. This retrospective study collected data from medical records of patients admitted to Hua Hin Hospital from January to December, 2012. Seventy- three patients with CRAB infections met the eligible criteria, while 57.5 and 61.6 % were death and treatment failure rate, respectively.

Risk factors associated with 30-day mortality were appropriate antimicrobial therapy (OR 0.22; 95% CI 0.08-0.62) and shock (OR 5.80; 95% CI 1.19-28.20). In addition, the appropriate antimicrobial therapy (OR 0.11; 95% CI 0.03-0.37) and shock (OR 10.97; 95% CI 1.35-89.34) were also predictors for treatment failure.

In multivariate analysis, a factor associated with 30-day mortality and treatment failure remained the appropriate antimicrobial therapy. In conclusion, the appropriate antimicrobial treatment was a strategy associated with better treatment outcomes in patients with CRAB infections.

Key Words: Acinetobacter baumannii; Carbapenem-resistant; Clinical outcomes; Risk factors Introduction

Acinetobacter baumannii, an aerobic Gramnegative bacilli, is a major cause of nosocomial infections, especially in the respiratory tract, urinary system, bloodstream and central nervous system (Bergogne-Berezin and Towner, 1996). With such infections, A. baumannii is a major public health problem in various parts of the world, including Thailand. Wisplinghoff et al. (2004) performed the nationwide surveillance study (SCOPE study) to

Silpakorn U Science & Tech J 8(1): 9-17, 2014

examine the causative pathogens in nosocomial bloodstream infection among 24,179 cases during March, 1995 to September, 2002. This study showed that A. baumannii was the second cause of death in patients admitted to intensive care unit. According to Alvarez-Lerma et al . (2007) study, which reported the national rates of acquired invasive device-related infections in the ICU during 2003-2005, A. baumannii was the third predominant etiology of pneumonia related with mechanical ventilation in

ISSN 1905-9159

Silpakorn U Science & Tech J Vol.8(1), 2014

Clinical Outcomes and Risk Factors Affecting 30-day Mortality

Spain. Similar to the other countries, in Thailand, A. bauamannii was identified in the sputum during year 2004 and 2006 at a rate of 14 and 17% of pathogenic organisms, respectively. In addition, this organism was ranked the fourth of isolated clinical pathogens in Thailand (National Antimicrobial Resistance Surveillance Thailand Center, 2010).

Besides the increasing rate of A. baumannii infections, the multi-mechanisms of antimicrobial resistance might also enhance problematic treatment such as producing enzymes (especially carbapenemases) destroying drugs, reducing amount of the drug into the cells (porin loss), driving drug out of the cell (efflux pump), or changing the target site of antimicrobial action (Bergogne-Berezin et al., 1996; Bonomo and Szabo, 2006). Thus, A. baumannii infections were hardly treated by effective medications. From the National Antimicrobial Resistance Surveillance Thailand Centre (NARST), Dejsirilert et al. (2009) reported the 6 years period of antimicrobial resistance surveillance (in 2000-2005). The result showed the increasing rate of infections, by carbapenemresistant A. baumannii (CRAB), from 2.1 to 46.7% in 2000 and 2005, respectively.

Due to the increasing rates of both infection and the spreading of resistant pathogen, the therapeutic choices for A. baumannii eradication were scant. Cefoperazone/sulbactam, colistin and tigecycline remain the last three options in the resistant era. However, mortality rate and treatment failure have been reported in some previous studies, which lots of unfavorable factors revealed the treatment outcomes (Deris et al., 2009; Erbay et al., 2009; Livermore et al., 2010; Sheng et al., 2010; Santimaleeworagun et al., 2011a).

Currently, the risk factors associated with clinical outcomes includes age, shock, renal dysfunction, ICU stay, immunocompromised host, bloodsteam infection, mechanical ventilation use,

and the appropriate antimicrobial therapy (Deris et al., 2009; Erbay et al., 2009; Livermore et al., 2010; Sheng et al., 2010; Santimaleeworagun et al., 2011a). However, such reports were often done in medical schools or large-sized hospitals. Thus, severity of problems in the general hospital might be different.

Problem of antimicrobial resistance in Hua Hin Hospital, a general hospital located in Prachuap Khiri Khan Province, Thailand, is increasing. One percent of MDR-AB (defined as resistance to ceftazidime, ciprofloxacin and aminoglycoside) was only sensitive to imipenem in year 2010. This phenomenon could be considered as an urgent problem. Therefore, the objective of this present study was to retrospectively collect data of patients infected with CRAB, to determine risk factors associated with 30-day mortality and treatment failure. Our results might be useful to plan the patient care for reducing the unexpected outcomes in patient with CRAB infections.

Materials and Methods

This was a retrospective cohort study that gathered the data of patients infected with CRAB during January to December, 2012 from electronic medical records database. The protocol was approved by the institutional review board of Faculty of Pharmacy, Silpakorn University and Hua Hin Hospital with a waiver for informed consent.

Participants

This study was to identify risk factors associated with clinical outcomes of patients infected with CRAB. The inclusion criteria for CRAB infections consisted of 1) A. baumannii was resistant to carbapenems; 2) Patients had clinical signs of infection, such as systemic inflammatory syndrome (SIRS) at least 2 out of 4 items (body temperature > 38 or < 36 ÂşC, respiratory rate > 20 breaths/min, heart rate > 90 beats/min, leukocytosis

10

W. Santimaleeworagun et al.

Silpakorn U Science & Tech J Vol.8(1), 2014

defined as having normal body temperature, normal level WBC, and stable vital signs. Treatment failure included failure and death. Failure patients were ones with clinical symptoms got worse or antimicrobial therapy had to be changed or added to be against CRAB. Death defined as decease within 30 days of CRAB infection.

Data Collection Patient data were reviewed, via database and medical records from medical record unit, for clinical information, including age, sex, underlying diseases, admitted ward, mechanical ventilator use, shock, hepatic function, renal function, immunocompromized status, antimicrobial regimens (date of start, dosage, administration and duration),

> 12,000 cells/mL or < 4,000 cells/mL) with suspected source of infections (Levy et al., 2003; Calandra and Cohen, 2005); 3) Sepsis occurred 48 hours or more after admission. Patients had CRAB grew up in specimen without any signs and symptoms (also called A. baumannii colonization), patients died before the susceptibility test results came out, patients transferred between hospital, treatment was not be able to followed-up, or patients with incomplete medical records were excluded.

Definitions

Carbapenem-resistant Acinetobacter baumannii (CRAB) was referred to A. baumannii which resists to all carbapenems (imipenem and meropenem) based on disk diffusion method. Combination antimicrobial therapy were treatment with more than 2 agents which have scientific evidence to enhance the effect of the treatment. Appropriate antimicrobial therapy meant treatment with at least one active antimicrobial agent within 24 hours after reporting the CRAB susceptibility. Septic shock was diagnosed by a physician, but not includes the other shocks (hypovolemic shock, cardiogenic shock, and obstructive shock). Impaired renal function defined as serum creatinine greater than 100% of the baseline level. Impaired liver function were 5-times increase of ALT from the upper normal limit or 3-time increase of ALT concomitant with clinical symptoms of hepatitis. Immunocompromised host included systemic lupus erythematosus, human immunodeficiency virus (HIV) infection, cancer, patients with an absolute neutrophil count < 0.5Ă&#x2014;109 cells/L, patients with organ transplantation or immunosuppressive agents use (steroids at a dosage greater than 10 mg of prednisolone daily for more than 2 weeks or chemotherapy)

According to clinical outcome definitions; presumptive success was a cure or clinical improvement. Cure was classified as clinical improvement and microbiological success (culture negative after treatment). Clinical improvement

antimicrobial susceptibility, length of hospital stay, source of infections, vital signs and clinical outcomes. The primary outcome measurements were all-cause 30-day mortality and risk factors related to failure and mortality.

Statistical Analysis

Descriptive statistics were used for 30-day mortality and treatment failure rates of CRAB infections. Chi-square or Fisherâ&#x20AC;&#x2122;s exact test statistics was analyzed the relationship between the discrete factors and clinical outcomes. Kolmogorov smirnov Z test or Student t-test were used to compare the median or mean, respectively, between continuous data and clinical outcomes. All significant variables in the univariate analysis were considered for the multivariate analysis by logistic regression analysis. Analysis and data interpretation were processed via SPSS 17.0 for windows data analysis at Îą = 0.05 for statistical significance.

Results During the study period, 131 patients were included. Only 73 cases were eligible according to inclusion criteria (47 cases with A. baumannii colonization, 7 patients died before the susceptibility

11

Silpakorn U Science & Tech J Vol.8(1), 2014

Clinical Outcomes and Risk Factors Affecting 30-day Mortality

Predictive Factors of Clinical Outcomes

Univariate analysis found that significant risk factors associated with treatment failure and 30-day mortality were the appropriate antimicrobial therapy and shock. Whereas age ≤ 65 years, gender, the antimicrobial combination, more than one types of bacterial infection, duration of hospitalization, admitted at the ICU, renal function impairment, impaired liver function, diabetes, mechanical ventilation use, and immunocompromised factors were not found to be predictive factors of treatment failure or death (Table 2 and 3). For multivariate analysis, the factors found to reduce the rate of

test results came out and 3 of them were transferred out). Among 73 patients with CRAB infections, 35 patients (47.95%) were male, mean age was 64.7 years (SD ± 16.5), mean duration of hospitalization was 44.5 days (SD ± 23.79) and 32 patients (43.84%) admitted at ICU. Pneumonia was the most common site of infection (84.9%) (Table1).

Clinical Outcomes

Of the clinical outcomes among ones infected with CRAB, 30-day mortality and treatment failure rates were 57.5 and 61.6 %, respectively. The percentage of appropriate antimicrobial use was only 57.5%.

Table 1 Characteristics of patients with carbapenem-resistant Acinetobacter baumannii infections (CRAB) (n=73) Characteristics

Number (%)

Sex ; male

35 (47.95)

Age (years; Mean ±SD)

64.66 ± 16.53

Duration of hospitalization (days; Mean ±SD)

44.52 ± 23.79

Intensive care unit stay

32 (43.84)

Combination antimicrobial therapy

25 (34.25)

Appropriate antimicrobial use

42 (57.53)

Shock

16 (21.92)

Renal dysfunction

27 (36.99)

Hepatic dysfunction

3 (4.11)

Diabetes mellitus

25 (34.25)

Mechanical ventilator use

54 (73.97)

Immunocompromized status

5 (6.85)

Site of infections - Pneumonia

62 (84.9)

- Skin and Soft tissue

6 (8.2)

- Bloodstream

3 (4.1)

- Urinary tract

1 (1.4)

- Central nervous system

1 (1.4)

12

W. Santimaleeworagun et al.

Silpakorn U Science & Tech J Vol.8(1), 2014

Table 2 Univariate and multivariate analysis to identify risk factors influencing treatment failure (failure

or death) among patients with carbapenem-resistant Acinetobacter baumannii infections (N=73) Presumptive

Treatment

Crude odd ratio

Adjusted odd

success

failures

(95%CI)

ratio

(n=28)

(n=45)

Age â&#x2030;¤ 65 years

13 (17.8)

19 (26.0)

0.843 (0.33-2.18)

Sex ; Male

12 (16.4)

23 (31.5)

0.72 (0.28-1.85)

Intensive care unit stay

9 (12.3)

23 (31.5)

2.21 (0.82-5.91)

The combination antimicrobial therapy

14 (25.0)

11 (19.6)

0.65 (0.22-1.87)

The appropriate antimicrobial therapy

24 (32.9)

18 (24.7)

0.11 (0.03-0.37)

1 (1.4)

13 (17.8)

10.97(1.35-89.34)

10 (13.7)

17 (23.5)

1.09 (0.41-2.91)

0 (0)

3 (14.1)

1.07 (0.99-1.16)

Diabetes mellitus

9 (12.3)

16 (21.9)

1.17 (0.43-3.17)

Mechanical ventilator

19 (26.0)

35 (47.9)

1.66 (0.58-4.78)

1 (1.4)

4 (5.5)

2.63 (0.28-24.86)

Variable

Shock Renal dysfunction Hepatic dysfunction

Immunocompromized status

(95%CI)

0.14 (0.04-0.47)

Table 3 Univariate and multivariate analysis to identify risk factors influencing 30-day mortality among

patients with carbapenem-resistant Acinetobacter baumannii infections (N=73) 30-day Variable

Adjusted

Survivor

mortality

Crude odd ratio

odd ratio

(n=31)

(n=42)

(95%CI)

(95%CI)

Age â&#x2030;¤ 65 years

14 (19.2)

18 (24.7)

0.91 (0.36-2.32)

Sex ; Male

14 (19.2)

21 (28.8)

1.21 (0.48-3.08)

Intensive care unit stay

10 (13.7)

22 (30.1)

2.31 (0.88-6.07)

The combined antimicrobialtherapy

14 (25.0)

11 (19.6)

0.74 (0.26-2.12)

The appropriate antimicrobial use

24 (32.9)

18 (24.7)

0.22 (0.08-0.62)

2 (2.7)

12 (16.4)

5.80(1.19-28.20)

11 (15.1)

16 (21.9)

1.12 (0.43-2.93)

0 (0)

3 (4.1)

1.08 (0.99-1.17)

Diabetes mellitus

10 (13.7)

15 (20.5)

1.17 (0.44-3.12)

Mechanical ventilator use

21 (28.8)

33 (45.2)

1.75 (0.61-5.01)

2 (2.7)

3 (4.1)

1.12 (0.18-7.11)

Shock Renal dysfunction Hepatic dysfunction

Immunocompromized status

13

0.27 (0.09-0.79)

Silpakorn U Science & Tech J Vol.8(1), 2014

Clinical Outcomes and Risk Factors Affecting 30-day Mortality

in CRAB infection was 45 % but our result seems to have higher mortality rate than the previous two studies performed in Thailand, even as the university medical schools (33.8 and 30%, respectively) (Jamulitrat et al., 2007; Santimaleeworagun et al., 2011a). However, the results from different studies might vary in the mortality rate depending patient status, age, underlying diseases, source of infection, and appropriate antimicrobial agents (Apisarnthanarak and Mundy, 2009; Deris et al., 2009; Erbay et al., 2009; Livermore et al., 2010; Sheng et al., 2010). Santimaleeworagun et al.

(2011a) reported the overall mortality rate as 30% among cases with a higher percentage of appropriate antimicrobial use (82.7%) ) than in the present study (57.5%). Thus, this study revealed that the treatment outcome might be the higher mortality rate if the patients had more unfavorable factors, regardless types of hospital level.

According to predictive factors for treatment failure and mortality, the appropriate antimicrobial use remained only favorable factor in multivariate analysis. This result was accorded with that from the study by Deris et al. (2009). Falagas et al. (2006) and Santimaleeworagun et al. (2011a) indicated that appropriate antimicrobial agents for the treatment of Acinetobacter infections could significantly reduce the mortality.

The present study did not find the differences between patients given single and combination antimicrobial therapy. Even, presumptive or survivor groups were more likely to have higher percentage of combination regimen, but the authors could not detect the statistical difference. The explanation for insignificant analysis might result from a small sample size.

Another limitation of this study was the authors could not calculate APACHE that are the severity of illness scoring because of its retrospective study pattern and lack of Glasgow coma score.

treatment failure and 30-day mortality were the appropriate antibiotics therapy (adjusted OR 0.14; 95% CI 0.04-0.47 and adjusted OR 0.27; 95% CI 0.09-0.79, respectively).

Discussion

CRAB-causing nosocomial infections are becoming a major problem worldwide (Hsueh et al., 2005; Marshall et al., 2007). Therefore, this important issue is challenging for medical treatment.

The main reason making CRAB treatment complicated is the pathogen carrying multimechanisms of resistance. As in previous studies, the resistant mechanism in A. baumannii, were identified as producing enzymes destroying antimicrobial agents (OXA, metallo-b-lactamase, Amp-C, aminoglycoside-modifying enzyme), loss of porins, efflux pumps or changing antibiotic target (penicillin-binding proteins), ribosomal RNA, or DNA gyrase) (Bergogne-Berezin et al., 1996; Bonomo et al., 2006). For β-lactam antibiotics, the highly efficient enzyme is carbapenemases which inactive carbapenems, cephalosporins and penicillins. OXA-23, is the most common type of carbapenemase found in Thailand, (Niumsup et al., 2009; Thapa et al., 2010;Santimaleeworagun et al., 2011b)

Owing to lack of carbapenem activity against A. baumannii, sulbactam, colistin and tigecycline are still the active antimicrobials. Using available agents for CRAB infection, treatment outcomes have been unsuccessful with high mortality rate.

Hello et al. showed that the cases infected with CRAB had significantly higher 30-day mortality rate than ones of patients infected with other MDR bacteria (Hello et al., 2010). This present study indicated that overall mortality rate for CRAB infections was 57.5% in the general hospital. This findings was similar with results from a study of Lee et al (2013)., which revealed that the mortality rate

14

W. Santimaleeworagun et al.

Silpakorn U Science & Tech J Vol.8(1), 2014

features. Clinical Microbiology Reviews 9(2): 148-165.

Bonomo, R. A. and Szabo, D. (2006) Mechanisms of multidrug resistance in Acinetobacter species and Pseudomonas aeruginosa.

Clinical Infectious Diseases 43 Suppl 2: S4956.

Calandra, T. and Cohen, J. (2005) The international sepsis forum consensus conference on definitions of infection in the intensive care unit. Critical Care Medicine 33(7): 15381548.

Dejsirilert, S., Tiengrim, S., Sawanpanyalert, P., Aswapokee, N., and Malathum, K. (2009) Antimicrobial resistance of Acinetobacter baumannii: six years of National Antimicrobial Resistance Surveillance Thailand (NARST) surveillance. Journal of the Medical Association of Thailand 92 Suppl 4: S34-45.

Deris, Z. Z., Harun, A., Shafei, M. N., Rahman, R. A., and Johari, M. R. (2009) Outcomes and appropriateness of management of nosocomial Acinetobacter bloodstream infections at a teaching hospital in northeastern Malaysia. Southeast Asian Journal of Tropical Medicine and Public Health 40(1): 140-147.

Erbay, A., Idil, A., Gozel, M. G., Mumcuoglu, I., and Balaban, N. (2009) Impact of early appropriate antimicrobial therapy on survival in Acinetobacter baumannii bloodstream infections. International Journal of Antimicrobial Agents 34(6): 575-579.

Falagas, M. E., Kasiakou, S. K., Rafailidis, P. I., Zouglakis, G., and Morfou, P. (2006) Comparison of mortality of patients with Acinetobacter baumannii bacteraemia receiving appropriate and inappropriate empirical therapy. Journal of Antimicrobial

Obviously, APACHE II scores was proved to be associated with mortality in CRAB infections (Prates et al., 2010). However, the characteristic data (including; age, immunocompromized status, renal or liver function, mechanical ventilator use or shock) were important parameters in APACHE II scores, had been herein analyzed.

Conclusion

The appropriate antimicrobial treatment was associated with better treatment outcomes in patients with CRAB infections. Thus, this is a strategy to improve outcomes of CRAB treatment. A larger study could identify the risk factors for clinical outcomes based on severity of disease and the benefit of combination therapy.

Acknowledgements

We would like to thank the director of Hua Hin Hospital and Mrs. Onanong hongchumpae, head of department of pharmacy, Hua Hin Hospital, for giving us convenience for the process of study. We also thank the staffs in medical records unit for finding the document to fulfill the data analysis.

References Alvarez-Lerma, F., Palomar, M., Olaechea, P., Otal, J. J., Insausti, J., and Cerda, E. (2007) National Study of Control of Nosocomial Infection in Intensive Care Units. Evolutive report of the years 2003-2005. Medicina Intensiva 31(1): 6-17.

Apisarnthanarak, A. and Mundy, L. M. (2009) Mortality associated with Pandrug-resistant Acinetobacter baumannii infections in Thailand. American Journal of Infection Control 37(6): 519-520.

Bergogne-Berezin, E. and Towner, K. J. (1996) Acinetobacter spp. as nosocomial pathogens: microbiological, clinical, and epidemiological

15

Silpakorn U Science & Tech J Vol.8(1), 2014

Clinical Outcomes and Risk Factors Affecting 30-day Mortality

Chemotherapy 57(6): 1251-1254.

Hello, S. L., Falcot, V., Lacassin, F., Mikulski, M., and Baumann, F. (2010) Risk factors for carbapenem-resistant Acinetobacter baumannii infections at a tertiary care hospital in New Caledonia, South Pacific.

Scandinavian Journal of Infectious Diseases 42(11-12): 821-826.

Hsueh, P. R., Chen, W. H., and Luh, K. T. (2005) Relationships between antimicrobial use and antimicrobial resistance in Gram-negative bacteria causing nosocomial infections from 1991-2003 at a university hospital in Taiwan.

International Journal of Antimicrobial Agents 26(6): 463-472.

Jamulitrat, S., Thongpiyapoom, S., and Suwalak, N. (2007) An outbreak of imipenem-resistant Acinetobacter baumannii at Songklanagarind Hospital: the risk factors and patient prognosis. Journal of the Medical Association of Thailand 90(10): 2181-2191.

Lee, Y. T., Tsao, S. M., and Hsueh, P. R. (2013) Clinical outcomes of tigecycline alone or in combination with other antimicrobial agents for the treatment of patients with healthcareassociated multidrug-resistant Acinetobacter baumannii infections. European Journal of Clinical Microbiology & Infectious Diseases 32(9):1211-1220.

Levy, M. M., Fink, M. P., Marshall, J. C., Abraham, E., Angus, D., Cook, D., Cohen, J., Opal, S. M., Vincent, J. L., and Ramsay, G. (2003) 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference.

Critical Care Medicine 31(4): 1250-1256.

Livermore, D. M., Hill, R. L., Thomson, H., Charlett, A., Turton, J. F., Pike, R., Patel, B. C., Manuel, R., Gillespie, S., Balakrishnan, I., Barrett, S. P., Cumberland, N., and Twagira, M. (2010) Antimicrobial treatment and

clinical outcome for infections with carbapenem- and multiply-resistant Acinetobacter baumannii around London.

International Journal of Antimicrobial Agents 35(1): 19-24.

Marshall, C., Richards, M., Black, J., Sinickas, V., Dendle, C., Korman, T., and Spelman, D. (2007) A longitudinal study of Acinetobacter in three Australian hospitals. Journal of Hospital Infection 67(3): 245-252.

National Antimicrobial Resistance Surveillance Center (2007) Result of antimicrobial resistantce surveillance. [Online URL: http:// narst.dmsc.moph.go.th/] accessed on April 1, 2013.

National Antimicrobial Resistance Surveillance Center (2010) Result of antimicrobial resistantce surveillance. [Online URL: http:// narst.dmsc.moph.go.th/] accessed on April 1, 2013.

Niumsup, P. R., Boonkerd, N., Tansawai, U., and Tiloklurs, M. (2009) Carbapenem-resistant Acinetobacter baumannii producing OXA-23 in Thailand. Japanese Journal of Infectious Diseases 62(2): 152-154.

Prates, C. G., Martins, A. F., Superti, S. V., Lopes, F. S., Ramos, F., Cantarelli, V. V., and Zavascki, A. P. (2010) Risk factors for 30-day mortality in patients with carbapenemresistant Acinetobacter baumannii during an outbreak in an intensive care unit.

Epidemiology and Infection 139(3): 411-418.

Santimaleeworagun, W., Wongpoowarak, P., Chayakul, P., Pattharachayakul, S., Tansakul, P., and Garey, K. W. (2011a) Clinical outcomes of patients infected with carbapenem-resistant Acinetobacter baumannii treated with single or combination antibiotic therapy. Journal of the Medical Association of Thailand 94(7): 863-870.

16

W. Santimaleeworagun et al.

Silpakorn U Science & Tech J Vol.8(1), 2014

Journal of Infectious Diseases 14(9): e764-e769.

Thapa, B., Tribuddharat, C., Srifuengfung, S., and Dhiraputra, C. (2010) High prevalence of bla(OXA)-23 in oligoclonal carbapenemresistant Acinetobacter baumannii from Siriraj Hospital, Mahidol University, Bangkok, Thailand. Southeast Asian Journal of Tropical Medicine and Public Health 41(3): 625-635.

Wisplinghoff, H., Bischoff, T., Tallent, S. M., Seifert, H., Wenzel, R. P., and Edmond, M. B. (2004) Nosocomial bloodstream infections in US hospitals: analysis of 24,179 cases from a prospective nationwide surveillance study. Clinical Infectious Diseases 39(3): 309-317.

Santimaleeworagun, W., Wongpoowarak, P., Chayakul, P., Pattharachayakul, S., Tansakul, P., and Garey, K. W. (2011b) In vitro activity of colistin or sulbactam in combination with fosfomycin or imipenem against clinical isolates of carbapenem-resistant Acinetobacter baumannii producing OXA-23 carbapenemases. Southeast Asian Journal of Tropical Medicine and Public Health 42(4): 890-900.

Sheng, W. H., Liao, C. H., Lauderdale, T. L., Ko, W. C., Chen, Y. S., Liu, J. W., Lau, Y. J., Wang, L. H., Liu, K. S., Tsai, T. Y., Lin, S. Y., Hsu, M. S., Hsu, L. Y., and Chang, S. C. (2010) A multicenter study of risk factors and outcome of hospitalized patients with infections due to carbapenem-resistant Acinetobacter baumannii. International

17

Research Article Factors Influencing the Success of an ERP System: A Study in the Context of an Agricultural Enterprise in Thailand Somsit Duangekanong Faculty of Science and Technology, Assumption University of Thailand Corresponding author. E-mail address: ben@moleculesys.com Received June 20, 2013; Accepted September 20, 2013

Abstract

The study examined factors in user satisfaction with ERP implementation pig farming and processing organization in northern Thailand. The target population included ERP users and government advisers involved with its implementation. A theoretical model with six determinants (Business Process of Reengineering, Top Management Support, Education and Training, Information Quality, System Quality and Perceived Usefulness) of User Satisfaction was developed. Data was collected using a questionnaire (n =228). From a theoretical perspective the findings supported most of those in previous studies whereby Top Management Support had the greatest effect on User Satisfaction followed in order by Perceived Usefulness, System Quality, and Information Quality while Business Process Reengineering and Education and Training had relatively small effects on User Satisfaction. New findings showed that contrary to those in previous studies: Top Management Support and Education and Training did not have significant effects on System Quality; Business Process Engineering only had significant indirect effects on Perceived Usefulness and User Satisfaction; and Education and Training only had a significant indirect effect on User Satisfaction. From a practical perspective the findings enabled the formulation of a hierarchy of objectives aimed at increasing user satisfaction each with an associated hierarchy of actions.

Key Words: Enterprise resource planning (ERP); Critical success factors (CSF); Information and system quality and net benefits Introduction

An Enterprise Resource Planning (ERP) system is an information technology based system used to collect data about business processes spanning supply chain management, production, distribution, marketing and sales, and administrative practices. The organization then uses the information collected by the system to control and refine its

Silpakorn U Science & Tech J 8(1): 18-45, 2014

existing practices and modify them in order to improve efficiency and productivity. Implemented properly, an ERP system may provide a substantial competitive advantage for the organization due to increased knowledge about the organization and the ability to implement changes in its operation based on these findings (Umble, et al., 2003). ERP systems also improve production efficiency by rationalizing

ISSN 1905-9159

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

work processes, reducing or eliminating duplication, and improving record keeping compared to manual systems or piecemeal software programs (such as accounting systems, manufacturing control systems, and so on) (Umble, et al., 2013). ERP systems may also offer user benefits such as improved IT department interaction and improved and more efficient workflow processes (Longinidis & Gotzamani, 2009). Given these potential benefits, many organizations are interested in the use of an ERP system.

Knowing how many organizations use an ERP system is difficult because of the diffusion of the market. A report by Panorama Consulting (2012) indicated that the largest ERP vendors by market share include SAP (22 percent), Oracle (15 percent), and Microsoft Dynamics (10 percent). However, the composition of the remainder of the market (53 percent) includes a wide range of mid-level, small or targeted, and open source systems, which are not necessarily tracked in terms of implementation (Monk and Wagner, 2008). This means that there are few statistics about how many companies have implemented ERP systems globally. However, there is some evidence for its popularity in specific areas. For example, one industry survey found that 70 percent of top firms use ERP systems to drive ordering, production, fulfillment, and billing cycles (Aberdeen Group, 2007). These firms see substantial benefits from the implementation, including complete and on-time shipment, inventory accuracy, and increased efficiency in payment collections (Aberdeen Group, 2007). A more recent study by Panorama Consulting (2012) suggested that organizations expected to realize increased information availability and interaction between business units and decreased labor costs and production lead time. Thus, there are many reasons for an organization to implement an ERP system.

While there are significant benefits from an ERP system, it is not an unproblematic choice for an organization. The implementation of ERP systems is exceptionally prone to failure and even when implementation is completed it can be substantially over time and over budget. A rough estimate of failure rates for large-scale software programs including ERP systems is that about 60 percent of all such projects fail (Simon, 2010).

One recent implementation survey of the most frequently used large ERP systems (SAP, Oracle, and Microsoft Dynamics) found that successful Oracle implementations took on average four months longer than the planned 18 months, while both SAP and Microsoft Dynamics took two or three months longer than expected (Panorama Consulting, 2012). This study also showed that the cost of implementation was high, with payback periods averaging 2.4 years and 29 percent of the organizations experienced no financial benefit from the implementation (Panorama Consulting, 2012).

Even when implementation succeeds in terms of time and budget, only 3 percent of organizations realize 81 percent or more of the expected benefits.

Particularly problematic are benefits such as decreased labor costs and improved lead time, which are only realized in 7 percent of the organizations (Panorama Consulting, 2012).

As a study in Jordanian manufacturing shows, ERP failure can be particularly problematic in developing countries due to a significant gap between the organizationâ&#x20AC;&#x2122;s capacity and resources and the requirements and assumptions of the ERP software (Hawari and Heeks, 2010). Overall, this means that firms in Thailand that are hoping to implement ERP software have a significant challenge in realizing this goal given the wide range of potential failure points for implementation. This high rate of failure and the potential gap between

19

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

Related Literature

The review includes an overview of previous studies related to the implementation of ERP systems. Studies related to influential models and important variables concerned with User Satisfaction are examined in order to form a basis for the development of a theoretical model which is presented in section 4.

An Overview of Previous Studies

Table 1 summarizes some of the key attributes of previous studies related to ERP systems.

The summary illustrates the breadth of possible factors that have been identified as issues in ERP implementation success or failure. The studies span the period from 2002-2012 and include studies conducted in Thailand. Most studies are either case studies or small-scale quantitative studies. All data collection was done using some combination of interviews, secondary data (archive data, reports, and other information), and quantitative surveys.

resources and ERP design requirements mean that Thai companies hoping to implement ERP systems need to be aware of important success factors in implementation. Identifying these important success factors for an ERP implementation in a specific sector (the agricultural production sector) was the main aim of this study.

A description of the research design and methodology for the study is presented next (section 2). This is followed by a review of the related literature (section 3) which forms the basis for the formulation of the theoretical model for the study (section 4). Section 5 presents the results of the data preparation and preliminary statistical analyses of the prepared data. This leads to the analysis and development of the theoretical model in order to produce a final model (section 6). The findings of the study are discussed in section 7 and conclusions are drawn in section 8.

Table 1 An overview of previous studies of ERP implementation Factors Studied

Top management support, Change management, Culture, Teamwork, Software development Business practices, System integration needs

Research Approach/ Strategy

Data Gathering Method

Case study, qualitative Interviews in two Saudi Arabian approach organizations with failed ERP implementations Grounded theory, Interviews of managers and qualitative approach employees at 16 firms in Thailand Quantitative survey Questionnaire directed to Midwestern United States

Project management principles, ERP project evaluation, Business process reengineering, Top management support, Costs, Consulting services Top management support, Project Quantitative survey management, Business process reengineering, Suitability of systems, Education and training, User involvement Business process reengineering, Case study, qualitative Organization size, Existing IT systems approach maturity, National culture

20

References

Aldammas and Al-Mudimigh (2011) Arunthari and Hasan (2005) Ehie and Madsen (2005)

Questionnaire distributed to Finnish firms

Jiang (2005)

Interviews and supporting documentation from six Thai firms

Kanthawongs (2010)

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

Table 1 An overview of previous studies of ERP implementation (continued) Factors Studied

Top management support, Leadership, Training and development, Decision making, Power sharing, Communication, Risk and conflict tolerance Readiness for change, Perceived organizational commitment, Individual competence Change management, Cultural readiness, Project implementation scope (controlled or exploratory vs. uncontrolled or revolutionary) Change management, Cultural readiness, Network relationships

Functional performance, Acquisition cost, Operating cost, Ease of use, Reliability, Serviceability, Compatibility Cultural factors (partnership, business process reengineering, human resources, reports and tables, language), Environmental factors (price, cost control) Cultural issues, Technical capability, Business process reengineering

Research Approach/ Strategy

Data Gathering Method

References

Theoretical discussion Literature review (secondary research)

Ke and Wei (2008)

Quantitative survey Questionnaire (n = 312) of 72 Korean organizations Case study, qualitative Interviews and supporting approach documentation in two organizations

Kwakh and Lee (2008)

Exploratory case study, qualitative approach

Motwani et al.

(2005)

Interviews and supplementary data (memos, reports, feasibility studies, and other documentation) from four firms (country not disclosed) Case study, qualitative Interviews with managers and approach secondary data in two Thai organizations

Motwani et al.

(2002)

Suebsin and Gerdsri (2009)

Case study, qualitative Interviews in five Chinese firms Xue et al. approach with failed ERP implementations (2005)

Case study, qualitative Interviews and secondary approach documentation from a single firm (Rolls-Royce) that had a troubled, though ultimately successful, ERP implementation

The studies in Table 1 show a number of clusters of common factors, although these factors were often conceptualized slightly differently in different studies. The first is a cluster of cultural factors (leadership and management support, national and organizational culture, effective change management, and training and development).

Yusuf et al.

(2004)

A second cluster of factors concerned business needs and resources (cost and budget, availability of consulting resources, and technical capability).

The third cluster focused on IT-related processes and resources (business process re-engineering, project management, and existing IT systems).

Overall, little attention was given to business

21

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

requirements although a few studies mentioned system integration needs (Arunthari and Hasan, 2005), feasibility evaluation (Ehie and Madsen, 2005), and organizational commitment (Kwahk and Lee, 2008). Factors were studied primarily as qualitative constructs and the number of quantitative studies was relatively small.

Models and Model Variables

An influential model of the success of the adoption of an information system in an organization is the DeLone and McLean (1992) model (D&M).

This model was based on a comprehensive review of information system success frameworks that had been used up to the early 1990s in an attempt to identify critical success factors. The initial model specification involved the variables System Quality, Information Quality, Use, and User Satisfaction as determinants of User Impact, which in turn affected Organizational Impact.

The D&M model has not remained static and its development continued with empirical testing and the identification of additional success factors and relationships. Seddon and Kiew (1996) confirmed the importance of three variables (System Quality, Information Quality, and Importance of the System) as determinants of User Satisfaction directly or indirectly through their influence of Usefulness. Seddon (1997) further simplified and respecified the D&M model in order to eliminate ambiguous or duplicated meanings of the constructs and to provide measurable constructs that could be more easily tested. This respecification was required because at the time there were several different notions for the variable Use in the D&M model (Seddon, 1997). Seddon found that the D&M model combined and confused process and output variables making it unclear as to what were intended to be the outputs from model. Ultimately, Seddon concluded that the original D&M model represented at least three distinct concepts of use and success.

By respecifying the model in an empirically testable way Seddon (1997) created a more usable model of information systems success which was influential in the development of the theoretical model used in this study.

A recent comprehensive literature review by Petter et al. (2008) of 180 studies using the D&M model found that Seddon (1997) was not alone in identifying the need to respecify and reconfigure this foundational model. Some other variations of the D&M model have included the addition of constructs such as Service Quality, and Net Benefits (Petter et al., 2008). Petter et al. (2008) also found that various researchers had proposed and tested a number of different relationships between the upstream variables (System Quality, Information Quality, Perceived Usefulness, and User Satisfaction) and downstream variables (Individual Impact and Organizational Impact) in the D&M model or Net Benefits in the Seddon (1997) respecification. Changes to the D&M model have met with varying success. However, the outcomes of Seddonâ&#x20AC;&#x2122;s (1997) respecification related to the use of a set of variables addressing Net Benefits other than only individual and organizational impacts were included in a combined model by Petter et al. (2008).

In addition to the role of System Quality, Information Quality, and Perceived Usefulness in explaining User Satisfaction Seddonâ&#x20AC;&#x2122;s (1997) respecification of the D&M model allows for the inclusion of behavioral constructs as part of the explanation. However, it does not specify in detail which behavioral constructs should be used.

Based on the overview of previous studies of ERP systems (Table 1) three behavioral critical success factors were identified (Top Management Support, Business Process Reengineering, and Education and Training).

In previous studies Top Management Support was established as a factor that contributed to the

22

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

success or failure of the ERP implementation process.

It had a positive influence on Perceived Usefulness and User Satisfaction (Seddon, 1997) and Ifinedo (2008) and Umble et al. (2003) found that it had a positive effect on System Quality. Business Process Reengineering was an acknowledged part of an ERP implementation, as well as other organizational change processes. It can significantly shift the organizationâ&#x20AC;&#x2122;s work routines and practices through the stages of: identifying vision, business objectives, and processes; measuring existing processes and understanding how information technology can be used as a lever for improvement; and then making and testing changes to the new process (Swamidass, 2000). Business Process Reengineering can be prone to failure, particularly if there is insufficient attention paid to existing processes and the role of information technology within them, if change management is insufficient, or if other opportunities for process involvement are missed (Eardley et al.,

2008). Seddon (1997) identified the positive influence of Business Process Reengineering on User Satisfaction and its positive influences on Perceived Usefulness and System Quality were suggested by Finney and Corbett (2007). Although effects of Education and Training were not described

by Seddon (1997) they were identified in several studies related to ERP systems (Ehie and Madsen, 2005; Finney and Corbett, 2007; Jiang, 2005; Ke and Wei, 2008). It has been identified to have important positive influences on Perceived Usefulness and User Satisfaction.

Based on the overview of previous studies (Table 1), the respecification of the D&M model by Seddon (1997), and the identification of behavioral constructs (Top Management Support, Business Process Reengineering, and Education and Training) a theoretical model aimed at determining User Satisfaction was formulated.

Figure 1 Theoretical model

23

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

Theoretical Model

The theoretical model is shown in Figure 1.

The model is notated to indicate the direct causal effects associated with the 18 research hypotheses

effects among the four variables System Quality, Information Quality, Perceived Usefulness, and User Satisfaction. In addition, the model incorporates three variables (Business Process Reengineering, Top Management Support, and Education and Training) which represent important critical success factors identified in previous studies to have an influence on at least two of the three variables (System Quality, Perceived Usefulness, and User Satisfaction).

in Table 2.

The theoretical model in Figure 1 was strongly influenced by the detailed restructuring of the DeLone and McLean (2003) model of information systems success by Seddon and Kiew (1996) and Seddon (1997). This is evident in the

Table 2 Research hypotheses associated with the theoretical model Research Hypothesis

Reference

H1: System Quality has a significant positive direct effect on Perceived Usefulness H2: System Quality has a significant positive direct effect on User Satisfaction H3: Information Quality has a significant positive direct effect on Perceived Usefulness H4: Information Quality has a significant positive direct effect on User Satisfaction H5: Perceived Usefulness has a significant positive direct effect on User Satisfaction H6: Business Process Reengineering has a significant positive direct effect on System Quality H7: Business Process Reengineering has a significant positive direct effect on Perceived Usefulness H8: Business Process Reengineering has a significant positive direct effect on User Satisfaction H9: Top Management Support has a significant positive direct effect on System Quality H10: Top Management Support has a significant positive direct effect on Perceived Usefulness H11: Top Management Support has a significant positive direct effect on User Satisfaction H12: Education and Training has a significant positive direct effect on Perceived Usefulness H13: Education and Training has a significant positive direct effect on User Satisfaction

Seddon (1997), Seddon and Kiew (1996) Seddon (1997), Seddon and Kiew (1996), DeLone and McLean (2003) Seddon (1997), Seddon and Kiew (1996) Seddon (1997), Seddon and Kiew (1996) Seddon (1997), Seddon and Kiew (1996) Swamidass (2000), Eardley et al. (2008), Finney and Corbett (2007) Finney and Corbett (2007), Aldammas and Al-Mudimigh (2011) Seddon (1997) Umble, et al. (2003), Ifinedo (2008) Seddon (1997), Aldammas and Al-Mudimigh (2011), Motwani et al. (2002) Seddon (1997) Jiang (2005), Arunthari and Hasan (2005) Arunthari and Hasan (2005)

Note: Significant effects are statistically significant at a level of 0.05 or less.

Table 3 presents the operational definition used for each of the seven variables in the theoretical

model. References to previous studies are included as the sources for the definitions.

24

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

Table 3 Operational definitions and labels for model variables Categories of Variables

Critical Success Factors

Variable

Aldammas and Al-Mudimigh, (2011), Ehie and Madsen (2005), Jiang (2005), Ke and Wei (2008), Umble, et al. (2003)

Business The extent to which the organizationâ&#x20AC;&#x2122;s Process business processes were reengineered to Reengineering align with the ERP system software.

Aldammas and Al-Mudimigh (2011), Arunthari and Hasan (2005), Ehie and Madsen (2005), Jiang (2005), Kanthawongs (2010), Xue, et al. (2005), Yusuf, et al. (2004)

Information Quality

System Quality

Net Benefits

References

Top The extent to which the ERP project Management received approval and support from top Support management.

Education and Training Information and System Quality

Operational Definition

The extent to which ERP concepts were Ehie and Madsen (2005), Finney and introduced to users and training was Corbett (2007), Jiang (2005), Ke and provided on features of the ERP software. Wei (2008) The extent to which an individual believes DeLone and McLean (1992), DeLone that the information produced by the ERP and McLean (2003), Seddon and Kiew system meets their needs and is relevant, (1996), Seddon (1997) comprehensible, accurate, complete, reliable, and timely.

The extent to which and individual believes that the ERP system is well documented, easy to use and learn, user friendly, accessible, and free of bugs.

DeLone and McLean (1992), DeLone and McLean (2003), Seddon and Kiew (1996), Seddon (1997)

Perceived Usefulness

The extent to which an individual believes Seddon and Kiew (1996), DeLone and that using the ERP system has enhanced McLean (2003), Seddon (1997), Davis their job performance, increased their (1989) productivity at work, and made them more effective in their work.

User Satisfaction

The userâ&#x20AC;&#x2122;s overall level of satisfaction with the efficiency and effectiveness of the ERP system and the manner in which the system satisfies their information processing needs.

Table 4 shows details associated with the measurement of each of the seven variables in the theoretical model. Labels for the variables are shown with references to existing measuring instruments

Seddon (1997), DeLone and McLean (2003), Seddon and Kiew (1996)

which were used as sources for the questions and measurement scales used in the study questionnaire (Appendix A1).

25

Silpakorn U Science & Tech J Vol.8(1), 2014

Table 4

Factors Influencing the Success of an ERP System

Measurement of model variables Variable (Label)

Measuring Instrument

Interval Scale Variables Top Management Support (TMS)

Yingjie (2005)

Business Process Reengineering (BPR)

Yingjie (2005)

Education and Training (ET)

Yingjie (2005)

Latent Variables

Number of Indicators and Labels

Information Quality (IQ)

10 indicators; IQ1 to IQ10

Seddon and Kiew (1996)

System Quality (SQ)

8 indicators; SQ1 to SQ8

Seddon and Kiew (1996)

Perceived Usefulness (PU)

6 indicators; PU1 to PU6

Seddon and Kiew (1996)

User Satisfaction (US)

4 indictors; US1 to US4

Seddon and Kiew (1996)

In Table 4 each of the three interval scale variables was measured on a five-point Likert scale with the measures treated as interval scale measures in the analyses. For the four latent variables each indicator was measured on a five-point Likert scale with the measures treated as interval scale measures in the analyses.

Research Design and Methodology

The research was: partly basic and applied; partly descriptive and explanatory; and crosssectional in time. The study used descriptive statistical techniques for data preparation and analysis and structural equation modeling (SEM) techniques for the analysis and further development of a theoretical model which was derived from existing theory.

The target population was individuals that were involved with an ERP system implemented in the largest pig farming and processing organization in northern Thailand. These individuals had variable levels of involvement with the ERP system so it was decided that in addition they must be fulltime or part-time employees of the organization or government advisers who worked with the company and its subsidiaries to implement the ERP system.

Individuals must be: at least 18 years of age; have at least one year of work experience; and have at least one year of experience with the ERP system. These

constraints aimed to ensure that the participants understood organizational practices and procedures with some understanding of the potential issues that faced the organization and sufficient knowledge and experience related to the ERP system. The size of this target population was estimated to be 300-350 individuals. Based on a level of precision of 0.05 and a 95 percent confidence interval the sample size for the study was determined to be at least 180 (Israel, 2013). This minimum sample size also ensured the statistical validity of the SEM and other statistical techniques used in the data analysis (Kline, 2005).

A self-administered questionnaire was prepared in the English language in order to measure characteristics of the respondents and the variables in the theoretical model. The questionnaire was reviewed by a focus group of five individuals with expertise in questionnaire design and knowledge of the organization and the ERP system. Suggested modifications were included in a revised version of the questionnaire which was then administered in a pretest study using a sample of 10 suitable participants. Their responses and comments were noted and any modifications were incorporated into the final version of the questionnaire which was then used in the full study. A notated version of the questionnaire is included in Appendix A1.

26

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

It was possible to distribute questionnaires to almost the entire target population by making contact with participants through access provided by the organization. Questionnaires were made available in both hard and soft copy forms and a cover letter introduced the purpose of the study and provided instructions for its completion and return and a contact address for enquiries. Three hundred and twenty two questionnaires were returned and among these 23 were from respondents with less than one year of experience with the ERP system and this group also included all of the 11 respondents who had less than one year of work experience.

None of the respondents was less than 18 years of age. These 23 respondents were removed from the sample leaving a sample size of 299. Data was entered into an SPSS (Version 19) worksheet and a random 10 percent of the responses were checked for accuracy of data entry and no errors were found.

Nineteen questionnaires were found to include at least one outlier value for a model variable (i.e. a value 3 or more standard deviations from the mean).

These 19 questionnaires were removed from the sample to give a final sample size of 280 which satisfied the minimum sample size of 180 described above.

Usefulness (3) were deleted because they did not meet the requirements for satisfactory construct validity. However, among the four latent variables in the model each was measured finally using at least three indicators with satisfactory construct validity.

The internal consistency reliability of the indicators for the latent variables which displayed satisfactory construct validity was measured using Cronbach alpha coefficients. The coefficients are shown in Appendix Table A2 and the reliability of the sets of indicators was at least acceptable and mainly good (George and Mallery, 2003).

Characteristics of Respondents

Appendix Table A3 displays personal and work characteristics of the respondents determined from the responses to the items in section 1 of the questionnaire. The sample of 280 individuals included 153 males (55 percent) and 127 females (45 percent). The average age of respondents was 39 years with respondents mainly in the age categories 33-37 years (25 percent) and 38-42 years (20 percent) and 75 percent were between age 28 years and age 47 years. The sizes of the age groups were consistent with the age groups in the Thai population as a whole (NationMaster, 2013a). Sixty percent of the respondents had a Bachelor degree and all of the respondents had a Bachelor degree or higher. Thus the respondents were well educated particularly in comparison to the Thai population as a whole where the rate of tertiary education in Thailand is currently 36 percent (NationMaster, 2013b). In summary, the sample was approximately evenly divided between male and female respondents, the respondents were close to a mid-point in their careers according to their age, and they were well educated compared to the population as a whole.

In terms of work characteristics the employees of the organization were divided almost equally between full-time (46 percent) and part-time (49

Data Preparation and Preliminary Analyses

Data Preparation

Principal Component factor analysis was used to test the construct (discriminant and convergent) validity of the measures of the latent model variables. Satisfactory construct validity required that each latent variable was measured by a set of indicators with factor loadings of magnitude at least 0.4 and an associated eigenvalue of at least 1 (Straub et al., 2004). The results for the final factor analysis are shown in Appendix Table A1 where it is seen that some of the indicators for Information Quality (7), System Quality (5), and Perceived

27

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

percent) employment and only 5 percent of the respondents were government advisors. With regard to work experience 44 percent had less than five years experience and a further 39 percent had 5-10 years experience but only 17 percent had 10 years or more experience. This was consistent with age distribution of the respondents, their educational status, and their career stage. There was a very shallow depth of experience with ERP systems. Sixty three percent had less than five years experience with ERP systems and all of the respondents had less than 10 years experience. However, this was expected given that ERP systems were a relatively new innovation in Thailand and it was consistent with the respondentsâ&#x20AC;&#x2122; levels of work experience. In summary, the work characteristics of the participants indicated that: most were full-time or part-time employees of the organization with only a small number of government advisors; most had less than 10 years work experience; and most had less than five years experience with ERP systems.

Descriptive Analysis of Model Variables

Appendix Table A4 shows the values of descriptive statistics for each of the model variables.

In the case of the four latent variables these statistics are shown for each of the indicators. In addition, each of the four latent variables was reduced to a single interval scale variable with values determined for each respondent by taking the mean of the values that the respondent assigned to the indicators.

Descriptive statistics for these single interval scale measures of the four latent variables are also shown in Table A4. These simplified measures of the latent variables were used in the preliminary descriptive analyses presented in this section but the full set of measures for the indicators were used in the SEM analyses throughout section 6. From Appendix Table A4 it is seen that the magnitudes of the values for skewness and kurtosis are well within the acceptable limits of 3 and 7, respectively, required for the use

of maximum likelihood estimation in subsequent SEM analyses (Kline, 2005).

The model variables and indicators were measured on 5-point Likert scales where 3 represented a neutral attitude to the construct being measured. T-tests showed that the mean values of all of the model variables and their indicators were significantly greater than a neutral value (p < 0.001).

This indicated that the respondents expressed very positive attitudes towards the range of aspects of the ERP system measured by the model variables.

T-tests were used to compare the mean values for males and females of the four profile variables (Age, Education, Work Experience, and ERP Experience) and the seven model variables. There was a statistically significant difference between males and females for only the two model variables Top Management Support and Business Process Reengineering and in both cases the mean for females was significantly greater than the mean for males (p < 0.01).

Appendix Table A5 shows the correlations among profile and model variables. Not surprisingly, there were significant positive correlations (p < 0.05) among the profile variables Age, Level of Education, Work Experience, and ERP Systems Experience. Other significant correlations involving profile variables were between: both Level of Education and Work Experience and Information Quality; and Work Experience and both Top Management Support and User Satisfaction. There were significant positive correlations among the three critical success factors (Top Management Support, Business Process Reengineering, and Education and Training). There were significant correlations between Systems Quality and Perceived Usefulness and between Perceived Usefulness and User Satisfaction which were associated with causal effects in the theoretical model (Figure 1). Although significant correlations do not guarantee significant

28

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

causal effects they often suggest significant causal relationships and the causal relationships proposed in the theoretical model are examined in detail in the next section.

followed by the symbol *, ** or *** if the effect was statistically significant at a level of 0.05, 0.01, or 0.001, respectively, and no symbol indicates that the unstandardized effect was not statistically significant at a level of 0.05 or less; (b) In parentheses the standardized effect is shown with S, M, or L to indicate that the magnitude of the effect is small (less than 0.1), medium (0.1 or greater but less than 0.5), or large (at least 0.5), respectively (Cohen, 1988).

Model Analysis and Development

Figure 2 shows the direct effects determined by the SEM analysis of the theoretical model. In Figure 2 and throughout the following sections the notation suggested by Kline (2005) was used for effects: (a) The unstandardized effect is shown first

Figure 2 SEM analysis of the theoretical model

Table 5 presents the values of the range of fit statistics recommended by Kline (2005) that are

associated with the theoretical model in Figure 1.

Table 5 Fit statistics for the theoretical model Model Theoretical Model

N

Nc

NC (Ď&#x2021;2/df)

RMR

GFI

AGFI

NFI

IFI

CFI

RMSEA

280

314

98.830/88 = 1.123

.009

.934

.915

.934

.944

.944

.025

R2: System Quality (0.42); Perceived Usefulness (0.51); User Satisfaction (0.57)

Note: R2 is the proportion of the variance of each endogenous variable that is explained by the variables affecting it.

29

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

From Table 5 it is seen that, although the fit statistics are reasonably satisfactory, in Figure 2 there are six highlighted direct effects that are small in magnitude and not statistically significant at a level of 0.05 or less. This raises the possibility that their removal from the theoretical model may produce a simpler model with an improved set of fit statistics. The six direct effects were made optional

and, using the specification search facility in Amos 18 computer software, the resulting hierarchy of 26 (64) models was analyzed and in accordance with the recommendation by Kline (2005) the model with the smallest value for Normed Chi-square (NC) was selected as the final model shown in Figure 3 with the fit statistics in Table 6.

Figure 3 Final model Table 6 Fit statistics for the final model Model

N

Nc

Final

280

313

Model

NC (Ď&#x2021;2/df)

RMR

GFI

AGFI

NFI

IFI

CFI

RMSEA

102.948/92 = 1.119

.008

.957

.936

.958

.994

.994

.021

R : System Quality (0.43); Perceived Usefulness (0.58); User Satisfaction (0.61) 2

Note: R2 is the proportion of the variance of each endogenous variable that is explained by the variables affecting it.

From Figure 3 it is seen that four of the six direct effects in the theoretical model which were considered for removal remained in the final model and all of the direct effects in the final model are medium in magnitude and statistically significant at a level of 0.05 or less. From Table 6 it is seen that the final model has very acceptable and improved fit statistics and reasonable proportions of the variance

of the endogenous variables are explained.

All the effects in the final model are shown in Table 7 where: (a) the variables on indirect paths are shown; (b) the same notations are used for effects as used in Figures 2 and 3; and (c) all of the effects are statistically significant at a level of 0.05 or less.

The determination of the statistical significance of effects followed the methods proposed by Sobel

30

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

(1986) for indirect effects with one intervening variable, Cohen and Cohen (1983) for indirect effects with more than one intervening variable,

and the results of nonparametric bootstrapping with Amos 18 using 1,000 random samples for the totals of indirect effects and the totals of all effects.

Table 7 Analysis of the final model Variable

Effect Direct

Intervening

Dependent

System Quality

Perceived Usefulness

User Satisfaction

.183*(.190M)

Nil

Nil BPR-SQ-US

Business Process

Indirect

Nil

BPR-SQ-PU

.017*(.027S)

.017*(.023S)

BPR-SQ-PU-US

Reengineering

.004*(.005S) Nil

017*(.023S)

.021*(.032S)

Total

.183*(.190M)

017*(.023S)

021*(.032S)

Direct

Nil

.139**(.206M)

.184**(.201M)

Indirect

Nil

Nil

Total Indirect

Nil

Nil

.034**(.042S)

Total

Nil

.139 (.206M)

.218**(.243M)

Direct

Nil

.067*(.107M)

Nil

Indirect

Nil

Nil

Total Indirect

Nil

Nil

.016*(.022S)

Total

Nil

.067*(.107M)

.016*(.022S)

Direct

Nil

.088**(.180M)

.078*(.106M)

Indirect

Nil

Nil

Total Indirect

Nil

Nil

.022**(.037S)

Total

Nil

.088**(.180M)

.100*(.143M)

Direct

Nil

.093*(.126M)

.095**(.140M)

Indirect

Nil

Nil

Total Indirect

Nil

Nil

.023*(.026S)

Total

Nil

.093 (.126M)

.118*(.166M)

Direct

Nil

Nil

.245**(.206M)

Perceived

Indirect

Nil

Nil

Nil

Usefulness

Total Indirect

Nil

Nil

Nil

Total

Nil

Nil

.245 (.206M)

Total Indirect

Top Management Exogenous

Support

Education and Training

Information Quality

System Quality Intervening

31

**

*

TMS-PU-US .034**(.042S)

ET-PU-US .016*(.022S)

IQ-PU-US .022**(.037S)

SQ-PU-US .023*(.026S)

**

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

Discussion of the Findings

Characteristics of the Respondents

The characteristics of the respondents identified them as sufficiently qualified, experienced, and mature to be able to provide answers to the issues raised about the ERP system and its implementation in the questionnaire. Respondents indicated a very positive attitude to the features of the ERP system and the issues concerned with its implementation.

There were only two variables (Top Management Support and Business Process Reengineering) where there was a significant difference between the male and female respondents. Although both groups considered that these two critical success factors had been addressed very well as part of the ERP system implementation the females felt this more so than

the males. Correlations showed expected significant positive associations among the respondents: ages, levels of education, amounts of work experience, and amounts of ERP experience. In addition, those with high (low) levels of education or with more (less) work experience considered the quality of the information provided by the ERP system to be high (low). Also, those with more (less) work experience considered the level of top management approval and support for the ERP project to be high (low) and expressed high (low) overall satisfaction with the ERP system.

Interpretation of Causal Effects

Table 8 presents the effects in the final model based on the total effects shown in Table 7.

Table 8 Summary of effects in the final model Variable

Intervening Variable

Dependent Variable

System Quality

Perceived Usefulness

User Satisfaction

Medium, only direct

Small, only indirect

Small, only indirect

Top Management Support

Nil

Medium, only direct

Medium, mainly direct

Education and Training

Nil

Medium, only direct

Small, only indirect

Information Quality

Nil

Medium, only direct

Medium, mainly direct

System Quality

Nil

Medium, only direct

Medium, mainly direct

Perceived Usefulness

Nil

Nil

Medium, only direct

Business Process Reengineering

Exogenous Variable

Intervening Variable

Note: All effects are statistically significant at a level of 0.05 or less and all effects are positive.

The final model included a single dependent variable User Satisfaction defined as the level of satisfaction with the efficiency and effectiveness of the system and the manner in which the system satisfies the userâ&#x20AC;&#x2122;s information processing needs. Top Management Support had the most

important influence on User Satisfaction followed in decreasing order of importance by the medium effects of Perceived Usefulness, System Quality, and Information Quality, and finally the small effects due to Business Process Reengineering and Education and Training.

32

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

There were two intervening variables (Perceived Usefulness and System Quality).

Perceived Usefulness was defined as the perception that the ERP system enhanced job performance, productivity and effectiveness. Perceived Usefulness only influenced User Satisfaction but as an intervening variable it was involved in the indirect effects of all of the other variables on User Satisfaction. The influences on Perceived Usefulness in decreasing order of importance were due to the medium effects of Top Management Support, Information Quality, System Quality, and Education and Training as well as the small effect of Business Process Reengineering. The other intervening variable System Quality referred to the extent to which an individual found the ERP system to be well-documented, easy to use and learn, userfriendly, accessible, and bug-free. It only had effects on User Satisfaction and Perceived Usefulness both of which were medium and the effect on User Satisfaction was larger. The only variable that had an influence on System Quality was Business Process Reengineering and it was medium and direct.

There were four exogenous independent variables (Business Process Reengineering, Top Management Support, Education and Training, and Information Quality) and they influenced at least one of the intervening variables. The first three variables represented critical success factors and they are discussed first. Business Process Reengineering was defined as a process of analysis and redesign of business processes in order to improve efficiency,

reduce redundancy, and fit outlines of tasks to specific requirements of the ERP system. Business Process Reengineering only had indirect small effects on Perceived Usefulness and User Satisfaction but as noted it was the only variable to have an effect on Systems Quality. Top Management Support was defined as vocal and material support for the ERP implementation process. As noted above Top Management Support had important medium effects on User Satisfaction and Perceived Usefulness.

Education and Training referred to the pre and postimplementation provision of information about the new ERP system and the ways it could be used.

Education and training had a small effect on User Satisfaction, similar to that of Business Process Reengineering, but it had a direct medium effect on Perceived Usefulness. The other exogenous variable was Information Quality which was defined as the extent to which the individual finds the information produced by the ERP system relevant, comprehensible, accurate, complete, and timely.

Information Quality, like Top Management Support, had medium effects on Perceived Usefulness and User Satisfaction.

Comparison of the Findings with those from

Previous Studies

There were thirteen hypotheses (Table 2) associated with direct effects among the variables in the theoretical model. Tables 9(a), (b), and (c) summarize the hypotheses that were fully supported, partially supported, or not supported, respectively.

33

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

Table 9(a) Hypotheses supported by the findings Research Hypothesis

Source

Magnitude of the Effect

H1: System Quality has a significant positive direct Seddon (1997), Seddon and Kiew (1996) effect on Perceived Usefulness

Medium

H2: System Quality has a significant positive direct Seddon (1997), Seddon and Kiew effect on User Satisfaction (1996), DeLone and McLean (2003)

Medium

H3: Information Quality has a significant positive direct effect on Perceived Usefulness

Seddon (1997), Seddon and Kiew (1996)

Medium

H4: Information Quality has a significant positive direct effect on User Satisfaction

Seddon (1997), Seddon and Kiew (1996)

Medium

H5: Perceived Usefulness has a significant positive Seddon (1997), Seddon and Kiew (1996) direct effect on User Satisfaction

Medium

H6: Business Process Reengineering has a significant positive direct effect on System Quality

Swamidass (2000), Eardley et al. (2008), Finney and Corbett (2007)

Medium

H10: Top Management Support has a significant positive direct effect on Perceived Usefulness

Seddon (1997), Aldammas and AlMudimigh (2011), Motwani et al. (2002)

Medium

H11: Top Management Support has a significant positive direct effect on User Satisfaction

Seddon (1997)

Medium

H12: Education and Training has a significant positive direct effect on Perceived Usefulness

Jiang (2005), Arunthari and Hasan (2005)

Medium

From Table 9(a) it is seen that there was full support for nine of the 13 hypotheses proposed in the theoretical model. Hypotheses 1 through 5 and hypotheses 10 and 11 were directly based on either the original model of information systems adoption success proposed by DeLone and McLean (D&M) (1992) or the reformulations by Seddon and Kiew (1996) and Seddon (1997) which introduced the effects of Top Management Support specified in hypotheses 10 and 11. The support for these nine hypotheses confirmed a basic level of validity in the Seddon (1997) reformulation of the D&M model.

Hypotheses 6 and 12 concerned causal effects due to the two exogenous variables Business Process Reengineering and Education and Training, respectively. Business Process Reengineering was

a factor that was specific to ERP systems and referred to preparatory activity that is required for success. Thus it was expected to have a significant positive direct effect on System Quality since it is a process that is directly designed to improve system quality. Education and Training had a significant positive direct effect on Perceived Usefulness as proposed by Arunthari and Hasan (2005) and Jiang (2005). The support for Hypotheses 6 and 12 was compatible with the general notion in the literature that preparing the workforce for the introduction of the ERP system through education and training and the realignment of work practices to better fit the ERP system have important positive influences on the usersâ&#x20AC;&#x2122; perceptions of the usefulness of the system and its quality.

34

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

Table 9(b) Hypotheses partially supported by the findings Research Hypothesis

Reference

Comment

H7: Business Process Reengineering has a significant positive direct effect on Perceived Usefulness

Finney and Corbett (2007), Aldammas and Al-Mudimigh (2011)

Statistically significant, positive, but only indirect and small

H8: Business Process Reengineering has a significant positive direct effect on User Satisfaction

Seddon (1997)

Statistically significant, positive, but only indirect and small

H13: Education and Training has a significant positive direct effect on User Satisfaction

Arunthari and Hasan (2005)

Statistically significant, positive, but only indirect and small

Hypotheses 7, 8, and 13 were only partly supported by the findings. In each case the effects were statistically significant. However, they were indirect rather than direct and they were small in magnitude which means that it may be difficult in practice to identify these effects. Notably, the two variables Business Process Reengineering and Education and Training in these hypotheses were not core to the D&M model. With respect to Business Process Reengineering based on the supported hypotheses in Table 9(a) the finding was that it had an effect on System Quality (hypothesis 6), which in turn had an effect on Perceived Usefulness

(hypothesis 1), which in turn had an effect on User Satisfaction (hypothesis 5). Consequently, Business Process Reengineering had a significant indirect influence on User Satisfaction. A similar situation applied to the effect of Education and Training on User Satisfaction (hypothesis 13) where there was a small statistically significant indirect effect on User Satisfaction through Perceived Usefulness specified by hypotheses 12 and 5, respectively. These partially supported hypotheses did not threaten the validity of the final model but they did indicate that the effects of some variables may not be as evident in practice as others.

Table 9(c) Hypotheses not supported by the findings Research Hypothesis

Source

H9: Top Management Support has a significant positive direct effect on System Quality

Hypothesis 9 was the only hypothesis associated with the theoretical model that was not supported. Although Top Management Support was found to be a very influential variable in the final model its influence on System Quality was not sufficient for it to remain in the final model where the only effect on System Quality was due Business

Umble, et al. (2003), Ifinedo (2008)

Comment Not included in the final model

Process Reengineering (hypothesis 6). It is argued that the lack of support for hypothesis 9 does not threaten the validity of the final model.

New Findings

Table 10 summarizes findings which have not been reported in previous studies.

35

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

Table 10 Summary of new findings Intervening Variable

Exogenous Variable

System Quality

Business Process

Top Management Support Education and Training

Perceived Usefulness Small, positive, only

-

Reengineering

Dependent Variable

indirect

Not included in the final model Not included in the final model

As shown in Table 10, most of the unexpected findings were associated with small, positive, and indirect effects even though they were statistically significant. This means that they may be difficult to detect in practice. However, they do pose some avenues for further exploration of relationships among variables to be examined in subsequent studies. The strength of these relationships may vary in different contexts.

Business Process Reengineering had small, positive, indirect effects on Perceived Usefulness and User Satisfaction and previous studies suggested that these effects would be direct (Aldammas & AlMudimigh, 2011; Finney & Corbett, 2007; Seddon, 1997). Instead, effects due to Business Process Reengineering resulted from its direct influence on System Quality which indicated that the relationships between Business Process Reengineering and the outcomes of an ERP implementation were not directly due to improvements in Perceived Usefulness or User Satisfaction. Instead, Business Process Reengineering served to improve perceptions about aspects of the performance of the system captured by the mediator System Quality, which in turn led to improved outcomes. Thus, Business Process Reengineering should be viewed as an important means of improving System Quality rather than

User Satisfaction Small, positive, only indirect

-

-

-

Small, positive, only indirect

user centered outcomes directly. Top Management Support and Education and Training are both people related constructs and they did not have important influences on the performance oriented construct System Quality. Instead, both had important effects on the user centered construct Perceived Usefulness and, in particular, Top Management Support had a very important effect on User Satisfaction.

Conclusion

The study aimed to develop theoretical knowledge with practical implications about factors that influence the extent to which users are satisfied with an ERP system. The theoretical contribution has been discussed in detail in the preceding section and it may be concluded that the findings were largely consistent with the findings of previous studies and with theoretical models of information systems adoption success.

The practical implications of the findings are explained in the following Table 11 where the findings have been deconstructed into a hierarchy of practical objectives each with an associated hierarchy of actions arranged in decreasing order of their likely effect on the achievement of the objective.

36

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

Table 11 Hierarchies of practical objectives and actions Objective

Primary Objective: Increase the userâ&#x20AC;&#x2122;s satisfaction with the ERP system

Action

Relevant Model Variable

1.1 Ensure that there is support for senior management for the ERP project.

Top Management Support

1.2 Ensure that users perceive the ERP system to be useful. See Secondary Objective 1.

Perceived Usefulness

1.3 Ensure that users perceive that the ERP system is of high quality. See Secondary Objective 2.

System Quality

1.4 Ensure that users perceive that the information provided by the ERP system is of high quality.

Information Quality

1.5 Conduct appropriate business process reengineering as part of the ERP project.

Business Process Reengineering

1.6 Provide users with appropriate education and training Education and to use the ERP system.

Training 2.1 Ensure that there is support for senior management for the ERP project.

Top Management Support

2.2 Ensure that users perceive that the information provided by the ERP system is of high quality.

Information Quality

2.5 Conduct appropriate business process reengineering as part of the ERP project.

Business Process Reengineering

3.1 Conduct appropriate business process reengineering as part of the ERP project.

Business Process Reengineering

Secondary Objective 1: Increase 2.3 Ensure that users perceive that the ERP system is of the userâ&#x20AC;&#x2122;s perception that the ERP System Quality high quality. See Secondary Objective 2. system is useful.

2.4 Provide users with appropriate education and training Education and to use the ERP system.

Training

Secondary Objective 2: Ensure that users perceive that the ERP system is of high quality.

The information in Table 11 offers important practical information for managers and others on the range of issues that need attention for an effective ERP system implementation. The importance of education and training for staff members is particularly important. As the literature review and research has shown, the benefits of ERP implementation cannot be realized if the staff members intended to use the system cannot do so because they do not know how.

Courses such as Basic ERP Use, ERP Reporting, and others of concern to the users would be very helpful. Providing extensive training in the use of

the ERP system, whether this is through in-house or outsourced programs, is essential for success.

This study did not set out to conduct an in-depth examination of implementation success factors from a technical perspective (e.g. user interface selection and design, network and database design, and other technical aspects) and further studies that consider these technical implementation issues would complement the findings from this study and provide further information on how an organization can engage in a successful ERP system implementation. There has not been much

37

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

research done on ERP implementation in Thailand, which means that there could be a useful expansion of the current study into other industrial sectors.

For example, the same study could be performed in the manufacturing sector, which is likely to be an area where ERP implementation is critical to organizational competitiveness. There is also the question of user benefits. To date, a limited amount of research has been done on user benefits of ERP implementation, as most research has focused on organizational benefits (Longinidis & Gotzamani, 2009). There is also the need for further research about the knowledge management (KM) plays in the effective implementation of ERP systems, which has also been under-examined in the literature. Such further studies may use the same research design but there are also some possible modifications. For example, additional exogenous variables may be identified in other contexts. The pool of respondents could also be widened, and this may result in an increased ability to detect effects. The repetition of this study and its expansion into other sectors may produce more generalizable outcomes than was possible from this single study and further test the validity and reliability of the measures and overall design of the study.

study of ERP adoption and vendor selection in Thailand. ACIS 2005 Proceedings, (p.

Paper 3).

Cohen, J. (1988) Statistical Power Analysis for the Behavioral Sciences, 2nd ed., Academic Press, New York.

Cohen, J. and Cohen, P. (1983) Applied Multiple Regression/correlation Analysis for the Behavioral Sciences, 3rd ed., Mahwah, NJ, Erlbaum.

Davis, F. (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly: Management Information Systems 13: 319-339 DeLone, W. H. and McLean, E. R. (1992) Information systems success: The quest for the dependent variable. Information Systems Research 3: 60-95.

DeLone, W. H. and McLean, E. R. (2003) The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems 19(4): 9-30.

Eardley, A., Shah, H., and Radman, A. (2008) A model for improving the role of IT in BPR. Business Process Management Journal 14(5): 629-653.

Ehie, I. C. and Madsen, M. (2005) Identifying critical issues in enterprise resource planning (ERP) implementation. Computers in Industry 56: 545-577.

Finney, S. and Corbett, M. (2007) ERP implementation: A compilation and analysis of critical success factors. Business Process Management Journal 13(3): 329-347.

George, D., Mallery, P., (2003) SPSS for Windows step by step: A simple guide and reference.

11.0 update, Allyn and Bacon, Boston.

Hawari, A. and Heeks, R. (2010) Explaining ERP failure in developing countries: A Jordanian

References Aberdeen Group. (2007) Aberdeen: 70% of top companies use integrated ERP for orderto-cash cycle. [Online URL: www.ihs.com/ news/aberdeen-integrated-erp.htm.] accessed on March 10, 2013.

Aldammas, A. and Al-Mudimigh, A. S. (2011) Critical success and failure factors of ERP implementations: Two cases from Kingdom of Saudi Arabia. Journal of Theoretical and Applied Information Technology 28(2): 73-82.

Arunthari, S. and Hasan, H. (2005) A grounded

38

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

Case Study. Working Paper. Manchester, UK: Manchester Centre for Development Informatics.

Ifinedo, P. (2008) Impacts of business vision, top management support, and external expertise on ERP success. Business Process Management Journal 14(4): 551-568.

Israel, G. (2013) Determining sample size. [Online URL: www. edis.ifas.ufl.edu/pd006.] accessed on March 10, 2013.

Jiang, Y. (2005) Critical success factors in ERP implementation in Finland (Masterâ&#x20AC;&#x2122;s thesis).

Swedish School of Economics and Business Administration.

Kanthawongs, P. (2010) Organizational and cultural factors influencing ERP systems implementation in a developing country: Case studies from Thailand. IADIS International Conference e-Society 2010 (pp. 173-179).

Ke, W. and Wei, K. K. (2008) Organizational culture and leadership in ERP implementation.

Decision Support Systems 45(2): 208-218.

Kline, R.B., (2005) Principles and Practice of Structural Equation Modeling, Guilford Press, London.

Kwahk, K. and Lee, J. (2008) The role of readiness for change in ERP implementation: Theoretical bases and empirical validation.

Information and Management 45: 474-481.

Longinidis, P. and Gotzamani, K. (2009) ERP user satisfaction issues: Insights from a Greek industrial giant. Industrial Management and Data Systems 109(5): 628-645.

Monk, E. F. and Wagner, B. J. (2008) Concepts in Enterprise Resource Planning. Cengage Learning EMEA., London.

Motwani, J., Subramanian, R., and Gopalakrishna, P. (2005) Critical factors for successful ERP implementation: Exploratory findings from four case studies. Computers in Industry 56:

529-544.

NationMaster. (2013a). Age Distribution, Thailand.

[Online URL:www.nationmaster.com/ country/th/Age_distribution.] accessed on March 16, 2013.

NationMaster. (2013b). Education in Thailand.

[Online URL:http://www.nationmaster.com/ country/th-thailand/edu-education.] accessed on March 16, 2013.

Oliver, P. (2012) Suceeding with your literature review: A handbook for students. McGrawHill International, London.

Panorama Consulting. (2012) Clash of the titans: An independent comparison of SAP, Oracle, and Microsoft Dynamics. [Online URL: www. panorama-consulting.com/Documents/Clashof-the-Titans-2012.pdf.] accessed on March 15, 2013.

Petter, S., DeLone, W., & McLean, E. (2008) Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems 17: 236-263.

Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research 8(3): 240-253.

Seddon, P. B. and Kiew, M. -Y. (1996) A partial test and development of Delone and McLeanâ&#x20AC;&#x2122;s model of IS success. Australian Journal of Information Systems 4(1): 90-109.

Simon, P. (2010) The Next Wave of Technologies: Opportunities from Chaos. John Wiley & Sons, London.

Sobel, M. E. (1986) Some new results on indirect effects and their standard errorsin covariance structure models, In N. B. Tuma (Ed.), Sociological Methodology, San Francisco, Jossey-Bass, 159-186.

Straub, D., Boudreau, M-C., Gefen, D., (2004)

39

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

Validation Guidelines for IS Positivist Research, Communications of the Association of Information Systems 13: 380-427.

Suebsin, C. and Gerdsri, N. (2009) Key factors driving the success of technology adoption: Case examples of ERP adoption. PICMET 2009 Proceedings, (pp. 2638-2643). Portland, Oregon, US. Swamidass, P. M. (Ed.). (2000) Encyclopedia of Production and Manufacturing Management.

Springer, London.

Umble, E. J., Haft, R. R., and Umble, M. M. (2003) Enterprise resource planning: Implementation

procedures and critical success factors.

European Journal of Operational Research 146: 241-257.

Xue, Y., Liang, H., Boulton, W. R., and Snyder, C. A. (2005) ERP implementation failures in China: Case studies with implications for ERP vendors. International Journal of Production Economics 97: 279-295.

Yusuf, Y., Gunasekaran, A., and Abthorpe, M. S. (2004) Enterprise information systems project implementation: A case study of ERP in Rolls-Royce. International Journal of Production Economics 87: 251-266.

APPENDIX A1. Notated Questionnaire Notations include labels for variables/indicators and measuring scales.

Section 1: Personal Information 1.1 Gender (G): □ Male (1) □ Female (2) 1.2 Age in Years (A): □ 18-22 (20) □ 23-27 (25) □ 28-32 (30) □ 33-37 (35) □ 38-42 (40) 43-47 (45) □ 48-52 (50) □ 53-57 (55) □ 58-62 (60) □ more than 62 (65) 1.3 What is your highest level of formal education (E)? □ High school (12) □ Bachelor degree (16) □ Master degree (18) □ Doctoral degree (22) 1.4 Type of current work position in the organization (WP): □ Full-time (1) □ Part-time (2) □ Government advisor (3) 1.5 Years of work experience (WE): □ Less than 1 year (1) □ 1 to less than 5 years (3) □ 5 to less than 10 years (8) □ 10 years or more (13) 1.6 Years of work experience in using the ERP system (SE): □ Less than 1 year (1) □ 1 to less than 5 years (3) □ 5 to less than 10 years (8) □ 10 years or more (13)

□

Section 2: A five-point measurement scale was used: (1) Very Bad (2) Bad (3) Neutral (4) Good (5) Excellent Please check (√) the answer which is the best match with your opinion about the implementation of the ERP system: Top management support (TMS) refers to the fact that the ERP project needs to receive approval from top management. Please rate the level of top management approval and support for the ERP project.

Business process reengineering (BPR) refers to aligning the company’s business processes with the ERP software that will be implemented. Please rate the degree of business process reengineering that occurred in the ERP project.

Education and training (ET) refers to the introduction of the ERP concepts to the future users and to providing training with regard to the features of the ERP software. Please rate the level of education and training provided in the ERP project.

40

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

Throughout Sections 3, 4 and 5 a five-point measurement scale was used: (1) Strongly Disagree (2) Disagree (3) Neutral (4) Agree (5) Strongly Agree Section 3: Quality of the Information (IQ) provided by the ERP system Indicator

Please check (â&#x2C6;&#x161;) the answer which best fits with your opinion about each statement:

for IQ IQ1

The output from the ERP system is presented in a useful format.

IQ2

You are satisfied with the accuracy of the information provided by the ERP system.

IQ3

Clear information is provided by the ERP system.

IQ4

The information provided by the ERP system is accurate.

IQ5

The ERP system provides sufficient information.

IQ6

The ERP system provides up-to-date information.

IQ7

The ERP system provides the information needed in time.

IQ8

The ERP system provides reports that are just about exactly what is needed.

IQ9

The ERP system provides precise information.

IQ10

The information content provided by the ERP system meet your needs.

Section 4: Quality of the ERP System (SQ) Indicator

Please check (â&#x2C6;&#x161;) the answer which best fits with your opinion about each statement:

for SQ SQ1

The ERP system is easy to use.

SQ2

The ERP system is user friendly.

SQ3

Compared to other computer software, the ERP system is easy to learn.

SQ4

I find it easy to get the ERP system to do what I want.

SQ5

It is easy for me to become skilful at using the ERP system.

SQ6

The ERP system is not cumbersome to use.

SQ7

Using the ERP system does not require a lot of mental effort.

SQ8

Using the ERP system is not frustrating.

41

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

Section 5: Usefulness of the ERP System (PU) Indicator

Please check (â&#x2C6;&#x161;) the answer which best fits with your opinion about each statement:

For PU PU1

Using the ERP system in my job enables me to accomplish my tasks more quickly.

PU2

Using the ERP system improves my job performance.

PU3

Using the ERP system in my job increases my productivity.

PU4

Using the ERP system enhances my effectiveness in the job.

PU5

Using the ERP system makes it easier to do my job.

PU6

Overall, I find the ERP system useful in my job.

Section 6: User Satisfaction (US) A five-point measurement scale was used: (1) Very Dissatisfied (2) Dissatisfied (3) Neutral (4) Satisfied (5) Very Satisfied.

Indicator Please check (â&#x2C6;&#x161;) the answer which best fits with your opinion about each characteristic: For US US1

How adequately the ERP system meets the information processing needs.

US2

The efficiency of the ERP system.

(Efficiency refers to achieving maximum productivity with minimum wasted effort or expense)

US3

The effectiveness of the ERP system.

(Effectiveness refers to success in producing a desired or intended result without concerning on waste and expense)

US4

Overall, how satisfied are you with ERP system?

42

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

Table A1 Final factor analysis Indicator US4 US1 US3

Latent Variable Information Quality Perceived Usefulness .008 .038 .072 .045 -.012 .147

User Satisfaction .853 .833 .798

System Quality .015 -.038 .023

US2 IQ9 IQ8

.792

.032

.061

.142

.051 .024

.914 .913

.073 .037

.050 -.006

IQ10 PU5 PU6

.004

.872

-.019

.011

.016 .144

-.005 .028

.872 .864

.081 .023

PU4 SQ2 SQ4

.060

.058

.793

.065

-.015 .037

.018 -.003

.035 .075

.881 .789

SQ3

.071

.035

.051

.781

Total Variance Explained Latent Variable User Satisfaction Information Quality Perceived Usefulness System Quality

Total

Initial Eigenvalues Percentage of Variance

Cumulative Percentage

Rotation Sums of Squared Loadings Percentage of Cumulative Total Variance Percentage

3.138

24.139

24.139

2.718

20.911

20.911

2.369

18.224

42.364

2.443

18.789

39.700

2.084

16.028

58.392

2.182

16.781

56.481

1.795

13.810

72.201

2.044

15.720

72.201

Notes: (a) Extraction Method: Principal Component Analysis; (b) Rotation Method: Equamax with Kaiser Normalization. Rotation converged in 5 iterations; (c) Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.725.

Bartlettâ&#x20AC;&#x2122;s Test of Sphericity: Approx. Chi-Square 1479.292, Degrees of Freedom 78, Significance 0.000; (d) Only factors with eigenvalues of 1 or more are shown. The other factors extracted had eigenvalues less than 1 and none of the latent variable indicators loaded significantly on to any of these factors which collectively explained only 27.799 percent of the variance.

Table A2 Cronbach alpha coefficients Latent Variable Information

Indicators

Alpha

Interpretation

IQ 8, 9, 10

0.884

Good

Perceived

Quality User Satisfaction

Latent Variable

Indicators

Alpha Interpretation

PU 4, 5, 6

0.807

Good

SQ 2, 3, 4

0.757

Acceptable

Usefulness US 1, 2, 3, 4

0.842

Good

System Quality

Note: The interpretation of Cronbach alpha follows George and Mallery, 2003.

43

Silpakorn U Science & Tech J Vol.8(1), 2014

Factors Influencing the Success of an ERP System

Table A3 Personal and work characteristics of respondents Personal Characteristics Gender

Work Characteristics

Frequency

Percent

Male

153

54.6

Female

127

Total

280

Age

Work Position

Frequency

Percent

Full-time

130

46.4

45.4

Part-time

136

48.6

100.0

Government Advisor

14

5.0

Total

280

100.0

Cumulative

Frequency

Percent

18-22

7

2.5

2.5

23-27

15

5.4

7.9

28-32

43

15.4

23.2

33-37

69

24.6

47.9

38-42

56

20.0

67.9

43-47

42

15.0

82.9

ERP System Experience (Years)

48-52

6

2.1

85.0

1 to less than 5 years

(Years)

Percent

53-57

32

11.4

96.4

58-62

10

3.6

100.0

Work Experience (Years) 1 to less than 5 years 5 to less than 10 years 10 years or more Total

5 to less than 10 years Total

Mean 39.3 Total

280

100.0

years, Std. Dev. 9.5

Level of Education (Degree) Bachelor

167

59.6

59.6

Master

110

39.3

98.9

3

1.1

100.0

280

100.0

-

Doctoral Total

44

Cumulative

Frequency

Percent

123

43.9

43.9

108

38.6

82.5

49

17.5

100.0

280

100.0

Percent

Mean 6.7 years, Std. Dev. 3.7

175

62.5

62.5

105

37.5

100.0

280

100.0

Mean 4.9 years, Std. Dev. 2.4

S. Duangekanong

Silpakorn U Science & Tech J Vol.8(1), 2014

Table A4 Descriptive statistics for model variables Model Variable/Indicator

Mean

Standard Deviation

Skewness

Kurtosis

Top Management Support

4.73

.477

-1.427

.943

Business Process Reengineering

4.66

.537

-1.315

.770

Education and Training

4.65

.508

-.943

-.383

Information Quality

4.64

.434

-.578

-1.455

IQ8

4.66

.474

-.683

-1.545

IQ9

4.63

.484

-.535

-1.726

IQ10

4.63

.485

-.519

-1.743

System Quality

4.55

.408

-.175

-1.563

SQ2

4.57

.495

-.305

-1.921

SQ3

4.57

.496

-.275

-1.938

SQ4

4.50

.501

.000

-2.014

Perceived Usefulness

4.48

.420

.103

-1.607

PU4

4.60

.490

-.426

-1.832

PU5

4.39

.489

.441

-1.818

PU6

4.43

.503

.190

-1.757

User Satisfaction

4.61

.400

-.557

-1.299

US1

4.55

.499

-.188

-1.979

US2

4.61

.488

-.457

-1.804

US3

4.63

.483

-.551

-1.709

US4

4.67

.472

-.717

-1.497

Table A5 Correlations among profile and model variables

Variable

Profile

Variable Age (A)

Profile Variable A

E

Variable

ERP TMS BPR

ET

IQ

SQ

PU

1

Level of Education (E)

.191

1

Work Experience (WE)

.297

.213

1

ERP System Experience (ERP)

.298

.775

.247

Top Management Support (TMS)

-.017 .045

1

.142 -.008

1

.003

.819

1

Education and Training (ET)

-.017 .026 -.097 .045

.564

.549

Information Quality (IQ)

.036

.125

.137

.068

.055

.056 -.003

System Quality (SQ)

-.049 -.029

.046

.003

.036

.090

Perceived Usefulness (PU)

.058

.045

.053 -.062 -.009 .053 .068 .134

User Satisfaction (US)

.070 -.014 .206 -.023 .069

Business Process Reengineering (BPR) -.027 .011 Model

WE

Model Variable

.015

.083

.021

1 1

.067 .044

1 1

.006 .064 .806 .178

Notes: (a) Highlighted correlation coefficients are statistically significant at a level of 0.05; (b) Shaded cells indicate significant correlations associated with causal effects in the theoretical model.

45

Research Article Solving the Course - Classroom Assignment Problem for a University Kanjana Thongsanit Department of Industrial Engineering and Management, Faculty of Engineering and Technology, Silpakorn University, Nakhon Pathom, Thailand Corresponding author. E-mail address: kanjanath7@yahoo.com Received July 26, 2013; Accepted November 4, 2013

Abstract

A large number of courses and the different classroom capacities with difference in study periods make the assignment between classrooms and courses complicated. The scheduler always takes long time to solve the problem. The purpose of the study is to develop mathematical model and design methods to solve the problem. The assignment problem uses the information from the Faculty of Engineering and Industrial Technology at Silpakorn University in the first semester, 2012. Excelâ&#x20AC;&#x2122;s Premium Solver is applied in this study. It was found that Excelâ&#x20AC;&#x2122;s Premium Solver can solve this classroom allocation problem with the process time in seconds. The total cost was reduced 27,920 baht / semester.

Key Words: Classroom timetable; Integer linear programming Introduction

Nowadays an officer of Faculty of Engineering and Industrial Technology has primary responsibility for doing the timetable scheduling before a semester starts. The timetables are prepared manually which is a high time-consuming process since a scheduler has to concern limitations. For example, the classroom capacity should be compatible with the number of enrolled students in each course.

The scheduler expects that each course should be assigned a classroom. The increasing number of students, a large number of courses and the different classroom capacities make the assignment complicated. The scheduler usually takes time at least one week to solve the problem. Furthermore, cost is another important factor. The faculty spends approximately five hundred thousand baht a year

Silpakorn U Science & Tech J 8(1): 46-52, 2014

for the classroom cost. Consequently, this study presents a guideline to improve the solutions for classroom allocation problem and the goal is to assign courses to classrooms to minimize the cost.

The university timetabling problem is defined as the process of assigning university courses to specific time periods throughout the five working days of the week and to specify classrooms suitability for the number of registered students and the requirements of each course. In practice, there are three main steps. First, each department processes the lecturers and course assignment depending on their skills and experience. In this step, the balancing load is considered based on the department policy.

The second step is that the department will specify the day and study period in that day based on the lecturer and studentsâ&#x20AC;&#x2122; availability. Finally, in the

ISSN 1905-9159

K. Thongsanit

Silpakorn U Science & Tech J Vol.8(1), 2014

components (Taha, 2003).

1) Objective of goal that is aimed to optimize the problem.

2) Constraints or restrictions that are needed to satisfy, for example a limited amount of raw materials or labors.

3) Decision variables or the solutions, the non-negativity restrictions accounting for this requirement.

Integer linear programming (ILP) is linear programming in which some or all the variables are restricted to integer value.

Assignment Problem

An assignment problem is a special case of a transportation model in which the workers represent

third step all the information of each department will come together to the scheduler for assigning all courses to the suitable classrooms. Since the courses are different in period, it is complicated for the scheduler. For example, the course 612 410 is set in 8:30 - 9:20 period, the course 615 451 is set in 8:30 - 11:10 period and the course 618342 is set in 9:25-12:05 period. This shows that the length of each period, the duration and the starting time are all different.

This paper focuses on the third step, i.e. assigning the courses to the classrooms in order to minimize cost. This study develops a mathematical model to solve the courses - classrooms assignment problem. The data input required in the model consists of the number of courses, the number of enrolled students in each course and the capacity of each classroom. The classrooms have the different costs which depend on the number of seats or classroom capacity with the higher expense for the larger-size classrooms. The objective of the model is to minimize cost. Integer Linear Programming is applied and solved by the computer program with Premium Solver for Excel software.

the sources and the jobs represent the destinations (Ragsdale et al., 2004) e.g. the resource allocation of labors, equipments or machine to workplaces.

The course time tabling problem is an assignment of courses to classrooms and time slots with restrictions in order to minimizing cost.

Literature Review

There are two major approaches to solve the university timetabling problems as instance of the discrete constrained optimization problem. The second approach involves some local search method such as the GA or SA and other heuristics and also a Hybrid Algorithm (Gunawan et al., 2007). Sarin et al. (2010) developed the integer programming formulation for a university-timetabling problem with the objective minimizing the total distance traveled by the lecturer from their offices to the classroom. Oladokun and Badmus (2008) studied about assigning a number of courses to classrooms taking into consideration constraints like classroom capacities and university regulations and using Integer linear programming (ILP) to solve the problem. Daskalaki et al. (2004) proposed ILP of the problem with consider the preferences regarding teaching periods or days of the week.

Theory and Literature Review

Integer Linear Programming (ILP)

Optimization problem is the problem involving one or more decisions with restrictions.

A goal or an objective is considered. The objective is represented by an objective function which identifies the function of the decision variables.

The decision maker may want to either maximize or minimize the objective e.g. minimizing cost and maximize profit. The constraint is represented in a mathematical model, e.g. f(x1,x2,.., xn) â&#x2030;¤ b , f(x1,x2,.., xn) â&#x2030;Ľ b (Ragsdale et al., 2004).

Linear programming (LP) involves an optimization problem with linear objective functions and linear constraints. LP model has three basic

47

Silpakorn U Science & Tech J Vol.8(1), 2014

Solving the Course - Classroom Assignment Problem for a University

Research Methodology

1. Interviewing the scheduler and gathering the information as data input for the mathematical model as follows: - The courses of all departments opened in the first semester in year 2012 of the Faculty of Engineering and Industrial Technology.

- Classroom capacity, classroom rate.

2. Designing the solving method and the mathematical model.

3. Running Premium Solver for Excel software.

4. Comparing the solution to the previous results.

room no. T.144-6 was booked for course 618 342 on Tuesday at 9:25-12:05.

2) There were 115 courses. The courses were grouped by date, Monday to Friday. Then the courses were sorted by the period.

3) The sorted courses of each day were grouped by the time. The courses were sorted by time which consisted of the time before lunch and time after lunch as seen in Table 1. It was found that the courses were dissimilar in period.

For example 8:30 - 9:20 had one study period and 8:30 -11.10 had two study periods. The idea of the assignment is to assign the long study period first then assigning the courses which have shorter study period in order to fill the empty period after the long courses were assigned.

Problem Statement

This is an assignment of courses to the classrooms where the number of the registered students and the period time of each course are known. The day and period in that day are already specified in each course based on the lecturer and students’ availability as shown in Table 1. The number of the students in a course is varied which depends on the number of registered students. There are i courses and j class rooms. The capacity of each class room is known. The cost of each classroom depends on its capacity. Cost of classroom is money charge by the Faculty of Engineering or the other faculties due to classroom utilization.

This research intends to solve the problem where all courses must be assigned to classrooms.

Each class room in a period must be used for only one course. The class room capacity is compatible for the number of students.

1. The proposed method 1) The data such as courses and period of each course was collected. Table 1 shows the collected data. It was found that some courses had already been specified the classrooms. For example,

Table 1 The courses on Tuesday

48

Code

No. Student

Period

Class room

1

612410

101

8.30-9.20

Meeting

2

615451

45

8.30-11.10

3

615321

39

8.30-11.10

4

618342

125

9.25-12.05

T.144-6

5

619351

82

9.25-12.05

T.137

6

619441

53

9.25-12.05

7

619452

80

9.25-12.05

8

615 211

45

9.25-12.05

9

615 231

40

9.25-12.05

10

611 201

169

9.25-12.05

Meeting

11

612 332

186

9.25-12.05

SR.1201

12

614392

57

9.25-12.05

13

614201

80

9.25-12.05

14

619441

65

13.00-15.40

K. Thongsanit

Silpakorn U Science & Tech J Vol.8(1), 2014

2) Objective Function

Minimizing Classroom cost

4) In each step of the assignment, a mathematical model was used to find the solution.

The process started with the courses having longer than 2 study periods as the input for a mathematical model. Then the available classroom was updated based on the solution from the previous run. Then the courses which had shorter study period were assigned to the empty period by the mathematical model.

5) Finally, the assignment step no.3-4 was applied for the other days.

2. Mathematical Model

The mathematical model presented the model solving the problem in each day within one time. This is the generalized assignment problem with objective minimizing cost. The mathematical model presented below determines which course {i =1,…,I} has to be assigned to a classroom {j =1,…,J}. The model uses a binary decision variable (xij),

The integer linear programming problem for this problem will be defined using the following notations: Indices i = Course , i = 1, 2, …, I j = Class room , j = 1, 2, …, J p = study period , p = 1, 2, …, P

3) Constrained (1) Constraint 1 shows the capacity limits. The number of students in each class rooms has to be less than classroom capacity.

Constraint 2 forces all classrooms to be assigned to at least one course.

Constraints 3 represents that all courses have to be assigned in the timetable.

Result and Conclusion

To test the improvement of the solution obtained from the mathematical model by Excel’Premium Solver for Excel software, the courses of the Faculty of Engineering and Industrial Technology at Silpakorn University in the first semester, 2012 were applied. An example of the course - classroom assignment problem on Monday is presented in this topic. Table 2-3 provides the example of data applied in the problem. The solver setting is applied to solve this problem as shown in Figure 1. Excel’s Premium Solver was used to test the solutions obtained from the mathematical model.

The solutions of the example are showed in Figure 2.

The binary number 1 indicates that the assignment on Monday, e.g. Course 612 321 was assigned to classroom no. T.144-6.

Parameters Vj = Capacity of class room j Ni = The registered student of course i Spip = The study period p of each course i Cj = Classroom Cost per period j Npi = The number of period of each course i 1) Decision Variables

49

Silpakorn U Science & Tech J Vol.8(1), 2014

Solving the Course - Classroom Assignment Problem for a University

The solution from the model was compared to the solution from the practical use or the manual assignment with total 115 courses (all courses from Monday to Friday). The findings are as follows.

Table 2 Classroom capacity and classroom cost

a) The solutions from the manual assignment are investigated. It was found that some solutions violated the constraints in the model For example, the study periods were overlapped and the number of students of the assigned course was over classroom capacity in some courses. This problem can be eliminated by the use of the proposed method. b) In practical use, some classrooms were unavailable since they were specified or reserved for some courses before doing courses - classrooms assignment. On average, 33 percent of all courses were already reserved. This reservation had reduced the quality of the solution and increased the operation cost. The mathematical model was used to solve the problem and compared the quality of the solution between with and without the reservation. It was found that the cost of assignment reduced 11% on average or 27,920 Baht/semester (16 weeks) as shown in Table 4. c) The method can solve the problem in short time while the manual assignments consume time for a week to solve the problem. Figure 3 shows the running time from the software output which are scheduled on Monday. The proposed method consumed 3.16 seconds of the running time for solving the problem as shown in Table 5.

Classroom ( j)

Classroom capacity (Vj)

Cost (Cj) (baht/ hr.)

T.135

85

30

T.136

85

30

T.138

85

30

T.139

85

30

T.142-3

120

40

T.144-6

180

60

Meeting

200

120

KT

415

120

O50-522

72

65

O50-523

73

65

O50-517

42

45

O50-518

47

45

SR 1201

265

120

Table 3 Example data of registered students and the study periods of each course No. student (Ni) 60

50

Course ( Spi ) 612312

8:30 9:30

9:25 10:15

10:20 11:10

1

1

1

11:15 12:05

60

623231

1

1

1

30

615211

1

1

1

45

614392

1

1

1

30

615231

1

1

1

120

611202

1

1

1

30

614241

1

1

1

30

614101

1

1

1

120

614201

1

1

1

65

613311

1

1

K. Thongsanit

Silpakorn U Science & Tech J Vol.8(1), 2014

Table 4 Cost comparison (baht/week)

Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Sunday

Cost :Manual(Baht)

3,360

2,640

4,155

2,400

1,590

1,080

630

Cost Software (Baht)

3,080

2,515

3,555

2,340

1,270

720

630

280

125

600

60

320

360

0

% save cost

8%

5%

14%

3%

20%

33%

0%

Table 5 The running time (second)

Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Sunday

Total

Solving time (sec.)

0.56

0.35

0.72

0.44

0.35

0.35

0.39

3.16

Figure 1 The Excel solver setting

51

Silpakorn U Science & Tech J Vol.8(1), 2014

Solving the Course - Classroom Assignment Problem for a University

Figure 2 The results of assignment courses to classrooms on Monday (xij) Gunawan, A., Ng, K. M., and Poh, K. L. (2007) Solving the Teacher Assignment-Course Scheduling Problem by a Hybrid Algorithm.

World Academy of Science, Engineering and Technology 33: 259-264.

Oladokun, V. O. and Badmus, S. O. (2008) An Integer Linear Programming Model of a University Course Timetabling Problem. The Pacific Journal of Science and Technology 9: 426-431.

Ragsdale, C. T. (2004) Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Management Science, 4th ed., Thomson Southwestern, Ohio.

Sarin, S. C., Wang, Y., and Varadarajan, A. (2010) A university-timetabling problem and its solution using Bendersâ&#x20AC;&#x2122; partitioning -a case study. Journal of Scheduling 13: 131-141.

Taha H. A. (2003) Operations Research: An Introduction, Prentice Hall, New Jersey.

Figure 3 The software output References Daskakaki, S., Birbas, T., and Housos, E. (2004) An integer programming formulation for a case study in university time tabling.

European Journal of Operational Research 153: 117-135.

52

Research Article The Development of Web-Oriented Decision Support System for Supporting a Single-Level Task Assignment Process Patravadee Vongsumedh Department of Information Technology, School of Science and Technology, Bangkok University, Bangkok, Thailand Corresponding author. E-mail address: patravadee.v@bu.ac.th Received June 11, 2013; Accepted November 18, 2013 Abstract

Selecting subordinates for assigning tasks is considered an important process of human resource management and can be found generally in all workplaces. The task assignment must be done based on the concept of “putting the right man on the right job” so that the subordinates who are selected can perform the task to their full potential. This, finally, reflects an effective operation and highly productive results. This research aims to 1) study the process involved and find general criteria that the supervisors use for selecting subordinates for a certain task, 2) develop a Decision Support System (DSS) prototype used for supporting the single-level task assignment, and 3) evaluate the users’ opinions on the system prototype. The research was divided into four phases which were 1) asking opinions of three domain experts about the process and the criteria that supervisors used for selecting subordinates for a task 2) analyzing and designing a DSS prototype in order to identify the system scope and the structure of system components 3) constructing a DSS prototype 4) testing and evaluating the DSS prototype with groups of users from five supportive organizations (specifically six supervisors and 31 operative subordinates). After the system testing, focus group interviews were conducted in order to get users’ opinions in the areas of the Input Model, the Output Model, and the Process Model of the system. The result shows that the DSS prototype can present practical criteria consistent with employee selection in a real situation. Moreover, the system effectively supports a single-level task assignment process, and also makes the operation of recording, accessing and retrieving the data of the task and the operating employees more systematic, convenient, and reliable.

Key Words: Decision support system; Web-oriented DSS; Task assignment process

Silpakorn U Science & Tech J 8(1): 53-66, 2014

ISSN 1905-9159

Silpakorn U Science & Tech J Vol.8(1), 2014

The Development of Web-Oriented Decision Support System

Introduction

Selecting subordinates for assigning task is considered as an important process in any workplaces or organizations. After having analyzed the task to specify the task skills and responsibilities of people essential to the task operation, supervisors will select appropriate subordinates to operate the task under their charge. The task assignment process must be based on the concept of putting the right man on the right job. This can be done by considering the efficiency and effectiveness of the work, and the appropriateness of workload distribution in the workplace (Jackson and Schuler, 2003; Snell and Bohlander, 2007).

Although the decision making in a task assignment process happens all the times in all organizations, the criteria that supervisors of each organization use during the process are different.

This depends on the types of business, work rules and regulations of the organization and the supervisors’ experiences (Sauter, 1997; Turban et al., 2011).

However, there are some criteria which were mentioned in the theory of task analysis (Cascio, 2003; Department of Human Services (State of Michigan), 2011; Ivancevich, 2004; Schneier et al., 1995), for example, required task skills, work duration, work experience and amounts of responsible tasks, and many researchers used those criteria to support the task assignment process. For example, Trivedi and Warners (1976) developed a system called Nursing Allocation System to plan the schedules for the float nurses to work with the nurses in each ward of the hospital. Gopalakrishnan et al. (1993) conducted research on using DSS to help scheduling the work of temporary employees of a local newspaper publisher in Alabama, USA. Juette et al. (2011) developed a DSS for supporting the crew scheduling for operating trains at DB Schenker. Also, Schniederjans and Carpenter (1996)

developed a DSS that helped scheduling the work shifts in a factory. The results of their research papers showed that general criteria could help supervisors to select employees and assign tasks more rationally and systematically, which was consistent with the concept of “putting the right man on the right job” although the final decision making depended on the experience and judgment of the supervisors. Moreover, those research papers showed that the DSS enabled supervisors to select, assign or schedule tasks more efficiently, and the organization spent less on operation and employment.

Although many researchers used DSS to support the task assignment process, the DSS which were developed in their research papers did not focus on the activities and processes after the task assignment, for example, the communication between the supervisors and the subordinates involved during the task operation, the task progress report, or the evaluation of the operative subordinates.

These processes contributed to the success and effectiveness of the task operation and might affect the next task assignments. Therefore, these were the starting point of this research and development which aimed to develop the DSS that supports the task assignment process and the activities after the task assignment. The research started at studying the task assignment process in order to find out the general criteria that the supervisors in general organizations used for selecting subordinates, and then, to identify the procedural steps or the workflow of task assignment process. The findings will be used as a guideline in developing the single-level task assignment DSS. In a general task assignment process, there are two involved parties, which are “supervisors” who are the ones to select the appropriate subordinates for a task under a certain circumstance and “ subordinates” who are the ones to perform the assigned task, report the operating results to their supervisors and inform the supervisors

54

P. Vongsumedh

Silpakorn U Science & Tech J Vol.8(1), 2014

of their personal data and working abilities so that the supervisors can use the data to consider during the task assignment process. Therefore, using DSS for supporting the task assignment process can help the task assignment to be done systematically and effectively. The recording, accessing and retrieving of data relating to the tasks and the subordinates can be done conveniently. The system also helps to provide appropriate workload distribution in the organization, both in terms of the appropriateness of task skills and the proper workloads assigned.

This results in the quality of the task operation and the satisfaction of the employees. Furthermore, the “communicative components” which is integrated to the basic components of the DSS enables the people involved in the task assignment process to communicate more conveniently, resulting in the effectiveness of the employees’ future work.

Phase 1: Asking Opinions from Domain Experts, the tool used was “the interview questions” for asking opinions from domain experts about the task assignment process.

Phase 2: Analyzing and Designing the DSS, the modeling tool used was Microsoft Office XP.

Phase 3: Creating the DSS Prototype, the tools used were Microsoft Visual Studio.NET 2005 (VB.NET), Hypertext Markup Language, Java Script, Microsoft SQL Server 2005 Enterprise Edition, Microsoft Windows Server 2003 Enterprise Edition, and MasterChartDemo (Chart Generator.)

Phase 4: Testing and Evaluating the DSS Prototype, the tool used was “the interview questions” for asking opinions from users about the Input Model, the Output Model, and the Process Model of the prototype.

Purposes of the Research

The purposes of this research were as follows:

1. To study the operative process related to the task assignment and find the general criteria that the supervisors used in task assignment process

2. To develop a DSS that supported a singlelevel task assignment process

3. To evaluate users’ opinions on the DSS that supported the task assignment.

Process of the Research

The research was divided into four phases as follows:

Phase 1: Asking Opinions from Domain Experts

After studying the theories and reviewing the research papers related to task analysis and factors in the task assignment process, the interview issues and questions were identified. The semi-structured interviews, then, were conducted with three domain experts who were supervisors and had more than five-year experiences in the task assignment. This was to study “the workflow related to the task assignment process” and to find out “the criteria that the supervisors used to assign tasks”. The interview questions could be categorized into three groups, which were “the subordinates’ personal data and the job profiles used for consideration during the task assignment process”, “the concerned factors and criteria used in the task assignment process”, and “the methodology the supervisors used for selecting subordinates to assign tasks.”

The Scope of the Research

- The research population was the supervisors and the operative subordinates in general organizations.

- The research samples were six supervisors and 31 operative subordinates from five supportive organizations. The purposive sampling was used.

Research Tools

According to the four research phases, the tools of this research were as follows:

55

Silpakorn U Science & Tech J Vol.8(1), 2014

The Development of Web-Oriented Decision Support System

Phase 2: Analyzing and Designing the DSS

The operation of the DSS supporting the task assignment was analyzed to identify the target users and necessary functions of the system. After that, the structure of the identified system components was designed.

Phase 3: Constructing the DSS Prototype

q The result from phase 2 was used to construct the DSS prototype. The implemented tools were listed as follows: - Microsoft Visual Studio .NET 2005 (VB. NET), Hypertext Markup Language, Java Script and MasterChartDemo (Chart Generator) for program coding.

- Microsoft SQL Server 2005 Enterprise Edition for managing the system database.

- Microsoft Windows Server 2003 Enterprise Edition as an operation system on the web server.

q The prototype was periodically tested and verified by the domain experts in order to ensure the appropriateness of the user interface, the input and output format, and the accuracy of the system operation.

Phase 4: Testing and Evaluating the DSS Prototype

q The interview questions for asking users’ opinions about the DSS prototype were identified.

The interview questions focused on three areas of evaluation, which were the input model, the output model, and the process model of the DSS prototype.

Here are some examples of the interview questions.

1.

“The Input Model”: to evaluate the appropriateness of the system input and commands.

Some interview questions are listed as follows: - “Do you think the menu and the commands listed in the DSS prototype are appropriate (in terms of the meaning and the font formatting) and are sufficient to support the task assignment process? Why or why not?”

- “Do you think the stored data are relevant, appropriate, and sufficient to support the task assignment process? Why or why not?”

2.

“The Output Model”: to evaluate the appropriateness of the output represented or generated by the system. Some interview questions are listed as follows: - “Do you think the output generated by the DSS prototype is intuitive, easy to read, and consistent with your needs? Why or why not?”

- “Do you think the output generated by the DSS prototype completely fulfill your needs in making decisions during the task assignment process? Why or why not?”

3.

“The Process Model”: to evaluate the DSS prototype’s efficiency and effectiveness. Some interview questions are listed as follows: - “Do you think the workflows or the procedural steps of the DSS prototype are consistent with the task assignment process that actually takes place in your organization? Why or why not?”

- “Do you think the DSS prototype provides practical and sufficient criteria used for comparing subordinates in the task assignment process? Are the criteria consistent with the criteria that you (supervisors) use in the real situation? Why or why not?”

- “Do you think the DSS prototype provides good results on the task assignment process? Why or why not?”

q The sample users (six supervisors and 31 subordinates) from five supportive organizations were invited to test the DSS prototype. After the prototype testing, the focus group interviews were conducted on sites by using the prepared interview questions.

Research Results

1. Based on the domain experts’ interviews, the concerned factors, the comparing criteria, and

56

P. Vongsumedh

Silpakorn U Science & Tech J Vol.8(1), 2014

the workflows of the task assignment process can be concluded as follows:

q The factors affecting the task assignment process can be divided into two groups: - “Task-related Factors” refer to the characteristics of the task, which are “skills required by the task”, “work duration”, “the difficulty level of the task (set by supervisors)”, and “the workplace (which affects the physical suitability of the subordinate who is assigned to perform a certain task.)”

- “Employee-related Factors” refer to the characteristics of the subordinate, which are “skills occupied by the subordinate”, “tasks in responsibility,” “work performance,” “work experiences,” “physical characteristics (e.g., gender, age, status of marriage)” and “work positions.”

The factors found will be set as “general criteria” that supervisors can use for considering and comparing subordinates in the task assignment process.

q Criterion Scoring Methodology The criterion scoring methodology that the supervisors use in comparing subordinates can be divided into two modes: - “Manual Criterion Scoring” refers to the way in which the supervisors analyze the data of both the task and the subordinate by using their experiences and attitudes. Then, the supervisors will score each selected criterion (e.g. work experience, work positions, or physical characteristics of the subordinates) by themselves.

- “Automatic Criterion Scoring” refers to

the way in which the system analyzes the quantitative data stored in the system, and then, uses the proper “Quantitative Model” to calculate scores for the selected criteria (e.g. the percentage of skill matching, the percentage of the work success, the work performance of the subordinates.)

q The workflow of the task assignment process

Figure 1 shows the workflow of the task assignment process. Before the task assignment, the supervisor will create a profile for each new task by identifying the task details, required skills, and the work rules, while the subordinates have to inform their supervisor of their characteristics, occupied skills, and report the outcome or the progress of the task in responsibility.

At the initial step of the task assignment process, the supervisor initiates the screening process by comparing the skills required by the task and the skills occupied by the subordinates (A). The initial screening process helps supervisors to eliminate the unqualified subordinates and to reduce the scope of possible alternatives’ determination. After that, the subordinates in the list of possible alternatives (or the suitable candidates) will be compared in terms of appropriateness of task skills (or the percentage of skill matching) (B) and the criteria identified by supervisors (C).

Finally, the supervisor selects the most appropriate subordinate to perform the concerned task (E), based on the evaluation of appropriateness from all aspects (i.e. task-related factors, and employeerelated factors) (D).

57

B) and the criteria identified by supervisors (C). Finally, the supervisor selects the most

ubordinate to perform the concerned task (E), based on the evaluation of appropriateness from The Development of Web-Oriented Decision Support System Silpakorn U Science & Tech J Vol.8(1), 2014

e. task-related factors, and employee-related factors) (D).

decide to choose the most appropriate employee to be responsible for the task, the supervisor will Job Supervisor Subordinates inform the subordinate of the task details and record all information that derives during the process as an Inform personal Report the Identify the task’s characteristics and procedural steps detail and the criteria occupied task skills. and the progress of to perform task. evidence to use in the next task assignment period. the current work.

2. The DSS prototype supporting a singleTask’s procedural steps level task assignment was developed using Microsoft Employee’s Data All Tasks’ and progress report.

Assigned Task’s (II) Data (III) (I) Data (IV) Visual Studio .NET 2005 (VB.NET), Hypertext Markup Language, Java Script and MasterChartDemo Initiate the task assignment process.

(Chart Generator). When the development was finished, it was installed on the server which was (A) Do the operated using the Microsoft Windows Server 2003 No Yes subordinate’s task skills match the task Enterprise Edition. The DSS prototype relates to the requirement?

Update target users and the components as follows: task status.

q Target Users, which can be divided into two groups.

(B) Evaluate the degree (%) of skill - “Supervisors” can use the following appropriateness for each subordinate, based on the matched task skills. system functions.

1. Creating and recording the task (C) Identify the criteria (e.g. current assigned task, work performance, work experience, physical Assign subordinate profile, such as the start date, work duration, required a task based on the ability) and the degree of importance for each ad-hoc criteria. criterion. These criteria are used for considering task skills, and the difficulty level of the task, etc. and comparing all possible subordinates.

2. Selecting subordinates for a certain task.

(D) Evaluate the degree (%) of appropriateness of being assigned for each possible subordinate by considering both the appropriateness of 3. Checking or following the task task skill and the appropriateness based on the co-criteria. progress after the task assignment in order to use Inform the assigned Select the most appropriate subordinate. subordinate. data in the next task assignment period.

4. Approving the submitted task which Record the details of the task assignment. The recorded detail would be used for considering and selecting the appropriate subordinate in the causes the task status to change. next task assignment period.

5. Communicating with the people involved in the task operation.

Figure 1: The Workflow of the Task Assignment Process Figure 1 The Workflow of the Task Assignment

- “Subordinates” can use the following ep (A) – (D), the factors related to both the task and the subordinate are considered and calculated.)

Process (Remark: In step (A)-(D), the system functions: factors related to both the task and the 1. Informing the supervisor of their subordinate are considered and calculated.) personal data and task skills.

2. Recording task operation details.

3. Reporting the outcome, task progress While considering the identified factors (C),

or problems occurring during the operation to the different supervisors may select different factors to supervisor compare subordinates, depending on the type of 4. Submitting the completed task to the business, work rules of the organization and the supervisor experience of the supervisors. After the supervisor

58

P. Vongsumedh

Silpakorn U Science & Tech J Vol.8(1), 2014

5. Communicating with the people involved in the task operation.

q DSS Prototype’s Components

Based on the architecture of the DSS (Dong and Loo, 2001; Sauter, 1997; Sprague, 1980; Turban et al, 2011; Vongsumedh, 2007), the DSS prototype is composed of four components, which are “Data Component”, “Model Component”, “User Interface”, and “Communicative Component”, (as shown in Figure 2). Each component has a role in 8the system operation as- follows:

User Interface

Target Users (Job Supervisors or Subordinates)

Communicative Component

all subordinates in a specific business unit, such as, age, gender, job positions, task skills occupied by subordinates, work-starting dates, and so on.

III. The Task Profiles: The third data repository stores characteristics of all tasks in a specific business unit, such as, task skills required by any task, task’s working duration, task’s started dates, task’s submitted dates, task’s status, and so on.

IV. The Task in Responsibility: The last data repository stores all assigned task’s data. It plays an important role in identifying tasks undertaken by a particular subordinate at any given time. These data show the relationship between the task and the subordinate. Moreover, the given data are necessary for the task assignment process, since the job supervisor must take them into account in the next task assignment.

Model Component

Component 2: “Model Component”

The component provides the analysis Knowledge capability for the DSS in order to compare the Data Component Component possible alternatives (or subordinates) in steps A to D. Based on the theory of job analysis and Figure 2 2: Components of of thethe DSS Prototype information gathered from the experts, the Figure Components DSS Prototype quantitative models used for comparing and ent 1: “Data Component” considering the alternatives are created. These

Component 1: “Data Component” ponent deals with the data used for deals supporting thedata workflow of the task assignment process models generate the mathematic results by analyzing The component with the used for data are classified and stored in four data as follows: the related quantitative data stored in the data supporting the workflow of repositories the task assignment repositories.

The general 1). These classified ask Operationprocess Details (Figure and Progress: Thedata dataare stored in the and first repository are composed of results are in the form of scores so that the job supervisor can use them in stored in fourand dataprocedural repositories as follows: ’s status, task’s progress, steps of any task. While the subordinates are being comparing and selecting the appropriate employee.

I. The Task Operation Details and Progress: hey can report these data back to the job director. Therefore, the job supervisor can check the The examples of the model used during the system The data stored in the first repository are composed ask, and keep tracks of events occurred during task’s working duration. operation are as follows: of the on-going task’s status, task’s progress, and Employee Profiles: The steps second datatask. repository stores characteristics and capabilities of allScore (S): The model helps Skill Matching procedural of any While the subordinates job supervisor to identify a specific business suchtasks, as, age, gender, position, task skills occupied by the subordinate who has are beingunit, assigned they can reportjob these data the required task skills by considering the connection back to the job director. Therefore, the job supervisor rk-starting date, and so on. “the task skills of the employee comparing canThe check the data progress of any task, andcharacteristics keep tracks of between Task Profiles: third repository stores all tasks in a specific to the task skills required for the task.” This is given of events occurred during duration. ch as, task skills required by any task,task’s task’sworking working duration, task’s started date, task’s by:

II. The Employee Profiles: The second data ask’s status, and so on.

Os) ´ 100 repository stores characteristics and capabilities of (1) S = (____ Task in Responsibility: The last data repository stores all assigned task’si data.Bs It plays an

identifying tasks undertaken by a particular subordinate at any given time. These data show

between the task and the subordinate. Moreover, the given data59are necessary for the task

ss, since the job supervisor must take them into account in the next task assignment.

Silpakorn U Science & Tech J Vol.8(1), 2014

The Development of Web-Oriented Decision Support System

i: The employee’s number, when i = 1, 2, 3, ...

Os: The number of task skills that the employee has corresponding to the requirement, when Os = 1, 2, 3, ...

Bs: The number of all task skills that the task requires, when Bs = 1, 2, 3, ...

(*Note*) This model is automatically activated and used in step (B) of the workflow shown in Figure 1.

- The Percentage of Success in Task Operation (Psuc): The percentage of success in task operation is considered by the ratio between “the number of accomplished tasks performed by specific employee” and “the number of all tasks assigned to a specific employee.” This is given by: Psuci =

(

A ) ´ 100 B

Tsi

Tpi = Tst

(3)

Ts: The number of tasks that the employeei can complete and submit on time.

Tst: The standard number of tasks that the employee must be able to finish within the timeframe.

i: The employee’s number, when i = 1, 2, 3, ...

The value of Tpi is beween 0 - 1.

(*Note*) In case of converting and displaying the value of Tpi in the form of task performance score, the value of Tpi is multiplied by 100.

However, though the supervisor in each organization will choose the same criteria in considering and selecting the subordinates during the task assignment, the model that each organization has created to analyze the data can be different. This depends on the work policies, regulations or the work procedures within the organization. Therefore, the DSS developers also need to consider these differences and provide the tailor-made models according to the user’s needs and the requirements of the organization.

(2)

i: The employee’s number, when i = 1, 2, 3, ...

A: The number of accomplished tasks performed by an employeei, when A = 1, 2, 3 …

B: The number of all tasks assigned to the employeei, both the finished and the on-going tasks, when B = 1, 2, 3 …

A≤B

Component 3: “User Interface”

Both supervisors and employees operate the system using the Menu-oriented Format so that both groups of user can scope out the system functions.

Moreover, the Input-Output Structure Format is provided in order to allow the users to fill in the data necessary for the system operation and the task assignment process, as shown in Figure 3-5.

- Task Performance (Tp): The efficiency of task operation by considering “the number of tasks that the employee can complete and submit on time” comparing to “the standard number of tasks that the employee must be able to finish within the timeframe.

This is given by:

60

P. Vongsumedh

Silpakorn U Science & Tech J Vol.8(1), 2014

System Menu (Supervisors): * Manage Task’s Detail * Search Subordinate * Check Task Progress * Print Task Report * Manage Task Skills * Search Tasks * etc.

Start considering and selecting the appropriate subordinate.

Figure 3 Input-Output Structure Format for Recording Task’s Detail (Remark: This screen supports the step (A) of the workflow shown in Figure 1.)

System Menu (Subordinates) * Create Profile * Update Profile * Update Task Skills * Search Assigned Tasks * Report Task’s Progress * Communicate with Supervisors

* etc

Figure 4 Input-Output Structure Format for Task’s Progress Report

61

Silpakorn U Science & Tech J Vol.8(1), 2014

The Development of Web-Oriented Decision Support System

The degree (%) of skill matching (skills required by the task), automatically calculated by Model (1), for each possible alternative (subordinate).

The identified co-criteria and the degree (%) of each criterion used for considering and comparing all possible subordinates. The corresponding quantitative models, such as Model (2) and Model (3), are activated to calculate the score of appropriateness on the co-criteria.

Figure 5 Steps for Identifying the Co-Criteria Used for Alternatives/Subordinates Comparison (Remark: This screen supports the step (B) and (C) of the workflow shown in Figure 1.)

The system output will be presented in the form of a graphical format (Figure 6) together with

a conclusion table (Figure 7) to facilitate the comparison of appropriate employees.

Represent the degree (%) of appropriateness of being assigned for each possible alternative (subordinate).

Figure 6 Result of Alternative Comparison (Graph)

62

P. Vongsumedh

Silpakorn U Science & Tech J Vol.8(1), 2014

The corresponding quantitative models used for summarizing the degree of appropriateness of being assigned are activated. The appropriateness of task skills and the appropriateness based on the co-criteria are both considered.

Represent the degree (%) of appropriateness of being assigned for each possible alternative (subordinate).

Assign task to the selected subordinate.

Figure 7 Result of Alternative Comparison (Table) (Remark: This screen supports the step (D) of the workflow shown in Figure 1.)

Component 4: “Communicative Component”

The DSS prototype supporting the singlelevel task assignment process was designed and developed as a web-based application so that the users can conveniently and flexibly use this system anywhere anytime. In addition, both groups of user can send emails and personal messages to one another during the assignment process, the task operation, and the task progress report.

(*Note*) The new trend of DSS development is trying to converge with the concept of Knowledge Management (KM) that enables the organization to transmit personal knowledge to organizational knowledge. The given convergence brings out the new DSS component called “Knowledge Component”. This component may facilitate the knowledge capturing, knowledge organizing,

knowledge refinement, and knowledge sharing which come up with the useful knowledge for the decision making process. However, this component and the KM concept were excluded from this research and development.

3. The opinions of the users both supervisors and subordinates on the DSS prototype were concluded in two points.

q Strength of the System

- “Supervisors” commented that the system enabled them to access and retrieve subordinates’ data easily and conveniently. With the support of the retrieved data (e.g. employee’s task skills, details of the assigned task), the job supervisors could assign the task to the subordinates properly.

- “Supervisors” commented that the system enabled them to check the task progress report (or operation report) conveniently. Therefore, they

63

Silpakorn U Science & Tech J Vol.8(1), 2014

The Development of Web-Oriented Decision Support System

could make a decision in the next task assignment quickly, based on these data.

- “Supervisors” commented that the system could provide various criteria that were relevant to the task assignment process. The supervisors in each organization, who might concern on different criteria, could select the criteria that were consistent with their task assignment process and regulations.

- “Supervisors” commented that the system enabled the task assignment process to be more systematic and more reliable since the employees who were evaluated at the same time would be evaluated using the same criteria.

- “Supervisors” commented that the system could help assuring that the selected employees were the ones who had knowledge and skills that matched the task.

- “Supervisors” and “subordinates” commented that the web-based DSS enabled them to flexibly and conveniently perform all activities related to the task assignment process (e.g. assigning task, keeping track of the assigned task, reporting and communicating to stakeholders).

- “Supervisors” and “subordinates” commented that the communicative component allowed them to communicate with each other easily. q Weakness of the System

- “Supervisors” commented that the system could not support “the multi-leveled job assignment process”, which was normally found in the projectbased workflow. Moreover, the system did not allow supervisors to identify the level of complexity for each task. Therefore, the current system could work well in the situation that all assigned tasks had the same level of complexity.

- “Supervisors” in the field of engineering and science commented that in some cases, the system could not compare the employee’s occupied task skills properly since the system did not

categorize the skill levels or the competency level for each skill occupied by the employee (for example, Very Poor, Poor, Satisfactory, Good, Very Good). Therefore, there was no difference in skill capability between the two (or more) employees who had the same skills.

- “Supervisors” commented that the system did not give the supervisor a chance to set up the scores or clarify the level of the task quality after submission. Task quality scores might be used to support the next task assignment.

- “Supervisors” and “Subordinates” commented that the system did not give employees a chance to access the details of the task procedural steps or the solutions of the previous tasks that were similar to the ones they were working on. Therefore, the knowledge sharing was still not supported by the current DSS prototype.

Conclusion and Discussion

The result from studying the task assignment process and the general criteria used during then shows that the factors used for considering and comparing subordinates during the task assignment process can be divided into two categories (i.e. taskrelated factors and employee-related factors.)

During the task assignment process, both categories will be considered in order to compare and select the appropriate employee to perform the task. In order to make a decision during the task assignment process, the supervisors will combine the consideration on work regulations and the recorded quantitative data of the task performance together with their experience and attitudes. This is consistent with the research of Trivedi and Warner (1976) and the research of Kaixuan (1994) which found that the basic criteria to select an appropriate person to do a certain task (for example, task skills, work experience or physical suitability of the employee) made the basic task assignment process more

64

P. Vongsumedh

Silpakorn U Science & Tech J Vol.8(1), 2014

Acknowledgement

I would like to thank the five supportive organizations, which were First Logic Co. Ltd., Thaicom Management Group Co., Ltd., DHL (Thailand), eProfessional Co. Ltd., and ISS Consulting (Thailand) Ltd., for providing the venues, personnel and useful information for this research.

appropriate and systematic. However, the final decision would depend on the experience and consideration of the supervisor.

After implementing the DSS prototype, the prototype was tested by the users, which were 6 supervisors, and 31 subordinates, from 5 supportive organizations. The focus-group interviews were, then, set up to ask users’ opinions on the areas of the Input Model, the Output Model, and the Process Model of the system. The interview’s result shows that the web-based feature of the prototype enables both groups of user to access the system and perform task-related activities easily and conveniently. In addition, both supervisors and subordinates found that the communicative component of the system allowed them to easily communicate with the people involved. This is consistent with Turban et al. (2011) and the findings of Dong and Loo (2001), and Gudigantala et al. (2011). Moreover, the supervisors expressed that the provided system functions allowed the task assignment process to be performed conveniently, reliably, and systematically. This finding is consistent with the works of Basnet and Ellison (1998), and also consistent with Juette et al.

(2011).

Reference Basnet, C. and Ellison, P. (1998) “A Manpower Planning Decision Support System for MQM Met Services.” Computers and Electronics in Agriculture 21: 181-194.

Cascio, W. F. (2003) Managing Human Resources: Productivity, Quality of work Life, Profits, 6th ed., McGraw-Hill Boston,.

Department of Human Services, State of Michigan.

(2002) Guidelines for Performance Evaluation: Job Performance Factors.

[Online URL: www.mfia.state.mi.us/olmweb/ ex/AHP/692-2.pdf] accessed on December 15, 2011.

Dong, C. J. and Loo, G. S. (2001) “Flexible WebBased Decision Support System Generator (FWDSSG) Utilizing Software Agents.” The 12th International Workshop on Database and Expert Systems Applications (DEXA’01).

September. pp. 892-896.

Gopalakrishnan, M., Gopalakrishnan, S., and Miller, D. M. (1993) “A Decision Support System for Scheduling in Newspaper Publishing Environment.” Interfaces 23(4): 104-115.

Gudigantala, N., Song, J., and Jones, D. (2011) “User satisfaction with Web-based DSS: The role of cognitive antecedents.” International Journal of Information Management 31(4): 327-338.

Ivancevich, J. M. (2004) Human Resource Management. 9th ed. Boston: McGraw-hill.

Future Work

To improve the task assignment capability, the supervisors should be able to perform the multileveled task assignment process which is found in the project workflow. In addition, to clearly and precisely identify the appropriate subordinates during the task assignment process, the level of the employee’s task skills (or the degree of work competency) and the quality of task operation should be set by the supervisors. Moreover, the system should enable supervisors to identify the task’s levels of complexity, which affects the determination of task operation’s quality after task submission.

65

Silpakorn U Science & Tech J Vol.8(1), 2014

The Development of Web-Oriented Decision Support System

Snell, S. and Bohlander, G. (2007) Human Resource Management: International Student Edition.

Australia: Thomson Corporation.

Sprague, R. H. (1980) “A Framework for the Development of Decision Support Systems.”

MIS Quarterly 4(4) December: 1-26.

Trivedi, V. M. and Warners, D. M. (1976) “A Branch and Bound Algorithm for Optimum Allocation of Float Nurses.” Management Science May; 22(9): 972-981.

Turban, E., Sharda, R., and Delen, D. (2011) Decision Support and Business Intelligence Systems. 9th ed. Boston: Pearson Education,

Jackson S. E. and Schuler, R. S. (2003) Managing Human Resource Through Strategic Partnerships. 8th ed. Australia: Thomson South-Western.

Juette, S., Albers, M., Thonemann, U. W., and Haase, K. (2011). “Optimizing Railway Crew Scheduling at DB Schenker.” Interfaces 41(2), March-April: 109-122.

Kaixuan Z. (1994) Some Issues Concerning Job Assignment for College Graduates. Chinese Education & Society May/Jun; 27(3): 19-21.

Sauter, V. (1997) Decision Support Systems: an Applied Managerial Approach. New York: John Wiley & sons.

Schneier, C. E., Baird, L. S., Beatty, R. W., and Shaw, D. G. (1995) Performance, Measurement, Management, and Appraisal Sourcebook. Massachusetts: HRD Press.

Schniederjans, M. J. and Carpenter, D. A. (1996) “A Heuristic Job Scheduling Decision Support System a Case Study.” Decision Support Systems 18: 159-166.

Inc. Vongsumedh, P. (2007) A Framework for Building a Web-based Decision Support System for Job Assignment. In Proceeding of the 3rd International Conference on Web Information Systems and Technologies (Filipe J, Cordeiro J, Encarnacao B, Pedrosa V, eds.), Mar 3-6, pp.136-141. Spain: INSTICC Press.

66

Research Article Agronomic Traits and Fruit Quality of Pineapple with Different Levels of Chicken Manure Application Auraiwan Isuwan Faculty of Animal Sciences and Agricultural Technology, Silpakorn University, Petchaburi Campus, Petchaburi, Thailand Corresponding authorE-mail address: auraiwan_i@hotmail.com Received September 5, 2013; Accepted November 11, 2013 Abstract

The responses of the pineapple (Ananas comosus (L.) Merr cv. Pattavia) grown in a fine-silty soil to various levels of chicken manure, in terms of plant growth, and fruit yield and quality, were investigated at a farm plantation in Petchaburi, Thailand. A randomized complete block design with 4 replications was used.

Treatments were the chicken manure applied in levels equivalent to 0 (control), 3, 6 and 9 g nitrogen (N) plant-1. The manure was manually and thoroughly mixed with the soils, which was incubated for 30 days prior to pineapple planting. Agronomic traits of the pineapple plants and chemical composition of the pineapple fruits were determined. Plant growth and fruit size were linearly increased (p<0.05) with the increased manure levels. Fresh fruit weight from the highest level of manure application was 60% higher than that of the control treatment. Similarly, citric acid and vitamin C contents were linearly increased (p<0.05) as the manure rates increased. It can be concluded that growth and yield of the pineapple positively respond to increased chicken manure application. In terms of productivity, the moderate application rate of chicken manure cannot yet be recommended because curvilinear response has not been detected in this study.

Therefore, further study with respect to a wider range of application rate is required to find out the most appropriate rate.

Key Words: Ananas comosus; Chicken manure; Plant growth; Yield; Fine-silty soil Introduction

It has been reported that pineapple (Ananuscomosus (L.) Merr.) products were the third most important tropical fruit produced in the world, after banana and citrus (Malezieux and Bartholomew, 2003). Globally, similar to other food products, a demand for the pineapple products is substantially increasing worldwide. In general, farmers improve productivity of pineapple by intensifying their

Silpakorn U Science & Tech J 8(1): 67-73, 2014

farming practices, mainly through increased use of synthetic fertilizers. However, a long term use of such fertilizers can degrade qualities of soils, which, in turn, results in a reduction in productivity of pineapple in long run. In addition, in terms of soil fertility and conservation, heavy use of synthetic fertilizers can induce soil impaction and acidification (Havlin et al., 2005).

Isuwan (2007) reported a notable reduction

ISSN 1905-9159

Silpakorn U Science & Tech J Vol.8(1), 2014

Agronomic Traits and Fruit Quality of Pineapple with Different Levels

Materials and Methods

Location, Experimental Plot and Soil Property

The experiment was conducted at farmerâ&#x20AC;&#x2122;s pineapple field in Petchaburi province, Thailand (12.90N; 99.63E), 190 km SSW of Bangkok. Soil samples were collected and subjected to chemical analysis before commencing the experiment. The chemical composition of the experimental soil is presented in Table 1. The soil was identified as fine-silty soil (Fine-silty, mixed, semiactive, isohyperthermic Typic Haplustalfs) and low fertility (Havlin et al., 2005). Therefore, before starting the experiment, soil property was improved by using green manure (Crotolaria mucronata). These plants were grown, tilled and subsequently allowed to decompose for 49 days, as described by Isuwan (2008). Significantly, soil fertility was improved after green manure application (Table 1).

Experimental Plan and Treatment

A randomized complete block design with 4 replications was used. Treatments were 4 rates of chicken manure application: 0 (control), 3, 6, and 9 g N plant-1, equivalent to 0, 131.25, 262.50 and 393.75 kg N ha-1, respectively. The incremental rates of manure application were set at equal interval

in soil fertility arising from repeated pineapple cropping on the same area. She also illustrated that, in Thailand, yield and quality of pineapple have diminished due to the degradation of soil quality.

Alternatively, in order to improve soil fertility as well as to fulfill a demand for nutrients of pineapple plants, the application of organic fertilizers is one of the most effective methods to address soil problems (Maillard and Angers, 2013). Moreover, the application of animal excreta, including chicken manure, has been reported to increase soil organic matter and to provide trace elements to crops (Havlin et al., 2005 and Brar et al., 2013) and to some varieties of pineapple (Daramola et al., 2013; Lui et al., 2013 and Omotoso and Akinrinde, 2013).

This study examined the effects of chicken manure levels on the improvement in agronomic and production traits of pineapple grown in low fertility soils. Therefore, the objectives of this experiment were: (i) to determine the response of agronomic and production traits of pineapple to the increased application rates of chicken (layer) manure, and (ii) to evaluate the optimal level of manure application for pineapple plants grown in poor-quality fine-silty soils.

Table 1 Properties of the experimental soil before and after amendment with green manure Before amendment

After amendment*

4.83

4.96

1.092

1.351

Electrical conductivity (dS m )

0.466

0.454

Total nitrogen (%)

0.051

0.072

Available phosphorus (mg kg-1)

8.334

nd.1

Exchangeable potassium (mg kg-1)

47.82

nd.

Exchangeable calcium (mg kg-1)

141.6

nd.

12.33

nd.

pH Organic matter (%) -1

Exchangeable magnesium (mg kg ) -1

49 days after green manure application;1nd.-not determined

*

68

A. Isuwan

Silpakorn U Science & Tech J Vol.8(1), 2014

after planting and a week before fruit harvesting, were measured. Plant dry weights (inclusive root) were carried out by randomly sampling 3 plants plot -1. They were cut into small pieces and

aiming at determining the optimal application levels in case of the quadratic response exists. Chicken manure was sampled and subjected to chemical analysis. Later, the manure was manually mixed with the soil. The mixture was incubated for 30 days prior to pineapple planting. Chemical composition of the manure is presented in Table 2.

Planting and Management

The pineapple suckers (cv. Pattavia) were treated with phos-ethyl-aluminium (80% WP) solution before transplanting. The suckers were planted at a spacing of 30×50×80 cm, which equals to the planting density of 45,000 plant ha-1. The plants were fertilized with urea fertilizer (46-0-0) at the rate of 312.50 kg ha-1, a month after planting.

Also, obvious weeds were manually controlled 1 month after planting. Later, the plants were induced for flowering with calcium carbine (CaC2), at 8 months after planting, at the rate of 1-2 g plant-1, twice at 5 days interval.

Measurement of Agronomic Traits and Sampling

Agronomic traits of the pineapple, including plant widths and heights, at 6 month after planting, and fruit perimeters and lengths, at 9 and 11 month Table 2

subsequently dried in the force air oven at 60° C for 72 h to determine dry weight. Fruits were harvested at 13 months after planting. Fresh fruits were weighed and randomly sampled. Subsequently, they were subjected to chemical analysis.

Chemical Analysis

Soil samples were air-dried and ground to pass through a 2 mm sieve. Subsequently, pH (1:1, soil: water) (Mclean, 1982), electrical conductivity (ECe) (Richards, 1954), organic matter (OM) (Walkley, 1947), total nitrogen (Bremmer and Mulvaney, 1982), available phosphorus (Bray II) (Bray and Kurtz, 1945), exchangeable potassium, calcium and magnesium (Peech et al., 1947) were determined.

Manure samples were air-dried and analyzed for pH (1:10 of manure: water) (AOAC, 2000), electrical conductivity (ECe, 1:10 of manure: water) (Jackson, 1958), organic matter (Walkley, 1947), total nitrogen (Bremmer and Mulvaney, 1982), total phosphorus and total potassium (AOAC, 2000),

Physical and chemical properties of experimental chicken manure

Items pH Organic matter (%) Electrical conductivity (dS m-1) Total nitrogen (%) Total phosphorus (%) Total potassium (%) Total calcium (%) Total magnesium (%) Total sulfur (%) Moisture content (%) C/N ratio

Chicken manure 8.21 64.34 6.83 1.972 0.723 2.610 1.941 0.606 0.097 11.34 16.32

Criteria for organic fertilizer as legally announced by Department of Agriculture (2005)

*

69

Criteria* 5.5-8.5 > 30% < 6.0 > 1.0% > 0.5% > 0.5% < 35% < 20:1

Silpakorn U Science & Tech J Vol.8(1), 2014

Agronomic Traits and Fruit Quality of Pineapple with Different Levels

Results and Discussion

Agronomic Traits of Pineapple Plant and Fruit

Agronomic traits of the plants at 6 months and of the fruits at 9, 11 months after planting and a week before fruit harvesting are presented in Table 3 and Table 4. Plant heights, widths and weights were linearly increased (p<0.05) as a result of the increased levels of manure application (Table 3).

Accordingly, fruit heights and perimeters were linearly increased (p<0.05) according to the increased rates of manure application (Table 4).

In general, chicken manure contains not only nitrogen but also other required plant nutrients, including macro and trace elements (Table 2).

total calcium and total magnesium (AOAC, 1990a), moisture (Hesse, 1971), and C:N ratio (Anderson and Ingram, 1993).

Fruit samples were subjected to chemical analysis, including total soluble solids (AOAC, 1990b), total sugars (Hodge and Hofreiter, 1962), citric acid (AOAC, 1990b) and vitamin C (AOAC, 1990a).

Statistical Analysis

Data were subjected to analysis of variance.

The response of pineapple to the increased rates of manure application was determined using pre-plan orthogonal polynomial analysis, including linear, quadratic and cubic trends (Muller and Fetterman, 2003).

Table 3 Botanical traits of pineapple received different levels of chicken manure at 6 month after planting Manure application rates (g N plant-1)

SEM@

0

3

6

9

Plant height (cm)

70.90

75.55

80.30

82.65

Plant width (cm)

93.55

110.55

121.00

1.70

2.08

187.50

224.00

Plant fresh weight (kg plant ) -1

Plant dry weight (g plant-1) standard error of the mean (n= 4);

@

Contrast linear

quadratic

cubic

0.91

***

ns

ns

125.33

1.47

***

***

ns

2.40

2.58

0.07

***

ns

ns

249.50

275.75

11.16

***

ns

ns

= p value < 0.001, ns= non-significant.

***

Table 4 Fruit physical traits of pineapple received different levels of chicken manure Manure application rates (g N plant-1)

SEM@

0

3

6

9

Height (cm)

12.80

15.33

15.30

14.88

Perimeter (cm)

29.38

31.75

31.18

Height (cm)

15.58

17.10

Perimeter (cm)

35.55

Height (cm) Perimeter (cm)

Contrast linear

quadratic

cubic

0.57

*

*

ns

31.53

0.68

ns

ns

ns

19.50

20.30

0.32

***

ns

ns

38.03

40.98

41.75

0.73

***

ns

ns

16.93

18.23

20.60

21.05

0.47

***

ns

ns

37.48

39.43

42.30

43.00

0.65

***

ns

ns

At 9 months after planting

At 11 months after planting

A week before harvesting

standard error of the mean (n= 4); = p value < 0.05,

@

*

= p value < 0.001, ns= non-significant

***

70

A. Isuwan

Silpakorn U Science & Tech J Vol.8(1), 2014

fresh fruit production, equivalent to 34,650 kg ha-1.

Therefore, the manure can provide a variety of nutrients to plants, which subsequently results in an improvement in growth performance of the pineapple (Malezieux and Bartholomew, 2003).

Moreover, Omotoso and Akinrinde (2013) reported that physical traits of the pineapple (such as numbers of leaves, leaf lengths and leaf areas) are increased as a result of the increased rates of nitrogen fertilizers. In addition, it has been reported that agronomic traits of the pineapple (for instance, plant heights, leaf and root lengths, leaf widths, numbers of leaves, and fresh weights of aboveground and belowground components) can be improved when the plants receiving either composts (Lui et al., 2013) or poultry manure (Daramola et al., 2013).

Fruit Yield and Chemical Composition

Fresh yield and chemical composition of the fruits from the plants receiving different rates of manure are presented in Table 5. Average fresh weight of pineapple fruits was linearly improved (p<0.05) as the rates of manure application increased.

While total soluble solids and total sugars of the fruits were not significantly affected (p>0.05) by manure application, citric acid and vitamin C contents were linearly increased (p<0.05).

The application of chicken manure at the level of 9 g N plant-1 to the plants was 63% higher

This is possibly due to the improved plant growth which can support increased fruit production. It has been reported that there was highly positive relationship between plant weight and fruit weight (Hepton, 2003). In addition, Daramola et al. (2013) reported that the pineapple plants received higher rates of poultry manure can have bigger fruit sizes and higher numbers of fruits. This is because chicken manure is a good source of plant nutrients. The manure contains not only macro elements but also trace elements (which may frequently be deficient in tropical soils) required by plants for their growth and development (Daramola et al. (2013).

In this experiment, total soluble solids and total sugars were not affected (p>0.05) by manure application. Malezieux and Bartholomew (2003) reported that nitrogen mineral application in the plots, which had enough potassium, did not affect the total solids of pineapple fruits. The application of nitrogen and potassium in low potassium soils can result in the improvement in the total soluble solids and total titratable acids (Spironello et al., 2004). Therefore, it can be assumed that potassium content (47.82 mg kg-1, Table 1) of the soil used in this experiment had enough potassium for pineapple cultivation.

Table 5 Fruit weight and chemical composition of pineapple received different levels of manure Manure application rate (g N plant-1)

SEM@

0

3

6

9

Fresh fruit weight (kg fruit )

1.23

1.45

1.80

2.00

Soluble solids ( brix)

10.50

11.75

12.25

Citric acid (%)

0.37

0.50

Vitamin C (g ml-1)

0.88 11.94

-1

째

Total sugars (mg g FW ) -1

standard error of the mean (n= 4);

@

Contrast Linear

quadratic

cubic

0.10

***

ns

ns

12.25

0.64

ns

ns

ns

0.60

0.69

0.02

***

ns

ns

1.10

1.14

1.51

0.07

***

ns

ns

11.96

12.25

12.09

0.22

ns

ns

ns

= p value < 0.001, ns= non-significant

***

71

Silpakorn U Science & Tech J Vol.8(1), 2014

Agronomic Traits and Fruit Quality of Pineapple with Different Levels

Conclusion

Based on the results of this experiment, it can be concluded that the agronomic traits of pineapple plants and fruits, and fresh fruit production and the concentrations of citric acid and vitamin C of pineapple fruits were improved according to increased rates of chicken manure application. At the highest rates (9 g Nplant-1), fruit production was

carbon pools in a rice-wheat cropping system: Effect of long-term use of inorganic fertilizers and organic manure. Soil & Tillage Research 128: 30-36.

Bray, R. H. and Kurtz, L. T. (1945) Determination of total, organic, and available forms of phosphorus in soils. Soil Science 59: 39-46.

Bremmer, J. M. and Mulvaney, C. S. (1982) Nitrogen total. In Methods of Soil Analysis: Agron (Page, A. L. ed.), No. 9, part 2: Chemical and Microbiological Properties. Am. Soc. Agron.

Madison, WI. Daramola F. Y., Afolami S. O., Idowu A. A. and Odeyemi I. S. (2013) Effects of poultry manure and carbofuran soil amendments on soil nematode population and yield of pineapple. International Journal of Agri Science 3: 298-307.

Havlin, J. L., Beaton, J. D., Tisdale, S. L. and Nelson, W. L. (2005) Soil Fertility and Fertilizers; An Introduction to Nutrient Management. Pearson Education, Inc. New Jersey.

Hepton, A. (2003) Cultural system. In The Pineapple; Botany, Production and Uses (Barthomew, D. P., Paull, R. E., Rohrbach, K. G. eds.), pp: 109-142. CABI Publishing.

Oxon, UK. Hesse, P. R. (1971) A Textbook of Soil Chemical Analysis. John Murray. London.

Hodge, J. E. and Hofreiter, B. T. (1962) Method in Carbohydrate Chemistry. In Determination of Reducing Sugar and Carbohydrate (Whistler, R. L., Be Miller, J. N. eds.), Academic Press. New York.

Isuwan, A. (2007) Effect of pineapple cropping on soil chemical and physical changes in tha-yang soil series, Petchaburi province.

Songklanakarin Journal of Science and Technology 29: 297-305.

63% higher than the control. However, the optimal rate could not be determined in this experiment as the response of pineapple to the manure application remained linear over the ranges of manure applied.

Moreover, the economic and environmental traits should be elaborated for improving the entire food chain of pineapple.

Acknowledgment

This study was funded by the Faculty of Animal Sciences and Agricultural Technology, Silpakorn University, Petchaburi IT campus. The author would like to thank Mr. Jeerasak Chobtang for his help with the statistical analysis. The contributions of Mr. Chatree Janlarpwattanakul and Mr. Titipong Laohaudomchok are acknowledged.

References Anderson, J. M. and Ingram, J. S. I. (1993) Tropical Soil Biology and Fertility: A Handbook of Methods. CAB International. Wallingford, UK. AOAC. (1990) Official Methods of Analysis of AOAC International. 15th ed., Association of Official Analytical Chemists, Washington, DC. AOAC. (2000) Official Methods of Analysis of AOAC International. 17th ed., Association of Official Analytical Chemists, Gaithersburg, MD. Brar, B. S., Singh, K., Dheri, G. S. and BalwinderKumar. (2013) Carbon sequestration and soil

72

A. Isuwan

Silpakorn U Science & Tech J Vol.8(1), 2014

Muller, K. E. and Fetterman, B. A. (2003) Regression and Anova: An Integrated Approach Using SAS Software: Joinly-copublished by John Wiley & Sons Inc. and SAS Institute Inc. Cary, NC. Omotoso S. O. and Akinrinde, E. A. (2013) Effect of nitrogen fertilizer on some growth, yield and fruit quality parameters in pineapple (Ananascomosus L. Merr.) plant at Ado-Ekiti Southwestern, Nigeria. International Research Journal of Agricultural Science and Soil Science 3: 11-16.

Peech, M., Alexander, L. T., Dean, L. A., and Reed, J. F. (1947) Method of Soil Analysis for Soil Fertility Investigations. US. Dept.

Isuwan, A. (2008) Effects of soil moisture and microbial inoculation on crotolaria mucronata decomposition and soil fertility.

In Proceedings of 2nd Silpakorn University Research Fair, Thapra, Bangkok. pp. 273277.

Jackson, M. L. (1958) Soil Chemical Analysis.

Prentice Hall, Inc. N. J. Liu, C. H., Liu, Y., Fan, C. and Kuang, S. Z. (2013) The effects of composted pineapple residue return on soil properties and the growth and yield of pineapple. Journal of Soil Science and Plant Nutrition 13: 433-444.

Malezieux, E. and Bartholomew, D. P. (2003) Plant Nutrition. In The Pineapple; Botany, Production and Uses (Barthomew, D.P., Paull, R.E., Rohrbach, K.G. eds.), pp. 143166.

CABI Publishing. Oxon, UK. Mclean, E. O. (1982) Soil pH and lime requirement. In Methods of Soil Analysis: Agron (Page A.L. ed.), 2nd ed., pp. 199-224. No. 9, part 2: Chemical and microbiological properties. Am. Soc. Agron.

Madison, WI. Maillard, E. and Angers, D. A. (2013) Animal manure application and soil organic carbon stocks: a meta-analysis. Global Change Biology doi: 10.1111/gcb.12438.

Agric. Circ.Washington, DC. Richards, L. A. (1954) Diagnosis and Improvement of Saline and Alkali Soil. USDA Agric.

Washington, DC. Spironello, A., Quaggio, J. A., Teixeira, L. A. J., Furlani, P. R., and Sigrist, J. M. M. (2004) Pineapple yield and fruit quality effected by npk fertilization in a tropical soil. Revista Brasileira de Fruticultura 26: 155-159.

Walkley, A. (1947) A critical examination of a rapid method for determining organic carbon in soils-effect of variations in digestion conditions and of inorganic soil constituents.

Soil Science 63: 251-264.

73

Research Article Effects of Asparagus Trims By-Product Supplementation in Laying Hens Diets on Nutrient Digestibility and Productive Performance Manatsanun Nopparatmaitree1*, Anunya Panthong2, Siwaporn Paengkoum1 and Pornpan Saenphoom1 Faculty of Animal Sciences and Agricultural Technology, Silpakorn University, Phetchaburi IT Campus, Phetchaburi, Thailand 2 Faculty of Animal Science, Phetchaburi College of Agricultural and Technology, Phetchaburi, Thailand * Corresponding author. Email address: Manatsanun@su.ac.th 1

Received August 13, 2013; Accepted December 16, 2013 Abstract

An experiment was conducted to examine the utilization of asparagus trims by-product as alternative feedstuffs in laying hen diets. Two hundred and forty laying hens (ISA-Brown strain), 40 weeks of age were raised under ambient temperature and assigned in a completely randomized design (CRD) with four dietary treatments and three replications per treatment. Each treatment contains different levels of asparagus trims by-product (0, 1, 2 and 3% TAP). All birds were fed with diets containing 18% CP and 11.9 MJ/kg (ME) of laying hens diet to meet nutrient requirements of poultry according to NRC (1994). Diets were restricted (110 g/h/d) throughout the study (42 days) and drinking water was offered ad libitum to the bird. Results showed that total hen-day egg production, egg mass, feed conversion ratio per one dozen of egg (FCR, feed:gain) and feed cost per gain (FCG) per one dozen of egg were not significantly different (P>0.05) among treatments. The average egg weight values differ significantly among levels of asparagus trims (P < 0.01) (58.29, 60.28, 59.94 and 60.72 g, respectively). In addition, egg from different treatment shows significant different on whole egg weight, yolk weight and albumen weight (P<0.01). Nutrients digestibility were not significantly different (P>0.05) among levels of asparagus trims by-products. However, fiber digestibility of birds fed with 3% of asparagus trims were higher than those with 2% asparagus trim, and significantly higher than those in control groups and 1% of asparagus trims (P<0.01) (44.80, 48.77, 51.50 and 56.69%, respectively). Furthermore, results also shows that four levels of asparagus trims has no effect on lipid oxidation (TBARs) (P>0.05). Nevertheless, asparagus trims by-product is suitable alternative feedstuffs in laying hen diets.

Key Words: Asparagus trims; Laying hen; Hen-day egg production; Egg weight; Digestibility

Silpakorn U Science & Tech J 8(1): 74-82, 2014

ISSN 1905-9159

M. Nopparatmaitree et al.

Silpakorn U Science & Tech J Vol.8(1), 2014

Introduction

Agriculture by-products are widely used as feed for livestock in the developing countries because of their availability and affordable price.

Western Thailand produces asparagus for local and export markets especially to Japan and Taiwan. In 2012, approximately 6,650 ton of asparagus were produced, which is 7% lower compared to the production in year 2011. Asparagus trim, a byproduct from the processing of asparagus prior to export, contains moderate protein (15-23%) and high crude fiber (>50%) (Fuentes-Alventosa et al., 2013). Green asparagus stem contains 32.7% crude protein, 18.5% crude fiber, 3.4% crude fat and 16.08 MJ/kg gross energy (Aberoumand and Deokule, 2010). At least 10% of asparagus is loss from stem cutting (Eveli et al., 2013). In addition, Asparagus contains fructooligosaccharides (FOS) (Yamamori et al., 2002). The use of FOS as prebiotics has attracted considerable interest, primarily because they can act as a modulator of colonic bacterial population and fermentation end-products (Czarneki-Maulden, 2000). This includes the reduction of pathogenic bacteria population (Barley et al., 1991) and increasing beneficial micro-flora population (bifidobacteria and lactobacilli) in large intestine (Williums et al., 1994), which can effectively improve health and performance of poultry (Juskiewicz et al., 2006; Fuguta et al., 1999).

FOS is not digested in small intestine by endogenous enzymes which will then enters the large intestine, eventually will be fermented by beneficial microflora to produce volatile fatty acid (VFA), lactate and gases (Twomey et al., 2003). The health benefits are mainly due to an antibacterial effect on potentially pathogenic bacteria through the production of acid which cause a reduction in intestinal pH, reduction of ammonia level through protonation of NH4+, production of B group vitamins,

and Roberfroid, 1995).

Therefore, aim of this study was to investigate the dietary effect of asparagus trims as alternative feedstuffs on productive performance, nutrient digestibility, egg quality trait and storage time of eggs in laying hens.

Material and Methods

Animal and Feeding Management

Asparagus trims collected from Hup-krapong, Phetchaburi province under the His Majestic King of Thailand royal project, the asparagus trims were sliced, spread on plastic sheet and sun dried for three days followed by oven dried at 60oC for three days.

The dried asparagus trims were ground to uniform size about of 2 mm for use in this experiment. In addition, the dried asparagus trims were analyzed for chemical composition.

The asparagus trims were used to substitute yellow corn in the diets at 3 levels (1, 2 and 3%) on dry basis and diets with no asparagus trims as control. One hundred and eighty ISA-Brown laying hens of 40 weeks of age were randomly assigned to control or experimental diets, with three replicates per treatment. The birds were housed in individual battery cage (50 x 40 x 40 cm) under photoperiod throughout the 28 days experiment. All of birds were kept under ambient temperature and were fed with corn-soybean based diet (Table 1) formulated to achieve 18% crude protein and 11.9 MJ/kg for laying hens to meet their nutrients requirement according to NRC (1994). During the 28 days experimental period, all hens were given 110 g/h/d of experimental diets and clean drinking water was offered ad-libitum. Limitation on the experimental diets given was because heavy weight might affect egg production.

Egg production, egg weight and feed intake were recorded daily. Hen-day egg production, average egg weight, egg mass [(Average Egg Weight

and immunomodulation in the gut mucosa (Gibson

75

Silpakorn U Science & Tech J Vol.8(1), 2014

Effects of Asparagus Trims by-Product Supplementation in Laying Hens Diets

treatment group were pooled and stored immediately at -20 °C until analysis. The fecal samples were later

x Hen-day production) / 100] were calculated according to the method of Uuganbayar et al. (2005) whereas the cost per egg production (FCR x Cost 1 kg of feed) were calculated as described by Chinrasri (2003). FCR (kg of feed needed to produce a kg of eggs) was calculated according to Yang et al. (2006).

Egg Quality Analysis

During the last five days of the experimental period, eggs were collected daily and five eggs were randomly selected from each treatment to determine egg characteristic. Egg weight was measured after washing and drying with cool air to remove contaminants from shell. Egg yolk was separated from the albumen and weighed. Shell weight was measured after removal of remaining albumen with water. The weight of albumen was calculated by subtracting the weights of yolk and shell from the weight of whole egg. Thickness of the shell was obtained by averaging measurement from three areas; blunt end, pointed end and middle part of the egg using a digit meter as described by Hatice and Muhlis (2012). The thickness of the albumen was measured on glass plate with an auto tri-pod micrometer (Chatcharee, 2003). Haugh unit was calculated from the thickness albumen and weight of egg using the following formula proposed by Haugh (1937); H.U. = 100 log [Albumen height in millimeter + 7.57 x 1.7 Weight of egg in gram0.37] according to the method described by Ragabe et al.

(2012) using the Eggware software program.

Nutrient Digestibility Analysis:

To determine nutrient digestibility, each treatment consisted of 3 replications and 15 birds per replicate making up a total of 45 birds per treatment, were transferred to battery cages. Chromic oxide (0.30%) was added to the experimental diets as external marker and was fed to the birds for 10 days with the first seven days as adaptation period, and the last three days were the test period. Fecal sample were collected daily and sample from same

dried in oven at 60 °C and ground for later use.

Samples of experimental diet and feces were analyzed for dry matter (DM), crude protein (CP), ether extract (EE), crude fiber (CF) and gross energy (GE) according to AOAC (2000). Apparent digestibility of nutrients was calculated using method as described by Fenton and Fenton (1979).

Chromium concentration was estimated by the absorbance readings in spectrophotometer (CE 3021, Cecil, England) at 390 nm.

Oxidative Stability Analysis

At day 28, six eggs per replicate were randomly collected and stored at room temperature.

For lipid oxidation, two eggs were analyzed weekly using thiobabituric acid reaction substance (TBARs) according to the method described by Buege and Aust (1978). Butylated hydroxytoluene (0.03% by weight) was used to prevent oxidation prior to homogenization with 25 ml of TBARs solutions (0.375% TBA, 15% TCA and 0.25 N HCl) at 11,000 rpm for 1 min. The mixture was heated at 90 oC for 10 min to develop a pink color, which is then cooled in ice water bath. The absorbance of the solutions was measured in a spectrophotometer (CE 3021, Cecil, England) at 538 nm. The TBARs value was calculated from the standard curve of malondialdehyde (MDA) and expressed as mg MDA/kg sample according to the method described

by Marshell et al. (1994) and Atchariya et al. (2011).

Statistical Analysis

The experimental data were subjected to analysis of variance (ANOVA) using the general liner models procedure (Monchai, 2001). Differences among treatment means were compared using Duncan’s multiple range test (DMRT) as described

by Steel and Torrie (1992). A significance levels P<0.05 was used to differentiate between means.

76

M. Nopparatmaitree et al.

Silpakorn U Science & Tech J Vol.8(1), 2014

Table 1 Ingredients and chemical composition of experimental diets Ingredients (%) Corn Soybean meal 44% Asparagus trims Rice bran meal Canola meal Corn-DDGS Rice bran oil CaCo3 (Fine) CaCo3 (Flake) MCP (P21) NaCl Choline-Choride DL-Methionine 99% L-Lysine Premix1 Total Analysis nutrient content Dry matter (%) Crude Protein (%) Ether Extract (%) Crude fiber (%) Ash (%) Ca (%) P (%) Gross Energy (MJ/kg)

Levels of asparagus trims 1% 2% 34.15 33.15 14.38 14.38 1.00 2.00 10.00 10.00 12.50 12.50 15.00 15.00 1.84 1.84 2.73 2.73 6.38 6.38 0.78 0.78 0.28 0.28 0.01 0.01 0.27 0.27 0.22 0.22 0.46 0.46 100.00 100.00

0% 35.15 14.38 10.00 12.50 15.00 1.84 2.73 6.38 0.78 0.28 0.01 0.27 0.22 0.46 100.00 91.22 18.78 2.17 5.31 5.73 4.39 0.93 15.22

91.06 18.31 2.03 5.89 5.24 4.51 0.91 15.94

91.08 18.41 2.06 6.20 5.90 4.44 0.97 15.72

3% 32.15 14.38 3.00 10.00 12.50 15.00 1.84 2.73 6.38 0.78 0.28 0.01 0.27 0.22 0.46 100.00 91.36 18.79 2.01 6.93 5.71 4.76 0.94 15.81

Each one kilogram of vitamin-mineral premix contained 22.75 MIU of retinal palmitate, 5.46 MIU of cholecalciferol,

1

54.60 g of DL-3-tocophyryl acetate, 5.46 g of phylloquinone, 1.82 g of thiamine, 7.28 g of riboflavin, 27.30 g of Ca-D-pantothenate, 10.92 g of pyridoxine, 72.80 g of niacin, 2.184 g of folic acid, 36.40 mg of cobalamin, 455 mg of D-biotin, 800 g of manganese, 2 g of selenium, 800 g of zinc, 2.5 g of cobalt, 150 g of copper, 700 g of ferrous, 10 g of iodine.

77

Silpakorn U Science & Tech J Vol.8(1), 2014

Effects of Asparagus Trims by-Product Supplementation in Laying Hens Diets

Results

Laying Performance

Chemical composition analysis of dried asparagus trims sample shows that dried asparagus trims contains 90.26% dry mater, 13.59% crude protein, 4.47% ether extract, 32.41% crude fiber, 9.45% crude ash, 0.79% calcium and 1.08% phosphorus. Average egg weight of chicken fed with asparagus trims diet was significantly higher than (60.28, 59.94 and 60.72 g, for 1, 2 and 3% supplementation, respectively) the control diet (58.29%) (P<0.01). In addition, FCR of chicken fed with 3% asparagus trims diet was the lowest (2.02) as compared with other treatments (2.16, 2.06 and 2.15 for 0, 1 and 2% supplementation, respectively (Table 2). However, hen-day egg production and egg mass were not significantly different among treatments (P>0.05).

Egg Quality

Egg quality parameters were not significantly different among treatments (P>0.05) except for whole egg, yolk weight and albumin weight (Table 3). Whole egg weight, yolk weight and albumin weight of chicken fed asparagus trims diet were significantly higher than asparagus trims free diet (control) (P<0.01). The average whole egg weight of each treatment was 59.33, 62.03, 62.38 and 62.19 g, respectively.

Nutrient Digestibility

Nutrient digestibility of asparagus trims diets are shown in Table 4. Nutrient digestibility was not significantly different among treatments (P>0.05), except CF digestibility. The CF digestibility of chicken fed asparagus trims diets at 2 and 3% were significantly higher than asparagus trims free diet, and asparagus trims diet at 1% (P<0.01). They were 44.80, 48.77, 51.50 and 56.69%, respectively.

Oxidative Stability

Oxidation stability of egg from different storage times are shown in Table 5. TBARs values

were not significantly different among treatments (P>0.05). However, the TBARs values of egg from asparagus trims diets group were slightly higher than asparagus trims free diet.

Discussion

Asparagus trim is a by-product from the processing of asparagus for export. It contains moderate crude protein (13.59%) and abundant crude fiber (32.41%). Up to date, only a few studies reported the use of asparagus trims as feedstuffs substitute. Thus, there is a limited amount of information regarding its use as feedstuffs.

For productivity performance, the increase in average egg weight, egg mass, yolk and albumin weight may be due to the high energy in asparagus diets (average 15 MJ/kg) (Table 1, 2 and 3).

Furthermore, the improve in FCR was due to the high CF digestibility of asparagus trim diet which the soluble fiber content of asparagus trim is mainly in the form of FOS. Asparagus is a food rich in prebiotics such as FOS and inulin. FOS and mannooligosaccharides (MOS) are prebiotic oligosaccharides which help to promote beneficial micro-flora (Bifidobacteria and Lactobacilli) in large intestine (Williums et al., 1994). Bifidobacteria are able to use FOS as an energy source owing to its ability to hydrolyze β-2,1-glycosidic bond (Berg et al., 2005). Moreover, the Bifidobacteria produces volatile fatty acid and lactic acid that tends to lower pH in large intestine, which in turn will inhibits the growth of pathogenic bacteria (Berg et al., 2005). A previous study by Kalavathy et al. (2003) found that Lactobacilli can increase weight gain and feed conversion in broiler chicken. Similarly, it has been reported that prebiotic could improve the egg production when 0.025 to 0.05% of prebiotics were included into layer henâ&#x20AC;&#x2122;s diets (Woo et al., 2007; Kim et al., 2011).

78

M. Nopparatmaitree et al.

Silpakorn U Science & Tech J Vol.8(1), 2014

Table 2 The effect of asparagus trims supplementation on productive performance of in laying hens Productive performance

0% Feed intake (g/d) Hen-day production % Average egg weight (g)

Levels of asparagus trims 1% 2% 3%

110.00 110.00 87.32 88.40 58.29b 60.28a

110.00 85.35

SEM1

110.00 89.3 83.47

59.94a 60.72a 0.65 50.90 53.30 51.14 54.28 2.14 b ab b 2.16 2.06 2.15 2.02a 0.06 33.48 31.93 33.33 31.31 0.91

Egg mass (g) FCR (Feed intake/Egg mass) FCG per 1 Kg of egg (Bath ) 1USD = 30.509 of Thai bath, 1

pool standard error of means, a, b, c Mean values on the same row with different superscripts differ significantly (P < 0.01).

Table 3 The effect of asparagus trims supplementation on egg quality of in laying hens Egg quality

0%

Levels of asparagus trims 1%

Whole egg weight (g)

2%

59.33b 62.03a Shell weight (g) 7.8 17.90 b Yolk weight (g) 15.53 16.04a Albumen weight (g) 35.99b 38.09a Albumen height (mm) 7.26 7.44 Haugh unit 84.32 84.69 Egg york color 11.76 11.83 Egg shell thickness (mm) 0.344 0.333

62.38a 7.79 15.76ab 38.83a 7.41 84.62 11.84 0.32

SEM1

3% 62.19a 0.19 8.05 0.28 15.99a 0.57 a 38.15 0.41 7.26 0.34 84.71 2.05 11.66 0.11 00.337 0.013

pool standard error of mean, a, b, c Mean values on the same row with different superscripts differ significantly (P < 0.01).

1

For nutrients digestibility, although most of nutrients digestibility were not significantly different but CF digestibility had increased in asparagus trims diet. One of the reasons for high CF digestibility was might be due to mostly is soluble fiber.

asparagus trims had improved feed conversion ratio and no adverse effect on other performance parameter, nutrients digestibility, egg quality and oxidative stability.

Acknowledgements The author greatly acknowledges the partial financial support by Silpakorn University Research and Development Institute, Thailand and Faculty of Animal Sciences and Agricultural Technology, Silpakorn University, Phetchaburi IT Campus,

Conclusion Asparagus trims by-product could be used as alternative feedstuffs in laying hen diets based on the increased average egg weight, yolk and albumin weight in laying hen shown in this study. In addition,

79

Silpakorn U Science & Tech J Vol.8(1), 2014

Effects of Asparagus Trims by-Product Supplementation in Laying Hens Diets

Table 4 The effect of asparagus trims supplementation on nutrients digestibility in laying hens Nutrients digestibility (%)

Levels of asparagus trims

0% Dry matter Crude fiber Ether extract Gross energy Crude protein

1%

83.02 44.80b 78.99 82.73 80.06

2%

SEM1

3%

82.56

82.68

48.77b 74.08 82.35 83.30

51.50ab 77.59 81.70 86.49

82.62

0.87

56.69a 77.65 81.54 83.97

3.82 3.46 0.72 4.90

pool standard error of mean, a, b, c Mean values on the same row with different superscripts differ significantly (P < 0.01).

1

Table 5 The effect of asparagus trims supplementation on oxidative stability of egg from different storage times TBARs (mg/kg)

1

Levels of asparagus trims

SEM1

0%

1%

2%

3%

0 day

1.19

1.27

1.81

1.26

0.032

7 day

4.77

4.98

4.47

5.02

0.041

14 day

5.14

5.38

5.44

5.29

0.017

pool standard error of mean International Journal of Poultry Sciences 10(1): 12-18.

Baley, J. S., Blankenship, L. C., and Cox, N. A. (1991) Effects of fructooligosaccharide on salmonella colonization of chicken intestine.

Poultry Science 70: 2433-2438.

Berge, E. L., Fu, C. J., Porter, J. H., and Kerley, M. S . (20 0 5 ) F ru cto o lig o -s acchar id e supplementation in the yearling horse: Effect on fecal pH, microbial content, and volatile fatty acid concentrations. Journal of Animal Science 83: 1549-1553.

Buege, J. A. and Aust, S. D. (1978) Microsomal lipid peroxidation. Method Enzymol 52: 302-304.

Chatcharee, H. (2003) QCD/EQM and QCM+ Eggware Software Manual, 1st ed., p. 31.

Bangkok.

Chinrasri, O. (2003) Poultry production technology,

Thailand. We would like to express my sincere appreciation to research trainee, Miss. Sunisa Pungtrakul and Miss. Nitchanun Sae-Aung.

References Aberoumand, A., and Deokule, S. S. (2010) Preliminary studies on proximate and mineral composition of marchubeh stem (Asparagus officinalis) vegetable consumed in the Behbahan of Iran. World Applied Sciences Journal 9(2): 127-130.

AOAC. (2000) Official Methods of Analysis of AOAC International. 17th ed., Associate of Analysis Chemistry, Gaithersburg, MD. Atchariya, C., Wattanachant, S., and Benjakul, S. (2011) Quality characteristics of raw and cooking spent hen Pectoralis major muscle during chilled storage: Effect of tea catechins.

80

M. Nopparatmaitree et al.

Silpakorn U Science & Tech J Vol.8(1), 2014

1st ed., p. 206. Mahasarakham University Mahasarakham, Thailand.

Czarnacki-maulden, G. (2000) The use of prebiotic in prepared pet food. Veterinary Internaional 12: 19-23.

Eveli, S., Paul, L., and Gabriele, W. B. (2013) Food waste in the supply chain-impacts on the product carbon footprint. The 6th International conference on life cycle management in Gothenburg, Sweden.

Fukata, T., Sasai, K., Miyamoto T., and Baba, E. (1999) Inhibitory effects of competitive exclusion and fructooligosacharide, singly and in combination, on Salmonella colonize of chicks. Journal of food Protection 62: 229-233.

Fenton, T. W., and Fenton, M. (1979) An improved method for chromic oxide determination in feed and feces. Canadian Journal of Animal Science 59: 631-634.

Fuentes-Alventosa, J. M., Jaramillo-Carmona, S., Rodrìguez-Gutiérrez, G., Guillén-Bejarano, R., Jiménez-Araujo, A., Fernández-Bolaños, J., and Rodríguez-Arcos, R. (2013) Preparation of bioactive extracts from asparagus by-product. Food and Bioproducts Processing 91: 74-82.

Gibson, G. R. and Roberfroid, M. S. (1995) Dietary modulation of the human colonic microbiota: introducing the concept of prebiotics. Journal of Nutrition 125: 1401-1412.

Hatice, K. and Muhlis, M. (2012) Effect of inclusion of garlic (Allium sativum) powder at different level and copper into diets of hens on performance egg quality traits and yolk cholesterol content. International Journal of Poultry Science 10(1): 12-18.

Haugh, R. R. (1937). The Haugh unit for measuring egg quality. U.S. Egg and Poultry Magazine 43: 552-572.

Juskiewicz, J., Jankoski, J., Zdunczyk, Z., and

Mikulski, D. (2006) Performance and gastrointestinal tract metabolism of turkeys fed diet with difference contents of fructooligosacchride. Poultry Science 85: 886-891.

Kalavathy, R., Abdullah, N., and Ho. Y. W. (2003).

Effect of Lactobacillus cultures on growth performance, abdominal fat deposition, serum lipid and weight of organs of broiler chickens. British Poultry Science 44: 139144.

Kim, G. B., Seo, Y. M., Kim, C. H., and Paik, I. K. (2011) Effect of dietary prebiotic supplementation on the performance, intestinal microflora and immune response of broilers. Poultry Science 90: 75-82.

Marshell, A. C., Sams, A. R., and Van Elswyk, M. E. (1994) Oxidative stability and sensory quality of stored egg from hen fed 1.5 % menhaden oil. Journal of Food Science 59: 561-563.

Monchai D. (2001). SAS Enterprise Guide for Analysis. Nanawittaya publishing company, Khon Kaen.

NRC. (1994) National Reseach Council, Nutrient Requirement of Poutry, 9th ed., pp.11-19.

National Academy Press, Washington, D. C. Ragab, H. I., Abdel Ati, K. A., Kijora C., and Ibrahim, S. (2012) Effect of difference level of the processed Lablab purpureus seed on laying performance, egg quality and serum paramaters. International Journal of Poultry Science 11(2): 131-137.

Steel, R. G. D. and Torrie, J. H. (1992) Principles and Rocedure Statistic, 2nd ed., McGrew-Hill Book, Singapore.

Twomey L. N., Pluske J. R., Rowe J. B., Choct, M., Brown W., and Pethick, D. W. (2003) The effect of added fructooligosaccharide (Raftilose® P95) and inulinase on feacal quality and digestibility in dogs. Animal Feed 81

Silpakorn U Science & Tech J Vol.8(1), 2014

Effects of Asparagus Trims by-Product Supplementation in Laying Hens Diets

immune response in laying hens. Korean Journal of Animal Science Technology 49: 481-490.

Yamamori, A., Onodera, S., Kikuchi M., and Shiomi N. (2002) Two novel oligo-saccharide formed by 1 F - fructo-syltransferase purified fom root of asparagus (Asparagus officinalis L.). Bioscience Biotechnology Biochemistry 66(6): 1419-1422.

Yang, Y. X., Kim, Y. J., Jin, Z., Lohakare, J. D., Kim, C. H., Ohh S. H., Lee, S. H., Choi, J. Y., and Chae, B. J. (2006) Effects of dietary supplementation of astaxanthin on production performance, egg quality in layers and meat quality in finishing pigs. Australasian Journal Animal Science 7: 1019 - 1025.

Science Technology Technology 108: 83-93.

Uuganbayar, D., Bea I. H., Choi K. S., Shin I. S., Firman J. D., and Yang, C. J. (2005) Effect of green tea powder on laying performance and egg quality in laying hens. Asian Australasian Journal Animal Science 18: 1769-1774.

Williums, C. H., Witherly S. A., and Buddington, R. K. (1994) Influence of dietary neosugar on selected bacterial groups of the human fecal microbiota. Microbial Ecology 7: 91-97.

Woo, K. C., Kim, C. H., and Paik, I. K. (2007) Effects of supplementary immune modulator (MOS, Lectin) and organic acid mixture (organic acid F, organic acid G) on the performance, profile of leukocytes and erythrocytes, small intestinal microflora and

82

ISSN 1905-9159

Silpakorn University

Science and Technology Journal Volume 8 Number 1 (January-June) 2014

Clinical Outcomes and Risk Factors Affecting 30-day Mortality and Treatment Failure of Patients Infected with Carbapenem-Resistant Acinetobacter baumanii in a General Hospital Wandee Sumret, Kanokwan Limsubjaroen, Nattaporn Ruangnara, Parada Sujarittham, Ploypailin Mulmek, Weerayuth Saelim and Wichai Santimaleeworagun

Factors Influencing the Success of an ERP System: A Study in the Context of an Agricultural Enterprise in Thailand Somsit Duangekanong

Solving the Course - Classroom Assignment Problem for a University Kanjana Thongsanit -8-

The Development of Web-Oriented Decision Support System for

User Interface

Supporting a Single-Level Task Assignment Process Patravadee Vongsumedh

Target Users (Job Supervisors or Subordinates)

Agronomic Traits and Fruit Quality of Pineapple with Different Levels of Chicken Manure Application Auraiwan Isuwan

Data Component

Communicative Component

Model Component

Knowledge Component

Figure 2: Components of the DSS Prototype Component 1: “Data Component” The component deals with the data used for supporting the workflow of the task assignment process

(Figure 1). These data are classified and stored in four data repositories as follows:

Effects of Asparagus Trims By-Product Supplementation in Laying I.Hens onDetails Nutrient Digestibility The TaskDiets Operation and Progress: The data stored inand the first repository are composed of Productive Performance

the on-going task’s status, task’s progress, and procedural steps of any task. While the subordinates are being assigned tasks, they can report these data back to the job director. Therefore, the job supervisor can check the

progress of any task, and and keep tracks of events occurred during task’s working duration.

Manatsanun Nopparatmaitree, Anunya Panthong, Siwaporn Paengkoum Pornpan Saenphoom

II. The Employee Profiles: The second data repository stores characteristics and capabilities of all

subordinates in a specific business unit, such as, age, gender, job position, task skills occupied by subordinates, work-starting date, and so on.

III. The Task Profiles: The third data repository stores characteristics of all tasks in a specific

http://www.surdi.su.ac.th http://www.journal.su.ac.th http://www.tci-thaijo.org/index.php/sustj

business unit, such as, task skills required by any task, task’s working duration, task’s started date, task’s submitted date, task’s status, and so on.

IV. The Task in Responsibility: The last data repository stores all assigned task’s data. It plays an important role in identifying tasks undertaken by a particular subordinate at any given time. These data show the relationship between the task and the subordinate. Moreover, the given data are necessary for the task assignment process, since the job supervisor must take them into account in the next task assignment.

Component 2: “Model Component” The component provides the analysis capability for the DSS in order to compare the possible alternatives (or subordinates) in steps A to D. Based on the theory of job analysis and information gathered