International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12
line decor
  
line decor

Volume 4, Issue 11 (November 2017), Pages: 54-64

----------------------------------------------

Original Research Paper

Title: Examining the determinants of information systems utilization in the public sector of developing countries

Author(s): Esmat A. Wahdain 1, *, Ahmad Suhaimi Baharudin 1, Mohammad Nazir Ahmad 2

Affiliation(s):

1School of Computer Sciences, University of Sciences Malaysia (USM), Gelogur, Penang, Malaysia
2Faculty of Computing, University of Technology Malaysia (UTM), Skudai, Johor Bahru, Malaysia

https://doi.org/10.21833/ijaas.2017.011.009

Full Text - PDF          XML

Abstract:

There is plenty of research about information systems adoption and utilization in the extant literature; however, most of it is focused on the context of developed countries. Less attention has been paid for studying the utilization determinants on the context of public organizations in the least developed countries. This paper tries to shed some light on IS utilization determinants in a 3rd world country with special economic and cultural characteristics, Yemen. The study amended the well-known technology acceptance model (TAM) by adding the factors: Organizational culture, Individual factors, Gender, and Perceived Personal Benefit to the original version. Data was collected quantitatively from 139 employees of the Ministry of Social Affairs and Labour (MoSAL) – Yemen, whom their jobs involve using IT. Using SmartPLS software, PLS-SEM method was used to check the reliability of measurement model, and to assess the structural model. The results provided a statistical evidence of the proposed hypotheses, as organizational culture was influential in deciding perceived usefulness and perceived personal benefit for the respondents, which is consistent with previous research. The results also demonstrated the role of gender in moderating both hypothesized relationships; this emphasized the importance of gender in the context of the study and similar contexts, which was rarely focused on in the previous research. Finally, the model showed a good predictive power since 65% of the focal factor, behavioral intention, was explained by its relationships with the other factors. 

© 2017 The Authors. Published by IASE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Technology acceptance model, Technology adoption, Public organizations, Organizational culture, Information systems utilization

Article History: Received 4 June 2017, Received in revised form 15 September 2017, Accepted 22 September 2017

Digital Object Identifier: 

https://doi.org/10.21833/ijaas.2017.011.009

Citation:

Wahdain EA, Baharudin AS, and Ahmad MN (2017). Examining the determinants of information systems utilization in the public sector of developing countries. International Journal of Advanced and Applied Sciences, 4(11): 54-64

Permanent Link:

http://www.science-gate.com/IJAAS/V4I11/Wahdain.html

----------------------------------------------

References (35)

  1. Ajzen I (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2): 179-211. https://doi.org/10.1016/0749-5978(91)90020-T 
  2. Awang Z (2012). Structural equation modeling using AMOS graphic. Universiti Teknologi MARA, Selangor, Malaysia.     
  3. Chandio FH (2011). Studying acceptance of online banking information system: A structural equation model. Ph.D. Dissertation, Brunel Business School, Brunel University London, London, UK.     
  4. Chin WW (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2): 295-336.     
  5. Chuttur MY (2009). Overview of the technology acceptance model: Origins, developments and future directions. Sprouts: Working Papers on Information Systems, 9(37): 1–21. Indiana University, USA.     
  6. Davis FD (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3): 319-340. https://doi.org/10.2307/249008 
  7. Dijk AJV (2009). Success and failure factors in ICT projects: A Dutch perspective. Ph.D. dissertation, Middlesex University, London, UK.     
  8. Dubey R, Gunasekaran A, Helo P, Papadopoulos T, Childe SJ, and Sahay BS (2017). Explaining the impact of reconfigurable manufacturing systems on environmental performance: The role of top management and organizational culture. Journal of Cleaner Production, 141: 56-66. https://doi.org/10.1016/j.jclepro.2016.09.035 
  9. Farrell AM and Rudd JM (2009). Factor analysis and discriminant validity: A brief review of some practical issues. In the Australia and New Zealand Marketing Academy Conference, Melbourne, Australia.     
  10. Fishbein M and Ajzen I (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley, Reading, Boston, USA.     
  11. Gefen D and Straub DW (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1(1): 8-30.     
  12. Hair JF (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications, London, UK.     
  13. Hair JF, Ringle CM, and Sarstedt M (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2): 139-152. https://doi.org/10.2753/MTP1069-6679190202 
  14. Hamner M and Qazi RuR (2009). Expanding the technology acceptance model to examine personal computing technology utilization in government agencies in developing countries. Government Information Quarterly, 26(1): 128-136. https://doi.org/10.1016/j.giq.2007.12.003 
  15. Ifinedo P (2011). Examining the influences of external expertise and in-house computer/IT knowledge on ERP system success. Journal of Systems and Software, 84(12): 2065-2078. https://doi.org/10.1016/j.jss.2011.05.017 
  16. Igbaria M and Chakrabarti A (1990). Computer anxiety and attitudes towards microcomputer use. Behaviour and Information Technology, 9(3): 229-241. https://doi.org/10.1080/01449299008924239 
  17. ILO (2014). Labour force using WB population estimates. International Labour Organization, Genève, Switzerland. Available online at: www.ilo.org     
  18. ITU (2011). Least developed countries (LDC). International Telecommunication Union, Genève, Switzerland. Available online at: www.itu.int 
  19. Jarvenpaa SL, Tractinsky N, and Vitale M (2000). Consumer trust in an internet store. Information Technology and Management, 1(1-2): 45-71. https://doi.org/10.1023/A:1019104520776 
  20. Ko DG, Kirsch LJ, and King WR (2005). Antecedents of knowledge transfer from consultants to clients in enterprise system implementations. MIS Quarterly, 29(1): 59-85.     
  21. Krejcie RV and Morgan DW (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3): 607-610. https://doi.org/10.1177/001316447003000308 
  22. Lin TC, Wu S, and Lu CT (2012). Exploring the affect factors of knowledge sharing behavior: The relations model theory perspective. Expert Systems with Applications, 39(1): 751-764. https://doi.org/10.1016/j.eswa.2011.07.068 
  23. MacKenzie SB, Podsakoff PM, and Podsakoff NP (2011). Construct measurement and validation procedures in mis and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2): 293-334.     
  24. Mathieson K, Peacock E, and Chin WW (2001). Extending the technology acceptance model: the influence of perceived user resources. ACM SigMIS Database, 32(3): 86-112. https://doi.org/10.1145/506724.506730 
  25. Nawi HAS, Rahman AA, and Ibrahim O (2011). Government's ICT project failure factors: A revisit. In the International Conference on Research and Innovation in Information Systems, IEEE, Kuala Lumpur, Malaysia: 1-6. https://doi.org/10.1109/ICRIIS.2011.6125738 
  26. RAEng (2004). The challenges of complex IT projects. The Royal Academy of Engineering, London, UK. Available online at: www.raeng.org.uk     
  27. SGII (2014). Chaos report. The Standish Group International Inc., Boston, USA. Available online at: https://www.standishgroup.com/ 
  28. Shipley B (2000). Cause and correlation in Biology: A user's guide to path analysis, structural equations, and causal inference. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9780511605949 
  29. Steel RG and James H (1960). Principles and procedures of statistics: With special reference to the biological sciences (No. 519.5 S314p). McGraw-Hill, New York, USA.     PMCid:PMC2974360     
  30. TSG (2015). Chaos Report. TSG International, Armidale Australia. Available online at: https://tsg.international/ 
  31. Venkatesh V and Davis FD (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2): 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926 
  32. Wahdain EA and Ahmed M (2015). Examining the determinants of users' acceptance of IT in the Yemeni public sector: pilot study. In the 1st ICRIL-International Conference on Innovation in Science and Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia: 373-376. Available online at: http://eprints.usm.my/33139/ 
  33. Wahdain EA, Ahmad MN, and Zakaria NH (2014). Using TAM to study the user acceptance of IT in the Yemeni public sector. International Journal of Computer and Communication Engineering, 3(3): 160-165. https://doi.org/10.7763/IJCCE.2014.V3.312 
  34. Xu Lu DV (2008). Factors influencing the adoption of mobile learning. In the 19th Australasian Conference on Information Systems, Christchurch, New Zealand: 597-606.     
  35. Yeo KT (2002). Critical failure factors in information system projects. International Journal of Project Management, 20(3): 241-246. https://doi.org/10.1016/S0263-7863(01)00075-8