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End-user computing effectiveness: A structural equation model

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  • Igbaria, M

Abstract

The paper reports the results of a field study investigating the determinants of End-User Computing (EUC) effectiveness among 187 end-users. A conceptual structural model was developed and tested using Partial Least Squares (PLS). The results show that computer anxiety and attitudes toward EUC are significantly affected by work/life experiences, end-user training, computer experience, and information center support. Computer anxiety also affected attitudes towards EUC. In addition, the number of tasks for which computers are used is directly affected by end-user training, computer experience, organizational support provided by information center and top management, task structure, and attitudes toward EUC. Furthermore, system usage, end-user satisfaction, and perceived effectiveness are strongly affected by end-user training, computer experience, top management support, information center support, task structure, task variety and attitudes towards EUC.

Suggested Citation

  • Igbaria, M, 1990. "End-user computing effectiveness: A structural equation model," Omega, Elsevier, vol. 18(6), pages 637-652.
    • Handle: RePEc:eee:jomega:v:18:y:1990:i:6:p:637-652
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    Cited by:

    1. Minkyung Choy & Gunno Park, 2016. "Sustaining Innovative Success: A Case Study on Consumer-Centric Innovation in the ICT Industry," Sustainability, MDPI, vol. 8(10), pages 1-13, September.
    2. Frambach, Ruud T. & Schillewaert, Niels, 2002. "Organizational innovation adoption: a multi-level framework of determinants and opportunities for future research," Journal of Business Research, Elsevier, vol. 55(2), pages 163-176, February.
    3. Hou, Chung-Kuang, 2012. "Examining the effect of user satisfaction on system usage and individual performance with business intelligence systems: An empirical study of Taiwan's electronics industry," International Journal of Information Management, Elsevier, vol. 32(6), pages 560-573.
    4. Norzaidi Mohd Daud, 2024. "Manager’s Performance and Technology Withdrawal: Are They Related? An Empirical Study in the Maritime Industry," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(8), pages 2965-2972, August.
    5. Zhong, Yongping & Oh, Segu & Moon, Hee Cheol, 2021. "Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model," Technology in Society, Elsevier, vol. 64(C).
    6. Li, L. X., 1997. "Relationships between determinants of hospital quality management and service quality performance--a path analytic model," Omega, Elsevier, vol. 25(5), pages 535-545, October.
    7. Rajiv Sabherwal & Anand Jeyaraj & Charles Chowa, 2006. "Information System Success: Individual and Organizational Determinants," Management Science, INFORMS, vol. 52(12), pages 1849-1864, December.
    8. Igbaria, M. & Iivari, J., 1995. "The effects of self-efficacy on computer usage," Omega, Elsevier, vol. 23(6), pages 587-605, December.
    9. Lee, Sang-Yong Tom & Kim, Hee-Woong & Gupta, Sumeet, 2009. "Measuring open source software success," Omega, Elsevier, vol. 37(2), pages 426-438, April.
    10. Zheng Li & Xiaodong Lou & Minwei Chen & Siyu Li & Cixian Lv & Shuting Song & Linlin Li, 2023. "Students’ online learning adaptability and their continuous usage intention across different disciplines," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    11. Adi Alsyouf & Awanis Ku Ishak & Abdalwali Lutfi & Fahad Nasser Alhazmi & Manaf Al-Okaily, 2022. "The Role of Personality and Top Management Support in Continuance Intention to Use Electronic Health Record Systems among Nurses," IJERPH, MDPI, vol. 19(17), pages 1-30, September.

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