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Elucidation of IS project success factors: an interpretive structural modelling approach

Author

Listed:
  • D. Laurie Hughes

    (Swansea University Bay Campus)

  • Nripendra P. Rana

    (Swansea University Bay Campus)

  • Yogesh K. Dwivedi

    (Swansea University Bay Campus)

Abstract

This study extends the debate surrounding the components of IS project success by reviewing success factors from the perspective of their interdependency and influence on each other. This research utilises interpretive structural modelling as the methodology and framework to develop the relationships between the selected factors. This approach is presented as a mechanism that can provide greater insight to the underlying causal interrelationships associated with IS project success and the successful transition to operations. The findings identify a number of key outcomes that have significant driving influence on other interconnected factors in the final model. This study highlights the benefits of an interpretive approach where IS factor interrelationships can be modelled to demonstrate potential influence on other connected factors thereby, increasing the chances of project success.

Suggested Citation

  • D. Laurie Hughes & Nripendra P. Rana & Yogesh K. Dwivedi, 2020. "Elucidation of IS project success factors: an interpretive structural modelling approach," Annals of Operations Research, Springer, vol. 285(1), pages 35-66, February.
    • Handle: RePEc:spr:annopr:v:285:y:2020:i:1:d:10.1007_s10479-019-03146-w
    DOI: 10.1007/s10479-019-03146-w
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    2. Elvira Ismagilova & Laurie Hughes & Nripendra P. Rana & Yogesh K. Dwivedi, 2022. "Security, Privacy and Risks Within Smart Cities: Literature Review and Development of a Smart City Interaction Framework," Information Systems Frontiers, Springer, vol. 24(2), pages 393-414, April.
    3. Sorooshian, Shahryar & Tavana, Madjid & Ribeiro-Navarrete, Samuel, 2023. "From classical interpretive structural modeling to total interpretive structural modeling and beyond: A half-century of business research," Journal of Business Research, Elsevier, vol. 157(C).
    4. A. M. Alamdari & Y. Jabarzadeh & B. Adams & D. Samson & S. Khanmohammadi, 2023. "An analytic network process model to prioritize supply chain risks in green residential megaprojects," Operations Management Research, Springer, vol. 16(1), pages 141-163, March.
    5. Garg, Aashish & Goel, Pooja & Sharma, Anuj & Rana, Nripendra P., 2022. "As you sow, so shall you reap: Assessing drivers of socially responsible investment attitude and intention," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    6. Elad Harison & Yael Lahav, 2024. "Finding the “Secret Sauce” for Organizational Sustainability: Towards Successful Completion of IT Implementation Projects," Sustainability, MDPI, vol. 16(18), pages 1-20, September.
    7. Kiebler, Lorenz & Moroff, Nikolas Ulrich & Jacobsen, Jens Jakob, 2022. "Preliminary analysis on data quality for ML applications," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 207-236, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    8. M. Suguna & Bhavin Shah & S. Karthik Raj & M. Suresh, 2022. "A study on the influential factors of the last mile delivery projects during Covid-19 era," Operations Management Research, Springer, vol. 15(1), pages 399-412, June.
    9. Yigang Jiang & Guanxin Yao & Jing Xu & Yue Tian, 2021. "Study in Driving Strategy and Analysis of Sustainable and Symbiosis Development Relationship between Agricultural Industrial Clusters and Agricultural Logistics Industry," Sustainability, MDPI, vol. 13(24), pages 1-21, December.
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