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Information systems project failure – analysis of causal links using interpretive structural modelling

Yes / The analysis of the root causes of information systems project failure has been the subject of intense scrutiny for some time within industry and the academic community. Researchers have developed various models, notions of failure and categorisations to succinctly classify project failure into a set of key factors for organisations and project managers to focus on in their attempts to avoid failure. This study incorporates a technique titled: interpretive structural modelling as the methodology to formalise the relationships between the selected failure factors. This approach is positioned as a mechanism that can yield greater insights into the relationships between the factors surrounding project failure, thereby developing a better understanding of how these relationships can have a bearing on project outcomes. The findings identify key driving variables that are presented as having significant impact on the other factors within the model. A number of variables are also identified as being heavily dependent on other connected factors highlighting that a failure in one or more of these connected factors is likely to result in a failure in one or more of the dependent factors unless timely steps are taken to address these key issues. This research details a number of practical implications for senior management and project managers as well as the academic community. These considerations form an underlying thread within this study as specific practice-related implications are highlighted and discussed throughout the study.

Identiferoai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/18079
Date25 September 2020
CreatorsHughes, D.L., Dwivedi, Y.K., Rana, Nripendra P., Simintiras, A.C.
Source SetsBradford Scholars
LanguageEnglish
Detected LanguageEnglish
TypeArticle, Accepted manuscript
Rights© 2016 Taylor & Francis. The Version of Record of this manuscript has been published and is available in Production Planning and control, 2016, https://doi.org/10.1080/09537287.2016.1217571.

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