Research on information systems failures and successes: Status update and future directions

By Yogesh Kumar Dwivedi, David Graham Wastell, Sven Laumer, Helle Zinner Henriksen, Michael D. Myers, Deborah Bunker, Amany R. Elbanna, Mayasandra-Nagaraja Ravishankar, Shirish C. Srivastava. Information Systems Frontiers, 2014

481 citations recorded by the source corpus.

Information systems success and failure are among the most prominent streams in IS research. Explanations of why some IS fulfill their expectations, whereas others fail, are complex and multi-factorial. Despite the efforts to understand the underlying factors, the IS failure rate remains stubbornly high. A Panel session was held at the IFIP Working Group 8.6 conference in Bangalore in 2013 which forms the subject of this Special Issue. Its aim was to reflect on the need for new perspectives and research directions, to provide insights and further guidance for managers on factors enabling IS success and avoiding IS failure. Several key issues emerged, such as the need to study problems from multiple perspectives, to move beyond narrow considerations of the IT artifact, and to venture into underexplored organizational contexts, such as the public sector.

Subfields: Information Systems and Management, Management Information Systems, Political Science and International Relations

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Citations in the corpus

Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action (2022, methodology): “The perceived unevenness of the logical flow inherent with this approach is countered by the capturing of the distinctive orientations of the expert perspectives related to the chosen topic (Dwivedi et al. 2015, 2020, 2021a, 2021b, 2022).”
Achieving superior organizational performance via big data predictive analytics: A dynamic capability view (2019, background): “Another factor contributing to the failure of IT systems in organizations is that organizations often become biased while choosing investment options and end up investing considerable resources in technological infrastructure and giving less importance to acquiring technical skills (Dwivedi et al., ”
Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling (2020, background): “This awareness of the users may be developed with the help of publication of product brochures, success stories and live demonstration (Alshare and Lane 2011; Dwivedi et al. 2015).”
Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life (2020, methodology): “Although this approach can present an inherent unevenness within the logical flow, this method captures the distinctive orientations of the expert perspectives related to the chosen topic (Dwivedi et al., 2015; 2019; 2020; Kizgin et al., 2020).”
Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model (2019, background): “This would lead to higher level of customer satisfaction and usage (Dwivedi et al., 2015).”
Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda (2019, background): “Overall, factors affecting the use, impact, success and failure of information systems have been studied extensively (Dwivedi et al. 2015; 2017ab; Hughes et al. 2016; 2017; 2019).”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, methodology): “…from a media + policy perspective shows the crystallisation of a third AI hype (after the pre-Lighthill and Alvey Programme ones described in Dwivedi et al., 2015b), established through public commentary by prestigious public personas such as Stephen Hawking, Elon Musk, and Bill Gates who,…”
Citizens’ adoption of an electronic government system: towards a unified view (2015, methodology): “…of these addresses the individual acceptance of technology, using intention or usage as a dependent variable (e.g., Ajzen 1991; Davis et al. 1989; Dwivedi et al. 2015a, b; Seethamraju 2015), whereas other streams have focused on the success of the systems, using IS success models (e.g., Barclay…”