AI Strategy and Concepts Bibliography

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Aversa, P., Cabantous, L., & Haefliger, S. (2018). When decision support systems fail: Insights for strategic information systems from formula 1. The Journal of Strategic Information Systems, 27(3), 221–236. 
Added by: SijanLibrarian (2021-03-29 17:19:37)   Last edited by: SijanLibrarian (2021-03-29 17:21:28)
Resource type: Journal Article
BibTeX citation key: Aversa2018
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Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, General
Subcategories: Augmented cognition, Big data, Decision making, Deep learning, Human decisionmaking, Machine learning, Psychology of human-AI interaction, Q-learning
Creators: Aversa, Cabantous, Haefliger
Collection: The Journal of Strategic Information Systems
Views: 47/54
Views index: 18%
Popularity index: 4.5%
Abstract

Decision support systems (DSS) are sophisticated tools that increasingly take advantage of big data and are used to design and implement individual- and organization-level strategic decisions. Yet, when organizations excessively rely on their potential the outcome may be decision-making failure, particularly when such tools are applied under high pressure and turbulent conditions. Partial understanding and unidimensional interpretation can prevent learning from failure. Building on a practice perspective, we study an iconic case of strategic failure in Formula 1 racing. Our approach, which integrates the decision maker as well as the organizational and material context, identifies three interrelated sources of strategic failure that are worth investigation for decision-makers using DSS and big data: (1) the situated nature and affordances of decision-making; (2) the distributed nature of cognition in decision-making; and (3) the performativity of the DSS. We outline specific research questions and their implications for firm performance and competitive advantage. Finally, we advance an agenda that can help close timely gaps in strategic IS research.


  
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