Generative mechanisms of AI implementation: A critical realist perspective on predictive maintenance
Document Type
Article
Publication Date
2-21-2024
Keywords
Artificial intelligence ; Causal loop diagramming ; Experimentation ; Generative mechanisms ; Predictive maintenance ; Techno-organizational context
Abstract
Artificial intelligence (AI) promises various new opportunities to create and appropriate business value. However, many organizations – especially those in more traditional industries – struggle to seize these opportunities. To unpack the underlying reasons, we investigate how more traditional industries implement predictive maintenance, a promising application of AI in manufacturing organizations. For our analysis, we employ a multiple-case design and adopt a critical realist perspective to identify generative mechanisms of AI implementation. Overall, we find five interdependent mechanisms: experimentation; knowledge building and integration; data; anxiety; and inspiration. Using causal loop diagramming, we flesh out the socio-technical dynamics of these mechanisms and explore the organizational requirements of implementing AI. The resulting topology of generative mechanisms contributes to the research on AI management by offering rich insights into the cause-effect relationships that shape the implementation process. Moreover, it demonstrates how causal loop diagraming can improve the modeling and analysis of generative mechanisms.
Citation
Alexander Stohr, Philipp Ollig, Robert Keller, Alexander Rieger, Generative mechanisms of AI implementation: A critical realist perspective on predictive maintenance, Information and Organization, Volume 34, Issue 2, 2024, 100503, https://doi.org/10.1016/j.infoandorg.2024.100503.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
