Alexander Stohr , Philipp Ollig , Robert Keller , Alexander Rieger
{"title":"Generative mechanisms of AI implementation: A critical realist perspective on predictive maintenance","authors":"Alexander Stohr , Philipp Ollig , Robert Keller , Alexander Rieger","doi":"10.1016/j.infoandorg.2024.100503","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":47253,"journal":{"name":"Information and Organization","volume":"34 2","pages":"Article 100503"},"PeriodicalIF":5.7000,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1471772724000034/pdfft?md5=1be2de8ad628224aa34934e2d4f69c3d&pid=1-s2.0-S1471772724000034-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information and Organization","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1471772724000034","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Advances in information and communication technologies are associated with a wide and increasing range of social consequences, which are experienced by individuals, work groups, organizations, interorganizational networks, and societies at large. Information technologies are implicated in all industries and in public as well as private enterprises. Understanding the relationships between information technologies and social organization is an increasingly important and urgent social and scholarly concern in many disciplinary fields.Information and Organization seeks to publish original scholarly articles on the relationships between information technologies and social organization. It seeks a scholarly understanding that is based on empirical research and relevant theory.