How Artificial Intelligence affords digital innovation: A cross-case analysis of Scandinavian companies

•Explore AI-enabled innovation process through multiple case study.•Identify AI affordances that drive digital innovation.•Develop AI-afforded digital innovation framework.•Discuss theoretical and practical implications of using AI to drive digital innovation. Artificial Intelligence (AI) is fuellin...

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Bibliographic Details
Published in:Technological forecasting & social change Vol. 173; p. 121081
Main Authors: Trocin, Cristina, Hovland, Ingrid Våge, Mikalef, Patrick, Dremel, Christian
Format: Journal Article
Language:English
Published: New York Elsevier Inc 01.12.2021
Elsevier Science Ltd
Subjects:
Actualisation
Affordance
Artificial intelligence
Artificial Intelligence (AI)
Case studies
Data analysis
Data collection
Data integrity
Data processing
Decision making
Digital innovation (DI)
Grounded theory
Grounded theory (GT)
Human resource management
Human Resource Management (HRM)
Human resources management
Information processing
Innovations
Legitimacy
Organizations
Resource management
Theory
Grounded theory (GT)
Artificial Intelligence (AI)
Actualisation
Affordance
Digital innovation (DI)
Human Resource Management (HRM)
ISSN:0040-1625, 1873-5509
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Summary:•Explore AI-enabled innovation process through multiple case study.•Identify AI affordances that drive digital innovation.•Develop AI-afforded digital innovation framework.•Discuss theoretical and practical implications of using AI to drive digital innovation. Artificial Intelligence (AI) is fuelling a new breed of digital innovation in Human Resource Management (HRM) by creating new opportunities for complying with General Data Protection Regulation (GDPR) during data collection and analysis, decreasing biases, and offering targeted recommendations. However, AI is also posing challenges to organisations and key assumptions about digital innovation processes and outcomes, making it unclear how to combine AI affordances with actors, goals, and tasks. We conducted a qualitative multiple-case study in Scandinavian organisations offering HR services. Grounded theory guided our data collection and analysis. Input-Process-Output framework and affordance theory supported the analysis of specific information processing constraints and enablers. We developed a framework to explain how AI affordances enable digital innovation and address the calls about definitional boundaries between innovation processes and outcomes. We showed how AI affordances are actualised and how this leads to reontologising decision-making and providing data driven legitimisation. Our study contributes to digital innovation research by elucidating AI affordances and their actualisation in organisations. We conclude with the implications to theory and practice, limitations, and suggestions for future research.
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ISSN:0040-1625
1873-5509
DOI:10.1016/j.techfore.2021.121081