TY - JOUR
T1 - How Artificial Intelligence affords digital innovation
T2 - a cross-case analysis of Scandinavian companies
AU - Trocin, Cristina
AU - Hovland, Ingrid Våge
AU - Mikalef, Patrick
AU - Dremel, Christian
N1 - Publisher Copyright:
© 2021
PY - 2021/12
Y1 - 2021/12
N2 - Artificial Intelligence (AI) is fuelling a new breed of digital innovation in Human Resource Management by creating new opportunities for complying with General Data Protection Regulation 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.
AB - Artificial Intelligence (AI) is fuelling a new breed of digital innovation in Human Resource Management by creating new opportunities for complying with General Data Protection Regulation 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.
KW - Artificial Intelligence (AI)
KW - Digital innovation (DI)
KW - Affordance
KW - Actualisation
KW - Grounded theory (GT)
KW - Human Resource Management (HRM)
UR - http://www.scopus.com/inward/record.url?scp=85111999523&partnerID=8YFLogxK
U2 - 10.1016/j.techfore.2021.121081
DO - 10.1016/j.techfore.2021.121081
M3 - Article
SN - 0040-1625
VL - 173
SP - 1
EP - 12
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
M1 - 121081
ER -