Cultural Tightness and its Effects on AI Reliance in AI-Augmented Decision-Making

Publication: Contribution to conferenceConference paper


This study investigates the influence of culture on AI-augmented decision-making. Drawing on the cultural tightness and looseness theoretical framework, we examine how such cultural schemas affect decision-makers´ reliance on AI advice, and how two contextual boundary conditions – i.e., governmental endorsement of AI and decision uncertainty– moderate this relationship. We conduct a vignette experiment with 95 MBAs and master´s students with prior business experience, where they act as venture capital trainees evaluating startups with the aid of AI. The results confirm our hypotheses that cultural tightness is negatively related to AI reliance and that governmental support for AI exacerbates tight cultures´ reluctance towards AI reliance. We do not find support for our hypothesis that higher decision uncertainty amplifies the impact of tightness on AI reliance. This study contributes to the literature on cross-cultural decision-making by investigating cross-cultural differences in the so far unexplored AI-augmented decision-making context. Furthermore, it contributes to the literature on AI in decision-making by introducing an important new contextual factor, i.e., the cultural heterogeneity of decision-makers. Finally, it provides insights for practitioners and government bodies (thinking about) introducing AI in AI-augmented decision-making contexts.
Original languageEnglish
Publication statusAccepted/In press - 2024
EventAcademy of Management (AOM) 2024: Annual Meeting of the Academy of Management 2024 - Chicago, Chicago, United States
Duration: 9 Aug 202413 Aug 2024
Conference number: 84


ConferenceAcademy of Management (AOM) 2024
Abbreviated titleAOM
Country/TerritoryUnited States
Internet address

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102001 Artificial intelligence
  • 501011 Cognitive psychology


  • AI reliance
  • cultural tightness
  • augmented decision-making
  • experiment

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