Understanding Managers’ Attitudes and Behavioral Intentions towards Using Artificial Intelligence for Organizational Decision-Making

Abstract

While using artificial intelligence (AI) could improve organizational decision-making, it also creates challenges associated with the “dark side” of AI. However, there is a lack of research on managers’ attitudes and intentions to use AI for decision making. To address this gap, we develop an integrated AI acceptance-avoidance model (IAAAM) to consider both the positive and negative factors that collectively influence managers’ attitudes and behavioral intentions towards using AI. The research model is tested through a large-scale questionnaire survey of 269 UK business managers. Our findings suggest that IAAAM provides a more comprehensive model for explaining and predicting managers’ attitudes and behavioral intentions towards using AI. Our research contributes conceptually and empirically to the emerging literature on using AI for organizational decision-making. Further, regarding the practical implications of using AI for organizational decision-making, we highlight the importance of developing favorable facilitating conditions, having an effective mechanism to alleviate managers’ personal concerns, and having a balanced consideration of both the benefits and the dark side associated with using AI.

Publication DOI: https://doi.org/10.1016/j.technovation.2021.102312
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2021 The Authors. This is an open access article under the CC BY-NC-ND license
Uncontrolled Keywords: AI adoption,Artificial intelligence,Integrated AI acceptance-Avoidance model (IAAAM),Organizational decision-making,Technology threat avoidance theory (TTAT),Unified theory of acceptance and use of technology (UTAUT),Engineering(all),Management of Technology and Innovation
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Related URLs: https://www.sci ... 166497221000936 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-08
Published Online Date: 2021-06-08
Accepted Date: 2021-05-22
Authors: Cao, Guangming
Duan, Yanqing
Edwards, John (ORCID Profile 0000-0003-3979-017X)
Dwivedi, Yogesh Kumar

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