Technophobia and the manager’s intention to adopt generative AI: The impact of self-regulated learning and open organisational culture

Abstract

Purpose — Using the Cognitive-Affective-Normative (CAN) model, this study highlights the role of self-regulated learning (SRL) and organisational culture and delves into the link between technophobia and a manager’s intention to adopt generative artificial intelligence (AI) in management practices. Design/methodology/approach — An empirical study was conducted through a survey of 528 business managers from China. Findings — The study reveals that technophobia is negatively related to a manager’s intention to adopt generative AI, while SRL is positively related to the intention to adopt generative AI. Moreover, SRL reduces the negative impact of technophobia on AI adoption. Open organisational cultures reduce the need for SRL. Originality — This study goes beyond a purely technical perspective towards a "human-side" view on understanding managers’ adoption of generative AI. This study is an early attempt to apply the CAN model to analysing the connection between technophobia, SRL, organisational culture, and the intention to adopt generative AI.

Publication DOI: https://doi.org/10.1108/JMP-05-2024-0371
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences
College of Business and Social Sciences > Aston Business School > Marketing & Strategy
Aston University (General)
Additional Information: Copyright © 2025, Emerald Publishing Limited. This author's accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com
Uncontrolled Keywords: Technophobia; self-regulated learning (SRL); innovation adoption; generative AI; Cognitive-Affective-Normative (CAN) model
Publication ISSN: 1758-7778
Last Modified: 01 Apr 2025 07:11
Date Deposited: 28 Jan 2025 18:25
Full Text Link:
Related URLs: https://www.eme ... -0371/full/html (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2025-02-06
Published Online Date: 2025-02-06
Accepted Date: 2025-01-16
Authors: Zhao, Li
He, Qile
Kamal, Mohammad
O'Regan, Nicholas (ORCID Profile 0000-0003-3014-0373)

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