Cross-Cultural Differences in AI Acceptance among Leaders: A UTAUT-Based Study of Western and Eastern Perspectives

Abstract

Artificial intelligence (AI) is spreading rapidly in organizational settings, yet limited research examines how culture shapes leaders' readiness to adopt these technologies. The current study addresses that gap by exploring cross-cultural differences in AI acceptance among 434 leaders from Western and Eastern regions, guided by the unified theory of acceptance and use of technology (UTAUT). A cross-sectional, quantitative design, supplemented by short, open-ended responses, assessed five UTAUT constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention. Results showed that Western leaders report significantly higher average effort expectancy, social influence, facilitating conditions, and behavioral intention than Eastern leaders, although both groups find AI beneficial. Multiple regression analyses reveal that Western leaders' intention to adopt AI is primarily related to ease of use, whereas Eastern leaders intention is related to organizational support and peer encouragement. Open-ended responses demonstrate that leaders across regions share ethical and privacy concerns, but Western participants emphasize security and training, while Eastern leaders highlight transparency and real-time insights. These results imply that AI implementation strategies require cultural adaptation, such as prioritizing the quality of user interfaces and training for Western leaders and ensuring organizational endorsements for Eastern contexts. By identifying how leaders evaluate and integrate AI, the current research delivers practical insights for multinational organizations and deepens theoretical dialogues on leadership and technology acceptance. These findings also address current leadership journal calls by spotlighting AI bias, inclusivity, and ethical governance in distinct regional settings.