Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Traditional leadership research assumes leadership decisions rest with humans. This article posits that generative AI (genAI), which creates content or predicts outcomes from data, has significant potential for leadership in the future of work. We examine genAI’s contribution to leadership through an online experiment with critical (n=304) leadership scenarios, assessing genAI versus human leadership. Additionally, we explore leader acceptance of AI leadership through another online experiment (n=301). Results indicate leaders prefer AI-generated decisions when the source is unknown but favor human leaders when identities are disclosed. Our findings highlight a preference for human leaders despite recognizing AI’s potential effectiveness, suggesting that a purely AI-based or human-based approach is insufficient. Future research should explore an AI-supported leadership model integrating AI-generated insights with human decision-making to enhance both decision-making and follower acceptance in the evolving workplace.
Recommended Citation
Stock-Homburg, Ruth, "AI Leadership: Investigating the Discrepancy between Perceived and Actual Effectiveness" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 4.
https://aisel.aisnet.org/hicss-58/da/social_robots/4
AI Leadership: Investigating the Discrepancy between Perceived and Actual Effectiveness
Hilton Waikoloa Village, Hawaii
Traditional leadership research assumes leadership decisions rest with humans. This article posits that generative AI (genAI), which creates content or predicts outcomes from data, has significant potential for leadership in the future of work. We examine genAI’s contribution to leadership through an online experiment with critical (n=304) leadership scenarios, assessing genAI versus human leadership. Additionally, we explore leader acceptance of AI leadership through another online experiment (n=301). Results indicate leaders prefer AI-generated decisions when the source is unknown but favor human leaders when identities are disclosed. Our findings highlight a preference for human leaders despite recognizing AI’s potential effectiveness, suggesting that a purely AI-based or human-based approach is insufficient. Future research should explore an AI-supported leadership model integrating AI-generated insights with human decision-making to enhance both decision-making and follower acceptance in the evolving workplace.
https://aisel.aisnet.org/hicss-58/da/social_robots/4