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Paper Type
ERF
Abstract
If artificially intelligent agents autonomously make decisions, which might personally affect employees, their perceptions are crucial. By applying a quasi-experimental survey design, we will compare to which degree varying levels of transparency and complexity influence trust towards human supervisors and intelligent systems. Our findings would contribute to the understanding of individual-level perceptions regarding situational and task-related antecedents of trust towards intelligent systems in a hierarchical, organizational context. Moreover, the comparative approach could highlight (dis)advantages of artificially intelligent agents in comparison to human supervisors under specific circumstances. Thereby, we could validate the MABA-HABA framework and identify opportunities for the application of intelligent systems.
Recommended Citation
Grotenhermen, Jan-Gerrit; Bruckes, Maike; and Schewe, Gerhard, "Are we Ready for Artificially Intelligent Leaders? A Comparative Analysis of Employee Perceptions Regarding Artificially Intelligent and Human Supervisors" (2020). AMCIS 2020 Proceedings. 12.
https://aisel.aisnet.org/amcis2020/ai_semantic_for_intelligent_info_systems/ai_semantic_for_intelligent_info_systems/12
Are we Ready for Artificially Intelligent Leaders? A Comparative Analysis of Employee Perceptions Regarding Artificially Intelligent and Human Supervisors
If artificially intelligent agents autonomously make decisions, which might personally affect employees, their perceptions are crucial. By applying a quasi-experimental survey design, we will compare to which degree varying levels of transparency and complexity influence trust towards human supervisors and intelligent systems. Our findings would contribute to the understanding of individual-level perceptions regarding situational and task-related antecedents of trust towards intelligent systems in a hierarchical, organizational context. Moreover, the comparative approach could highlight (dis)advantages of artificially intelligent agents in comparison to human supervisors under specific circumstances. Thereby, we could validate the MABA-HABA framework and identify opportunities for the application of intelligent systems.
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