Proactive and Reactive Help from Intelligent Agents in Identity-Relevant Tasks

Date
2024-01-03
Authors
Goutier, Marc
Diebel, Christopher
Adam, Martin
Benlian, Alexander
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401
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Abstract
Enabled with artificial intelligence (AI), intelligent agents in information systems have developed from passive tools that only help in return to user prompts (i.e., reactive help) to intelligent agents that can help without requiring user requests (i.e., proactive help). Yet, it is unclear how users react to these different types of help and whether the task creates or reinforces the users’ identity (i.e., identity-relevance). Against this backdrop, we drew on self-affirmation and identity theory and conducted a vignette-based online experiment (n = 135). Our results show that proactive (vs. reactive) help decreases users’ willingness to accept help because of users’ higher perceived self-threat (i.e., threat to their self-image). Identity-relevance of the task moderates this effect – high (vs. low) identity-relevance causes a greater increase in self-threat through proactive (vs. reactive) help. Our study contributes to a better understanding of help from intelligent agents and their implications for effective human-AI collaboration.
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Collaboration with Intelligent Systems: Machines as Teammates, identity-relevant tasks, intelligent agents, proactive and reactive help, self-affirmation theory
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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