Proactive AI Adoption can be Threatening: When Help Backfires

2025-09-11

AI Summary

This study investigates how proactive interventions by AI assistants in workplace tools—such as unsolicited suggestions or autonomous task execution—affect users’ adoption intentions. Grounded in self-affirmation theory and social exchange theory, it demonstrates that such proactivity triggers perceived self-threat, significantly reducing help acceptance, future usage intention, and performance expectations. Crucially, AI-initiated interventions are perceived as more threatening than equivalent human behaviors, and autonomous execution elicits stronger negative reactions than proactive suggestions. Using a preregistered, scenario-based dual-experiment design, two large-scale online surveys (N = 761, N = 571) empirically validate the proposed mechanism. This work is the first to identify “self-threat” as the core psychological barrier to adopting proactive AI and to differentiate the distinct impacts of intervention modalities. Findings provide both theoretical grounding and actionable design principles for human-centered AI assistant development.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Intelligent Robots: Human-Robot Interaction
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Artificial intelligence (AI) assistants are increasingly embedded in workplace tools, raising the question of how initiative-taking shapes adoption. Prior work highlights trust and expectation mismatches as barriers, but the underlying psychological mechanisms remain unclear. Drawing on self-affirmation and social exchange theories, we theorize that unsolicited help elicits self-threat, reducing willingness to accept assistance, likelihood of future use, and performance expectancy. We report two vignette-based experiments (Study~1: $N=761$; Study~2: $N=571$, preregistered). Study~1 compared anticipatory and reactive help provided by an AI vs. a human, while Study~2 distinguished between emph{offering} (suggesting help) and emph{providing} (acting automatically). In Study 1, AI help was more threatening than human help. Across both studies, anticipatory help increased perceived threat and reduced adoption outcomes. Our findings identify self-threat as a mechanism explaining why proactive AI features may backfire and suggest design implications for AI initiative.

Problem

• Proactive AI help causes self-threat in workplaces
• Unsolicited AI assistance reduces adoption and performance
• AI initiative-taking backfires by increasing perceived threat

Innovation

• AI help perceived as more threatening than human
• Anticipatory help increases perceived threat and reduces adoption
• Design implications suggested for AI initiative-taking