{"ID":2872147,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.09309","arxiv_id":"2509.09309","title":"Proactive AI Adoption can be Threatening: When Help Backfires","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, thereby reducing willingness to accept help, likelihood of future use, and performance expectancy of AI. 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 reactive help was more threatening than reactive human help. Across both studies, anticipatory help increased user's self-threat and reduced adoption outcomes. Our findings identify self-threat as a mechanism through which anticipatory help, a proactive AI feature, may backfire, and suggest design implications to be tested in interactive systems.","short_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 exch...","url_abs":"https://arxiv.org/abs/2509.09309","url_pdf":"https://arxiv.org/pdf/2509.09309v2","authors":"[\"Dana Harari\",\"Ofra Amir\"]","published":"2025-09-11T09:52:27Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}