Proactive AI Adoption can be Threatening: When Help Backfires
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914507170775040 |
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| author | Harari, Dana Amir, Ofra |
| author_facet | Harari, Dana Amir, Ofra |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_09309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Proactive AI Adoption can be Threatening: When Help Backfires Harari, Dana Amir, Ofra Human-Computer Interaction 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. |
| title | Proactive AI Adoption can be Threatening: When Help Backfires |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.09309 |