← 研踪 PaperTrailarXiv:2611.16931 · v1
Beyond Curiosity: Habit and Organizational Learning Capability as Predictors of Shadow AI
首次提交 2026-11-12 23:18
版本更新 2026-11-12 23:18
雷达观测 2026-11-13 14:15
摘要
Generative AI has entered the workplace faster than organizations can govern it. A large share of employees now use AI tools their employer has not sanctioned, and many actively conceal that use, but the psychology of this covert technology behavior remains poorly understood. A common assumption, in popular accounts and early commentary, treats unauthorized (shadow) AI use as curiosity-driven exploration by adventurous individuals. We test this against a competing account grounded in the psychology of habitual technology use, in which automaticity rather than exploratory disposition sustains the behavior. Using survey data from 510 employed generative AI users across multiple countries, we adjudicated among individual and contextual predictors (workplace curiosity, habit, technology-acceptance beliefs, and organizational learning capability) with hierarchical regression, relative weights analysis with bootstrap confidence intervals, and full-subset dominance analysis. Habit was the strongest predictor, accounting for 28.3% of explained variance, and completely dominated joyous exploration, the curiosity dimension most aligned with the exploration account, across all 512 possible predictor subsets. Joyous exploration contributed 3.4% (95% CI [1.6, 7.4]) and fell to nonsignificance once use behavior and habit were modeled. Learning-supportive organizational conditions accounted for a further 24.1% and operated additively rather than by moderating dispositions. Parallel models decomposing the outcome showed the same dominance pattern for unauthorized use and concealment, and perceived social support for AI use was protective specifically against concealment. These findings reframe shadow AI as a habit-and-environment phenomenon rather than an individual-exploration phenomenon. The cross-sectional design means causal claims are exploratory.
作者备注：Preprint: submitted version (10 June 2026), not peer reviewed. 51 pages
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v1 · 2026-11-12 23:18 · 最新观测版本
Beyond Curiosity: Habit and Organizational Learning Capability as Predictors of Shadow AI
Generative AI has entered the workplace faster than organizations can govern it. A large share of employees now use AI tools their employer has not sanctioned, and many actively conceal that use, but the psychology of this covert technology behavior remains poorly understood. A common assumption, in popular accounts and early commentary, treats unauthorized (shadow) AI use as curiosity-driven exploration by adventurous individuals. We test this against a competing account grounded in the psychology of habitual technology use, in which automaticity rather than exploratory disposition sustains the behavior. Using survey data from 510 employed generative AI users across multiple countries, we adjudicated among individual and contextual predictors (workplace curiosity, habit, technology-acceptance beliefs, and organizational learning capability) with hierarchical regression, relative weights analysis with bootstrap confidence intervals, and full-subset dominance analysis. Habit was the strongest predictor, accounting for 28.3% of explained variance, and completely dominated joyous exploration, the curiosity dimension most aligned with the exploration account, across all 512 possible predictor subsets. Joyous exploration contributed 3.4% (95% CI [1.6, 7.4]) and fell to nonsignificance once use behavior and habit were modeled. Learning-supportive organizational conditions accounted for a further 24.1% and operated additively rather than by moderating dispositions. Parallel models decomposing the outcome showed the same dominance pattern for unauthorized use and concealment, and perceived social support for AI use was protective specifically against concealment. These findings reframe shadow AI as a habit-and-environment phenomenon rather than an individual-exploration phenomenon. The cross-sectional design means causal claims are exploratory.
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