Work Design and Multidimensional AI Threat as Predictors of Workplace AI Adoption and Depth of Use

TL;DR

This study investigates how job characteristics and AI threat perceptions influence employees' adoption and use of AI tools at work, highlighting the importance of work design and perceived threats.

Contribution

It offers new insights into how motivational job features and multidimensional AI threats jointly predict AI adoption and depth of use in workplace settings.

Findings

Job variety and autonomy positively relate to AI adoption.

Threat perceptions influence depth of AI use.

Perceived work changes increase frequency and duration of AI use.

Abstract

Artificial intelligence tools are increasingly embedded in everyday work, yet employees' uptake varies widely even within the same organization. Drawing on sociotechnical and work design perspectives, this research examines whether motivational job characteristics and multidimensional AI threat perceptions jointly predict workplace AI adoption and depth of use. Using cross-sectional survey data from 2,257 employees, we tested group differences across role level, years of experience, and region, along with multivariable predictors of AI adoption and use depth, specifically frequency and duration. Across models, job design, especially skill variety and autonomy, showed the most consistent positive associations with AI adoption, whereas threat dimensions exhibited differentiated patterns for depth of use. Perceived changes in work were positively associated with frequency and duration, while status threat showed a negative but not consistently significant relationship with deeper use. Findings are correlational given the cross-sectional and self-report design. Practical implications emphasize aligning AI enablement efforts with work design and monitoring potential workload expansion alongside adoption initiatives.