Paper Type
ERF
Abstract
This paper examines whether firm-level AI investment is associated with voluntary employee turnover and how the mechanism works through employee satisfaction. Although AI investment can improve productivity and innovation, it can also raise job demands, automation pressure, and job insecurity, which can lower employee satisfaction and, in turn, increase voluntary turnover. Based on the Job Demands Resources (JD-R) model, we hypothesize that employee satisfaction mediates the relationship between AI investment and voluntary turnover. Moreover, we assume that human capital intensity is a boundary condition, meaning that AI investments are more likely to improve employee satisfaction in knowledge intensive firms where employees have complementary skills. Using archival panel data from several secondary sources, we examine these hypotheses using fixed effects models. By exploring the relationship between AI investment and employee attitudes and turnover outcomes, this paper contributes to the literature on the organizational impact of AI investment, moving beyond the conventional performance perspective.
Paper Number
1322
Recommended Citation
Ibrahim, Fatima and Yoo, Chul Woo, "AI Investment: Innovation or Employee Exit?" (2026). AMCIS 2026 Proceedings. 7.
https://aisel.aisnet.org/amcis2026/conftheme/conftheme/7
AI Investment: Innovation or Employee Exit?
This paper examines whether firm-level AI investment is associated with voluntary employee turnover and how the mechanism works through employee satisfaction. Although AI investment can improve productivity and innovation, it can also raise job demands, automation pressure, and job insecurity, which can lower employee satisfaction and, in turn, increase voluntary turnover. Based on the Job Demands Resources (JD-R) model, we hypothesize that employee satisfaction mediates the relationship between AI investment and voluntary turnover. Moreover, we assume that human capital intensity is a boundary condition, meaning that AI investments are more likely to improve employee satisfaction in knowledge intensive firms where employees have complementary skills. Using archival panel data from several secondary sources, we examine these hypotheses using fixed effects models. By exploring the relationship between AI investment and employee attitudes and turnover outcomes, this paper contributes to the literature on the organizational impact of AI investment, moving beyond the conventional performance perspective.
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