Curse or Blessing: Investigating the Influence of Firms’ Artificial Intelligence Adoption on Employee Job Satisfaction

Schulz, Colin; Bendig, David; Bräunche, Antonio; Kindermann, Bastian

Abstract

Artificial intelligence’s (AI’s) growing influence in business has introduced a pivotal shift in workplace dynamics. However, the understanding of how AI adoption influences employee job satisfaction remains inconclusive. Drawing on job characteristics theory, we argue that with increasing levels of adoption, the relationship between employees’ perceived benefits and costs of AI changes, resulting in an inverted U-shaped relationship between AI adoption and job satisfaction. We further propose that the firm-level contingencies exploration orientation and data governance moderate the effects of AI adoption on job satisfaction. Using longitudinal data from 509 publicly listed US firms between 2009 and 2020, we find broad support for our hypotheses. To better understand how specific job characteristics may explain these relationships, we conducted follow-up interviews with employees from our sample firms. Our study contributes to the AI adoption literature by highlighting the previously neglected interplay of enrichment and impairment effects that drive job satisfaction at varying levels of adoption. We also show that firm-level strategies shape how employees perceive AI-driven changes to their jobs and provide a nuanced view of how a job characteristics perspective can help organizational scholars and practitioners understand the multifaceted effects of AI on work environments.