Symbolizing Artificial Intelligence at Work: How Leaders Shape Opportunity and Threat in Employees’ Performance

• In Event: Organizations, Occupations, and Work Section Roundtables
  In Refereed Roundtable (90 minute): Table 8: Supervisor-Subordinate Communication

Abstract

Based on social learning theory and self-determination theory, this study investigates the dual pathways influence mechanism of leaders’ AI symbolization on employee task performance. It also examines the moderating role of leader–member exchange (LMX) in shaping these effects. We conducted two studies: a scenario-based experiment (N = 243) and a multi-wave field survey (N = 379) with matched leader–employee pairs. Regression analyses, structural equation modeling, and bootstrapping were employed to test our hypotheses. Leaders’ AI symbolization has a double-edged effect on employee task performance. It enhances performance by increasing perceived AI utility and encouraging promotion-oriented task crafting, but it also heightens job insecurity and prevention-oriented task crafting, which affect performance through a different pathway. High-quality LMX strengthens the positive pathway and buffers the negative pathway. This study uniquely integrates dual pathways to clarify how leaders’ AI symbolization impacts employees, highlighting LMX as a crucial relational boundary shaping employee responses to AI-driven workplace changes. Beyond its theoretical contributions, the study offers practical guidance for organizations by suggesting ways to foster employee adaptability and proactive behaviors in AI-enabled workplaces.

Authors

• Xinglan Yang

• tiezeng Jin, Harbin Institute of Technology

• Jiajing Wang, Harbin Institute of Technology

• Li Zhang