Paper Type

ERF

Abstract

The current study explores employees’ resistance to the use of Gen AI in the workplace, highlighting functional barriers and task-related dynamics. Drawing on Innovation Resistance Theory (IRT) and Cognitive Load Theory (CLT), we argue that Gen AI's agentic and opaque nature creates unique challenges that amplify resistance beyond deterministic and traditional IT contexts. This study proposes that usage, value, and risk barriers shape resistance, while explainable Gen AI may reduce resistance by lowering uncertainty, particularly under high-complexity conditions. To test our hypothesis, a 2x2 between-subjects experiment manipulating task complexity (low vs. high) and Gen AI explainability (absent vs. present) has been designed for employees in business analytics roles using Gen AI. The results of the study will contribute to the literature by shifting the focus from adoption to resistance, examining how the explainability of agentic Gen AI and task conditions jointly influence employees’ responses to Gen AI in organizational settings.

Paper Number

1912

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Aug 15th, 12:00 AM

Decoding Individuals’ Resistance toward the Adoption of Gen AI Technology in the Workplace

The current study explores employees’ resistance to the use of Gen AI in the workplace, highlighting functional barriers and task-related dynamics. Drawing on Innovation Resistance Theory (IRT) and Cognitive Load Theory (CLT), we argue that Gen AI's agentic and opaque nature creates unique challenges that amplify resistance beyond deterministic and traditional IT contexts. This study proposes that usage, value, and risk barriers shape resistance, while explainable Gen AI may reduce resistance by lowering uncertainty, particularly under high-complexity conditions. To test our hypothesis, a 2x2 between-subjects experiment manipulating task complexity (low vs. high) and Gen AI explainability (absent vs. present) has been designed for employees in business analytics roles using Gen AI. The results of the study will contribute to the literature by shifting the focus from adoption to resistance, examining how the explainability of agentic Gen AI and task conditions jointly influence employees’ responses to Gen AI in organizational settings.