# The Impact of AI-Enabled Job Characteristics on Manufacturing Workers’ Work-Related Flow: A Dual-Path Perspective of Challenge–Hindrance Stress and Techno-Efficacy

## TL;DR

This study explores how AI in manufacturing affects workers' flow by examining stress and confidence factors, revealing complex impacts.

## Contribution

The study introduces a dual-path perspective combining stress and techno-efficacy to explain AI's impact on work-related flow.

## Key findings

- AI job features increase flow via challenge stress but decrease it via hindrance stress.
- Techno-efficacy reduces the negative impact of AI on hindrance stress.
- Four causal configurations for high work-related flow are identified using fsQCA.

## Abstract

The integration of artificial intelligence (AI) in the manufacturing industry is increasingly prevalent, presenting both ongoing opportunities and challenges for organizations while also significantly impacting worker behavior and psychology. Drawing on data from 405 workers in China, this study employs hierarchical regression analysis and fuzzy-set qualitative comparative analysis (fsQCA) to investigate the influence mechanism of AI-enabled job characteristics on work-related flow. Key findings reveal that: AI-enabled job characteristics positively predict work-related flow by increasing perceived challenge stress, yet simultaneously exert a negative influence by exacerbating perceived hindrance stress; techno-efficacy significantly alleviates the relationship between AI-enabled job characteristics and perceived hindrance stress but does not moderate the path via perceived challenge stress; fsQCA identifies four distinct causal configurations of antecedents leading to high work-related flow. This research elucidates the complexities of AI-enabled job characteristics and their dual-faceted impact on work-related flow. By integrating AI into the study of worker psychology and behavior, it extends the contextual scope of job characteristics research. Furthermore, the application of fsQCA provides novel insights into the antecedent conditions and configurational pathways for achieving work-related flow, offering significant theoretical and practical implications.