# AI Self-Efficacy and Innovative Work Behavior in Hospitality and Tourism: A Job Demands-Resources Perspective on Work Engagement and Schedule I-Deals

## TL;DR

This study explores how confidence in using AI affects innovative work in hospitality, showing that training and schedule flexibility boost employee engagement and creativity.

## Contribution

The study introduces a moderated mediation model linking AI self-efficacy to innovative work behavior through work engagement and schedule i-deals.

## Key findings

- AI self-efficacy directly and indirectly predicts innovative work behavior through work engagement.
- Schedule i-deals moderate the relationship, strengthening the indirect effect of AI self-efficacy on innovative work behavior.
- Training and schedule autonomy are recommended to support AI integration in hospitality.

## Abstract

As artificial intelligence becomes increasingly embedded in hospitality and tourism services, it is reshaping employees’ innovative work behavior. Grounded in the Job Demands-Resources perspective, this study examines how AI self-efficacy affects innovative work behavior and proposes a moderated mediation model to investigate the mediating role of work engagement and the boundary condition of schedule idiosyncratic deals. Using a three-wave time-lagged design, the study collected data from 300 employees working in the hospitality and tourism industry in Korea. The findings show that AI self-efficacy positively predicts innovative work behavior both directly and indirectly through increased work engagement. Furthermore, this mediating process is strengthened by higher levels of schedule i-deals, confirming a positive moderating effect. Theoretically, this study extends human-AI collaboration research by broadening the explanatory scope of the Job Demands-Resources model in the AI context. Practically, organizations undergoing digital transformation should provide training that strengthens employees’ confidence in using AI and grant greater autonomy over work schedules. Such practices help create a supportive environment that enables AI self-efficacy to translate into work engagement and ultimately innovative work behavior.