Service robots, artificial intelligence awareness, self-efficacy and work engagement

Purpose The rise of artificial intelligence (AI) technology has sparked concerns across various industries, especially within frontline service sectors, as it fosters a sense of insecurity among practitioners. However, prior research on how and when AI impacts employees’ psychological and work states remains insufficient. This study aims to explore the relationships of AI awareness with job insecurity and work engagement, with a particular focus on how self-efficacy affects these dynamics. Design/methodology/approach A questionnaire survey was employed to collect 302 responses from frontline employees in the hotel industry in China. Data analysis was conducted using SPSS 21.0 and AMOS, employing hierarchical regression analysis and Johnson-Neyman tests to validate the main, mediating, and moderating effects. Findings The findings indicate that AI awareness negatively influences work engagement by triggering job insecurity. Self-efficacy serves as a mitigating factor, alleviating the adverse impact of AI awareness on work engagement. Notably, the Johnson-Neyman test reveals a positive shift in the relationship between AI awareness and work engagement when self-efficacy exceeds a certain threshold. Originality/value The present study is among the first to explore the relationship between AI awareness and work engagement, contributing to a growing body of research in this area. It contributes not only by enriching the existing literature on AI and employee work states but also by introducing new perspectives, thereby providing valuable insights for both scholars and practitioners.

Authors

Publication Details

Journal
Management Decision
Published
2025-04-09
DOI
https://doi.org/10.1108/md-05-2024-1058
Citations
7
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
17.37
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Service robots, artificial intelligence awareness, self-efficacy and work engagement

Chunhao Ma, Xiang‐Peng Kong, Wei Yuan
7 citations
Management Decision
AI in Service Interactions
17.37
article

Service robots, artificial intelligence awareness, self-efficacy and work engagement

Chunhao Ma, Xiang‐Peng Kong, Wei Yuan
article en
7 citations

Abstract

Purpose The rise of artificial intelligence (AI) technology has sparked concerns across various industries, especially within frontline service sectors, as it fosters a sense of insecurity among practitioners. However, prior research on how and when AI impacts employees’ psychological and work states remains insufficient. This study aims to explore the relationships of AI awareness with job insecurity and work engagement, with a particular focus on how self-efficacy affects these dynamics. Design/methodology/approach A questionnaire survey was employed to collect 302 responses from frontline employees in the hotel industry in China. Data analysis was conducted using SPSS 21.0 and AMOS, employing hierarchical regression analysis and Johnson-Neyman tests to validate the main, mediating, and moderating effects. Findings The findings indicate that AI awareness negatively influences work engagement by triggering job insecurity. Self-efficacy serves as a mitigating factor, alleviating the adverse impact of AI awareness on work engagement. Notably, the Johnson-Neyman test reveals a positive shift in the relationship between AI awareness and work engagement when self-efficacy exceeds a certain threshold. Originality/value The present study is among the first to explore the relationship between AI awareness and work engagement, contributing to a growing body of research in this area. It contributes not only by enriching the existing literature on AI and employee work states but also by introducing new perspectives, thereby providing valuable insights for both scholars and practitioners.

Management Decision
Openalex Percentile: Top 1%
AI in Service Interactions
17.37
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.