Exploring the double-edged sword effect of perceived organizational AI adoption on employee knowledge behaviors

Purpose The purpose of this study is to investigate how and under what conditions perceived organizational artificial intelligence (AI) adoption influences employees’ knowledge sharing and knowledge hiding behaviors, drawing upon transactional theory of stress and coping. Design/methodology/approach The study gathered survey data from 324 research and development employees in China across three-time points. Using SPSS 25.0 and PROCESS 3.3, the authors validated the hypotheses using path analysis and a bootstrapping-based analytical method. Findings The results suggest that the relationship between perceived organizational AI adoption and employees’ challenge or hindrance appraisals, and their subsequent knowledge behaviors, varies depending on employees’ regulatory focus. Specifically, for employees with a promotion focus, perceived organizational AI adoption tends to be associated with higher levels of challenge appraisal, which are positively related to knowledge sharing behaviors. Conversely, for those with a prevention focus, perceived organizational AI adoption is associated with hindrance appraisal, which is in turn related to knowledge hiding behaviors. Originality/value There is limited research on investigating the influence of perceived organizational AI adoption in the field of knowledge management, especially the impact on individual knowledge strategies of employees. The authors carried out this study to provide evidence of how and when perceived organizational AI adoption affects employee knowledge sharing and hiding.

Authors

Institutions

Publication Details

Journal
Chinese Management Studies
Published
2026-06-13
DOI
https://doi.org/10.1108/cms-03-2025-0258
Primary Topic
Knowledge Management and Sharing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the double-edged sword effect of perceived organizational AI adoption on employee knowledge behaviors

Feng Xu, Jing Qin, Dayong Zhang
Chinese Management Studies
Knowledge Management and Sharing
article

Exploring the double-edged sword effect of perceived organizational AI adoption on employee knowledge behaviors

Feng Xu, Jing Qin, Dayong Zhang
article en

Abstract

Purpose The purpose of this study is to investigate how and under what conditions perceived organizational artificial intelligence (AI) adoption influences employees’ knowledge sharing and knowledge hiding behaviors, drawing upon transactional theory of stress and coping. Design/methodology/approach The study gathered survey data from 324 research and development employees in China across three-time points. Using SPSS 25.0 and PROCESS 3.3, the authors validated the hypotheses using path analysis and a bootstrapping-based analytical method. Findings The results suggest that the relationship between perceived organizational AI adoption and employees’ challenge or hindrance appraisals, and their subsequent knowledge behaviors, varies depending on employees’ regulatory focus. Specifically, for employees with a promotion focus, perceived organizational AI adoption tends to be associated with higher levels of challenge appraisal, which are positively related to knowledge sharing behaviors. Conversely, for those with a prevention focus, perceived organizational AI adoption is associated with hindrance appraisal, which is in turn related to knowledge hiding behaviors. Originality/value There is limited research on investigating the influence of perceived organizational AI adoption in the field of knowledge management, especially the impact on individual knowledge strategies of employees. The authors carried out this study to provide evidence of how and when perceived organizational AI adoption affects employee knowledge sharing and hiding.

Chinese Management Studies
Harbin Institute of Technology (CN)
Openalex Percentile: Top 2%
Knowledge Management and Sharing
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.