TY - JOUR
T1 - From despair to hope
T2 - the nonlinear impact of AI-induced job insecurity on employees’ innovative behavior
AU - Zhong, Huanyi
AU - Lou, Guobiao
AU - Wei, Kexin
AU - Yang, Tianan
AU - Deng, Jianwei
AU - Liu, Ran
N1 - Publisher Copyright:
© 2026 Emerald Publishing Limited
PY - 2026
Y1 - 2026
N2 - Purpose – This study aims to investigate the complex relationship between artificial intelligence (AI)-induced job insecurity and employee innovative behavior. It examines a potential U-shaped relationship between these variables, with knowledge hiding behavior as a mediator and trait resilience as a moderator. The research specifically explores whether trait resilience might amplify rather than mitigate the positive effect of AI-induced job insecurity on knowledge hiding behavior, revealing its potential dark side in organizational contexts. Design/methodology/approach – A two-wave survey was conducted through the Credamo platform, collecting data from 485 Chinese office employees across multiple industries. The study used established scales to measure AI-induced job insecurity, knowledge hiding behavior, innovative behavior and trait resilience. Data analysis was performed using SPSS 27.0 and Amos 27.0, using hierarchical regression and bootstrap methods to test the hypothesized relationships. Findings – Results confirmed a significant positive U-shaped relationship between AI-induced job insecurity and innovative behavior. Knowledge hiding behavior partially mediated this relationship. Notably, trait resilience positively moderated the connection between AI-induced job insecurity and knowledge hiding behavior. Employees with higher trait resilience demonstrated a stronger tendency to engage in knowledge hiding when experiencing AI-induced job insecurity, revealing an unexpected negative aspect of this typically positive trait. Originality/value – This study breaks new ground by identifying a U-shaped relationship between AI-induced job insecurity and innovative behavior. It reveals the dark side of trait resilience, demonstrating its potential to strengthen counterproductive knowledge behaviors. These findings provide novel theoretical insights into employee adaptation to AI technologies and offer important practical implications for organizational management in the digital transformation era.
AB - Purpose – This study aims to investigate the complex relationship between artificial intelligence (AI)-induced job insecurity and employee innovative behavior. It examines a potential U-shaped relationship between these variables, with knowledge hiding behavior as a mediator and trait resilience as a moderator. The research specifically explores whether trait resilience might amplify rather than mitigate the positive effect of AI-induced job insecurity on knowledge hiding behavior, revealing its potential dark side in organizational contexts. Design/methodology/approach – A two-wave survey was conducted through the Credamo platform, collecting data from 485 Chinese office employees across multiple industries. The study used established scales to measure AI-induced job insecurity, knowledge hiding behavior, innovative behavior and trait resilience. Data analysis was performed using SPSS 27.0 and Amos 27.0, using hierarchical regression and bootstrap methods to test the hypothesized relationships. Findings – Results confirmed a significant positive U-shaped relationship between AI-induced job insecurity and innovative behavior. Knowledge hiding behavior partially mediated this relationship. Notably, trait resilience positively moderated the connection between AI-induced job insecurity and knowledge hiding behavior. Employees with higher trait resilience demonstrated a stronger tendency to engage in knowledge hiding when experiencing AI-induced job insecurity, revealing an unexpected negative aspect of this typically positive trait. Originality/value – This study breaks new ground by identifying a U-shaped relationship between AI-induced job insecurity and innovative behavior. It reveals the dark side of trait resilience, demonstrating its potential to strengthen counterproductive knowledge behaviors. These findings provide novel theoretical insights into employee adaptation to AI technologies and offer important practical implications for organizational management in the digital transformation era.
KW - AI-induced job insecurity
KW - Innovative behavior
KW - Knowledge hiding behavior
KW - Trait resilience
UR - https://www.scopus.com/pages/publications/105040536349
U2 - 10.1108/JKM-10-2025-1491
DO - 10.1108/JKM-10-2025-1491
M3 - Article
AN - SCOPUS:105040536349
SN - 1367-3270
SP - 1
EP - 24
JO - Journal of Knowledge Management
JF - Journal of Knowledge Management
ER -