LEVERAGING AI INTEGRATION FOR FOSTERING INNOVATIVE WORK BEHAVIOR: A HYBRID SEM-ANN APPROACH IN THE IT SECTOR

This study examines the relationship between Perceived Artificial Intelligence (AI) Integration and Innovative Work Behavior (IWB) among IT professionals in India by investigating the mediating role of Employee Engagement (EE) and the moderating role of AI Literacy (AIL), drawing on Ability–Motivation–Opportunity (AMO) Theory and Social Exchange Theory (SET). A hybrid Structural Equation Modelling-Artificial Neural Network (SEM-ANN) approach was employed to examine and predict IWB. Data collected from 630 IT professionals revealed that Perceived AI Integration positively influences IWB, with EE partially mediating this relationship. AIL negatively moderates this relationship, weakening the positive relationship between Perceived AI Integration and IWB. The structural model explained 73.1% of the variance in IWB, while the ANN analysis provided complementary evidence of predictive stability within the study sample and identified the relative predictive importance of the model predictors. The study extends the AI and innovation literature by explaining how perceived AI-enabled work opportunities and EE contribute to IWB, while identifying AIL as a boundary condition. The findings provide practical guidance for managers seeking to support EE, develop AI capabilities, and design AI-enabled work environments that facilitate innovation.