Exploring compensation fairness, emotional engagement, and task performance of enterprise employees: a moderated-moderation approach with emotional intelligence and artificial intelligence adoption as moderators

Abstract

The rapid development of artificial intelligence technology has profoundly impacted the behavior of enterprise employees. How to improve employee engagement and performance in the new era is an important issue in enterprise management. Grounded in the Group Engagement Model (GEM), this study investigates the impact of various dimensions of employee compensation fairness (CF) on task performance (TP) via emotional engagement (EE), with emotional intelligence (EI) and artificial intelligence adoption (AIA) as moderators. This study employs structural equation modeling and three-way interaction analysis to examine data from 311 employees in Chinese media enterprises. The results indicate that distributive fairness (DF), procedural fairness (PF), and interactional fairness (IF) are positively associated with EE, which is positively linked to TP. Among the three dimensions of CF, DF demonstrated the strongest positive relationship with EE, and PF exhibited the largest total effect on TP. The moderating effect of EI on the relationship between IF and EE is significantly and positively related to AIA; and the moderated-moderated mediation effects exists. These findings extend the GEM and suggest enterprise managers should tailor their compensation systems and technology strategies, foster a favorable working environment, to enhance employee productivity, and promote sustainable development of the enterprise.