The Impact of Compensation Fairness on Task Performance: The Role of Behavioral Engagement and Emotional Intelligence

Summary

The research paper, "The Impact of Compensation Fairness on Task Performance: The Role of Emotional Engagement and Emotional Intelligence" by Qiu and Rojniruttikul, published in 2025, delves into the complex interplay between perceived compensation fairness and employee task performance. Grounded in the Group Engagement Model (GEM), the study aims to understand how various dimensions of compensation fairness—distributive fairness (DF), procedural fairness (PF), and interactional fairness (IF)—influence task performance (TP) indirectly through emotional engagement (EE). Additionally, it examines the moderating effects of emotional intelligence (EI) and artificial intelligence adoption (AIA) on these relationships. The authors highlight that improving employee engagement and performance in the modern era, particularly with the rapid advancement of AI technology, is a critical issue for enterprise management. For its methodology, the study employed a quantitative approach, collecting data from 311 employees within Chinese media enterprises through an online survey conducted between May and July 2024. The researchers utilized structural equation modeling and three-way interaction analysis to test their conceptual model. Measurement scales for the variables were adapted from existing literature: compensation fairness dimensions (DF, PF, IF), emotional engagement, task performance, emotional intelligence, and artificial intelligence adoption, all assessed using a 5-point Likert-type scale. The sampling method was convenience sampling, and measures were taken to address potential common method bias. The key findings reveal several significant relationships. All three dimensions of compensation fairness—distributive, procedural, and interactional fairness—were found to be positively associated with emotional engagement. Crucially, emotional engagement, in turn, positively linked to task performance. Among the dimensions of compensation fairness, distributive fairness demonstrated the strongest positive relationship with emotional engagement, while procedural fairness exhibited the largest total effect on task performance. The study also uncovered nuanced moderating effects: the impact of emotional intelligence on the relationship between interactional fairness and emotional engagement was significantly and positively related to artificial intelligence adoption, indicating a moderated-moderated mediation effect. These results extend the Group Engagement Model by providing empirical evidence for the specific mechanisms through which compensation fairness influences employee outcomes. The implications of this research are significant for both theoretical understanding and practical application in human resource management. Theoretically, the study enriches the Group Engagement Model and Equity Theory by empirically demonstrating the mediating role of emotional engagement and the moderating roles of emotional intelligence and AI adoption in the compensation fairness-task performance linkage. Practically, the findings suggest that enterprise managers should prioritize fostering a perception of fairness in their compensation systems, with particular attention to distributive and procedural aspects, to enhance employee emotional engagement. Tailoring compensation strategies and integrating technology (like AI) in a way that aligns with employees' emotional intelligence can create a more supportive work environment, ultimately optimizing employee productivity and promoting the sustainable development of the enterprise.