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Author: search.filters.author.Qiu, Yingming

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    Exploring compensation fairness, emotional engagement, and task performance of enterprise employees: a moderated-moderation approach with emotional intelligence and artificial intelligence adoption as moderators
    (2025-01-01)
    Qiu, Yingming
    ;
    Rojniruttikul, Nuttawut
    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.
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    The Impact of Compensation Fairness on Task Performance: The Role of Behavioral Engagement and Emotional Intelligence
    (2025-01-01)
    Qiu, Yingming
    ;
    Rojniruttikul, Nuttawut
    This study explores the impact of various dimensions of compensation fairness (CF) on behavioral engagement (BE) and task performance (TP) of employees in Chinese media enterprises, as well as the moderating role of emotional intelligence (EI) in the relationship between various dimensions of CF and BE. The conceptual model is based on Equity Theory and the Group Engagement Model (GEM). Using data collected from 298 media enterprise employees in China, the theoretical model is tested using the structural equation modeling approach. The linear regression analysis reveals that interactional fairness (IF) is positively significantly associated with BE, whereas distributive fairness (DF) and procedural fairness (PF) are not. BE significantly impacts TP and fully mediates the relationship between IF and TP. EI positively moderates the relationship between IF and BE, as well as the relationship between DF and BE. The findings provide insight into the impact of different dimensions of CF on BE and extend the theory of GEM. The results contribute to both theoretical understanding and practical application. Enterprise managers should prioritize IF to enhance BE, thereby improving TP. These insights empower organizations to effectively tailor their compensation strategies and technological implementations, creating a supportive work environment and optimizing employee productivity.
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    The Acceptance Model of Mobile Shopping Apps in China
    (2021-06-23)
    Lin, Xiaojian
    ;
    Qiu, Yingming
    ;
    Chaveesuk, Singha
    ;
    Chaiyasoonthorn, Wornchanok
    With the higher users' rate of mobile internet technology, more and more Chinese people start to use smartphones. E-commerce is gradually changing to mobile commerce in China. As a new shopping tool, mobile shopping Apps are more and more popular in China. The challenges of promoting mobile shopping Apps have brought challenges to the company promoting it. In this study, we propose a conceptual framework of the adoption model of mobile shopping (M-shopping) Apps based on UTAUT2 integrated with factor Corporate Reputation and factor Personal Innovativeness in IT to examine the correlative factors influencing users' willingness to use the mobile shopping applications before quantitative analysis, providing some suggestions on how to improve M-shopping Apps for the retailers and Apps developers.