Search results

This study examines the relationship between guanxi human resource management (HRM) practices and psychological withdrawal behavior and its mechanism, and examines the mediating role of psychological contract breach and the moderating role of employee resilience.

This study collected 287 three-stage questionnaires from 62 teams from public institutions, large state-owned enterprises and private enterprises in Sichuan Province, and used regression analysis, PROCESS and Amos structural equation model to test the research hypothesis.

Guanxi HRM practices positively influenced the employees’ psychological withdrawal behavior, and psychological contract breach played a mediating role in the relationship. Employee resilience not only moderated guanxi HRM practice and psychological contract breach but also moderated the mediating effect of psychological contract breach between guanxi HRM practice and psychological withdrawal behavior.

This study revealed the impact of guanxi HRM practices on employees’ psychological withdrawal behavior, which often serves as an early indicator of mental health issues. This finding has important implications for the research on relation-oriented HRM practices.

Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the variability in features and the intrinsic correlation among diverse aspect categories in ACSA tasks. To address these problems, this paper aims to propose a novel integrated framework.

The integrated framework consists of three modules: text feature extraction and fusion, adaptive feature selection and category-aware decision fusion. First, text features from global and local views are extracted and fused to comprehensively capture the potential information in the different dimensions of the review text. Then, an adaptive feature selection strategy is devised for each aspect category to determine the optimal feature set. Finally, considering the intrinsic associations between aspect categories, a category-aware decision fusion strategy is constructed to enhance the performance of ACSA tasks.

Comparative experimental results demonstrate that the integrated framework can effectively detect aspect categories and their corresponding sentiment polarities from review texts, achieving a macroaveraged F1 score (Fmacro) of 72.38% and a weighted F1 score (F1) of 79.39%, with absolute gains of 2.93% to 27.36% and 4.35% to 20.36%, respectively, compared to the baselines.

This framework can simultaneously detect aspect categories and corresponding sentiment polarities from review texts, thereby assisting e-commerce enterprises in gaining insights into consumer preferences, prioritizing product improvements, and adjusting marketing strategies.