Introduction: The integration of artificial intelligence (AI) into workplaces has transformed organizational operations, yet its impact on employee knowledge sharing remains underexplored. While AI adoption enhances learning and collaboration, the extent to which employees engage in knowledge sharing depends on leadership styles and attitudes toward technology. This study investigates how AI adoption promotes knowledge sharing through employee learning opportunities, while considering the moderating roles of paradoxical leadership and technophilia.Methods: A survey was conducted with 364 employees across various organizations to examine the proposed relationships. Structural equation modeling (SEM) was employed to test the mediation effect of learning opportunities and the moderating effects of paradoxical leadership and technophilia.Results: The findings reveal that AI adoption positively influences employee knowledge sharing, with learning opportunities serving as a key mediating factor. Furthermore, paradoxical leadership and technophilia amplify this relationship, indicating that employees with a strong affinity for technology and those working under paradoxical leaders are more likely to leverage AI for knowledge sharing.Discussion: These results provide important implications for organizations seeking to maximize the benefits of AI adoption. Managers should foster a paradoxical leadership style and support employees in developing a positive attitude toward technology to enhance knowledge-sharing behaviors. Future research should explore additional contextual factors influencing AI-driven knowledge sharing.

Integrating artificial intelligence (AI) and cause-related marketing has reshaped corporate social responsibility practices while triggering a conflict between technological instrumental rationality and moral value transmission. Building on the Persuasion Knowledge Model (PKM) and AI aversion literature, this research employs two experiments to reveal that AI disclosure exerts a unique inhibitory effect on consumers’ purchase intentions in cause-related marketing contexts compared to non-cause-related marketing scenarios. Further analysis uncovers a chain mediation pathway through consumer skepticism and advertisement attitudes, explaining the psychological mechanism underlying AI disclosure’s impact on purchase intentions. The study also identifies the moderating role of AI aversion within this chain model. The findings provide a new theoretical perspective for integrating AI disclosure, consumer psychological responses, and marketing effectiveness while exposing the “value-instrumentality” conflict inherent in AI applications for cause-related marketing. This research advances the evolution of the PKM in the digital era and offers practical insights for cause-related marketing enterprises to balance AI technology application with optimized disclosure strategies.

Purpose: The research aims to explore the influence mechanism of peer feedback and users' knowledge contribution behavior. This study draws on the social identity theory and considers social identity as a mediating factor into the research framework.Design/methodology/approach: This paper collected users' activity data of 142,191 ideas submitted by 76,647 users from the MIUI community between October 2010 and May 2018 via Python software, and data were processed using Stata 16.0.Findings: The results indicate that knowledge feedback and social feedback positively influence users' knowledge contribution (quantity and quality), respectively. User's cognitive identity positively mediates the relationship between peer feedback and knowledge contribution behavior, affective identity positively mediates the relationship between peer feedback and knowledge contribution behavior, while evaluative identity positively mediates the relationship between peer feedback and knowledge contribution quality, but there is no mediating effect between peer feedback and knowledge contribution quantity.Originality/value: This study advances knowledge management by highlighting peer feedback on online innovation communities. By demonstrating the significant mediating effect of social identity, this study empirically clarifies the relationships of peer feedback (knowledge feedback and social feedback) to specific dimensions of knowledge contribution, thereby providing managerial guidance to the online innovation community on incentivizing and managing user interaction to foster the innovation development of firms.

This study focuses on the factors influencing consumers’ continuous participation willingness in online pre-sale activities. Based on perceived value and social exchange theory, it analyzes how perceived benefits (including practical benefits, hedonic benefits, and social benefits) and perceived costs (including search costs, waiting costs, and adjustment costs) affect consumer satisfaction and their continuous participation intention in online pre-sales. A total of 527 valid questionnaires were collected, and structural equation modeling was used for data analysis. The results indicate that perceived benefits are significantly positively correlated with consumer satisfaction and their willingness to continue participating, while perceived costs are significantly negatively correlated with both aspects. Additionally, the study found that product type moderates the effect of perceived benefits and perceived costs on consumer satisfaction. This research helps retailers understand consumers’ willingness to continuously participate in online pre-sales and provides valuable insights for developing pre-sale strategies.

As a critical external mechanism driving green innovation, institutional and competitive pressure often coexist and jointly shape firms’ strategic responses. However, existing studies primarily focus on the individual effects of these pressures, with limited attention to their interactive impacts on green innovation. Drawing on optimal distinctiveness theory, this study proposes a “pressure–response” analytical framework that classifies institutional and competitive pressure combinations into congruent (i.e., high–high or low–low) and incongruent (i.e., high–low or low–high) pressure contexts based on their relative intensities. It further examines how these distinct configurations affect two types of green innovation: strategic green innovation (StrGI) and substantive green innovation (SubGI). Using panel data from Chinese A-share listed firms between 2010 and 2022, the empirical results reveal that under congruent pressure contexts, the alignment of institutional and competitive pressures tends to suppress green innovation. In contrast, under incongruent contexts, the misalignment between the two pressures significantly promotes green innovation. Regarding innovation motivation, the high institutional–low competitive pressure context more significantly promotes StrGI, while the low institutional–high competitive pressure context has a more prominent effect on SubGI. In addition, this study also investigates the mediating roles of StrGI and SubGI on ESG performance. The findings provide theoretical support and policy implications for improving green transition policies and institutional frameworks, as well as promoting sustainable corporate development.