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The potential of generative AI (GenAI) to stimulate employee creativity has received extensive attention from industry and academia. However, there is still limited research on strategically using GenAI to leverage its positive effects on employee creativity. This study aims to clarify the effects of different GenAI use purposes on employee creativity.

Based on self-determination theory, this study explores the effects of work-related and nonwork-related GenAI use on incremental and radical creativity through the mediator role of exploratory and exploitative learning and the boundary role of perceived ease of use. This study constructs a theoretical model and uses structural equation modeling to test the model by analyzing survey data from 330 employees.

(1) Work-related and nonwork-related GenAI use positively impacts incremental and radical creativity through exploratory and exploitative learning; (2) work-related GenAI use contributes more to exploitative learning than to exploratory learning, while nonwork-related GenAI use contributes more to exploratory learning than to exploitative learning; (3) exploitative learning has a stronger positive impact on incremental creativity, and exploratory learning has a stronger positive impact on radical creativity; (4) perceived ease of use weakens the positive effects of nonwork-related GenAI use on exploratory and exploitative learning.

First, this study enriches employee creativity research by revealing the relationship between different GenAI use purposes and incremental and radical creativity. Second, this study enriches employee creativity research by revealing the mediating role of exploratory and exploitative learning between GenAI use and incremental and radical creativity. Finally, this study enriches GenAI use research by revealing the moderating role of perceived ease of use between GenAI use and employee learning.

Work interruptions (WIs) due to social media are becoming more and more common in the daily lives of organizations. However, the relationship between WI and work performance of employees is still unclear. This study aims to investigate the effects of WIs due to social media on employees' work performance in terms of different mechanisms; it also considers the moderating role of social media usage.

Using the jobs demands-resource (JD-R) model, this paper proposes a research model to investigate the effects of WIs on employee work performance from the perspective of the enabling mechanism and burden mechanism. Structural equation modeling (SEM) was used to analyze the data of 444 employees.

The results show that (1) with regard to the enabling mechanism path, WI has a positive effect on employees' sense of belonging, which further has a positive effect on employees' work performance; (2) with regard to the burden mechanism path, WI has a positive effect on employees' interruption overload; however, the effect of employee interruption overload on employees' work performance is not significant, and (3) social media used for either work or social purposes can strengthen the relationship between WI and interruption overload, while social media used for work-related purposes can reduce the relationship between WI and a sense of belonging.

First, this paper contributes to the WI literature by clarifying how WI affects employees' work performance through different mechanisms, namely the enabling mechanism and the burden mechanism. Second, this paper contributes to the WI literature by revealing a boundary condition, namely social media use, between WI and a sense of belonging and between WI and employees' interruption overload.

This study aims to explore the intricate relationship between uncertainty indicators and volatility of commodity futures, with a specific focus on agriculture and energy sectors.

The authors analyse the volatility of Indian agriculture and energy futures using the GARCH-MIDAS model, taking into account different types of uncertainty factors. The evaluation of out-sample predictive capability involves the application of out-sample R-squared test and computation of various loss functions.

The research outcomes underscore the significant impact of diverse uncertainty factors such as domestic economic policy uncertainty (EPU), global EPU (GEPU), US EPU and geopolitical risk (GPR) on long-run volatility of Indian energy and agriculture (agri) futures. Additionally, the study demonstrates that GPR exhibits superior predictive capability for crude oil futures volatility, while domestic EPU stands out as an effective predictor for agri futures, particularly castor seed and guar gum.

The study offers practical implications for market participants and policymakers to adopt a comprehensive perspective, incorporating diverse uncertainty factors, for informed decision-making and effective risk management in commodity markets.

The research makes an inaugural attempt to examine the impact of domestic and global uncertainty indicators on modelling and predicting volatility in energy and agri futures. The distinctive feature of considering an emerging market also adds a novel dimension to the research landscape.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

The aim of this research was to investigate fans’ responses toward a sponsor that has had a long-standing sponsorship deal with a club and decided also to sponsor the club’s rival.

A long-term sponsorship deal between a retsina wine company and a popular football club and a newly established deal between the company and the main rival club were selected as the research setting. Data were collected from a total sample of 302 participants, fans of the two teams, using an online survey and PLS-SEM was employed to test the relationships of the proposed structural model.

The results provided evidence for the importance of the inclusion of perceptions of fit for both teams to the model as it impacted the responses in the joint sponsorship. Team identification emerged significant for improving fans perceptions of fit between the sponsor and their favorite club but also led fans of the long-term sponsored club to feel betrayed from the sponsor. The sense of betrayal impacted the level of fit, the rejection of sponsorship but did not emerge significant for driving negative responses toward the sponsor’s brand. The same held for the rejection of the joint sponsorship.

This is the very first study that incorporated the effects of the perceptions of fit of two rival clubs to test the effect of sponsorship for a sponsor brand of a deal that includes a longtime sponsored football club and its rival as a newly sponsored one. It is also one of the first attempts that explores relationships between perceptions of fit, sense of betrayal and rejection of a joint sport sponsorship in a rivalry context, highlighting the importance of preventing fans' betrayal.

This study explores the effects of gender and participation in agricultural cooperatives on biodiversity conservation farming practices in vegetable production.

This study used data collected from a survey of 627 vegetable farmers in Viet Nam and employed the Ordered Probit regression model to examine the effects of gender and participation in agricultural cooperatives on biodiversity conservation farming practices.

We find that female vegetable farmers are more likely to conduct biodiversity conservation farming practices than male farmers. This gender difference is, however, removed when participation in agricultural cooperatives is controlled, suggesting that agricultural cooperatives effectively facilitate biodiversity conservation farming practices.

It is noted that our study is not free from some limitations. First, we conducted our study on vegetable farmers only. The biodiversity conservation practices in vegetable cultivation might be different from other types of farming. Future studies should be conducted with other types of agricultural cultivation. Second, we do not have enough data to explain why female farmers are more likely to adopt biodiversity conservation practices than male farmers. Future studies should capture biological and social aspects of gender differences to address this limitation.

This study contributes to the literature on biodiversity conservation by presenting empirical evidence on the effects of gender and agricultural cooperatives. Participation in agricultural cooperatives is revealed to facilitate the adoption of biodiversity conservation practices. In addition, we find that the education of farmers, the number of years that farmers have been living in the local area and the quality of land and water are positively related to the adoption of biodiversity conservation practices in vegetable production.

Gamification has been widely applied in mobile fitness apps to motivate users to exercise continuously. Based on the affordances–psychological outcomes–behavioral outcomes framework, this study explores the roles of three specific gamification affordances (competition, visibility of achievement and interactivity) in self-health management (continuous use behavior and health behavior) from the perspectives of achievement satisfaction and gamification exhaustion.

We test the research model using a structural equation model (SEM) with 505 self-reported data points. Furthermore, we apply fuzzy-set qualitative comparative analysis (fsQCA) to explore configurations of gamification affordances associated with self-health management behavior, reinforcing the SEM results.

Results indicate that competition, visibility of achievement and interactivity can enhance achievement satisfaction, which further boosts self-health management behavior. However, competition and interactivity can also cause gamification exhaustion, which undermines self-health management behavior to some extent. Overall, the positive impacts of the three affordances outweigh the negative impacts.

This study provides new insights for relevant practitioners on designing gamification affordances, aiding the sustainable development of mobile fitness apps and their long-term effects on self-health management. Visibility of achievement should be emphasized, and competition and interactivity should be thoughtfully designed to minimize their negative effects.

This study extends the affordances–psychological outcomes–behavioral outcomes framework and the literature on gamification and health management by applying both SEM and fsQCA methodologies to examine the relationship between specific gamification affordances and self-health management behavior.

In recent decades, advancements in biosensors technology have made fluorescent biosensor pivotal for biomolecular recognition. This paper aims to provide an in-depth analysis of polymerase chain reaction (PCR) fluorescent biosensor detection technology for identifying Escherichia coli (E. coli), setting the stage for future developments in the field.

The review of literature on PCR fluorescent biosensor detection technology for E. coli over the past decades includes discussions on traditional biological fluorescent detection, quantitative PCR fluorescent detection and digital fluorescent detection technology.

Advancements in fluorescent biosensor technology enable precise measurement of fluorescent signals, and when integrated with microfluidic technology, produce compact, reagent-efficient digital sensor devices.

This paper provides a comprehensive review of recent fluorescent detection technology for pathogenic E. coli, assessing method efficiencies and offering insights to advance the field.

The landscape of health information acquisition has shifted from offline to online, and online question-and-answer (Q&A) communities have emerged as prominent sources of health information; however, it is unclear how users identify satisfactory health information. This paper identifies factors that influence users’ adoption of health information in the context of online Q&A communities.

Based on the elaboration likelihood model (ELM) and opinion leader theory, we construct a research model to examine how information quality (complexity, image structure and emotional change) and source credibility (authentication status, follower number) affect health information adoption behavior. We verify the hypotheses by Poisson regression and zero-inflation Poisson regression using the data collected from an online Q&A community.

The empirical results indicate that both information quality and source credibility positively affect users’ adoption of health information.

This research can assist designers and managers of online Q&A communities to better comprehend users’ health information needs and their preferences for adoption. This enhanced understanding can facilitate the provision of superior online health information.

This study identifies a methodology to explore the issues of enterprise innovation ecosystem health (EIEH).

First, this study constructs the indicator system of EIEH based on the research objective; second, the dynamic vertical projection method (DVPM) and entropy weight method are proposed to analyze the status and influencing factors of EIEH; finally, the future development of EIEH is analyzed using GM (1,1).

In terms of methodology, the DVPM can effectively analyze EIEH, which can not only analyze the development status and potential of EIEH every year but also analyze the comprehensive state of EIEH for many years. In terms of practice, the value and grade of EIEH in China have been gradually increasing from 2016 to 2020, but the overall development is unbalanced, and five key factors affecting EIEH have been identified. The EIEH in China is predicted to steadily grow from 2021 to 2025.

The analytical method employed in this study can effectively analyze EIEH, which provides a new analytical perspective for the evaluation of EIEH and enriches the research content of the enterprise innovation ecosystem (EIE). By analyzing the results, we can gain a comprehensive understanding of the state of different EIEs, enabling each EIE to design tailored remedial measures to enhance EIEH and achieve sustainable development.