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This study aims to examine the role of location autonomy (i.e. autonomy over where to work) in shaping employee mental distress during their working days.

A total of 316 employees from 6 organizations in the UK provided data for 4,082 half-day sessions, over 10 working days. Random intercept modeling is used to analyze half-day data nested within individuals.

Results show that location autonomy, beyond decision-making autonomy and work-method autonomy, is positively associated with the perception of task-environment (TE) fit which, in turn, contributes to lower mental distress during each half-day session. Results of supplementary analysis also show that location autonomy can contribute to higher absorption, task proficiency and job satisfaction via TE fit during each half-day session.

This study reveals the importance and uniqueness of location autonomy in shaping employees' outcomes, offering implications for how organizations can use this in the work–life flexibility policies to support employee mental health.

This study aims to understand the impact of negative supervisor gossip on job performance among South Indian hotel employees. The focus is not just on the direct influence, but also on the mediating role of feedback-seeking behaviour (FSB) and the moderating effects of the agreeableness trait.

Through purposive sampling, data was garnered from South Indian hotel employees. Comprehensive analyses were performed using partial least squares structural equation modelling.

The analysis shows that FSB plays a mediating role in the positive relationship between negative supervisor gossip and job performance. In addition, the influence of gossip on FSB and subsequent job performance was more pronounced for employees with high agreeableness.

This research underscores the complex interplay between negative supervisor gossip and job performance, revealing that such gossip can catalyze FSB process in employees. It suggests that under certain conditions, negative gossip can be transformed into a constructive force that enhances job performance, challenging traditional perceptions of gossip in the workplace.

The findings underscore the importance of understanding the effects of workplace dynamics, like supervisor gossip, on employee behaviour and performance. Recognizing the influence of individual personality traits, such as agreeableness, can guide management strategies for fostering a productive work environment.

This research sheds light on the intricate interplay between negative supervisor gossip, FSB and agreeableness, offering a novel perspective on their combined impact on job performance. It not only enriches the existing literature on workplace communication but also broadens the understanding of the role of personality traits in shaping employee responses and outcomes.

Based on the JD-R model and process-focused HRM perspective, this research paper aims to investigate the processes underlying the relationship between AI-enabled HR analytics and employee well-being outcomes (resilience) that received less attention in the AI-driven HRM literature. Specifically, this study aims to examine the indirect effect between AI-enabled HR analytics and employee resilience via job crafting, moderated by HRM system strength to highlight the contextual stimulus of AI-enabled HR analytics.

The authors adopted a time-lagged research design (one-month interval) to test the proposed hypotheses. The authors used two-wave surveys to collect data from 175 full-time hotel employees in China.

The findings indicated that employees' perceptions of AI-enabled HR analytics enhance their resilience. This study also found the mediation role of job crafting in the mentioned relationship. Moreover, the positive effects of AI-enabled HR analytics on employee resilience amplify in the presence of a strong HRM system.

Organizations that aim to utilize AI-enabled HR analytics to achieve organizational missions should also dedicate attention to its associated employee well-being outcomes.

This study enriched the literature with regard to AI-driven HRM in that it identifies the mediating role of job crafting and the moderating role of HRM system strength in the relationship between AI-enabled HR analytics and employee resilience.

This study, rooted in affordance-actualization theory and communication theory, aims to critically examine how ChatGPT influences users’ transition from new adopters to loyal advocates within the context of travel decision-making. It incorporates constructs including communication quality, personalization, anthropomorphism, cognitive and emotional trust (ET), loyalty and intention to adopt into a comprehensive model.

This study used quantitative methods to analyze data from 477 respondents, collected online through a self-administered questionnaire by Embrain, a leading market research company in South Korea. Lavaan package within R studio was used for evaluating the measurement model through confirmatory factor analysis and using structural equation modeling to examine the proposed hypotheses.

The findings reveal a pivotal need for enhancing ChatGPT’s communication quality, particularly in terms of accuracy, currency and understandability. Personalization emerges as a key driver for cognitive trust, while anthropomorphism significantly impacts ET. Interestingly, the study unveils that in the context of travel recommendations, users’ trust in ChatGPT predominantly operates at the cognitive level, significantly impacting loyalty and subsequent adoption intentions.

The findings of this research provide valuable insights for improving Generative AI (GenAI) technology and management practices in travel recommendations.

As one of the few empirical research papers in the burgeoning field of GenAI, this study proposes a highly explanatory model for the process from affordance to actualization in the context of using ChatGPT for travel recommendations.

Despite ongoing reports of insider-driven leakage of confidential data, both academic scholars and practitioners tend to focus on external threats and favour information technology (IT)-centric solutions to secure and strengthen their information security ecosystem. Unfortunately, they pay little attention to human resource management (HRM) solutions. This paper aims to address this gap and proposes an actionable human resource (HR)-centric and artificial intelligence (AI)-driven framework.

The paper highlights the dangers posed by insider threats and presents key findings from a Leximancer-based analysis of a rapid literature review on the role, nature and contribution of HRM for information security, especially in addressing insider threats. The study also discusses the limitations of these solutions and proposes an HR-in-the-loop model, driven by AI and machine learning to mitigate these limitations.

The paper argues that AI promises to offer many HRM-centric opportunities to fortify the information security architecture if used strategically and intelligently. The HR-in-the-loop model can ensure that the human factors are considered when designing information security solutions. By combining AI and machine learning with human expertise, this model can provide an effective and comprehensive approach to addressing insider threats.

The paper fills the research gap on the critical role of HR in securing and strengthening information security. It makes further contribution in identifying the limitations of HRM solutions in info security and how AI and machine learning can be leveraged to address these limitations to some extent.

This study examines the role of social media adoption (SMA), opportunity recognition (OR) and opportunity exploitation (OE) in mediating the relationship between entrepreneurial orientation (EO) and the performance of newly established small and medium-sized enterprises (SMEs) in emerging economies, with a particular emphasis on Ghana.

This study adopts a post-positivist philosophical stance and uses a quantitative approach and a survey design. A purposive sampling technique was used to select 336 SME owners and managers from Ghana’s manufacturing, trading and service sectors. Questionnaires were administered to source the empirical data for this study. Structural equation modelling (SEM) was used to analyse the proposed hypotheses.

The results reveal that EO positively and significantly influences the performance of new-born SMEs. SMA, OR and OE partially mediated this relationship.

This study is a wakeup call to policymakers, practitioners, managers and owners of recently established businesses. Policymakers should provide support and resources for newly established SMEs to adopt effective social media marketing strategies, bolstering their online presence and customer engagement. Simultaneously, they should invest in entrepreneurship education and create an environment conducive to innovation to cultivate an entrepreneurial mindset among fresh SMEs. Business owners and managers should proactively monitor market trends and consumer preferences, adapting their strategies to identifying and seizing emerging opportunities.

This study introduces a significant novelty to previous literature and one of the first to employ the dynamic capability theory to examine the interplay between EO, SMA, OR and OE in influencing the performance of new SMEs in the context of emerging markets. Furthermore, it extends the scope of understanding of the mechanisms through which SMEs can prosper in these dynamic environments. This unique combination of theoretical framework, comprehensive variables and contextual focus sets this study apart from existing research, enriching the literature on SME performance in emerging markets.

This study aims at investigating how hybrid intelligence might enhance employee engagement in breakthrough innovation. Specifically, it empirically examines the mediating role of self-extinction and moderating role of social intelligence.

This study, using the lens of socio-technical system (STS) theory, collected data from 317 employees through cross-sectional survey. The hypotheses were tested using MPlus 8.3 by applying Structural Equation Modelling (SEM).

The results support the proposed model, suggesting that hybrid intelligence fosters employees' breakthrough innovation engagement and such a relationship is fully mediated by self-extinction. Besides, the findings provide support for the positive moderating impact of social intelligence on such indirect relationships in a way that high social intelligence will further strengthen the relationship.

As a pioneering contribution, the study uncovers the social mechanism that underlies hybrid intelligence–breakthrough innovation engagement relationship via self-extinction. The research suggests managers leveraging employees' social intelligence for playing a critical role in countering the negative impact of self-extinction by enhancing the employees' engagement in the breakthrough innovation process.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.