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The key to preventing employees from engaging in deceptive knowledge hiding is fostering a responsible environment. Drawing on social cognitive theory, this study aims to explore the factors that inhibit deceptive knowledge hiding and to construct potential pathways for enhancing individual moral cognition. This study further analyzes the moderating effect of leader–follower value congruence on these relationships.

Using data from 341 full-time employees in various service industries in China, this study conducted path analysis, the product-of-coefficients method and bootstrapping to test the hypotheses through a three-stage, time-lagged survey.

The empirical results show that responsible leadership is negatively associated with employees’ deceptive knowledge hiding. Employee moral reflectiveness mediates this relationship, whereas leader–follower value congruence moderates the indirect effect of responsible leadership on deceptive knowledge hiding through moral reflectiveness.

First, this study extends field research by introducing positive leadership factors to reduce deceptive knowledge hiding, whereas prior studies focused mainly on negative leadership antecedents. Second, this study sheds light on the underlying moral cognitive mechanisms and explains how responsible leadership can prevent implicit unethical behavior. Third, it reveals how leader–follower value congruence can enhance the impact of responsible leadership on moral reflectiveness, offering novel insights into the role of value-based fit in reducing deceptive knowledge hiding.

This paper aims to contribute to the performance improvement and the broader application of hot-dip galvanized coating.

First, the ability to provide barrier protection, galvanic protection, and corrosion product protection provided by hot-dip galvanized coating is introduced. Then, according to the varying Fe content, the growth process of each sublayer within the hot-dip galvanized coating, as well as their respective microstructures and physical properties, is presented. Finally, the electrochemical corrosion behaviors of the different sublayers are analyzed.

The hot-dip galvanized coating is composed of η-Zn sublayer, ζ-FeZn13 sublayer, δ-FeZn10 sublayer, and Γ-Fe3Zn10 sublayer. Among these sublayers, with the increase in Fe content, the corrosion potential moves in a noble direction.

There is a lack of research on the corrosion behavior of each sublayer of hot-dip galvanized coating in different electrolytes.

It provides theoretical guidance for the microstructure control and performance improvement of hot-dip galvanized coatings.

The formation mechanism, coating properties, and corrosion behavior of different sublayers in hot-dip galvanized coating are expounded, which offers novel insights and directions for future research.

Social media use is prevalent today, but there is a possibility that it might go out of control and cause negative consequences. Furthermore, by using social media at work, businesses may develop their networks, communicate in a productive manner and ultimately expand the efficiency. The purpose of this study is to investigate the effect of social media use (SMU) on job performance (JP) through sequential mediators such as social capital dimensions (SC), self-efficacy (SE), job satisfaction (JS) and knowledge sharing (KS) in Indian Public Universities.

Serial mediation model has been used in the study to analyse the relationship. Data is collected from teaching faculty (n = 702) who use social media in Indian public universities. The study has assessed the association between variables using structural equation modelling.

The findings suggest that the dimensions of SC, SE, JS and KS sequentially mediated the effect of SMU on JP. In light of the results, the SMU specifies prerequisites for the development of various dimensions of SC. Similarly, the rest of the mediating constructs further affect the other constructs, which ultimately positively affect JP. The final result shows that the indirect effect between social media use and job performance is positive and significant.

The study provides practical suggestions for university administration regarding the use of social media for teaching faculty.

No research has been done regarding social media use affecting the job performance of teaching faculty through serial mediation in public universities. In this respect, this study represents an original attempt to conduct such research.

The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.

The approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).

The proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.

This work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

Purpose: This piece delves into the transformative potential of artificial intelligence (AI) in the healthcare field within the emerging realm of Industry 5.0, highlighting a people-focused and eco-friendly approach.

Need for the study: While Industry 4.0 set the foundation for digitization in healthcare, it frequently overlooked the human factor and concerns about sustainability. Industry 5.0 tackles these deficiencies by giving importance to human welfare, efficiency in resource usage, and societal consequences alongside technological progress.

Methodology: This research utilizes a survey of existing written works on Industry 5.0, AI in healthcare, and associated empowering technologies. It also leans on insights from recent investigations and business actions to pinpoint current patterns and future paths.

Findings: This chapter showcases how AI-driven solutions can greatly alter various facets of healthcare. Some of these healthcare facets encompass personalized medicine and treatment, intelligent diagnostics and decision support, robot-supported surgery and care, and enhanced availability and affordability.

Practical applications: This piece offers valuable perspectives for healthcare investors. These investors cover healthcare suppliers, technology creators, rule creators, and patients. By embracing the standards of Industry 5.0, the merging of AI into healthcare brings significant potential for crafting a more competent, sustainable, and people-centered healthcare network that benefits both patients and society as a complete unit. This research investigates the stance, viewpoints, and potential impacts of machine intelligence (MI) in health with an emphasis on Industry 5.0.

The chapter explored integrating smart construction techniques in achieving stealth construction objectives, emphasising the development of building cross-sections, visibility management, energy transmission optimisation, and countermeasure implementation. It delved into the multifaceted aspects of smart construction towards achieving stealth construction goals, including environmental protection, enhanced construction safety, accelerated construction duration, cost-effectiveness, and aesthetic considerations. Furthermore, the chapter underscores the importance of leveraging innovative approaches and advanced technologies to meet the evolving demands of stealth construction projects and pave the way for sustainable, safe, and aesthetically pleasing built environments.

Wine tourism is gaining increasing popularity among Chinese tourists, making it necessary to thoroughly examine tourist behavior. While online reviews posted by wine tourists have been extensively studied from the perspectives of destinations and wineries, the perspective of the tourists themselves has been overlooked. To address this gap, this study aims to identify significant attributes intrinsic to the tourism experiences of Chinese wine tourists by adopting a text-mining approach from a tourist-centric perspective.

The authors use topic modeling to extract these attributes, calculate topic intensity to understand tourists’ attention distribution across these attributes and conduct topical sentiment analysis to evaluate tourists’ satisfaction levels with each attribute. The authors perform importance-performance analyses (IPAs) using topic intensity and sentiment scores. Furthermore, the authors conduct semistructured in-depth interviews with Chinese wine tourists to gain insights into the underlying reasons behind the key findings.

The study identifies eleven attributes for domestic wine tourists and seven attributes for outbound wine tourists. From the reviews of both domestic and outbound tourists, three common attributes have been identified: “scenic view”, “wine tasting and purchase” and “wine knowledge”.

According to the results of the IPAs, there is a pressing need for enhancements in the wine tasting and purchasing experience at domestic wine attractions. Additionally, managers of domestic wine attractions should continue to prioritize the positive aspects of the family trip experience and scenic views. On the other hand, for outbound wine attractions, it is crucial for managers to maintain their efforts in providing opportunities for wine knowledge acquisition, ensuring scenic views and upholding the reputation of wine regions.

First, this study breaks new ground by adopting a tourist-centric perspective to extract significant attributes from real wine tourism reviews. Second, the authors conduct a comparative analysis between Chinese wine tourists who travel domestically and those who travel abroad. The third novel aspect of this study is the application of IPA based on textual review data in the context of wine tourism. Fourth, by integrating topic modeling with qualitative interviews, the authors use a mixed-method approach to gain deeper insights into the experiences of Chinese wine tourists.

The purpose of this study is to empirically examine the cause–effect relationships between workplace friendship and knowledge-sharing behavior, from a static perspective. Furthermore, it investigates the bi-directional relationship between the increase in both workplace friendship and knowledge-sharing behavior over same time periods, and also endeavors to identify whether there is a significant negative lagged effect of the increase in both workplace friendship on knowledge-sharing behavior, and vice versa, across time from a dynamic perspective.

The study conducts a three-wave questionnaire survey to test the research model. A latent change score approach was used to test the direct relationship between changes in workplace friendship and changes in knowledge-sharing behavior.

The findings reveal that knowledge-sharing behavior fosters workplace friendship and workplace friendship promotes the emergence of knowledge-sharing behavior. An increase in workplace friendship promotes an increase in knowledge-sharing behavior over same time periods. However, an increase in workplace friendship will lead to a lagged decrease of knowledge-sharing behavior across time, and vice versa.

The time interval in this study is a little short to capture the full changes in workplace friendship. Some important control factors and mediating mechanisms are not included in the research model.

This study guides managers to focus on various motivators to better strengthen workplace friendship and knowledge-sharing behavior and to consider and effectively respond to the negative side of workplace friendship and knowledge-sharing behavior across time.

This study emphasizes the predictivity of one important interaction patterns, namely, knowledge-sharing behavior on friendship at the workplace, from a static perspective. This study also shows the benefits of an increase in workplace friendship for the development of knowledge-sharing behavior in the same time period. Furthermore, the study presents a counterintuitive finding when taking the lag effect into consideration in exploring the relationship between changes both in workplace friendship and knowledge-sharing behavior, and identifies a negative side of both when viewed over longer periods.

An emerging body of literature has pinpointed the role of supply chain structure in influencing the extent to which supply chain members disclose information about their internal practices and performance. Nevertheless, empirical research investigating the effects of firm-level relational embeddedness on network-level transparency still lags. Drawing on social network analysis, this research examines the effect of relational embeddedness on supply chain transparency and the contingent role of digitalization in the context of environmental, social and governance (ESG) information disclosure.

In their empirical analysis, the authors collected secondary data from the Bloomberg database about 2,229 firms and 14,007 ties organized in 107 extended supply chains. The authors employed supplier and customer concentration metrics to measure relational embeddedness and performed multiple econometric models to test the hypothesis.

The authors found a positive effect of supplier concentration on supply chain transparency, but the effect of customer concentration was not significant. Additionally, the digitalization of focal firms reinforced the impact of supplier concentration on supply chain transparency.

The study findings contribute by underscoring the critical effect of relational embeddedness on supply chain transparency, extending prior literature on social network analysis, providing compelling evidence for the intersection of digitalization and supply chain management, and drawing important implications for practices.

This paper explores the cognitive impacts of social media on employees in workplace environments, focusing on how digital interactions disrupt cognitive functions and employee engagement. It bridges theoretical models of cognitive psychology with practical human resource (HR) strategies to mitigate these effects.

The methodology includes a review of relevant cognitive and psychological theories – namely cognitive load theory (Sweller, 1988), distraction-conflict theory (Baron, 1986), and engagement theory (Schaufeli et al., 2002). The discussion extends to practical interventions such as policy adjustments, employee training and technological solutions, assessing their application within organizational contexts to address social media challenges.

The research findings have significant practical implications. The author reveals that continuous social media notifications can lead to distraction conflicts and decreased employee engagement, impacting cognitive load. Effective HR interventions, such as clear guidelines, digital detox initiatives and technology-based social media access control, can enhance focus and productivity, improving workplace outcomes.

This paper contributes to discussions on social media’s role in workplace dynamics by combining cognitive psychological theories with practical HR applications. It presents a structured approach to understanding and managing digital distractions, offering a new framework for organizations aiming to balance technological advancements with employee well-being.