The current study employs communication visibility theory to examine the nexus between task structure, knowledge transfer, and employee agility. We further investigated the role of ESM in facilitating knowledge transfer and its effects on employee agility. Utilising cross-sectional data from Chinese employees working in different organisations, structural equation modelling (SEM) was applied to 425 samples using AMOS version 24.0. Results reveal that task interdependence significantly influences both knowledge-sharing and knowledge-hiding behaviours in organisations. Moreover, task complexity positively affects knowledge sharing but negatively impacts knowledge hiding. The results also indicate that knowledge sharing positively correlates with employee agility, while knowledge hiding has a negative association with employee agility. Additionally, knowledge sharing mediates the relationship between task interdependence, task complexity, and employee agility, as does knowledge hiding. Furthermore, ESM usage strengthens the relationship between knowledge sharing and employee agility, although it does not moderate the link between knowledge hiding and employee agility. The study concludes with substantial theoretical and practical implications and directions for future research.

Previous research evident that social media provides a platform to individuals social comparison, namely social comparison on social media (USC-SM). Drawing from social comparison theory, this study explores the creative consequences of such USC-SM, focusing on the underlying mechanism of emotional exhaustion. Additionally, the study examines how social media dependency influences these emotional experiences resulting from USC-SM. Results of data collected from 435 employees and their supervisors through a multi-source, two-wave survey reveal that employees who engage in USC-SM platforms tend to experience emotional exhaustion, thereby detrimentally impacting their creativity. Furthermore, this research found that the extent of an employee’s dependence on social media for work tasks serves as moderator which strengthens the underlying relationship. This study makes a significant contribution to the existing literature on social media use and its effect on employee creativity. Theoretical contributions and practical guidelines are discussed in the subsequent sections.

In spite of the massive adoption of AI technologies in organizational settings, there is a mix of empirical evidence on how such technologies affect employee performance. Underpinned theoretically by social exchange theory, this study investigates how AI affects knowledge sharing and work engagement among employees. The present study also investigates the moderating role of perceived risk in the relationships between AI and knowledge sharing and work engagement. For analysis of the data collected from a sample of 447 Chinese workers, Structural Equation Modeling (SEM) was used. The results found a positive link between AI and both knowledge sharing and work engagement. It was also found that knowledge sharing and work engagement significantly influence employee performance. Moreover, the results suggest a positive correlation between knowledge sharing and work engagement. Additionally, the research has drawn attention to a fact that perceived risk moderates negatively the relationship between AI and knowledge sharing and work engagement. From these findings emanate important management implications for both management and employees on the strategic use of AI technology to enhance employee performance.

Abstract The sinkhole attacks for Internet of Things (IoT) situations can overcome the network and interrupt communication. Sinkhole attacker nodes can advertise the best possible shortest path towards the destination, and once the normal node starts transferring the packets by the given path, the attacker node starts troublemaking the flow of the network. In this environment, the destination is unable to receive proper information or may not receive complete information. Sinkhole attacked network keeps the problems like the enhanced end‐to‐end delay, less throughput, and reduced packet delivery. Furthermore, it can affect other network constraints. So, it has become important to design an effective model to prevent the IoT environment from sinkhole attacks. In this article, an intrusion detection model is proposed to protect the IoT environment from sinkhole attacks. A model proposed by using rich resourced edge nodes to detect various kinds of sinkhole attacker nodes by exchanging messages. A well‐known NS2 simulator is used for the practical implementation of the model. The proposed model attains more than 95% detection rate with a near about 1.4% false‐positive rate, which seems better than previously given schemes. Finally, the proposed scheme is the appropriate match for a sensitive platform like a monitoring and security system.