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This study aims to investigate the predictive role of team personality composition in facilitating shared leadership through team member exchange (TMX), while also to examine the moderating effect of organizational culture.

The authors conducted a two-stage online survey and selected the customer service teams, claims teams and financial teams of 26 Chinese insurance companies as the research samples. The authors finally obtained validated questionnaires from 107 teams with 457 members. The hypothesized relationships were tested using SPSS 25.0 and Mplus.

The results indicate that both team relationship-oriented and task-oriented personality composition have significant positive effects on shared leadership with team-member exchange serving as a full mediator for both paths. As a boundary condition, organizational culture (i.e. including internal integration values and external adaptation values) has a moderating effect on the influence of TMX on shared leadership.

The study investigates the predictive role of team personality composition on shared leadership, which complements the empirical studies of shared leadership antecedents in the literature. Drawing on social exchange perspective, the authors find out that TMX serves as a mediator between team personality composition and shared leadership. The authors also identify the moderating effect of organizational culture on the emergence of shared leadership. The research emphasizes the contextual boundary condition in this process.

Based on upper echelons theory, this paper aims to explore the mixed impacts of chief executive officer (CEO) Machiavellianism on new venture performance. At the same time, this paper tests the mediating and suppression effect of top management team (TMT) collective organizational engagement, and the moderating effect of entrepreneurial orientation.

The authors conducted a three-wave survey of a sample of 1,550 enterprises established within three years, finally retained the full sample of 216 companies (216 CEOs, 733 vice presidents) with complete responses in all surveys. By using SPSS 26.0 and Amos 26.0 software to conduct data analysis, the authors empirically tested the hypothesized relationships.

Regression results show that CEO Machiavellianism negatively affects new venture performance through TMT collective organizational engagement, whereas there is a direct positive relationship between CEO Machiavellianism and new venture performance when TMT collective organizational engagement is controlled for. In addition, entrepreneurial orientation plays a boundary role in this mechanism, which can weaken the negative effect of CEO Machiavellianism on TMT collective organizational engagement.

By expanding the application contexts of the upper echelons theory, this paper enriches the research on Machiavellianism in the organizational research and further clarified the simultaneous positive and negative effects of CEO Machiavellianism on new venture performance.

With the rapid growth of the gig economy worldwide, gig workers’ perceived algorithmic control has been proven to have a crucial impact on the service performance, well-being and mental health of gig workers. However, the literature suggests that gig workers’ perceived algorithmic control may be a double-edged sword. The purpose of this research is to explore how the perceived algorithmic control of gig workers can accelerate thriving at work.

Based on the model of proactive motivation and work design literature, a three-wave survey was employed, yielding 281 completed responses. The structural equation modeling method was used to test the theoretical hypothesis.

The results indicate that gig workers’ perceived algorithmic control has positive and indirect effects on thriving at work through the mediating role of job crafting. In addition, job autonomy can moderate the mediated relationship; specifically, when job autonomy is high, this mediated relationship will be stronger.

The health and well-being of gig workers is a concern around the world. The findings provide insights for service platform enterprises and gig workers.

Perceived algorithmic control is critical to mental health and positive work experiences during a gig worker’s service process. However, the current literature focuses more on the negative aspects of algorithmic control. This paper provides a comprehensive research agenda for how to accelerate thriving at work for gig workers.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.

This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.

DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.

To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.