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A country’s institutional environment influences every facet of its business. This paper aims to identify institutional factors (state ownership, government attention on employment and employees’ educational background) that affect the asymmetric cost behavior in China.

Using 2,570 listed firms’ data between 2002 and 2015, we use empirical models to explore the effects of state ownership, government attention on employment and employees’ educational background on the asymmetric cost behavior in China.

This study found that the asymmetric cost behavior of central state-owned enterprises (CSOEs) is greater than local state-owned enterprises (LSOEs). Meanwhile, the empirical results show that government attention on employment is reflected in five-year government plans, and employees’ educational backgrounds are positively associated with asymmetric cost behavior.

This study contributes to the economic theory of sticky costs, institutional theory and asymmetric cost behavior literature by providing evidence that shows how government intervention and employee educational background limit the flexibility of corporate cost adjustments. Additionally, this study provides guidance to policymakers by showing how government long-term plans affect firm-level resource adjustment decisions.

This paper aims to further develop stator permanent magnet (PM) type memory machines by providing generalized design guidelines for double-stator memory machines (DSMMs) with hybrid PMs. This paper discusses the design experience of DSMMs and presents a comparative study of radial magnetization (RM) and circumferential magnetization (CM) types.

It begins with an introduction to RM and CM operating principles and magnetization mechanisms. Then, a comparative study is conducted for one of the RM-DSMM rotor pole pairs, inner and outer stator clamping angles and low coercive force PMs thickness. Finally, the two machines’ finite element simulation performance is compared. The validity of the proposed machine structure is demonstrated.

In this paper, the double-stator structure is extended to parallel hybrid PM memory machines, and two novel DSMMs with RM and CM configurations are proposed. Two types of DSMMs have PMs and magnetizing windings on the inner stator and armature windings on the outer stator. The main difference between the two is the arrangement of PMs on the inner stator.

Conventional stator PM memory machines have geometrical space conflicts between the PM and armature windings. The proposed double-stator structure can alleviate these conflicts and increase the torque density accordingly. In addition, this paper contributes to comparing the arrangement of hybrid PMs for DSMMs.

The purpose of this study is to improve the thermal management of lithium-ion batteries. The phase change material (PCM) cooling does not require additional equipment to consume energy. To improve the heat dissipation capacity of batteries, fins are added in the PCM to enhance the heat transfer process.

Computational fluid dynamics method is used to study the influence of number of vertical fins and ring fins (i.e. 2, 4, 6 and 8 vertical fins, and 2, 3, 4 and 5 ring fins) and the combination of them on the cooling performance.

The battery maximum temperature can be decreased by the PCM with vertical or ring fins, and it can be further decreased by the combination of them. The PCM with eight vertical fins and five ring fins reduces the battery maximum temperature by 5.21 K. In addition, the temperature and liquid-phase distributions of the battery and PCM are affected by the design of the cooling system.

This work can provide guidelines for the development of new and efficient PCM cooling systems for lithium-ion batteries.

The combination of PCM and fins can be used to reduce the battery maximum temperature and temperature difference.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

This study examines how leaders’ narcissistic rivalry (LNR) affects the in-role performance (IRP) and proactive customer service performance (PCSP) of employees in the hospitality industry. Specifically, this study investigates the mediating role of psychological distress and the moderating role of locus of control (LOC) in the aforementioned relationships.

This study administered a multi-wave, multi-source questionnaire survey with 323 employees working in 11 full-service hotels in China. Statistical analyses were performed using the PROCESS macro in SPSS 26 software and structural equation modeling using Mplus 8.3 software.

The authors' results suggest that LNR can negatively affect hospitality employees’ IRP and PCSP and that these relationships are mediated by psychological distress. Additionally, the impact of LNR on psychological distress can be lessened by internal LOC.

This study contributes to the literature on leader narcissism by investigating how LNR affects IRP and PCSP among hospitality employees. Drawing on conservation of resources theory, this study also identifies a novel mediating mechanism (psychological distress) connecting LNR to hospitality employees’ service outcomes. Furthermore, this study reveals the moderating role of LOC in the relationship between LNR and psychological distress.

Gossip spreads like wildfire, damaging relationships, decaying trust and creating a negative work environment. This study aims to investigate the relationship between negative workplace gossip (NWG) and quiet quitting (QQ), while considering the mediating effects of workplace stress and emotional exhaustion (EE).

Drawing upon the conservation of resource theory, the study aimed to comprehend this association in the context of 267 employees from diverse sectors in India, including health care, IT, banking and education. Through a three-wave time lagged survey design, using partial least squares structural equation modeling, significant findings were uncovered.

The results revealed a positive link between NWG and QQ. There was also a positive correlation between NWG and workplace stress. In addition, workplace stress and EE were found to mediate the relationship between NWG and QQ.

The findings have implications for both theory and practice. Organizations should consider implementing strategies to mitigate the prevalence of negative gossip and foster a healthier work environment, promoting employee well-being and retention.

The study reveals the “black box” between NWG and QQ, adding to the body of knowledge on the novel concept of QQ. Second, the study expands the literature on NWG, by examining impact path of how it leads to stress and EE, leading to QQ.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

People are more likely to participate in work-related events that might cause positive and negative affective reactions. Prior research linked coworker friendship with incivility; however, few studies investigated negative workplace gossip. Simultaneously, linking coworker friendship with incivility through positive/negative affective responses is lacking. As such, this paper aims to examine this relationship via the dual mediation effect of positive and negative workplace gossip.

A total of 398 subordinates at family restaurants in Greater Cairo were surveyed, and data was analyzed using SmartPLS4.

Coworker friendship significantly influences coworker incivility via positive and negative workplace gossip and other underlying mechanisms.

Managers should take the initiative to decrease gossip by sharing information promptly and thoroughly and establishing effective channels for information exchange. In the case of an informal plan, restaurant managers may seek to create a welcoming and motivating corporate atmosphere and cultivate social ties among subordinates to prevent the creation of negative gossip. Restaurant managers should give victims of negative gossip timely psychological counseling.

This paper contributes to the restaurant literature on affective emotional responses to coworkers’ judgment-driven behavior from new perspectives.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.