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Drawing on conservation of resources (COR) theory, this study examined the effect of developmental human resource (HR) practices on employee workplace procrastination and investigated the mediation effect of boredom at work and the moderation effects of exploitative leadership and self-leadership.

Data were collected from 443 employees across companies in China. Hypotheses were tested using hierarchical regression analysis and indirect effect testing via bootstrapping in SPSS and Mplus.

This study found that developmental HR practices were negatively related to employee workplace procrastination and that boredom at work mediated the relationship between developmental HR practices and employee workplace procrastination. Moreover, exploitative leadership strengthened the negative relationship between developmental HR practices and boredom at work, whereas self-leadership weakened the positive relationship between boredom at work and employee workplace procrastination. The indirect relationship between developmental HR practices and employee workplace procrastination through boredom at work was moderated by exploitative leadership and self-leadership.

This study extended the literature on the antecedents of employee workplace procrastination. Moreover, by investigating the mediation effect of boredom at work, this study extended the underlying mechanism by which developmental HR practices affect subsequent employee outcomes. Finally, by testing the moderation effect of exploitative leadership and self-leadership, respectively, this study offered insights into the boundary conditions resultant from developmental HR practices.

Drawing on social information processing theory, this study constructs a multilevel moderated mediation model. This model seeks to delve into the intricate and previously overlooked interplay between supervisor bottom-line mentality (BLM) and knowledge hiding. Within this context, we introduce self-interest as a mediating factor and incorporate performance climate as a team-level moderating variable.

The time-lagged data involve 336 employees nested in 42 teams from 23 automobile sales companies in five regions of China. The analysis was meticulously executed using Hierarchical Linear Modeling, complemented by bias-corrected bootstrapping techniques.

The findings reveal that self-interest acts as a full mediator in the positive link between supervisor BLM and knowledge hiding. Furthermore, the performance climate plays a moderating role in both the relationship between supervisor BLM and self-interest, and the entire mediation process. Notably, these relationships are intensified in environments with a high performance climate compared to those with a low one.

This research stands as one of the pioneering efforts to integrate supervisor BLM into the discourse on knowledge hiding, elucidating the underlying psychological mechanisms and delineating the boundary conditions that shape the “supervisor BLM–knowledge hiding” relationship. Further, our insights provide organizations with critical guidance on strategies to curtail knowledge hiding among their employees.

This paper aims to propose a stable and precise recognition and pose estimation method to deal with the difficulties that industrial parts often present, such as incomplete point cloud data due to surface reflections, lack of color texture features and limited availability of effective three-dimensional geometric information. These challenges lead to less-than-ideal performance of existing object recognition and pose estimation methods based on two-dimensional images or three-dimensional point cloud features.

In this paper, an image-guided depth map completion method is proposed to improve the algorithm's adaptability to noise and incomplete point cloud scenes. Furthermore, this paper also proposes a pose estimation method based on contour feature matching.

Through experimental testing on real-world and virtual scene dataset, it has been verified that the image-guided depth map completion method exhibits higher accuracy in estimating depth values for depth map hole pixels. The pose estimation method proposed in this paper was applied to conduct pose estimation experiments on various parts. The average recognition accuracy in real-world scenes was 88.17%, whereas in virtual scenes, the average recognition accuracy reached 95%.

The proposed recognition and pose estimation method can stably and precisely deal with the difficulties that industrial parts present and improve the algorithm's adaptability to noise and incomplete point cloud scenes.

In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

Choosing the proper selling format for online retail has long been a critical issue for many platforms to consider, whereas the emergence and popularity of live-streaming have had a massive impact on the platform's business. As a result, selecting the suitable operating strategy for the live channel has become another critical issue for platforms. In such a context, what will be the impact of live-streaming on selling formats?

In order to explore these issues, we identified two selling formats (wholesale reselling or agency selling) as well as two operating strategies (introduce or discard). Thereby, four channel-structures are constructed, namely the reselling-discard model (WN), the reselling-introduce model (WL), the agency-discard model (AN), and the agency-introduce model (AL). We comprehensively compare how different structures affect stakeholders' interests, consumer surplus, and social welfare through equilibrium analyses.

These results help clarify the impact of critical factors (e.g. self-effort attribute, cross-effort attribute, and commission ratio) on the choice of models. We find that regardless of the selling agreement between the manufacturer and the platform, the introduction of a live store is necessary; specifically, when the commission ratio is high, the platform's optimal decision is first to sign an agency agreement and then apply live selling (AL); conversely, when the commission ratio is low, the platform's optimal strategy is first to enable the live channel and then to select the reselling format (WL), together, this also reveals, from a theoretical perspective.

Our study includes the dual analysis of selling formats and channel operations, considering the inherent dual attributes of service efforts and the external competitive environment.

In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.

(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.

By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.

This paper provides a comprehensive overview of the definition, connotations, characteristics and key technologies of digital twin technology. It also conducts a thorough analysis of the current state of digital twin applications, with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure. Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study, the paper details the construction process of the twin system from the perspectives of system architecture, theoretical definition, model construction and platform design.

Digital twin technology can play an important role in the whole life cycle management, fault prediction and condition monitoring in the field of high-speed rail operation and maintenance. Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.

This paper systematically summarizes the main components of digital twin railway. The general framework of the digital twin bridge is given, and its application in the field of intelligent operation and maintenance is prospected.

Responding to the need for exploration of psychological predictors influencing innovation, this study aims to examine the impact of psychological ownership (PO) in employee innovation behavior (EIB) with a focus on psychological empowerment (PE) and employee resilience (ER) as mediators.

Data was collected from full-time employees working in travel agencies and five-star hotels in Egypt. A Partial Least Squares Structural Equation Modeling analysis was performed on 409 valid responses.

The findings of this study revealed PO has a positive effect on EIB. Such relationship is partially mediated by PE and ER.

Theoretically, this study offers valuable insights into the underlying mechanisms that elucidate how PO influences employees' innovation behavior and resilience, while aligning with the principles of self-determination theory.

The research explores how artificial intelligence-driven natural language processing can be integrated into digital game-based learning to enhance science education worldwide. It conducted a thorough literature review focusing on electronic bibliographic databases, identifying nine key themes. The chapter discusses the potential of digital games to improve science education, including theoretical frameworks, integration techniques and problem-solving methods. It also examines the impact of gaming on critical thinking and natural language processing. The findings suggest that integrating natural language processing into digital game-based learning shows promise for improving inquiry-based teaching and student performance in science education. Digital platforms can offer personalized feedback, support natural language interactions and customize content for each student. However, addressing pedagogical concerns and technological limitations is crucial for fully realizing these benefits. Practically, the research offers guidance for educators, curriculum developers and policymakers. It emphasizes the importance of educating educators on natural language processing, developing inquiry-driven digital learning platforms and establishing supportive policies. This signifies a move toward student-centered learning and the integration of innovative technologies into education. In terms of originality and value, the study contributes to the existing knowledge by discussing challenges and potential strategies for integrating digital game-based learning with natural language processing globally. It suggests that future research and practical efforts in this area can benefit from a comprehensive understanding of the opportunities and implications of this educational approach, informed by extensive research across various themes.

Common institutional ownership is a phenomenon that has extended throughout the capital markets in recent years and has a significant impact on business strategy decisions. The study intends to investigate the effect of common institutional ownership on corporate over-financialization and potential functioning mechanisms.

Using panel data from Chinese-listed companies over the period of 2003–2021, the authors conduct regression models which controlled year-, industry- and regional fixed effects to explore the impact of common institutional ownership on corporate over-financialization.

This study concludes that corporate over-financialization may be prevented via common institutional ownership. The mechanism test suggests that common institutional ownership inhibits corporate over-financialization by improving internal control quality and enhancing financial flexibility. Besides, heterogeneity analysis shows that the inhibiting effect of common institutional ownership on corporate over-financialization is more pronounced in stability-oriented institutional investors and high financing constraints firms.

This paper makes a valuable contribution to the current studies on effective strategies to prevent enterprises from becoming overly financialized by recognizing common institutional ownership. Furthermore, this paper adds to the research on common institutional ownership’s economic consequences. Finally, this study provides management implications for how to optimize corporate governance structures, curb the financialization of entities in practice and promote the development of the real economy.