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Based on integrating social exchange theory and input-mediator-output-input model, the current study proposes a model to explore the relationship between benevolent leadership and creative work behavior of Vietnamese university staffs.

A printed questionnaire was developed and directly distributed to 503 Vietnamese university staffs from 1st to 27th October in 2023 using the quota sampling method. After removing 61 missing data and outlier cases, a total of 442 valuable cases were used for further investigation. SPSS 22 and SMARTPLS version 3.2.2 was employed for data analysis.

Our study found the positive relationship between benevolent leadership and creative work behavior through the mediation of innovative climate and fear of failure. The findings also indicated that benevolent leadership fosters an innovative climate and reduces subordinates’ fear of failure, then increases subordinates’ creative work behavior.

The present study explored mechanisms of benevolent leadership effect on creative work behavior of university staff. From that, contributing several practical implications for university leaders and educators in fostering university staff’s creative work behavior. This research contributed to the knowledge domain of leadership and creative work behavior of employees in higher education.

Lean manufacturing (LM) is essential for businesses to remain competitive in today’s global economy and to meet the needs of consumers from three separate perspectives: price, dependability and production schedules. A fundamental goal of this research is to how lean management in manufacturing organization may improve product value for the customer, address customer concerns, minimize costs and boost the firm’s profitability.

The extensive literature analysis identified a number of LM enablers and manufacturing industry factors that might favorably affect the organizations operational performance. Initially, 16 enablers of LM and 16 factors operational performance were identified, which were later reduced to 8 factors each. After that, Grey-DEMATEL technique was applied to investigate the relationships between the factors by categorizing elements into two groups (cause and effect) and ranking them within each category.

The results show that F4 (Work Force Development) and F7 (Six Sigma) were the key enablers of LM. Similarly, F12 (Maintain Better inventory control/optimize inventory level) and F14 (Reduce conversion cost) are the key effect factors of operational performance. It eliminates inefficiencies in the production process and internal storage requirements while retaining a high level of dependability and flexibility in response to customer demands.

LM has unquestionably been a popular method for improving the production efficiency of industrial sectors for the last two decades. Despite the fact that LM has helped several firms reduce waste and thereby improve a range of efficiency metrics, many businesses are still struggling to effectively transform into lean firms. While previous studies have explored LM’s significance and its influence on different aspects of organizational metrics in various industries, this research pioneers in probing into the nuanced relationship between LM enablers and OP in a critical and procedure-intensive industry.

This study aims to examine the direct impact of perceived service quality (PSQ) on satisfaction and the intention to use accommodation sharing (IAS). Additionally, it investigates the mediating role of satisfaction between PSQ and IAS. To extend the findings, the study also analyzes the moderating roles of behavioral change and family status in the relationship between satisfaction and IAS within a collective group.

This study includes 413 accommodation sharing (AS) users in the USA using data collected through MTurk. The authors analyzed the data using the ordinary least squared regressions path analytical framework and bootstrapping methods in PROCESS Macro to estimate the hypothesized mediated moderation models.

The findings underscore the significant impact of PSQ on satisfaction and IAS. Moreover, they reveal the crucial role of satisfaction as a mediator in this relationship and the moderating influence of family status and behavior change on the observed indirect relationship. These insights are invaluable for understanding and navigating the dynamics of the accommodation-sharing industry.

This paper is a pioneering work that sheds new light on the roles of change in behavior and family status in the context of AS. It makes significant contributions to the sharing economy and offers valuable managerial insights for companies operating in this market. The study s originality lies in its explanation of the mediator role of satisfaction in the sharing economy and the moderator mechanism of change in behavior and family status on IAS.

The surging market demand for green construction materials has brought opportunities for construction materials enterprises' greenwashing behavior (GWB). This study aims to establish the causal relationship among the influencing factors of GWB and reveal the key influencing factors from the perspective of Chinese construction materials enterprises under multi-agent interactions.

This study is based on stakeholder theory, resource-based theory and the green development behavior and performance of industrial enterprises (GDBP-IE). First, with the literature analysis, an index framework of the influencing factors of enterprises' GWB was constructed from five dimensions (including 15 factors): environmental regulation, public scrutiny, market environment, corporate resources and corporate green development (GD) performance. Second, the interactive relationship among influencing factors was obtained by a questionnaire survey. Finally, the data are processed and analyzed with the grey-DEMATEL (Decision-making Trial and Evaluation Laboratory) method.

Among the factors, corporate information transparency has the greatest impact on the other factors, and consumer green preferences are most influenced by others. The most critical and important factor is the corporate social performance factor. In China, corporate social performance, corporate information transparency, corporate size and media supervision are the key factors influencing the GWB of construction materials enterprises.

This study provides a new perspective on the literature related to GWB by considering multi-agent interactions and extends the evidence from the construction materials industry for research on the drivers or influencing factors of enterprises' bad environmental behavior. Furthermore, it adds insights from China for further research on the governance strategies of GWB in other countries.

Despite firms’ growing investments, process improvement (PI) programs often fail to deliver the expected benefits. In this paper, we argue that the widespread adoption in PI research of a paradigm founded in positivism plays a primary role in deriving these conclusions and limits the development of PI theory and practice.

We examine the main characteristics of the dominant paradigm in PI research and then propose an alternative perspective drawing on research in management innovation and complexity. Findings from two empirical case studies in a pharmaceutical and an automotive firm are reported to support our theoretical argument.

The proposed perspective highlights the interaction between various PI approaches – such as lean, Six Sigma and total quality management (TQM) – and the context-dependence and experiential aspects of PI. We argue that this perspective can better account for where, by whom and how PI approaches are shaped and used and, ultimately, can more effectively advance both theory and practice.

This study suggests that PI approaches should be considered as adaptable rather than static, in combination rather than as single entities and as continuously interpreted and translated by organizational actors rather than homogeneously diffused across companies and business units. In this paper, we discuss the substantial conceptual, methodological and practical implications of adopting this perspective.

Given a baffling contradiction that the availability of safety knowledge may not necessarily lead to workers' safety behavior, this study aims to develop an exploratory two-step working model of safety knowledge in translating safety climate into safety behavior. In particular, this study highlights the importance of articulating tacit safety knowledge and improving workers' systematic problem solving (SPS) capacity in a favorable safety climate.

This study uses 110 valid responses from Pakistan-based construction workers to test five hypotheses which embody the exploratory two-step working model of safety knowledge. The partial least squares structural equation modeling (PLS-SEM) is used to analyze the data.

The results of this study support the two-step working mechanism of safety knowledge in translating safety climate into safety behavior. Furthermore, results suggest that safety climate as a job resource facilitates converting construction workers' tacit safety knowledge into explicit safety knowledge (i.e. safety knowledge articulation) and then enabling them to spot non-conformities in safety management practices (i.e. SPS) and consequently to work safely (i.e. safety behavior).

The study has both theoretical and practical significance. In theory, it extends organizational learning theory and job demands-resources (JD-R) theory in the construction safety research domain and elaborates on the mediating role of safety knowledge articulation and SPS for the relationship between safety climate and safety behavior. In practice, it highlights the importance of continuous articulation of tacit safety knowledge and accumulation and use of explicit safety knowledge in construction safety management practices.

The concept of Construction 5.0 has emerged as the next frontier in construction practices and is characterized by the integration of advanced technologies with human-centered approaches, sustainable practices and resilience considerations to build smart and future-ready buildings. However, there is currently a gap in research that provides a comprehensive perspective on the opportunities and challenges of facilitating Construction 5.0. This study aims to explore the opportunities and challenges in facilitating Construction 5.0 and its potential to implement smart, sustainable and resilient buildings.

The structural equation modeling (SEM) method was used to evaluate the research model and investigate the opportunities and challenges related to Construction 5.0 in its implementation for smart, sustainable and resilient buildings.

The results show that adopting human-centric technology, sustaining resilience and maintaining sustainability in the architecture, engineering and construction (AEC) industry seizes the opportunities to overcome the challenges for facilitating Construction 5.0 in the implementation of smart, sustainable and resilient buildings.

The AEC industry facilitating Construction 5.0 has the potential to redefine the future of construction, creating a built environment that is not only intelligent, sustainable and resilient but also deeply connected with the well-being and values of the communities it serves.

The research illuminates the path forward for a holistic understanding of Construction 5.0, envisioning a future where smart, sustainable and resilient buildings stand as testaments to the harmonious collaboration between humans and technology.

Megaprojects exert a significant impact on sustainable development, and it is imperative for stakeholders to collectively ensure their development occurs in a socially responsible manner. While there has been a growing focus on the involvement of megaprojects in social responsibility, scant attention has been given to understanding the collective actions of stakeholders in implementing social responsibility within these projects. Specifically, the institutional mechanism leading megaproject stakeholders to engage in socially responsible collective action is largely unexplored. To fill this gap, this study primarily aims to explore the institutional antecedents influencing socially responsible collective action in megaprojects.

Drawing on institutional theory, this study empirically examines the factors influencing socially responsible collective action in megaprojects. An online questionnaire survey was administered to collect data from 365 participants engaged in mega water transfer projects in China. The data analysis employed the partial least squares structural equation modeling technique.

The findings from the partial least squares analyses indicate that coercive isomorphism, mimetic isomorphism, and normative isomorphism all demonstrate positive associations with stakeholders’ intention to engage in socially responsible collective action. Moreover, the findings also show a positive correlation between stakeholders’ intention and their behavior in participating in socially responsible collective action within megaprojects. Additionally, coercive isomorphism positively moderates the connection between mimetic isomorphism and the intention to engage in SRCA, while negatively moderates the relationship between normative isomorphism and the intention to undertake socially responsible collective action.

This study enriches the existing body of knowledge by identifying coercive, mimetic, and normative isomorphism as antecedents to adopting socially responsible collective action in megaprojects. Furthermore, the study enhances our comprehension by demonstrating that stakeholders’ intention to fulfill social responsibility translates into tangible actions. The implications and recommendations provided shed light on how various types of institutional isomorphism can be used to encourage stakeholders to embrace socially responsible collective action in megaproject management.

Smallholders are essential in ensuring food security; however, smallholder-dominated food production often involves high resource-environmental costs. This study analyzed the factors that differentiate horticultural practices, willingness to adopt technology and social networks between optimized practices (OPT) and farmer practices (FP) to provide localized and systematic solutions for the sustainable apple production.

To explore the approach of smallholder-dominated sustainable apple production, 257 apple producers in the Bohai Bay region, a major apple planting area in China, were investigated. Life cycle assessment (LCA), emergy analysis and social network analysis methods were used for evaluation.

The results showed that the net economic profit and emergy sustainability index (ESI) in OPT was 15.8 × 104 RMB·ha-1 and 1.2, respectively, which were 126.9 and 128.0% higher than FP. In contrast, greenhouse gas (GHG) emissions under OPT was 29.3% lower than those under FP. OPT has a higher percentage of adoption of scientific fertilizer application and water-saving irrigation technologies compared to FP. OPT has strong learning abilities, more social resources (such as technical training and sharing technical experience with others) and connections with stakeholders in the apple supply chain. Optimizing smallholders' social capital, willingness to adopt technology, behavioral willingness and technological awareness can promote sustainable apple production.

Considering the horticultural practices employed by smallholders in conjunction with their social networks, these factors contributed to the transition of smallholder-led apple production toward sustainability. The findings provided viable options and a theoretical basis for smallholder-dominated crop production to move toward sustainability, with significant implications for policymakers.

In traditional Chinese medicine (TCM), the mechanism of disease (MD) constitutes an essential element of syndrome differentiation and treatment, elucidating the mechanisms underlying the occurrence, progression, alterations and outcomes of diseases. However, there is a dearth of research in the field of intelligent diagnosis concerning the analysis of MD.

In this paper, we propose a supervised Latent Dirichlet Allocation (LDA) topic model, termed MD-LDA, which elucidates the process of MDs identification. We leverage the label information inherent in the data as prior knowledge and incorporate it into the model’s training. Additionally, we devise two parallel parameter estimation algorithms for efficient training. Furthermore, we introduce a benchmark MD identification dataset, named TMD, for training MD-LDA. Finally, we validate the performance of MD-LDA through comprehensive experiments.

The results show that MD-LDA is effective and efficient. Moreover, MD-LDA outperforms the state-of-the-art topic models on perplexity, Kullback–Leibler (KL) and classification performance.

The proposed MD-LDA can be applied for the MD discovery and analysis of TCM clinical diagnosis, so as to improve the interpretability and reliability of intelligent diagnosis and treatment.