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This study introduces the heritage city risk dimension of the urban rail transit (URT) projects. It aims to identify the risk factors affecting URT projects within the unique context of heritage-rich cities, exploring their interrelation and evaluating critical factors.

The research adopts a multi-case exploratory study to identify the unique challenges faced by URT projects in heritage-rich environments, followed by a comprehensive risk assessment framework integrating Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP) and Risk Interaction Network (RIN) analysis to assess identified risks in the context of Kathmandu Valley. Additionally, a risk response action is simulated using RIN analysis.

About 16 risk factors were identified from the case studies and evaluated using the proposed risk assessment methodology. The study reveals a highly interconnected risk environment, with heritage impact-related factors exerting the strongest causative influence on cost and social engagement factors. Community opposition (R8) shows the highest betweenness centrality, indicating its central position in risk propagation across the network. Cost-related risk, social demand contingency (R2) ranked as the most critical. Simulations of a targeted risk avoidance strategy showed that addressing only three key high-betweenness centrality factors (R5, R8 and R15) reduced overall risk interactions by 46%, simplifying the risk network, reducing project complexity and improving manageability.

The findings emphasize that project managers, urban planners and policymakers should integrate heritage preservation concerns when planning and executing URT projects in heritage-rich cities. Moreover, the research highlights that effective community engagement serves as a key strategy for reducing risk propagation and plays a crucial role in overall project risk management.

The study contributes to the underexplored context of URT projects in heritage-rich cities, providing a comprehensive risk management framework for identifying and assessing project risks intersecting with urban development imperatives and heritage conservation objectives.

This study aims to propose a cloud platform architecture considering information sharing based on blockchain to realize the security and convenience of enterprise information sharing in the automotive supply chain.

A bilateral matching model considering enterprises information contribution stimulates information sharing and improves the efficiency and quality of supply and demand matching. Three smart contracts are used to complete the information sharing process and match supply and demand in the automotive supply chain.

The system is tested on the local Ganache private chain, and the decentralized web page is designed based on the architecture prototype.

Solve the problem of information island in automobile supply chain.

(1) The shear lag effect and its additional deflection contribution to composite beams based on spatial grid elements were presented. (2) A refined spatial grid element analysis method that can simultaneously obtain the internal forces, displacements and stresses of various parts of a composite beam.

A refined spatial grid element analysis method.

The proposed method can directly obtain the internal forces and displacements of the joints of the composite beam roof, floor and web.

To comprehensively comprehend the mechanical behavior of double-girder steel plate composite girder bridge structures and facilitate refined analysis, this paper introduces a refined spatial grid element analysis model applicable to both the global and local domains.

This study aims to design a novel seasonal discrete grey model for forecasting monthly natural gas consumption by incorporating damping accumulation and time-polynomial term.

Considering the principle of new information priority and nonlinear patterns in the original series of monthly natural gas consumption, we establish a novel discrete seasonal grey model by adding the damping accumulation and time-polynomial term into the existing model. In addition, the order of damping accumulation and the coefficient of time-power term can be determined by the moth flame optimization (MFO) algorithm.

The empirical cases show that the proposed model has a better prediction performance when compared with other benchmark models, including six seasonal grey models, one statistical model and one artificial intelligent model. Based on forecasts, the proposed model can be considered a promising tool for monthly natural gas consumption (NGC) in US.

By combining the damping accumulation and the time-polynomial term, a new discrete seasonal grey model for improving the prediction performance of the existing grey model is proposed. The properties of the proposed model are given, and the newly-designed model is initially applied to predict monthly NGC in US.

As quality becomes increasingly prioritized in supply chain management, understanding how supply chain quality risk management (SCQRM) practices impact quality performance (QP) is essential. This study investigates the effects of two SCQRM practices – risk prevention (RP) and proactive product recall (PPR) – on QP, with a particular focus on the mediating role of supply chain quality integration (SCQI).

A structured survey was administered to gather data from 400 Chinese manufacturing firms. Structural equation modeling was employed to evaluate the proposed relationships among SCQRM practices (RP and PPR), SCQI and QP.

The findings reveal that both RP and PPR significantly and positively influence QP. Specifically, in the structural model, RP exerts a positive effect on SCQI, while PPR also positively impacts SCQI. Additionally, SCQI serves as a mediator between RP and QP, as well as between PPR and QP.

This study contributes to the supply chain management literature by elucidating the beneficial effects of RP and PPR on QP and identifying SCQI as a key mediating factor in these relationships. Leveraging information processing theory (IPT), the study provides new theoretical insights into the mechanisms through which SCQRM enhances QP via SCQI.

Implementing a green strategy to enhance the competitiveness of enterprises is a hot topic in current research. Although most enterprises have formed a green strategy orientation (GSO), it has not been transformed into green competitiveness (GC). Prior studies have not thoroughly studied the effect and mechanism of GSO on GC. To fill this research gap, based on optimal distinctiveness theory, this paper discusses the mediating role of two kinds of green innovation (GI) in the GSO–GC relationship and the moderating role of big data capability (BDC).

This study adopts the quantitative research methods of multiple linear regression, Bootstrap and structural equation modeling (SEM). Data were collected through a questionnaire and a random sampling method was used to survey middle and senior managers and professionals in manufacturing enterprises. About 400 questionnaires were distributed, and 342 valid questionnaires were collected.

The conclusions show that GSO significantly positively affects GI and GC. Still, it turns out that only strategic green innovation (SGI) mediates the GSO–GC relationship. BDC can positively moderate the mediation effect of SGI between GSO and GC, thus supporting the moderated mediation model.

This study used a survey questionnaire from Chinese manufacturing enterprises to collect data, but the sample size was limited. Furthermore, the mediating mechanism by which GSO affects GC requires further exploration. This study directly establishes the GSO–GC relationship based on the optimal distinctiveness theory, making an essential contribution to the literature on GSO and GC. At the same time, this paper uses GI as a bridge to connect the relationship between GSO and GC, enriching the literature on GI. In addition, we consider BDC to be a moderator, expanding the boundaries of the GSO–GC relationship.

This study provides new knowledge and insights for manufacturing enterprises to construct and implement green strategies to achieve GC. More importantly, managers should attach great importance to the critical role of SGI and BDC.

This study understands the importance of GSO, SGI and BDC to GC in theory and practice.

During the COVID-19 pandemic, an infodemic erupted on social media, leading to a surge in negative disclosure behaviors such as expressing dissatisfaction and releasing negative emotions. By extending the elaboration likelihood model and the Big Five personality theory to the domain of online self-disclosure, we aimed to identify the factors that influence negative disclosure behavior.

We investigated how the features of negative information content, information sources and recipients’ social perceptions influence how social media users disclose negative information. We also examined the moderating roles of personality traits in this process. To validate the model and test our hypotheses, we collected cross-sectional data from 456 social media users.

Empirical results reveal that (1) information overload, topic relevance, attractiveness of information sources, peer approval of negative disclosure and social influence on negative information strengthen the intention to disclose negative information. (2) The perception of social risk weakens the intention to disclose negative information. (3) Openness to experience, extraversion and neuroticism strengthen the relationship between the intention to disclose negative information and actual disclosure behavior.

Our results not only provide new perspectives on the decision-making mechanisms behind negative disclosure behavior but also extend personality research within the context of the dissemination of negative information. Furthermore, it offers insights into negative information dissemination on social media platforms, with significant implications for various stakeholders.

Based on the collective agentic perspective of social cognitive theory and supplemented by social support theory, this study aims to investigate how the entrepreneurial collective efficacy of entrepreneurial teams impacts team effectiveness. Specifically, this study hypothesizes that entrepreneurial collective efficacy is a critical factor affecting effectiveness through the mechanism of instrumental support to varying extents, depending on team size.

The hypotheses were tested on a Chinese sample of new venture teams (N teams = 81; N individuals = 335). A multisource questionnaire that included questions for team leaders and other team decision makers was designed. The data on entrepreneurial collective efficacy (six items) and instrumental support (four items) comprised the aggregate responses from all team members; the data on team effectiveness (seven items) was reported by team leaders.

The findings indicate that entrepreneurial collective efficacy is positively associated with team effectiveness through instrumental support, especially in small teams.

This study makes important contributions to the research related to exploring in uncertain environments (entrepreneurship) how (instrumental support) entrepreneurial collective efficacy impacts team effectiveness, as well as more particularly under what conditions (team size), all within the specific context of collectivistic cultures (China).

This study aims to understand how Gen Z consumers perceive fashion brands’ corporate social responsibility (CSR) communication through emotional appeals on Instagram and how such perception affects their overall behaviour towards the brand.

The study adopts a qualitative research approach through photo-elicitation and 14 semi-structured in-depth interviews with members of Gen Z, using one of the world’s largest fast fashion brands, H&M, as the study context.

It is increasingly difficult to capture the attention of Gen Z as they have become immune to the typical CSR messages despite attempting to appeal to their emotions. This makes CSR communication alone challenging in influencing brand perception. However, behaviour towards the brand, such as purchase intention, is not necessarily dependent on whether consumers are convinced of the brand’s CSR activities, as greater value is placed on fast fashion’s price and availability. For Gen Z, such elements surpass their concern for sustainable fashion. Since more emphasis is placed on neutral endorsers due to their trustworthiness, CSR efforts may be disseminated through such third parties to achieve desired outcomes.

Understanding consumers’ perceptions of the current CSR effort allows brand managers to reevaluate their CSR communication strategies to appeal to Gen Z and encourage positive brand behaviour.

Contrary to previous studies, which have focused on organisational outcomes, the study has in-depth explored consumers’ perception of CSR efforts on Instagram and the implications of such perceptions for long-term brand building.

This study aims to investigate the mechanical and tribological behavior of 70Mn steel with different laser re-melted textured patterns.

Laser surface re-melting (LSR) was used to manufacture various textured patterns (i.e. line, grid and mixed) on both the original and heat-treated 70Mn steel plates. The micro-hardness, microstructure, tensile strength, yield strength, elongation, coefficients of friction (COF) and worn morphologies were characterized to evaluate the impact of different textured patterns on the overall performance.

The results show that re-melted unit exhibited the highest surface hardness on the subsurface. The increase in surface hardness of the re-melted unit for the heat-treated 70Mn steel samples was much lower than that of the original ones. The re-melted textured patterns did not improve the tensile strength, yield strength and elongation of either original or heat-treated 70Mn steel samples. The re-melted textured patterns effectively reduced the average COFs of heat-treated 70Mn steel samples, but increased friction of the non-heat-treated samples.

This study provides valuable insights into enhancing the mechanical properties and tribological characteristics of 70Mn steel, particularly in the automotive, heavy machinery and high-load application sectors. These industries have stringent requirements for durability and friction control, and the findings of this research are expected to effectively extend the lifespan of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0443/