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Given the scarcity of studies regarding religious food as a destination attraction and limited research on tourist halal food experience, this study aims to explore and compare halal food experience perceived by Muslim and non-Muslim tourists in a non-Islamic destination.

This study was carried out in a halal food street in Yuanjia Village, China. It used a qualitative approach and interviewed 16 Muslim tourists and 20 non-Muslim tourists.

Six themes and 18 attributes of halal food experience were identified. The findings revealed that Muslim tourists saw the reassuring options and religious value of halal food as important experiences. By contrast, the experiences of abundant choices, value for money, sensory pleasure and unique charm were frequently mentioned by non-Muslim tourists. The nature of halal food, the context of China (i.e. Chinese halal food culture) and the feature of research site (i.e. food operation of Yuanjia Village) work together to create such experiences.

To the best of the authors’ knowledge, this is one of the first studies to explore and compare halal food experiences of Muslim and non-Muslim tourists in a non-Islamic country (China). This study suggests that halal food could be an appealing destination attraction, even in non-Islamic destinations. Thus, this study contributes to a better understanding of the halal food experiences and assists destination marketers in promoting halal food.

As global supply chains continue to develop, uncertainty grows and supply chains are frequently threatened with disruption. Although big data technology is being used to improve supply chain resilience, big data technology's role in human–machine collaboration is shifting between “supporters” and “substitutes.” However, big data technology's applicability in supply chain management is unclear. Choosing appropriate big data technology based on the enterprise's internal and external environments is important.

This study built a three-factor structural model of the factors “management support,” “big data technology adoption” and “supply chain resilience”. Big data technology adoption was divided into big data-assisted decision-making technology (ADT) and big data intelligent decision-making technology (IDT). A survey was conducted on more than 260 employees from supply chain departments in Chinese companies. The data were analyzed through structural equation modeling using Analyze of Moment Structures (AMOS) software.

The study's empirical results revealed that adopting both ADT and IDT improved supply chain resilience. The effects of both types of big data were significant in low-dynamic environments, but the effect of IDT on supply chain resilience was insignificant under high-dynamic environments. The authors also found that government support had an insignificantly effect on IDT adoption but significantly boosted ADT adoption, whereas management support factors promoted both ADT and IDT adoption.

By introducing two types of big data technology from the perspectives of the roles in human–machine collaborative decision-making, the research results provide a theoretical basis and management implications for enterprises to reduce the supply chain risk of enterprises.

Organizations working in high-hazard environments contribute significantly to modern society and the economy, not only for the valuable resources they hold but also for the indispensable products and services they provide, such as power generation, transportation and defense weapons. Therefore, the main purpose of this study is to develop a framework that outlines future research on systems safety and provides a better understanding of how organizations can effectively manage hazard events.

In this research, we developed the high hazard theory (HHT) and a theoretical framework based on the grounded theory method (GTM) and the integration of three established theoretical perspectives: normal accident theory (NAT), high reliability theory (HRT) and resilience engineering (RE) theory.

We focused on the temporal aspect of accidents to create a timeline showing the progression of hazard events and the factors contributing to safety and hazards in organizations. Given the limitations of the previous theories in providing a coherent explanation of hazard event escalation in high-hazard organizations (HHOs), we argue that the highlighted theories can be more complementary than contradictory regarding their standpoints on disasters and accident prevention.

A proper appreciation of the hazard nature of organizations can help reduce their susceptibility to failure, prevent outages and breakdowns of systems, identify areas for improvement and develop strategies to enhance performance.

By developing HHT, we contribute to systems safety research by developing a new, refined theory and enrich the theoretical debate. We also expand the understanding of scholars and practitioners about the characteristics of organizations working in high-hazard environments.

The purpose of this paper is twofold: first, it explores the influencing factors of the government’s trust decision-making in the private sector; second, it explores how these influencing factors affect the government’s trust decisions.

A theoretical model was established, and a questionnaire survey was conducted among 152 professionals. The collected datas were analyzed by the structural equation modeling (SEM) method.

The study identified four critical factors that influence the government’s decision to trust the private sector in public-private-partnership (PPP) projects. All the four factors have a positively correlated impact on the government’s trust decision-making. The structural equation path analysis shows that the most important factor affecting the government’s trust decision-making is the trustee’s (private sector) trustworthy characteristics, and the path coefficient is 0.92. The path coefficients of risk perception and the trustor’s trust tendency are 0.83 and 0.74, respectively. The influence of the legal system environment on government trust decision-making is moderate, with a path coefficient of 0.68.

This paper contributes to the literature in two aspects. First, the factors influencing decision-making to government trust in the private sector in PPP projects have been identified. Second, a comprehensive view of the mechanism of government trust in the private sector in PPP projects has been theorized by the SEM method.

The purpose of this study is to explore the impact of asymmetric trust on construction project management performance in China's construction industry. Moreover, the authors explore the mediating role of two types of knowledge sharing (explicit knowledge sharing and tacit knowledge sharing) in explaining the association between asymmetric trust and project management performance.

A theoretical model based on the research hypotheses proposed in this study was developed and a questionnaire survey was conducted with 271 professionals. The data collected was analyzed by the structural equation modeling (SEM) technique.

The results of this study indicate that there is a significant and negative association between asymmetric trust and project management performance. Moreover, two types of knowledge sharing (explicit knowledge sharing and tacit knowledge sharing) have different degrees of impact on improving project management performance. In addition, tacit knowledge sharing is a mediator between asymmetric trust and project management performance.

The data used in this study is from Chinese scenarios, so the research conclusions and application effects based on this are bound to have certain regional limitations. Besides, there are many factors that affect project management performance improving, and the relationships among them are so complex. The theoretical model proposed in this study may not be fully considered. Therefore, follow-up researchers can consider bringing more suitable variables into their researches, so that the theoretical researches can be more in line with the actual project management practice, and the specific mechanism for improving project management performance can be explained more deeply.

This research's value is as follows: Firstly, this paper contributes to the trust and relational governance literature by expanding the research perspective of mutual trust to asymmetric trust. Specially, this research designs a measurement scale for asymmetric trust and then reveals the impact mechanism of it on project management performance, which will certainly promote research paradigm change of trust. Secondly, this research is beneficial to knowledge sharing literature in the construction management field by expanding the research scope of knowledge sharing from a cross-organizational perspective.

Shifting to OLSL classes during the pandemic can bring learners ambivalent experiences: negative, positive or both appraisals toward the technologies. However, few studies have examined how ambivalent experiences can influence students' learning behaviors, specifically cyberslacking and active participation. Using the challenge-hindrance stressor framework, this study investigates the impact of challenge and hindrance appraisals on these learning behaviors.

This study uses a mixed methods approach to answer research questions. An interview was conducted to identify the key components of ambivalent appraisals, and a survey was conducted to empirically examine the impact of challenge and hindrance appraisals on learners' behaviors in online live streaming learning (OLSL) contexts. The data of 675 university students were analyzed using structural equation modeling.

This study found that hindrance appraisal leads to cyberslacking while challenge appraisal leads to active participation, but it can also cause cyberslacking. Social presence has a double-edged effect, acting as both a facilitator and inhibitor, strengthening the effect of hindrance appraisal on cyberslacking and the impact of challenge appraisal on active participation.

Prior studies have primarily focused on the negative side (techno-distress) of technology appraisals. This study simultaneously examines the positive side, techno-eustress, on learners' behaviors in OLSL contexts, and explores the moderating effects of social presence. This study contributes to the technostress and technology adaptation literature by revealing how technology-induced ambivalent appraisals impact behavioral responses. It offers important theoretical and practical implications for education tool designers.

As a high-performance engineering plastic, polyimide (PI) is widely used in the aerospace, electronics and automotive industries. This paper aims to review the latest progress in the tribological properties of PI-based composites, especially the effects of nanofiller selection, composite structure design and material modification on the tribological and mechanical properties of PI-matrix composites.

The preparation technology of PI and its composites is introduced and the effects of carbon nanotubes (CNTs), carbon fibers (CFs), graphene and its derivatives on the mechanical and tribological properties of PI-based composites are discussed. The effects of different nanofillers on tensile strength, tensile modulus, coefficient of friction and wear rate of PI-based composites are compared.

CNTs can serve as the strengthening and lubricating phase of PI, whereas CFs can significantly enhance the mechanical properties of the matrix. Two-dimensional graphene and its derivatives have a high modulus of elasticity and self-lubricating properties, making them ideal nanofillers to improve the lubrication performance of PI. In addition, copolymerization can improve the fracture toughness and impact resistance of PI, thereby enhancing its mechanical properties.

The mechanical and tribological properties of PI matrix composites vary depending on the nanofiller. Compared with nanofibers and nanoparticles, layered reinforcements can better improve the friction properties of PI composites. The synergistic effect of different composite fillers will become an important research system in the field of tribology in the future.