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It is the aim of this paper to discuss the value of trust and the effects of transaction specific investments for the relative degree of collaborative joint efforts, and also to assess the moderating effect of the information network on such joint efforts.

The paper takes the perspective of the buyer making the joint effort decision and draws on transaction cost economics, relational exchange and network perspectives to develop the hypotheses of the conceptual framework. Wholesalers and other merchant‐distributors in the Dutch flower industry provided the data to test the hypotheses.

The results show the importance of trust in coordinating the joint efforts and the joint effort response in terms of safeguarding and better integrating the transaction specific investments.

While the information network does not moderate the relation between trust and joint efforts, there is a significant moderating effect of the network on the relation between transaction specific investments and joint effort. This result suggests that buyers temper their specific investments to the degree of joint effort according to the information that is obtained in the network. This implies that coordinating collaborative joint efforts with suppliers is more than just buying well. The degrees of trust, specific investments and the information from the network have managerial implications for the coordination of a buyer‐supplier relationship.

The purpose of this paper is to adopt a three-stage cloud-based management system for optimizing greenhouse gases (GHG) emission and marketing decisions with supplier selection and product family design in a multi-level supply chain with multiple suppliers, one single manufacturer and multiple retailers.

The manufacturer purchases optional components of a certain functionality from his alternative suppliers and customizes a set of platform products for retailers in different independent market segments. To tackle the studied problem, a hierarchical analytical target cascading (ATC) model is proposed, Jaya algorithm is applied and supplier selection and product family design are implemented in its encoding procedure.

A case study is used to verify the effectiveness of the ATC model in solving the optimization problem and the corresponding algorithm. It has shown that the ATC model can not only obtain close optimization results as a central optimization method but also maintain the autonomous decision rights of different supply chain members.

This paper first develops a three-stage cloud-based management system to optimize GHG emission, marketing decisions, supplier selection and product family design in a multi-level supply chain. Then, the ATC model is proposed to obtain the close optimization results as central optimization method and also maintain the autonomous decision rights of different supply chain members.

The purpose of this paper is to explore and investigate the measurement of a carbon footprint and environmental program in supply chain management.

The study uses a case study methodology and employs the qualitative methods of interviews and document analysis to collect data on Hyundai Motors Co. (HMC) and its key first‐tier supplier, referred to here as Supplier A, in the Korean automobile industry.

The results of the study show that a key strategic action to implement carbon management is to identify and measure the carbon footprint of products and processes within the supply chain. A carbon footprint measurement framework and different levels of CO₂ adoption categories developed at HMC are presented. By monitoring and evaluating suppliers' CO₂ emissions performance, a focal company may avoid carbon‐related risk and retain competitiveness based on its supply chain.

Developing a carbon footprint measurement and evaluation program in the supply chain provides a track record to improve carbon and energy efficiency. This may lead companies to develop and exploit greater energy efficiency to tackle carbon emission challenges in the supply chain.

This paper provides academics and managers with a new approach to consider carbon management and green supply chain management.

The purpose of this paper is to analyse the factors that influence food safety reporting intention and behaviour of the public.

Data used in this study came from a questionnaire survey conducted in Shandong Province, China. The 642 qualified samples were analysed through structural equation model based on the expanded theory of planned behaviour to study public food safety reporting behaviour and its influencing factors.

Results indicated that participation attitude, subjective norm, perceived behavioural control (PBC) and moral norm had significantly positive effects on public reporting intention, which had a direct effect on behaviour. Among subjective norm, descriptive norm had a more significant influence on the intention to report than injunctive norm. PBC indirectly affected reporting behaviour through participation intention, and directly affected participation behaviour. Socio-demographic variables had significant influence on participation attitude, injunctive norm and PBC, whereas these variables had no influence on descriptive norm and moral norm.

This research is of academic value and of value to policy makers. To promote public participation in food safety reporting, the government should consider influencing factors of food safety reporting.

The COVID-19 pandemic has showcased the lack of resilience found in the global value chains (GVCs) of multinational enterprises (MNEs). Existing evidence shows that MNEs have only recently and slowly started recovering and attempting to rebuild the resilience of their GVCs. This paper analyzes the challenges/inhibitors faced by MNEs in building their resilience through their GVCs.

A four-stage hybrid model was used to identify the interrelationship among the identified inhibitors and to distinguish the most critical ones by ranking them. In the first stage, we employed a modified total interpretive structural modeling (m-TISM) approach to determine the inter-relationship among the inhibitors. Additionally, we identified the inhibitors' driving power and dependency by performing a matrix multiplication applied to classification (MICMAC) analysis. In the second stage, we employed the Pythagorean fuzzy analytic hierarchy process (PF-AHP) method to determine the weight of the criteria. The next stage followed, in which we used the Pythagorean fuzzy combined compromise solution (PF-CoCoSo) method to rank the inhibitors. Finally, we performed a sensitivity analysis to determine the robustness of the framework we had built based on the criteria and inhibitors.

We find business sustainability to have the highest importance and managerial governance as the most critical inhibitor hindering the path to resilience. Based on these insights, we derive four research propositions aimed at strengthening the resilience of such GVCs, followed by their implications for theory and practice.

Our findings contribute to the extant literature by uncovering key inhibitors that act as barriers to MNEs. We link out our findings with a number of propositions that we derive, which may be considered for implementation by MNEs and could help them endow their GVCs with resilience.