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To motivate the supplier to exert more corporate social responsibility (CSR) effort, the manufacturer offers it either a revenue sharing contract or a cost sharing contract. We study the contract choice of the manufacturer.

We develop game theoretic models to investigate the manufacturer’s optimal contract choice and examine whether there is a conflict of contract preference between the manufacturer and the supplier.

First, the revenue sharing contract has more strict conditions regarding the unit cost of the supplier’s CSR effort and the manufacturer’s retail price. Second, the cost sharing contract enables the manufacturer to achieve a “win-win” performance in terms of both profitability and CSR effort. Finally, the supplier prefers the cost sharing contract when the manufacturer’s price is low, otherwise, it prefers the revenue sharing contract.

Differing from the papers on CSR, our paper focuses on the supplier CSR management problem, and analyzes the optimal contract to motivate the supplier to exert more CSR effort.

To study these issues, the authors chose a GFSC with one producer and one material supplier as research object, the supplier will offer green material to the producer and the producer will make green food using green production technology. Then, the authors proposed that consumers' perceived value was determined by the trustworthiness levels of the related green and quality-safety information provided by the supplier and the producer. Then, considering the trustworthiness levels of the green and quality information provided by the supplier and the producer, the authors improved the demand function. Afterwards, we constructed four investment models and their income models are built and then a cost-sharing and revenue-sharing contract (hereafter, CSRS) was adopted to coordinate the GFSC.

With the growth of consumers environmental awareness and life level, consumers' requirements for green and high quality food are growing. In recently years, to increase consumers' perceived trustworthiness on the product greenness and quality levels, stakeholders in green food supply chain (hereafter, GFSC) start to adopt the blockchain-based traceability system (hereafter, BLTS). For investors, they need to know the investment conditions and how to coordinate the GFSC.

(1) When the revenue-sharing coefficient is less than three-fourths and higher then a certain vaule, the cost-sharing and revenue-sharing contract can make the GFSC coordinate. (2) The investment cost threshold of the BLTS has a positive relationship with the trustworthiness improvement levels of the green and quality information, the green degree of food products and the quality of food products. (3) In the proposed four investment situations, as the growth of consumers perceived credibility coefficient about the greenness information and the quality information, chain members' revenues will increase. In addition, comparing with co-investing the BLTS, benefits of chain members are lower than them in the sole investment model.

(1) The demand function we proposed can help chain members forecast market demand to support production or ordering decisions. (2) The investment decision policies can offer a theoretical reference for chain members to use the BLTS. (3) The CSRS will offer the theoretical reference for coordinating the supply chain after using the BLTS. Furthermore, our study method can be referenced by other scholars. (4) The study method can offer a method reference for researchers who do a similar discussion in a manufacturing supply chain. Although, our research cannot guide the industrial practices, it can serve as a reference of the similar research in industry.

When developing a new product, a buying firm solicits revenue sharing bids from two competing suppliers. Bidding behaviors of suppliers do not always align with predictions from rational agent models due to task uncertainty and bounded rationality, which could result in non-optimal supplier offers and ultimately hurt buying firm interests. This paper aims to discuss the aforementioned issues.

The authors built an analytical model that considers the impact of supplier technological risk, buyer–supplier coordination cost and supplier loss aversion on the optimal bid of the supplier. Next, using limited information processing capacity as a theoretic lens, the authors explore antecedents to the size of a focal supplier's bidding error, the absolute difference between the actual bid and the optimal bid. The authors used quantitative lab experimental data to test the hypotheses.

(1) Bounded rational bidders often fail to differentiate between relevant and irrelevant competitive information when placing bids, (2) loss aversion of a bidder significantly affects not only levels of bids, particularly for bidders with competitive disadvantages, but also sizes of the bidding error and (3) competitive information that has clearer performance implications are more influential in reducing sizes of bidding errors.

The results provide a comprehensive view of the bidding behaviors of a bounded rational supplier in an innovation outsourcing context with competition. With the results, managers now have a better understanding of behavioral influencers behind non-optimal supplier bids in an innovation outsourcing context.

The objective is to have a better understanding of the impacts of the COVID-19 pandemic on food supply chain in Wuhan.

Through a simplified flow, the authors qualitatively analyze the impacts of the COVID-19 pandemic on food supply chain. Data was gathered through a telephone survey of food suppliers in Wuhan.

The prevention measures of the COVID-19 pandemic had negative impacts on food supply chain in Wuhan. About 83.1% of food suppliers experienced a decrease in revenues. This is influenced by factors including food category on sale, purchase channel of food, food supplier's household registration and the number of the COVID-19 patients in the located community.

Due to the limitation of available data, there is a lack of quantitative analysis on the impact on food supply chain. The sample size of food suppliers is limited.

This study identifies the challenges in the food supply chain resulting from the control measures implemented during the COVID-19 pandemic in Wuhan and provides a reference for the design of control measures in other regions.

This study supplements the literature regarding the impact of public health emergencies such as the COVID-19 pandemic on food supply chain, especially food suppliers' revenues.

Research involving interorganizational relationships (IORs) has grown at an impressive rate. Several datasets have been used to understand the nature and performance implications of these relationships. Given the importance of such relationships, we describe a relatively new dataset, Bloomberg SPLC, which contains data regarding the percentage of costs and revenues attributed to suppliers and customers, as well as allows researchers to construct a comprehensive dataset of IORs of buyer–supplier networks. Because of this, Bloomberg SPLC data can be used to uncover new and exciting theoretical and empirical implications. This chapter provides background information about this dataset, guidance on how it can be leveraged, and new theoretical terrain that can be charted to better understand IORs.

Purpose: The purpose of this paper is to describe a decision support tool based on various types of contracts in a two‐level supply chain. A supply chain (SC) consists of disparate but interdependent members, dependent on each other to manage various resources (inventory, money and information). The conflicting objectives between these members may cause uncertainties in supply and demand, which can be managed by adopting coordination with the help of contracts (such as buyback, revenue sharing and quantity flexibility). Design/methodology/approach: A decision support tool for SC coordination using contracts (DSTSCCC) has been developed to explore the applicability of contracts and to compare different types of contracts in various situations. The DSTSCC is comprised of an analytical module, which is an extension of the classical newsboy problem and a simulation module. Findings: DSTSCCC helps in determining decision variables for different scenarios of contracts in the best interest of all SC members as well as whole SC. Practical implications: DSTSCCC is a simple‐to‐use and easy‐to‐implement decision making tool which helps in taking decisions prior to the actual start of SC activities. The prior decisions may help to handle future exceptions. SC members may jointly select the most profitable contract to share risks and rewards. Originality/value: DSTSCCC comprised of analytical module and simulation module presents an Integrative framework which cannot be dealt in isolation. The output of analytical module becomes input for simulation to quantify performance measures. The improvement in performance measures after satisfying the objectives of all SC members helps in realizing coordination in SC.

To counteract the effects of global competition, many organizations have extended their enterprises by forming supply chain networks. However, as organizations increase their dependence on these networks, they become more vulnerable to their suppliers' risk profiles. The purpose of this paper is to present a methodology for modeling and evaluating risk profiles in supply chains via Bayesian networks.

Empirical data from 15 casting suppliers to a major US automotive company are analyzed using Bayesian networks. The networks provide a methodological approach for determining a supplier's external, operational, and network risk probability, and the potential revenue impact a supplier can have on the company.

Bayesian networks can be used to develop supplier risk profiles to determine the risk exposure of a company's revenue stream. The supplier risk profiles can be used to determine those risk events which have the largest potential impact on an organization's revenues, and the highest probability of occurrence.

A limitation to the use of Bayesian networks to model supply chain risks is the proper identification of risk events and risk categories that can impact a supply chain.

The methodology used in this study can be adopted by managers to formulate supply chain risk management strategies and tactics which mitigate overall supply chain risks.

The methodology used in this study can be used by organizations to reduce supply chain risks which yield numerous societal benefits.

As part of a comprehensive supplier risk management program, organizations along with their suppliers can develop targeted approaches to minimize the occurrence of supply chain risk events.

We conducted this study to examine the relationship between revenue concentration and a firm's financial leverage. We aimed to analyze whether revenue concentration influences a firm's capital structure decisions and whether this relationship is driven by customer-specific investments or the direct effect of revenue concentration itself. Additionally, we investigated the role of asset redeployability in mediating or moderating the relationship between revenue concentration and financial leverage.

The paper investigates the relationship between revenue concentration and a firm's financial leverage. The results indicate a negative association between revenue concentration and financial leverage. This finding holds across various regression models and is statistically significant. Furthermore, the paper explores the potential role of asset redeployability in explaining the relationship between revenue concentration and financial leverage. The results indicate that even after controlling for asset redeployability, the negative relationship between revenue concentration and leverage remains significant, suggesting that revenue concentration affects capital structure decisions independently of the risks associated with relationship-specific investments. Robustness tests are conducted using a three-stage least squares approach to account for the simultaneity between revenue concentration, asset redeployability and capital structure.

Our findings demonstrate that revenue concentration is negatively associated with financial leverage, even after accounting for asset redeployability. This suggests that revenue concentration affects capital structure decisions independently of the risks associated with customer-specific investments. Furthermore, we performed robustness tests to address potential simultaneity issues between revenue concentration, asset redeployability and capital structure.

The study relies on available data sources, which may have inherent limitations in terms of accuracy, completeness or consistency. The quality of the data used in the analysis could impact the robustness of the findings. Time Period: The study focuses on more recent years, which might limit the ability to compare the findings with studies conducted over different time periods. Historical trends or structural changes that could impact the relationship between revenue concentration and financial leverage might not be fully captured.

Firms with higher revenue concentration tend to have lower financial leverage. Recent years show a negative relationship between profitability and market leverage compared to earlier periods. Revenue concentration has a distinct effect on financial leverage, not fully explained by risks from relationship-specific investments or asset redeployability. Insights for firms in managing capital structure decisions, considering revenue concentration and its implications for leverage.

This research is one of the first papers that investigates the impact of revenue concentration on the capital structure choices of firms. By exploring the relationship between revenue concentration and financial leverage, the study contributes to the existing literature by shedding light on an underexplored area. Thus, this study adds originality to the field by addressing a research gap and contributing to the understanding of the relationship between revenue concentration and capital structure choices.

The purpose of this paper is to compare the pricing decisions and earning potential of the software supplier and the smart device manufacturer in different software promotion strategies.

Based on game theory, the authors formulate two promotion models, that is, the supplier implements software promotion activities individually (SP model) or outsources the promotion activity to the manufacturer under profit-sharing contract (MP model) when taking different channel power structures into consideration. Besides, in order to test the robustness of the conclusions, the authors also extend the basic model to the following situations: (1) the customers have different price elasticity toward service fee and product price; (2) the revenue sharing contract is employed by the supply chain members; and (3) the manufacturer's product promotion practice is taken into consideration.

The optimal service fee (product price) of the supplier (manufacturer) under SP model is always lower (higher) than that under MP model. Surprisingly, if the supplier is the channel leader and the profit sharing ratio exceeds certain threshold, the manufacturer's profit decreases in profit sharing ratio, which remains robust in three extension models. Moreover, the supply chain's profit in supplier-led game is always lower than that in Nash game irrespective of the promotion strategy in profit sharing context. When revenue sharing contract is adopted, the result holds only when the revenue sharing ratio is relatively low.

The authors originally explore two promotion strategies of the software supplier when taking the channel power structures into considerations, which has not been explored in the literature to the best of the authors' knowledge.

The importance of carbon reduction has become a global consensus, and more and more countries are implementing the cap-and-trade mechanism, including China. The purpose of this paper is to investigate the optimal carbon emission allowances (CEA) purchasing decisions of supply chain members under the cap-and-trade mechanism in China.

An evolutionary game model is established to analyze the CEA purchase strategy choices of suppliers and manufacturers in the supply chain. The influence of the key parameters on the evolutionary game results is analyzed by numerical simulations.

The supply chain system always evolves towards neither supplier nor manufacturer purchasing CEA or both purchasing CEA. Illegal production behavior and excessive CEA costs are key factors that hinder parties from purchasing CEA. High revenue from purchasing CEA for production, high supply chain losses and high governmental penalties can promote parties to purchase CEA.

The results help supply chain members make better CEA purchasing decisions and also benefit the development of China’s carbon trading market and environmental protection.