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With the pressing need for environmental conservation, regulatory authorities are actively looking for measures to prevent global warming. In the proposed inventory model for deteriorating items, demand is dependent on the selling price and green technology investment (or carbon reduction investment) for the green product (GP), as well as an investment in price-based preservation technology to slow down the pace of deterioration. Furthermore, emission reduction measures are put in place to reduce carbon emissions (CEs).

The current study executed a thorough literature review to determine how to improve supply chain management performance. Furthermore, assumptions are made to fill research gaps, and a mathematical model is created to address the problem mentioned above. To collect the data, the available inventory literature was reviewed. Additionally, numerical illustrations and sensitivity analyses are presented to emphasize the model's robustness.

The research indicates that it is more prudent to invest in preservation technology based on its selling price in order to control the rate of deterioration. In addition, the proposed model facilitates the management of deteriorated waste through salvage trading and emission reduction investment. The findings validate sustainable practices with a 20.86% increase in profit and a 21.4% decrease in CEs, thereby signifying environmental and economic benefits.

The proposed model enhances understanding of the impact of investments in price-based preservation technology and carbon reduction efforts on consumer perceptions of their intention to purchase GPs. Moreover, the study provides valuable insights by identifying important recommendations for policymakers regarding areas that require further investigation. This guideline can help identify both current and unexplored gaps, enabling researchers to direct future research efforts toward producing new products.

Good management strives to align and corporate processes for more attention being paid to supply chain management. Firms realize that greater co-operation and improved coordination can help to manage the entire supply chain more efficiently. The imperfect quality item is one of the most important issues that affect the expected profit of green supply chain. The imprecise cost with screening process of poor quality items posed in supply chain is the subject of this study.

The present study explores production model for imperfect items having uncertain cost parameters with three-layer supply chain encompassing supplier, manufacturer and retailer. The model is considering the impact of business tactics such as order size, production rate, production cost and appropriate times in various sectors on collaborative marketing systems. Due to imprecise cost parameters, the pentagonal fuzzy numbers are set to fuzzify the total cost and defuzzifition by using graded mean integration.

This study offers an explicit condition in uncertain environment to manage the imperfect quality item to increase the potential profit of the supply chain. The influence of changes in parameter values on the optimal inventory policy under fuzziness is provided managerial insights.

This model makes a significant contribution to fuzzy inference. The results of the study provide a trading strategy for the industry to avoid losses. The prescribed study can be suitable for the industries like sculpture, jewelry, pottery, etc.

This study aims to focus on the optimal green R&D of a capital-constrained supply chain under different channel power structures as well as the impact of capital constraint, financing cost, channel power structure and cost-reducing efficiency on green R&D and supply chain profitability.

A two-echelon supply chain is considered. The upstream firm engages in green R&D but has capital constraints that can be overcome by external financing. Green R&D is beneficial to reduce production costs and increase consumer demand. Based on whether or not the upstream firm is capital constrained and dominates the supply chain, four models are developed.

Capital constraints significantly lower green R&D and supply chain profitability. Transferring leadership from the upstream to the downstream firms leads to higher green R&D levels and downstream firm profitability, whereas the upstream firm's profitability is increased (decreased) if green R&D investment efficiency is high (low) enough. Greater financing costs reduce green R&D and downstream firm profitability; however, the upstream firm's profitability under the model in which it functions as the follower increases if the initial capital is sufficient. More importantly, empirical analysis based on practice data is used to verify the theoretical results reported above.

This study reveals how upstream firms in supply chains decide green R&D decisions in situations with capital constraints, providing managers and governments with an understanding of the impact of capital constraint, channel power structure, financing cost and cost-reducing efficiency on supply chain green R&D and profitability.

The major contributions are the exploration of supply chain green R&D by taking into consideration channel power structures and cost-reducing efficiency and the validation of theoretical results using practice data.

Supply chain network is complicated to manage due to the involvement of a number of agents. Formation of virtual organization using Industry 4.0 (I4.0) is an approach to improve the efficiency and effectiveness and to overcome the complexities of the channel. However, the task of managing the channel further becomes complicated after incorporating sustainability into the supply chain. To fill this gap, this paper focuses on designing of mechanism and demonstration of I4.0-based virtual organization to coordinate sustainable supply chain.

In this paper, we model and compare I4.0-based virtual organization models using four other traditional contracts with centralized supply chain. The non-cooperative game theoretic approach has been used for the analysis of models.

Our game-theoretic analysis shows that investment in I4.0 and sustainable innovation are beneficial for the overall supply chain. Our results show that linear two-part tariff contract and I4.0-based virtual organization model can perfectly coordinated with the supply chain.

This study consider deterministic model settings with full information game. Therefore researchers are encouraged to study I4.0-based coordination models under information asymmetry and uncertain situations.

The paper includes implications for the development of I4.0-based coordination model to tackle the problems of channel coordination.

This study proposes I4.0-based game-theoretic model for the sustainable supply chain coordination.

The purpose of this study is to investigate a joint economic lot-size model with the possibility of cofinancing between members of a three-echelon supply chain (SC) including one supplier, one manufacture and one retailer. Given the differences in credit as well as differences in access to capital markets, SC members will be able to create a financial alliance to maximize the profits of each member. This study proposed a model to maximize the annuity stream of the SC by considering the financial interaction between SC members.

This joint economic lot-sizing problem was described and modeled mathematically. To evaluate the mathematical model, different scenarios were considered with (and without) the possibility of financial interaction.

It is suggested that, in addition to the goods and information flow among SC members, proper financial flow can also have an impact on the improvement of SC performance.

While previous studies consider cofinancing between members of a two-echelon SC, this paper considers a three-echelon SC including one supplier, one manufacturer and one retailer where financial cooperation between different levels of the SC in both upstream and reverse directions will be possible.

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

This paper investigates how the channel leadership strategies develop a post-coronavirus disease (COVID-19) resilient agri-supply chain, which reduces supplier and retailer's price loss and enhances the logistics service quality level considering logistics outsourcing of agri-product especially for the rapidly changing market condition.

Based on the classical leadership theory, two channel leadership strategies, i.e. LPL and SL, are considered. The proposed framework first derives the equilibrium price and service quality level decision among the supplier, the logistics provider and the retailer. Then it compares both leadership strategies in terms of the equilibrium prices and service quality theoretically. This article also presents a case study of Arabian dates pricing and supply chain to test the theoretically derived propositions.

Selection of suitable leadership strategy is a critical factor for profit maximization of the supply chain drivers and proper optimization of equilibrium price and service quality. Here, the product's quality and the market's socio-economic condition play an important role in selecting a suitable leadership strategy. A random transformation of the physical market to an e-commerce portal creates a wide variation of the market's socio-economic parameters, affecting the equilibrium pricing and the logistics provider's service quality.

This study proposes a post-COVID-19 resilient agri-supply chain framework considering price and quality-dependent stochastic market demand, incorporating a wide range of socio-economic factors in the model to counteract the effect of rapid behavior change of agri-market due to COVID-19 norms. This research examines the effect of different channel leadership strategies to facilitate suitable decisions on prices and service quality and retrieve the profit of the supplier, retailer and logistics provider. The future models can incorporate competitiveness in logistics outsourcing, fourth-party logistics (4PL) and contract farming in the agri-supply chain. Each of the extensions can open avenues in different directions.

As the post-COVID-19 market and the customer behavior is randomly changing, and the traditional market is rapidly converting into supermarkets and e-commerce portals, this paper examines the model with a wide variety of e-commerce portals with multi-variation of product. It is conclusive that the product's quality and the market's socio-economic behavior significantly impact the equilibrium decision. The drivers of the supply chain must take them into account before choosing a particular channel leadership strategy.

This study considers a multi-product and multi-market (e-commerce) model by integrating a wide variety of products and the market's socio-economic parameters. The model is tested in a price and quality-dependent stochastic market condition, contributing to the literature by reconciling two different channel leadership strategies into the global logistics of fresh agri-product.

Drawing on information processing theory, the linkage between buffering and bridging and the ability on the part of procurement to resolve demand–supply imbalances is investigated, as well as contexts in which these strategies may be particularly useful or detrimental. Buffering may be achieved through demand change or redundancy, while bridging may be achieved by the means of collaboration or monitoring.

This study employs a hierarchical regression analysis of a survey of 150 Finnish and Swedish procurement and sales and operations planning professionals, each responding from the perspective of their own area of supply responsibility.

Both the demand change and redundancy varieties of buffering are associated with procurement's ability to resolve demand–supply imbalances without delivery disruptions, but not with cost-efficient resolution. Bridging is associated with the cost-efficient resolution of imbalances: while collaboration offers benefits, monitoring seems to make things worse. Dynamism diminishes, while the co-management of procurement in S&OP improves procurement's ability to resolve demand–supply imbalances. The most potent strategy for tackling problematic contexts appears to be buffering via demand change.

The results highlight the importance of procurement in the S&OP process and suggest tactical measures that can be taken to resolve and reduce the effects of supply and demand imbalances.

The results contribute to the procurement and S&OP literature by increasing knowledge regarding the role and integration of procurement to the crucial process of balancing demand and supply operations.

Literature sparsely documents the association between the deviant behavior of a firm and its financial policies. Trade credit is one of the most critical financial policies of a firm. In this study, the authors examine the association between strategic deviance and trade credit.

The authors explore a strategy-based explanation for trade credit by examining whether strategic deviance affects trade credit using a sample of 33 countries from 1996 to 2020. The authors test the hypothesis using static OLS regression models. To address autocorrelation and endogeneity issues, the authors use dynamic OLS models, lag models, and instrumental variable approach.

The authors find that an increase in strategic deviance reduces both demand and supply of trade credit, and the study’s results indicate that a one standard deviation increase in strategic deviance leads to a 1.34% decrease in the demand for trade credit. Also, a one standard deviation increase in strategic deviance leads to a 2.26% fall in the supply of trade credit.

This study facilitates managers to formulate trade credit policies when choosing a deviant strategy.

To the best of the authors’ knowledge, this is the first study to explore the association between strategic deviance and trade credit policies.

This study examines the relationship between policyholders’ psychographic characteristics and demand decisions for life insurance products in Uganda.

The study is based on a cross-sectional survey. Using a purposive sampling method, 389 questionnaires were administered to life insurance policyholders in the four geographical regions of Uganda. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the primary data, specifically to test the relationships between the dependent and independent variables.

The findings indicate a positive and significant influence of psychographic characteristics on demand decisions for life insurance products. In addition, the analysis indicates that the two first-order constructs of psychographic characteristics, namely price consciousness and consumer innovativeness, are positive and significant predictors of demand decisions for life insurance products. In contrast, the third first-order construct religious salience, exhibits a negative and nonsignificant effect on demand decisions for life insurance products.

For insurance practitioners, to influence demand decisions, they should emphasize premium-related appeals in their marketing messages (price consciousness) ignore product decisions based on religious beliefs and norms (religious salience). They should also ensure that insurance products are highly trustable and experiential (consumer innovativeness). For insurance policymakers, it offers an in-depth understanding of customer psychographic characteristics, which can be used to identify exploitative information embedded in certain marketing campaigns targeting specific psychographic characteristics, for better regulation.

The study provides a basis for understanding lifestyle and personality characteristics (psychographics), which may influence demand decisions for life insurance products in a developing country like Uganda, where the insurance industry is at an early stage of development.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0440