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There is great uncertainty and volatility in the evaluation and measurement of green supplier satisfaction. The purpose of this paper is to fill this gap based on the information entropy theory (IET) to describe the probability of green supplier satisfaction degree.

The authors introduce a formal model using analytic hierarchy process (AHP), IET and entropy technique for order preference by similarity to an ideal solution (TOPSIS) method to evaluate green supplier satisfaction and promote them for the better implementation of green supply chain management practices.

The first finding is developing an effective framework for green supplier satisfaction, incorporating various measures of environmental dimension. Second, a hybrid uncertainty decision method is introduced, by integrating AHP and IET and entropy-TOPSIS.

One of the main limitations of the research is that the authors introduced a conceptual example. Real-world applications need to investigate the accuracy and effectiveness of these measures, and the operational feasibility of this method.

This is one of the first works to provide a comprehensive appraisal model for evaluation of green supplier satisfaction. This study and research method can form general guidelines, and organizations can increasingly benefit from using green supplier satisfaction evaluation as a management tool. Green supplier satisfaction evaluation is just the beginning.

Based on natural-resource-based view (NRBV), the purpose of this paper is to clarify the dimensions of green procurement and the mechanisms involved in the relationship between green procurement and firm performance.

Secondary data were to measure all the variables in 206 Chinese A-share companies’ annual reports, social responsibility reports, environmental reports, and sustainability reports, which were published by Guotai Junan Securities Co., Ltd and Wind Information Co., Ltd.

The results indicate that although both product-based and process-based green procurement have a positive effect on firm performance, these Chinese companies focus on the impact of product-based green procurement on their operational efficiency as well as the moderating role played by stakeholder satisfaction.

Although studies on green procurement have increased in recent years, issues such as how to measure green purchasing behaviors and how green procurement affects firms’ operational and financial performance are still unexplored. Based on previous literature, two dimensions have been defined for green procurement in this study to develop corresponding measurements, namely, product-based green procurement and process-based green procurement. Besides, this study considers operational efficiency as the mediator and stakeholder satisfaction as the moderator when constructing a new conceptual model to illustrate the relationship between green procurement and firm performance.

In the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves diverse sets of classical and environmental parameters, which are originated from a complex, ambiguous and inconsistent decision-making environment. Arguably, supply chain management is fronting the next industrial revolution, which is named industry 4.0, due to the fast advance of digitalization. Considering the latter's rapid growth, current supplier selection models are, or it will, inefficient to assign the level of priority of each supplier among a set of suppliers, and therefore, more advanced models merging “recipes” of sustainability and industry 4.0 ingenuities are required. Yet, no research work found towards a digitalized, along with sustainability's target, sourcing.

A new framework for green and digitalized sourcing is developed. Thereafter, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating. The proposed approach is implemented in consultation with the procurement department of a food processing company willing to develop a greener supply chain in the era of industry 4.0.

The proposed approach is capable to recognize the most important evaluation criteria, explain the ambiguity of experts' expressions and having better discrimination power to assess suppliers on operational efficiency and environmental and digitalization criteria, and henceforth enhances the quality of the sourcing process. Sensitivity analysis is performed to help managers for model approval. Moreover, this work presents the first attempt towards green and digitalized supplier selection. It paves the way towards further development in the modelling and optimization of sourcing in the era of industry 4.0.

Competitive supply chain management needs efficient purchasing and production activities since they represent its core, and this arises the necessity for a strategic adaptation and alignment with the requirement of industry 4.0. The latter implies alterations in the avenue firms operate and shape their activities and processes. In the context of supplier selection, this would involve the way supplier assessed and selected. This work is originally initiated based on a joint collaboration with a food company. A hybrid decision-making approach is proposed to evaluate and select suppliers considering operational efficiency, environmental criteria and digitalization initiatives towards digitalized and green supplier selection (DG-SS). To this end, supply chain management in the era of sustainability and digitalization are discussed.

The purpose of this study is to develop a scale and model to measure suppliers' satisfaction index (SSI) in buyer‐suppliers relationships.

Based on the literature review and opinion of the suppliers, this paper first explores the factors that influence suppliers' satisfaction (SS). Subsequently a questionnaire is designed based on these factors and survey was conducted among 300 suppliers' organizations. Partial least square (PLS) approach is used to validate the model and investigate the relationships of factors or constructs with SS. Finally, the authors compute the extent of SSI using analytical hierarchy process (AHP).

The results indicate that purchase policy, payment policy, coordination policy and corporate image of the buying firm have positive impact on SS. The purchase policy and corporate image have strong impact on SS compared to other factors. The computed value of SSI in this study is 65.25 on the scale of 1‐100.

The data used in this study are collected only from the suppliers of an Indian PSU. The proposed method can also be used by other industries for measuring the value of SS. In the future, one can carry out a study by measuring the satisfaction of both parties simultaneously. Moreover, generalizability of the findings to other industries or other countries is open to scrutiny.

This paper develops a new scale to measure SS and explores the relationships of different factors with SS and among each other. Finally, a method is proposed to compute the current level of SSI.

Supplier segmentation provides companies with suitable policies to control each segment, thereby saving time and resources. Sustainability has become a mandatory requirement in competitive business environments. This study aims to develop a clustering-based approach to sustainable supplier segmentation.

The characteristics of the suppliers and the aspects of the purchased items were considered simultaneously. The weights of the sub-criteria were determined using the best-worst method. Then, the K-means clustering algorithm was applied to all company suppliers based on four criteria. The proposed model is applied to a real case study to test the performance of the proposed approach.

The results prove that supplier segmentation is more efficient when using clustering algorithms, and the best criteria are selected for sustainable supplier segmentation and managing supplier relationships.

This study integrates sustainability considerations into the supplier segmentation problem using a hybrid approach. The proposed sustainable supplier segmentation is a practical tool that eliminates complexity and presents the possibility of convenient execution. The proposed method helps business owners to elevate their sustainable insights.

The objective of the study is to develop a methodology to strategically rank store locations using criteria such as population, store site characteristics, economic considerations, competition and so on to select the most optimal retail convenience store location.

A case of National Capital Region, India, for a 24-h convenience store was considered for the study and the major criteria that affect the performance of a convenience store are identified, such as population characteristics, economic criteria, competition, consumer accessibility, store size, total cost, site attractiveness and security. Fuzzy AHP is utilized to find the weightage for each criteria and a combination of fuzzy TOPSIS and grey relational analysis (GRA) is applied to rank the alternative using these criteria weight. Further, results obtained are compared with results from fuzzy TOPSIS and fuzzy VIKOR methods. Sensitivity analysis is also performed for ensuring the robustness of the framework.

It is observed that outcomes do not change under various settling coefficient values, demonstrating that the methodology is very robust. The developed framework will be quite useful to diverse retailers looking to expand and generate substantial profits.

A large sample size of number of locations encourages generalization of results. Strategic ranking of the selected locations is carried out on a few selected criteria. The study was limited by the designated geographical area.

The study contributes to the few available articles on convenience store selection using combination of fuzzy AHP, fuzzy TOPSIS and GRA for a developing country.

Clean and sustainable energy becomes an alternative to differentiate electricity suppliers, but it is necessary to have a better understanding of their behaviour to achieve green customer loyalty. This paper aims to deploy a behavioural model that helps explain loyalty of customers towards green electricity providers by including a series of antecedents such as trust, satisfaction, perceived environmental impact, propensity to trust and perceived risk.

The paper deploys a behavioural model that it is tested through structural equation modelling to a sample of 231 German electricity consumers with green contracts. The data analysis included two steps: first, the development and validation of the scales used to measure the constructs proposed in the model, and second, the model test.

Results demonstrate that trust and satisfaction directly influence loyalty, while satisfaction and the other variables included in the model have an indirect relationship with loyalty mediated by trust and satisfaction. As green characteristics of electricity are difficult to evaluate, managers should demonstrate in their communication the environmental effects of their activities while emphasising their capacity to attend to supply requirements for building long-term customer relationships.

The paper is focused on the understanding of those consumers who have signed a green electricity contract and the antecedents associated to their loyalty. The behavioural model helps identify how managers should apply marketing strategies to foster green consumers loyalty.

The purpose of this study is to explore green supply chain management (GSCM) practices and their relationship with organizational performance. More specifically, this research explores the effect of GSCM efforts and other organizational factors on firm performance of small and medium enterprises (SMEs) that serve as suppliers to large customer firms in the electronics industry.

This study developed a research model relating GSCM practice and business performance through three organizational variables (employee satisfaction, operational efficiency, and relational efficiency) as moderators. Statistical analyses were based on the data collected, through survey questionnaires, from 223 SMEs in the electronics industry in Korea. Reliability, validity, and goodness‐of‐fit of the research model were tested by the widely accepted statistical tools. To test the hypotheses relating GSCM practice implementation and business performance, structural equation modeling was used.

The most anticipated finding of the study was a direct link between GSCM practice implementation and business performance. However, no statistical significance was found. Instead, significant indirect relationships were found between GSCM practice implementation and business performance through mediating variables of operational efficiency and relational efficiency. This result indicates that business performance will be improved when GSCM enhances operational efficiency and operational efficiency.

Research on GSCM is still at the early stage. Further refinement of the questionnaire is needed. Generalizability of the findings is also limited because of data collected from electronics firms in Korean. This study shed several important insights. The findings of this study are generally consistent with prior studies in other parts of the world. SMEs in the Korean electronics industry believe that GSCM practices help generate new opportunities to attract clients in addition to complying with the buyer firms' demand. It was also found that implementation of GSCM practices help improve operational and relational efficiencies of supplier firms.

Few empirical studies have been done in GSCM based on the conceptual footing of resource dependence theory. Also, this study was conducted from the supplier's perspective in examining the weaknesses of SME suppliers. Thus, the authors emphasize the importance of support from large buying firms for improving SME suppliers' green management capabilities.

Today, proficient practices are required to stimulate along various boundaries of the supply chain (SC) to exploit manufacturing resources economically, effectually and gracefully for retaining operational excellence. Accordingly, varieties of paramount practices, i.e. Lean, Agile, Resilient and Green practices, are integrated in present study with the objective to develop a Decision Support Framework (DSF) to select robust supplier under the extent of Lean-Agile-Resilient-Green (LARG) practices for a manufacturing firm. The framework is developed and validated in the Indian automotive sector, where the primary data is collected based on perceptions of the respondents working in an automotive company.

LARG metrics can ponder ecological balance, customer satisfaction, associations, effectiveness and sustainability and thus, the study consolidated LARG practices in one umbrella to develop a DSF. The analytical approach under DSF is developed by the integration AHP, DEMATEL, ANP, Extended MOORA and SAW techniques in present study to evaluate a robust supplier under the aegis of LARG practices in SC. DSF is developed by scrutinizing and categorizing LARG characteristics, where the selected LARG characteristics are handled by fuzzy sets theory to deal with the impreciseness and uncertainty in decision making.

The study has identified 63 measures (15 for Lean, 15 for Agile, 14 for resilient and 19 for Green) to support the robust supplier selection process for manufacturing firms. The findings of study explicate “Internal communication agility”, “Interchangeability to personnel resources”, “Manufacturing flexibility”, “degree of online solution”, “Quickness to resource up-gradation”, “Manageability to demand and supply change”, “Overstocking inventory practices” as significant metrics in ranking order. Additionally, “Transparency to share information”, “Internal communication agility”, “Manufacturing Flexibility”, “Green product (outgoing)” are found as influential metrics under LARG practices respectively.

A technical DSF to utilize by the managers is developed, which is connected with knowledge-based theory and a case of an automobile manufacturing firm is presented to illustrate its implementation. The companies can utilize presented DSF to impose service excellence, societal performance, agility and green surroundings in SC for achieving sustainable outcomes to be welcomed by the legislations, society and rivals. The framework represents an important decision support tool to enable managers to overcome imprecise SC information sources.

The study presented a proficient platform to review the most significant LARG alternative in the SC. The study suggested a cluster of LARG metrics to support operational improvement in manufacturing firms for shifting gear toward sustainable SC practices. The present study embraces its existence in enrolling a high extent of collaboration amongst clients, project teams and LARG practices to virtually eradicate the likelihood of absolute project failure.

The principal research objective entails examining the nexus between green supplier selection and green performance while scrutinizing the moderating role of governance mechanisms, specifically process control and outcome control, in shaping this association.

To assess our hypotheses, this study obtained data from Chinese manufacturing sectors and utilized regression analysis on a dataset consisting of 295 samples.

This study enriches the sustainable supply chain management literature by emphasizing the influence of green supplier selection on a firm’s green performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.

This study enriches the sustainable supply chain management literature by emphasizing the influence of supplier selection on a firm’s environmental performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.