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Performance assessment of green supply chain management (GSCM) requires a systematic approach because of its interdisciplinary and multi-objective nature. The purpose of this paper is to propose a model to the performance assessment of GSCM.

A model is proposed, grounded on a literature review on GSCM performance, after which the causal relationships and prioritization of the sub-criteria are analyzed by fuzzy Decision Making Trial and Evaluation Laboratory technique in a company operating in the cement industry.

An integrated holistic performance assessment model incorporating specifically six criteria and 21 sub-criteria is applied, which represents causal relationships and prioritization of sub-criteria.

The proposed model can be generalized, because an integrative framework can be used in future empirical studies to analyze performance of GSCM. However, the causal relationships and prioritization among sub-criteria are analyzed based on the needs and capabilities of the individual company; therefore, the causal relationships found are company specific.

The proposed model can be hired and implemented by companies striving for GSCM. This model allows companies to assess their current GSCM performance, analyze causal relationships, and prioritize sub-criteria.

Several studies have analyzed performance assessment in green supply chains; however, to the best of the authors’ knowledge, no study has taken an approach to performance assessment in GSCM that combines environmental, economics/financial, logistics, operational, organizational and marketing in the same framework. In addition, the cause-effect relationships identified will be the base for performance improvement.

Today, information systems and technology provides a wide set of tools for companies to increase the efficiency of their businesses. Although technology offers many benefits to businesses, it also brings risks as the information systems security breaches. Security breaches and their financial impact is a constant concern of the researchers and practitioners. This paper explores information systems breaches and their financial impacts on the publicly traded companies in different sectors.

After a comprehensive data collection process, data from 192 events are analyzed by employing Event Study Methodology and a comparison of the results between the four highly affected sectors (Consumer Goods, Technology, Financial and Communications) is presented. The abnormal returns on the prices of stocks after the events are calculated with the Market Model. Also, the results of the Market Adjusted Model and Mean Adjusted Model are presented to support the results.

While information systems security breaches have a significant negative impact on the Financials and the Technology sectors for all the event windows in the study ([−5, 0], [−5, 1], [−5, 5], and [−5, 10]), the significant negative impact is observed only on the [−5, 5] and [−5, 10] event windows for the Consumer Goods sector. No significant negative impact is observed in the Communications sector, in fact, the cumulative abnormal returns are positive for this sector.

The contribution of this paper to provide evidence about the financial impacts of the information systems breaches for businesses in different sectors. While there are studies that have previously focused on the information systems breaches and their financial impacts on businesses, to the best of our knowledge, this is the first study that compares this effect between the four highly impacted sectors. With a relatively larger sample size and broader event windows than the past studies in the literature, statistical evidence is provided to managers to justify their investments in information security and build preventive measures to secure the market value of their firms.

The aim of the present study is to overcome some of the limitations of the FMEA method by presenting a theoretical base for considering risk evaluation into its assessment methodology and proposing an approach for its implementation.

Fuzzy AHP is used to calculate the weights of the likelihood of occurrence (O), severity (S) and difficulty of detection (D). Additionally, the prospect-theory-based TODIM method was integrated with fuzzy logic. Thus, fuzzy TODIM was employed to calculate the ranking of potential failure modes according to their risk priority numbers (RPNs). In order to verify the results of the study, in-depth interviews were conducted with the participation of industry experts.

The results are very much in line with prospect theory. Therefore, practitioners may apply the proposed method to FMEA. The most crucial failure mode for a firm's attention is furnace failure followed by generator failure, crane failure, tank failure, kettle failure, dryer failure and operator failure, respectively.

The originality of this paper consists in integrating prospect theory with the FMEA method in order to overcome the limitations naturally inherent in the calculation of the FMEA's RPNs.

This study aims to propose a novel framework for barriers to circularity within cooperative supply chains. The barriers in the adoption and implementation of circular economy principles are examined within a framework.

Fuzzy best-worst method is used to calculate the weights of barriers and identify the prioritization of barriers to circularity within cooperative supply chain.

“Insufficient implementation of circular economy laws” was found as the most important barrier, followed by “Lack of information”, “Ineffective recycling policies”, “Lack of awareness for circular economy”, “Remanufacturing is a labor-intensive procedure”, “Inconsistent price policies in sources and products”, “Lack of environmental management system”, “Cost of implementation for green activities” and “Lack of R&D capability” barriers, respectively.

The number of participant professionals limits the conclusions of the study and reaching more general conclusions. A comprehensive research can be conducted by the participation of a greater number of professionals.

Several studies analyzed the barriers to circularity; however, to the best of the authors' knowledge, no study has been taken an approach for barriers to circularity for cooperatives or cooperative supply chains.

The main aim of this study is to understand the role of smart technologies and show the rankings of various smart technologies in collection and classification of electronic waste (e-waste).

This study presents a framework integrating the concepts of collection and classification mechanisms and smart technologies. The criteria set includes three main, which are economic, social and environmental criteria, including a total of 15 subcriteria. Smart technologies identified in this study were robotics, multiagent systems, autonomous tools, smart vehicles, data-driven technologies, Internet of things (IOT), cloud computing and big data analytics. The weights of all criteria were found using fuzzy analytic network process (ANP), and the scores of smart technologies which were useful for collection and classification of e-waste were calculated using fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR).

The most important criterion was found as collection cost, followed by pollution prevention and control, storage/holding cost and greenhouse gas emissions in collection and classification of e-waste. Autonomous tools were found as the best smart technology for collection and classification of e-waste, followed by robotics and smart vehicles.

The originality of the study is to propose a framework, which integrates the collection and classification of e-waste and smart technologies.

This study aims to propose an electronic waste collection and classification system to enhance social, environmental and economic sustainability by integrating data-driven technologies in emerging economies.

GM (1, 1) model under grey prediction is used in this study in order to estimate the trend of the amount of collected electronic waste in emerging economies.

It is revealed that the amount of collected electronic waste is increasing day by day, and within the framework of sustainability in the process of collecting and classification of electronic waste, digital technologies were found to be lacking. It has been determined that this deficiency, together with the increasing amount of electronic waste, has caused environmental, social and economic damage to emerging economies.

The main originality of this study is integrating electronic waste collection and classification processes with data-driven technologies and sustainability, which is a relatively new subject.

The volume of data being generated by various sectors in recent years has increased exponentially. Consequently, professionals struggle to process essential data in the current competitive world. The purpose of the study is to explore and provide insights into the Big Data Analytics (BDA) studies in different sectors.

This study performs a systematic literature review (SLR) with bibliometric analysis of BDA adoption (BDAA) in the supply chain and its applications in various sectors from 2014 to 2018. This paper focuses on BDAA studies have been carried out across different countries and sectors. Also, the paper explores different tools and techniques used in BDAA studies.

The benefits of adopting BDA, coupled with a lack of adequate research in the field, have motivated this study. This literature review categorizes paper into seven main areas and found that most of the studies were carried out in manufacturing and service.

This research insight and observations can provide practitioners and academia with guidance on implementing BDA in different sustainable supply chain sectors. The article indicates a few remarkable gaps in the future direction and trends regarding the integration of BDA and sustainable supply chain development.

The study derives a new categorization of BDA, which investigates how data is generated, organized, captured, interpreted and evaluated to give valuable insights to manage the sustainable supply chain.

Performance assessment of layouts requires a systematic approach because of its multi-objective nature. The purpose of this paper is to propose a framework to the performance assessment of layout designs.

A layout performance assessment framework is proposed, grounded on a literature review. Then, the causal relationships and prioritization of the sub-criteria are analyzed by fuzzy Decision Making Trial and Evaluation Laboratory technique in an elevator and escalator-manufacturing firm.

An integrated holistic performance assessment framework, specifically, the 7 criteria, 19 sub-criteria and 112 measures, are studied in this model which represents causal relationships and prioritization of sub-criteria.

The proposed framework can be generalized, because an integrative framework can be used in future empirical studies to analyze performance of layout design. However, the causal relationships and prioritization among sub-criteria are analyzed based on the needs and capabilities of the individual company; therefore, the results of the causal relationships are company specific.

With this framework, the companies may assess their current layout’s performance, may analyze causal relationships and prioritization of sub-criteria.

There are very few models or frameworks regarding the performance assessment of layout designs. In this paper, a new conceptual holistic framework was proposed as three-dimensional hierarchy, which includes the main criteria, sub-criteria and the measures, respectively. Cost, flow, flexibility, surrounding environment, environment quality, time and characteristics are identified as the main criteria for the layout design performance assessment. In addition, cause-effect relationships, which will be the base for improvement of the performance, are found.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.