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This study proposes a systematic and comprehensive circular economy (CE) performance measure as an instrument to operationalize and quantify circularity. It seeks to evaluate the relative contribution of blockchain technology to evaluate various measures in this study. A general research agenda for investigating blockchain capabilities to performance assessment in CE is presented.

Empirical survey data from 32 CE and blockchain experts are collected to inform this study. Inductive reasoning, heatmaps, and a middle-range theory building approach are utilized to generalize theoretical patterns for blockchain support of CE performance assessment and measurement. A series of propositions are then developed as a middle-range theory for the true, false, perceptible, and hidden affordances of blockchain technology capabilities for CE performance measurement.

Overall, sixteen performance measures and metrics are identified and examined. The authors find most of these measures and metrics – based on literature and expert opinion – can be supported using blockchain technology capabilities. Four major blockchain capabilities, transparency and traceability, reliability and security, smart contracts, and incentivization and tokenization are shown to have varying potential support for CE performance assessment. There needs to be an evaluation of true, false, perceptible, and hidden affordances of blockchain technology capabilities for CE performance measurement in future studies.

Blockchain application for CE, and specifically performance measurements, is a new area. Research and practice evaluation on this issue is important but needs substantially additional investigation to help CE progress. This study provides a framework for evaluation and a foundation for future research at the nexus of CE, blockchain technology, and performance measurement.

This study provides a reflective overview on the role of traditional and emergent digitalization and information technologies for leveraging environmental supply chain sustainability – while reflecting on potential trade-offs and conflicts of digitalization and greening.

The authors use relevant literature and literature from Industrial Management and Data Systems (IMDS) research published in this journal over the past 50 years. They also use their knowledge and over 30 years of research experience in the field to provide professional scholarly reflections and perspective.

The authors provide a focused and succinct evaluation for research directions. A pressures, practices and performance framework sets the stage for pertinent research questions and theoretical needs to investigate the nexus of digitalization and green supply chain management. The authors provide two frameworks with exemplary practices and research for traditional and emergent digitalization and information technology. Their reflection concludes with a summary and steps forward.

The authors show how research and practice can be used to affect supply chain greening with digitalization and information technology. They observe that care should be taken given that these technologies can paradoxically simultaneously offer solutions to environmental degradation and potentially be a source of environmental degradation across the supply chain.

This work provides a summary and unique perspective that links traditional and emergent digitalization technology to green and environmental sustainability work. The area has not seen a clear summary and path forward and shows how IMDS literature has contributed to the field for decades.

In response, the purpose of this paper is to provide theoretical frameworks about the organizational uncertainty behind what and when to adopt blockchain technology and their implications on transaction costs. The immature nature and the absence of standards in blockchain technology lead to uncertainty in government organizations concerning the adoption (“what to adopt”) and the identification of the right time (“when to start”).

Using transaction cost theory and path dependency theory, this paper proposes two frameworks: to assess transaction cost risks and opportunities costs; and to depict four different types of transaction costs outcomes regarding blockchain adoption.

This paper identifies various theoretical concepts that influence blockchain adoption and combine the two critical constructs of “bounded rationality” and the “lock-in effect” to categorize the multiple transaction costs outcomes for blockchain adoption.

Although existing research in blockchain highlights mainly the potential benefits of blockchain applications, only a little attention has been given to frameworks that categorize potential transaction costs outcomes under uncertainty, in particular from organizational theorists.

Both frameworks advance the understanding of the decision-making behind blockchain adoption and synthesize the current literature to offer conceptual clarity regarding the varied implications and outcomes linked to the uncertainty regarding transactions costs stemming from blockchain technology.

Overconsumption of resources has become a global issue. To deal with resource depletion and mitigate these impending crises, the circular economy (CE) holds some promise. A wide range of performance measurements for CE have emerged over the years. However, with increasing complexity of supply chains, appropriate and potentially new performance measurements are needed for effective CE management. Blockchain is an innovative technology that may advance CE development. This chapter provides an overview of the potential linkages between blockchain technology and CE from sustainability perspectives – the specific focus will be on the performance measurement of reverse logistics activities. One of the main findings indicates that both blockchain and CE performance measurements – especially reverse logistics processes – are still evolving in both theory and practical developments. Future directions with a critical analysis including research and theoretical applications will conclude this chapter.

The topic of Lean Six Sigma (LSS) implementation in a supply chain context is an emerging research stream comprising of diverse objectives and complex processes thereby presenting opportunities for further exploration and organizational process improvement. Thus, this study proposes an integrated multi-criteria decision-making methodology to determine what can facilitate the successful implementation of LSS as an organizational change strategy in the manufacturing supply chain.

The proposed methodology based on Decision-Making Trial and Evaluation Laboratory and hierarchical Evaluation Based on Distance to Average Solution is employed to ascertain the relative importance and priorities of an identified framework of factors with the aid of opinions of managers in the Nigerian plastics industry.

The results show a high significance of institution-based factors (e.g. government regulations) and present relevant implications to the policymakers as well as the managers and practitioners of the plastics manufacturing industry.

This study indicates a possible pathway to accurately evaluate a framework of critical factors to integrate and institutionalize LSS in the manufacturing supply chain for organizational performance improvement.

This study endeavours to (a) develop a comprehensive interpretive structural modelling (ISM) toolkit containing sufficient details about the suitability and procedural aspects of each ISM approach and offer points of reference for budding researchers, (b) highlight the compatibility of ISM approaches with other qualitative and quantitative approaches, and (c) chalk-out an agenda for future research.

This study is based on an extensive review of 74 studies where researchers have used one or more ISM approaches. These studies span across the different industry sectors.

There exists a huge void in terms of the methodological synthesis of ISM approaches. ISM approaches are frequently used in sync with other qualitative and quantitative approaches. Furthermore, it highlights the need of improving the robustness of the proposed ISM models by sharing the critical details of research process.

Being a review-based work, it could not illustrate the discussed ISM approaches with real data. However, it offers a research agenda for the prospective researchers.

The prerequisites, pitfalls, suitability and the procedural aspects of various ISM approaches contained in this toolkit are equally useful for the academicians as well as practitioners.

In the absence of a synthesized framework, this study contributes a comprehensive ISM toolkit which will help the researchers to choose a suitable ISM approach in a given case.

In recent years, a number of researchers have attempted to make an integration of sustainability with supply chain risk management. These studies have led to valued insights into this issue, though there is still a lack of knowledge about the mechanisms by which sustainability-related issues are materialized as risks in the supply chain management.

The paper aims to provide a comprehensive framework to evaluate the sustainability risk in the supply chain management mechanism. To do so, a novel approach using the double normalization-based multiple aggregation (DNMA) approach under the intuitionistic fuzzy (IF) environment is extended to identify, rank and evaluate the sustainability risk factors in supply chain management.

To provide comprehensive sustainability risk factors, this study has conducted a survey using interview and literature review. In this regard, this study identified 36 sustainability risk factors in supply chain management of the manufacturing firms in five different groups of risk, including sustainable operational risk factors, economic risk factors, environmental risk factors, social risk factors, and sustainable distribution and recycling risk factors. The results of this paper found that the poor planning and scheduling was the important sustainability risk in supply chain management of the manufacturing firms, followed by the environmental accidents, production capacity risk, product design risk and exploitative hiring policies. In addition, the results of the study found that the extended approach was effective and efficient in evaluating the sustainability risk factors in supply chain management of the manufacturing firms.

Three aggregation methods based on the normalization techniques are discussed. A DNMA method is proposed under intuitionistic fuzzy sets (IFSs). To propose a broad procedure for identifying and classifying sustainability risk factors (ESFs) in supply chain management. To rank the sustainability risk factor, the authors utilize a procedure for evaluating the significance degree of the sustainability risk factor in supply chain management.