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Investment decisions hold immense significance for investors and eventually affect their portfolio performance. Investors are advised to weigh the costs and benefits associated with every decision in order to make rational investment decisions. However, behavioral finance research reveals that investors' choices often stem from a blend of economic, psychological and sociological factors, leading to irrationality. Moreover, environmental, social and corporate governance (ESG) factors, aligned with behavioral finance hypotheses, also sway opinions and stock prices. Hence, this study aims to identify how individual equity investors prioritize key determinants of investment decisions in the Indian stock market.

The current research gathered data from 391 individual equity investors through a structured questionnaire. Thereafter, a fuzzy analytic hierarchy process (F-AHP) was used to meet the purpose of the research.

Information availability, representative heuristics belonging to psychological factors and macroeconomic indicators falling under economic factors were discovered to be the three most prioritized criteria, whereas environmental issues within the realm of ESG factors, recommendations of brokers or investment consultants of sociological factors, and social issues belonging to ESG factors were found to be the least prioritized criteria, respectively.

Only active and experienced individual equity investors were surveyed in this study. Furthermore, with a sample size of 391 participants, the study was confined to individual equity investors in one nation, India.

This research has implications for individual investors, institutional investors, market regulators, corporations, financial advisors, portfolio managers, policymakers and society as a whole.

To the best of the authors' knowledge, no real attempt has been made to comprehend how active and experienced individual investors prioritize critical determinants of investment decisions by taking economic, psychological, sociological and ESG factors collectively under consideration.

The purpose of this study is to examine the impact of data analytics, collaboration and flexibility on supply chain resilience (SCR) performance in the current dynamic global market.

This study uses a partial least squares modeling approach to analyze the relationships defined in the conceptual model. This data was organized through a survey questionnaire shared with the professionals working in different industries and belonging to supply chain functions. This survey was designed to measure data analytics capability (DAC), supply chain collaboration (SCC), supply chain flexibility (SCF), industry dynamism (INY) and SCRP, consisting of 29 items. This analysis included involved assessing measurement model for reliability and validity and a structural model for hypothesis testing.

This research empirically examines that collaboration and flexibility are significantly enhanced by advanced DACs, generating superior SCRP. Furthermore, the findings validate that cooperation and adaptability among the supply chain are necessary to reinforce this inherent resilience. The relation of SCC, SCF and the SCRP was significantly moderated with the INY.

This study complements the extant literature by providing empirical evidence of the tangible effects of data analytics on SCR. The study demonstrates the need for the alignment of supply chain strategies with the INY, giving some directions on how businesses can tailor their practices to specific market environments for enhanced resilience.

This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions.

This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool.

The technique is easy to apply using “jrank” software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process.

The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process.

The stormwater industry grapples with numerous environmental challenges resulting from producing and using storm materials. Green building materials (GBMs) offer a more ecologically friendly alternative to conventional construction materials. However, establishing criteria for selecting GBMs and assessing their sustainability has proven to be a complex endeavor. Therefore, this paper aims to assess the suitability of GBMs in stormwater management projects.

This study investigates and identifies the green storm drainage materials criteria based on previous literature and an extensive survey involving 140 stakeholders from the Egyptian industry, including facilities managers, asset managers, engineers and policymakers. A comprehensive model employing partial least squares structural equation modeling and artificial neural network is developed to assess the suitability of GBMs in stormwater management projects.

The study’s findings emphasize the pivotal role of social factors in the practical implementation of green material selection criteria. Understanding the intricate interplay among economic, environmental and social dimensions becomes crucial as stakeholders in the stormwater industry navigate the transition toward sustainable storm materials.

This research highlights the importance of integrating social factors into decision-making, contributing to more holistic and effective strategies for sustainable stormwater management. The study’s originality lies in its innovative approach to assessing the suitability of GBMs in stormwater management projects and its novel insights into the complex dynamics of sustainable materials selection, addressing a significant research gap in the field.

This research aims to identify and model metrics and sub-metrics that enhance sustainable performance measurement in agri-food supply chains.

The study evaluates five key metrics and 18 sub-metrics critical to this industry, establishing interrelationships among them to ensure a successful sustainable performance measurement system. The decision-making trial and evaluation laboratory technique was employed, integrated with fuzzy theory and expert opinions.

The findings suggest that metrics like information technology and organizational productivity, alongside the sub-metric of information integration, significantly contribute to sustainable supply chain performance.

This study proposes a performance measurement system that enables organizations to achieve optimal performance levels through a sustainable supply chain (SCC) and supply chain agility (SCA) framework, supported by digital technologies.

This study aims to evaluate the sustainable supply chain performance indicators. At a macro level, the identification of the sustainable supply chain management (SSCM) performance indicators is done through exhaustive literature survey and interviews with experts. Furthermore, these indicators are evaluated through a hybrid approach, i.e. total weighted interpretive structural modelling (TWISM) followed by analytic hierarchical process (AHP).

Micro small and medium enterprises (MSMEs) in India are a major contributor to nation’s GDP. However, this sector struggles to comprehend benefits from implementation of SSCM due to a lack of appropriate performance evaluation metrics. The purpose of this paper is to contribute to the body of knowledge in SSCM by proposing and evaluating a set of SSCM performance indicators.

The paper highlights the SSCM performance indicators and concludes that business strategies, implementation planning and impact of stakeholders are the top SSCM performance indicators (SPIs). Therefore, the decision-makers must initially focus on strategic requirements which foster the implementation of SSCM, thereby ensuring profitability for all stakeholders.

Although the proposed framework was validated through a case study on Indian automobile component manufacturing MSMEs, future research would explore the extension of the framework to other industries.

The originality of this study lies in the application of the novel TWISM-AHP tool. Furthermore, the SPIs identified in the study, consider the integration of the triple bottom line from the MSME perspective. The TWISM-AHP analysis will be beneficial for SC decision-makers to enhance the SSCM performance based on the identified indicators and their criticality.

Given the sudden disruption caused by COVID-19, knowledge sharing between organizations has become a meaningful way to improve supply chain resilience. However, there is still a lack of in-depth research on how to reduce the threat to knowledge sharing caused by increased levels of relational risk. With the emergence of new digital technologies, whether blockchain governance can control relational risk and replace traditional relational governance remains to be demonstrated.

This study uses a cross-sectional survey approach in which quantitative data are collected from 300 participants from Chinese manufacturing enterprises to test the hypotheses.

The results show that relational and blockchain governance can significantly and complementarily reduce the level of relational risk in knowledge sharing. When the relational risk is at a low, medium or high level, the best matches of relational and blockchain governance are low-level relational governance–low-level blockchain governance, high-level relational governance–low-level blockchain governance and high-level relational governance–high-level blockchain governance, respectively.

The findings of this study have important practical implications for manufacturing enterprises in terms of how to choose reasonable governance modes to manage relational risk behaviour according to different relational risk levels to better understand the positive role of knowledge sharing in supply chain resilience.

The antecedent variables of knowledge sharing in previous studies are based on transaction cost theory or relational theory and have not moved beyond the original theoretical framework. This paper addresses this limitation, puts knowledge sharing in the academic context of digital technology, considers blockchain governance into the process of relational risk-knowledge sharing and defines blockchain governance, which is a novel approach in the supply chain resilience management literature.

This study aims to identify and understand critical success factors for implementing and sustaining circular economy (CE) practices in manufacturing small and medium-sized enterprises (SMEs). More precisely, this study examines the complex interplay between micro-, meso- and macro-level success factors that are deemed critical for implementing and sustaining CE practices.

The study is based on a two-stage methodology that combines a comprehensive literature review and an interview study with 12 Swedish manufacturing SMEs that implement CE practices.

The study identifies and categorizes success factors for implementing and sustaining CE practices in manufacturing SMEs. Based on the findings, a holistic framework is proposed that takes into account multiple perspectives, i.e. at the micro, meso and macro levels. This framework enables a deeper understanding and thus a more nuanced discussion of the complexity inherent in the transition to a CE from the perspective of manufacturing SMEs.

This study contributes to the growing body of research on CE transition. By focusing on SMEs in particular, the paper adds the needed diversity to the study of CE practices and influencing factors at different levels.

Current studies show that the Lean Six Sigma (LSS) methodology is used all over the world, by different types of companies in different countries. However, this is not yet the case for certain developing countries such as Morocco, where this methodology is still being discovered and applied and where also the relationship between the adoption of this methodology and sustainable performance is not yet clear. Thus, the aim of this study is to investigate the impact of LSS tools, used by industrial companies in Morocco, on the three dimensions of performance: economic, social and environmental.

This study used partial least squares-based structural equation modeling (PLS-SEM) to conduct an empirical examination of the impact of LSS tools used by Moroccan manufacturing industries on their sustainable performance. Data were collected using a semistructured questionnaire, with a total of 121 valid responses collected for this study.

The results showed that the adoption of LSS tools has a positive effect on the sustainable performance of these industries. The analysis of the collected data also revealed that this effect is most significant when it comes to social performance, followed by environmental and finally economic performance. It was also found that Lean Management tools have a greater impact than Six Sigma tools.

The results of this study may encourage Moroccan industries that are new to LSS to adopt it, as it proves to have positive results not only on the economic aspects of the firm but also on the improvement of employee well-being and the protection of our planet’s environment. In addition, this study gives them an idea of the tools that are most widely used in their Moroccan context, thus facilitating the choice of tools with which they can begin to embed a continuous improvement mindset.

Although several studies have already analyzed the relationship between the LSS approach and sustainable performance, these studies have generally focused on developed countries that are well advanced in the use of these tools. Hence, the originality of this study is its relevance to the Moroccan context, which still needs more studies in the fields of continuous improvement and sustainability.

Recent worldwide developments have altered how businesses operate. As a result, when making business decisions, the emphasis for many industries has shifted towards digital adoption to ensure sustainability, and the food supply chain is no exception. However, a substantial gap exists in assessing the barriers to a digitised food supply chain enabled by Industry 5.0 technologies. This study strives to bridge the gap by identifying and assessing the barriers to improved traceability.

For this study, a mixed method approach was used encompassing both qualitative and quantitative techniques, including an online survey, exploratory factor analysis (EFA), and the fuzzy evidential reasoning approach (FERA). The literature survey and expert opinion first yielded a list of 18 barriers, which were subsequently examined using EFA. As a result, four barriers were removed. The remaining 14 barriers were then assessed using FERA from the perspectives of the Technology, Organisation and Environment (TOE) framework. Further, a sensitivity analysis was performed to test the model’s reliability.

The present study resulted in the prioritisation of barriers from the TOE perspective. According to the findings, the top three barriers that impede the traceability of Industry 5.0-enabled digital food supply chains are Limited Digital and Physical Infrastructure, Inadequate Capital Investment, and the Intricate Supply Chain Framework.

The findings from this research will prove valuable for decision-makers, practitioners and policymakers in developing methods for improving traceability within the digital food supply chain. Concerned stakeholders may use the findings to identify and take immediate action for better decision-making.

This study’s originality lies in its position as one of the first to identify and examine the challenges to better traceability in an Industry 5.0-enabled digital food supply chain. It also adds value by broadening the TOE framework’s scope in the Industry 5.0-enabled digital food supply chain context.