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This research develops a theoretical framework to understand the role of big data analytics capability (BDAC) in enhancing supply chain sustainability and examines the moderating effect of green supply chain management (GSCM) practices on this relationship.

Guided by the dynamic capability view (DCV), we formulated a theoretical model and research hypotheses. We used partial least square-based structural equation modeling (PLS-SEM) to analyze data collected from 159 survey responses from Bangladeshi ready-made garments (RMG).

The statistical analysis revealed that BDAC positively impacts all three dimensions of supply chain sustainability: economic, social and environmental. Additionally, GSCM practices significantly moderate the relationship between BDAC and supply chain sustainability.

This study makes unique contributions to the operations and supply chain management literature by providing empirical evidence and theoretical insights that extend beyond the focus on single sustainability dimensions. The findings offer valuable guidelines for policymakers and managers to enhance supply chain sustainability through BDAC and GSCM practices.

This study advances the current understanding of supply chain sustainability by integrating BDAC with GSCM practices. It is among the first to empirically investigate the combined effects of BDAC on the three dimensions of sustainability – economic, social and environmental – while also exploring the moderating role of GSCM practices. By employing the DCV, this research offers a robust theoretical framework highlighting the dynamic interplay between technological and environmental capabilities in achieving sustainable supply chain performance.

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.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

This study aims to examine the mediating effects of two corporate social responsibility factors – leadership mindset and corporate commitment – on the relationship between sustainable orientation toward Industry 4.0 and environmental decision-making effectiveness.

The research model was tested using structural equation modeling based on survey data from 175 companies serving the Hajj and Umrah sector in Saudi Arabia, a sector recognized for its high level of digitalization.

The results indicate that a sustainable orientation toward Industry 4.0 and digitalization positively influences environmental decision-making effectiveness. Additionally, Corporate commitment was found to have a direct positive effect on environmental decision-making effectiveness, while leadership mindset showed no significant effect.

This study highlights the critical role of sustainable Industry 4.0, driven by digitalization, in enhancing service quality and competitive value in companies serving the Hajj and Umrah sector. These companies see digitalization as an opportunity to improve business outcomes through effective environmental strategic decisions, though its application remains challenging. The study contributes to the existing body of knowledge by providing empirical insights into the impact of digitalization on environmental decision-making effectiveness within this specific context.

The purpose of this study is to explore how big data analytics (BDA) as a potential information technology (IT) innovation can facilitate the retail logistics supply chain (SC) from the perspective of outbound logistics operations in the United Kingdom. The authors' goal was to better understand how BDA can be integrated to streamline SCs and logistical networks by using the technology, organisational and environmental model.

The authors applied existing theoretical foundations for theory building based on semi-structured interviews with 15 SC and logistics managers.

The perceived benefits of using BDA in outbound retail logistics comprised the strongest predictor amongst technological, organisational and environmental issues, followed by top management support (TMS). A framework was proposed for the adoption of BDA in retail logistics. Contextual concepts from previous literature have helped us understand how environmental changes impact BDA decision-making, as such: (i) SC maturity levels and connectivity affect BDA utilisation, (ii) connected SCs improve data accessibility and information exchange, (iii) the benefits of BDAs also affect adoption and (iv) outsourcing complex tasks to experts allows companies to focus on core businesses instead of investing in IT infrastructure.

Outside the key findings listed, this study shows that there is no one-size-fits-it-all approach for use within all organisational settings. The proposed framework reveals that the perceived benefit of BDA is non-transferrable and requires top-level management support for successful implementation.

The existing literature focusses on the approaches to applying BDA in SC and logistics but fails to present a deep dive into retail outbound logistics activity. This study addresses the “how” and proposes a social-inclusive framework for a technology-enabled topic.

This paper aims to examine the direct and indirect impact of competitive intelligence (CI) practices on sustainable competitiveness (SC) and firm performance (FP) mediated by strategic design collaboration (SDC).

This empirical study is based on a survey of 179 respondents comprising senior managers from pharmaceutical companies operating in Egypt across three sectors: public, private and multinational corporations (MNCs).

The results show that CI has a positive and significant impact on SDC, which, in turn, positively impacts SC and FP. The study also shows that SDC mediates the relationship between CI and both SC and FP.

This study sheds light on the significant and mediating role of SDC in explaining the relationship between CI and both SC and FP.

This paper aims to systematically review the managerial literature on Industry 4.0 (I4.0) in emerging markets (EMs) through bibliometric analyses to identify incipient research streams and literature gaps and recommend avenues for future research.

This research uses bibliographic coupling analysis (BCA) to obtain a comprehensive view of the intellectual contours within the addressed literature. The primary source utilised is the Web of Science database. A total of 345 peer-reviewed journal articles were retrieved. Complementing BCA, we use social network analysis and the content analysis of articles to study the resulting literature clusters.

The results reveal four thematic clusters: (1) Adoption of I4.0 in EMs; (2) impact of I4.0 on organisational aspects and financial performance of companies and supply chains in EMs; (3) I4.0, lean management and operational performance in EMs and (4) I4.0 and the development of sustainable practices in EMs. We supplement these results with the proposal of different future avenues of research, both general and specific, for each identified cluster.

The current study has certain limitations arising from using the bibliometric method and techniques employed in the analyses.

To the best of the authors’ knowledge, there is no comprehensive literature review article on this subject. This research is deemed valuable for future scholars as it facilitates the identification of research fronts that define the forefront of knowledge, reveals current trends and sets the stage for further exploration of key issues in the field. This, in turn, can offer valuable insights to academics, policymakers and practitioners.

Supply chain disruptions are a significant risk to businesses in a global marketplace because they make it more challenging for suppliers to effectively transport goods and services to customers. Therefore, it is essential to comprehend how these disruptions affect the retail food supply chain during pandemics and explore how digitalization might help to mitigate these issues in the future.

A hybrid systematic review and analysis was conducted by retrieving data set from the scopus database using strong keyword search strategy. Later a content analysis was also done to gain more insights on the proposed research.

The results show that there are several possibilities enabling optimal scenario planning supply chain disruptions and mitigation. In this area, digitalization improves customer satisfaction and logistical efficiency, particularly in transportation and network optimization. In order to cope with uncertainty and grasp significant enhancements proactive strategies and collaboration that are guided by scenario planning and digitalization assist in developing robust supply chains that are sufficiently adaptable to adapt to shifting market conditions.

The study is limited to research papers indexed in Scopus from 2015 to 2023 with a more comprehensive review of retail food supply chain disruptions.

This research provides practical insights for retail food supply chain managers, highlighting the importance of digital maturity and scenario planning by leveraging digital tools and proactive strategies to improve logistical efficiency.

This study helps in building resilient supply chains ensures the reliable availability, and food security of essential goods, particularly during crises.

This research uniquely links digitalization and scenario planning to managing supply chain disruptions, highlighting how digital tools and strategic planning enhance resilience and adaptability in the retail food supply chain.

This study aims to investigate the mediating role of different types of intellectual capital (human, structural and relational) in the relationship between artificial intelligence-driven supply chain analytics capability (AI-SCAC) and various supply chain decision-making processes, specifically rational, bounded and tacit decision-making.

The study used a quantitative survey strategy to collect the data. A total of 320 valid questionnaires were received from manufacturing companies. The data were analysed using structural equation modeling with partial least squares (PLS-SEM) approach through SmartPLS software.

The results indicate that human and structural capital significantly mediate the relationship between AI-SCAC and rational and bounded decision-making processes. However, structural capital does not mediate the relationship between AI-SCAC and the tacit decision-making process. Moreover, relational capital does not show a significant mediating effect on all of the decision-making processes. Notably, structural capital has the strongest impact on rational and bounded decision-making, while human capital plays a critical role across all three decision-making processes, including tacit decision-making.

This study contributes to the literature by providing a nuanced understanding of the differentiated impact of intellectual capital components on various decision-making processes within the context of AI-SCAC. While previous studies have broadly acknowledged the role of intellectual capital in decision-making, this research provides more understanding of how specific types of intellectual capital interact with AI to influence distinct decision-making processes. Notably, the differential impact of structural capital on rational and bounded decision-making versus tacit decision-making highlights the need for organisations to adopt a more tailored approach in leveraging their intellectual capital.

The purpose of this study is to explore the disclosure of sustainable development goals (SDGs) by Indian firms and to examine the association between sustainable business practices and the disclosure of SDGs.

The study is based on large Indian non-financial firms listed in Bombay Stock Exchange 200 for six years from 2016–2017 to 2021–2022. Sustainable business practices are measured using four important indicators - the quality of sustainability discourse, compliance with Global Reporting Initiative guidelines, adoption of the guidelines of the International Integrated Reporting Council and external assurance of published reports. Content analysis is used to compute the disclosure score of SDGs and corporate sustainability performance. The authors have used a fixed effects regression model followed by Tobit model and two-stage least square model to examine the association between sustainable business practices and the disclosure of SDGs.

The results indicate an increasing trend of disclosure of SDGs by Indian firms. The empirical findings suggest a positive impact of sustainable business practices on the SDGs disclosure after controlling for firm-specific and corporate governance variables. Among the components of corporate sustainability, social and environmental factors positively influence the SDGs.

The study is a noble attempt to enrich the extant literature by providing empirical evidence on the association between sustainable business practices and disclosure of SDGs considering four important indicators of sustainable practices. The findings are useful for theoretical and practical implications.