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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 measure scientific literature on the emerging research area of “big data” in the field of “library and information science” (LIS).

This study used the “bibliometric method” for data curation. Web of Science and altmetric.com were used. Data analysis and visualisation were done using three widely used powerful data analytics software, R-bibliometrix, VOSviewer and Statistical Package for Social Sciences.

This study revealed the most preferred venues for publication. Furthermore, this study highlighted an association between the Mendeley readers of publications and citations. Furthermore, it was evident that the overall altimetric attention score (AAS) does not influence the citation score of publications. Other fascinating findings were moderate collaboration patterns overall. Furthermore, the study highlighted that big data (BD) research output and scientific influence in the LIS sector are continually increasing.

Findings related to BD analytics in LIS techniques can serve as helpful information for researchers, practitioners and policymakers.

This study contributes to the current knowledge accumulation by its unique manner of blending the two approaches, bibliometrics and altmetrics.

This study aims to enhance our understanding of customer expectations and experiences in grocery shopping within the metaverse. It investigates factors influencing customer satisfaction and driving continued engagement with metaverse platforms, offering insights into the drivers of customer adoption and barriers to usage.

Adopting a qualitative netnographic approach, this study analysed customer reactions to Walmart’s virtual store demonstration. Data were collected from user comments on YouTube, TikTok, Twitter and Reddit. Thematic analysis was employed to identify key factors contributing to satisfaction and dissatisfaction with metaverse grocery shopping experiences.

The study reveals three major drivers shaping customer satisfaction and subsequent positive intentions toward grocery shopping in the metaverse: social, functional and hedonic stimuli. Eight critical barriers affecting the metaverse shopping experience are identified: functional, hedonic, social, financial, privacy, safety, ownership and store atmospherics concerns, including tactile, acoustic and visual elements.

The findings are derived from a qualitative analysis of customer comments on social media platforms, which may limit generalisability. Future studies could adopt a mixed-methods approach to validate these findings across broader datasets.

This work is the first research to examine customer satisfaction with grocery shopping in the metaverse. It offers valuable insights into customer expectations, adoption drivers and critical barriers, laying the groundwork for further exploration of metaverse applications in retail.

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.

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 paper aims to explain how consumer persuasion knowledge and perceived deception in advertisements can influence consumers’ future evaluation of fake news about a brand.

This research develops a conceptual model using widely used persuasion knowledge theory and confirmation bias theory. A questionnaire-based online survey (n = 410) was conducted by displaying an advertisement stimulus followed by a fake news stimulus to test the model. Covariance-based structural equation modeling was used to analyze the hypothesized research model.

The results demonstrate that consumers with high persuasion knowledge are more likely to trust and adopt fake news about an advertised brand through the mediation of perceived deception in the advertisement. Additionally, perceived deception indirectly affects information adoption through the mediation of news credibility.

Theoretically, this study contributes to the existing body of literature on advertising deception and fake news. This research also extends theory of persuasion knowledge in understanding adoption of fake news. Practically, this study has significant implications for various stakeholders, including brands, social media corporations and consumers.

This research adds novel insights in the relationship of consumers’ persuasion knowledge and credibility and adoption of fake news. Furthermore, the investigation of the relationship between the perceived deception in advertising and the adoption of fake news has not been explored, which is also novel.

This study aims to investigate the relationship between patient satisfaction (PS) and the parameters in healthcare and supply chain management (HLSCM).

The structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) method have been employed to identify correlation and possible configuration of causal factors that influence PS, including lack of resilience (LS), lack of visibility (LV), cost management (CM) and integration and interoperability (II).

The results from SEM confirmed that PS is highly correlated with lack of visibility, CM and II as critical parameters. Moreover, fsQCA findings state that the configuration of high levels of both resilience and lack of visibility, as well as high levels of II, are crucial for PS.

The researchers also identified the configuration of factors that lead to low PS. The study’s results could assist healthcare providers in improving their supply chain operations, resulting in more effective and efficient healthcare service delivery and ultimately improving PS.

The fsQCA method used in the study provides a more nuanced understanding of the complex interplay between these factors. The inclusion of supply chain management characteristics as parameters in the evaluation of PS is a novel aspect of this research. Previous studies largely focused on more traditional factors such as physical care, waiting times and hospital amenities. By considering supply chain management factors, this study provides insights into an under-explored area of PS research, which has important implications for healthcare providers looking to improve their operations and PS.

The construction industry is often criticised due to inherited challenges: poor payment practices, inadequate collaboration, etc. Blockchain has the potential to address these issues with its salient features. Nonetheless, adopting blockchain and smart contracts (B&SC) within the construction industry is a comprehensive endeavour due to its intricate nature. The situation is bleak in Sri Lanka due to numerous barriers such as limited technical expertise, cost implications and many others. Hence, this paper aims to examine these barriers, appropriate strategies to overcome them, explore potential blockchain-applicable areas and formulate a framework to adopt B&SC in the construction industry of Sri Lanka (CISL).

This study employed a mixed research approach. The barriers for the adoption of B&SC to the CISL and their applicable areas were distinguished through a questionnaire survey and analysed using the Relative Importance Index. Strategies to overcome them were identified through expert interviews and analysed utilising manual-content analysis.

The study identified 15 barriers, 12 strategies and 9 areas for integrating B&SC for the CISL. The analysis indicated that having fewer blockchain-powered applications, the reluctance of the enterprises to bear costs to integrate blockchain and sluggish adaptation to new digital technologies are significant barriers. Further, conducting an industry-wide digitalisation analysis, developing an industry-wide digitalisation strategy and recruiting skilful IT staff were pointed out as the pivotal strategies. Moreover, payment and supply chain management were identified as areas with high potential.

This study unveils an analysis of barriers, strategies and areas in adopting B&SC for CISL and formulates a framework. It can be deployed as a guideline for implementing B&SC in the CISL.

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.