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In the wake of the COVID-19 pandemic, remote working (RW) has emerged as a viable alternative to working employees in general and knowledge workers in particular. However, previous researchers have worked on the concept, development and facilitation of RW since the 1970s. Therefore, this study aims to review the existing literature on RW to ascertain the evolution of the concept in the business and management domain and provide for requisite arguments to extend the settings for future research agendas.

The authors based this study on a bibliometric analysis of articles (n = 349) retrieved from the Web of Science database published between January 1990 and October 2021. The authors have used a bibliometric toolbox comprising performance analysis, science mapping and network analysis in various software namely, VOSviewer, Gephi and Biblioshiny package in R.

The study’s results accentuated important themes like work–life balance, strengthening digital infrastructure, performance and productivity, hybrid work models and well-being and clustered them under four heads with proposed future research questions.

The study is based on a single database; the authors have used an extensive but not exhaustive list of keywords to retrieve the articles. The analysis employs certain threshold limits while using the science mapping technique.

This study would enable managers and academics to comprehensively understand remote work and offer logical implications to appreciate its nuances.

This study is unique as it recognizes the intellectual structure in the existing literature on RW and traces the advancements and exponential growth post-COVID-19. The authors recapitulated the literature as network analysis of the RW facilitation model comprising the antecedents, outcomes, mediators and moderators.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.