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Grey Systems: Theory and Application (GSTA) journal started publication in 2011 and completed a decade in 2021. The purpose of this study is to provide a detailed bibliometric analysis of the articles published in GSTA and their content primary trends and themes.

This study uses the Web of Science (WoS) database to analyze the content of published articles. A range of bibliometric analyses and indicators are applied to analyze the GSTA article content using science mapping tools of the Bibliometrix package in the R environment.

The GSTA publishes around 28 articles each year with citations of this work steadily growing over time. The impact of these publications is measured as total mean citations which increased from 0 to 11. The journal has attracted contributors from around the globe, most often affiliated with China, India and Europe. Thematic evolution of the journal's themes reveals that it has expanded its scope to include topics such as relational analysis, decision making, incidence analysis, and forecasting, hybrid grey-fuzzy or grey-rough modeling, etc.

The study is majorly based on GSTA data available on the WoS database.

This study provides the first overview of GSTA's publication and citation trends as well as the evolution of its thematic structure. It also suggests future directions that the journal might take to strengthen its position.

This study aims to address the adoption issues of green and sustainable practices in the hotel industry. The study identifies critical performance indicators (CPIs) and utilizes Hotel Carbon Management Initiative (HCMI) framework to prioritize CPIs for achieving a robust adoption framework for green and sustainable practices.

The hotel industry is driven by changing ecological degradation, and it is necessary to achieve feasible development goals. This research article formulates the CPIs derived from HCMI and decision-making model is created using the Analytic Hierarchy Process (AHP).

In this research, CPIs of HCMI are considered and aim to formulate five major CPIs of HCMI, namely air pollution, energy efficiency, water conservation, noise pollution and waste management. The study identifies the need for better control and sustainable growth in the Indian hotel industry with minimum carbon emissions coupled with the green approach adoption.

The CPIs work on minimization of risks and maximizing optimality of return on investment. The development of the hotel industry will be improved and immensely welcomed by capping the carbon emission with the green initiatives. This research is limited as urban hotels are surveyed in this study.

This work makes a valid argument to establish HCMI as a model initiative for environment quality improvement and further extension of other activities in the hospitality sector and scale-up sustainable practices for future-ready circular economies.

Circular supply chain management (CSCM) is proving to be a strong contributor towards sustainable development. The purpose of this study is to analyse the circular practices in CSCM implementation towards sustainability in the Indian auto sector; these practices are at the initial stage in a developing economy.

In this research work, a framework has been developed to enrich understanding of various CSCM practices for sustainability. To develop a causal framework, a Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL) methodology has been adopted.

The results produce 32 practices with cause and effect groups and their importance/priorities. Based on F-DEMATEL, these 32 practices are grouped into cause and effect groups based on R + C and R − C values.

The findings will help managers and decision-makers to gain a deeper understanding of the practices and their nature; the governmental and financial practices are identified as the most critical factors that need more attention. The results will help strategy makers to plan accordingly.

This research has identified and analysed 32 basic practices and their interrelationships to achieve sustainability in CSCM.

The study aims to examine the influence of Sustainable Human Resource Management (SHRM) practices and Industry 4.0 Technologies (I4Te) adoption on the Employability Skills (ES) of the employees. The study has undertaken four major SHRM practices – Training (TR), Flexibility (FL), Employee Participation (EP) and Employee Empowerment (EE) to measure its impact on the ES along with I4Te.

A survey approach method was designed on the identified constructs from existing literature based on SHRM, I4Te and ES. The survey resulted into 198 valid responses. The study used confirmatory factor analysis (CFA) and structural equation modelling (SEM) using SPSS 25.0 and AMOS 25.0 for constructs validation and hypothesis testing.

The current study reveals that all the four SHRM practices (TR, FL, EP and EE) along with I4Te directly influence ES in the organisation. The I4Te along with the SHRM practices may bring enhancement in the skills and competencies of the employees that is the requirement of future organisations.

Considering the results, the SHRM practices aligned with I4Te may directly influence the employee's ES including core skills, IT skills and personal attributes. The SHRM practices in the organisation will enhance the opportunities for the employees and bring long-term association with the employees.

For the development of the economy and the individual, the SHRM practices need to conduct themselves in more socially responsible ways along with the I4Te to enhance the ES of the employees. The individual development will bring sustainable behavioural changes in the employees.

There has been no research conducted on exploring SHRM, I4Te and ES together. This is the pioneer in the HRM fields that explores the interrelationships and influence amongst the five constructs undertaken in the study.

The purpose of this research is to establish a conceptual model to understand the impact of Total Productive Maintenance (TPM) and Industry 4.0 (I4.0) on the transition of a Circular Economy (CE). Also, the paper explores the combined impact of TPM, I4.0 and CE on the sustainability performance (SP) of manufacturing firms.

The conceptual model is proposed using the dynamic capability view (DCV) and empirically validated by partial least squares-structural equation modelling (PLS-SEM) using 304 responses from Indian manufacturing firms.

The results suggest that I4.0 positively impacts TPM, CE and SP, also showing TPM's positive impact on CE and SP. In addition, CE has a positive influence on the SP of manufacturing firms. Furthermore, CE partially mediates the relationship between I4.0 and SP with TPM and SP. The study also identifies TPM, I4.0 and CE as a new bundle of dynamic capabilities to deliver SP in manufacturing firms.

The present research adds to the knowledge and literature on DCV by identifying the importance of CE in the settings of I4.0 and TPM, especially in the context of sustainability. Also, the current study offers a new set of dynamic capabilities and provides some significant future recommendations for researchers and practitioners.

The main aim of this paper is to examine the claim that economic value added (EVA) advocates its superiority over the traditional accounting-based financial performance measures, i.e. profit after tax (PAT), earnings per share (EPS), return on assets (ROA), return on equity (ROE) and return on investment (ROI) in the Indian manufacturing sector and at the same time, give empirical facts. It also tests and examines the information content of various performance measures and their relationship with stock returns.

The paper uses the sample of 534 Indian manufacturing companies from the Bombay Stock Exchange (BSE) during the period 2000–2018. Multiple regression models are applied to examine the information content of EVA and traditional performance measures in explaining shareholders’ returns.

Relative information content tests revealed that traditional accounting-based measures such as EPS, ROE and ROA performed better than EVA in explaining the returns of Indian manufacturing companies. Incremental information content of EVA adds little contribution to information content above traditional performance measures. The claim of superiority of EVA over accounting-based measures in association with shareholder returns is proved invalid in Indian manufacturing companies.

This study concludes that EVA has no superiority over traditional accounting-based financial performance measures in explaining stock returns of Indian manufacturing companies. To achieve heftiness in outcomes, panel data are tested by using Breusch–Pagan–Godfrey (BPG) test for heteroskedasticity, Hausman’s test for fixed and random effect, variance inflation factor (VIF) test for multicollinearity and Durbin–Watson test for autocorrelation.

In this paper, the authors emphasize that COVID-19 pandemic is a serious pandemic as it continues to cause deaths and long-term health effects, followed by the most prolonged crisis in the 21st century and has disrupted supply chains globally. This study questions “can technological inputs such as big data analytics help to restore strength and resilience to supply chains post COVID-19 pandemic?”; toward which authors identified risks associated with purchasing and supply chain management by using a hypothetical model to achieve supply chain resilience through big data analytics.

The hypothetical model is tested by using the partial least squares structural equation modeling (PLS-SEM) technique on the primary data collected from the manufacturing industries.

It is found that big data analytics tools can be used to help to restore and to increase resilience to supply chains. Internal risk management capabilities were developed during the COVID-19 pandemic that increased the company's external risk management capabilities.

The findings provide valuable insights in ways to achieve improved competitive advantage and to build internal and external capabilities and competencies for developing more resilient and viable supply chains.

To the best of authors' knowledge, the model is unique and this work advances literature on supply chain resilience.

Coronavirus disease 2019 (COVID-19) has disrupted global supply chains, revealing dreadful gaps and exposing vulnerabilities. Retailers are challenged to tackle risks and organise themselves to fit into the “new normal” scenario. This global outbreak has established a volatile environment for supply chains; it has raised the question of survival in the market, forcing companies to rethink resilient strategies to be adopted for the post-pandemic situation to mitigate the long-term effects of this virus. This study explores the priorities for retail supply chains (RSCs) to align their business operations and strategies for the post-pandemic world.

This study has utilised integrated full consistency model (FUCOM) – best–worst method (BWM) for assessment of RSCs to enhance their business performance irrespective of pandemic disruptions. The FUCOM has been employed to identify the priorities of determinants enhancing business performance, whereas RSC strategies are evaluated using the BWM method.

The current study identifies “Collaboration Efficiency” as the main criterion for accelerating the performance of RSCs in a dynamic social environment. Also, the study concludes that “Order Fulfilment” and “Digital RSCs” are the most appropriate resilient business strategies to mitigate the long-term effects.

Supply-demand balancing is a challenging task at the moment, but highly significant for the future. The pandemic disruptions have placed intense pressure on retailers to deliver products as per consumers' changing behaviours towards the purchase of essentials and other products. Hence, “Order Fulfilment” and “Digital RSCs” should be adopted for meeting customer requirements and to ensure sustainability in the post-pandemic business world.

This work sets out a comprehensive framework which will be helpful for accelerating RSCs performance against pandemic disruption by adopting resilient strategies to mitigate the long-term effects.

The case offers a study of change management in the pharmaceutical industry in India.

The case is designed for undergraduate and postgraduate students to examine strategic decisionmaking in the context of mergers and acquisitions (M&As), firm capabilities and management practices. In particular, it has important pedagogical lessons for businesses eager to start operations in emerging countries. Students learn to recognize the unique nature of the pharmaceutical market and the factors affecting the demand and supply of drugs, including the economics of generics. The case can be discussed in one class session of approximately one-and-a-half to two hours duration.

In 2012, the pharmaceutical industry in India was undergoing dynamic changes. There was keen interest among MNC pharmaceutical giants to buy up Indian generic manufacturing companies since their revenues were drying up with the impending patent expirations of many blockbuster brand name drugs. Japan's Daiichi Sankyo's had taken over the largest Indian pharmaceutical company, Ranbaxy Laboratories, known for its heritage of process innovations and market leadership. However, after the acquisition, Ranbaxy slipped to third position in the domestic market and was facing multiple problems including net losses and falling share prices, cultural differences in management practices, recall of drugs from foreign markets and a US FDA ban on its manufacturing plants. Further, Ranbaxy had always been viewed as a national champion and a customer-friendly company but drug prices had increased after the merger causing problems of affordability. The new CEO of Ranbaxy was facing a dilemma: how to regain the company's position as the market leader. Students are asked to advise the CEO of Ranbaxy how to tackle the challenges arising from the integration of an Indian company with a Japanese company. More specifically, the case focuses on M&A as a strategy for growth and also touches on issues related to competition, regulation, innovation and corporate governance.

The case discusses the different motives behind the deal for Daiichi Sankyo and Ranbaxy and why it was a strategic move by both the alliance partners. The case also raises issues of corporate governance for the management of Ranbaxy and the need for a proactive corporate social responsibility (CSR) strategy. The case provides students with the opportunity to develop their analytical skills in a real-life setting and apply theoretical concepts to the consideration of the various issues raised by the acquisition deal.

Teaching notes are available; please consult your librarian for access.

The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM).

Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia.

The results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM.

The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics.

The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.