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The issue of food security is one of the critical global challenges. The Government and the industries have begun apprehending the importance of green supply chain management (GSCM) implementation in their supply chains. There are various drivers or performance indicators (PIs) of GSCM in the agro-sector. This paper aims to analyse 14 PIs using an interpretive structural modelling (ISM) approach.

In this study, the PIs of GSCM were identified through a literature survey and opinions of field experts. The identified 14 PIs were modelled by applying an ISM methodology for establishing the interrelationship between the PIs and to identify the PIs having high influential power.

The result of the investigation underlined that three PIs, namely, environmental management (PI 1), regulatory pressure (PI 3) and competitive pressure (PI 2) are the significant PIs having high driving power.

The experts’ judgments were used for the development of the structural model, which could be biased influencing the reliability of the model. Also, only 14 significant PIs were considered for the analysis. This research is intended to help the policymakers, managers and supply chain designers in the food industry and in agribusiness in formulating the policies and strategies for achieving food security, conservation of the environmental resources and for improving the financial performance of the industry.

It is pioneering research focusing on the analysis of the PIs towards the implementation of GSCM in the Indian agro-industries context using an ISM approach. This research adds value to the existing knowledge base by identifying the crucial PIs, exploring their mutual relationship and highlighting their level of influence in the case sector.

The purpose of this paper is to identify the critical factors influencing the cloud computing adoption (CCA) in the manufacturing micro, small and medium enterprises (MSMEs) by employing a decision-making trial and evaluation laboratory (DEMATEL) methodology.

Through literature review and expert opinions, 30 significant factors were identified, and then a DEMATEL approach was applied for exploring the cause–effect relationship between the factors.

The results of study highlighted that five factors, namely, “hardware scalability and standardisation”, “cost (subscription fees, maintenance cost and implementation cost (CS1)”, “innovation”, “installation and up gradation (CS28)”, and “quality of service” were the most significant factors influencing the CCA in the case sector.

The DEMATEL model was developed by considering expert inputs, and these inputs could be biased which can influence the reliability of the model. This study guides the organisational managers, cloud service providers and governmental organisations in formulating the new policies/strategies or modifying the existing ones for the effective CCA in the case sector.

For the first time. interdependency between the critical factors influencing CCA was discussed by employing the DEMATEL approach in the Indian manufacturing MSMEs context.

The purpose of this paper is to identify and model the evaluation criteria for the selection of third-party logistics service provider (3PLSP) by an interpretive structural modelling (ISM) approach in the pharmaceutical sector.

Delphi technique was used for identifying the most significant criteria, and the ISM method was employed for developing the interrelationship among the criteria. Also, the critical criteria for having high influential power were identified by using the Matrice d’Impacts Croisés Multiplication Appliqués à un Classement analysis.

The most significant factors, namely, capability of robust supply network/distribution network, quality certification and health safety, service quality and environmental quality certifications, were found to have a high driving power, and these factors demand the maximum attention of the decision makers.

As the ISM approach is a qualitative tool, the expert opinions were used for developing the structural model, and the judgments of the experts could be biased influencing the reliability of the model. The developed hierarchical concept is proposed to help the executives, decision and policy makers in formulating the strategies and the evaluation of sustainable 3PLSP.

It is an original research highlighting the association between the sustainable 3PLSP evaluation criteria by employing ISM tool in the pharmaceutical industry. This paper will guide the managers in understanding the importance of the evaluation criteria for the efficient selection of 3PLSP.

The objective of this article is to carry out the driving power and dependency analysis of green human resource management (GHRM) indicators of the automotive service sector to identify the most significant ones.

The GHRM indicators were identified through exhaustive literature search and validated through the semi-structured interview with 15 domain experts. The ‘Total Interpretive Structural Modelling (TISM)’ approach was applied for exploring the contextual relationship between the indicators and simultaneously developing their structural hierarchy. The MICMAC analysis was used for categorising the indicators based on their ability to influence the other ones.

In the present study, indicators namely ‘Green organisational culture and adoption of green strategy (C5)’ and ‘Green training and development (C1)’ were found to be the significant ones, whereas ‘Green employee relations and union-management (C10)’ was found to be highly dependent on the rest of the indicators.

The proposed model has been developed in the Indian context and is limited to the automotive sector. However, the same model may apply to other domains of different economies by carrying out slight modifications to the same. Also, the inputs taken from the experts of the case sector could be biased. For the HR professionals, the present study helps to identify the key indicators which need to be considered for enlightening the environmental performance of the service organisation.

This research adds a significant assessment to the current knowledge base by assessing the contextual relationship between the indicators of GHRM as none of the past studies focused on the same by using the TISM method in the Indian service sectors context.

This study aims to identify and analyse critical barriers to cloud manufacturing (CM) adoption for achieving sustainability amongst manufacturing micro, small and medium-sized enterprises (MSMEs) in an emerging economy.

Initially, through a literature survey and expert inputs, this study identified and finalized 21 critical barriers to CM adoption, which were further analysed by employing a hybrid multiple criteria decision-making (MCDM) approach. In this approach, the best–worst method (BWM) was used to rank the identified barriers. However, to analyse the interrelation amongst these barriers and classify them into the cause-effect group, the decision-making trial and evaluation laboratory (DEMATEL) method was employed. Furthermore, the study recommended various strategies that can help overcome high-priority barriers and enhance CM adoption within firms.

The results of the study show that organizational, legal and economic barriers are the high-priority barriers that need to be addressed to enhance CM adoption.

The findings of the study will help managers and decision-makers in manufacturing MSMEs develop effective strategies and policies to enhance CM adoption.

Notably, this investigation is one of the preliminary research activities to explore the sustainability aspect of CM adoption amongst MSMEs in an emerging economy.

Textile and Apparel (T&A) sector significantly influences socio-economic and environmental dimensions of the sustainability. The purpose of this paper is proposed to establish the interrelationship among the critical barriers to the sustainable development of T&A supply chains by using a multi-criteria decision-making approach and to obtain a ranking of the barriers.

In the present investigation through literature review and from expert opinions, 14 significant challenges to the sustainable growth of T&A sector have identified. For establishing the interrelationship and for developing a structural model of the identified challenges, interpretive structural modelling (ISM) methodology is employed.

The results of the investigation revealed that lack of effective governmental policies (B8), poor infrastructure (B4), lack of effective level of integration (B6), low foreign investment (B13) and demonetization (B12) are the top most significant challenges.

The model development based on the expert inputs from the industry and academia, these inputs could be biased influencing the accuracy of the model. Also, inclusion more factors for the analysis will improve the reliability of the model.

This research is intended to guide the policy and decision makers for improving overall the growth of the T&A supply chain.

Cloud technology is extremely critical for the continuing progress of Industry 4.0 and it helps in pooling centralized information for the business. Further, it offers a platform for collaboration for improving the performance of the industry. This paper aims to investigate the factors affecting the implementation of cloud technology for boosting Industry 4.0 adoption in micro, small and medium enterprises (MSMEs) of the manufacturing sector.

A total of 14 enablers were considered for the case study which were obtained from the literature survey and shortlisted by the experts of the domain (academia and industries). The interpretive structural modelling (ISM) approach has been used as a methodology for exploring the relationships between the enablers. Matrices impact croises multiplication applique and classment (MICMAC) analysis has been carried out for the validation of the developed structural model. MICMAC analysis helps to identify driving power and dependency potential of all considered enablers.

The results of the investigation indicate that three factors, namely, system integration, project management and competitive pressure, were significant. These factors drive all other considered factors in the implementation process.

The opinions of the experts from the industry and academia were analyzed for the development of the hierarchical model and these inputs could be biased. This investigation intends to help the decision makers in the effective adoption of the cloud and Industry 4.0 technologies and for the formulation of the efficient implementation policies.

The present study aims to focus on cloud technology as well as Industry 4.0 in the context of manufacturing MSMEs and none of the previous investigations analyzed the enablers that influence the adoption of cloud technology for boosting Industry 4.0, especially using the ISM approach.

The purpose of this study is to determine the significant factors affecting the adoption of Cloud of Things (CoT) by Indian small and medium-sized enterprises, using exploratory and confirmatory factor analysis.

Significant factors that impact CoT implementation were identified through a detailed literature survey. A conceptual framework and hypotheses were proposed for linking the significant factors so identified, namely, cost saving, relative advantage, sharing and collaboration, reliability, security and privacy, technical issues and adoption intention. The data were collected from 270 Indian SMEs using an online survey. Structural equation modelling (SEM) was used to test the proposed model.

It was observed that factors such as “sharing and collaboration”, “cost saving” and “relative advantage” had a positive influence on CoT adoption. Findings of the study also supported the hypothesis that “security and privacy” were the prime concerns for CoT adoption.

Sample coverage across different geographical areas with qualitative data can be helpful. The SEM methodology is only capable of verifying linear relationships; to counter this, a hybrid approach with tools such as artificial neural network and multiple linear regression can be used.

This study intends to guide the managers of SMEs, cloud service providers and regulatory organisations for formulating an effective strategy to adopt CoT. It may be noted that CoT is the prime building block of Industry 4.0 and SMEs will benefit from government support for the same.

This paper highlights the influence of factors on the adoption intention of CoT with a focus on the SMEs of a developing country like India.

This study aims to provide an overview of what characterizes the current state of research in the field of cloud computing use in human resource management (HRM) with the identification, analysis and classification of the existing literature and lines of research addressed and to provide guidance for future research.

The systematic literature review (SLR) technique has been used to identify, select, analyze and evaluate the existing publications on cloud computing and HRM. A total of 35 papers published up to December 2020 have been obtained from the Web of Science (WoS) scientific database. The research design has allowed us to determine what characterizes the current state of research on the use of cloud computing in HRM and obtain a novel classification of the literature that identifies four lines of research and the contributions in each line and has allowed us to define the future research agenda.

The four groups into which the papers on the cloud computing-HRM relationship have been classified are: (1) studies focused on the development of cloud platforms for HRM that highlight technical aspects, (2) papers that focus on the concept of human resource elasticity, (3) papers on the adoption and/or implantation of cloud platforms for HRM and (4) studies that highlight the effects or implications of cloud platforms for HRM. This paper proposes some new opportunities for future research and presents some helpful implications from the theoretical and management perspectives.

This study uses only scientific articles in the WoS database with a Journal Citation Report (JCR) or SCImago Journal Rank (SJR) impact.

This paper provides an overview of the knowledge on cloud computing and HRM research and offers recommendations for future research.

The purpose of this paper is to identify and model critical barriers to sustainable practices implementation in Indian oil and gas sectors by the interpretive structural modeling (ISM) approach.

In this paper, through exhaustive literature survey and experts opinions, 14 critical barriers were identified, and the ISM tool, which is a multi-criteria decision-making approach, was used to establish interrelationship among the identified barriers and to determine the key barriers having high driving power.

After analyzing, it was found that six barriers, namely, market competition and uncertainty (B7), shortage of resources (B8), governmental rules and regulations (B1), knowledge and training (B2), financial implications (B3), and management commitment and leadership (B5) were found to have high influencing power. These barriers need the maximum attention and organizations need to overcome these hindrances for the effective implementation of sustainable practices. From the driving and dependence power diagram, two barriers, namely, management commitment and leadership (B5) and knowledge and training (B2) were found to have the highest driving power and two barriers, namely, lack of green initiatives (B9) and lack of corporate social responsibility (B14) were found to have highest dependence power.

The presently developed model is based on the experts’ opinions, which may be biased, influencing the final output of the structural model. The research implications of the developed model are to help managers of the organization in understanding significance of the barriers, to prioritize or eliminate the same for the practical implementation of sustainability.

This is for the first time an attempt has been made to apply the ISM methodology to explore the interdependencies among the critical barriers for Indian oil and gas industries. This paper will guide the managers at various levels of an organization for effective implementation of the sustainable practices.