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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.

In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).

This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.

This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.

This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

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.

A reconfigurable manufacturing system (RMS) can provide manufacturing flexibility, meet changing market demands and deliver high performance, among other benefits. However, adoption and performance improvement are critical activities in it. The current study aims to identify the important factors influencing RMS adoption and validate a conceptual model as well as develop a structural model for the identified factors.

An extensive review of RMS articles was conducted to identify the eight factors and 47 sub-factors that are relevant to RMS adoption and performance improvement. For these factors, a conceptual framework was developed as well as research hypotheses were framed. A questionnaire was developed, and 117 responses from national and international domain experts were collected. To validate the developed framework and test the research hypothesis, structural equation modeling was used, with software tools SPSS and AMOS.

The findings support six hypotheses: “advanced technologies,” “quality and safety practice,” “strategy and policy practice,” “organizational practices,” “process management practices,” and “soft computing practices.” All of the supported hypotheses have a positive impact on RMS adoption. However, the two more positive hypotheses, namely, “sustainability practices” and “human resource policies,” were not supported in the analysis, highlighting the need for greater awareness of them in the manufacturing community.

The current study is limited to the 47 identified factors; however, these factors can be further explored and more sub-factors identified, which are not taken into account in this study.

Managers and practitioners can use the current work’s findings to develop effective RMS implementation strategies. The results can also be used to improve the manufacturing system’s performance and identify the source of poor performance.

This paper identifies critical RMS adoption factors and demonstrates an effective structural-based modeling method. This can be used in a variety of fields to assist policymakers and practitioners in selecting and implementing the best manufacturing system.

In the digitalization era, supply chain processes and activities have changed entirely, and smart technology impacts each sustainable supply chain movement. The warehouse and distribution of various organizations have started adopting smart technologies globally. However, the adoption of smart technologies in the Indian warehousing industry is minimal. The study aims to identify the implementation barriers of smart technology in the Indian warehouse to achieve sustainability.

This study employs an integrated Delphi-ISM-ANP research approach. The study uses the Delphi approach to finalize the barriers identified from the detailed literature review and expert opinion. The finalized 17 barriers are modeled using interpretive structural modeling (ISM) to get the contextual relationship. The ISM method's output and analysis using the analytical network process (ANP) illustrate priorities.

The study's findings showed that the lack of government support, lack of vision and mission and the lack of skilled manpower are the most significant barriers restricting the organization from implementing smart and sustainable supply chain practices in the warehouse.

This study would help the practitioners enable the sustainable warehousing system or convert the existing warehouse into a smart and sustainable warehouse by developing an appropriate strategy. This study would also help reduce the impact of different barriers that would strengthen the chance of technology adoption in the warehouses.

The literature related to adopting smart and sustainable practices in the warehouse is scarce. Modeling of adoption barrier for smart and sustainable warehouse using an integrated research approach is the uniqueness of this study that have added value in the existing scientific knowledge.

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

This study initially aims to identify the barriers to the big data analytics (BDA) initiative and further evaluates the barriers for knowing their interrelations and priority in improving the performance of manufacturing firms.

A total of 15 barriers to BDA adoption were identified through literature review and expert opinions. Data were collected from three types of industries: automotive, machine tools and electronics manufacturers in India. The grey-decision-making trial and evaluation laboratory (DEMATEL) method was employed to explore the cause–effect relationship amongst barriers. Further, the barrier's influences were outranked and cross-validated through analytic network process (ANP).

The results showed that “lack of data storage facility”, “lack of IT infrastructure”, “lack of organisational strategy” and “uncertain about benefits and long terms usage” were most common barriers to adopt BDA practices in all three industries.

The findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context.

The paper is one of the initial efforts in evaluating the barriers to BDA in improving the performance of manufacturing firms in India.

Digital supply chains require nascent technologies like blockchain and Internet of Things (IoT). There is a need to develop a roadmap for the implementation of these technologies, as they require a huge amount of resources and infrastructure. The purpose of this paper is to analyze the challenges of implementing blockchain-IoT integrated architecture in the green supply chain and develop strategies for the same.

After a thorough literature survey of Scopus-indexed journals and books, 37 barriers were identified, which were then brought down to 15 barriers after confirming with industry and academic experts using the Delphi method. Using the total interpretive structural modeling (TISM) method and cross-impact matrix multiplication applied to classification (MICMAC) analysis, the barriers were modeled, and finally, strategies were formulated using a concept map to handle the barriers in the blockchain-IoT integrated architecture for a green supply chain.

This paper presents the research on barriers that can be considered for incorporating blockchain and IoT in the green supply chain. It was found from the TISM model that environmental concerns are Level-1 barriers and need to be addressed by developing appropriate technology and allocating funds for the same. An integrated ecosystem with blockchain and IoT is developed.

The focus of this study was on the challenges of blockchain and IoT; hence, it is required to extend the research and find challenges for different industries and also analyze the criteria using other multi-criteria decision-making (MCDM) methods. Further research is required for the integration of blockchain-IoT with supply chain functions.

The transformation of a traditional supply chain into a green supply chain is possible with the integration of technologies. This research work and the strategies developed are useful to managers and practitioners working on technology implementation. Planning resources and addressing key barriers is possible with the concept maps and architecture developed.

Green supply chain management (SCM) is gaining importance in industry as well as the academic sector due to government Policies and norms worldwide for reducing emissions and encouraging environment-friendly production systems. Incorporating blockchain and IoT in a green supply chain will further digitize and increase transparency in supply chains.

We have done a categorization of all barriers based on the expert survey by academicians and industry experts from industries in India. The concept map helps in identifying possible solutions for the challenges and initiatives to be taken for the smooth integration of technologies in the green supply chain.

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.

This study aims to investigate the mediating role of “Big Data Analytics” played between “Project Performance” and nine factors including top management, project knowledge management focus on sustainability, green purchasing, environmental technologies, social responsibility, project operational capabilities, project complexity, collaboration and explorative learning, and project success.

A sample of 321 responses from 106 Indian manufacturing small and medium-scaled enterprises (SMEs) was collected. Data were analyzed using empirical analysis through structural equation modeling.

The result shows that project knowledge management, green purchasing and project operational capabilities require the mediating support of big data analytics. The adoption of big data analytics has a positive influence on project performance in the manufacturing sector.

This study is useful to SMEs managers, practitioners and government policymakers to develop an understanding of big data analytics, eliminate challenges in the adoption of big data, and formulate strategies to handle projects efficiently in SMEs in the context of Indian manufacturing.

For the first time, big data for manufacturing firms handing innovative projects was discussed in the Indian SME context.