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This study aims to uncover and develop explicit knowledge of existing smart communities (SCs) to guide services and business solutions for enterprises and serve community users in a well-thought-out manner. These sagacious frameworks will assist in analyzing trends and reaching out to pre-existing setups with different degrees of expertise.

A systematic overview is provided in this paper to unify insights and competencies toward building SCs; a hybrid analytical approach is used consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose.

SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving business processes and efficiency. This system of SC is an evolution of the conventional method. It provides a foundation for intelligent community services based on individual users and technologies such as the Internet of Things (IoT), artificial intelligence, cloud computing and big data. Manufacturing-based, service-based, retail-based, resource management and infrastructure-based SCs exist in the literature.

The paper summarizes a conceptual framework of SCs based on existing works around SCs. To the best of the authors’ knowledge, this is the first systematic literature review that uses a hybrid approach of topic modeling and bibliometric analysis to understand SCs better.

Due to the rapid surge in the number of COVID-19 cases in India, the health-care supply chain (HCSC) disruptions and uncertainties have increased manifold posing severe challenges to health-care facilities and significantly hampering the functioning of the health industry. This study aims to propose a hierarchical structural model of enablers of HCSC in the COVID-19 outbreak and identifies inter-relationships among them in the health-care market.

Enablers of emergency HCSC have been identified through extensive literature review and experts’ opinions. Subsequently, total interpretive structural modeling (TISM) and cross-impact matrix-multiplication (MICMAC) analysis have been implemented to determine the hierarchical inter-relationships among enablers and classify them according to their contribution to the overall system.

The research has identified and validated 15 enablers of the emergency supply chain in health-care businesses. The study resulted in a seven-level hierarchical structural model based on enabler’s driving and dependence powers. Further, the application of MICMAC analysis resulted in the classification of enablers into four groups, namely, autonomous, dependent, linkage and independent group.

This study would help health professionals, policymakers and academia to implement the theoretical model constructed to alleviate the effect of COVID-19 by improving the HCSC performances in pandemic situations. This study has social and economic implications in terms of cost-effective and efficient delivery of care services in health emergencies.

The proposed theoretical model constructed is a new effort addressing the issues of HCSC in the COVID-19 crisis. Procedural implementation of TISM and MICMAC analysis in this study would help researchers to grasp concepts in a very lucid manner. The present study is one of the very few studies analyzing enablers in pandemic situations by implementing the TISM approach.

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.

This research looks into the revolutionary potential of Industry 5.0, healthcare, sustainability and the metaverse, with a focus on the transformation of healthcare firms through cutting-edge technologies such as artificial intelligence (AI) and Internet of Things (IoT). The study emphasizes the significance of sustainability, human-machine collaboration and Industry 5.0 in the development of a technologically advanced, inclusive and immersive healthcare system.

The study surveyed 354 medical professionals and used structural equation modeling (SEM) to investigate healthcare sustainability, Industry 5.0 and the metaverse, emphasizing the integration of modern technology while maintaining ethical issues.

The findings highlight Industry 5.0’s and the metaverse’s transformational potential in healthcare firms. The study finds that human centricity (HC) has only a minor direct impact on healthcare sustainability, whereas intelligent automation (IA) and innovation (INN) play important roles that are regulated by external factors.

Utilizing IA inside healthcare organizations can result in significant industrial advancements. However, these organizations must recognize the importance of moderating factors and attempt to find a balance between INN and thesev restraints.

This study makes a substantial contribution to the field by investigating the potential of Industry 5.0, healthcare, sustainability and the metaverse. It discusses how these advances can transform healthcare firms, with an emphasis on patient-centered treatment, environmental sustainability and data ethics. The study emphasizes the importance of having a thorough awareness of these trends and their implications for healthcare practices.

During pre-vaccine era, pharmaceutical supplies [self-care essentials (SCEs)] have been proved to be a major deflector, protector and safety guard against novel coronavirus disease (COVID-19). Hence, the objective of the study is to provide a comprehensive socio-technological decision-making framework based on multiple criteria for selecting the suppliers of pharmaceuticals, such as SCEs, by multi-brand enterprises (distributors) in the pandemic environment.

A hybrid methodology of Bayesian best worst method (BWM) and multi-attributive border approximation area comparison (MABAC) method has been applied for carrying out the study. Bayesian BWM has been applied for computing the importance of criteria identified for the selection of SCEs' suppliers during pandemic environment and MABAC method evaluated the suppliers of the SCEs.

In the study, the authors have identified eight criteria such as disinfection and sanitization of vehicles, social conscience of suppliers, brand (Technological recognition) of SCEs and logistics and distribution network, among others, which are critical to the selection of a supplier for the supply of SCEs. The application of the proposed hybrid model revealed that lead time and quality of SCEs are of utmost concern for pharmacies in a pandemic environment. Among the ten suppliers, results showed that Suppliers 2, 4 and 5 have been ranked first for supplying hand wash, hand sanitizer and face mask, respectively.

The proposed model has helped the multi-brand distributors of pharmaceuticals in selecting suppliers during the ongoing crisis of COVID-19. In addition to that, in future the outcomes of the study would be helpful for multi-brand distributors as well as pharmacies and hospitals in selecting the best suppliers. Policy makers will be able to make and revise the policies immediately with the help of the proposed decision-making framework.

The paper makes a novel contribution towards theory with the criteria identified for selecting best suppliers during the pandemic COVID-19. Additionally, the proposed hybrid model helps multi-brand distributors of pharmaceuticals in making decisions that lead to a huge social and economic success in pandemic time.

Managing risks is becoming a highly focused activity in the health service sector. In particular, due to the complex nature of processes in the pharmaceutical industry, several risks have been associated to its supply chains. The purpose of this paper is to identify and analyze the risks occurring in the supply chains of the pharmaceutical industry and propose a decision model, based on the Analytical Hierarchy Process (AHP) method, for evaluating risks in pharmaceutical supply chains (PSCs).

The proposed model was developed based on the Delphi method and AHP techniques. The Delphi method helped to select the relevant risks associated to PSCs. A total of 16 sub risks within four main risks were identified through an extensive review of the literature and by conducting a further investigation with experts from five pharmaceutical companies in Bangladesh. AHP contributed to the analysis of the risks and determination of their priorities.

The results of the study indicated that supply-related risks such as fluctuation in imports arrival, lack of information sharing, key supplier failure and non-availability of materials should be prioritized over operational, financial and demand-related risks.

This work is one of the initial contributions in the literature that focused on identifying and evaluating PSC risks in the context of Bangladesh. This research work can assist practitioners and industrial managers in the pharmaceutical industry in taking proactive action to minimize its supply chain risks. To the end, the authors performed a sensitivity analysis test, which gives an understanding of the stability of ranking of risks.

Sustainable consumption and production (SCP) assist managers to improve their operational efficiency while aiming at reducing the generation of waste. The purpose of this paper is to construct a structural model of the SCP practices in a supply chain context.

The work is based on the experience of supply chain professionals, a case study and literature review related to SCP. The present work recommends an assessment framework by prioritizing the SCP oriented practices using best-worst method.

The current work is an effort to understand the significance of SCP practices and to reveal their level of influence in developing a sustainable culture in value chains. Data for this work are derived from an automotive company operating in India. Findings reveal that the “resource efficiency” acquires the highest rank and “sharing assets” acquires the last lank among all SCP practices.

It is difficult to finalize the SCP practices. This work uses the expert’s approach for this. In this way, the process needs to be conducted very carefully.

This research can assist automotive managers and practitioners in efficiently utilizing their companies’ resources, which would result in superior business effectiveness by generating higher employment opportunities in value chains.

Efforts have been made to contribute in the identification and analysis of SCP oriented practices. The developed structural model will help in understanding the ranking of practices.

Consumers have the multiple options to choose their products and services, which have a significant impact on the pattern of consumer decision making in digital market and further increases the challenges for the service providers to predict their buying pattern. In this sense, the purpose of this paper is to propose a structural hierarchy model for analyzing the changing pattern of consumer decision making in digital market by taking an Indian context.

To accomplish the objectives, the research is conducted in two phases. An extensive literature review is performed in the first phase to list the factors related to the changing pattern of consumer decision making in digital market and then fuzzy Delphi method is applied to finalize the factors. In the second phase, fuzzy analytic hierarchy process (AHP) is employed to find the priority weights of finalized factors. The fuzzy set theory allows capturing the vagueness in the data.

The findings obtained in this study shows that consumers are much conscious about innovative and trendy products as well as brand and quality; therefore, the service providers must think about these two most important factors so that they can able to retain their consumer in their online portal.

The analysis shows that “innovative and trendy” is the first priority factor for the consumers followed by “brand and quality” and “fulfilment and time energy.” The proposed model can help the marketers and service providers in predicting customers’ preferences and their changing pattern efficiently under vague surroundings. The outcomes of this research work not only help the service provider to update their products and services according to consumers’ needs but can also help them to increase profit and minimize their risk.

This work contributes to consumer research literature focusing on problem evaluation in the context of changing pattern of consumer decision making in digital era.