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Immunization is one of the most cost-effective ways to save lives while promoting good health and happiness. The coronavirus disease 2019 (COVID-19) pandemic has served as a stark reminder of vaccines' ability to prevent transmission, save lives, and have a healthier, safer and more prosperous future. This research investigates the sustainable development (SD) of the COVID-19 vaccine supply chain (VSC).

This study investigates the relationship between internal process, organizational growth, and its three pillars of SD environmental sustainability, economic sustainability and social sustainability. Survey-based research is carried out in the hospitals providing COVID-19 vaccines. Nine hypotheses are proposed for the study, and all the hypotheses got accepted. The survey was sent to 428 respondents and received 291 responses from health professionals with a response rate of 68%. For the study, the healthcare professionals working in both private and public hospitals across India were selected.

The structural equation modelling (SEM) approach is used to test the hypothesis. All nine hypotheses are supported. This study examines a link between internal processes and organizational learning and the three sustainability pillars (environmental sustainability, economic sustainability and social sustainability).

This study will help the management and the policymakers to think and adopt SD in the COVID-19 VSC. This paper also implies that robust immunization systems will be required in the future to ensure that people worldwide are protected from COVID-19 and other diseases.

This paper shows the relationship between organizational learning and internal process with environmental sustainability, economic sustainability and social sustainability for the COVID-19. Studies on VSC of COVID-19 are not evident in any previous literature.

This paper aims to investigate the firm performance of micro, small and medium enterprises (MSMEs) by using artificial intelligence-based supply chain resilience strategies. A theoretical framework shows the relationship between artificial intelligence, supply chain resilience strategy and firm performance.

A questionnaire is developed to survey the MSMEs of India. A sample size of 307 is considered for the survey. The employees working in MSMEs are targeted responses. The conceptual model developed is tested empirically.

The study found that eight hypotheses were accepted and two were rejected. There are five mediating variables in the current study. Artificial intelligence, the independent variable, positively affects all five mediators. Then, according to the survey and analysis of the final 307 responses from MSMEs, the mediating variables significantly impact the dependent variable, firm performance.

This study is limited to emerging markets only. Also this study used only cross sectional data collection methods.

This study is essential for supply chain managers and top management willing to adopt the latest technology in their organisation or firmfor a better efficient supply chain process.

This study investigated artificial intelligence-based supply chain resilience for improving firm performance in emerging countries like India. This study tried to fill the research gap in artificial intelligence and supply chain resilience.

The purpose of this paper is to find the driving factors for effective and efficient management of international manufacturing network (IMN) which has become increasingly important due to the intensive competition existing in the manufacturing industry. This paper presents a magnified view of different constructs of IMN and identifies the qualitative factors which are broadly classified under different constructs like political, economic, social, technological, legal and environmental.

Principal component analysis is applied to club identified factors into political, economic, social, technological and legal categories. PESTLE–SWOT approach is used to shortlist the identified factors using the elimination algorithm. Using analytical hierarchy process, weightages and rank of the identified factors are evaluated. Interpretive structural modeling is applied to understand inter-relationship among factors, and to analyze the driving power and dependence of the factors.

The results obtained from the above approaches are compared to identify most significant factors and the list of IMN enablers is presented using Venn Diagram. Government stability, Economic freedom, economic stability, environmental sustainability and raw material availability are coming out to be the driving factors across all techniques. Finally, the paper provides weights of the driving indicators common across all multi-criteria decision-making techniques considered.

The proposed work provides significant information about the qualitative factors needed to be considered while designing and developing IMN. As identified by literature review and through expert opinions, not all 29 factors responsible for IMN development are crucial. Many factors are either interdependent or driven by others. The paper provides a useful analysis about the driving factors that need to be taken into account.

The study presents a comprehensive analysis of the IMN enablers. Furthermore, it provides managerial and theoretical implications to deal with the complexities encountered while establishing IMN.