PurposeThe purpose of this paper is to evaluate the interplay of various measures used by different governments around the world in combatting COVID-19.Design/methodology/approachThe research uses the interpretative structural modelling (ISM) for assessing the powerful measures amongst the recognized ones, whereas to establish the cause-and-effect relations amongst the variables, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used. Both approaches utilized in the study aid in the comprehension of the relationship amongst the assessed measures.FindingsAccording to the ISM model, international support measures have the most important role in reducing the risk of COVID-19. There has also been a suggestion of a relationship between economic and risk measures. Surprisingly, no linkage factor (unstable one) was reported in the research. The study indicates social welfare measures, R&D measures, centralized power and decentralized governance measures and universal healthcare measures as independent factors. The DEMATEL analysis reveals that the net causes are social welfare measures, centralized power and decentralized government, universal health coverage measure and R&D measures, while the net effects are economic measures, green recovery measures, risk measures and international support measures.Originality/valueThe study includes a list of numerous government measures deployed throughout the world to mitigate the risk of COVID-19, as well as the structural links amongst the identified government measures. The Matrice d'Impacts croises-multiplication applique and classment analysis can help the policymakers in understanding measures used in combatting COVID-19 based on their driving and dependence power. These insights may assist them in employing these measures for mitigating the risks associated with COVID-19 or any other similar pandemic situation in the future.

Purpose This study aims to examine which organisational and other factors can facilitate the adoption of artificial intelligence (AI) in Indian management institutes and their interrelationship. Design/methodology/approach To determine the factors influencing AI adoption, a synthesis-based examination of the literature was used. The interpretative structural modelling (ISM) method is used to determine the most effective factors among the identified ones and the inter-relationship among the factors, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyse the cause-and-effect relationships among the factors in a quantitative manner. The approaches used in the analysis aid in understanding the relationship among the factors affecting AI adoption in management institutes of India. Findings This study concludes that leadership support plays the most significant role in the adoption of AI in Indian management institutes. The results from the DEMATEL analysis also confirmed the findings from the ISM and Matrice d’ Impacts croises- multiplication applique and classment (MICMAC) analyses. Remarkably, no linkage factor (unstable one) was reported in the research. Leadership support, technological context, financial consideration, organizational context and human resource readiness are reported as independent factors. Practical implications This study provides a listing of the important factors affecting the adoption of AI in Indian management institutes with their structural relationships. The findings provide a deeper insight about AI adoption. The study's societal implications include the delivery of better outcomes by Indian management institutes. Originality/value According to the authors, this study is a one-of-a-kind effort that involves the synthesis of several validated models and frameworks and uncovers the key elements and their connections in the adoption of AI in Indian management institutes.

Purpose The purpose of this paper is to explore the motives of Indian firms for engaging with corporate social responsibility (CSR) practices and their interplay by using interpretive structural modelling methodology (ISM) and Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis. Design/methodology/approach The research uses ISM and Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis to find the structural relationship among the CSR motives of the Indian firms identified from the past literature and agreed upon by the experts. Findings The ISM model indicates that firms primarily engage in CSR either because of top management commitment to certain values, to meet the legal mandate or of the pressure from the NGOs. The top management commitment gives a strategic orientation to CSR, which results in community engagement by the firm as one of the important components of the strategy. The community engagement helps in engaging with its employees and investors along with finding sources of innovations, which, in turn, help the firm in engaging its customers, managing corporate reputation and getting a cost advantage. Collectively, these help them in improving their financial performance. However, the model highlights two autonomous sources, meeting legal mandate and pressure from NGOs also motivate firms to engage in CSR without having any strategic thought or engagement with its strategic system. Originality/value The study provides a comprehensive listing of CSR motives of Indian firms along with the structural relationships among the identified CSR motives. The model developed provides CSR professionals and policymakers an understanding of the primary CSR motives along with their driving power and dependence. This insight will help them in manipulating these motives for better CSR engagement by the Indian firms.

PurposeSmall and medium enterprises (SMEs) across the world are generally found to have a limited interest in wider social issues. SMEs face many barriers in operating in a socially responsible and sustainable manner despite it making a good business sense. This paper explores the barriers and challenges faced by Indian SMEs for engaging in corporate social responsibility (CSR) practices.Design/methodology/approachThe research uses interpretive structural modelling (ISM) to explore the structural relationship among barriers faced by Indian SMEs in their CSR engagement which were identified from the past literature and validated by the experts.FindingsThe study identified thirteen variables as important barriers resulting in a lower CSR engagement by Indian SMEs. The ISM model indicates that Indian SMEs focus on tactical rather on strategic needs along with their limited information and knowledge about CSR are the main driving forces which keep them away from an active and meaningful CSR engagement. Their limited CSR engagement capabilities, limited need to engage with their workforce and lower CSR perceived benefits also constrain their CSR engagement. The Indian SMEs also do not see a need for CSR engagement because of lower community and governmental pressure.Originality/valueThe study provides a comprehensive listing of CSR engagement barriers faced by Indian SMEs along with the structural relationships among them. The model developed provides CSR professionals and policymakers an understanding of the important impediments in CSR engagement of Indian SMEs based on their driving power and dependence. This insight will help them in designing initiatives to influence identified barriers to promote CSR engagement by Indian SMEs.

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