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.
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.
Electric mobility has been around for a long time. In recent years, with advancements in technology, electric vehicles (EVs) have shown a new potential to meet many of the challenges being faced by humanity. These challenges include increasing dependence on fossil fuels, environmental concerns, challenges posed by rapid urbanization, urban mobility, and employment. However, the adoption of electric vehicles has remained challenging despite consumers having a positive attitude toward EVs and big policy pushes by governments in many countries. Marketers from the electric vehicle (EV) industry are finding it difficult to identify genuine buyers for their products. In this context, the present study attempts to develop a machine learning model to predict whether a person would “Buy” or “Won’t Buy” an electric vehicle in India. To develop the model, an exploration of EV context was done first by conducting a text analysis of online content relating to electric vehicles. The objective was to find frequently occurring words to gain a meaningful understanding of the consumer’s interests and concerns relating to electric vehicles. The machine learning model indicates that age, gender, income, level of environmental concerns, vehicle cost, running cost, vehicle performance, driving range, and mass behavior are significant predictors of electrical vehicle purchase in India. The level of education, employment, and government subsidy are not significant predictors of EV uptake.
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