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The pharmaceutical industry faces multiple risks that adversely affect its performance. Within these risks, some dependencies have been observed, which help in streamlining the mitigation efforts. Therefore, the present work identifies and categorizes various risks/sub-risks in cause–effect groups, considering uncertainty in the decision-making process.

An extensive literature review and experts' opinions were utilized to identify and finalize the risks faced by the pharmaceutical industry. For further analysis, data collection was done using a questionnaire focusing on finalized risks. Based on the data, the causal relation under uncertainty between various risks/sub-risks was identified using a multi-criteria decision making (MCDM) technique, i.e. intuitionistic fuzzy DEMATEL, in a pairwise manner.

The results show that the three most prominent risk categories are operational, demand/customer/market and financial. Also, out of the seven main risks, only supplier and operational are categorized within the effect group and the rest, i.e. financial, demand, logistics, political and technology within the cause group. The sub-risks within each category have also been categorized into cause–effect groups. The mitigation of cause group risks will help in economize the financial resources and improve the performance and resilience of the industry.

There is insufficient research on identifying the causality among the pharmaceutical industry risks. Additionally, an extensive discussion on the identified cause–effect groups is also missing in the literature. Therefore, in this work, efforts have been made to determine the prominent risks for the Indian pharmaceutical industry that will be helpful for channelizing the resources to mitigate risks for a resilient industry.

The research empirically examines the role of artificial intelligence (AI) integrated with social customer relationship management (SCRM) in multinational enterprises (MNEs) towards international relationship management under social distancing conditions due to the COVID-19 pandemic.

The study initially undertakes pertinently focused theoretical research in the fields of international marketing, knowledge management, and customer relationship management. And, utilizing the theories of resource-based view (RBV) and dynamic capability view (DCV) theory, the study develops a theoretical model that is subsequently empirically validated through a survey and structural equation modeling.

The study highlights the importance and means of adopting AI-integrated social CRM by MNEs, in the context of international relationship management, under the Covid-19 social distancing conditions. The study more specifically elucidates the role and significance of MNE leadership approach and support towards the adoption of AI-integrated social CRM systems and, ultimately, performance improvement of MNEs under such conditions.

The study presents insights and prescriptive explications on a topic at the heart of state-of the-art technology-based international marketing in the explicit context of the primary business-defining environment of the Covid-19 pandemic. The research provides practicable suggestions to MNEs' leadership towards the adoption of an AI-integrated social CRM system. And the study presents a unique model for international relationship management under social distancing conditions, potentially applicable during other crises.

The research is original and on a ‘fresh’ topic that combines the latest technological advancements in business (AI-integrated CRM) with the present critical business context (pandemic). The research develops a tested theoretical model that (a) is unique in its field; (b) provides a solid foundation for further research; (c) bears generic value and application during other-than-Covid-19 conditions; and (d) enhances the understanding of important fields of international marketing, including international customer relationship management and global knowledge management.