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Purpose: The purpose of this study is to examine how leading a childhood characterized by transnational mobility affects youths’ understanding of and relationship to their ethnic identity.

Study approach: This study examines the effects of transnational mobility on ethnic identity by focusing on the specific case of Indian Americans who grew up in the USA and Bangalore, a city in southwest India, before relocating to the USA for college. The analysis for this chapter comes from semi-structured, in-depth interviews with 20 transnational Indian American youth.

Findings: The data analysis reveals that by spending part of their childhood in India, transnational Indian American youth were able to learn more about their Indian ethnic identity, which helped them resolve issues related to their status as an ethnic minority in the USA, reframe how they define their ethnic identity, and reevaluate the status of their ethnic identity relative to their counterparts in the USA.

Originality: This study focuses on the unique case of Indian American youth who had a childhood characterized by transnational mobility. As such, this work contributes to the literature on children and youths’ transnational mobility through its focus on the migration patterns of relatively elite and socially privileged children and youth. Additionally, it adds to our understanding of the effects of migration between the USA and India by addressing how these processes affect children and youth. Last, it adds to the literature on Indian Americans by focusing on an understudied subpopulation within this group. The study motivates future research on the diversity that exists among transnationally mobile Indian American children and youth.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.