Adoption of artificial intelligence-integrated CRM systems in agile organizations in India

Sheshadri Chatterjee, Ranjan Chaudhuri, Demetris Vrontis, Alkis Thrassou and Soumya Kanti Ghosh

Technological Forecasting and Social Change, 2021, vol. 168, issue C

Abstract: Artificial intelligence integrated with customer relationship management (CRM) systems has revolutionized organizations’ means of analyzing their huge volumes of customer data. To effectively respond to and manage the opportunities and challenges that arise from this, organizations are developing competencies and processes that evolve their agility, fine-tuning them to the artificial intelligence customer service system (AICS) and wider digitalization setting. In this context, this study identifies the factors impacting the adoption of an AI-integrated CRM system (AICS) in agile organizations as a part of their digitalization strategy. Methodologically, the research builds its theoretical foundation on extant works to develop hypotheses and a corresponding conceptual model. The model is quantitatively validated through a survey across the spectrum of Indian companies, following expert-based pretesting and pilot testing, and subsequently it is statistically tested using the partial least squares structural equation modeling (PLS-SEM) technique. The results, contextualized against the backdrop of organizational agility, identify and elucidate the relationship between stakeholders and perceived value and easiness of AICS, between employee trust and attitude, and the influence of attitude and behavioral intention as key mediators towards AICS adoption. The findings are conclusively transcribed into tangible implications for practice and explicit avenues for future research.

Keywords: AI; CRM; Digitalization; Systems adoption; Agility (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:168:y:2021:i:c:s0040162521002158

DOI: 10.1016/j.techfore.2021.120783

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