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

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

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.

Suggested Citation

  • Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris & Thrassou, Alkis & Ghosh, Soumya Kanti, 2021. "Adoption of artificial intelligence-integrated CRM systems in agile organizations in India," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:tefoso:v:168:y:2021:i:c:s0040162521002158
    DOI: 10.1016/j.techfore.2021.120783
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