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THE ROLE OF ARTIFICIAL INTELLIGENCE IN VALUE CO-CREATION: A THEORETICAL PERSPECTIVE FROM SERVICE MARKETING

📘 Volume 13 📄 Issue 5 📅 May 2026

👤 Authors

Dr. G. Arun Kumar 1
1. Associate Professor, Department of Business Administration, Business Administration, Vijayanagara Sri Krishnadevaraya University, Ballari, Karnataka

📄 Abstract

Artificial Intelligence (AI) is reshaping service marketing by transforming customer–firm interactions into intelligent, data-driven, and personalized engagements. While existing research primarily emphasizes operational efficiency and automation benefits, limited attention has been paid to AI’s theoretical role in value co-creation within service ecosystems. Grounded in Service-Dominant Logic (SDL), Relationship Marketing Theory, and Technology Acceptance Theory (TAM), this conceptual paper develops an integrative framework explaining how AI capabilities facilitate value co-creation. The study conceptualizes AI as an operant resource that enhances interactive intelligence, predictive analytics, personalization capability, and adaptive learning, thereby strengthening customer engagement and relational outcomes. The paper proposes a structured set of theoretical propositions linking AI capabilities to customer engagement, co-creation outcomes, relational performance, and firm performance. The study contributes to service marketing literature by extending SDL into AI-mediated service ecosystems and provides managerial insights for designing AI-enabled relational strategies. Directions for empirical validation and future research are also discussed.

🏷️ Keywords

Artificial Intelligence Value Co-Creation Service-Dominant Logic Customer Engagement Relationship Marketing Technology Acceptance.

🔗 DOI

DOI - (https://doi.org/10.36713/epra27703)

📚 How to Cite:

Dr. G. Arun Kumar , THE ROLE OF ARTIFICIAL INTELLIGENCE IN VALUE CO-CREATION: A THEORETICAL PERSPECTIVE FROM SERVICE MARKETING , Volume 13 , Issue 5, May 2026, EPRA International Journal of Economics, Business and Management Studies (EBMS) , DOI: https://doi.org/10.36713/epra27703

🔗 PDF URL

https://cdn.eprapublishing.org/article/202605-07-027703.pdf

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