An Exploratory Study on Generative AI-Driven Customer Experience in B2B Markets: Adoption, Service Quality, and Ethical Implications

Abstract

The widespread uptake of Generative Artificial Intelligence (GenAI) in service delivery has redefined how organizations shape and manage customer experiences. While prior research has largely examined GenAI adoption and applications in consumer-facing contexts, despite this, there is still a lack of empirical insight of how GenAI-driven services are experienced and trusted in business-to-business (B2B) markets, where service quality, ethical governance, and trust are critical relational outcomes. Responding to this gap, the study explores how the adoption of GenAI affects customer experience and trust in B2B service settings by combining perspectives from technology acceptance, service quality, and ethical AI.
Adopting an exploratory and predictive research design, the study develops and empirically tests an integrated model linking technology acceptance factors (system beneficial and easy to operate), service performance quality, customer contentment, ethical AI, and customer trust. Data were collected from B2B service users and were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM). Model evaluation included assessment of reliability and validity, structural relationships, predictive relevance (Q²), and Importance–Performance Matrix Analysis (IPMA).
The study demonstrates that users’ technology acceptance strongly affects perceived service quality, leading to higher levels of customer satisfaction and trust. The relationship between service quality and trust is significantly mediated by customer satisfaction. Contrary to normative assumptions, ethical AI does not exert a significant direct effect on customer trust; however, it plays a significant moderating role by stabilizing trust and reducing customers’ reliance on service quality cues. The model demonstrates strong predictive relevance, particularly for customer trust, highlighting its practical and theoretical robustness.
This study advances theoretical understanding by adapting the Technology Acceptance Model and SERVQUAL frameworks to GenAI-based B2B service environments. reconceptualizing ethical AI as a contextual moderator rather than a direct trust antecedent, and advancing an integrative framework for understanding trust formation in AI-mediated services. The results suggest that, in practice, organizations should place strong emphasis on enhancing service quality and ensuring customer satisfaction while embedding robust ethical AI governance to ensure sustainable trust in GenAI-driven B2B services.
Keywords: Generative Artificial Intelligence, B2B Customer Experience, Service Quality, Ethical AI, Customer Satisfaction, Customer Trust, PLS-SEM