Impact of AI-Enabled Tax Services on Perceived Service Value and Client Trust: An Empirical Study
Impact of AI-Enabled Tax Services on Perceived Service Value and Client Trust: An Empirical Study
A Conceptual Framework and Research Agenda
AUTHOR-1
Sumukh Hegde
Dayananda Sagar College of Engineering
AUTHOR - 2
Jayashree K
jayashree-mba@dayanandasagar.edu
Assistant Professor Department of Management Studies
Dayananda Sagar College of Engineering
Abstract
Artificial intelligence (AI) is rapidly reshaping the delivery of tax advisory and compliance services, moving firms from document-heavy, labour-intensive workflows toward automated return preparation, machine-learning-based risk flagging, natural-language query systems, and conversational tax chatbots. While industry surveys consistently report that trust — not capability
— is the principal barrier to client acceptance of AI-enabled tax services, academic theorising on how such systems shape two pivotal client-side constructs, perceived service value and client trust, remains fragmented. This chapter develops an integrative conceptual framework that draws on the Technology Acceptance Model, service-quality and perceived-value theory, and the trust-in-automation literature to explain how specific attributes of AI-enabled tax services — accuracy and reliability, transparency and explainability, personalisation, responsiveness, data security, and the degree of human-AI collaboration — influence clients' perceived utilitarian and epistemic value and their cognitive and affective trust, and how these, in turn, shape satisfaction, continuance intention, advocacy, and firm reputation. The framework further specifies boundary conditions, including client AI literacy, task complexity, perceived risk, and preference for human interaction. Nine research propositions are advanced, and a mixed-methods empirical agenda — including a validated multi-item survey instrument, scenario-based experiments, and longitudinal designs — is proposed to test the framework. The chapter contributes a theoretically grounded, testable model for scholars and offers tax practice leaders a diagnostic lens for designing AI-enabled services that build rather than erode client trust.
Keywords: Artificial intelligence, tax services, perceived service value, client trust, technology acceptance, algorithm aversion, professional services, accounting technology