Consumer Trust Formation in Artificial Intelligence–Enabled Service Encounters

Ahmad Farid

Consumer Trust Formation in Artificial Intelligence–Enabled Service Encounters

Author(s)	Prof. Ahmad Farid
Country	United States
Abstract	Artificial intelligence has become an increasingly visible component of contemporary service encounters through conversational agents, recommendation systems, automated support interfaces, intelligent service robots, virtual assistants, and decision-support technologies. While these applications can improve responsiveness, personalization, accessibility, and operational efficiency, their effectiveness depends substantially on whether consumers consider the AI-enabled interaction sufficiently trustworthy. Trust formation in such encounters is complex because consumers simultaneously evaluate the functional capability of the AI system, the intentions and accountability of the service provider, the fairness of algorithmic processes, the security of personal information, and the availability of human assistance when automated interaction becomes inadequate. This study develops an integrated framework for understanding consumer trust formation in artificial intelligence–enabled service encounters. The framework conceptualizes trust as an evolving judgment influenced by perceived competence, responsiveness, appropriate transparency, fairness, privacy assurance, and accessible human support. Anthropomorphic and empathetic characteristics are treated as contextual trust cues rather than universally beneficial design features. Because no original consumer dataset was supplied, the analytical component employs a transparent simulation-based design involving 600 synthetic service-interaction profiles divided equally across low, moderate, and high trust-support conditions. A Consumer Trust Index was modeled on a standardized 0–100 scale. The simulated analysis produced trust scores of 48.5, 67.1, and 82.8 across the three conditions, indicating a coherent theoretical relationship between stronger assurance mechanisms and greater modeled consumer trust. These figures are methodological outputs and should not be interpreted as population estimates or causal effects. The study further argues that organizations should pursue calibrated trust rather than indiscriminate trust maximization. Transparency without understandable explanation, anthropomorphism without competence, personalization without privacy assurance, or automation without effective human escalation may weaken rather than strengthen consumer confidence. The proposed framework contributes to service and consumer-behavior research by integrating technical, relational, ethical, and organizational trust mechanisms within a single service-encounter perspective and offers propositions for future empirical validation.
Keywords	Consumer Trust, Artificial Intelligence, AI-Enabled Services, Service Encounters, Chatbots, Transparency, Perceived Competence, Algorithmic Fairness, Privacy Assurance, Human–AI Interaction
Field	Engineering
Published In	Volume 1, Issue 6, November-December 2020
Published On	2020-11-22

View / Download PDF File

About AJAMIR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@ajamir.com

American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Consumer Trust Formation in Artificial Intelligence–Enabled Service Encounters

Share this