Outlet Title

International Journal of Advanced Trends in Multidisciplinary Researc

Document Type

Article

Publication Date

Summer 6-15-2026

Abstract

Conversational AI systems are now a primary interface for customer service across telecommunications, banking, insurance, and healthcare sectors, yet their limitations become most consequential in emotionally sensitive, ambiguous, or highstakes interactions. This study introduces and operationalizes the concept of emotional boundary failure (EBF) — instances in which AI agents misread customer emotion, fail to recognize escalating frustration, or produce responses that are procedurally correct but socially or relationally inappropriate. Drawing on 184,200 customer service interactions, sentiment trajectory data, escalation records, and post-interaction surveys (n = 26,830) from four service providers spanning telecommunications, banking, insurance, and healthcare, the study compares AI-only, hybrid (AI-with-human-escalation), and human-only service models. Regression results show that issue emotional complexity (IECS) significantly moderates the effect of service model on satisfaction, escalation, complaint filing, retention, and EBF incidence: for low-complexity issues, AI-only service models perform comparably to or better than human agents on efficiency-related outcomes, but for high-complexity issues, AI-only models show significantly worse satisfaction (interaction β = –0.19, p < .001), higher escalation (β = 0.05, p < .001), higher complaint rates (β = 0.014, p < .001), lower retention (β = –0.022, p < .001), and dramatically higher EBF incidence (β = 0.047, p < .001) relative to human agents — a pattern substantially attenuated, though not eliminated, in hybrid service models. Sentiment trajectory analysis reveals that AI-only interactions involving high-complexity issues exhibit a pronounced negative sentiment slope across conversation turns that human and hybrid interactions do not replicate. Thematic analysis of 37 customer and frontline-agent interviews identifies six themes, including a 'sincerity gap' in AI-delivered empathy and a 'context collapse' problem in AI-to-human handoffs. The study develops a theory of socio-emotional fit, proposing that conversational AI performance depends on the alignment between an interaction's emotional complexity and the AI system's socio-emotional capability, and offers a five-principle design framework for responsible conversational AI deployment in emotionally consequential service contexts.

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