Apology Strategies in Commercial Chatbots: A Speech Act Analysis of Formulaicity, Pragmatic Deficits, and Design Implications for Customer-Service Conversational AI Systems

Abstract

This study evaluates apology strategies deployed by commercial customer-service chatbots in English-language interactions, drawing on speech act theory (Austin, 1962; Searle, 1969) and the cross-cultural apology research tradition (Olshtain & Cohen, 1983; Trosborg, 1995). A corpus of 600 chatbot–customer interaction transcripts collected from six major multinational companies was analyzed to examine the pragmatic structure, frequency, and functional adequacy of apology realizations in automated customer-service discourse. The analysis focuses on the distribution of apology components, including expression of regret, explanation of cause, acknowledgment of responsibility, and offer of repair. Findings indicate a systematic over-reliance on formulaic apology expressions such as fixed “I’m sorry” templates, often deployed irrespective of contextual severity or interactional breakdown. Conversely, there is a notable underrepresentation of elaborated apology components, particularly explanations and offers of repair, which are essential for achieving pragmatic completeness and restoring interactional trust. This imbalance results in apologies that are linguistically frequent but pragmatically shallow, limiting their effectiveness in managing customer dissatisfaction. Based on these findings, the study proposes a pragmatic quality framework for evaluating chatbot apology performance, incorporating four dimensions: sincerity (perceived authenticity of regret), adequacy (completeness of apology components), responsiveness (context-sensitive adaptation), and cultural sensitivity (alignment with interactional norms across user groups). The framework is intended to guide both linguistic evaluation and computational design. The study contributes to speech act research, pragmatics, and human–computer interaction by demonstrating how current chatbot systems operationalize apology as a template-driven routine rather than a contextually grounded communicative act, with direct implications for improving conversational AI in customer-facing environments.