Customer Forgiveness After Automated Service Failure: The Roles of Apology, Recovery Transparency, Perceived Control, and Trust Repair

Réka Szabó

Customer Forgiveness After Automated Service Failure: The Roles of Apology, Recovery Transparency, Perceived Control, and Trust Repair

Author(s)	Dr. Réka Szabó
Country	United States
Abstract	Automated service technologies increasingly perform customer-facing tasks that were historically managed by human employees, including order processing, account assistance, appointment scheduling, payment support, recommendations, and complaint triage. Their efficiency, availability, and scalability create substantial value, but automation also introduces distinctive service-recovery challenges when systems provide incorrect information, reject valid requests, repeatedly misunderstand customers, produce unsuitable recommendations, or prevent access to human assistance. This study develops a simulation-based framework for examining customer forgiveness following automated service failure, with particular attention to apology quality, recovery transparency, response speed, perceived control, failure severity, and trust repair. A synthetic dataset representing 360 hypothetical customers was constructed using seven-point measurement scales. The analytical model treats forgiveness as a post-failure relational response rather than simple satisfaction and evaluates whether customers become more willing to restore the relationship when automated recovery acknowledges the failure, explains what occurred, provides meaningful corrective options, and allows escalation to human support. Simulated results indicate that customer forgiveness rises from 2.41 under very low recovery quality to 5.84 under very high recovery quality. Trust repair produces the strongest positive standardized association with forgiveness (β = 0.331), followed by apology quality (β = 0.300), recovery transparency (β = 0.205), and perceived control (β = 0.183). Failure severity has a negative association (β = −0.244). The full model explains approximately 49.0% of synthetic variation in forgiveness. The findings suggest that effective automated recovery requires more than rapid system correction. Customers may be more forgiving when organizations combine technical resolution with accountability, understandable explanations, restoration of agency, and credible trust-repair mechanisms. The framework offers a basis for future empirical investigation of forgiveness in artificial intelligence, chatbot, self-service, and service-robot environments.
Keywords	automated service failure; customer forgiveness; service recovery; trust repair; artificial intelligence; chatbots; perceived control; service automation
Field	Engineering
Published In	Volume 6, Issue 1, January-February 2025
Published On	2025-01-06

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American Journal of Advanced Multidisciplinary Innovation and Research

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Customer Forgiveness After Automated Service Failure: The Roles of Apology, Recovery Transparency, Perceived Control, and Trust Repair

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