Year: 2026
Type: article
Study design: Observational Study
Abstract: Chatbots are increasingly part of digital customer service, yet their evaluation is often reduced to response speed, availability, or automation rate. This study assumes that customers do not experience the algorithm directly, but the language through which the system communicates. The empirical analysis is based on survey data from 411 respondents. Composite indices were constructed for language quality, trust, satisfaction, service communication performance, and customer re-engagement intention, all showing acceptable internal consistency. Regression models indicate that language quality is positively associated with trust ( $$\:\beta\:=0.283;p<0.001$$ ). Language quality and trust jointly explain 50.7% of the variance in satisfaction. Customer re-engagement intention is most closely linked to satisfaction ( $$\:\beta\:=0.287;p<0.001$$ ), while the direct relationship between trust and re-engagement becomes non-significant after satisfaction is included. Mediation results support a sequential mechanism in which language quality is associated with re-engagement via trust and satisfaction. The study shifts chatbot evaluation from technical efficiency toward language quality as part of managing digital service operations.
Source: Management & Marketing
Authors: Pavol Lipták, Filip Kalaš
Institution: Bratislava University of Economics and Business
Language: English
Topic: AI in Service Interactions
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $323
Award: G6318157479
OpenAlex ID: W7212227864