A Quantitative Study on the Effect of Chat-Based Customer Service Tone of Voice on Customer Satisfaction

Abstract

The rapid expansion of digital commerce has increased the strategic importance of chat-based customer service, where customers evaluate not only the accuracy and efficiency of responses but also the manner in which communication is delivered. This study examines the effect of perceived tone of voice in chat-based customer service on customer satisfaction. A quantitative explanatory design with a cross-sectional survey approach was employed. Data were collected from customers with prior experience of interacting with chat-based customer service in digital business and e-commerce environments. From 223 collected responses, data screening resulted in 215 valid observations. Tone of voice was conceptualized as a multidimensional construct comprising friendliness, empathy, clarity, professionalism, personalization, and responsiveness, whereas customer satisfaction reflected evaluations of interaction quality, expectation fulfillment, overall satisfaction, service experience, and intention to reuse the service. The measurement instruments demonstrated strong psychometric properties, with all indicators meeting the validity criterion and Cronbach's alpha coefficients of .954 for tone of voice and .963 for customer satisfaction. Classical assumption testing confirmed the adequacy of the regression model. The results of simple linear regression revealed that perceived tone of voice positively and significantly predicted customer satisfaction (b = .731, SE = .066, t = 11.073, p < .001). The findings indicate that more favorable perceptions of communicative quality are associated with substantially higher levels of customer satisfaction. The study contributes by empirically positioning tone of voice as an explicit communication construct rather than merely an implicit component of broader digital service quality. Practically, the findings highlight the importance of integrating empathy, clarity, professionalism, personalization, responsiveness, and friendliness into human-agent and artificial-intelligence-supported customer service design.