| Abstract [eng] |
AI and chatbots are changing customer service in e-commerce. Chatbots have useful features, such as 24/7 availability, the ability to respond to numerous questions at the same time, and the potential to adapt the experience to the customer; however, the underlying reasons for customer satisfaction when interacting with chatbots remain under-researched, especially in Central and Eastern Europe. Most of the research was conducted in the USA, South Korea and China with similar student populations, and have almost never studied the functional trust and perceived trust together. This research intends to bridge this gap, studying e-commerce users from Lithuania, who are particularly influenced by GDPR data protection and privacy regulation. The study focuses on the effects of engaging with chatbots on customer satisfaction in e-commerce, analyzing the mediating effects of functional trust and perceived trust. The purpose is to understand the role of the trust dimensions in a chatbot interaction and their influence on customer satisfaction. Thus, an extensive literature review on the Technology Acceptance Model (TAM), trust and service quality theories, and human-computer interaction was performed. In total, 46 survey questions using a Likert scale were developed to assess nine latent constructs. The survey was distributed online among Lithuanians who have interacted with an e-commerce chatbot in the last six months. After applying PLS-SEM in R to the data, 198 samples were determined to be sufficient for the study. The findings confirm five hypotheses out of the eight. For the functional trajectory, the only significant predictor of functional trust was expertise (β = 0.586, p < 0.001). The variables of trust were defined both in terms of responsiveness and ease of use, and neither variable reached significance when expertise was included. For the perceived trust, the predictors were both the humanness (β = 0.274, p < 0.001) and the transparency (β = 0.284, p = 0.011). The impact of personalization was also eliminated when both of the trust constructs were integrated. The trust constructs also had a significant and comparable impact on customer satisfaction; functional trust (β = 0.467) and perceived trust (β = 0.434). The model of customer satisfaction had a 72.4% variance (R² = 0.724). The findings show that Lithuanian e-commerce chatbot users express satisfaction through two parallel and equally important trust pathways: a cognitive-functional one dominated by expertise, and a perceived- one dominated by humanness and transparency. Notably, transparency acts as a suppressing mediator — its direct effect on satisfaction is negative, but its indirect effect through perceived trust is positive and significant. This is a theoretically coherent result within Lithuania's GDPR-sensitive environment. The empirically grounded dual mediation model extends previous studies that analyzed functional and perceived trust dimensions separately, and provides a basis for practical recommendations for Lithuanian e-commerce companies. The main body spans 75 pages, including 20 tables and 5 figures, with 86 references and 1 appendix. |
