Evaluating Trust and Satisfaction in AI Chatbots in Customer Service: A User-Centric Study
2024 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE credits
Student thesis
Abstract [en]
Artificially intelligent chatbots are rapidly gaining popularity in customer service, and they can offer businesses benefits such as increased stock prices and low-cost, efficient service. However, customer trust and satisfaction with these chatbots are often low. Most current research focuses on which business benefits artificially intelligent systems can yield, and less focus lies on how to create satisfactory experiences for the customers. This study examines which chatbot characteristics that build customer trust and satisfaction, and identifies the types of inquiries where customers prefer artificially intelligent chatbots over human customer service agents. An online questionnaire was conducted with 47 participants, primarily 18-24-year-old females from Sweden, with a university degree and high comfort with modern technology. The data analysis, using the Wilcoxon signed-rank test and an investigation of the mean and median scores, revealed no characteristic was significantly more important for trust than for enhancing experience. However, anthropomorphism was indicated to have some importance for trust, though not statistically significant. Machine reading comprehension, perceived accuracy, and privacy concerns were found to be important for a positive customer experience. Participants preferred artificially intelligent chatbots over human customer service agents only for basic information inquiries. The findings support existing theories such as the service quality model and can provide guidance for businesses wanting to implement artificially intelligent chatbots in customer service.
Place, publisher, year, edition, pages
2024. , p. 53
Keywords [en]
Artificial Intelligence, AI Chatbot, Customer Service, Customer Trust, Customer Satisfaction, Trust in AI, Chatbot Characteristics, SERVQUAL
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hj:diva-64705OAI: oai:DiVA.org:hj-64705DiVA, id: diva2:1866271
Subject / course
JTH, Informatics
Supervisors
Examiners
2024-06-102024-06-062025-10-13Bibliographically approved