When Loyalty Speaks Louder Than the Machine: A Quantitative Study on How Pre-existing Brand Loyalty Shapes Perceived AI Chatbot Attributes and Customer Satisfaction
(2025) BUSN39 20251Department of Business Administration
- Abstract
- Purpose: This thesis aims to investigate how customers’ pre-existing brand loyalty influences their perceptions of four characteristics – Perceived Usefulness (PU), Perceived Ease of Use (PEoU), Perceived Trustworthiness (PT), and Perceived Human-likeness (PH) – of AI chatbots used in customer service. In turn, we strive to examine how those perceptions affect overall customer satisfaction towards the brand or company. Theoretical perspective: Our study combines the Technology Acceptance Model (TAM) with the Expectation Confirmation Theory (ECT). Drawing on TAM, we explore cognitive (PU, PEoU) and affective (PT, PH) constructs of AI customer service chatbots, with brand loyalty shaping these factors. Based on ECT, we position brand loyalty... (More)
- Purpose: This thesis aims to investigate how customers’ pre-existing brand loyalty influences their perceptions of four characteristics – Perceived Usefulness (PU), Perceived Ease of Use (PEoU), Perceived Trustworthiness (PT), and Perceived Human-likeness (PH) – of AI chatbots used in customer service. In turn, we strive to examine how those perceptions affect overall customer satisfaction towards the brand or company. Theoretical perspective: Our study combines the Technology Acceptance Model (TAM) with the Expectation Confirmation Theory (ECT). Drawing on TAM, we explore cognitive (PU, PEoU) and affective (PT, PH) constructs of AI customer service chatbots, with brand loyalty shaping these factors. Based on ECT, we position brand loyalty as the source of customer expectation and show that perceived AI chatbot attributes influence customer satisfaction. Together, this integrated framework explains how existing brand loyalty can affect perceptions of AI chatbots and ultimately, customer satisfaction with the brand itself. Methodology: We employed a deductive reasoning approach and a quantitative research method, utilizing a cross-sectional design. A Qualtrics questionnaire was distributed via different social media platforms to collect as much data as possible. Next, we analyzed the consumers who have interacted with a company’s AI chatbot for customer service purposes (N = 141) by running PLS-SEM’s path analysis to test the hypotheses. Findings: Brand loyalty has a significantly positive effect on three of the examined variables: PU, PEoU, and PT. However, the results do not support the enhancement of PH as a result of brand loyalty. Furthermore, none of the considered AI chatbot’s attributes have a statistically significant impact on the overall customer satisfaction. Original/value: This study uniquely examines brand loyalty as an independent driver of AI chatbot perceptions and links these perceptions to overall customer satisfaction, not just satisfaction with the technology – offering fresh insights for companies integrating AI into customer service. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9206476
- author
- Yoon, Jeewon LU and Nguyen, Tuong-Yên LU
- supervisor
- organization
- course
- BUSN39 20251
- year
- 2025
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Customer Service, AI chatbot, Customer Satisfaction, Brand Loyalty, Perceived Ease of Use, Perceived Trustworthiness, Perceived Usefulness, Perceived Human-likeness
- language
- English
- id
- 9206476
- date added to LUP
- 2025-06-30 12:16:53
- date last changed
- 2025-06-30 12:16:53
@misc{9206476,
abstract = {{Purpose: This thesis aims to investigate how customers’ pre-existing brand loyalty influences their perceptions of four characteristics – Perceived Usefulness (PU), Perceived Ease of Use (PEoU), Perceived Trustworthiness (PT), and Perceived Human-likeness (PH) – of AI chatbots used in customer service. In turn, we strive to examine how those perceptions affect overall customer satisfaction towards the brand or company. Theoretical perspective: Our study combines the Technology Acceptance Model (TAM) with the Expectation Confirmation Theory (ECT). Drawing on TAM, we explore cognitive (PU, PEoU) and affective (PT, PH) constructs of AI customer service chatbots, with brand loyalty shaping these factors. Based on ECT, we position brand loyalty as the source of customer expectation and show that perceived AI chatbot attributes influence customer satisfaction. Together, this integrated framework explains how existing brand loyalty can affect perceptions of AI chatbots and ultimately, customer satisfaction with the brand itself. Methodology: We employed a deductive reasoning approach and a quantitative research method, utilizing a cross-sectional design. A Qualtrics questionnaire was distributed via different social media platforms to collect as much data as possible. Next, we analyzed the consumers who have interacted with a company’s AI chatbot for customer service purposes (N = 141) by running PLS-SEM’s path analysis to test the hypotheses. Findings: Brand loyalty has a significantly positive effect on three of the examined variables: PU, PEoU, and PT. However, the results do not support the enhancement of PH as a result of brand loyalty. Furthermore, none of the considered AI chatbot’s attributes have a statistically significant impact on the overall customer satisfaction. Original/value: This study uniquely examines brand loyalty as an independent driver of AI chatbot perceptions and links these perceptions to overall customer satisfaction, not just satisfaction with the technology – offering fresh insights for companies integrating AI into customer service.}},
author = {{Yoon, Jeewon and Nguyen, Tuong-Yên}},
language = {{eng}},
note = {{Student Paper}},
title = {{When Loyalty Speaks Louder Than the Machine: A Quantitative Study on How Pre-existing Brand Loyalty Shapes Perceived AI Chatbot Attributes and Customer Satisfaction}},
year = {{2025}},
}