AI-based service quality, digital experience, and perceived fair pricing provenly act as primary drivers in boosting customer satisfaction through the mediating role of customer trust
Society's need for stable internet connectivity continues to surge alongside massive digital transformation across various sectors
To examine this phenomenon, the study adopted a quantitative approach involving 180 active IndiHome customers in Kediri
Data analysis results indicate several key findings as follows:
- AI-based service quality, digital experience, and perceived fair pricing positively and significantly enhance customer trust and satisfaction
. - Customer trust variables are proven capable of significantly mediating the relationship of AI-based service quality, digital experience, and fair pricing toward customer satisfaction
. - This research model explains 78.2% of the variance in customer satisfaction and 46.2% of the variance in customer trust
.
These findings confirm that digital service strategies will not yield optimal impact unless balanced with price transparency and system reliability capable of fostering a sense of security among users. Telecommunication companies are advised to continuously strengthen artificial intelligence capabilities and maintain reasonable tariff rates to preserve long-term loyalty amid tight industry competition
Author Profiles
- Bunga Ayuwangi – Student in the Management Study Program, Faculty of Economics and Business, Universitas 17 Agustus 1945 Surabaya
. - Dr. Tri Andjarwati, M.M. – Lecturer and researcher at the Faculty of Economics and Business, Universitas 17 Agustus 1945 Surabaya
. - Dr. I Dewa Ketut Raka Ardiana, M.Si. – Lecturer and researcher at the Faculty of Economics and Business, Universitas 17 Agustus 1945 Surabaya
.
Research Sources:
- Article Title: The Effect of AI-Based Service Quality, Digital Experience, and Perceived Fair Pricing on Customer Satisfaction with Customer Trust as a Mediation Variable on Indihome Customer in Kediri
- Journal Name: International Journal of Management Analytics (IJMA)
- Publication Year: 2026
- DOI:
https://doi.org/10.59890/ijma.v4i3.16
0 Komentar