The Influence of E-Service Quality on Data-Driven Culture Moderated By BI Adoption & AI Readiness in Indonesian Transportation Support Services Provider

Ridho Ilham Putra Satyanegara (Faculty of Economics and Business, Padjadjaran University Jl. Dipati Ukur No. 46, Bandung 40321, West Java, Indonesia)
Popy Rufaidah (Faculty of Economics and Business, Padjadjaran University Jl. Dipati Ukur No. 46, Bandung 40321, West Java, Indonesia)

Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal
Vol. 12 No. 1 (2026): JABM, Vol. 12 No. 1, Januari 2026

Background: This study examines e-service quality, BI, and AI in the digital food ordering services of a state-owned transportation company.Purpose: This study aims to examine the direct effect of e-service quality on data-driven culture and the moderating roles of business intelligence (BI) implementation and artificial intelligence (AI) readiness.Design/Methodology/Approach: This study used a descriptive quantitative method. The sample was selected using purposive sampling method. A total of 63 respondents were involved in the study. The sample size is acceptable as PLS-SEM is robust with small samples and meets the 10-times rule. Primary and secondary data were used. The researcher obtained primary data using questionnaires. Secondary data were obtained from the company’s internal information system. Data were collected through a literature review and questionnaires. The data were analyzed using the PLS-SEM method.Findings/Results: This study found that service quality positively affects a company’s data-driven culture. Based on respondents’ perceptions, AI readiness, and BI implementation, the correlation between electronic service quality and data-driven culture is supported. However, BI implementation has a more decisive influence than AI readiness. This condition is likely due to earlier implementation, stronger integration, and greater organizational readiness.Conclusions: Theoretically, this study extends technology adoption and service quality theories by demonstrating that improving e-service quality, combined with BI and AI readiness, enhances a data-driven decision-making culture in the public sector. Practically, this study recommends improving system availability and responsiveness, optimizing BI for real-time analytics, and accelerating AI implementation, such as chatbots and predictive analytics, to increase customer satisfaction and operational efficiency. Keywords:   business intelligence, artificial intelligence, e-service quality, data-driven culture, transportation services