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

Abstract

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