Modeling Impact of Perceived Service Quality on Revisit Intention: A Health Information Management Perspective from Primary Care - Halimatusa Diah; Martin N Kariuki; Joan S Awingan; John van der Graff (Journal of Health Innovation and Environmental Education, 2025)
Purpose of the study: The present investigation was conducted to construct an empirical model explaining how patients intention to return is shaped by multiple dimensions of perceived service quality. Methodology: This research applied a quantitative method using a cross-sectional design to explore the associations among the studied variables at one specific point in time. A total of 75 outpatient respondents participated in the study. Data were gathered through a structured questionnaire consisting of closed questions measured likert scale. Collected responses were processed and analyzed using partial least squares–structural equation modeling (PLS-SEM) with assistance of SmartPLS version 4 to assess the suitability of the measurement model and to examine magnitude relationships among constructs. Main Findings: The analysis confirmed that every construct satisfied the established criteria for reliability and validity. Composite reliability values were all above 0.70, while.average variance extracted.(AVE) for each variable exceeded 0.50, indicating adequate convergent validity. Within the structural model, the independent variables jointly accounted for 68.4% of the variance in patients’ intention to revisit healthcare services (R² = 0.684), demonstrating substantial explanatory capacity. Each hypothesized path showed a positive direction and achieved statistical significance (p < 0.05). Of all examined determinants, service interaction quality emerged as the strongest predictor of revisit intention. Novelty/Originality of this study: The originality research lies in its theoretical contribution, as it broadens the application of the DeLone. and McLean information systems success model by adapting and contextualizing it within a healthcare service setting to better understand patient behavioral intentions.