Factors Affecting Purchase Decisions for Health Services via Online Platforms in the Bangkok Metropolitan Region
DOI:
https://doi.org/10.65205/jmsr.2026.e286587Keywords:
Health Services, Online Platforms, Marketing Technology, Marketing mixAbstract
This quantitative research investigated factors affecting purchase decisions for Bangkok Metropolitan Region (BMR) online platform health services. The goals were to 1) study and develop knowledge of factors influencing purchase decisions for health services via online platforms in the BMR; to 2) formulate strategic guidelines for healthcare service providers and online platform developers to design services and business strategies meeting consumer lifestyles and needs as well as improving messaging and response to target customers, while improving the effectiveness of customer engagement and accessibility. The sample comprised 482 working-age consumers chosen by purposive sampling, aged between 21 and 59 who lived in the Bangkok Metropolitan Region, were seeking healthcare services, and had previously purchased products or services online. Data were collected by structured online questionnaires with closed-ended questions. The content validity index (IOC) of all questionnaire items ranged from 0.600 to 1.000, while the reliability test yielded a 0.976 Cronbach’s alpha. Data were analyzed by descriptive and inferential statistics, including structural equation modeling (SEM), to evaluate relationships among variables. Results were that the model fit the empirical data well, with model fit indices: CMIN/df = 1.960; GFI = 0.971; AGFI = 0.945; and RMSEA = 0.045, meeting acceptable criteria. These findings suggest that perceived usefulness (PU) and perceived ease of use (PEOU) positively influenced intention to use, explaining 52% of variance, with PEOU most impactful. Additionally, user characteristics (health motivation, information seeking behavior, cues to action, provider preference) and the 4E’s marketing mix (experience, exchange, everywhere, evangelism) jointly influenced PEOU, accounting for 63% of variance, with user characteristics most influential. PEOU, technology quality (information quality, system quality, service quality), and the 4E’s marketing mix also collectively influenced PU, explaining 59% of variance.
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