Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach

Shounak Pal, Baidyanath Biswas, Rohit Gupta, Ajay Kumar and Shivam Gupta

Journal of Business Research, 2023, vol. 156, issue C

Abstract: Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms. Using topic-modelling techniques, we identified the impacting factors (predictors) and categorised them into two major dimensions based on strategic adoption and motivational association. Findings from our study suggest that time and money, convenience, responsiveness, and availability emerge as significant predictors for delivering a positive user experience on m-health platforms. Next, we identified substantial moderating effects of review polarity on the predictors related to brand association and hedonic motivation, such as online booking and video consultation. Further, we also identified the top predictors for successful user experience across these platforms. Recommendations from our study will benefit business managers by offering an improved service design leading to higher user satisfaction across these m-health platforms.

Keywords: Mobile health; Service-dominant logic; Machine learning; Text analytics; Proportional-odds logit (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:156:y:2023:i:c:s0148296322009493

DOI: 10.1016/j.jbusres.2022.113484

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