LAUSR

Abstract In the digital era, online channels have become an inevitable part of healthcare services making healthcare/health-product e-commerce an important area of study. However, the reflections of customer-satisfaction and their difference in various subgroups of this industry is still unexplored. Additionally, extant literature has majorly focused on consumer surveys for customer-satisfaction research ignoring the huge data available online. The current study fills these gaps. With 186,057 reviews on 619 e-commerce firms from 29 subcategories of healthcare/health-product industry posted in a review-website between 2008 and 2018, we used text-mining, machine-learning and econometric techniques to find which core and augmented service aspects and which emotions are more important in which service contexts in terms of reflecting and predicting customer satisfaction. Our study contributes towards the healthcare/health-product marketing and services literature in suggesting an automated and machine-learning-based methodology for insight generation. It also helps healthcare/health-product e-commerce managers in better e-commerce service design and delivery.