Managing service flexibility in healthcare for improved customer experience: a data-driven approach

The purpose of this paper is to explore how customers perceive service providers' flexibility and evaluate the healthcare experience. The study applied a machine learning technique to develop a classification framework of service flexibility in healthcare where the studies are limited. The data mining was applied to 3600 stories from a healthcare firm using the Weka software. The study presents several facets of service flexibility in healthcare through topic modeling using Latent Dirichlet Allocation (LDA) theory. The relationship was gauged by utilizing structural equation modeling (PLSSEM). This paper is a first step to designing the marketing strategies from the flexibility perspective by adopting a customer's lens. The article demystifies a novel perspective on customer-perceived service flexibility by illuminating the underlying mechanisms through a data-driven approach. The implications are then discussed, emphasizing that healthcare service providers need to develop flexible competence to understand customer idiosyncrasies and enhance the service experience.