Title

Healthcare Service Quality in Emerging Economies: Perceptions via Social Media

Document Type

Article

Publication Date

2019

Abstract

In this study, we use a data analytics approach to explore the quality of public healthcare in emerging economies based on users’ perceptions as shared on social media. In most studies, the norm for determining service quality entails two main models; the SERVQUAL model (Parasuraman, Zeithaml, & Berry, 1988) and the SERVPERF model (Cronin & Taylor, 1992). Whereas both have their unique strengths and weaknesses, we opt for the SERVPERF model due to its parsimony in data collection. Also, its data collection parsimony is beneficial for a data-centric platform such as the Twitter social media platform. Using SERVPERF, we focus on understanding the perception of the general populace regarding the quality of service provided by public health facilities. We further evaluate the perception of quality in the light of the vision and mission statements of the health facilities in order to understand differences between the perceived quality of service quality rendered and the standard of service quality promised. Specifically, we ask the following questions: - What is the quality of service provided by health care facilities as perceived via social media? - What are the thematic sentiments and insights expressed by social media users on health care service quality in emerging economies? We focus on hospitals in Ghana, one of the fastest emerging economies in the world. We extract users’ perceptions on service quality as entailed in tweets regarding services at a set of major hospitals in metropolitan centers. Furthermore, we employ a text mining approach to analyze the tweets. As a methodology, text mining has been extensively used as an exploratory analysis tool for model development (Chen, Chiang, & Storey, 2012). Determining the perception of healthcare service quality in a wider population on social media is critical to designing effective remedial strategies that is beneficial to a greater portion of the population. Using text analytics, we also will be able to develop a preliminary understanding of different themes of discussions that surround the perception of care quality provided at public health institutions in emerging economies. Our social media analytics approach to understanding users’ perception of quality in the healthcare delivery system in Ghana will generate insight from a wider and more diverse audience. This would give hospitals an accurate reflection of their quality performance. Furthermore, it will enable them to revisit key organizational metrics as dictated by their mission and vision so as to improve on service delivery.


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