Communicating Value in Healthcare Marketing From a Social Media Perspective

dc.authorscopusid 57210113353
dc.authorscopusid 57203211930
dc.authorscopusid 6601980161
dc.contributor.author Çağlıyor, S.
dc.contributor.author Çağlıyor, Sendi
dc.contributor.author Tosun, P.
dc.contributor.author Tosun, Petek
dc.contributor.author Uray, N.
dc.contributor.author Uray, Nimet
dc.contributor.other Business Administration
dc.date.accessioned 2023-10-19T15:05:15Z
dc.date.available 2023-10-19T15:05:15Z
dc.date.issued 2022
dc.department-temp Çağlıyor, S., Kadir Has University, Istanbul, Turkey; Tosun, P., Kadir Has University, Istanbul, Turkey; Uray, N., Kadir Has University, Istanbul, Turkey en_US
dc.description.abstract Sustainable healthcare policies and a developed healthcare industry are vital to countries’ competitiveness and productivity. The ongoing transformations in healthcare services and advances in health technologies and analytics make it clear that there is a pressing need for more collaborative and interdisciplinary efforts in the industry. This study aims to explore the effectiveness of online marketing communication for healthcare services in Turkey with regard to the value-driven marketing approach utilized by leading chain hospitals through an examination of two research questions: (1) Which messages are emphasized in the social media marketing communications of hospitals? (2) Which factors increase engagement with healthcare consumers on social media? To that end, we compiled the Facebook and Twitter posts of three of the largest hospital chains in Turkey for the last 5 years along with the interaction metrics of the posts, ultimately generating a dataset consisting of 9212 posts in total. Using Latent Dirichlet Allocation, we identified four main topics: Posts on holidays and special days/weeks promoting healthy lifestyles, informative posts about the symptoms and treatments of illnesses, posts containing statistics about diseases, and posts including news about the hospital in question. In the following stage, we carried out predictive analysis using three tree-based machine learning algorithms (decision trees, random forests, and gradient boosting trees) to predict total interaction and relative variable importance. Our model performed at an accuracy rate of 70%. The findings of this study indicate that contextual factors such as the number of followers may have more predictive power than content or interactivity factors. Hospitals use social media to improve their brand reputation and increase public awareness about health and critical diseases. The posts about holidays and special days and using links in the posts resulted in the most interaction. Message source was identified as an important factor, so different social media platforms should be treated as separate mediums in the design of marketing communication strategies and the different dynamics of those platforms should be considered instead of posting the same content on various platforms. As such, this research has valuable implications for marketing managers and administrators working in healthcare in terms of the design of their online marketing communication strategies. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. en_US
dc.identifier.citationcount 3
dc.identifier.doi 10.1007/978-3-030-91851-4_6 en_US
dc.identifier.endpage 170 en_US
dc.identifier.issn 0884-8289
dc.identifier.scopus 2-s2.0-85133184141 en_US
dc.identifier.startpage 143 en_US
dc.identifier.uri https://doi.org/10.1007/978-3-030-91851-4_6
dc.identifier.uri https://hdl.handle.net/20.500.12469/4771
dc.identifier.volume 326 en_US
dc.khas 20231019-Scopus en_US
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.ispartof International Series in Operations Research and Management Science en_US
dc.relation.publicationcategory Kitap Bölümü - Uluslararası en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 3
dc.subject Decision tree en_US
dc.subject Gradient boosting tree en_US
dc.subject Healthcare marketing en_US
dc.subject Latent Dirichlet allocation en_US
dc.subject Marketing communication en_US
dc.subject Random forest en_US
dc.subject Social media en_US
dc.title Communicating Value in Healthcare Marketing From a Social Media Perspective en_US
dc.type Book Part en_US
dspace.entity.type Publication
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