Çağlıyor, Sendi

Profile Picture
Name Variants
Cagliyor,Sendi
Cagliyor S.
SENDI ÇAĞLIYOR
Çağliyor S.
Sendi Çağlıyor
Cağlıyor, Sendi
S. Cağlıyor
Cagliyor,S.
ÇAĞLIYOR, Sendi
Çağlıyor, SENDI
Çağlıyor,S.
Çaǧliyor S.
Çağlıyor S.
Sendi, Cagliyor
Sendi ÇAĞLIYOR
C.,Sendi
S. Çağlıyor
Cağlıyor, S.
Ç., Sendi
Çağlıyor, S.
ÇAĞLIYOR, SENDI
C., Sendi
Çağlıyor, Sendi
Sendi Cağlıyor
Cagliyor, Sendi
Job Title
Dr. Öğr. Üyesi
Email Address
sendi.cagliyor@khas.edu.tr
ORCID ID
0000-0003-1106-5531
Scopus Author ID
57210113353
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output

2

Articles

1

Citation Count

3

Supervised Theses

0

Filters

20221
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Book Part
    Citation Count: 3
    Communicating Value in Healthcare Marketing from a Social Media Perspective
    (Springer, 2022) Çağlıyor, Sendi; Tosun, Petek; Uray, Nimet
    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.