Çağlıyor, Sendi

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https://hdl.handle.net/20.500.12469/6068
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
Çağlıyora, Sandy
Çağliyor, S.
Job Title
Dr. Öğr. Üyesi
Email Address
[email protected]
Main Affiliation
Business Administration
Status
Current Staff
Website
ORCID ID
0000-0003-1106-5531
Scopus Author ID
57210113353
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
ERF-0115-2022

Sustainable Development Goals

15

LIFE ON LAND
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PEACE, JUSTICE AND STRONG INSTITUTIONS
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14

LIFE BELOW WATER
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CLEAN WATER AND SANITATION
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GOOD HEALTH AND WELL-BEING
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17

PARTNERSHIPS FOR THE GOALS
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QUALITY EDUCATION
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ZERO HUNGER
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REDUCED INEQUALITIES
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AFFORDABLE AND CLEAN ENERGY
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RESPONSIBLE CONSUMPTION AND PRODUCTION
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DECENT WORK AND ECONOMIC GROWTH
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SUSTAINABLE CITIES AND COMMUNITIES
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Scholarly Output

6

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WoS Citation Count

11

Scopus Citation Count

20

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2

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WoS Citations per Publication

1.83

Scopus Citations per Publication

3.33

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Challenging Discrimination in Different Areas: Turkey1
International Journal of Consumer Studies1
International Series in Operations Research and Management Science1
Journal of Intelligent & Fuzzy Systems1
Journal of Intelligent and Fuzzy Systems1
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Now showing 1 - 6 of 6
  • Thumbnail Image
    Book Part
    Citation - Scopus: 3
    Communicating Value in Healthcare Marketing From a Social Media Perspective
    (Springer, 2022) Çağlıyor, S.; Tosun, P.; Uray, N.
    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.
  • Thumbnail Image
    Book Part
    HIV/AIDS Mass Media Coverage and HIV-Related Discrimination in Turkey
    (Peter Lang AG, 2021) Çağliyor, S.
    HIV/AIDS is one of the most stigmatizing medical problems in modern history. Social prejudices developed due to society’s real and unrealistic fears against the disease also caused negative attitudes such as stigma and discrimination. Stigma and discrimination suffered by people living with HIV/ AIDS can be considered a problem as impactful as the disease itself. Although much progress has been made on this issue, HIV positive individuals still face severe discrimination in their professional and social lives. The fear of discrimination not only reduces the quality of life of people but also causes HIV positive individuals to avoid diagnostic tests or treatment. Based on the premise mass communication reflects fundamental frameworks in how the contemporary world is seen and perceived by society, this study aims to observe general trends in the news and how discrimination against people living with HIV or AIDS is portrayed in the mass media. To determine the general trends, Latent Dirichlet Allocation (LDA) topic modeling is applied to 770 news about AIDS or HIV published between 2010 and 2020, and a total of 6 general trends were obtained. Then, news containing discrimination and prejudice and social marketing campaigns are examined separately, and the perception of discrimination and stigma related to AIDS and HIV in the general population is discussed. © Peter Lang GmbH Internationaler Verlag der Wissenschaften Berlin 2021.
  • Thumbnail Image
    Article
    Citation - WoS: 1
    Citation - Scopus: 3
    Forecasting US movies box office performances in Turkey using machine learning algorithms
    (IOS PRESS, 2020) Çağlıyora, Sandy; Oztaysi, Briar; Sezgin, Selime
    The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.
  • Thumbnail Image
    Article
    Citation - Scopus: 3
    Forecasting Us Movies Box Office Performances in Turkey Using Machine Learning Algorithms
    (IOS Press BV, 2020) Çaǧliyor,S.; Öztayşi,B.; Sezgin,S.
    The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature. © 2020 - IOS Press and the authors. All rights reserved.
  • Thumbnail Image
    Article
    Citation - WoS: 3
    Citation - Scopus: 4
    Assessing the Effectiveness of Ott Services, Branded Apps, and Gamified Loyalty Giveaways on Mobile Customer Churn in the Telecom Industry: a Machine-Learning Approach
    (Elsevier Sci Ltd, 2024) Kirgiz, Omer Bugra; Kiygi-Calli, Meltem; Cagliyor, Sendi; El Oraiby, Maryam
    Telecom operators allocate a significant amount of resources to retain their customers as the organic growth in the number of customers is slowing down. Gamified loyalty programs, branded apps, and over-the-top (OTT) services emerged as ways to develop customer acquisition and retention strategies. Despite these strategies, some mobile customers still churn; therefore, churn prediction plays an essential role in the sustainable future of telecom businesses. Churn prediction is used both to detect customers with a high propensity to churn and to identify the reasons behind their churn behavior. This study examines several features affecting the churn behavior of mobile customers, including branded apps, gamified loyalty programs, and OTT services. In this study, the secondary data is provided by a telecom operator and contains the attributes of both churner and non-churner mobile customers. Logistic regression and random forest classifiers are compared in terms of their predictive power, and we used the latter as the machine learning algorithm in the churn prediction model. To understand the variable importance, mean decrease in impurity and permutation importance are performed. The key findings of this research reveal that while gamified loyalty giveaways and branded app strategies are effective, OTT service strategies show lower importance in predicting mobile customer churn behavior.
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    Article
    Citation - WoS: 7
    Citation - Scopus: 7
    Reducing Consumer-Brand Incongruity Through Corporate Social Responsibility and Brand Trust: Exploring Negative Word-Of (nwom)
    (Wiley, 2024) Tosun, Petek; Cagliyor, Sandy Ipeker; Gurce, Merve Yanar
    Drawing upon consumer-brand disidentification theory and balance theory, this study examines symbolic and ideological incongruity in consumer-brand relationships through an original conceptual model shaped by negative past experiences, brand trust, perceived corporate social responsibility (CSR), and negative word-of-mouth (NWOM). A preliminary study was conducted to explore the dimensions of consumers' negative past experiences by topic detection. Latent Dirichlet allocation (LDA) topic modeling was undertaken to analyze online consumer reviews (n = 6095) about a coffee chain brand. The dimensions detected in this preliminary study were included in the research model and further analyzed in the main study. The main study, a cross-sectional consumer survey (n = 522), tested the original research model by way of partial least squares structural equation modeling (PLS-SEM) on SmartPLS. The findings showed that negative past experiences consisted of product-related, service-related, and technology-related problems and negatively influenced brand trust. It was found that brand trust and perceived CSR negatively affected symbolic and ideological incongruity, while symbolic and ideological incongruity positively influenced NWOM. The findings provide empirical evidence for balance theory by showing that the three critical domains of consumer-brand relationships (ideological, symbolic, and experiential) provide a complex cognitive model that covers personal-symbolic and moral-societal aspects of consumer-brand disidentification and consequent NWOM intentions. In line with consumer-brand disidentification theory, the results contribute to the literature by demonstrating the direct negative impacts of brand trust and perceived CSR on symbolic and ideological incongruity, as well as the direct positive impacts of symbolic and ideological incongruity on NWOM.