Journals / Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi / 2017 / Cilt: 1 - Sayı: 1

DETERMINATION OF FACTORS AFFECTING UNIVERSITY PREFERENCE BY CHOICE BASED CONJOINT ANALYSIS

ÜNİVERSİTE TERCİHLERİNİN SEÇİME DAYALI KONJOİNT ANALİZİ İLE BELİRLENMESİ

Pages
65–84
Publication Date
2017-09-29
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is hoped that these results will be enlightening for both university and education administrators.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:State universities had ruled Turkey’s higher education system for years.
📊
Key Findings & Data:Conjoint analysis has been technically used since the early 1980's in determining consumer preferences and in marketing research, but its use in education is not widespread.
💡
Conclusion & Impact:It is hoped that these results will be enlightening for both university and education administrators.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Üniversite tercihi · Seçime Dayalı Konjoint Analizi · University selection

Abstract

State universities had ruled Turkey’s higher education system for years. Recently number of private (foundation) universities rise and they start to participate university candidates’ selection paradigm with their various marketing activities. In this context, determination of important factors according to the candidates' in university selection process gained importance for these schools. Conjoint analysis has been technically used since the early 1980's in determining consumer preferences and in marketing research, but its use in education is not widespread. In this research, factors affecting the preferences of university candidates regarding newly established private universities are tried to be determined by choice based conjoint analysis method. 296 university candidates participated in the study. According to results, some of the most important factors in preference process were appeared as, presence of the field wishing to be studied, academic reputation and campus facilities of the school respectively. It is hoped that these results will be enlightening for both university and education administrators.

Özet

Ülkemizde yükseköğretim alanında uzun yıllardır süren devlet üniversitesi yoğunluğu son yıllarda pek çok vakıf üniversitesinin kurulması ile azalmaya başlamıştır. Bu bağlamda üniversite adaylarının tercih sürecine çeşitli tanıtım faaliyetleri ile katılan bu okulların hangi faktörler gözetilerek tercih edildiği sorusu cevaplanmaya muhtaçtır. Konjoint analizi teknik olarak 1980’lerin başından itibaren tüketici tercihlerinin belirlenmesinde ve pazarlama araştırmalarında yaygın olarak kullanılmakta ise de eğitim alanında kullanımı pek yaygın değildir. Bu çalışmada üniversite adaylarının vakıf üniversitesi tercihlerini etkileyen faktörler seçime dayalı konjoint analizi yöntemi ile belirlenmeye çalışmıştır. 2016 yılı üniversite seçme sınavları sonrasında tercih sürecinde bulunan 296 öğrenci ile yapılan çalışma sonucunda görece yeni faaliyet göstermeye başlayan vakıf üniversitelerini tercih edecek aday öğrencilerin en önemli tercih nedenleri sırasıyla öğrenim görmek istenen bölümün mevcudiyeti, okulun akademik itibarı ve kampüs imkanları olarak belirlenmiştir. Elde edilen sonuçların hem okul yönetimleri hem de eğitim yöneticileri tarafından aydınlatıcı olması umulmaktadır.

🔖

Cite This Article

YAMAN, T. T., ÇAKIR, Ö. (2017). DETERMINATION OF FACTORS AFFECTING UNIVERSITY PREFERENCE BY CHOICE BASED CONJOINT ANALYSIS. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, Cilt: 1 - Sayı: 1, 65–84. https://ajindex.com/articles/790638

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (10)

  1. 2016 YGS Sonuçları Açıklandı – ÖSYM Duyurusu, (2016, 26 Mart), Milliyet. http://www.milliyet.com.tr/2016-ygs-sonuclari-aciklandi--egitim-2216231/(02 Nisan 2017).
  2. Adams, A. (2009). College Choice + Enrollment Management = Enrollment Choice, College & University, Washington, 84.4, 42-49.
  3. Akaah, J. P. and Korgaonkar, P. K. (1988). A Conjoint Investigation of the Relative Importance of Risk Relievers in Direct Marketing. Journal of Advertising Research. 28, 4. Cattin, P., Wittink, D. (1989). Commercial Use of Conjoint Analysis: An Update, Journal of Marketing, 53.3, 91–96. doi:10.2307/1251345 Chapman, D. W. (1981).
  4. A Model of Student College Choice, The Journal of Higher Education, 52.5,490-505.
  5. DeSarbo, W.S., Choi, S.C. (1993). Game Theoretic Derivations of Competitive Strategies in Conjoint Analysis, Marketing Letters, 4.4, 337-348. doi:10.1007/BF00994352 DesJardins, S. L., Ahlburg, D. A., & McCall, B. P. (1999).
  6. An Integrated Model of Application, Admission, Enrollment, and Financial Aid, The Journal of Higher Education, 77.3, 381-429. doi:10.1353/jhe.2006.0019 Domino, S., Libraire, T., Lutwiller, D., & Superczynski, S. (2006). Higher Education Marketing Concerns: Factors Influence Students' Choice Of Colleges, The Business Review, 6.2, 101-111.
  7. Ghansah, B., Benuwa, B. B., Ansah, E. K., Ghansah, N. E., Magama, C., & Ocquaye, E. N. N. (2016). Factors that Influence Students' Decision to Choose a Particular University: A Conjoint Analysis, International Journal of Engineering Research in Africa, 27, 147-157. doi: 10.4028/www.scientific.net/JERA.27.147 Green, P. and DeSarbo, W. S. (1978). Additive Decomposition of Perceptions Data Via Conjoint Analysis. Journal of Consumer Research. 5.1. doi: https://doi.org/10.1086/208714
  8. Green, P. E, Krieger, A. M., Wind, Y. (2001). Thirty Years of Conjoint Analysis: Reflections and Prospects, Marketing Research and Modeling: Progress and Prospects, 14, International Series in Quantitative Marketing, 117-139. doi:10.1007/978-0-387-28692-1_6 Green, P., Rao, V. (1971). Conjoint Measurement for Quantifying Judgemental Data. Journal of Product Innovation Management. 8, 189-202.
  9. Green, P.E., Srinivasan, V. (1990). Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice, Journal of Marketing, 54, 3-19.
  10. Hair, F. H., Black, W. C., Babin, B. J., & Anderson, R. E. (1995). Multivariate Data Analysis with Readings, 4th Edition, New Jersey: Prentice Hall,. Hooley, G. J., Lynch, J. E. (1981). Modellin