Research Article
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DETERMINATION OF FACTORS AFFECTING UNIVERSITY PREFERENCE BY CHOICE BASED CONJOINT ANALYSIS

Year 2017, Volume: 1 Issue: 1, 65 - 84, 29.09.2017
https://doi.org/10.31200/makuubd.337287

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.
   

References

  • 2016 YGS Sonuçları Açıklandı – ÖSYM Duyurusu, (2016, 26 Mart), Milliyet. http://www.milliyet.com.tr/2016-ygs-sonuclari-aciklandi--egitim-2216231/(02 Nisan 2017).
  • Adams, A. (2009). College Choice + Enrollment Management = Enrollment Choice, College & University, Washington, 84.4, 42-49.
  • 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). A Model of Student College Choice, The Journal of Higher Education, 52.5,490-505.
  • 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). 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.
  • 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
  • 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.
  • Green, P.E., Srinivasan, V. (1990). Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice, Journal of Marketing, 54, 3-19.
  • 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). Modelling the Student University Choice Process Through the Use of Conjoint Measurement Techniques, European Research, 9.4, 158.
  • Hossler, D., Gallagher, K. S. (1987). Studying Student College Choice. A Three Phase Model And The Implications For Policy Makers. College and University, 2.3, 207- 221.
  • Hossler, D., Braxton, J., & Coopersmith, G. (1989). Understanding Student College Choice. In: Smart, J. C. (ed.), Higher education: Handbook of theory and research (IV), New York: Agathon.
  • Hoyt, J.E., Brown, A.B., (2003). Identifying College Choice Factors to Successfully Market Your Institution. College and University, 78.4, 3-10.
  • Hur, J. S., Pak, R. J. (2007). Conjoint Analysis For The Preferred Subjects Of Elementary School Computer Education, Journal of the Korean Data and Information Science Society, 18.2, 357-364
  • Joseph, M., Joseph, B. (2000), Indonesian Students’ Perceptions of Choice Criteria in The Selection of a Tertiary Institution: Strategic Implications, The International Journal of Educational Management, 14.1, 40-44. doi: 10.1108/09513540010310396
  • Kahneman, D., Tvertsky, A. (1979). Prospect Theory: An Analysis of Decision Under Risk, Econometrica, 47.2, 263-291. doi:10.2307/1914185
  • Kallio, R. E. (1995). Factors Influencing The College Choice Decisions of Graduate Students, Research in Higher Education, 36.1, 109-124. doi:10.1007/BF02207769
  • Kim, A., Son, Y. D., & Sohn, S. Y. (2009). Conjoint Analysis Of Enhanced English Medium. Instruction For College Students. Expert Systems with Applications, 36.6, 10197-10203. doi: 10.1016/j.eswa.2009.01.080
  • Krampf, R. F., Heinlein, A. C. (1981). Developing Marketing Strategies and Tactics in Higher Education Through Target Market Research, Decision Sciences, 12, 175-192. doi:10.1111/j.1540-5915.1981.tb00074.x
  • Kuhfeld, W.F., Tobias, R. D., & Garratt, M. (1994), Efficient Experimental Design with Marketing Research Applications, Journal of Marketing Research, 31, 545-557.
  • Kuzmanovic, M., Savic, G., Martic, M. (2012). A New Approach to Evaluation of University Teaching Considering Heterogeneity of Students’ Preferences, Procedia - Social and Behavioral Sciences, 64.9, 402-411. doi: 10.1016/j.sbspro.2012.11.047
  • Luce, R.D., Tukey, J.W. (1964). Simultaneous Conjoint Measurement: A New Type of Fundamental Measurement, Journal of Mathematical Psychology, 1, 1-27.
  • Popović, M., Vagić, M., Kuzmanović, M., & Anđelković Labrović, J. (2016). Understanding Heterogeneity Of Students' Preferences Towards English Medium Instruction: A Conjoint Analysis Approach, Yugoslav Journal of Operations Research, 26.1, 91-102. doi: 10.2298/YJOR140915009P
  • Raposo, M., Alves, H. (2007). A Model of University Choice: An Exploratory Approach, MPRA Paper, 1, 5523, 203-218.
  • Sawtooth Software (2001). Choice Based Conjoint Analysis, Technical Paper Series, Sequim, WA: Sawtooth Software.
  • Sawtooth Software, (2017). Testing the CBC Design, https://www.sawtoothsoftware.com/help/issues/ssiweb/online_help/index.html?hid_web_cbc_designs_6.htm (02 Mayıs 2017).
  • Sohn, S. Y., Ju, Y. H. (2010). Conjoint Analysis For Recruiting High Quality Students For College Education, Expert Systems with Applications, 37.5, 3777-3783. doi:10.1016/j.eswa.2009.11.043
  • Soutar, G. N., Turner, J. P. (2002). Students Preferences For University: A Conjoint Analysis, International Journal of Educational Management, 16. 1, 40-45. doi: 10.1108/09513540210415523
  • Sullivan, E., Ferguson, S., & Donndelinger, J. (2011). Exploring Differences in Preference Heterogeneity Representation and Their Influence in Product Family Design, ASME, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 5: 37th Design Automation Conference, Parts A and B, 81-92. doi:10.1115/DETC2011-48596.
  • Şen, H., Çemrek, F. (2004). Konjoint Analizi ve Özel Dershane Tercihine Yönelik Bir Uygulama, Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 5.2, 105-120.
  • Tuncalı, T. (2007). Seçime Dayalı Konjoint Analizi Yöntemi İle GSM Servis Sağlayıcı Seçiminde Etkili Olan Faktörlerin Araştırılması ve Uygulama, (Yayımlanmamış Yüksek Lisans Tezi). Marmara Üniversitesi, SBE, İstanbul.
  • Veloutsou, C., Lewis, J. W., & Pton, R. A. (2005). Consultation and Reliability of Information Sources Pertaining to University Selection: Some Questions Answered?, International Journal of Educational Management, 19.4, 279-291.
  • Yamamoto, G.T. (2006). University evaluation-selection: A Turkish case. International Journal of Educational Management, 20.7, 559-569. doi:10.1108/09513540610704654
  • Yılmaz, Ö. (2012). Öğrencilerin Üniversite Tercihini Etkileyen Kriterlerin Belirlenmesinde Analitik Hiyerarşi Proses Uygulaması ve Süleyman Demirel Üniversitesi Örneği, (Yayımlanmamış Yüksek Lisans Tezi). Süleyman Demirel Üniversitesi, SBE, Isparta.

ÜNİVERSİTE TERCİHLERİNİN SEÇİME DAYALI KONJOİNT ANALİZİ İLE BELİRLENMESİ

Year 2017, Volume: 1 Issue: 1, 65 - 84, 29.09.2017
https://doi.org/10.31200/makuubd.337287

Abstract

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.

References

  • 2016 YGS Sonuçları Açıklandı – ÖSYM Duyurusu, (2016, 26 Mart), Milliyet. http://www.milliyet.com.tr/2016-ygs-sonuclari-aciklandi--egitim-2216231/(02 Nisan 2017).
  • Adams, A. (2009). College Choice + Enrollment Management = Enrollment Choice, College & University, Washington, 84.4, 42-49.
  • 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). A Model of Student College Choice, The Journal of Higher Education, 52.5,490-505.
  • 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). 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.
  • 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
  • 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.
  • Green, P.E., Srinivasan, V. (1990). Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice, Journal of Marketing, 54, 3-19.
  • 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). Modelling the Student University Choice Process Through the Use of Conjoint Measurement Techniques, European Research, 9.4, 158.
  • Hossler, D., Gallagher, K. S. (1987). Studying Student College Choice. A Three Phase Model And The Implications For Policy Makers. College and University, 2.3, 207- 221.
  • Hossler, D., Braxton, J., & Coopersmith, G. (1989). Understanding Student College Choice. In: Smart, J. C. (ed.), Higher education: Handbook of theory and research (IV), New York: Agathon.
  • Hoyt, J.E., Brown, A.B., (2003). Identifying College Choice Factors to Successfully Market Your Institution. College and University, 78.4, 3-10.
  • Hur, J. S., Pak, R. J. (2007). Conjoint Analysis For The Preferred Subjects Of Elementary School Computer Education, Journal of the Korean Data and Information Science Society, 18.2, 357-364
  • Joseph, M., Joseph, B. (2000), Indonesian Students’ Perceptions of Choice Criteria in The Selection of a Tertiary Institution: Strategic Implications, The International Journal of Educational Management, 14.1, 40-44. doi: 10.1108/09513540010310396
  • Kahneman, D., Tvertsky, A. (1979). Prospect Theory: An Analysis of Decision Under Risk, Econometrica, 47.2, 263-291. doi:10.2307/1914185
  • Kallio, R. E. (1995). Factors Influencing The College Choice Decisions of Graduate Students, Research in Higher Education, 36.1, 109-124. doi:10.1007/BF02207769
  • Kim, A., Son, Y. D., & Sohn, S. Y. (2009). Conjoint Analysis Of Enhanced English Medium. Instruction For College Students. Expert Systems with Applications, 36.6, 10197-10203. doi: 10.1016/j.eswa.2009.01.080
  • Krampf, R. F., Heinlein, A. C. (1981). Developing Marketing Strategies and Tactics in Higher Education Through Target Market Research, Decision Sciences, 12, 175-192. doi:10.1111/j.1540-5915.1981.tb00074.x
  • Kuhfeld, W.F., Tobias, R. D., & Garratt, M. (1994), Efficient Experimental Design with Marketing Research Applications, Journal of Marketing Research, 31, 545-557.
  • Kuzmanovic, M., Savic, G., Martic, M. (2012). A New Approach to Evaluation of University Teaching Considering Heterogeneity of Students’ Preferences, Procedia - Social and Behavioral Sciences, 64.9, 402-411. doi: 10.1016/j.sbspro.2012.11.047
  • Luce, R.D., Tukey, J.W. (1964). Simultaneous Conjoint Measurement: A New Type of Fundamental Measurement, Journal of Mathematical Psychology, 1, 1-27.
  • Popović, M., Vagić, M., Kuzmanović, M., & Anđelković Labrović, J. (2016). Understanding Heterogeneity Of Students' Preferences Towards English Medium Instruction: A Conjoint Analysis Approach, Yugoslav Journal of Operations Research, 26.1, 91-102. doi: 10.2298/YJOR140915009P
  • Raposo, M., Alves, H. (2007). A Model of University Choice: An Exploratory Approach, MPRA Paper, 1, 5523, 203-218.
  • Sawtooth Software (2001). Choice Based Conjoint Analysis, Technical Paper Series, Sequim, WA: Sawtooth Software.
  • Sawtooth Software, (2017). Testing the CBC Design, https://www.sawtoothsoftware.com/help/issues/ssiweb/online_help/index.html?hid_web_cbc_designs_6.htm (02 Mayıs 2017).
  • Sohn, S. Y., Ju, Y. H. (2010). Conjoint Analysis For Recruiting High Quality Students For College Education, Expert Systems with Applications, 37.5, 3777-3783. doi:10.1016/j.eswa.2009.11.043
  • Soutar, G. N., Turner, J. P. (2002). Students Preferences For University: A Conjoint Analysis, International Journal of Educational Management, 16. 1, 40-45. doi: 10.1108/09513540210415523
  • Sullivan, E., Ferguson, S., & Donndelinger, J. (2011). Exploring Differences in Preference Heterogeneity Representation and Their Influence in Product Family Design, ASME, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 5: 37th Design Automation Conference, Parts A and B, 81-92. doi:10.1115/DETC2011-48596.
  • Şen, H., Çemrek, F. (2004). Konjoint Analizi ve Özel Dershane Tercihine Yönelik Bir Uygulama, Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 5.2, 105-120.
  • Tuncalı, T. (2007). Seçime Dayalı Konjoint Analizi Yöntemi İle GSM Servis Sağlayıcı Seçiminde Etkili Olan Faktörlerin Araştırılması ve Uygulama, (Yayımlanmamış Yüksek Lisans Tezi). Marmara Üniversitesi, SBE, İstanbul.
  • Veloutsou, C., Lewis, J. W., & Pton, R. A. (2005). Consultation and Reliability of Information Sources Pertaining to University Selection: Some Questions Answered?, International Journal of Educational Management, 19.4, 279-291.
  • Yamamoto, G.T. (2006). University evaluation-selection: A Turkish case. International Journal of Educational Management, 20.7, 559-569. doi:10.1108/09513540610704654
  • Yılmaz, Ö. (2012). Öğrencilerin Üniversite Tercihini Etkileyen Kriterlerin Belirlenmesinde Analitik Hiyerarşi Proses Uygulaması ve Süleyman Demirel Üniversitesi Örneği, (Yayımlanmamış Yüksek Lisans Tezi). Süleyman Demirel Üniversitesi, SBE, Isparta.
There are 39 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Tutku Tuncalı Yaman

Özgür Çakır This is me

Publication Date September 29, 2017
Acceptance Date September 30, 2017
Published in Issue Year 2017 Volume: 1 Issue: 1

Cite

APA Tuncalı Yaman, T., & Çakır, Ö. (2017). ÜNİVERSİTE TERCİHLERİNİN SEÇİME DAYALI KONJOİNT ANALİZİ İLE BELİRLENMESİ. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, 1(1), 65-84. https://doi.org/10.31200/makuubd.337287


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