• ISSN: 2148-2225 (online)

Ulaştırma ve Lojistik Kongreleri

alphanumeric journal

The Journal of Operations Research, Statistics, Econometrics and Management Information Systems

A Game-Theoretical Approach to Conjoint Analysis


Tutku Tuncalı Yaman, Ph.D.

Özgür Çakır, Ph.D.


The study aims to combine the results of Conjoint Analysis, which is frequently used to determine customer preferences in marketing and market research areas, with Game Theory as suggested in the article of Choi and DeSarbo (1993). In this context, the application of the proposed approach was made within the framework of the factors affecting private university preferences of university candidates and the marketing decisions of school administrations. Student preferences were determined by the choice-based conjoint analysis method. As a result of a study conducted with 296 prospective students who were in the selection process after the 2016 university entrance exams. The reasons for preference in order of importance were determined as the availability of the program to be studied, the academic reputation of the school, and campus facilities. The data relating to the characteristics of university managements care in their marketing activities obtained from the interviews with the school administrators and the data obtained from the Conjoint Analysis of students’ reasons for preference were used as input in the payoff matrix organized in the context of Game Theory, and the solution of the game was completed as a two-person zero-sum game. As a result of the application of the method with empirical data, it is observed that how student preferences will change when the weights of strategic marketing factors change in the decisions taken by school administrations from the business point of view. In addition, with the help of this approach and by obtaining competitor data, it allows to describe the situation of the market in general and to make a comparative evaluation of each university on its own.

Keywords: Conjoint Analysis, Game Theory, University Preference

Jel Classification: C46

Suggested citation

Tuncalı Yaman, T. & Çakır, Ö. (). A Game-Theoretical Approach to Conjoint Analysis. Alphanumeric Journal, 9(2), 179-216. http://dx.doi.org/10.17093/alphanumeric.883432


  • Adams, A. (2009). College choice+ enrollment management= enrollment choice. College and University, 84(4), 42.
  • 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), 38-44.
  • Akdağ, Y. (2015). Oyun teorı̇sı̇ yaklaşımı ı̇le reklam aracı seçı̇m sürecı̇nı̇n ekonomı̇ye etkı̇lerı̇: bulanık TOPSIS yöntemı̇yle vakıf ünı̇versı̇telerı̇nı̇n eğı̇tı̇m sektörü üzerı̇ne bı̇r uygulama, Yayımlanmamış Yüksek Lisans Tezi, İstanbul Üniversitesi, İstanbul.
  • Aliev, R. A., & Huseynov, O. H. (2014). Decision theory with imperfect information (Vol. 10). World Scientific.
  • Angelou, G. N., & Economides, A. A. (2009). A multi-criteria game theory and real-options model for irreversible ICT investment decisions. Telecommunications Policy, 33(10-11), 686-705. https://doi.org/10.1016/j.telpol.2009.07.005
  • Aplak, H.S. (2010) Karar verme sürecinde bulanık mantık bazlı oyun teorisi uygulamaları, YayImlanmamiIş Doktora Tezi, Gazi Üniversitesi, Ankara
  • Arenoe, B., van der Rest, J. P. I., & Kattuman, P. (2015). Game theoretic pricing models in hotel revenue management: An equilibrium choice-based conjoint analysis approach. Tourism Management, 51, 96-102. https://doi.org/10.1016/j.tourman.2015.04.007
  • Arsenyan Üşenmez, J. A. (2011). Designing and implementing a collaborative structure for product development, Yayımlanmamış Doktora Tezi, Galatasaray Üniversitesi, İstanbul.
  • BittiLemke, S., Mazarakis, A., & Peters, I. (2021). Conjoint analysis of researchers' hidden preferences for bibliometrics, altmetrics, and usage metrics. Journal of the Association for Information Science and Technology.1-16. https://doi.org/10.1002/asi.2444516
  • Blokhuis, E. G. J., Snijders, C. C. P., Han, Q., & Schaefer, W. F. (2012). Conflicts and cooperation in brownfield redevelopment projects: Application of conjoint analysis and game theory to model strategic decision making. Journal of Urban Planning and Development, 138(3), 195-205. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000122
  • Can, M. (2015). Karar teorisi, (In: Çok Kriterli Karar Verme Yöntemleri), Ed: Bahadır Fatih Yıldırım, Emrah Önder, Dora Yayınları, Bursa.
  • Cattin, P., & Wittink, D. R. (1982). Commercial use of conjoint analysis: A survey. Journal of marketing, 46(3), 44-53. https://doi.org/10.1177/002224298204600308
  • Chapman C. N. and Love, E. (2012). Game theory and conjoint analysis: Using choice data for strategic decisions, B. Orme, ed. Proceedings of the 2012 Sawtooth Software Conference, Orlando, Florida.
  • Chapman, D. W. (1981). A model of student college choice. The Journal of Higher Education, 52(5), 490-505. https://doi.org/10.1080/00221546.1981.11778120
  • Choi, S. C., & DeSarbo, W. S. (1993). Game theoretic derivations of competitive strategies in conjoint analysis. Marketing Letters, 4(4), 337-348. https://doi.org/10.1007/BF00994352
  • Debnath, A., Bandyopadhyay, A., Roy, J., & Kar, S. (2018). Game theory based multi criteria decision making problem under uncertainty: a case study on Indian tea industry. Journal of Business Economics and Management, 19(1), 154-175. https://doi.org/10.3846/16111699.2017.1401553
  • Deng, X., Zheng, X., Su, X., Chan, F. T., Hu, Y., Sadiq, R., & Deng, Y. (2014). An evidential game theory framework in multi-criteria decision making process. Applied Mathematics and Computation, 244, 783-793. https://doi.org/10.1016/j.amc.2014.07.065
  • Desjardins, S. L., Ahlburg, D. A., & McCall, B. P. (2006). An integrated model of application, admission, enrollment, and financial aid. The Journal of Higher Education, 77(3), 381-429. https://doi.org/10.1080/00221546.2006.11778932
  • Domino, S., Libraire, T., Lutwiller, D., Superczynski, S., & Tian, R. (2006). Higher education marketing concerns: factors influence students’ choice of colleges. The Business Review, 6(2), 101-111.
  • Fitz-Gerald, A., & Tracy, M. (2008). Developing a decision-making model for security sector development in uncertain situations. Journal of Security Sector Management, 6(2), 1-37.
  • Green, P. E., & DeSarbo, W. S. (1978). Additive decomposition of perceptions data via conjoint analysis. Journal of Consumer Research, 5(1), 58-65.doi: https://doi.org/10.1086/208714
  • Green, P. E., & Rao, V. R. (1971). Conjoint measurement-for quantifying judgmental data. Journal of Marketing research, 8(3), 355-363. https://doi.org/10.1177/002224377100800312
  • Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: new developments with implications for research and practice. Journal of marketing, 54(4), 3-19. https://doi.org/10.1177/002224299005400402
  • Green, P. E., Krieger, A. M., & Wind, Y. (2001). Thirty years of conjoint analysis: Reflections and prospects. Interfaces, 31(3_supplement), S56-S73. https://doi.org/10.1287/inte.31.3s.56.9676
  • Hair, J. F., Anderson, R. E., Tatham, R., & Black, W. C. (1995). Multivar data analysis with readings. Indianapolis, Macmillan Pub..
  • Hansson, S. O. (2005). Decision theory. A brief introduction. Department of Philosophy and the History of technology. Royal Institute of Technology. Stockholm.
  • Hauser, J. R., Eggers, F., & Selove, M. (2019). The strategic implications of scale in choice-based conjoint analysis. Marketing Science, 38(6), 1059-1081.https://doi.org/10.1287/mksc.2019.1178
  • Hooley, G. J., & Lynch, J. E. (1981). Modelling the student university choice process through the use of conjoint measurement techniques. European Research, 9(4), 158-170.
  • Hossler, D., & Gallagher, K. S. (1987). Studying student college choice: A three-phase model and the implications for policymakers. College and university, 62(3), 207-21.
  • Hossler, D., Braxton, J., & Coopersmith, G. (1989). Understanding student college choice. Higher education: Handbook of theory and research, 5, 231-288.
  • Howell, J. R., Ebbes, P., & Liechty, J. C. (2021). Gremlins in the Data: Identifying the Information Content of Research Subjects. Journal of Marketing Research, 58(1), 74-94. https://doi.org/10.1177/0022243720965930
  • Hoyt, J. E., & Brown, A. B. (2003). Identifying college choice factors to successfully market your institution. College and University, 78(4), 3.
  • Işığıçok, E. (2015). Karar vermeye giriş, (In: Karar Verme), Ed: Prof. Dr. Mustafa Aytaç & Prof. Dr. Necmi Gürsakal) Dora Yayınları, Bursa.
  • Joseph, M., & Joseph, B. (2000). Indonesian students’ perceptions of choice criteria in the selection of a tertiary institution: Strategic implications. International Journal of Educational Management, 14(1), 40-44. https://doi.org/10.1108/09513540010310396
  • Kahneman, D., & Tvertsky, A., (1979). Prospect theory: An analysis of decision under risk, Econometrica, 47(2), 263-291.
  • Kallio, R. E. (1995). Factors influencing the college choice decisions of graduate students. Research in higher education, 36(1), 109-124. https://doi.org/10.1007/BF02207769
  • Krampf, R. F., & Heinlein, A. C. (1981). Developing marketing strategies and tactics in higher education through target market research. Decision sciences, 12(2), 175-192. https://doi.org/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(4), 545-557. https://doi.org/10.1177/002224379403100408
  • Kuzmanovic, M., & Martic, M. (2012). An approach to competitive product line design using conjoint data. Expert Systems with Applications, 39(8), 7262-7269. https://doi.org/10.1016/j.eswa.2012.01.097
  • Liu, Y. M., Brazell, J. D., & Allenby, G. M. (2020). An Integrative Model for Complex Conjoint Analysis. Available at SSRN 3696526. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3696526 (Accessed: 23.05.2021).
  • Luce, R. D., & Tukey, J. W. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of mathematical psychology, 1(1), 1-27. https://doi.org/10.1016/0022-2496(64)90015-X
  • Madani, K., & Lund, J. R. (2011). A Monte-Carlo game theoretic approach for multi-criteria decision making under uncertainty. Advances in water resources, 34(5), 607-616. https://doi.org/10.1016/j.advwatres.2011.02.009
  • ÖSYM (2017). 2016 YGS Sayısal Bilgiler, http://dokuman.osym.gov.tr/pdfdokuman/2016/YGS/2016_YGS_Sayisal_Bilgiler.pdf (Accessed: 21.09.2017).
  • ÖSYM. (2021). Yükseköğretim Kurumları Sınavı (YKS) Kılavuzu https://dokuman.osym.gov.tr/pdfdokuman/2021/YKS/kilavuz_04022021.pdf (Accessed: 09.02.2021)
  • Öztürk, A. (2001) Yöneylem araştırması, Ekin Kitabevi Yayınları, Bursa.
  • Perić, T. (2016). Vendor Selection and Supply Quotas Determination by Using a New Multi-Objective Programming Method Based on Cooperative Game Theory. Business Systems Research: International journal of the Society for Advancing Innovation and Research in Economy, 7(1), 104-118. https://doi.org/10.1515/bsrj-2016-0008
  • Raposo, M., & Alves, H. (2007). A model of university choice: an exploratory approach., MPRA Paper 5523, University Library of Munich, Germany.
  • Romp, G. (2011). Game theory: Introduction and applications. Oxford University Press, New York.
  • Sawtooth Software (2001). Choice based conjoint analysis, Technical Paper Series, Sawtooth Software Inc., Sequim, WA. https://sawtoothsoftware.com/conjoint-analysis/cbc
  • Sawtooth Software, (2021). Testing the CBC Design, https://sawtoothsoftware.com/help/lighthouse-studio/manual/hid_web_cbc_designs_6.html (Accessed: 02.02.2021)
  • Soutar, G. N., & Turner, J. P. (2002). Students’ preferences for university: A conjoint analysis. International journal of educational management, 16(1), 40-45. https://doi.org/10.1108/09513540210415523
  • Steiner, W. J. (2010). A Stackelberg-Nash model for new product design. OR spectrum, 32(1), 21-48. https://doi.org/10.1007/s00291-008-0137-4
  • Sullivan, E., Ferguson, S., & Donndelinger, J. (2011, January). Exploring differences in preference heterogeneity representation and their influence in product family design. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 54822, pp. 81-92). https://doi.org/10.1115/DETC2011-48596
  • Taha, H.A. (2007). Operations research an introduction, 8th Edition, Prentice – Hall Inc., NJ.
  • Turskis, Z., & Juodagalvienė, B. (2016). A novel hybrid multi-criteria decision-making model to assess a stairs shape for dwelling houses. Journal of Civil Engineering and Management, 22(8), 1078-1087. https://doi.org/10.3846/13923730.2016.1259179
  • Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323. https://doi.org/10.1007/BF00122574
  • Veloutsou, C., Paton, R. A., & Lewis, J. (2005). Consultation and reliability of information sources pertaining to university selection: some questions answered?. International Journal of Educational Management, 19(4), 279-291. https://doi.org/10.1108/09513540510599617
  • Von Neumann, J., & Morgenstern, O. (1947). Theory of games and economic behavior (2nd rev. ed.). Princeton University Press.
  • Wedel, M., & Kamakura, W. A. (2012). Market segmentation: Conceptual and methodological foundations (Vol. 8). Springer Science & Business Media.
  • Wilks, S. S. (1938). The large-sample distribution of the likelihood ratio for testing composite hypotheses. The annals of mathematical statistics, 9(1), 60-62. https://doi.org/10.1214/aoms/1177732360
  • Yamamoto, G.T. (2006). University evaluation-selection: A Turkish case. International Journal of Educational Management, 20(7), 559-569. https://doi.org/10.1108/09513540610704654
  • Yılmaz, Ö. (2012). Öğrencilerin üniversite tercihini etkileyen kriterlerin belirlenmesinde analitik hiyerarşi proses uygulaması ve Süleyman Demirel Üniversitesi örneği, Yüksek Lisans Tezi, Süleyman Demirel Üniversitesi, Isparta.
  • YÖK. (2016). Üniversitelerimiz. http://www.yok.gov.tr/web/guest/universitelerimiz (Accessed: 02.04.2017).

Volume 9, Issue 2, 2021


alphanumeric journal

Volume 9, Issue 2, 2021

Pages 179-216

Received: Feb. 20, 2021

Accepted: Sept. 22, 2021

Published: Dec. 31, 2021

Full Text [707.9 KB]

2021 Tuncalı Yaman, T., Çakır, Ö.

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