E-Ticarette Bilişsel Önyargılar ve Tüketici Kararlarına Etkileri

Abstract

Dijitalleşmeyle birlikte ticaret çevrimiçi platformlara taşınmış ve satın alma kararları yüz yüze etkileşim olmadan çevrimiçi olarak alınmaya başlanmıştır. Etkileşimin dijitalleşmesi, satıcılar ve tüketiciler arasındaki ürün özelinde ortaya çıkan bilgi asimetrisini tüketiciler lehine ortadan kaldırmaktadır. Öte yandan bireylerin dijital ortamlarda bıraktığı dijital ayak izleri ile internetin zamandan ve mekândan bağımsız olarak kişiselleştirilmiş içerikler sunabilmesi, yeni bir tür bilgi asimetrisinin ortaya çıkmasına neden olmuştur. Tüketici verilerinin işlenmesi, dijital dürtme mesajları ile bilişsel önyargılar içeren içeriklerin geliştirilmesini mümkün kılmış ve böylece tüketicilerin rasyonel olmayan tüketim kararlarına yönlendirilmesi riski ortaya çıkmıştır. Bu çalışmada öncelikle e-ticaret platformlarının ürün sayfaları incelenmiş ve tüketicileri akıl dışı tüketime yönlendirmek için sıklıkla kullanılan bilişsel önyargılar ortaya çıkartılmıştır. Akabinde, bilişsel önyargıların etkisi altında tüketicilerin karar verme sıklıkları, geliştirilen bir e-ticaret sayfasında gönüllü katılımcılarla yapılan deneyler ile belirlenmiştir. Ayrıca katılımcıların verileri ve katılımcıların rasyonel davranış skorları anket çalışması ile elde edilmiş ve rasyonellik puanı, yaş, cinsiyet, alışveriş sıklığı ve internette geçirilen günlük süre gibi kişisel veriler ile bilişsel önyargılar arasındaki ilişkiler ikili lojistik regresyon ile analiz edilmiştir. Çalışmanın sonucunda her bir bilişsel yanlılığı etkileyen faktörler tespit edilmiş ve ileride yapılacak araştırmalar için tavsiyelerde bulunulmuştur.

Keywords

• E-Ticaret
• Bilişsel Yanlılıklar
• Dijital Dürtmeler

The Cognitive Biases in E-Commerce and Effects on Consumer Decisions

Abstract

Commerce has been moved to online platforms, and purchasing decisions have begun to be made online without face-to-face interaction with digitalization. The digitalization of interaction eliminates the product-specific information asymmetry between sellers and consumers in favor of consumers. On the other hand, the digital footprints left by individuals in digital environments and the internet's ability to offer personalized content independent of time and place have led to the emergence of a new type of information asymmetry. The processing of consumer data has made it possible to develop digital nudge messages and content containing cognitive biases and thus posing the risk of directing consumers to irrational consumption decisions. In this study, first of all, the product pages of e-commerce platforms were examined, and frequently used cognitive biases to direct consumers to irrational consumption were revealed. Subsequently, the frequency of consumers' decision-making under the influence of cognitive biases was determined by experiments with volunteer participants on an e-commerce page developed. In addition, the data of the participants and the rational behavior scores of the participants were obtained by questionnaire study, and the relationships between cognitive biases and personal data such as rationality score, age, gender, shopping frequency, and daily time spent on the internet were analyzed by binary logistic regression. As a result of the study, the factors affecting each cognitive bias were determined, and recommendations were made for further researches.

Keywords

• E-Commerce
• Cognitive Biases
• Digital Nudges

References

1. Akerlof, G. A. (1970). The Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism. Quarterly Journal of Economics, 84(3), 488–500.
2. Akkuş, Ç. (2018). Turistik Satın Alma Karar Sürecinde Bedava Etkisi: Otel İşletmelerine Yönelik Bir Araştırma. Seyahat ve Otel İşletmeciliği Dergisi, 15(2), 386-403.
3. Alpar, R. (2021). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler. 6. Baskı, Ankara: Detay Yayıncılık.
4. Austin, P.C. ve Steyerberg, E.W. (2017). Events per Variable and the Relative Performance of Different Strategies for Estimating the Out-of-Sample Validity of Logistic Regression Models. Statistical Methods in Medical Research, 26(2), 796-808.
5. Backer, G. (2022). Cognitive Biases 2022: Complete List of 151 Biases. Erişim Tarihi:12.07.2022. https://gustdebacker.com/cognitive-biases/
6. Baddeley, M. (2010). Herding, Social Influence and Economic Decision-Making: Socio-Psychological and Neuroscientific Analyses. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1538), 281-290.
7. Blanco, F. (2017). Cognitive Bias. Editörler: Vonk, J. ve T. Shackelford, Encyclopedia of Animal Cognition and Behavior, (ss. 1-7), Springer, Cham.
8. Chen, J. ve Stallaert, J. (2014). An Economic Analysis of Online Advertising Using Behavioral Targeting. MIS Quarterly, 38(2), 429-450.

9. Christozov, D., Chukova, S. ve Mateev, P. (2006). A Measure of Risk Caused by Information Asymmetry in E-commerce. Issues in Informing Science and Information Technology, 3(1), 147-157.
10. Demidenko, E. (2007). Sample Size Determination for Logistic Regression Revisited. Statistics in Medicine, 26(18), 3385-3397.
11. Dimoka, A., Hong, Y. ve Pavlou, P. A. (2012). On Product Uncertainty in Online Markets: Theory and Evidence. MIS Quarterly, 36(2), 395-426.
12. Ertemel, A. V. (2016). Dijital Çağda İllüzyonel Pazarlama, Istanbul: Abaküs Yayın.
13. Faul, F., Erdfelder, E., Buchner, A. ve Lang, A.G. (2009). Statistical Power Analyses Using G*Power 3.1: Tests for Correlation and Regression Analyses. Behavior Research Methods, 41, 1149-1160.
14. Field, A. (2009). Discovering Statistics Using SPSS, 3. Baskı, London: SAGE.
15. Fonteyn, M. E., Kuipers, B. ve Grobe, S. J. (1993). A Description of Think Aloud Method and Protocol Analysis. Qualitative Health Research, 3(4), 430-441.
16. Gierl, H. ve Huettl, V. (2010). Are Scarce Products Always More Attractive? The Interaction of Different Types of Scarcity Signals with Products' Suitability for Conspicuous Consumption. International Journal of Research in Marketing, 27(3), 225-235.
17. GPower. (2021). Z test: Multiple Logistic Regression. G*Power 3.1 Manual, https://www.psychologie.hhu.de/fileadmin/redaktion/Fakultaeten/Mathematisch-Naturwissenschaftliche_Fakultaet/Psychologie/AAP/gpower/GPowerManual.pdf
18. Grabner-Kraeuter, S. (2002). The Role of Consumers' Trust in Online-Shopping. Journal of Business Ethics, 39(1), 43-50.
19. Griffin, R. (2015). Fundamentals of Management. Boston, MA: Cengage Learning.
20. Hamilton, R., Thompson, D., Bone, S., Chaplin, L. N., Griskevicius, V., Goldsmith, K., ... ve Zhu, M. (2019). The Effects of Scarcity on Consumer Decision Journeys. Journal of the Academy of Marketing Science, 47(3), 532-550.
21. Helion, C. ve Gilovich, T. (2014). Gift Cards and Mental Accounting: Green‐Lighting Hedonic Spending. Journal of Behavioral Decision Making 27.4 (2014): 386-393.
22. Hsieh, F.Y., Bloch, D.A. ve Larsen, M.D. (1998). A Simple Method of Sample Size Calculation for Linear and Logistic Regression. Statistics in Medicine, 17(14), 1623-1634.
23. Kahneman, D. (2015). Hızlı ve Yavaş Düşünme. Çev., Osman Ç. Deniztekin ve Filiz Deniztekin, 7. Baskı, İstanbul: Varlık Yayınları.
24. Katalin, E. (2000). Please, Keep Talking: The ‘Think-Aloud’ Method in Second Language Reading Research. Novelty, 7(3).
25. Levitt, S. D. ve List, J. A. (2008). Economics: Homo Economicus Evolves. Science, 319, 909-910. DOI: 10.1126/science.115364
26. Levitt, T. (2004). Marketing Myopia. Harvard Business Review, 82(7/8), 138-149.
27. Lim, S. H., Lee, S. ve Kim, D. J. (2017). Is Online Consumers’ Impulsive Buying Beneficial for E-Commerce Companies? An Empirical Investigation of Online Consumers’ Past Impulsive Buying Behaviors. Information Systems Management, 34(1), 85-100.
28. Liu, S., Gao, B., Gallivan, M. ve Gong, Y. (2020). Free Add-on Services and Perceived Value in Competitive Environments: Evidence from Online Hotel Reviews. International Journal of Hospitality Management, 90, 102611.
29. Liu, Z. (2020). Research on Information Asymmetry in c2c E-Commerce: based on the Case of Alibaba. International Conference on Financial Innovation and Economic Development (ICFIED 2020), 24-41, Atlantis Press.
30. Long S. L. (1997). Regression Models for Categorical and Limited Dependent Variables Advanced Quantitative Techniques in the Social Sciences. 7. Basım, Thousand Oak: SAGE.
31. Fleischmann, M., Amirpur, M., Benlian, A. ve Hess, T. (2014). Cognitive Biases in Information Systems Research: A Scientometric Analysis. Proceedings of the 22th European Conference on Information Systems (ECIS), 1-21. https://aisel.aisnet.org/ecis2014/proceedings/track02/5/
32. McConnell, J. D. (1968). An Experimental Examination of the Price-Quality Relationship. The Journal of Business, 41(4), 439-444.
33. Meske, C. ve Potthoff, T. (2017). The Dinu-Model: A Process Model for the Design of Nudges. Proceedings of the 25th European Conference on Information Systems (ECIS), 2587-2597. https://aisel.aisnet.org/ecis2017_rip/11
34. Milkman, K. L. ve Beshears, J. (2009). Mental Accounting and Small Windfalls: Evidence From an Online Grocer. Journal of Economic Behavior & Organization, 71(2), 384-394.
35. Nemes, S., Jonasson, J. M., Genell, A. ve Steineck, G. (2009). Bias in Odds Ratios by Logistic Regression Modelling and Sample Size. BMC Medical Research Methodology, 9(1), 1-5.
36. Pacini, R. ve Epstein, S. (1999). The Relation of Rational and Experiential Information Processing Styles to Personality, Basic Beliefs, and the Ratio-Bias Phenomenon. Journal of Personality and Social Psychology, 76(6), 972-987.
37. Pan, Y. (2014). E-Marketing Under the Adverse Selection Environment: Model and Case Study. International Journal of Networking and Virtual Organisations, 14(1/2), 111-128.
38. Peduzzi, P., Concato, J., Kemper, E., Holford, T. R. ve Feinstein A. R. (1996). A Simulation Study of the Number of Events Per Variable in Logistic Regression Analysis. Journal of Clinical Epidemiology, 49(2), 1373–1379.
39. Pritchard, E. (2022). How the Metaverse is Designed to Hijack Your Circuits, Erişim Tarihi: 01.08.2022. https://www.behavioraleconomics.com/how-the-metaverse-is-designed-to-hijack-your-circuits/
40. Schins, M. (2014). The Influence of Quantity Scarcity and Time Restrictions on Consumer Preference and Purchase Intention, Erişim Tarihi: 15.07.2022. https://edepot.wur.nl/313388
41. Schneider, C., Weinmann, M. ve Vom Brocke, J. (2018). Digital Nudging: Guiding Online User Choices Through Interface Design. Communications of the ACM, 61(7), 67-73.
42. Shampanier, K., Mazar, N. ve Ariely, D. (2007). Zero as a Special Price: The True Value of Free Products. Marketing Science, 26(6), 742-757.
43. Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99-118.
44. Tabachnick, B. G., Fidell, L. S. ve Ullman, J. B. (2013). Using Multivariate Statistics. 6. Baskı, Boston, MA: Pearson.
45. Thaler, R. (2019). Akılsız İnsanların Mantıksız Kararları. İstanbul: Pegasus Yayınları.
46. Türk, E. G. ve Gülleroğlu, D. (2014). Mantıksal Deneyimsel Düşünme Ölçeğinin Türkçe Formunun Geçerlik ve Güvenirlik Çalışması, Kastamonu Eğitim Dergisi, 22(2), 555-571.
47. Tversky, A. ve Kahneman, D. (1974). Judgment Under Uncertainty: Heuristics and Biases: Biases in Judgments Reveal Some Heuristics of Thinking Under Uncertainty. Science, 185(4157), 1124-1131.
48. Tversky, A. ve Kahneman, D. (1981). The Framing of Decisions and the Psychology of Choice. Science, 211(4481), 453-458.
49. Ural, A. ve Kılıç, İ., (2011). Bilimsel Araştırma Süreci ve SPSS ile Veri Analizi. Ankara: Detay Yayıncılık.
50. Van Smeden, M., de Groot, J. A., Moons, K. G., Collins, G. S., Altman, D. G., Eijkemans, M. J. ve Reitsma, J. B. (2016). No Rationale for 1 Variable Per 10 Events Criterion for Binary Logistic Regression Analysis. BMC Medical Research Methodology, 16(1), 1-12.
51. Weinmann, M., Schneider, C. ve Brocke, J. V. (2016). Digital Nudging. Business & Information Systems Engineering, 58(6), 433-436.
52. Yan, J., Liu, N., Wang, G., Zhang, W., Jiang, Y. ve Chen, Z. (2009). How Much can Behavioral Targeting Help Online Advertising?. Proceedings of the 18th international Conference on World Wide Web, pp.261-270.
53. Zaiontz, C. (2022). Logistic Regression Sample Size. Real Statistics Using Excel, Erişim Tarihi: 12.07.2022, https://www.real-statistics.com/logistic-regression/logistic-regression-sample-size/