THE EFFECT OF DYNAMIC PRICING ON CONSUMER PURCHASE INTENTION: A STUDY USING THE THEORY OF PLANNED BEHAVIOR

Yıl 2025, Cilt: 18 Sayı: 3, 567 - 598, 29.09.2025

Altuğ Ocak

Öz

The current research investigates dynamic pricing (DP) or real-time price change depending on demand and supply and its influence on consumers' purchase intention under the Theory of Planned Behavior (TPB). Employing an online survey of 428 consumers and Structural Equation Modeling, this research reveals that attitude towards DP, subjective norms and perceived behavioral control (PBC) all have significant and positive influences on purchase intention. Attitude plays the strongest role, followed by PBC and subjective norms. Mediation analyses show that PBC in part mediates the attitude-intention relationship and subjective norms in part mediate the PBC-intention relationship. Also, subjective norms amplify the positive influence of attitudes on intention. T-tests further suggest that purchase intention varies significantly according to age and the frequency of online shopping but not according to gender, educational level or income. These conclusions employ TPB in a modern price context and identify fairness perception, consumer empowerment and social validation as the most influential factors behind DP adoption. The research provides actionable advice for firms to develop straightforward and transparent pricing solutions that give customers confidence and stimulate take-up.

Anahtar Kelimeler

Dynamic Pricing , Consumer Behavior , Purchase Intentions , Theory of Planned Behavior , Perceived Behavioral Control

Etik Beyan

The ethics committee approval letter for this research was obtained from Istanbul Beykent University Social and Human Sciences Scientific Research and Publication Ethics Committee on January 9, 2025 with approval number 173816.

DİNAMİK FİYATLANDIRMANIN TÜKETİCİ SATIN ALMA NİYETİNE ETKİSİ: PLANLI DAVRANIŞ TEORİSİNİ KULLANAN BİR ARAŞTIRMA

Öz

Bu araştırma, dinamik fiyatlandırma (DP) veya talep ve arza bağlı gerçek zamanlı fiyat değişikliğini ve bunun Planlı Davranış Teorisi (TPB) kapsamında tüketicilerin satın alma niyetleri üzerindeki etkisini incelemektedir. 428 tüketicinin katıldığı çevrimiçi anket ve Yapısal Eşitlik Modellemesi kullanılarak yapılan bu araştırma, DP'ye yönelik tutum, öznel normlar ve algılanan davranış kontrolünün (PBC) satın alma niyeti üzerinde önemli ve olumlu etkileri olduğunu ortaya koymaktadır. Tutum en güçlü rolü oynarken, onu PBC ve öznel normlar izlemektedir. Aracılık analizleri, PBC'nin kısmen tutum-niyet ilişkisine, öznel normların ise kısmen PBC-niyet ilişkisine aracılık ettiğini göstermektedir. Ayrıca, öznel normlar tutumların niyet üzerindeki olumlu etkisini güçlendirmektedir. T-testleri, satın alma niyetinin yaş ve çevrimiçi alışveriş sıklığına göre önemli ölçüde değiştiğini, ancak cinsiyet, eğitim düzeyi veya gelire göre değişmediğini göstermektedir. Bu sonuçlar, TPB'yi günümüzün fiyatlandırma anlayışına uyarlamakta ve adalet algısı, tüketici güçlendirme ve sosyal onaylamayı DP'nin benimsenmesinde en etkili faktörler olarak belirlemektedir. Araştırma, firmalara müşterilere güven veren ve benimsenmeyi teşvik eden basit ve şeffaf fiyatlandırma çözümleri geliştirmeleri için uygulanabilir tavsiyeler sunmaktadır.Bu çalışma, dinamik fiyatlandırma kapsamında tüketici karar verme sürecinde psikolojik faktörlerin önemini vurgulamakta ve işletmelerin dinamik fiyatlandırma modellerini etkin bir şekilde uygulamaları için stratejiler önermektedir. Gelecekteki araştırmalar boylamsal etkileri, kültürel farklılıkları ve bireysel özelliklerin dinamik fiyatlandırma kabulündeki rolünü araştırmalıdır.

Anahtar Kelimeler

Dinamik Fiyatlandırma , Tüketici Davranışı , Satın Alma Niyetleri , Planlı Davranış Teorisi , Algılanan Davranışsal Kontrol

Etik Beyan

Bu araştırma için etik kurul onay yazısı , İstanbul Beykent Üniversitesi Sosyal ve Beşeri Bilimler Bilimsel Araştırma ve Yayın Etiği Kurulundan 9 Ocak 2025 tarih ve 173816 sayılı onay numarasıyla alınmıştır.

Kaynakça

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
• Amanda, T. A., & Marsasi, E. G. (2024). Exploration of perceived behavioral control and intention to purchase to increase actual behavior. Image: Jurnal Riset Manajemen, 12(1), 14–30. https://doi.org/10.17509/image.2024.002
• Anderson, E. T., & Simester, D. I. (2003). Effects of $9 price endings on retail sales: Evidence from field experiments. Quantitative Marketing and Economics, 1(1), 93–110. https://doi.org/10.1023/A:1023581927405
• Armitage, C. J., & Conner, M. (2001). Efficacy of the Theory of Planned Behaviour: A meta-analytic review. British Journal of Social Psychology, 40(4), 471–499. https://doi.org/10.1348/014466601164939
• Arokoski, E. (2023). Dynamic ticket pricing: How consumer perceptions of fairness affect purchase decisions [Bachelor’s thesis, Aalto University School of Business].
• Arora, N., Dreze, X., Ghose, A., Hess, J. D., Iyengar, R., Jing, B., ... & Zhang, Z. J. (2008). Putting one-to-one marketing to work: Personalization, customization and choice. Marketing Letters, 19(3–4), 305–321. https://doi.org/10.1007/s11002-008-9056-z
• Aydan, G., & Atılgan, K. Ö. (2023). Tüketicilerin kişiselleştirilmiş fiyatlandırma ile dinamik fiyatlandırma uygulamalarına yönelik tutumlarının incelenmesi. Pamukkale Üniversitesi İşletme Araştırmaları Dergisi, 10(1–Prof. Dr. Feyzullah EROĞLU Armağan Sayısı), 243–256. https://doi.org/10.47097/piar.1263342
• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16, 74–94. https://doi.org/10.1007/BF02723327
• Banerjee, P., Sayed, V., Samdariya, P., Jha, R. K., & Rajput, V. B. (2025). Impact of Dynamic Pricing on Consumer Loyalty in E-Commerce. Journal of Innovation and Entrepreneurship Research, 5(1). https://doi.org/10.52783/jier.v5i1.2316
• Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
• Bigne, E., Nicolau, J. L., & William, E. (2021). Advance booking across channels: The effects on dynamic pricing. Tourism Management, 86, 104341. https://doi.org/10.1016/j.tourman.2021.104341
• Cheung, C. M., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision support systems, 54(1), 461–470. https://doi.org/10.1016/j.dss.2012.06.008.
• Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich.
• Elmaghraby, W., & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices and future directions. Management Science, 49(10), 1287-1309. https://doi.org/10.1287/mnsc.49.10.1287.17315
• Ferguson, J. L., & Ellen, P. S. (2013). Transparency in pricing and its effect on perceived price fairness. Journal of Product & Brand Management, 22(5/6), 404–412. https://doi.org/10.1108/JPBM-06-2013-0323
• Findlay, A., & Sparks, L. (2008). ‘Switched’: Store-switching behaviours. International Journal of Retail & Distribution Management, 36(5), 375–386. https://doi.org/10.1108/09590550810870102
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• Garbarino, E., & Lee, O. F. (2003). Dynamic pricing in internet retail: Effects on consumer trust. Psychology & Marketing, 20(6), 495-513. https://doi.org/10.1002/mar.10084
• George, J. F. (2004). The theory of planned behavior and Internet purchasing. Internet Research, 14(3), 198–212. https://doi.org/10.1108/10662240410542634
• Hair, J. F., Black, W. C., Babin, B. J. anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis (7. Baskı). Pearson. Hallahan, TA, Faff, RW, McKenzie, MD (2004). An Empirical Investigation of Personal Financial Risk Tolerance. Financial Services Review-Greenwıch, 13(1), 57-78.
• Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature.
• Haws, K. L., & Bearden, W. O. (2006). Dynamic pricing and consumer fairness perceptions. Journal of Consumer Research, 33(3), 304–311. https://doi.org/10.1086/508435
• Haws, K. L., Bearden, W. O., & Nenkov, G. Y. (2012). Consumer spending self-control effectiveness and outcome elaboration prompts. Journal of the Academy of Marketing Science, 40, 695-710. https://doi.org/10.1007/s11747-011-0249-2
• Hinterhuber, A., & Liozu, S. (2012). Is it time to rethink your pricing strategy? MIT Sloan Management Review, 53(4), 69–77.
• Homburg, C., Koschate, N., & Hoyer, W. D. (2005). Do satisfied customers really pay more? A study of the relationship between customer satisfaction and willingness to pay. Journal of Marketing, 69(2), 84–96. https://doi.org/10.1509/jmkg.69.2.84.60760
• Hou, H., Wu, F., & Huang, X. (2024). Dynamic pricing strategy for content products considering consumer fairness concerns and strategic behavior. Industrial Management & Data Systems, 124(11), 3164-3196. https://doi.org/10.1108/IMDS-09-2023-0669
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
• Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1986). Fairness and the assumptions of economics. The Journal of Business, 59(4), S285–S300. https://doi.org/10.1086/296367
• Kannan, P. K., & Li, H. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22–45. https://doi.org/10.1016/j.ijresmar.2016.11.006
• Kimes, S. E. (2002). Perceived fairness of yield management. Cornell Hotel and Restaurant Administration Quarterly, 43(1), 21–30. https://doi.org/10.1177/0010880402431002
• Kopalle, P. K., Pauwels, K., Akella, L. Y., & Gangwar, M. (2023). Dynamic pricing: Definition, implications for managers and future research directions. Journal of Retailing, 99(4), 580–593. https://doi.org/10.1016/j.jretai.2023.11.003
• Kramer, A., Jung, M., & Burgartz, T. (2016). A small step from price competition to price war: understanding causes, effects and possible countermeasures. International Business Research, 9(3), 1-13. http://dx.doi.org/10.5539/ibr.v9n3p1
• Li, Y., Xu, Z., & Xu, F. (2018). Perceived control and purchase intention in online shopping: The mediating role of self-efficacy. Social Behavior and Personality: An International Journal, 46(1), 99-105. https://doi.org/10.2224/sbp.6377
• Liu, P., & Sun, W. W. (2025). Fairness-aware Contextual Dynamic Pricing with Strategic Buyers. arXiv preprint arXiv:2501.15338. https://doi.org/10.48550/arXiv.2501.15338
• Meng, F., Ma, Q., Liu, Z., & Zeng, X. J. (2023). Multiple dynamic pricing for demand response with adaptive clustering-based customer segmentation in smart grids. Applied Energy, 333, 120626. https://doi.org/10.1016/j.apenergy.2022.120626
• Milman, A., & Tasci, A. D. A. (2023). The influence of dynamic pricing on consumer trust, value and loyalty relationships in theme parks. Journal of Vacation Marketing, 29(3), 386–408. https://doi.org/10.1177/13567667221095583
• Neubert, M. (2022). A systematic literature review of dynamic pricing strategies. International Business Research, 15(4), 1–17. https://doi.org/10.5539/ibr.v15n4p1
• Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
• Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320. https://doi.org/10.1177/109467050024001
• Pena-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6), e04284. https://doi.org/10.1016/j.heliyon.2020.e04284
• Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717–731. https://doi.org/10.3758/BF03206553
• Priester, A., Robbert, T., & Roth, S. (2020). A special price just for you: Effects of personalized dynamic pricing on consumer fairness perceptions. Journal of Revenue and Pricing Management, 19(1), 1–12. https://doi.org/10.1057/s41272-019-00224-3
• Rohani, A., & Nazari, M. (2012). Impact of dynamic pricing strategies on consumer behavior. Journal of Management Research, 4(4), 143–155. https://doi.org/10.5296/jmr.v4i4.2009
• Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS Quarterly, 865-890. https://doi.org/10.2307/25148757
• Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144
• Teodorescu, D., Aivaz, K.-A., Vancea, D. P. C., Condrea, E., Dragan, C., & Olteanu, A. C. (2023). Consumer trust in AI algorithms used in e-commerce: A case study of college students at a Romanian public university. Sustainability, 15(15), 11925. https://doi.org/10.3390/su151511925
• Thompson, E. G., & Wilson, D. R. (2024). Dynamic pricing promotion strategies on consumer repeat purchase behavior in the United States. Frontiers in Management Science, 3(3), 19–30. https://doi.org/10.56397/FMS.2024.06.03
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
• Voss, K. E., Spangenberg, E. R., & Grohmann, B. (2003). Measuring the hedonic and utilitarian dimensions of consumer attitude. Journal of Marketing Research, 40(3), 310-320. https://doi.org/10.1509/jmkr.40.3.310.19238
• Wang, Y., Zhou, Y., Ding, J., & Ye, X. (2025). The Impact of Dynamic Pricing Strategies on the Operational Efficiency of Online Retail. International Theory and Practice in Humanities and Social Sciences, 2(4), 205–232. https://doi.org/10.70693/itphss.v2i4.324
• Weisstein, F. L., Monroe, K. B., & Kukar-Kinney, M. (2013). Effects of price framing on consumers’ perceptions of online dynamic pricing practices. Journal of the Academy of Marketing Science, 41(5), 501–514. https://doi.org/10.1007/s11747-013-0330-0
• Xia, L., Monroe, K. B., & Cox, J. L. (2004). The price is unfair! A conceptual framework of price fairness perceptions. Journal of Marketing, 68(4), 1–15. https://doi.org/10.1509/jmkg.68.4.1.42733