SavedAdd your journal

Journals / Pazarlama ve Pazarlama Araştırmaları Dergisi / 2026 / Cilt: 19 Sayı: 2

BEYOND TECHNOLOGY ACCEPTANCE: UNDERSTANDING HOW CONSUMPTION VALUES DRIVE AI ADOPTION IN RETAIL

TEKNOLOJİ KABULÜNÜN ÖTESİNDE: TÜKETİM DEĞERLERİNİN PERAKENDEDE YAPAY ZEKÂ BENİMSENMESİNE ETKİSİNİ ANLAMAK

Ozge Habiboglu(TEKİRDAĞ NAMIK KEMAL ÜNİVERSİTESİ)0000-0001-5312-5024
Pages
398–416
Publication Date
2026-05-20
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)

Practical implications are offered for designing AI-based retail experiences that enhance trust, curiosity, and situational value for consumers.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial intelligence (AI) is becoming increasingly integrated into retail environments; yet, little is known about how consumers with no prior AI shopping experience develop their attitudes and intentions toward such technologies.
📊
Key Findings & Data:This study aims to investigate how functional, emotional, social, epistemic, and conditional values influence consumers’ attitudes toward AI-assisted retail and how these attitudes shape their intention to use AI.
💡
Conclusion & Impact:Practical implications are offered for designing AI-based retail experiences that enhance trust, curiosity, and situational value for consumers.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Tüketim Değerleri Teorisi · Yapay Zekâ · Kullanma Niyeti

Abstract

Artificial intelligence (AI) is becoming increasingly integrated into retail environments; yet, little is known about how consumers with no prior AI shopping experience develop their attitudes and intentions toward such technologies. This study aims to investigate how functional, emotional, social, epistemic, and conditional values influence consumers’ attitudes toward AI-assisted retail and how these attitudes shape their intention to use AI. Focusing specifically on consumers who have never used AI during their shopping process, the study aims to capture attitude formation at the early stage of AI adoption, where evaluations are not yet shaped by direct usage experience. To achieve this, consumers with no prior experience in AI shopping were introduced to AI-based retail scenarios and then completed a structured questionnaire. Structural Equation Modeling (SEM) was employed to examine both the measurement and structural models. The findings suggest that epistemic, functional, and conditional values enhance positive attitudes toward AI, whereas emotional and social values diminish them. Attitude, in turn, plays a central role in shaping consumers’ intention to adopt AI in retail settings. The study extends the application of the Theory of Consumption Values to AI adoption, highlighting the distinct mechanisms through which inexperienced users form their evaluations. Practical implications are offered for designing AI-based retail experiences that enhance trust, curiosity, and situational value for consumers.

Özet

Yapay zekâ (YZ) perakende deneyimlerine giderek daha fazla entegre edilmektedir; ancak YZ ile hiç alışveriş yapmamış tüketicilerin bu teknolojiyi benimsemeye yönelik tutum ve niyetlerini nasıl oluşturdukları yeterince bilinmemektedir. Bu çalışmanın amacı, fonksiyonel, duygusal, sosyal, epistemik ve durumsal değerlerin tüketicilerin YZ destekli alışverişe yönelik tutumları üzerindeki etkilerini ve bu tutumların YZ kullanma niyetini nasıl şekillendirdiğini incelemektir. Bu kapsamda, YZ ile daha önce alışveriş yapmamış tüketicilere YZ tabanlı alışveriş senaryoları tanıtılmış ve ardından yapılandırılmış bir anket uygulanmıştır. Ölçüm ve yapısal modelleri test etmek üzere Yapısal Eşitlik Modellemesi (YEM) kullanılmıştır. Bulgular, epistemik, fonksiyonel ve durumsal değerlerin tutumu güçlendirdiğini; duygusal ve sosyal değerlerin ise tutumu zayıflattığını göstermektedir. Tutumun YZ kullanma niyetinin oluşumunda merkezi bir rol oynadığı belirlenmiştir. Çalışma, Tüketim Değerleri Teorisi’nin YZ benimseme literatüründeki kullanımını genişletmekte ve deneyimsiz kullanıcıların değerlendirme süreçlerini açıklayan özgün bir bakış açısı sunmaktadır. Bulgular, perakendeciler için tüketicide güven, merak ve durumsal fayda yaratan YZ tabanlı deneyimlerin tasarlanmasına yönelik pratik öneriler sunmaktadır.

🔖

Cite This Article

Habiboglu, O., *, O. H. (2026). BEYOND TECHNOLOGY ACCEPTANCE: UNDERSTANDING HOW CONSUMPTION VALUES DRIVE AI ADOPTION IN RETAIL. Pazarlama ve Pazarlama Araştırmaları Dergisi, Cilt: 19 Sayı: 2, 398–416. https://ajindex.com/articles/1301023

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (49)

  1. Aravindan, K. L., Ramayah, T., Thavanethen, M., Raman, M., Ilhavenil, N., Annamalah, S., & Choong, Y. V. (2023). Modeling positive electronic word of mouth and purchase intention using the theory of consumption value. Sustainability, 15(4), 3009. https://doi.org/10.3390/su15043009
  2. Arık, A., & Zeren, D. (2023). Tüketicilerin sanal asistanları kullanma niyetinde etkili faktörlerin araştırılması. Alanya Akademik Bakış, 7(1), 27–49. https://doi.org/10.29023/alanyaakademik. 1051081
  3. Bagozzi, R. P., Y. Yi & L. W. Phillips. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421–458
  4. Bahoo, S., Umar, R. M., Mason, M. C., & Zamparo, G. (2024). Role of theory of consumption values in consumer consumption behavior: A review and agenda. The International Review of Retail, Distribution and Consumer Research, 34(4), 417–441. https://doi.org/10.1080/09593969.2023.2290657
  5. Bhagat, R., Chauhan, V., & Bhagat, P. (2023). Investigating the impact of artificial intelligence on consumer’s purchase intention in e-retailing. Foresight, 25(2), 249–263. https://doi.org/10.1108/FS-10-2021-0218
  6. Bilici, F., & İnam, S. (2024). Tüketicilerin akıllı robot süpürgelere yönelik tutum ve robot süpürgeleri kullanma niyeti üzerine bir araştırma. Uluslararası Yönetim İktisat ve İşletme Dergisi, 20(3), 552–586. https://doi.org/10.17130/ijmeb.1364854
  7. Biswas, A. (2017). A consumption value-gap analysis for sustainable consumption. Environmental Science and Pollution Research, 24(8), 7714–7725. https://doi.org/10.1007/s11356-016-8355-9
  8. Brislin, R. W. (1970). Back-Translation for Cross-Cultural Research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
  9. Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-Dependent Algorithm Aversion. Journal of Marketing Research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788
  10. Chakraborty, D., Mehta, P., & Khorana, S. (2025). Metaverse technologies in hospitality: Using the theory of consumption values to reveal consumer attitudes and trust factors. International Journal of Contemporary Hospitality Management. 37(4), 1276–1308. https://doi.org/10.1108/IJCHM-09-2023-1500
  11. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
  12. Durmuş, B., Yurtkoru, E. S., & Çinko, M. (2016). Sosyal Bilimlerde SPSS’le Veri Analizi. Beta
  13. Farha, A. K. A., El Hedhli, K., Alnawas, I., Zourrig, H., & Becheur, I. (2024). Drivers and outcomes of a shopper-retailer's app relationship. Journal of Retailing and Consumer Services, 81, 104002. https://doi.org/10.1016/j.jretconser.2024.104002
  14. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
  15. Foroughi, B., Nhan, P. V., Iranmanesh, M., Ghobakhloo, M., Nilashi, M., & Yadegaridehkordi, E. (2023). Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS. Journal of Retailing and Consumer Services, 70, 103158. https://doi.org/10.1016/j.jretconser.2022.103158
  16. Gonçalves, H. M., Lourenço, T. F., & Silva, G. M. (2016). Green buying behavior and the theory of consumption values: A fuzzy-set approach. Journal of Business Research, 69(4), 1484-1491. https://doi.org/10.1016/j.jbusres.2015.10.129
  17. Guerra-Tamez, C. R., Kraul Flores, K., Serna-Mendiburu, G. M., Chavelas Robles, D., & Ibarra Cortés, J. (2024). Decoding Gen Z: AI's influence on brand trust and purchasing behavior. Frontiers in Artificial Intelligence, 7, 1323512. https://doi.org/10.3389/frai.2024.1323512
  18. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage.
  19. Hur, W. M., Yoo, J. J., & Chung, T. L. (2012). The consumption values and consumer innovativeness on convergence products. Industrial Management & Data Systems, 112(5), 688–706. https://doi.org/10.1108/02635571211232271
  20. Huriah, T., Permana, I., Octavia, M., & Hamid, S. H. B. A. (2022). Understanding the purchasing behaviors of halal cosmetics of teenagers in Indonesia using the theory of planned behavior and theory of consumption value. Bali Medical Journal, 11(3), 1608–1613.
  21. Hwang, J., & Choe, J. Y. (2019). Exploring perceived risk in building successful drone food delivery services. International Journal of Contemporary Hospitality Management, 31(8), 3249–3269. https://doi.org/10.1108/IJCHM-07-2018-0558
  22. Jamrozy, U., & Lawonk, K. (2017). The multiple dimensions of consumption values in ecotourism. International Journal of Culture, Tourism and Hospitality Research, 11(1), 18–34. https://doi.org/10.1108/IJCTHR-09-2015-0114
  23. Jiang, Q., Gu, C., Feng, Y., Wei, W., & Tsai, W.-C. (2023). Study on the continuance intention in using virtual shoe-try-on function in mobile online shopping. Kybernetes, 52(10), 4551–4575. https://doi.org/10.1108/K-12-2021-1346
  24. Kaur, P., Dhir, A., Rajala, R., & Dwivedi, Y. (2018). Why people use online social media brand communities. Online Information Review, 42(2), 205–221. https://doi.org/10.1108/OIR-12-2015-0383
  25. Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling. Birleşik Krallık: Guilford Publications.
  26. Lee, J.-C., & Chen, X. (2022). Exploring users' adoption intentions in the evolution of artificial intelligence mobile banking applications: The intelligent and anthropomorphic perspectives. International Journal of Bank Marketing, 40(4), 631–658. https://doi.org/10.1108/IJBM-08-2021-0394
  27. Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650 https://doi.org/10.1093/jcr/ucz013
  28. Mehmood, K., Kautish, P., & Shah, T. R. (2024). Embracing digital companions: Unveiling customer engagement with anthropomorphic AI service robots in cross-cultural context. Journal of Retailing and Consumer Services, 79, 103825. https://doi.org/10.1016/j.jretconser.2024.103825
  29. Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service Robots Rising: How Humanoid Robots Influence Service Experiences and Elicit Compensatory Consumer Responses. Journal of Marketing Research, 56(4), 535–556. https://doi.org/10.1177/0022243718822827
  30. Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis testing approaches for RMSEA. Structural Equation Modeling, 11(3), 320–341.
  31. Mehta, P., Jebarajakirthy, C., Maseeh, H. I., Anubha, A., Saha, R., & Dhanda, K. (2022). Artificial intelligence in marketing: A meta‐analytic review. Psychology & Marketing, 39(11), 2013-2038.
  32. Özkaynar, K. (2024). Teknoloji kabul modeli çerçevesinde, tüketicilerin yapay zekâ araçlarini kullanimlarinda algilanan fayda, tutum ve niyetler arasindaki ilişki: Etik kaygilarin düzenleyici etkisi. Yönetim Bilimleri Dergisi, Özel Sayı: Endüstri 4.0 ve Dijitalleşmenin Sosyal Bilimlerde Yansımaları, 1472–1498. https://doi.org/10.35408/comuybd.1515898
  33. Pantano, E., Rese, A., & Baier, D. (2017). Enhancing the online decision-making process by using augmented reality: A two country comparison of youth markets. Journal of Retailing and Consumer Services, 38, 81–95. https://doi.org/10.1016/j.jretconser.2017.05.011
  34. Park, J., Hong, E., & Le, H. T. P. M. (2021). Adopting autonomous vehicles: The moderating effects of demographic variables. Journal of Retailing and Consumer Services, 63, 102687. https://doi.org/10.1016/j.jretconser.2021.102687
  35. Peng, N., & Chen, A. (2019). Examining consumers’ luxury hotel stay repurchase intentions-incorporating a luxury hotel brand attachment variable into a luxury consumption value model. International Journal of Contemporary Hospitality Management, 31(3), 1348-1366.
  36. Pham, H. C., Duong, C. D., & Nguyen, G. K. H. (2024). What drives tourists’ continuance intention to use ChatGPT for travel services? A stimulus-organism-response perspective. Journal of Retailing and Consumer Services, 78, 103758. https://doi.org/10.1016/j.jretconser.2024.103758
  37. Phau, I., Quintal, V., & Shanka, T. (2014). Examining a consumption values theory approach of young tourists toward destination choice intentions. International Journal of Culture, Tourism and Hospitality Research, 8(2), 125–139. https://doi.org/10.1108/IJCTHR-12-2012-0090
  38. Ray, A., Bala, P. K., & Dwivedi, Y. K. (2021). Exploring values affecting e-learning adoption from the user-generated-content: A consumption-value-theory perspective. Journal of Strategic Marketing, 29(5), 430–452. https://doi.org/10.1080/0965254X.2020.1749875
  39. Sheth, J. N., Newman, B. I., & Gross, B. L. (1991).Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159–170. https://doi.org/10.1016/0148-2963(91)90050-8
  40. Sousa, V. D., & Rojjanasrirat, W. (2011). Translation, adaptation and validation of instruments or scales for use in cross‐cultural health care research: a clear and user‐friendly guideline. Journal of Evaluation in Clinical Practice, 17(2), 268–274.
  41. Tabachnick, B. G. & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Allyn and Bacon.
  42. Tanrikulu, C. (2021). Theory of consumption values in consumer behaviour research: A review and future research agenda. International Journal of Consumer Studies, 45(6), 1176-1197.
  43. Tehseen, S., Ramayah, T., & Sajilan, S. (2017). Testing and controlling for common method variance: A review of available methods. Journal of Management Sciences, 4(2), 146-175.
  44. Van der Heijden, H. (2004). User Acceptance of Hedonic Information Systems. MIS Quarterly, 28(4), 695–704. https://doi.org/10.2307/25148660
  45. Wang, H. Y., Liao, C., & Yang, L. H. (2013). What affects mobile application use? The roles of consumption values. International Journal of Marketing Studies, 5(2), 11–22. https://doi.org/10.5539/ijms.v5n2p11
  46. Waytz, A., Cacioppo, J., & Epley, N. (2010). Who Sees Human?: The Stability and Importance of Individual Differences in Anthropomorphism: The Stability and Importance of Individual Differences in Anthropomorphism. Perspectives on Psychological Science, 5(3), 219–232. https://doi.org/10.1177/1745691610369336
  47. Xiao, G., & Kim, J.O. (2009). The investigation of Chinese consumer values, consumption values, life satisfaction, and consumption behaviors. Psychology & Marketing, 26(7), 610–624. https://doi.org/10.1002/mar.20291
  48. Yoo, W., Yu, E., & Jung, J. (2018). Drone delivery: Factors affecting the public’s attitude and intention to adopt. Telematics and Informatics, 35(6), 1687-1700. https://doi.org/10.1016/j.tele.2018.04.014
  49. Yuan, C., Wang, S., & Liu, Y. (2023). AI service impacts on brand image and customer equity: empirical evidence from China. Journal of Brand Management, 30, 61–76. https://doi.org/10.1057/s41262-022-00292-8