Elektronik Ticaret Alışverişinde Tüketicilerin Algıladığı Riskin Ölçümlenmesi: Çok Boyutlu Bir Yaklaşım

Year 2021, Volume: 12 Issue: 23, 96 - 11, 31.05.2021

İlhami Tuncer

https://doi.org/10.47129/bartiniibf.882877

Cited By: 1

https://izlik.org/JA33LX88ZJ

Abstract

Özellikle pandeminin etkisiyle elektronik ticaret (e-ticaret) sektörünün önemi gittikçe artmaktadır. Sektördeki gelişime paralel olarak tüketicilerin karşılaştığı risklerde farklılaşmakta ve satın alım sürecinden büyük sorunlar oluşturmaktadır. Literatürde bu konu ile ilgili çalışmalar var olmasına rağmen belirli risk boyutlarına odaklanıldığı gözlemlenmiştir. Daha kapsamlı risk boyutları ile ilgili çalışmaların oldukça kısıtlı olduğu anlaşılmaktadır. Dolayısıyla bu çalışma literatürdeki bu boşluğa önemli katkı sunması düşünülmektedir. Bu çalışmada e-ticaret tüketicilerinin algıladığı riskin ölçümlenmesi amaçlanmıştır. Bu doğrultuda 230 katılımcıdan veriler elde edilmiş ve bu verilerin analizinde en küçük kareler yöntemi ile yapısal eşitlik modeli kullanılmıştır. Bu analiz sonuçlarına göre bütün boyutlar algılana risk ana yapısıyla ilişki iken, tüketicilerin için en fazla riski oluşturan boyutlar benzer olmayan ürün, yetersiz hizmet ve işlem başarısızlığı olmuştur.

Keywords

Algılanan Risk , Elektronik Ticaret , Yapısal eşitlik Modeli

References

• Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing Construct Validity in Organizational Research. Administrative Science Quarterly, 36(3), 421–458. https://doi.org/10.2307/2393203
• Bashir, S., Khwaja, M. G., Mahmood, A., Turi, J. A., & Latif, K. F. (2021). Refining e-shoppers’ perceived risks: Development and validation of new measurement scale. Journal of Retailing and Consumer Services, 58, 102285.
• Bauer, R. A. (1960). Consumer behavior as risk taking. Içinde R. S. Hancock (Ed.), Dynamic Marketing for a Changing World (ss. 389–398). American Marketing Association.
• Bhatnagar, A., & Ghose, S. (2004). Segmenting consumers based on the benefits and risks of Internet shopping. Journal of Business Research, 57(12), 1352–1360.
• Chen, C. (2012). PRIS: A multiple-item scale for measuring perceived risk of internet shopping. Purdue University.
• Desrochers, J., & François Outreville, J. (2020). Perceived risk and insurance decision taking for small losses. Journal of Risk Research, 23(4), 447–460.
• Dholakia, U. M. (1997). An Investigation of the Relationship Between Perceived Risk and Product Involvement. Advances in Consumer Research, 24(1), 159–167.
• Dowling, G. R. (1986). Perceived risk: the concept and its measurement. Psychology & Marketing, 3(3), 193–210.
• 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
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.
• Hawes, J. M., & Lumpkin, J. R. (1986). Perceived risk and the selection of a retail patronage mode. Journal of the Academy of Marketing Science, 14(4), 37–42.
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115–135.
• Hussain, S., Ahmed, W., Jafar, R. M. S., Rabnawaz, A., & Jianzhou, Y. (2017). eWOM source credibility, perceived risk and food product customer’s information adoption. Computers in Human Behavior, 66, 96–102.
• Jacoby, J., & Kaplan, L. B. (1972). The Components of Perceived Risk. ACR Special Volumes, 382–393.
• Lutz, R. J., & Reilly, P. J. (1974). An exploration of the effects of perceived social and performance risk on consumer information acquisition. ACR North American Advances, 1, 393–405.
• Mello, S. C. B., & Collins, M. (1998). Risk Perception And Industrial Buyer Differences. Encontro Nacional da ANPAD.
• Prasad, V. K. (1975). Socioeconomic product risk and patronage preferences of retail shoppers. Journal of Marketing, 39(3), 42–47.
• Quintal, V. A., Lee, J. A., & Soutar, G. N. (2010). Tourists’ information search: the differential impact of risk and uncertainty avoidance. International Journal of Tourism Research, 12(4), 321–333.
• Ring, A., Shriber, M., & Horton, R. L. (1980). Some effects of perceived risk on consumer information processing. Journal of the Academy of Marketing Science, 8(3), 255–263.
• Rosa, E. A. (2003). The logical structure of the social amplification of risk framework (SARF): Metatheoretical foundations and policy implications. The social amplification of risk, 47, 47–49.
• Roselius, T. (1971). Consumer rankings of risk reduction methods. Journal of marketing, 35(1), 56–61.
• Samadi, M., & Yaghoob-Nejadi, A. (2009). A survey of the effect of consumers’ perceived risk on purchase intention in e-shopping. Business Intelligence Journal, 2(2), 261–275.
• Sarstedt, M., & Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: a software review. Journal of Marketing Analytics, 7(3), 196–202. https://doi.org/10.1057/s41270-019-00058-3
• Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197–211. https://doi.org/https://doi.org/10.1016/j.ausmj.2019.05.003
• Stone, R. N., & Grønhaug, K. (1993). Perceived risk: Further considerations for the marketing discipline. European Journal of marketing, 27(3), 39–50.
• Tseng, S.-Y., & Wang, C.-N. (2016). Perceived risk influence on dual-route information adoption processes on travel websites. Journal of Business Research, 69(6), 2289–2296. https://doi.org/https://doi.org/10.1016/j.jbusres.2015.12.044
• Zhang, L., Tan, W., Xu, Y., & Tan, G. (2012). Dimensions of perceived risk and their influence on consumers’ purchasing behavior in the overall process of B2C. Içinde Engineering education and management (ss. 1–10). Springer.