The role of perceived risk and trust in the effect of artificial intelligence marketing technology on online purchase intention

Year 2024, Volume: 26 Issue: Özel Sayı, 1 - 16, 21.10.2024

Ceylan Bozpolat

https://doi.org/10.33707/akuiibfd.1403109

Abstract

Artificial intelligence marketing technology provides online consumers with a personalized purchasing experience and businesses the opportunity to effectively meet customer needs. This study was conducted to reveal the role of trust in the relevant technology and perceived risk in the effect of artificial intelligence marketing technology experience on online purchasing intention. In this context, the experiential elements of artificial intelligence marketing technology are discussed in three dimensions: accuracy, insight, and interaction. A conceptual model based on the SOR model was developed, including trust, perceived risk, and purchase intention. Then, a quantitative study was conducted with a sample size of 480 people using an online survey method for people with online shopping history. Data collected by snowball sampling method were analyzed by Partial Least Squares Variance Based Structural Equation Modeling (PLS-SEM) using the SmartPLS 4 program. The results showed that the experience of insight and interaction positively affected trust in technology, while the experience of accuracy had no effect on trust. It has been determined that all the experiential elements of artificial intelligence marketing technology have a positive effect on reducing consumers' risk perceptions. Reduced perceived risk due to trust and experiential factors positively and significantly affects online purchase intention. In addition, it was found that trust and perceived risk variables had a serial mediating effect on the relationship between artificial intelligence marketing technology experiential elements and online purchasing intention.

Keywords

Artificial Intelligence Marketing Technology, Trust, Perceived Risk, Online Purchase Intention

Yapay zekâ pazarlama teknolojisinin çevrimiçi satın alma niyetine etkisinde algılanan risk ve güvenin rolü

Abstract

Yapay zekâ pazarlama teknolojisi, çevrimiçi alışveriş yapan tüketicilere kişiselleştirilmiş satın alma deneyimi, işletmelere ise müşteri ihtiyaçlarını etkin bir şekilde karşılama imkânı tanımaktadır. Bu çalışma, yapay zekâ pazarlama teknolojisi deneyiminin, çevrimiçi satın alma niyeti üzerindeki etkisinde ilgili teknolojiye olan güvenin ve algılanan riskin rolünü ortaya koymak amacıyla yapılmıştır. Bu bağlamda yapay zekâ pazarlama teknolojisinin deneyimsel unsurları doğruluk, içgörü ve etkileşim olmak üzere üç boyutta ele alınmış; güven, algılanan risk ve satın alma niyetinin de dahil olduğu SOR modeline dayanan kavramsal bir model geliştirilmiştir. Ardından çevrimiçi alışveriş geçmişi bulunan kişilere çevrimiçi anket yöntemi ile 480 örnek hacimli nicel bir çalışma gerçekleştirilmiştir. Kartopu örnekleme yöntemi ile toplanan veriler, SmartPLS 4 programı kullanılarak Kısmi En Küçük Kareler Varyans Temelli Yapısal Eşitlik Modellemesi (PLS-SEM) ile analiz edilmiştir. Sonuçlar, içgörü ve etkileşim deneyiminin teknolojiye olan güveni pozitif bir şekilde etkilediğini, doğruluk deneyiminin ise güven üzerinde herhangi bir etkisi olmadığını göstermiştir. Tüketicilerin risk algılarının azalmasında yapay zekâ pazarlama teknolojisinin deneyimsel unsurlarının tamamının pozitif bir etkisi olduğu tespit edilmiştir. Güven ve deneyimsel unsurlar dolayısıyla azalan algılanan risk, çevrimiçi satın alma niyetini pozitif ve anlamlı bir şekilde etkilemektedir. Ayıca yapay zekâ pazarlama teknolojisi deneyim unsurları ile çevrimiçi satın alma niyeti arasındaki ilişkide güven ve algılanan risk değişkenlerinin seri aracı etkisi olduğu bulunmuştur.

Keywords

Yapay Zeka Pazarlama Teknolojisi, Güven, Algılanan Risk, Çevrimiçi Satın Alma Niyeti

References

• Abu-Shamaa, R., Abu-Shanab, E., & Khasawneh, R. (2016). Payment methods and purchase intention from online stores: An empirical study in Jordan. International Journal of E-Business Research (IJEBR), 12(2), 31-44. https://doi.org/10.4018/IJEBR.2016040103
• Aini, Q., Sembiring, I., Setiawan, A., Setiawan, I., & Rahardja, U. (2023). Perceived accuracy and user behavior: Exploring the impact of AI-based air quality detection application (AIKU). Indonesian Journal of Applied Research (IJAR), 4(3), 209-218. https://doi.org/10.30997/ijar.v4i3.356
• Ajenaghughrure, I. B., da Costa Sousa, S. C., & Lamas, D. (2020). Risk and trust in artificial intelligence technologies: A case study of autonomous vehicles. 13th International Conference on Human System Interaction, Tokyo, Japan. https://doi.org/10.1109/HSI49210.2020.9142686
• Aksay, B., & Ünal, A. Y. (2016). Yapısal Eşitlik Modellemesi Kapsamında Formatif Ve Reflektif Ölçüm. Cag University Journal of Social Sciences, 13(2), 1-21. https://dergipark.org.tr/en/download/article-file/696237
• Alam, S. S., Masukujjaman, M., Mohamed Makhbul, Z. K., Helmi Ali, M., Ahmad, I., & Al Mamun, A. (2023). Experience, Trust, eWOM Engagement and Usage Intention of AI Enabled Services in Hospitality and Tourism Industry: Moderating Mediating Analysis. Journal of Quality Assurance in Hospitality & Tourism, 1-29. https://doi.org/10.1080/1528008X.2023.2167762
• Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548. 1-14. https://doi.org/10.1016/j.chb.2020.106548
• Antony, S., Lin, Z., & Xu, B. (2006). Determinants of escrow service adoption in consumer-to-consumer online auction market: an experimental study. Decision Support Systems, 42(3), 1889-1900. https://doi.org/10.1016/j.dss.2006.04.012
• Baber, R., & Baber, P. (2022). Influence of social media marketing efforts, e-reputation and destination image on intention to visit among tourists: application of SOR model. Journal of Hospitality and Tourism Insights, 6(5), 2298-2316. https://doi.org/10.1108/JHTI-06-2022-0270
• Bashir, S., Anwar, S., Awan, Z., Qureshi, T. W., & Memon, A. B. (2018). A holistic understanding of the prospects of financial loss to enhance shopper's trust to search, recommend, speak positive and frequently visit an online shop. Journal of Retailing and Consumer Services, 42, 169-174. https://doi.org/10.1016/j.jretconser.2018.02.004
• Beyari, H., & Garamoun, H. (2022). The Effect of Artificial Intelligence on End-User Online Purchasing Decisions: Toward an Integrated Conceptual Framework. Sustainability, 14(15), 1-17. https://doi.org/10.3390/su14159637
• Bhatnagar, A., Misra, S., & Rao, H. R. (2000). On risk, convenience, and Internet shopping behavior. Communications of the ACM, 43(11), 98-105. https://dl.acm.org/doi/fullHtml/10.1145/353360.353371
• Bhatti, A., Saad, S., & Gbadebo, S. M. (2019). Effect of financial risk, privacy risk and product risk on online shopping behavior. Pakistan Journal of Humanities and Social Sciences, 7(4), 342-356. https://doi.org/10.52131/pjhss.2019.0704.0091
• Bitkina, O. V., Jeong, H., Lee, B. C., Park, J., Park, J., & Kim, H. K. (2020). Perceived trust in artificial intelligence technologies: A preliminary study. Human Factors and Ergonomics in Manufacturing & Service Industries, 30(4), 282-290. https://doi.org/10.1002/hfm.20839
• Chen, J. S., Le, T. T. Y., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, 49(11), 1512-1531. https://doi.org/10.1108/IJRDM-08-2020-0312
• Chen, Y. S., & Huang, S. Y. (2017). The effect of task-technology fit on purchase intention: The moderating role of perceived risks. Journal of Risk Research, 20(11), 1418-1438. https://doi.org/10.1080/13669877.2016.1165281
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates, Publishers
• Crockett, K., Garratt, M., Latham, A., Colyer, E., & Goltz, S. (2020). Risk and trust perceptions of the public of artifical intelligence applications. International Joint Conference on Neural Networks (IJCNN), Glasgow, UK https://doi.org/10.1109/IJCNN48605.2020.9207654
• Curzon, J., Kosa, T. A., Akalu, R., & El-Khatib, K. (2021). Privacy and artificial intelligence. IEEE Transactions on Artificial Intelligence, 2(2),96-108. https://doi.org/10.1109/TAI.2021.3088084
• 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
• Friedman, A., Knijnenburg, B.P., Vanhecke, K., Martens, L., & Berkovsky, S. (2015). privacy aspects of recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (Eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_19
• Hafizoğlu, F. M., & Sen, S. (2019). Understanding the influences of past experience on trust in human-agent teamwork. ACM Transactions on Internet Technology (TOIT), 19(4), 1-22. https://doi.org/10.1145/3324300
• Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017a). A primer on partial least squares structural equation modeling (PLS-SEM). Second Edition, Sage Publications. Thousand Oaks, California.
• Hair, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107- 123. https://doi.org/10.1504/IJMDA.2017.087624
• 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. https://doi.org/10.1108/EBR-11-2018-0203
• Hair, J.F., Sarstedt, M., Hopkins, L. & Kuppelwieser, V.G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128
• Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
• Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119-132. https://doi.org/10.1016/j.ijin.2022.08.005
• Hasan, R., Shams, R., & Rahman, M. (2021). Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri. Journal of Business Research, 131, 591-597. https://doi.org/10.1016/j.jbusres.2020.12.012
• 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, 115-135. https://doi.org/10.1007/s11747-014-0403-8
• Hong, I. B., & Cha, H. S. (2013). The mediating role of consumer trust in an online merchant in predicting purchase intention. International Journal of Information Management, 33(6), 927-939. https://doi.org/10.1016/j.ijinfomgt.2013.08.007
• Jacoby, J., & Kaplan, L. B. (1972). The components of perceived risk. In Proceedings of the 3rd Annual Conference of the Association for Consumer Research, Chicago, IL, USA, 3–5 November, pp. 382–393.
• Jarek, K., & Mazurek, G. (2019). Marketing and artificial intelligence. Central European Business Review, 8(2). 46-55. https://doi.org/10.18267/j.cebr.213
• Khrais, L. T. (2020). Role of artificial intelligence in shaping consumer demand in E-commerce. Future Internet, 12(12), 1-14. https://doi.org/10.3390/fi12120226
• Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564. https://doi.org/10.1016/j.dss.2007.07.001
• Kim, J., Giroux, M., & Lee, J. C. (2021). When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychology & Marketing, 38(7), 1140-1155. https://doi.org/10.1002/mar.21498
• Ko, H., Jung, J., Kim, J., & Shim, S. W. (2004). Cross-cultural differences in perceived risk of online shopping. Journal of Interactive Advertising, 4(2), 20-29. https://doi.org/10.1080/15252019.2004.10722084
• Kopalle, P. K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., & Rindfleisch, A. (2022). Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, 39(2), 522-540. https://doi.org/10.1016/j.ijresmar.2021.11.002
• Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 1-21. https://doi.org/10.1177/0008125619859317
• Lăzăroiu, G., Neguriţă, O., Grecu, I., Grecu, G., & Mitran, P. C. (2020). Consumers’ decision-making process on social commerce platforms: Online trust, perceived risk, and purchase intentions. Frontiers in Psychology, 11, 890, 1-7. https://doi.org/10.3389/fpsyg.2020.00890
• Ling, K. C., Daud, D. B., Piew, T. H., Keoy, K. H., & Hassan, P. (2011). Perceived risk, perceived technology, online trust for the online purchase intention in Malaysia. International Journal of Business and Management, 6(6), 167-182. https://doi.org/10.5539/ijbm.v6n6p167
• Marsh, H. W., Balla, J. R., & Mcdonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103(3), 391-410. https://doi.org/10.1037/0033- 2909.103.3.391
• Martin, J., Mortimer, G., & Andrews, L. (2015). Re-examining online customer experience to include purchase frequency and perceived risk. Journal of Retailing and Consumer Services, 25, 81-95. https://doi.org/10.1016/j.jretconser.2015.03.008
• Maseeh, H. I., Jebarajakirthy, C., Pentecost, R., Arli, D., Weaven, S., & Ashaduzzaman, M. (2021). Privacy concerns in e‐commerce: A multilevel meta‐analysis. Psychology & Marketing, 38(10), 1779-1798. https://doi.org/10.1002/mar.21493
• Mathew, P. M., & Mishra, S. (2014). Online retailing in India: Linking internet usage, perceived risks, website attributes and past online purchase behaviour. The Electronic Journal of Information Systems in Developing Countries, 65(1), 1-17. https://doi.org/10.1002/j.1681-4835.2014.tb00466.x
• McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334-359. https://doi.org/10.1287/isre.13.3.334.81
• Ming, J., Jianqiu, Z., Bilal, M., Akram, U., & Fan, M. (2021). How social presence influences impulse buying behavior in live streaming commerce? The role of SOR theory. International Journal of Web Information Systems, 17(4), 300-320. https://doi.org/10.1108/IJWIS-02-2021-0012
• Mohseni, S., Jayashree, S., Rezaei, S., Kasim, A., & Okumus, F. (2018). Attracting tourists to travel companies’ websites: the structural relationship between website brand, personal value, shopping experience, perceived risk and purchase intention. Current Issues in Tourism, 21(6), 616-645. https://doi.org/10.1080/13683500.2016.1200539
• Munikrishnan, U. T., Huang, K., Mamun, A. A., & Hayat, N. (2023). Perceived risk, trust, and online food purchase intention among Malaysians. Business Perspectives and Research, 11(1), 28-43. https://doi.org/10.1177/22785337211043968
• Naiyi, Y. E. (2004). Dimensions of consumer's perceived risk in online shopping. Journal of Electronic Science and Technology, 2(3), 177-182.
• Nalbant, K. G., & Aydın, S. (2023). Development and transformation in digital marketing and branding with artificial intelligence and digital technologies dynamics in the Metaverse universe. Journal of Metaverse, 3(1), 9-18. https://doi.org/10.57019/jmv.1148015
• Overgoor, G., Chica, M., Rand, W., & Weishampel, A. (2019). Letting the computers take over: Using AI to solve marketing problems. California Management Review, 61(4), 156-185. https://doi.org/10.1177/0008125619859318
• Özbek, A., & Sırakaya, Ö. (2022). Türkiye’de kullanılan e-ticaret platformlarının performanslarının karşılaştırılması. Kırıkkale Üniversitesi Sosyal Bilimler Dergisi, 12(2), 469-492. https://dergipark.org.tr/en/download/article-file/2218809
• Peng, C., & Kim, Y. G. (2014). Application of the stimuli-organism-response (SOR) framework to online shopping behavior. Journal of Internet Commerce, 13(3-4), 159-176. https://doi.org/10.1080/15332861.2014.944437
• Pires, G., Stanton, J., & Eckford, A. (2004). Influences on the perceived risk of purchasing online. Journal of Consumer Behaviour: An International Research Review, 4(2), 118-131. https://doi.org/10.1002/cb.163
• Pitardi, V., & Marriott, H. R. (2021). Alexa, she's not human but… Unveiling the drivers of consumers' trust in voice‐based artificial intelligence. Psychology & Marketing, 38(4), 626-642. https://doi.org/10.1002/mar.21457
• Qalati, S. A., Vela, E. G., Li, W., Dakhan, S. A., Hong Thuy, T. T., & Merani, S. H. (2021). Effects of perceived service quality, website quality, and reputation on purchase intention: The mediating and moderating roles of trust and perceived risk in online shopping. Cogent Business & Management, 8(1), 1-20. https://doi.org/10.1080/23311975.2020.1869363
• Rohden, S. F., & Zeferino, D. G. (2023). Recommendation agents: A analysis of consumers’ risk perceptions toward artificial intelligence. Electronic Commerce Research, 23(4), 2035-2050. https://doi.org/10.1007/s10660-022-09626-9
• Rooij, S. V. (2022). Taking it personally? A study on the effects of trust and privacy in the context of AI-enabled personalization. Master Thesis, MSc Marketing, Radboud University.
• Schwerter, F., & Zimmermann, F. (2020). Determinants of trust: The role of personal experiences. Games and Economic Behavior, 122, 413-425. https://doi.org/10.1016/j.geb.2020.05.002
• Sharifpour, M., Walters, G., Ritchie, B. W., & Winter, C. (2014). Investigating the role of prior knowledge in tourist decision making: A structural equation model of risk perceptions and information search. Journal of Travel Research, 53(3), 307-322. https://doi.org/10.1177/0047287513500390
• Shavit, T., Lahav, E., & Rosenboim, M. (2016). Don’t fear risk, learn about it: How familiarity reduces perceived risk. Applied Economics Letters, 23(15), 1069-1072. https://doi.org/10.1080/13504851.2015.1133892
• Song, M., Xing, X., Duan, Y., Cohen, J., & Mou, J. (2022). Will artificial intelligence replace human customer service? The impact of communication quality and privacy risks on adoption intention. Journal of Retailing and Consumer Services, 66, 102900, 1-17. https://doi.org/10.1016/j.jretconser.2021.102900
• Stanciu, V., & Rîndaşu, S. M. (2021). Artificial Intelligence in retail: Benefits and risks associated with mobile shopping applications. Amfiteatru Economic, 23(56), 46-64. https://doi.org/10.24818/EA/2021/56/46
• Trivedi, J. (2019). Examining the customer experience of using banking chatbots and its impact on brand love: The moderating role of perceived risk. Journal of Internet Commerce, 18(1), 91-111. https://doi.org/10.1080/15332861.2019.1567188
• Tüik (2023, 21 Kasım). Hanehalkı Bilişim Teknolojileri Kullanım Araştırması. https://data.tuik.gov.tr/Bulten/Index?p=Hanehalki-Bilisim-Teknolojileri-(BT)-Kullanim-Arastirmasi-2023-49407
• Udo, G. J., Bagchi, K. K., & Kirs, P. J. (2010). An assessment of customers’e-service quality perception, satisfaction and intention. International Journal of Information Management, 30(6), 481-492. https://doi.org/10.1016/j.ijinfomgt.2010.03.005
• Ventre, I., & Kolbe, D. (2020). The impact of perceived usefulness of online reviews, trust and perceived risk on online purchase intention in emerging markets: A Mexican perspective. Journal of International Consumer Marketing, 32(4), 287-299. https://doi.org/10.1080/08961530.2020.1712293
• Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 1-8. https://doi.org/10.1016/j.jjimei.2020.100002
• Viberg, E., & Halldén, L. (2023). Do consumers trust it?: Exploring consumers trust in artificial intelligence personalization. Bachelor Thesis, Linnaeus University, Sweden.
• Vlačić, B., Corbo, L., e Silva, S. C., & Dabić, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187-203. https://doi.org/10.1016/j.jbusres.2021.01.055
• Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: service robots in the frontline. Journal of Service Management, 29(5), 907-931. https://doi.org/10.1108/JOSM-04-2018-0119
• Yin, J., & Qiu, X. (2021). AI technology and online purchase intention: Structural equation model based on perceived value. Sustainability, 13(10), 1-16. https://doi.org/10.3390/su13105671
• Zhu, B., Kowatthanakul, S., & Satanasavapak, P. (2019). Generation Y consumer online repurchase intention in Bangkok: Based on Stimulus-Organism-Response (SOR) model. International Journal of Retail & Distribution Management, 48(1), 53-69. https://doi.org/10.1108/IJRDM-04-2018-0071
• Zhu, D. S., O'Neal, G. S., Lee, Z. C., & Chen, Y. H. (2009). The effect of trust and perceived risk on consumers' online purchase intention. International Conference on Computational Science and Engineering, 4(August), Vancouver, BC, Canada, 771-776.