Pengaruh Ekosistem Komunikasi Pemasaran Berbasis Kecerdasan Buatan terhadap Perilaku Konsumen
DOI:
https://doi.org/10.38035/jafm.v7i4.3893Keywords:
Interaktivitas Kecerdasan Buatan, Personalisasi Kecerdasan Buatan, Keterlibatan Konsumen, Niat Beli, SEM-PLSAbstract
Kecerdasan buatan telah mengubah komunikasi pemasaran digital, tetapi pengaruh khusus interaktivitas dan personalisasi terhadap perilaku konsumen belum sepenuhnya dipahami. Penelitian ini menguji pengaruh interaktivitas dan personalisasi berbasis kecerdasan buatan terhadap keterlibatan konsumen dan niat beli pada 190 pengguna platform digital berbasis kecerdasan buatan di Indonesia. Data dikumpulkan melalui survei daring dengan teknik purposive sampling dan dianalisis menggunakan pemodelan persamaan struktural kuadrat terkecil parsial. Hasil penelitian menunjukkan bahwa interaktivitas berpengaruh positif kuat terhadap keterlibatan konsumen (koefisien = 1,081), sedangkan personalisasi berpengaruh negatif terhadap keterlibatan (koefisien = -0,382), yang menunjukkan paradoks personalisasi dan privasi. Namun, personalisasi berpengaruh langsung positif terhadap niat beli (koefisien = 0,325). Keterlibatan konsumen memediasi sebagian hubungan antara interaktivitas dan niat beli serta memediasi secara negatif hubungan antara personalisasi dan niat beli. Penelitian ini memperluas Model Penerimaan Teknologi dengan menunjukkan bahwa dua kemampuan kecerdasan buatan dapat bekerja melalui jalur psikologis yang berbeda dan berlawanan. Pemasar digital disarankan memprioritaskan interaksi responsif, transparansi penggunaan data, dan kontrol pengguna agar personalisasi berlebihan tidak melemahkan kepercayaan dan keterlibatan konsumen.
References
Bleier, A., Goldfarb, A., & Tucker, C. (2020). Consumer privacy and the future of data-based innovation and marketing. International Journal of Research in Marketing, 37(3), 466–480. https://doi.org/10.1016/j.ijresmar.2020.03.006
Cheng, Y., & Jiang, H. (2022). How do AI-powered digital assistants influence consumer PIs? An examination through the lens of the social presence theory. Journal of Retailing and Consumer Services, 65, 102823. https://doi.org/10.1016/j.jretconser.2022.102823
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008
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
Gao, W., Liu, Z., Liu, A., & Li, H. (2023). How does AI-powered recommendation affect PI? The role of trust and privacy concerns. Frontiers in Psychology, 14, 1091004. https://doi.org/10.3389/fpsyg.2023.1091004
Google, Temasek, & Bain & Company. (2022). e-Conomy SEA 2022: Through the waves, towards a sea of opportunity.
Hair, J. F., Henseler, J., Ringle, C. M., & Sarstedt, M. (2019). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 47(3), 1–22. https://doi.org/10.1007/s11747-019-00674-y
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hollebeek, L. D., Sprott, D. E., Andreassen, T. W., Costley, C., Klaus, P., Kuppelwieser, V., Karahasanovic, A., Taggart, L., Ul Islam, J., & Rather, R. A. (2019). Customer engagement in evolving technological environments: Synopsis and guiding propositions. European Journal of Marketing, 53(9), 2018–2023. https://doi.org/10.1108/EJM-09-2019-882
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Islam, J. U., & Rahman, Z. (2020). The impact of online brand community characteristics on customer engagement: An application of Stimulus-Organism-Response paradigm. Telematics and Informatics, 51, 101397. https://doi.org/10.1016/j.tele.2020.101397
Kietzmann, J., Paschen, J., & Treen, E. (2021). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 61(1), 6–16. https://doi.org/10.2501/JAR-2021-001
Liao, S. H., & Yang, L. L. (2020). Mobile payment and online to offline retail business models. Journal of Retailing and Consumer Services, 57, 102230. https://doi.org/10.1016/j.jretconser.2020.102230
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2022). Machines vs. humans: The impact of AI chatbot disclosure on customer purchases. Marketing Science, 38(6), 937–947. https://doi.org/10.1287/mksc.2019.1192
MarketsandMarkets. (2023). Artificial intelligence in marketing market: Global forecast to 2028.
Pavlou, P. A., & Fygenson, M. (2021). Understanding and predicting electronic commerce adoption. MIS Quarterly, 45(2), 543–578. https://doi.org/10.25300/MISQ/2021/14779
Prentice, C., Lopes, S. D., & Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29(7), 739–756. https://doi.org/10.1080/19368623.2020.1722304
Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. SmartPLS GmbH. https://www.smartpls.com
Sheng, J. (2022). Being yourself in the digital world: User engagement with AI-powered chatbots. Journal of Retailing and Consumer Services, 66, 102893. https://doi.org/10.1016/j.jretconser.2022.102893
Woehleke, G., Bermes, M., & Wirtz, B. W. (2023). AI-based personalization and consumer behavior: A systematic literature review. Electronic Commerce Research and Applications, 62, 101327. https://doi.org/10.1016/j.elerap.2023.101327
Yin, J., Qiu, X., & Zhang, X. (2022). Artificial intelligence and online PI: Based on the SOR model. Frontiers in Psychology, 12, 760437. https://doi.org/10.3389/fpsyg.2021.760437
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