KETIKA ALGORITMA MENGENAL KITA: PENGARUH ALGORITHMIC PERSONALIZATION TERHADAP PURCHASE INTENTION MELALUI PERCEIVED INTRUSIVENESS

Authors

  • Nulthazam Sarah Universitas Negeri Makassar
  • Nur Fadilah Ayu Sandira Universitas Negeri Makassar
  • Indri Iswardhani Universitas Negeri Makassar
  • Sri Astuti Nasir Universitas Negeri Makassar

DOI:

https://doi.org/10.52362/jisamar.v10i4.2667

Keywords:

algorithmic personalization; perceived intrusiveness; purchase intention; SEM-PLS; e-commerce

Abstract

Personalisasi berbasis algoritma (algorithmic personalization) telah menjadi strategi utama platform e-commerce di Indonesia dalam meningkatkan relevansi produk yang ditawarkan kepada konsumen. Di sisi lain, personalisasi yang terlalu mendalam berpotensi menimbulkan persepsi gangguan (perceived intrusiveness) yang dapat menekan minat beli (purchase intention). Penelitian ini bertujuan menganalisis pengaruh algorithmic personalization terhadap purchase intention secara langsung maupun melalui mediasi perceived intrusiveness. Penelitian menggunakan pendekatan kuantitatif asosiatif dengan populasi pengguna aktif e-commerce di Indonesia. Sampel sebanyak 150 responden dipilih melalui purposive sampling dan dianalisis menggunakan Structural Equation Modeling berbasis Partial Least Square (SEM-PLS). Data yang digunakan bersifat simulasi/hipotetis untuk mendemonstrasikan penerapan metode. Hasil analisis menunjukkan bahwa algorithmic personalization berpengaruh positif signifikan terhadap purchase intention dan terhadap perceived intrusiveness, sementara perceived intrusiveness berpengaruh negatif signifikan terhadap purchase intention. Pengujian mediasi menunjukkan bahwa perceived intrusiveness memediasi secara parsial pengaruh algorithmic personalization terhadap purchase intention, dengan arah pengaruh tidak langsung yang berlawanan dengan pengaruh langsungnya. Temuan ini menegaskan adanya paradoks personalisasi (personalization paradox) yang perlu dikelola melalui transparansi algoritma dan kontrol privasi bagi pengguna.

Downloads

Download data is not yet available.

References

[1] Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). “Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness”. Journal of Retailing, 91(1), 34-49.
[2] van Doorn, J., & Hoekstra, J. C. (2013). “Customization of Online Advertising: The Role of Intrusiveness”. Marketing Letters, 24(4), 339-351.
[3] Lee, J., Kim, C., & Lee, K. C. (2022). “Exploring the Personalization-Intrusiveness-Intention Framework to Evaluate the Effects of Personalization in Social Media”. International Journal of Information Management, 66, 102532.
[4] De Keyzer, F., Dens, N., & De Pelsmacker, P. (2022). “How and When Personalized Advertising Leads to Brand Attitude, Click, and Word-of-Mouth Intention”. Journal of Advertising, 51(1), 39-56.
[5] de Groot, J. I. M. (2022). “The Personalization Paradox in Facebook Advertising: The Mediating Effect of Relevance on the Personalization-Brand Attitude Relationship and the Moderating Effect of Intrusiveness”. Journal of Interactive Advertising, 22(1), 57-74.
[6] Ul Haq, I., Mazhar, B., Maqsood, F., Pokharel, M., Tareen, H. K., & Maaz ul Hassan, M. (2026). “Understanding Algorithmic Personalization in Instagram Ads: The Role of Perceived Creativity, Authenticity & Need for Cognition in Shaping Generation Z’s Purchase Intentions”. Telematics and Informatics Reports, 21, Article 100287. https://doi.org/10.1016/j.teler.2025.100287
[7] Saha, P., Sengupta, A., & Gupta, P. (2024). “Influence of Personality Traits on Generation Z Consumers’ Click-Through Intentions towards Personalized Advertisements: A Mixed-Methods Study”. Heliyon, 10(15), e34559.
[8] Ghozali, I., & Latan, H. (2015). Partial Least Squares: Konsep, Teknik dan Aplikasi Menggunakan Program SmartPLS 3.0 (2nd ed.). Semarang: Badan Penerbit Universitas Diponegoro.
[9] 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.
[10] Kotler, P., & Keller, K. L. (2016). Marketing Management (15th ed.). Boston: Pearson Education.
[11] Google, Temasek, & Bain & Company. (2025). e-Conomy SEA 2025: Streaming Towards 2030. Jakarta: Google, Temasek, Bain & Company.
[12] Culnan, M. J., & Armstrong, P. K. (1999). “Information Privacy Concerns, Procedural Fairness, and Impersonal Trust: An Empirical Investigation”. Organization Science, 10(1), 104-115.
[13] Brehm, J. W. (1966). A Theory of Psychological Reactance. New York: Academic Press.
[14] Kock, N. (2015). “Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach”. International Journal of e-Collaboration, 11(4), 1-10.
[15] Chin, W. W. (1998). “The Partial Least Squares Approach for Structural Equation Modeling”. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp. 295-336). Mahwah: Lawrence Erlbaum Associates.
[16] Sarah, N., Sandira, N. F. A., Iswardhani, I., Nasir, S. A., & Windarsari, W. R. (2025). “Efektivitas Kampanye Instagram Ads terhadap Brand Awareness di Kalangan Gen Z”. Jurnal Ekonomi, Akutansi dan Manajemen Nusantara (JEAMA), 4(1), 101-108. https://doi.org/10.55338/jeama.v4i1.249
[17] Sarah, N., Sandira, N. F. A., Iswardhani, I., Nasir, S. A., & Macenning, A. R. A. D. (2025). “Peran Customer Review dalam Meningkatkan Kepercayaan Konsumen pada Filan Official Store di Marketplace Shopee”. Jurnal Akutansi Manajemen Ekonomi Kewirausahaan (JAMEK), 5(2), 442-450. https://doi.org/10.47065/jamek.v5i2.2017

Published

2026-11-09