Analisis Faktor-Faktor Ekonomi Perilaku yang Mempengaruhi Keterlibatan Sharing Konten Produk di Tiktok
DOI:
https://doi.org/10.29313/bcses.v6i2.25776Keywords:
Behavioral Economics, Pre-purchase Phase, Engagement MetricsAbstract
Abstract. The transformation of the digital market has significantly altered consumer decision-making processes in the pre-purchase phase. Social media has become the primary source of information shaping consumer behavior. This study aims to analyze the effect of the number of likes, the number of saves, and product prices on the probability of product endorsement content sharing behavior on the TikTok platform. Data were collected from TikTok Insights across 57 endorsement videos from the account @aleen.4044 between June 2024 and December 2025. Data processing was conducted using an ordered logit regression model. The estimation results indicate that the variables like and ln_price have a positive effect on the probability of moving to a higher share behavior category, whereas the variable save has a negative effect. These findings demonstrate that sharing behavior is influenced by a combination of emotional responses, information evaluation processes, and perceptions of the product's economic attributes. From a Behavioral Economics perspective, saving activity represents bounded rationality, liking activity reflects the affect heuristic, while price information acts as an anchor in the product evaluation process through the anchoring effect.
Abstrak. Transformasi pasar digital telah mengubah proses pengambilan keputusan konsumen pada fase pre-purchase. Media sosial menjadi sumber informasi utama dalam membentuk perilaku konsumen. Penelitian ini bertujuan untuk menganalisis pengaruh jumlah like, jumlah save, dan harga produk terhadap probabilitas perilaku penyebaran konten produk endorsement (share) pada platform TikTok. Data diperoleh dari TikTok Insights pada 57 video endorsement akun @aleen.4044 periode Juni 2024 hingga Desember 2025. Pengolahan data dilakukan menggunakan model regresi ordered logit. Hasil estimasi model menunjukkan bahwa variabel like dan ln_price berpengaruh positif terhadap probabilitas peningkatan kategori perilaku share, sedangkan variabel save berpengaruh negatif. Temuan tersebut menunjukkan bahwa perilaku share dipengaruhi oleh kombinasi respons emosional, proses evaluasi informasi, dan persepsi terhadap atribut ekonomi produk. Dalam perspektif Behavioral Economics, aktivitas save merepresentasikan bounded rationality, aktivitas like mencerminkan affect heuristic, sedangkan informasi harga berperan sebagai anchor dalam proses evaluasi produk melalui anchoring effect.
References
Berger, J., & Milkman, K. L. (2012). What Makes Online Content Viral? Journal of Marketing Research, 49(2), 192–205. https://doi.org/10.1509/jmr.10.0353
Chevalier, J. A., & Mayzlin, D. (2006). The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research, 43(3), 345–354. https://doi.org/10.1509/jmkr.43.3.345
Goldfarb, A., & Tucker, C. (2019). Digital Economics. Journal of Economic Literature, 57(1), 3–43. https://doi.org/10.1257/jel.20171452
Kahneman, D. (2003). Maps of Bounded Rationality: Psychology for Behavioral Economics. American Economic Review, 93(5), 1449–1475. https://doi.org/10.1257/000282803322655392
Kemp, S. (2025). Digital 2025: Indonesia. https://datareportal.com/reports/digital-2025-indonesia?utm
Muntinga, D. G., Moorman, M., & Smit, E. G. (2011). Introducing COBRAs: Exploring Motivations for Brand-Related Social Media Use. International Journal of Advertising, 30(1), 13–46. https://doi.org/10.2501/IJA-30-1-013-046
Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2007). The affect heuristic. European Journal of Operational Research, 177(3), 1333–1352. https://doi.org/https://doi.org/10.1016/j.ejor.2005.04.006.
Stephen, A. T., & Berger, J. A. (2011). Creating Contagious: How Social Networks and Item Characteristics Combine to Drive Persistent Social Epidemics. SSRN Electronic Journal, 1–48. https://doi.org/10.2139/ssrn.1354803
Zhou, S., & Hudin, N. S. (2024). Advancing e-commerce user purchase prediction: Integration of time-series attention with event-based timestamp encoding and Graph Neural Network-Enhanced user profiling. PLoS ONE, 19(4). https://doi.org/https://doi.org/10.1371/journal.pone.0299087
