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All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Industri dan Inovasi (INVASI)
Muhammad Rezaldi
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Transportation

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STIMULUS-ORGANISM-RESPONSE FACTORS DRIVING IMPULSE BUYING IN LIVE-STREAM SHOPPING ENVIRONMENTS: A SYSTEMATIC REVIEW Muhammad Rezaldi
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 1 (2025): DECEMBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v5i1.1380

Abstract

This study reviews empirical research examining what triggers impulse buying in live-streaming commerce and how these triggers work through psychological processes. The review took a systematic approach, using Scopus and DOAJ as the main databases. The search yielded 249 results, of which 34 studies published between 2020 and 2025 were included after screening. The analysis applies the stimulus-organism-response (SOR) framework. Four main types of stimuli appear consistently across studies. Social stimuli include streamer influence, viewer interaction, and social proof. Platform stimuli stem from real-time features that enhance the user experience. Promotional stimuli include time pressure, flash deals, and limited stock displays. Product presentation stimuli include demonstrations and clear visuals that help viewers imagine using the product. These stimuli affect emotional, cognitive, and immersive states. The most common internal responses linked to impulse buying are excitement, trust, perceived value, and flow. The impact of these stimuli varies depending on consumer traits, platform design, and product type. However, most studies rely on cross-sectional surveys from Asian markets, which limits generalization. This review offers an updated model connecting external triggers with psychological mechanisms.
Data Mining dan Big Data Dalam Dunia Industri putera, Muzakkir; Newton, Newton; Parkhurst, Helen; Rezaldi, Muhammad; Lestari, Sri Indah; Oziana, Deea Rizki; Hulwani, Zati
Jurnal Industri dan Inovasi (INVASI) Vol 3, No 1 (2025): Vol 3, No 1 (September 2025)
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/invasi.v3i1.12756

Abstract

salah satu metode klastering yang paling populer dan banyak digunakan dalam analisis data tak berlabel. Metode ini bertujuan untuk membagi sekumpulan data ke dalam sejumlah klaster yang telah ditentukan sebelumnya, berdasarkan kedekatan data terhadap pusat klaster (centroid). Proses K-Means dimulai dengan menentukan jumlah klaster (k), kemudian memilih centroid awal secara acak. Setiap data kemudian diklasifikasikan ke klaster terdekat berdasarkan jarak Euclidean. Selanjutnya, centroid diperbarui berdasarkan rata-rata data dalam masing-masing klaster, dan proses ini diulang hingga pusat klaster tidak lagi berubah secara signifikan. Kelebihan metode ini adalah kesederhanaannya dan efisiensi komputasinya, namun K-Means juga memiliki keterbatasan seperti kepekaan terhadap pemilihan centroid awal dan ketidaksesuaian dalam menangani data non-linier atau berbentuk kompleks. Metode ini banyak diaplikasikan dalam segmentasi pasar, pengenalan pola, analisis citra, dan pengelompokan dokumen.
Co-Authors Hulwani, Zati Lestari, Sri Indah Newton, Newton Oziana, Deea Rizki Parkhurst, Helen Putera, Muzakkir