Pengaruh Artificial Intelligence (AI) terhadap Perilaku Konsumen pada E-Commerce Shopee

Artificial Intelligence (AI) Perilaku Konsumen E-Commerce Shopee

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Marcela, D., Yaqutunnafis, L., & Hully, H. (2026). Pengaruh Artificial Intelligence (AI) terhadap Perilaku Konsumen pada E-Commerce Shopee. Empiricism Journal, 7(3), 3240-3252. https://doi.org/10.36312/ej.v7i3.6559

Perkembangan Artificial Intelligence (AI) dalam perdagangan elektronik (e-commerce) telah mengubah cara konsumen mencari informasi, mengevaluasi produk, dan mengambil keputusan pembelian. Namun, kajian empiris mengenai hubungan AI dengan perilaku konsumen pada masyarakat pedesaan masih relatif terbatas. Penelitian ini bertujuan menganalisis hubungan dan kontribusi AI terhadap perilaku konsumen pengguna Shopee di Desa Jeringo Lauq, Kecamatan Gunung Sari, Kabupaten Lombok Barat. Penelitian menggunakan pendekatan kuantitatif dengan desain asosiatif. Sebanyak 100 responden dipilih menggunakan teknik purposive sampling. Variabel AI diukur melalui lima indikator, yaitu rekomendasi produk otomatis, personalisasi layanan, pelayanan pelanggan otomatis (chatbot), prediksi kebutuhan konsumen, serta kemudahan interaksi dan pengalaman pengguna. Data dikumpulkan menggunakan kuesioner dan dianalisis melalui uji validitas, reliabilitas, normalitas, linearitas, regresi linear sederhana, serta uji t menggunakan IBM SPSS Statistics versi 25. Hasil penelitian menunjukkan hubungan positif dan signifikan secara statistik antara AI dan perilaku konsumen, dengan koefisien regresi sebesar 0,947, nilai t hitung sebesar 14,713, dan tingkat signifikansi p < 0,001. Koefisien determinasi (R²) sebesar 0,688 menunjukkan bahwa AI mampu menjelaskan 68,8% variasi perilaku konsumen dalam model penelitian, sedangkan 31,2% sisanya tidak dijelaskan oleh model. Temuan ini memberikan bukti empiris mengenai keterkaitan AI dengan perilaku konsumen dalam konteks masyarakat pedesaan. Namun, penelitian ini belum mengidentifikasi kontribusi masing-masing fitur AI secara terpisah dan tidak membuktikan hubungan kausal. Hasil penelitian dapat menjadi dasar pengembangan kajian lanjutan mengenai penerimaan teknologi, kepercayaan digital, serta pengalaman pengguna dalam aktivitas belanja daring. Penelitian mendatang disarankan menggunakan model multivariat untuk menguji peran setiap fitur AI dan faktor kontekstual lainnya secara lebih mendalam.

The Influence of Artificial Intelligence (AI) on Consumer Behavior in Shopee E-commerce

Abstract

The advancement of Artificial Intelligence (AI) in e-commerce has transformed how consumers search for information, evaluate products, and make purchasing decisions. However, empirical research examining the relationship between AI and consumer behavior in rural communities remains relatively limited. This study aims to analyze the relationship between AI and consumer behavior among Shopee users in Jeringo Lauq Village, Gunung Sari District, West Lombok Regency, Indonesia. A quantitative approach with an associative research design was employed, involving 100 respondents selected through purposive sampling. AI was measured using five indicators: automated product recommendations, service personalization, automated customer service (chatbots), consumer needs prediction, and ease of interaction and user experience. Data were collected using questionnaires and analyzed through validity, reliability, normality, and linearity tests, followed by simple linear regression and a t-test using IBM SPSS Statistics version 25. The findings revealed a statistically significant positive relationship between AI and consumer behavior, with a regression coefficient of 0.947, a t-value of 14.713, and a significance level of p < 0.001. The coefficient of determination (R²) was 0.688, indicating that AI explained 68.8% of the variance in consumer behavior, while the remaining 31.2% was unexplained by the model. These findings provide empirical evidence of the relationship between AI and consumer behavior in a rural context. Nevertheless, the study neither identifies the individual contributions of specific AI features nor establishes causality. Future research should employ multivariate models to investigate individual AI features, technology acceptance, digital trust, and user experience.