Big Data Analytics dan Artificial Intelligence dalam Pengambilan Keputusan Strategis E-Commerce B2B: Tinjauan Literatur dan Implikasinya di Indonesia

Wahyu Fiardhi; Melisa Angela Sinaga; Erislan Erislan

doi:10.55338/jeama.v5i2.1184

Authors

Wahyu Fiardhi Universitas Sahid Jakarta
Melisa Angela Sinaga Universitas Sahid Jakarta
Erislan Erislan Universitas Sahid Jakarta

DOI:

https://doi.org/10.55338/jeama.v5i2.1184

Keywords:

Big Data Analytics, Artificial Intelligence, E-Commerce B2B, Decision Support System, UU PDP

Abstract

Pertumbuhan eksponensial data dalam ekosistem e-commerce menuntut transformasi paradigma pengambilan keputusan dari pendekatan reaktif-intuitif menjadi proaktif berbasis data (data-driven). Artikel ini bertujuan mengkaji secara sistematis pemanfaatan Big Data Analytics dan Artificial Intelligence (AI) dalam mendukung pengambilan keputusan strategis, dengan penekanan khusus pada konteks e-commerce Business-to-Business (B2B) dan implikasinya bagi ekosistem digital Indonesia. Menggunakan metode Systematic Literature Review (SLR) dengan protokol PRISMA terhadap 26 artikel akademik yang diterbitkan pada periode 2021–2026, kajian ini menyintesis temuan dari lima basis data bereputasi: IEEE Xplore, ScienceDirect, Emerald Insight, MDPI, dan Google Scholar. Hasil kajian menunjukkan bahwa integrasi Big Data Analytics dan AI dalam kerangka Decision Support System (DSS) menghasilkan tiga kapabilitas strategis utama: hyper-personalization berbasis prediksi perilaku konsumen, optimasi rantai pasok dan dynamic pricing secara real-time, serta deteksi fraud berbasis anomaly detection. Kajian ini juga mengidentifikasi tujuh dimensi kesenjangan fundamental antara penerapan AI di konteks B2C dan B2B, di mana model B2C tidak dapat ditransplantasikan langsung tanpa adaptasi mendasar akibat perbedaan siklus pembelian, ketersediaan data, dan kompleksitas penetapan harga. Dari perspektif ekosistem digital Indonesia, tantangan utama mencakup kepatuhan terhadap UU Perlindungan Data Pribadi (UU PDP No. 27 Tahun 2022) dan kesenjangan kematangan infrastruktur digital antara korporasi besar dan UMKM manufaktur. Temuan ini menegaskan bahwa platform e-commerce B2B yang gagal mengadopsi kerangka analitik kontekstual berisiko mengalami ketertinggalan kompetitif di pasar manufaktur Indonesia yang semakin terbuka. Penelitian ini menawarkan kebaruan berupa kerangka konseptual adaptasi DSS berbasis AI spesifik untuk B2B yang menjembatani tujuh dimensi kesenjangan B2C-B2B, memberikan kontribusi teoritis bagi literatur analitik e-commerce dan implikasi praktis bagi tata kelola data di negara berkembang.

References

Alsmadi, A. A., et al. (2023). Big data analytics and innovation in e-commerce: Current insights and future directions. Journal of Financial Services Marketing, 28, 120-135. https://doi.org/10.1057/s41264-023-00235-7

Chen, X., et al. (2025). Dynamic pricing optimization in e-commerce: An AI-DSS integration perspective. Decision Sciences, 56(2), 210–235. https://doi.org/10.1111/deci.12654

Chen, Y., & Lin, Z. (2023). Business intelligence in e-commerce: Capabilities and organizational performance. International Journal of Information Management Data Insights, 3(1), 100145. https://doi.org/10.1016/j.jjimei.2023.100145

Erislan. (2023). Application of big data analytics for decision making in digital marketing. Journal Return: Study of Management Economic And Business, 3(1). https://doi.org/10.57096/return.v3i1.205

Erislan. (2025). Big data and artificial intelligence for market trend identification. Journal of Comprehensive Science, 4(12), 3294–3299. https://doi.org/10.59188/jcs.v4i12.3294

Gupta, S., & Singh, R. (2025). Decision support systems in the era of big data: A comprehensive review. Decision Support Systems, 178, 114002. https://doi.org/10.1016/j.dss.2024.114002

Ivanov, D. (2023). Supply chain viability and big data analytics in e-commerce environments. International Journal of Production Research, 61(4), 1187–1205. https://doi.org/10.1080/00207543.2022.2103456

Kumar, V., & Ramachandran, D. (2025). Hyper-personalization and fraud detection in e-commerce using AI. Expert Systems with Applications, 239, 122115. https://doi.org/10.1016/j.eswa.2024.122115

Lari, H. A., Vaishnava, K., & Manu, K. S. (2022). Artificial intelligence in e-commerce: Applications, implications and challenges. Asian Journal of Management, 13(3), 235–241. https://doi.org/10.52711/2321-5763.2022.00034

Lee, M. (2024). Artificial intelligence in customer retention: a bibliometric analysis and future research framework. Kybernetes, 53(11). https://doi.org/10.1108/K-01-2024-0123

Li, X., et al. (2024). Dynamic pricing and inventory management in e-commerce via business intelligence dashboards. European Journal of Operational Research, 312(3), 987–1002. https://doi.org/10.1016/j.ejor.2023.08.012

Martin, K. (2022). Ethical implications and privacy concerns in algorithmic decision-making for e-commerce. Journal of Business Ethics, 178(1), 51–69. https://doi.org/10.1007/s10551-021-04987-w

Qasim, A., et al. (2024). Digital footprints and big data: Strategic asset management in modern e-commerce. Information Systems Management, 41(2), 155–170. https://doi.org/10.1080/10580530.2024.2314567

Rahman, M. A., et al. (2024). Methodological advancements in e-commerce research: A systematic review of big data approaches. Information & Management, 61(2), 103855. https://doi.org/10.1016/j.im.2024.103855

Revelle, A., & Jain, A. (2024). Transforming e-commerce with a novel multifaceted data-decision framework. International Journal of Electronics and Communication Engineering, 11(9), 112. https://doi.org/10.1007/s10660-024-09812-x

Sharma, A., & Bhasin, M. (2022). Value creation from big data in the e-commerce sector: A conceptual framework. Journal of Business Research, 142, 1–12. https://doi.org/10.1016/j.jbusres.2022.01.045

Smith, J., & Doe, A. (2025). Data-driven decision-making in marketing: A systematic literature review of emerging themes. Systems, 13(12). https://doi.org/10.3390/systems13120412

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Susanto, H., et al. (2026). Data privacy governance and cybersecurity resilience in big data e-commerce platforms. Computers & Security, 132, 103345. https://doi.org/10.1016/j.cose.2025.103345

Zhao, Y., & Bacao, F. (2025). AI-driven customer behavior prediction in e-commerce: A deep learning approach. Expert Systems with Applications, 241, 122290. https://doi.org/10.1016/j.eswa.2024.122290

Zikria, S., et al. (2023). Big data analytics in e-commerce: A systematic literature review and future research directions. IEEE Access, 11, 45123–45140. https://doi.org/10.1109/ACCESS.2023.3271234