Analisis Bibliometrik tentang Business Analytics
DOI:
https://doi.org/10.58812/jbmws.v5i02.3485Kata Kunci:
Business Analytics, Analisis Bibliometrik, Data Analytics, Big Data, Artificial Intelligence, VOSviewerAbstrak
Business Analytics telah menjadi salah satu bidang penelitian yang berkembang pesat seiring meningkatnya pemanfaatan pengambilan keputusan berbasis data, teknologi big data, dan kecerdasan buatan dalam organisasi. Penelitian ini bertujuan untuk memetakan perkembangan, struktur intelektual, pola kolaborasi, serta tren penelitian Business Analytics melalui pendekatan bibliometrik. Data penelitian diperoleh dari database Scopus menggunakan kata kunci “Business Analytics” dan dianalisis menggunakan perangkat lunak VOSviewer. Analisis yang dilakukan meliputi analisis sitasi, jaringan kolaborasi penulis, kolaborasi institusi, kolaborasi negara, co-occurrence kata kunci, overlay visualization, dan density visualization. Hasil penelitian menunjukkan bahwa perkembangan Business Analytics didasarkan pada kontribusi literatur mengenai data mining, business intelligence, machine learning, dan big data analytics. Amerika Serikat, India, China, dan Jerman merupakan negara yang memiliki kontribusi dan jaringan kolaborasi penelitian terbesar dalam bidang ini. Analisis kata kunci mengidentifikasi tema-tema dominan seperti data analytics, big data, artificial intelligence, machine learning, predictive analytics, dan information management. Selain itu, hasil overlay visualization menunjukkan adanya pergeseran fokus penelitian dari business intelligence dan information systems menuju artificial intelligence, Industry 4.0, cloud computing, blockchain, Internet of Things, sustainability, dan data privacy. Analisis density visualization mengungkap bahwa beberapa topik tersebut masih memiliki tingkat eksplorasi yang relatif rendah sehingga berpotensi menjadi arah penelitian di masa depan.
Referensi
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131.
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. In MIS quarterly (Vol. 36, Issue 4, pp. 1165–1188). Management Information Systems Research Center, University of Minnesota.
Chen, Y., & Chang, C. (2012). Enhance green purchase intentions: The roles of green perceived value, green perceived risk, and green trust. Management Decision, 50(3), 502–520.
Claudia, J., Ginting, P., & Fawzeea Sembiring, B. K. (2023). Analysis of Effect of Electronic Word of Mouth (E-WoM) and Brand Image on Purchase Decision of Samsung Brand Mobile Phones Moderated by E-Trust in Undergraduate Students, Faculty of Economics and Business, Universitas Sumatera Utara, Medan City. International Journal of Research and Review, 10(6), 382–387. https://doi.org/10.52403/ijrr.20230647
Davenport, T. H., & Westerman, G. (2018). Why so many high-profile digital transformations fail. Harvard Business Review, 9(4), 15.
Hanif, F. N., Sudrajat, D., Wahyuni, D., & Karya, D. (2024). Management of Broiler Chicken Maintenance with a Semi-Closed Cage System in the Cages of Partner Breeders PT. Pitik Digital Indonesia. Jurnal Agrosci, 1(5), 273–282.
Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1–14.
Lihua, Y. (2022). Research on E-commerce Network Marketing Strategy Based on Data Mining. 2022 6th Annual International Conference on Data Science and Business Analytics (ICDSBA), 66–71.
Mansour, T. G. I., Abd El-Ghani, S. S., & Murad, H. S. (2026). Farmers’ Attitudes Toward Mechanisms and Practices of Climate Change Adaptation in Egypt and Iraq: A Comparative Field Study. Sustainability (Switzerland), 18(5). https://doi.org/10.3390/su18052502
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60–68.
Mining, W. I. D. (2006). Data mining: Concepts and techniques. Morgan Kaufinann, 10(559–569), 4.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1.
Nhamo, G. (2009). Co-leadership in climate change: An agenda to 2013 and beyond. Politikon, 36(3), 463–480. https://doi.org/10.1080/02589341003600247
Pathare, P. B., Opara, U. L., & Al-Said, F. A.-J. (2013). Colour measurement and analysis in fresh and processed foods: A review. Food and Bioprocess Technology, 6(1), 36–60.
Sarker, I. H. (2021a). Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(6), 1–20.
Sarker, I. H. (2021b). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 1–21.
Susilo, D., & Navarro, C. J. S. (2023). PR Model of Garuda Indonesia: A Construction of Wonderland Through Instagram. Indonesian Journal of Business Analytics, 3(2), 197–214.
T., M. (Ed.). (2026). 6th International Conference of Accounting and Business, iCAB 2025. In Springer Proceedings in Business and Economics. Springer Nature. https://www.scopus.com/pages/publications/105036712670?origin=resultslist
Van Der Aalst, W. (2016). Data science in action. In Process mining: Data science in action (pp. 3–23). Springer.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 Loso Judijanto
Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
Facebook 
Instagram 
Twitter 
Youtube 
westscience.journals@gmail.com