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Business Intelligence Strategies, Best Practices, and Latest Trends: Analysis of Scientometric Data from 2003 to 2023 Using Machine Learning

Author

Listed:
  • Fatih Gurcan

    (Department of Management Information Systems, Faculty of Economics and Administrative Sciences, Karadeniz Technical University, Trabzon 61080, Turkey)

  • Ahmet Ayaz

    (Digital Transformation Office, Karadeniz Technical University, Trabzon 61080, Turkey)

  • Gonca Gokce Menekse Dalveren

    (Department of Software Engineering, Faculty of Engineering, Atilim University, Ankara 06830, Turkey)

  • Mohammad Derawi

    (Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 7034 Gjøvik, Norway)

Abstract

The widespread use of business intelligence products, services, and applications piques the interest of researchers in this field. The interest of researchers in business intelligence increases the number of studies significantly. Identifying domain-specific research patterns and trends is thus a significant research problem. This study employs a topic modeling approach to analyze domain-specific articles in order to identify research patterns and trends in the business intelligence field over the last 20 years. As a result, 36 topics were discovered that reflect the field’s research landscape and trends. Topics such as “Organizational Capability”, “AI Applications”, “Data Mining”, “Big Data Analytics”, and “Visualization” have recently gained popularity. A systematic taxonomic map was also created, revealing the research background and BI perspectives based on the topics. This study may be useful to researchers and practitioners interested in learning about the most recent developments in the field. Topics generated by topic modeling can also be used to identify gaps in current research or potential future research directions.

Suggested Citation

  • Fatih Gurcan & Ahmet Ayaz & Gonca Gokce Menekse Dalveren & Mohammad Derawi, 2023. "Business Intelligence Strategies, Best Practices, and Latest Trends: Analysis of Scientometric Data from 2003 to 2023 Using Machine Learning," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9854-:d:1175681
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    References listed on IDEAS

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    5. Fatih Gurcan & Gizem Dilan Boztas & Gonca Gokce Menekse Dalveren & Mohammad Derawi, 2023. "Digital Transformation Strategies, Practices, and Trends: A Large-Scale Retrospective Study Based on Machine Learning," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
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