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

被引:7
作者
Gurcan, Fatih [1 ]
Ayaz, Ahmet [2 ]
Dalveren, Gonca Gokce Menekse [3 ]
Derawi, Mohammad [4 ]
机构
[1] Karadeniz Tech Univ, Fac Econ & Adm Sci, Dept Management Informat Syst, TR-61080 Trabzon, Turkiye
[2] Karadeniz Tech Univ, Digital Transformat Off, TR-61080 Trabzon, Turkiye
[3] Atilim Univ, Fac Engn, Dept Software Engn, TR-06830 Ankara, Turkiye
[4] Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Dept Elect Syst, N-7034 Gjovik, Norway
关键词
business intelligence; topic modeling; text mining; trend analysis; machine learning; SEMANTIC CONTENT-ANALYSIS; BIG DATA ANALYTICS; EXPLORATORY ANALYSIS; SYSTEMS; MANAGEMENT; INTERESTS; EVOLUTION; INTENTION; IMPACT;
D O I
10.3390/su15139854
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
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页数:23
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