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

dc.authoridGURCAN, Fatih/0000-0001-9915-6686
dc.authoridMenekse Dalveren, Gonca Gokce/0000-0002-8649-1909
dc.authoridAyaz, Ahmet/0000-0003-1405-0546
dc.authorscopusid57194776706
dc.authorscopusid57338589200
dc.authorscopusid57201658878
dc.authorscopusid35408917600
dc.authorwosidGURCAN, Fatih/AAJ-7503-2021
dc.authorwosidMenekse Dalveren, Gonca Gokce/HHS-4591-2022
dc.authorwosidAyaz, Ahmet/JBJ-2146-2023
dc.contributor.authorDalveren, Gonca Gökçe Menekşe
dc.contributor.authorAyaz, Ahmet
dc.contributor.authorDalveren, Gonca Gokce Menekse
dc.contributor.authorDerawi, Mohammad
dc.contributor.otherInformation Systems Engineering
dc.date.accessioned2024-07-05T15:22:35Z
dc.date.available2024-07-05T15:22:35Z
dc.date.issued2023
dc.departmentAtılım Universityen_US
dc.department-temp[Gurcan, Fatih] Karadeniz Tech Univ, Fac Econ & Adm Sci, Dept Management Informat Syst, TR-61080 Trabzon, Turkiye; [Ayaz, Ahmet] Karadeniz Tech Univ, Digital Transformat Off, TR-61080 Trabzon, Turkiye; [Dalveren, Gonca Gokce Menekse] Atilim Univ, Fac Engn, Dept Software Engn, TR-06830 Ankara, Turkiye; [Derawi, Mohammad] Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Dept Elect Syst, N-7034 Gjovik, Norwayen_US
dc.descriptionGURCAN, Fatih/0000-0001-9915-6686; Menekse Dalveren, Gonca Gokce/0000-0002-8649-1909; Ayaz, Ahmet/0000-0003-1405-0546en_US
dc.description.abstractThe 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.en_US
dc.identifier.citation3
dc.identifier.doi10.3390/su15139854
dc.identifier.issn2071-1050
dc.identifier.issue13en_US
dc.identifier.scopus2-s2.0-85164913188
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/su15139854
dc.identifier.urihttps://hdl.handle.net/20.500.14411/2220
dc.identifier.volume15en_US
dc.identifier.wosWOS:001028127500001
dc.identifier.wosqualityQ2
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectbusiness intelligenceen_US
dc.subjecttopic modelingen_US
dc.subjecttext miningen_US
dc.subjecttrend analysisen_US
dc.subjectmachine learningen_US
dc.titleBusiness Intelligence Strategies, Best Practices, and Latest Trends: Analysis of Scientometric Data from 2003 to 2023 Using Machine Learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
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