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

dc.contributor.author Gurcan, Fatih
dc.contributor.author Ayaz, Ahmet
dc.contributor.author Dalveren, Gonca Gokce Menekse
dc.contributor.author Derawi, Mohammad
dc.contributor.other Information Systems Engineering
dc.date.accessioned 2024-07-05T15:22:35Z
dc.date.available 2024-07-05T15:22:35Z
dc.date.issued 2023
dc.description GURCAN, Fatih/0000-0001-9915-6686; Menekse Dalveren, Gonca Gokce/0000-0002-8649-1909; Ayaz, Ahmet/0000-0003-1405-0546 en_US
dc.description.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. en_US
dc.identifier.doi 10.3390/su15139854
dc.identifier.issn 2071-1050
dc.identifier.scopus 2-s2.0-85164913188
dc.identifier.uri https://doi.org/10.3390/su15139854
dc.identifier.uri https://hdl.handle.net/20.500.14411/2220
dc.language.iso en en_US
dc.publisher Mdpi en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject business intelligence en_US
dc.subject topic modeling en_US
dc.subject text mining en_US
dc.subject trend analysis en_US
dc.subject machine learning en_US
dc.title Business Intelligence Strategies, Best Practices, and Latest Trends: Analysis of Scientometric Data From 2003 To 2023 Using Machine Learning en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.id GURCAN, Fatih/0000-0001-9915-6686
gdc.author.id Menekse Dalveren, Gonca Gokce/0000-0002-8649-1909
gdc.author.id Ayaz, Ahmet/0000-0003-1405-0546
gdc.author.institutional Dalveren, Gonca Gökçe Menekşe
gdc.author.scopusid 57194776706
gdc.author.scopusid 57338589200
gdc.author.scopusid 57201658878
gdc.author.scopusid 35408917600
gdc.author.wosid GURCAN, Fatih/AAJ-7503-2021
gdc.author.wosid Menekse Dalveren, Gonca Gokce/HHS-4591-2022
gdc.author.wosid Ayaz, Ahmet/JBJ-2146-2023
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.description.department Atılım University en_US
gdc.description.departmenttemp [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, Norway en_US
gdc.description.issue 13 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q2
gdc.description.volume 15 en_US
gdc.description.wosquality Q2
gdc.identifier.wos WOS:001028127500001
gdc.scopus.citedcount 16
gdc.wos.citedcount 7
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