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Author: Gurcan, FatihHas files: true

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    Article
    Citation - WoS: 27
    Citation - Scopus: 43
    Digital Transformation Strategies, Practices, and Trends: a Large-Scale Retrospective Study Based on Machine Learning
    (Mdpi, 2023) Gurcan, Fatih; Boztas, Gizem Dilan; Dalveren, Gonca Gokce Menekse; Derawi, Mohammad
    The purpose of this research is to identify the areas of interest, research topics, and application areas that reflect the research nature of digital transformation (DT), as well as the strategies, practices, and trends of DT. To accomplish this, the Latent Dirichlet allocation algorithm, a probabilistic topic modeling technique, was applied to 5350 peer-reviewed journal articles on DT published in the last ten years, from 2013 to 2022. The analysis resulted in the discovery of 34 topics. These topics were classified, and a systematic taxonomy for DT was presented, including four sub-categories: implementation, technology, process, and human. As a result of time-based trend analysis, "Sustainable Energy", "DT in Health", "E-Government", "DT in Education", and "Supply Chain" emerged as top topics with an increasing trend. Our findings indicate that research interests are focused on specific applications of digital transformation in industrial and public settings. Based on our findings, we anticipate that the next phase of DT research and practice will concentrate on specific DT applications in government, health, education, and economics. "Sustainable Energy" and "Supply Chain" have been identified as the most prominent topics in current DT processes and applications. This study can help researchers and practitioners in the field by providing insights and implications about the evolution and applications of DT. Our findings are intended to serve as a guide for DT in understanding current research gaps and potential future research topics.
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    Article
    Citation - WoS: 9
    Citation - Scopus: 20
    Business Intelligence Strategies, Best Practices, and Latest Trends: Analysis of Scientometric Data From 2003 To 2023 Using Machine Learning
    (Mdpi, 2023) Gurcan, Fatih; Ayaz, Ahmet; Dalveren, Gonca Gokce Menekse; Derawi, Mohammad
    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.