Bibliometric Analysis to Understand Research Trends in Data-Driven Economics - Judijanto, Loso; Muni, Komang Shanty; Amir, Firlie Lanovia (West Science Journal Economic and Entrepreneurship, 2024)
The bibliometric analysis conducted in this study offers a nuanced understanding of the evolution and current state of research within data-driven domains, highlighting "data" as a central theme interconnected with diverse fields like business, smart cities, and machine learning. This research provides valuable insights into the dominant trends, collaboration patterns, and thematic priorities that shape the data-driven research landscape, underscoring the critical role of data in advancing both theoretical knowledge and practical applications across various sectors. The findings suggest significant opportunities for enhancing data analytics capabilities, developing targeted educational programs, and fostering interdisciplinary collaborations that can bridge existing gaps in the literature. However, the study also notes limitations inherent in bibliometric analyses, including potential biases toward more frequently cited or recent publications and the exclusion of works outside selected databases. Overall, this analysis not only reflects the dynamic and impactful nature of data-driven research but also guides future academic and practical endeavors in the field.