Bibliometric Analysis to Understand Research Trends in Data-Driven Economics

Authors

  • Loso Judijanto IPOSS Jakarta, Indonesia
  • Komang Shanty Muni IPBI
  • Firlie Lanovia Amir Institut Pariwisata dan Bisnis Internasional

DOI:

https://doi.org/10.58812/wsjee.v2i03.1198

Keywords:

Data-Driven Economics, Bibliometric Analysis, VOSviewer

Abstract

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.

References

C. I. Michael et al., “Data-driven decision making in IT: Leveraging AI and data science for business intelligence,” World J. Adv. Res. Rev., vol. 23, no. 1, pp. 472–480, 2024.

O. Abdul-Azeez, A. O. Ihechere, and C. Idemudia, “Enhancing business performance: The role of data-driven analytics in strategic decision-making,” Int. J. Manag. Entrep. Res., vol. 6, no. 7, pp. 2066–2081, 2024.

O. T. Joel and V. U. Oguanobi, “Data-driven strategies for business expansion: Utilizing predictive analytics for enhanced profitability and opportunity identification,” Int. J. Front. Eng. Technol. Res., vol. 6, no. 02, pp. 71–81, 2024.

A. Pabedinskaitė, V. Davidavičienė, and P. Milišauskas, “Big data driven e-commerce marketing,” 2014.

B. De Langhe and S. Puntoni, Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data. University of Pennsylvania Press, 2024.

A. Kusiak, “Innovation: A data-driven approach,” Int. J. Prod. Econ., vol. 122, no. 1, pp. 440–448, 2009.

O. Troisi and G. Maione, “Data-Driven Decision Making: Empowering Businesses through Advanced Analytics and Machine Learning,” J. Environ. Sci. Technol., vol. 3, no. 1, pp. 515–525, 2024.

N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” J. Bus. Res., vol. 133, pp. 285–296, 2021.

L. Einav and J. Levin, “Economics in the age of big data,” Science (80-. )., vol. 346, no. 6210, p. 1243089, 2014.

S. Mullainathan and J. Spiess, “Machine learning: an applied econometric approach,” J. Econ. Perspect., vol. 31, no. 2, pp. 87–106, 2017.

H. R. Varian, “Big data: New tricks for econometrics,” J. Econ. Perspect., vol. 28, no. 2, pp. 3–28, 2014.

D. Bholat, “Big data and central banks,” Big Data Soc., vol. 2, no. 1, p. 2053951715579469, 2015.

D. Zhao and A. Strotmann, “The knowledge base and research front of information science 2006–2010: An author cocitation and bibliographic coupling analysis,” J. Assoc. Inf. Sci. Technol., vol. 65, no. 5, pp. 995–1006, 2014.

L. Marinelli, “Tempo di bilanci. Dalla" lingua e letteratura polacca" agli" studi polacchi" e oltre,” Eur. Orient., vol. 39, pp. 9–22, 2020.

S. L. Brunton and J. N. Kutz, Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press, 2022.

F. Provost and T. Fawcett, “Data science and its relationship to big data and data-driven decision making,” Big data, vol. 1, no. 1, pp. 51–59, 2013.

J. Zhang, F.-Y. Wang, K. Wang, W.-H. Lin, X. Xu, and C. Chen, “Data-driven intelligent transportation systems: A survey,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 4, pp. 1624–1639, 2011.

T. Johns, Should you be persuaded: Two samples of data-driven learning materials, vol. 4. na, 1991.

J. N. Kutz, S. L. Brunton, B. W. Brunton, and J. L. Proctor, Dynamic mode decomposition: data-driven modeling of complex systems. SIAM, 2016.

P. Mohajerin Esfahani and D. Kuhn, “Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations,” Math. Program., vol. 171, no. 1, pp. 115–166, 2018.

S. Yin, S. X. Ding, X. Xie, and H. Luo, “A review on basic data-driven approaches for industrial process monitoring,” IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6418–6428, 2014.

K. Amasyali and N. M. El-Gohary, “A review of data-driven building energy consumption prediction studies,” Renew. Sustain. Energy Rev., vol. 81, pp. 1192–1205, 2018.

S. J. Qin, “Survey on data-driven industrial process monitoring and diagnosis,” Annu. Rev. Control, vol. 36, no. 2, pp. 220–234, 2012.

E. Brynjolfsson, L. M. Hitt, and H. H. Kim, “Strength in numbers: How does data-driven decisionmaking affect firm performance?,” Available SSRN 1819486, 2011.

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Published

2024-08-30

How to Cite

Judijanto, L., Muni, K. S., & Amir, F. L. (2024). Bibliometric Analysis to Understand Research Trends in Data-Driven Economics . West Science Journal Economic and Entrepreneurship, 2(03), 372–382. https://doi.org/10.58812/wsjee.v2i03.1198