Project Management: A Bibliometric Analysis of Publication Patterns and Research Frontiers

Authors

  • Loso Judijanto IPOSS Jakarta, Indonesia
  • Rini Fitrianingrum

DOI:

https://doi.org/10.58812/wsbm.v4i01.2768

Keywords:

Project Management, Bibliometric Analysis, Research Trends, VOSviewer

Abstract

This study aims to map the intellectual structure, publication patterns, and emerging research frontiers in project management through a comprehensive bibliometric analysis. Data were collected from the Scopus database and analyzed using VOSviewer to examine co-authorship networks, keyword co-occurrence, and thematic evolution. The results reveal that project management research is anchored by core themes such as decision making, information management, and risk assessment, which form a well-established knowledge base. At the same time, the field is experiencing significant transformation driven by the integration of emerging technologies, particularly artificial intelligence and machine learning, as well as a growing emphasis on sustainability and climate-related issues. The analysis also identifies a distinct yet connected research stream in healthcare and clinical management, highlighting the interdisciplinary application of project management principles. Furthermore, overlay and density visualizations indicate a temporal shift toward more technology-oriented and sustainability-focused topics, reflecting the evolving priorities of both academia and practice. This study contributes to the literature by providing a systematic overview of the development and current trends in project management research, offering insights into future research directions and potential areas for cross-disciplinary integration.

References

[1] M. Hueskes, K. Verhoest, and T. Block, “Governing public–private partnerships for sustainability: An analysis of procurement and governance practices of PPP infrastructure projects,” Int. J. Proj. Manag., vol. 35, no. 6, pp. 1184–1195, 2017.

[2] D. Grimsey and M. K. Lewis, “Evaluating the risks of public private partnerships for infrastructure projects,” Int. J. Proj. Manag., vol. 20, no. 2, pp. 107–118, 2002.

[3] L. D. Mubarik, B. K. Iskamto, and K. N. Sakib, “Entrepreneurial Competencies and Success of SMEs in Changwon, South Korea,” J. Entrep. Proj. Manag., vol. 7, no. 8 SE-Articles, pp. 1–11, Jul. 2023, doi: 10.53819/81018102t5206.

[4] P. Salwan, A. Patankar, B. Shandilya, S. Iyengar, and M. S. Thakur, “The interplay of knowledge management, operational and dynamic capabilities in project phases,” VINE J. Inf. Knowl. Manag. Syst., vol. 53, no. 5, pp. 923–940, 2023.

[5] R. Osei-Kyei and A. P. C. Chan, “Review of studies on the Critical Success Factors for Public–Private Partnership (PPP) projects from 1990 to 2013,” Int. J. Proj. Manag., vol. 33, no. 6, pp. 1335–1346, 2015.

[6] R. Ahmed, S. Shaheen, and S. P. Philbin, “The role of big data analytics and decision-making in achieving project success,” J. Eng. Technol. Manag., vol. 65, p. 101697, 2022.

[7] P. A. Harris, R. Taylor, R. Thielke, J. Payne, N. Gonzalez, and J. G. Conde, “Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support,” J. Biomed. Inform., vol. 42, no. 2, pp. 377–381, 2009.

[8] A. McKenna et al., “The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data,” Genome Res., vol. 20, no. 9, pp. 1297–1303, 2010.

[9] Q. Wang, G. M. Garrity, J. M. Tiedje, and J. R. Cole, “Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy,” Appl. Environ. Microbiol., vol. 73, no. 16, pp. 5261–5267, 2007.

[10] M. Abadi et al., “{TensorFlow}: a system for {Large-Scale} machine learning,” in 12th USENIX symposium on operating systems design and implementation (OSDI 16), 2016, pp. 265–283.

[11] E. Cerami et al., “The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data,” Cancer Discov., vol. 2, no. 5, pp. 401–404, 2012.

[12] D. Tranfield, D. Denyer, and P. Smart, “Towards a methodology for developing evidence‐informed management knowledge by means of systematic review,” Br. J. Manag., vol. 14, no. 3, pp. 207–222, 2003.

[13] D. N. Moriasi, J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith, “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations,” Trans. ASABE, vol. 50, no. 3, pp. 885–900, 2007.

[14] R. S. Kaplan and D. P. Norton, The balanced scorecard: measures that drive performance, vol. 70. Harvard Business Review Boston, MA, USA, 2005.

[15] J. G. Arnold, R. Srinivasan, R. S. Muttiah, and J. R. Williams, “Large area hydrologic modeling and assessment part I: model development 1,” JAWRA J. Am. Water Resour. Assoc., vol. 34, no. 1, pp. 73–89, 1998.

[16] D. Tilman, C. Balzer, J. Hill, and B. L. Befort, “Global food demand and the sustainable intensification of agriculture,” Proc. Natl. Acad. Sci., vol. 108, no. 50, pp. 20260–20264, 2011.

Downloads

Published

2026-03-31

How to Cite

Project Management: A Bibliometric Analysis of Publication Patterns and Research Frontiers (L. Judijanto & R. Fitrianingrum, Trans.). (2026). West Science Business and Management, 4(01), 234~242. https://doi.org/10.58812/wsbm.v4i01.2768