Деректерге негізделген жобаларды басқарудағы белгісіздікті төмендету: библиометриялық талдау

цифровая экономика, экономика управления, стратегическое управление проектами, эффективность проектов, интеллектуальная эволюция, искусственный интеллект, анализ данных, библиометрический анализ

1. Adebayo, Y., Udoh, P., Kamudyariwa, X. B., & Osobajo, O. A. (2025). Artificial Intelligence in Construction Project Management: A Structured Literature Review of Its Evolution in Application and Future Trends. Digital, 5(3), 26. https://doi.org/10.3390/digital5030026

2. Adeniran, I., Agu, E., Efunniyi, C., Osundare, O., & Iriogbe, H. (2024). The future of project management in the digital age: Trends, challenges, and opportunities. Engineering Science & Technology Journal, 5(8), 2632–2648. https://doi.org/10.51594/estj.v5i8.1516

3. Ajirotutu, R., Garba, B., & Johnson, S. (2024). AI-driven risk mitigation: Transforming project management in construction and infrastructure development. World Journal of Advanced Engineering Technology and Sciences, 13, 611–623. https://doi.org/10.30574/wjaets.2024.13.2.0628

4. Akhavian, R., & Behzadan, A. H. (2016). Smartphone-based construction workers’ activity recognition and classification. Automation in Construction, 71, 198–209. https://doi.org/https://doi.org/10.1016/j.autcon.2016.08.015

5. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/https://doi.org/10.1016/j.joi.2017.08.007

6. Bühler, M. M., Nübel, K., Jelinek, T., Köhler, L., & Hollenbach, P. (2025). Bridging the Construction Productivity Gap-A Hierarchical Framework for the Age of Automation, Robotics, and AI. Buildings, 15(16), 2899. https://doi.org/10.3390/buildings15162899

7. Cabeças, A., & Marques da Silva, M. (2021). Project Management in the Fourth Industrial Revolution. TECHNO REVIEW. International Technology, Science and Society Review, 9, 79-96. https://doi.org/10.37467/gka-revtechno.v9.2804

8. Chou, S. (2008). Uncertainty management in a process environment. Journal of the Chinese Institute of Engineers, 31(4), 625–638. https://doi.org/10.1080/02533839.2008.9671416

9. Costantino, F., Di Gravio, G., & Nonino, F. (2015). Project selection in project portfolio management: An artificial neural network model based on critical success factors. International Journal of Project Management, 33(8), 1744–1754. https://doi.org/https://doi.org/10.1016/j.ijproman.2015.07.003

10. Cucos, S., & Turcan, R. (2025). Role of the construction industry in economic growth and sustainable development. Journal of Social Sciences, 8(1), 25–38. https://doi.org/10.52326/jss.utm.2025.8(1).02

11. David, A., & Thaveeporn, P. (2005). Predicting the Outcome of Construction Litigation Using Boosted Decision Trees. Journal of Computing in Civil Engineering, 19(4), 387–393. https://doi.org/10.1061/(ASCE)0887-3801(2005)19:4(387)

12. De Meyer, A., Loch, C., & Pich, M. (2002). Managing Project Uncertainty: From Variation to Chaos. Engineering Management Review, IEEE, 30(3), 91. https://doi.org/10.1109/EMR.2002.1032403

13. Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal, 32(4), 869-896. https://doi.org/10.1108/TQM-10-2019-0243

14. El Khatib, M., & Falasi, A. (2021). Effects of Artificial Intelligence on Decision Making in Project Management. American Journal of Industrial and Business Management, 11(3), 251–260. https://doi.org/10.4236/ajibm.2021.113016

15. El Khatib, M., Zitar, R., Alnaqbi, K., Alnaqbi, W., Alharmoodi, S., & Baydoun, A. (2023). Effect of Big Data and Analytics on Managing Projects. International Journal of Computers and Their Applications, 30(2), 173–182.

16. Daraojimba, E., Nwasike, C., Adegbite, A., Ezeigweneme, C., & Gidiagba. J. (2024). Comprehensive review of agile methodologies in project management. Computer Science & IT Research Journal, 5(1), 190–218. https://doi.org/10.51594/csitrj.v5i1.717

17. Faghihi, V., Nejat, A., Reinschmidt, K. F., & Kang, J. H. (2015). Automation in construction scheduling: a review of the literature. The International Journal of Advanced Manufacturing Technology, 81(9), 1845–1856. https://doi.org/10.1007/s00170-015-7339-0

18. Hanafy, N. O., & Hanafy, N. O. (2025). An Extensive Examination of Uses of Machine Learning and Artificial Intelligence in The Construction Industry’s Project Life Cycle. Energy and Buildings, 345, 116094 https://doi.org/10.1016/j.enbuild.2025.116094

19. Hashfi, M. I., & Raharjo, T. (2023). Exploring the Challenges and Impacts of Artificial Intelligence Implementation in Project Management: A Systematic Literature Review. International Journal of Advanced Computer Science and Applications, 14(9). https://doi.org/10.14569/IJACSA.2023.0140940

20. Hoang, A. (2025). Evaluating Bibliometrics Reviews: A Practical Guide for Peer Review and Critical Reading. Evaluation Review, 49(6), 1074–1102. https://doi.org/10.1177/0193841X251336839

21. Hohl, P., Klünder, J., van Bennekum, A., Lockard, R., Gifford, J., Münch, J., Stupperich, M., & Schneider, K. (2018). Back to the future: origins and directions of the “Agile Manifesto” – views of the originators. Journal of Software Engineering Research and Development, 6(1), 15. https://doi.org/10.1186/s40411-018-0059-z

22. Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2020). Big Data analytics and Computational Intelligence for Cyber-Physical Systems: Recent trends and state of the art applications. Future Generation Computer Systems, 105, 766–778. https://doi.org/https://doi.org/10.1016/j.future.2017.10.021

23. Jørgensen, T., & Wallace, S. W. (2000). Improving project cost estimation by taking into account managerial flexibility. European Journal of Operational Research, 127(2), 239–251. https://doi.org/https://doi.org/10.1016/S0377-2217(99)00484-1

24. Koi-Akrofi, J., Henry Matey, A., & Koi-Akrofi, G. (2019). Understanding the Characteristics, Benefits and Challenges of Agile IT Project Management: A Literature Based Perspective. International Journal of Software Engineering & Applications, 10(5), 25–44. https://doi.org/10.5121/ijsea.2019.10502

25. Krasteva, I., & Ilieva, S. (2020). Adopting Agile Software Development Methodologies in Big Data Projects – a Systematic Literature Review of Experience Reports. 2020 IEEE International Conference on Big Data (Big Data), 2028–2033. https://doi.org/10.1109/BigData50022.2020.9378118

26. Kumar, R. (2025). Bibliometric Analysis: Comprehensive Insights into Tools, Techniques, Applications, and Solutions for Research Excellence. Spectrum of Engineering and Management Sciences, 3(1), 45–62. https://doi.org/10.31181/sems31202535k

27. Lenfle, S., & Loch, C.H. (2010). Lost Roots: How Project Management Came to Emphasize Control over Flexibility and Novelty. California Management Review, 53(1), 32–55. https://doi.org/10.1525/cmr.2010.53.1.32

28. Leong, J., Yee, K., Baitsegi, O., Palanisamy, L., & Ramasamy, R. K. (2023). Hybrid Project Management between Traditional Software Development Lifecycle and Agile Based Product Development for Future Sustainability. Sustainability, 15(2), 1121. https://doi.org/10.3390/su15021121

29. Marinho, M., Sampaio, S., & Moura, H. (2018). Managing uncertainty in software projects. Innovations in Systems and Software Engineering, 14(3), 157–181. https://doi.org/10.1007/s11334-017-0297-y

30. Marnewick, C., & Marnewick, A. L. (2022). Digitalization of project management: Opportunities in research and practice. Project Leadership and Society, 3, 100061. https://doi.org/https://doi.org/10.1016/j.plas.2022.100061

31. Martinsuo, M., Korhonen, T., & Laine, T. (2014). Identifying, framing and managing uncertainties in project portfolios. International Journal of Project Management, 32(5), 732–746. https://doi.org/10.1016/j.ijproman.2014.01.014

32. Moniruzzaman, A. B. M. (2013). Comparative Study on Agile software development methodologies. Cornell University. https://doi.org/10.48550/arXiv.1307.3356

33. Nachbagauer, A. G. M., & Schirl-Boeck, I. B. (2018). Organizational maturity beyond risk management: Successfully managing the unexpected. In Developing Organizational Maturity for Effective Project Management (pp. 78–103). IGI Global. https://doi.org/10.4018/978-1-5225-3197-5.ch005

34. Osemeike, G., Eyieyien, Eyieyien, O., Paul, P., & Ijomah, T. (2024). Strategic approaches for successful digital transformation in project management across industries. International Journal of Frontiers in Engineering and Technology Research, 7, 7. https://doi.org/10.53294/ijfetr.2024.7.1.0037

35. Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J.M., Grimshaw, J.M., Hrõbjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuinness, L.A., Stewart, L.A., Thomas, J., Tricco, A.C., Welch, V.A., Whiting, P.F., & Moher, D. (2020). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic Reviews, 10. https://doi.org/10.1186/s13643-021-01626-4

36. Pantović, V., Vidojević, D., Vujičić, S., Sofijanić, S., & Jovanović-Milenković, M. (2024). Data-Driven Decision Making for Sustainable IT Project Management Excellence. Sustainability, 16(7), 3014. https://doi.org/10.3390/su16073014

37. PMI. (2021). A Guide to the Project Management Body of Knowledge – Seventh Edition and The Standard for Project Management. Project Management Institute. Pospieszny, P., Czarnacka-Chrobot, B., & Kobylinski, A. (2018). An effective approach for software project effort and duration estimation with machine learning algorithms. Journal of Systems and Software, 137, 184–196. https://doi.org/https://doi.org/10.1016/j.jss.2017.11.066

38. Ranasinghe, U., Jefferies, M., Davis, P., & Pillay, M. (2021). Conceptualising project uncertainty in the context of building refurbishment safety: A systematic review. Buildings, 11(3), 1–15. https://doi.org/10.3390/buildings11030089

39. Salimimoghadam, S., Ghanbaripour, A. N., Tumpa, R. J., Kamel Rahimi, A., Golmoradi, M., Rashidian, S., & Skitmore, M. (2025). The Rise of Artificial Intelligence in Project Management: A Systematic Literature Review of Current Opportunities, Enablers, and Barriers. Buildings, 15(7), 1130. https://doi.org/10.3390/buildings15071130

40. Saunders, F. C., Gale, A. W., & Sherry, A. H. (2015). Conceptualising uncertainty in safety-critical projects: A practitioner perspective. International Journal of Project Management, 33(2), 467–478. https://doi.org/10.1016/j.ijproman.2014.09.002

41. Siddiqui, A., Chowdhary, H., Krishna, R., Muniyandy, E., & Maguluri, L. (2024). Harnessing Big Data: Strategic Insights for IT Management. International Journal of Advanced Computer Science and Applications, 15(7), 912. https://doi.org/10.14569/IJACSA.2024.0150790

42. Steegh, R., Van De Voorde, K., & Paauwe, J. (2025). Understanding how agile teams reach effectiveness: A systematic literature review to take stock and look forward. Human Resource Management Review, 35(1), 101056. https://doi.org/https://doi.org/10.1016/j.hrmr.2024.101056

43. Tynchenko, V., Lomazov, A., Lomazov, V., Evsyukov, D., Nelyub, V., Borodulin, A., Gantimurov, A., & Malashin, I. (2024). Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making. Big Data and Cognitive Computing, 8(11), 150. https://doi.org/10.3390/bdcc8110150

44. Vergara, D., del Bosque, A., Lampropoulos, G., & Fernández-Arias, P. (2025). Trends and Applications of Artificial Intelligence in Project Management. Electronics, 14(4), 800. https://doi.org/10.3390/electronics14040800

45. Wang, Y.-R., Yu, C.-Y., & Chan, H.-H. (2012). Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models. International Journal of Project Management, 30(4), 470–478. https://doi.org/10.1016/j.ijproman.2011.09.002

46. Wauters, M., & Vanhoucke, M. (2014). Support Vector Machine Regression for project control forecasting. Automation in Construction, 47, 92–106. https://doi.org/10.1016/j.autcon.2014.07.014

47. Wauters, M., & Vanhoucke, M. (2016). A comparative study of Artificial Intelligence methods for project duration forecasting. Expert Systems with Applications, 46, 249–261. https://doi.org/10.1016/j.eswa.2015.10.008

48. Wauters, M., & Vanhoucke, M. (2017). A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence. European Journal of Operational Research, 259(3), 1097–1111. https://doi.org/https://doi.org/10.1016/j.ejor.2016.11.018

49. Zhang, J., & El-Gohary, N. M. (2017). Integrating semantic NLP and logic reasoning into a unified system for fully-automated code checking. Automation in Construction, 73, 45–57. https://doi.org/10.1016/j.autcon.2016.08.027