A Systematic Review of The Literature on the TechnologyIts ‎Application and Integration Needs

  • Authors

    • Zulkieflimansyah Sumbawa University of Technology, Innovation Management Study Programme, Sumbawa, West Nusa Tenggara, Indonesia
    • Muammar Khadafie Sumbawa University of Technology, Communication Sciences study Programme, Sumbawa, West Nusa Tenggara, Indonesia
    • Dzulfikar Ahmad Furqon Sumbawa University of Technology, Entrepreneurship Study Programme, Maras Sumbawa, West Nusa Tenggara, Indonesia
    https://doi.org/10.14419/4t6x0421

    Received date: July 6, 2025

    Accepted date: August 11, 2025

    Published date: August 25, 2025

  • Artificial Intelligence; Open Innovation; Technology; Application; Integration

  • Abstract

    This research seeks to offer valuable Understanding to help organizations develop optimal approaches to integrating Artificial Intelligence ‎‎(AI) and enhancing project coordination procedures, with a particular focus on collaborative innovation initiatives. It utilizes a thorough and ‎structured review of existing literature, selecting 365 scholarly articles from an initial set of 1,265 scholarly works sourced from the academ-‎ic databases such as IEEE and Scopus. The investigation creates a structure for synthesizing the previous studies based on five investigative ‎questions, which cover artificial intelligence systems, project coordination activities, sectors implementing AI, and the conditions required to ‎ensure effective adoption. The examination shows that automated learning systems is commonly used in project coordination, particularly ‎for predictive data analysis, resource optimization, and risk management. AI enhances managing open innovation projects through the inte-‎gration of varied knowledge bases, fostering cooperation, and delivering planned decision-making understandings. Additionally, the study ‎finds that adoption of AI relies not limited to technical aspects factors such as framework, system combination and information prepared-‎ness as well as on leadership backing and strategic coordination, funding, skill growth and company culture. The results emphasize the need ‎to align AI efforts coupled with the specific demands of open innovation, characterized by collaboration, flexibility, and outside expertise ‎integration play key roles. The construction industry is leading the way in AI adoption. This research addresses a critical gap by determining ‎both the technological and non-technological conditions necessary for efficiently embedding integrating AI within open innovation project ‎management methods.

  • References

    1. Aktürk, C. (2021). Artifıcial intelligence in enterprise resource planning systems: A bibliometric study. Journal of International Logistics and Trade, 19(2), 69–82. https://doi.org/10.24006/jilt.2021.19.2.069.
    2. Alasadi, S., & Fatla, O. (2023). Utilizing Artificial Neural Networks for Predicting Delays and Assessing Contractor Performance in Roadworks Projects. https://doi.org/10.1109/ISNCC58260.2023.10323855.
    3. Ali, Z. H., Burhan, A. M., Kassim, M., & Al-Khafaji, Z. (2022). Developing an Integrative Data Intelligence Model for Construction Cost Estimation. Complexity, 2022. https://doi.org/10.1155/2022/4285328.
    4. Almahameed, B., & Bisharah, M. (2023). Applying Machine Learning and Particle Swarm Optimization for predictive modeling and cost optimization in construction project management. Asian Journal of Civil Engineering, 25. https://doi.org/10.1007/s42107-023-00843-7
    5. Angara, J., Prasad, S., & Sridevi, G. (2020). DevOPs project management tools for sprint planning, estimation and execution maturity. Cybernetics and Information Technologies, 20(2), 79–92. https://doi.org/10.2478/cait-2020-0018.
    6. Arsic, S., Mihic, M., Petrović, D., & Mitrovic, Z. (2022). Data driven analysis of lifecycle stages in Serbian SMEs. https://doi.org/10.1109/ICECCME55909.2022.9987806.
    7. Assefa, Y., Berhanu, F., Tilahun, A., & Alemneh, E. (2022). Software Effort Estimation using Machine learning Algorithm. https://doi.org/10.1109/ICT4DA56482.2022.9971209.
    8. Avazov, A. (2022). Centrale Lille Institute. https://centralelille.fr/
    9. B. Bharathi, P. Shareefa, P. Uma Maheshwari, B. Lahari, A. David Donald, & T. Aditya Sai Srinivas. (2023). Exploring the Possibilities: Reinforcement Learning and AI Innovation. International Journal of Advanced Research in Science, Communication and Technology, March, 356–362. https://doi.org/10.48175/IJARSCT-8837.
    10. Baharom, M., Rahman, M., Sabudin, A., & Nor, M. (2022). Decision Support Tools: Machine Learning Application in Smart Planner (pp. 753–760). https://doi.org/10.1007/978-981-19-1939-8_58.
    11. Baharum, Z., Ngadiman, S., & Mustaffa, N. (2015). Modelling of uncertainty on late delivery for construction industry in environmental issues: A preliminary review. Proceedings - 8th EUROSIM Congress on Modelling and Simulation, EUROSIM 2013, September 2013, 238–243. https://doi.org/10.1109/EUROSIM.2013.115
    12. Bahoo, S., Cucculelli, M., & Qamar, D. (2023). Artificial intelligence and corporate innovation: A review and research agenda. Technological Forecasting and Social Change, 188, 122264. https://doi.org/10.1016/j.techfore.2022.122264.
    13. Bang, S., Aarvold, M. O., Hartvig, W. J., Olsson, N. O. E., & Rauzy, A. (2022). Application of Machine Learning To Limited Datasets: Prediction of Project Success. Journal of Information Technology in Construction, 27(June), 732–755. https://doi.org/10.36680/j.itcon.2022.036.
    14. Bannerman, P. L., & Scientific, T. C. (2014). Defining Project Success : A Multi-Level Framework. July 2008.
    15. Barbier, L. M., Green, J. L., & Draper, D. S. (2022). The need for open access and natural language processing. Proceedings of the National Academy of Sciences of the United States of America, 119(15). https://doi.org/10.1073/pnas.2200752119.
    16. Barcaui, A., & Monat, A. (2023). Project planning by generative artificial intelligence and human project managers: A comparative study. Project Leadership and Society, 4, 100101. https://doi.org/10.1016/j.plas.2023.100101.
    17. Barrocas, A., Silva, A. R. da, & Saraiva, J. P. (2022). Data analysis and visualization techniques for project tracking: Experiences with the ITLingo-Cloud Platform. Quality of Information and Communications Technology (QUATIC’2023), 1–19. https://doi.org/10.1007/978-3-031-43703-8_11.
    18. Berezka, V. (2018). Application of the integrated decision support system for scheduling of development projects. MATEC Web of Conferences, 251, 1–9. https://doi.org/10.1051/matecconf/201825105033
    19. Bianchi, M. J., & Amaral, D. C. (2020). A systematic review of association rules in project management: opportunities for hybrid models. Product Management & Development, 18(2), 136–144. https://doi.org/10.4322/pmd.2020.033.
    20. Bley, K., Fredriksen, S., Skjærvik, M., & Pappas, I. (2022). The Role of Organizational Culture on Artificial Intelligence Capabilities and Organizational Performance (pp. 13–24). https://doi.org/10.1007/978-3-031-15342-6_2
    21. Broekhuizen, T., Dekker, H., de Faria, P., Firk, S., Nguyen, D. K., & Sofka, W. (2023). AI for managing open innovation: Opportunities, challenges, and a research agenda. Journal of Business Research, 167(November), 114196. https://doi.org/10.1016/j.jbusres.2023.114196.
    22. Bushuyev, S., Bushuiev, D., Bushuieva, V., Bushueva, N., & Murzabekova, S. (2024). The Erosion of Competencies in Managing Innovation Projects due to the Impact of Ubiquitous Artificial Intelligence Systems. Procedia Computer Science, 231, 403–408. https://doi.org/10.1016/j.procs.2023.12.225.
    23. Cancer, V., Tominc, P., & Rozman, M. (2023). Multi-Criteria Measurement of AI Support to Project Management. IEEE Access, 11, 142816–142828. https://doi.org/10.1109/ACCESS.2023.3342276.
    24. Capone, C., Talgat, S., Hazir, O., Abdrasheva, K., & Kozhakhmetova, A. (2024). Artificial Intelligence Models for Predicting Budget Expenditures. Eurasian Journal of Economic and Business Studies, 68(1), 32–43. https://doi.org/10.47703/ejebs.v68i1.331.
    25. Chang, Y., & Liang, Y. (2023). Intelligent Risk Assessment of Ecological Agriculture Projects from a Vision of Low Carbon. Sustainability (Switzerland), 15(7), 1–21. https://doi.org/10.3390/su15075765.
    26. Chen, Z., Qin, L., Sun, P., Zhao, Z., & Guo, W. (2025). Applied Mathematics and Nonlinear Sciences Research on Investment Measurement Model of Grid Infrastructure Projects Based on Improved Discrete Gray Forecasting. 10(1), 1–14. https://doi.org/10.2478/amns-2025-0340.
    27. Cheng, M.-Y., Hoang, N.-D., & Wu, Y.-W. (2015). Cash Flow Prediction for Construction Project Using a Novel Adaptive Time-Dependent Least Squares Support Vector Machine Inference Model. Journal of Civil Engineering and Management, In Press. https://doi.org/10.3846/13923730.2014.893906.
    28. Cheng, Z., Wu, Y., Li, C., & Liu, Z. (2023). Research on Project Management Systems and Key Technologies for High-Speed Railway Technical Renovation and Overhaul. https://doi.org/10.1109/BDAI59165.2023.10256833.
    29. Chou, J.-S., Cheng, M.-Y., Wu, Y.-W., & Pham, A.-D. (2014). Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification. Expert Systems with Applications, 41, 3955–3964. https://doi.org/10.1016/j.eswa.2013.12.035.
    30. Coelho, F. D., Reis, R. Q., & De Souza, C. R. B. (2019). A genetic algorithm for human resource allocation in software projects. Proceedings - 2019 45th Latin American Computing Conference, CLEI 2019, September 2019. https://doi.org/10.1109/CLEI47609.2019.235055.
    31. Corrales-Garay, D., Rodriguez-Sanchez, J. L., & Montero-Navarro, A. (2024). Co-Creating Value With Artificial Intelligence: A Bibliometric Approach to the Use of AI in Open Innovation Ecosystems. IEEE Access, 12(April), 56860–56871. https://doi.org/10.1109/ACCESS.2024.3391054.
    32. Davahli, M. R. (2020). The Last State of Artificial Intelligence in Project Management. 1–27.
    33. Deepa, G., Niranjana, A. J., & Balu, A. S. (2023). A hybrid machine learning approach for early cost estimation of pile foundations. Journal of Engineering, Design and Technology, 23. https://doi.org/10.1108/JEDT-03-2023-0097.
    34. Deshpande, M. (n.d.). Journal of Artificial Intelligence & Cloud Computing The AI-powered PM Toolkit - A Project Manager ’ s Guide to Thrive the Generative AI Wave. 2(4), 1–5.
    35. Devarajan, J., Arunmozhi, M., & Sreedharan, V. R. (2021). Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics. IEEE Transactions on Engineering Management, PP. https://doi.org/10.1109/TEM.2021.3076603.
    36. Diker, G., Frühbauer, H., & Bisso Bi Mba, E. M. (2021). Development of a Digital ESP Performance Monitoring System Based on Artificial Intelligence. https://doi.org/10.2118/207929-MS.
    37. Dritsas, E., Fazakis, N., Kocsis, O., Moustakas, K., & Fakotakis, N. (2021). Optimal Team Pairing of Elder Office Employees with Machine Learning on Synthetic Data. https://doi.org/10.1109/IISA52424.2021.9555511.
    38. Du, J., Leten, B., & Vanhaverbeke, W. (2014). Managing open innovation projects with science-based and market-based partners. Research Policy, 43(5), 828–840. https://doi.org/10.1016/j.respol.2013.12.008
    39. Elghaish, F., Matarneh, S. T., Talebi, S., Abu-Samra, S., Salimi, G., & Rausch, C. (2022). Deep learning for detecting distresses in buildings and pavements: a critical gap analysis. In Construction Innovation (Vol. 22, Issue 3). https://doi.org/10.1108/CI-09-2021-0171.
    40. Elkholosy, H., Ead, R., Hammad, A., & AbouRizk, S. (2024). Data mining for forecasting labor resource requirements: a case study of project management staffing requirements. International Journal of Construction Management, 24(5), 561–572. https://doi.org/10.1080/15623599.2022.2112898
    41. F. Fahmy, A., Mohamed, H., & Hassan Yousef, A. (2017). A data mining experimentation framework to improve six sigma projects. https://doi.org/10.1109/ICENCO.2017.8289795.
    42. Fan, X., Lv, S., Xia, C., Ge, D., Liu, C., & Lu, W. (2024). Strength prediction of asphalt mixture under interactive conditions based on BPNN and SVM. Case Studies in Construction Materials, 21, e03489. https://doi.org/10.1016/j.cscm.2024.e03489.
    43. Farhan, N. M., & Setiaji, B. (2023). Indonesian Journal of Computer Science. Indonesian Journal of Computer Science, 12(2), 284–301. http://ijcs.stmikindonesia.ac.id/ijcs/index.php/ijcs/article/view/3135.
    44. Farih, N. E., Nafil, K., & El Messousi, R. (2021). Effort Estimation in Agile Software Development: A Systematic Mapping Study. Frontiers in Artificial Intelligence and Applications, 337, 224–234. https://doi.org/10.3233/FAIA210022.
    45. Ferrer-rosell, B. (2023). Springer Proceedings in Business and Economics : Information and Communication Technologies in Tourism. In Encyclopedia of Tourism Management and Marketing. https://doi.org/10.1007/978-3-031-25752-0.
    46. Fotso, G., Pradhan, A., & Sukdeo, N. (2022). Importance of Artificial Intelligence in Technology Project Management. https://doi.org/10.46254/AU01.20220302.
    47. Fridgeirsson, T. V., Ingason, H. T., Jonasson, H. I., & Jonsdottir, H. (2021). An authoritative study on the near future effect of artificial intelligence on project management knowledge areas. Sustainability (Switzerland), 13(4), 1–20. https://doi.org/10.3390/su13042345.
    48. Garg, P. (2015). Effort Estimation with Neural Network Back Propagation. 3(10), 1–3.
    49. Geng, S., Huang, M., & Wang, Z. (2020). A Swarm Enhanced Light Gradient Boosting Machine for Crowdfunding Project Outcome Prediction (pp. 372–382). https://doi.org/10.1007/978-3-030-62463-7_34.
    50. Ghai, S., Lakhanpal, S., Ramola, B., R, R., Al-Taee, M., & Alazzam, M. (2023). Natural Language Processing in Solving Resource Constrained Project Scheduling Problems. https://doi.org/10.1109/ICACITE57410.2023.10182623.
    51. Golab, A., Sedgh Gooya, E., Alfalou, A., & Cabon, M. (2022). Investigating the performance of an artificial neural network for solving the resource constrained project scheduling problem (RCPSP). https://doi.org/10.1117/12.2618499.
    52. Gondia, A., Ezzeldin, M., & El-Dakhakhni, W. (2023). Machine Learning-based Construction Site Dynamic Risk Models. Technological Forecasting and Social Change, 189. https://doi.org/10.1016/j.techfore.2023.122347.
    53. González Moreno, J. J., Mesa Fernández, J. M., Morán Palacios, H., & Fernández Iglesias, A. (2022). Application of Artificial Intelligence Techniques To the Management of Innovation Projects. Proceedings from the International Congress on Project Management and Engineering, 2022-July, 173–187.
    54. Grosan, C. (2011). Intelligent Systems: A Modern Approach. https://doi.org/10.1007/978-3-642-21004-4.
    55. Hammad, M. (2023). Software Cost Estimation using Stacked Ensemble Classifier and Feature Selection. International Journal of Advanced Computer Science and Applications, 14(6), 183–189. https://doi.org/10.14569/IJACSA.2023.0140621.
    56. Hamza, H., Kamel, A., & Shams, K. (2013). Software Effort Estimation Using Artificial Neural Networks: A Survey of the Current Practices. In Proceedings of the 2013 10th International Conference on Information Technology: New Generations, ITNG 2013. https://doi.org/10.1109/ITNG.2013.111.
    57. Haris, M., Chua, F.-F., & Lim, A. (2023). An Ensemble-Based Framework to Estimate Software Project Effort. https://doi.org/10.1109/ICSECS58457.2023.10256337.
    58. Hassan, F. ul, Nguyen, T., Le, T., & Le, C. (2023). Automated prioritization of construction project requirements using machine learning and fuzzy Failure Mode and Effects Analysis (FMEA). Automation in Construction, 154(June). https://doi.org/10.1016/j.autcon.2023.105013
    59. HASSANI, R. (2019). Proposal of a framework and integration of artificial intelligence to succeed IT project planning. International Journal of Advanced Trends in Computer Science and Engineering, 8, 3396–3404. https://doi.org/10.30534/ijatcse/2019/114862019.
    60. Hefny, A. H., Dafoulas, G. A., & Ismail, M. A. (2021). A Proactive Management Assistant Chatbot for Software Engineering Teams: Prototype and Preliminary Evaluation. NILES 2021 - 3rd Novel Intelligent and Leading Emerging Sciences Conference, Proceedings, November, 295–300. https://doi.org/10.1109/NILES53778.2021.9600547.
    61. Heidari, M., Ding, L., Kheshti, M., Bao, W., Zhao, X., Popov, M., & Terzija, V. (2024). A review on application of machine learning-based methods for power system inertia monitoring. International Journal of Electrical Power & Energy Systems, 162, 110279. https://doi.org/10.1016/j.ijepes.2024.110279.
    62. Hsu, C., Kuo, H. A., Chien, J. C., Fu, W., Ma, K. T., & Chien, C. F. (2019). A machine learning based intelligent agent for human resource planning in IC design service industry. Proceedings of the International Conference on Industrial Engineering and Operations Management, 2019(MAR), 3758–3768.
    63. Huang, Y., Leng, C., & Zhan, L. (2021). Application of AI technology in management engineering. https://doi.org/10.1109/ICAICE54393.2021.00024.
    64. Hudaib, A., & Alhaj, F. (2019). Self-Organizing Maps for Agile Requirements Prioritization. https://doi.org/10.1109/ICTCS.2019.8923075
    65. Ikuabe, M., Aigbavboa, C., Oke, A., Thwala, W., & Balogun, J. (2023). Drivers of Machine Learning Applications in the Construction Industry of Developing Economies (pp. 343–350). https://doi.org/10.1007/978-3-031-35399-4_26.
    66. Insa-Iglesias, M., Jenkins, M. D., & Morison, G. (2021). 3D visual inspection system framework for structural condition monitoring and analysis. Automation in Construction, 128. https://doi.org/10.1016/j.autcon.2021.103755.
    67. Isreal, O. (2025). The Rise of AutoML in Predictive Analytics. May.
    68. Jallow, H., Renukappa, S., & Suresh, S. (2020). Knowledge Management and Artificial Intelligence (AI). Proceedings of the European Conference on Knowledge Management, ECKM, 2020-Decem(Eckm), 363–369. https://doi.org/10.34190/EKM.20.197.
    69. Jang, H.-C., & Wu, S.-C. (2022). Tracking of Hardware Development Schedule based on Software Effort Estimation. https://doi.org/10.1109/IEMCON56893.2022.9946524
    70. Jang, H. (2022). Predicting funded research project performance based on machine learning. Research Evaluation, 31, 257–270. https://doi.org/10.1093/reseval/rvac005
    71. Jayameena Desikan, & A. Jayanthila Devi. (2023). AI and ML-based Assessment to Reduce Risk in Oil and Gas Retail Filling Station: A Literature Review. Journal of Information Technology and Digital World, 4(4), 299–316. https://doi.org/10.36548/jitdw.2022.4.005.
    72. Jha, M. K., Wanko, N., & Bachu, A. K. (2023). A Machine Learning-Based Active Learning Framework to Capture Risk and Uncertainty in Transportation and Construction Scheduling. Lecture Notes in Civil Engineering, 347 LNCE(September), 167–178. https://doi.org/10.1007/978-981-99-2556-8_13.
    73. Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors. Business and Information Systems Engineering, 63(1), 5–20. https://doi.org/10.1007/s12599-020-00676-7
    74. Just, J. (2024). Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary. Technovation, 129, 102883. https://doi.org/10.1016/j.technovation.2023.102883.
    75. Kar, S., Roy, C., Das, M., Mullick, S., & Saha, R. (2023). AI Horizons: Unveiling the Future of Generative Intelligence. International Journal of Advanced Research in Science, Communication and Technology, 387–391. https://doi.org/10.48175/IJARSCT-12969.
    76. Karamthulla, M. J., & Anish Tadimarri, Ravish Tillu, M. M. (2024). Navigating the Future: AI-Driven Project Management in the Digital Era. International Journal For Multidisciplinary Research, 6(2). https://doi.org/10.36948/ijfmr.2024.v06i02.15295.
    77. Kharbat, F., Al-Shawakbeh, A., & Woolsey, M. L. (2020). Identifying gaps in using artificial intelligence to support students with intellectual disabilities from education and health perspectives. Aslib Journal of Information Management, ahead-of-p. https://doi.org/10.1108/AJIM-02-2020-0054
    78. Koc, K. (2023). Role of Shapley Additive Explanations and Resampling Algorithms for Contract Failure Prediction of Public-Private Partnership Projects. Journal of Management in Engineering, 39, 4023031. https://doi.org/10.1061/JMENEA.MEENG-5492.
    79. Kolbjørnsrud, V. (2019). The Promise of Artificial Intelligence. Texas Medicine, 115(10), 32–35. https://doi.org/10.7551/mitpress/12385.001.0001
    80. Kolyshkina, I., & Simoff, S. (2021). Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach. Frontiers in Big Data, 4(May). https://doi.org/10.3389/fdata.2021.660206
    81. Kühl, N., Schemmer, M., Goutier, M., & Satzger, G. (2022). Artificial intelligence and machine learning. Electronic Markets, 32(4), 2235–2244. https://doi.org/10.1007/s12525-022-00598-0
    82. Kumar, A., Devi, M. S., & Saltz, J. (2023). Bridging the Gap in AI-Driven Workflows: The Case for Domain-Specific Generative Bots. https://doi.org/10.1109/BigData59044.2023.10386894.
    83. Kuzior, A., Sira, M., & Brożek, P. (2023). Use of Artificial Intelligence in Terms of Open Innovation Process and Management. Sustainability (Switzerland), 15(9). https://doi.org/10.3390/su15097205
    84. Labd, Z., Bahassine, S., Housni, K., & Aadi, F. Z. A. (2024). Reassessing Glove Embeddings in Deep Learning: A Comparative Study with Classical ML Approaches. Procedia Computer Science, 251, 740–745. https://doi.org/10.1016/j.procs.2024.11.178
    85. Lagzi, M. D., sajadi, S. M., & Taghizadeh-Yazdi, M. (2024). A hybrid stochastic data envelopment analysis and decision tree for performance prediction in retail industry. Journal of Retailing and Consumer Services, 80, 103908. https://doi.org/10.1016/j.jretconser.2024.103908.
    86. Lam, L., Nguyen, P., Le, N., & Tran, K. (2021). The relation among organizational culture, knowledge management, and innovation capability: Its implication for open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 1–16. https://doi.org/10.3390/joitmc7010066.
    87. Li, Y. H., Huang, J. W., & Tsai, M. T. (2009). Entrepreneurial orientation and firm performance: The role of knowledge creation process. Industrial Marketing Management, 38(4), 440–449. https://doi.org/10.1016/j.indmarman.2008.02.004.
    88. Lin, Y., & Yu, Z. (2024). A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interactive Technology and Smart Education, 21, 189–213. https://doi.org/10.1108/ITSE-12-2022-0165.
    89. Lishner, I., & Shtub, A. (2022). Using an Artificial Neural Network for Improving the Prediction of Project Duration. Mathematics, 10(22). https://doi.org/10.3390/math10224189.
    90. Liu, H., Zhang, Y., Wang, S., & Zhao, H. (2024). Comprehensive evaluation of digital village development in the context of rural revitalization: A case study from Jiangxi Province of China. PLoS ONE, 19(5 May), 1–24. https://doi.org/10.1371/journal.pone.0303847
    91. Liu, L., Bingxuan, D., Yu, K., & Wei, W. (2023). Prediction of compressive strength of high-performance concrete via coupled meta-heuristic random forest regression techniques. Multiscale and Multidisciplinary Modeling, Experiments and Design, 7. https://doi.org/10.1007/s41939-023-00256-8.
    92. Liu, W. (2024). Energy Project Management with Artificial Intelligence. International Journal of Electric Power and Energy Studies, 2(2), 10–16. https://doi.org/10.62051/ijepes.v2n2.02.
    93. Lung, L. W., & Wang, Y. R. (2023). Applying Deep Learning and Single Shot Detection in Construction Site Image Recognition. Buildings, 13(4). https://doi.org/10.3390/buildings13041074.
    94. Maity, S. (2019). Identifying opportunities for artificial intelligence in the evolution of training and development practices. Journal of Management Development, 38, 651–663. https://doi.org/10.1108/JMD-03-2019-0069.
    95. Mancini, M., Mariani, C., & Manfredi, C. (2023). Nuclear decommissioning risk management adopting a comprehensive artificial intelligence framework: An applied case in an Italian site. Progress in Nuclear Energy, 158, 104589. https://doi.org/10.1016/j.pnucene.2023.104589
    96. Marchinares, A., & Rodriguez, C. (2021). Online Solution Based on Machine Learning for IT Project Management in Software Factory Companies. https://doi.org/10.1109/CICN51697.2021.9574682
    97. Mariani, C., & Mancini, M. (2023). Artificial Intelligence Adoption in Project Management: Are We Still Far from Practical Implementation? https://doi.org/10.5592/CO/SENET.2022.3
    98. Marmier, F., Cheikhrouhou, N., & Gourc, D. (2014). Improvement of the planning reliability by the integration of human skills in project risk management. Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014, 125–132. https://doi.org/10.1109/GOL.2014.6887429.
    99. Martínez-Rojas, M., Soto-Hidalgo, J. M., Marín, N., & Vila, M. A. (2018). Using Classification Techniques for Assigning Work Descriptions to Task Groups on the Basis of Construction Vocabulary. Computer-Aided Civil and Infrastructure Engineering, 33(11), 966–981. https://doi.org/10.1111/mice.12382
    100. Matos, R., Rodrigues, H., Costa, A., & Rodrigues, F. (2023). Convolutional Neural Networks and Regression Algorithms Supporting Buildings Facility Management. Buildings, 13(11). https://doi.org/10.3390/buildings13112805.
    101. Mccray, G., Purvis, R., & McCray, C. (2002). Project Management under Uncertainty: The Impact of Heuristics and Biases. Project Management Journal, 33, 49–57. https://doi.org/10.1177/875697280203300108.
    102. Megha, C. R., Madhura, A., & Sneha, Y. S. (2017). Cognitive computing and its applications. https://doi.org/10.1109/ICECDS.2017.8389625.
    103. Meharunnisa, Saqlain, M., Abid, M., Awais, M., & Stević, Ž. (2023). Analysis of Software Effort Estimation by Machine Learning Techniques. Ingenierie Des Systemes d’Information, 28(6), 1445–1457. https://doi.org/10.18280/isi.280602
    104. Mercier-Laurent, E., Owoc, M. L., & Boulanger, D. (2015). Preface. IFIP Advances in Information and Communication Technology, 469(January 2015), V–VI. https://doi.org/10.1007/978-3-319-28868-0.
    105. Merzouk, S., Gandoul, R., Marzak, A., & Sael, N. (2023). Toward new data for IT and IoT project management method prediction. Mathematical Modeling and Computing, 10(2), 557–565. https://doi.org/10.23939/mmc2023.02.557.
    106. Mills, C., Escobar-Avila, J., & Haiduc, S. (2018). Automatic traceability maintenance via machine learning classification. Proceedings - 2018 IEEE International Conference on Software Maintenance and Evolution, ICSME 2018, 369–380. https://doi.org/10.1109/ICSME.2018.00045
    107. Mohammed, B. H., Sallehuddin, H., Yadegaridehkordi, E., Safie Mohd Satar, N., Hussain, A. H. Bin, & Abdelghanymohamed, S. (2022). Nexus between Building Information Modeling and Internet of Things in the Construction Industries. Applied Sciences (Switzerland), 12(20). https://doi.org/10.3390/app122010629.
    108. Mohammed, M. Y., & Skibniewski, M. J. (2023). The Role of Generative AI in Managing Industry Projects: Transforming Industry 4.0 Into Industry 5.0 Driven Economy. Law and Business, 3(1), 27–41. https://doi.org/10.2478/law-2023-0006.
    109. Morozov, V., Kalnichenko, O., Proskurin, M., & Mezentseva, O. (2020). Investigation of Forecasting Methods of the State of Complex IT-Projects with the Use of Deep Learning Neural Networks. Advances in Intelligent Systems and Computing, 1020(November 2019), 261–280. https://doi.org/10.1007/978-3-030-26474-1_19.
    110. Nafil, K., Kobbane, A., Mohamadou, A.-B., Saidi, A., Yahya, B., & Oussama, L. (2023). Fall Detection System for Elderly People using IoT and Machine Learning technology. https://doi.org/10.1109/ICECCME57830.2023.10252183
    111. Nigar, N., Shahzad, M. K., Islam, S., Oki, O., & Lukose, J. M. (2023). A Novel Multi-Objective Evolutionary Algorithm to Address Turnover in the Software Project Scheduling Problem Based on Best Fit Skills Criterion. IEEE Access, 11, 89742–89756. https://doi.org/10.1109/ACCESS.2023.3306838.
    112. Nortje, M., & Grobbelaar, S. (2020). A Framework for the Implementation of Artificial Intelligence in Business Enterprises: A Readiness Model. https://doi.org/10.1109/ICE/ITMC49519.2020.9198436.
    113. O’brien, D., Manager, S., & Haskins, L. (2014). Who are your presenters? 1–33.
    114. Olorunshogo Benjamin Ogundipe, Azubuike Chukwudi Okwandu, & Sanni Ayinde Abdulwaheed. (2024). Optimizing construction supply chains through AI: Streamlining material procurement and logistics for project success. GSC Advanced Research and Reviews, 20(1), 147–158. https://doi.org/10.30574/gscarr.2024.20.1.0258
    115. Olukoga, T. A., & Feng, Y. (2022). Determination of miscible CO2 flooding analogue projects with machine learning. Journal of Petroleum Science and Engineering, 209, 109826. https://doi.org/10.1016/j.petrol.2021.109826
    116. Opoku, D. G. J., Perera, S., Osei-Kyei, R., Rashidi, M., Famakinwa, T., & Bamdad, K. (2022). Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review. Buildings, 12(2). https://doi.org/10.3390/buildings12020113
    117. Otero-Mateo, M., Cerezo-Narváez, A., Pastor, A., Castilla Barea, M., & Ramirez, M. (2023). Stakeholder Management in Technological Projects and the Opportunity of Artificial Intelligence. A Case Study (pp. 297–318). https://doi.org/10.1007/978-3-031-30351-7_23.
    118. Özden, M., Liu, X., Wilkinson, T., Üstün-Yavuz, M., & Morley, N. (2024). Predictive modelling of laser powder bed fusion of Fe-based nanocrystalline alloys based on experimental data using multiple linear regression analysis. Heliyon, 10, e35047. https://doi.org/10.1016/j.heliyon.2024.e35047.
    119. Ozlati, S., & Yampolskiy, R. (2017). The formalization of ai risk management and safety standards. AAAI Workshop - Technical Report, WS-17-01-, 127–131.
    120. Parr, C. S., Lemay, D. G., Owen, C. L., Woodward-Greene, M. J., & Sun, J. (2021). Multimodal AI to Improve Agriculture. IT Professional, 23(3), 53–57. https://doi.org/10.1109/MITP.2020.2986122
    121. Pathak, A., & Bansal, V. (2024). Factors Influencing the Readiness for Artificial Intelligence Adoption in Indian Insurance Organizations. IFIP Advances in Information and Communication Technology, 698 AICT, 43–55. https://doi.org/10.1007/978-3-031-50192-0_5
    122. Perera, A. D., Jayamaha, N. P., Grigg, N. P., Tunnicliffe, M., & Singh, A. (2021). The application of machine learning to consolidate critical success factors of lean six sigma. IEEE Access, 9, 112411–112424. https://doi.org/10.1109/ACCESS.2021.3103931.
    123. Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J. (2020). Industrial Artificial Intelligence in Industry 4.0 -Systematic Review, Challenges and Outlook. IEEE Access, 220121–220139. https://doi.org/10.1109/ACCESS.2020.3042874.
    124. Peretz, O., Koren, M., & Koren, O. (2024). Naive Bayes classifier – An ensemble procedure for recall and precision enrichment. Engineering Applications of Artificial Intelligence, 136, 108972. https://doi.org/10.1016/j.engappai.2024.108972
    125. Pham, H., & Han, S. (2023). Natural Language Processing with Multitask Classification for Semantic Prediction of Risk-Handling Actions in Construction Contracts. Journal of Computing in Civil Engineering, 37. https://doi.org/10.1061/JCCEE5.CPENG-5218
    126. PMI. (2011). Project Management Professional (PMP). Examination Content Outline. January.
    127. Prieto, A. J., & Alarcon, L. (2023). Using Fuzzy Inference Systems for Lean Management Strategies in Construction Project Delivery. Journal of Construction Engineering and Management, 149. https://doi.org/10.1061/JCEMD4.COENG-12922.
    128. Rahman, M. S. A., Jamaludin, N. A. A., Zainol, Z., & Sembok, T. M. T. (2021). Machine Learning Algorithm Model for Improving Business Decisions Making in Upstream Oil Gas. 2021 International Congress of Advanced Technology and Engineering, ICOTEN 2021, July. https://doi.org/10.1109/ICOTEN52080.2021.9493499
    129. Rahman, M. S. A., Jamaludin, N. A. A., Zainol, Z., & Sembok, T. M. T. (2023). The Application of Decision Tree Classification Algorithm on Decision-Making for Upstream Business. International Journal of Advanced Computer Science and Applications, 14(8), 660–667. https://doi.org/10.14569/IJACSA.2023.0140873
    130. Raj, A. (2019). A Review on Machine Learning Algorithms. International Journal for Research in Applied Science and Engineering Technology, 7, 792–796. https://doi.org/10.22214/ijraset.2019.6138.
    131. Rajadhyaksha, C., & Saini, J. (2022). Robotic Process Automation for Software Project Management. https://doi.org/10.1109/I2CT54291.2022.9823972
    132. Ramana, B., & Narsimha, G. (2022). Identification of the Ideal Team Capabilities and Predictive Success Measure for Software Projects Using Machine Learning (pp. 593–608). https://doi.org/10.1007/978-981-16-8987-1_64
    133. Rane, N. (2023). Role of ChatGPT and Similar Generative Artificial Intelligence (AI) in Construction Industry. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4598258.
    134. Ranesh, M., Samuel, S., Natchadalingam, R., & Jeyanthi, P. (2022). Information Technology (IT) Governance Framework with Artificial Neural Network and Balance Scorecard to Improve the Success Rate of Software Projects. https://doi.org/10.1109/ICECA55336.2022.10009299.
    135. Rathod, K., & Sonawane, A. (2022). Application of Artificial Intelligence in Project Planning to Solve Late and Over-Budgeted Construction Projects. https://doi.org/10.1109/ICSCDS53736.2022.9761027.
    136. Reim, W., Åström, J., & Eriksson, O. (2020). Implementation of Artificial Intelligence (AI): A Roadmap for Business Model Innovation. AI (Switzerland), 1(2), 180–191. https://doi.org/10.3390/ai1020011.
    137. Relich, M. (2016a). A computational intelligence approach to predict- ing new product success. January 2015.
    138. Relich, M. (2016b). Computational Intelligence for Estimating Cost of New Product Development. Foundations of Management, 8(1), 21–34. https://doi.org/10.1515/fman-2016-0002.
    139. Relich, M., & Nielsen, I. (2021). Estimating Production and Warranty Cost at the Early Stage of a New Product Development Project. IFAC-PapersOnLine, 54(1), 1092–1097. https://doi.org/10.1016/j.ifacol.2021.08.128.
    140. Rzevski, G., Skobelev, P., Zhilyaev, A., Lakhin, O., Mayorov, I., & Simonova, E. (2018). Ontology-Driven Multi-Agent Engine for Real Time Adaptive Scheduling. Proceedings - 2018 International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2018, November 2020, 14–22. https://doi.org/10.1109/ICCAIRO.2018.00011
    141. Salama, A. (2024). Evaluating the impact of construction delays on project duration using machine learning and multi-criteria decision analysis. Asian Journal of Civil Engineering, 26, 389–399. https://doi.org/10.1007/s42107-024-01196-5.
    142. 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), 1–32. https://doi.org/10.3390/buildings15071130.
    143. Samira, T. (2022). The Role of Culture in Managing Open Innovation Projects within Regional Clusters. European Project Management Journal, 12, 46–60. https://doi.org/10.56889/iaor2455.
    144. Sarafanov, E., Valilai, O. F., & Wicaksono, H. (2024). Causal Analysis of Artificial Intelligence Adoption in Project Management. Lecture Notes in Networks and Systems, 822(January), 245–264. https://doi.org/10.1007/978-3-031-47721-8_17
    145. Saura, J. R., Palacios-Marqués, D., & Ribeiro-Soriano, D. (2023). Exploring the boundaries of open innovation: Evidence from social media mining. Technovation, 119(December 2021), 102447. https://doi.org/10.1016/j.technovation.2021.102447
    146. Senivongse, C., Bennet, A., & Mariano, S. (2017). Utilizing a systematic literature review to develop an integrated framework for information and knowledge management systems. VINE Journal of Information and Knowledge Management Systems, 47. https://doi.org/10.1108/VJIKMS-03-2017-0011
    147. Serrano, W., & Barnett, J. (2023). Verification and Validation for a Project Information Model Based on a Blockchain (pp. 219–233). https://doi.org/10.1007/978-981-19-1610-6_19.
    148. Shaik, N. B., Jongkittinarukorn, K., & Bingi, K. (2024). XGBoost based enhanced predictive model for handling missing input parameters: A case study on gas turbine. Case Studies in Chemical and Environmental Engineering, 10, 100775. https://doi.org/10.1016/j.cscee.2024.100775.
    149. Shang, G., Low, S., & Lim, X. (2023). Prospects, drivers of and barriers to artificial intelligence adoption in project management. Built Environment Project and Asset Management, 13. https://doi.org/10.1108/BEPAM-12-2022-0195.
    150. Shetty, P., Udhayakumar, R., Patil, A., Manwal, M., Vadar, P., & Nooji, P. (2023). Application of Natural Language Processing (NLP) in Machine Learning. https://doi.org/10.1109/AECE59614.2023.10428345
    151. Shi, T., & Wu, J. (2021). Application of Artificial Intelligence in Water Conservancy Project Management. https://doi.org/10.1109/ICBASE53849.2021.00109
    152. Shu, P. G., Yeh, Y. H., Chiu, S. B., & Yang, Y. W. (2015). Board external connectedness and earnings management. Asia Pacific Management Review, 20(4), 265–274. https://doi.org/10.1016/j.apmrv.2015.03.003
    153. Sirisha Maddula, S. (2018). The Impact of AI and Reciprocal Symmetry on Organizational Culture and Leadership in the Digital Economy. Engineering International, 6(2), 201–210. https://doi.org/10.18034/ei.v6i2.703.
    154. Sousa, A., Faria, J., Moreira, J., Gomes, D., Henriques, P., & Graça, R. (2021). Applying Machine Learning to Risk Assessment in Software Projects (pp. 104–118). https://doi.org/10.1007/978-3-030-93733-1_7.
    155. Suma, V., Pushphavathi, T. P., & Ramaswamy, V. (2014). An Approach to Predict Software Project Success Based on Random Forest Classifier. Advances in Intelligent Systems and Computing, 249 VOLUME, 329–336. https://doi.org/10.1007/978-3-319-03095-1_36
    156. Sun, W., Bocchini, P., & Davison, B. D. (2020). Applications of artificial intelligence for disaster management. Natural Hazards, 103(3), 2631–2689. https://doi.org/10.1007/s11069-020-04124-3.
    157. Taboada, I., Daneshpajouh, A., Toledo, N., & de Vass, T. (2023). Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Applied Sciences (Switzerland), 13(8). https://doi.org/10.3390/app13085014
    158. Tan, X. (2021). Intelligent construction integrated management system based on BIM and Internet of Things. https://doi.org/10.1109/ICCES51350.2021.9489201
    159. Tanbour, Z., Khudarieh, D., Abuodeh, H., & Hawash, A. (2022). Forming Software Development Team: Machine-Learning Approach. 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022, October, 104–110. https://doi.org/10.1109/ICETSIS55481.2022.9888936
    160. Tao, F., Pi, Y., Deng, M., Tang, Y., & Yuan, C. (2023). Research on Intelligent Grading Evaluation of Water Conservancy Project Safety Risks Based on Deep Learning. Water (Switzerland), 15(8). https://doi.org/10.3390/w15081607
    161. Tominc, P., Oreški, D., & Rožman, M. (2023). Artificial Intelligence and Agility-Based Model for Successful Project Implementation and Company Competitiveness. Information (Switzerland), 14(6), 1–26. https://doi.org/10.3390/info14060337.
    162. Truong, Y., & Papagiannidis, S. (2022). Artificial intelligence as an enabler for innovation: A review and future research agenda. Technological Forecasting and Social Change, 183, 121852. https://doi.org/10.1016/j.techfore.2022.121852
    163. Umer, Q., Liu, H., & Sultan, Y. (2018). Emotion based automated priority prediction for bug reports. IEEE Access, 6(c), 35743–35752. https://doi.org/10.1109/ACCESS.2018.2850910.
    164. Ummah, M. S. (2019). Evolving physical infrastructure to support open innovation in the digital age: case studies from the UK. Sustainability (Switzerland), 11(1), 1–14. http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017-Eng-8ene.pdf?sequence=12&isAllowed=y%0A. Ahttps://www.researchgate.net/publication/305320484_SISTEM_PEMBETUNGAN_TERPUSAT_STRATEGI_MELESTARI.
    165. van Lieshout, J. W. F. C., van der Velden, J. M., Blomme, R. J., & Peters, P. (2021). The interrelatedness of organizational ambidexterity, dynamic capabilities and open innovation: a conceptual model towards a competitive advantage. European Journal of Management Studies, 26(2/3), 39–62. https://doi.org/10.1108/EJMS-01-2021-0007
    166. Varghese, R., Deshpande, A., Digholkar, G., & Kumar, D. (2023). Deciphering the Role of Artificial Intelligence in Health Care, Learning and Development (pp. 149–179). https://doi.org/10.1108/978-1-80455-662-720230010
    167. Viktor, M., Anna, K., & Olga, M. (2021). Development of a model for evaluating the effectiveness of innovative startups based on information cycles and using neural networks. Indonesian Journal of Electrical Engineering and Computer Science, 23(1), 396–404. https://doi.org/10.11591/ijeecs.v23.i1.pp396-404
    168. Villarroel, L., Bavota, G., Russo, B., Oliveto, R., & Di Penta, M. (2016). Release planning of mobile apps based on user reviews. https://doi.org/10.1145/2884781.2884818.
    169. Wang, J., & Qin, S. (2024). A recurrent neural network approach for nonconvex interval quadratic programming. Neurocomputing, 588, 127636. https://doi.org/10.1016/j.neucom.2024.127636.
    170. Waqar, A., Andri, Qureshi, A. H., Almujibah, H. R., Tanjung, L. E., & Utami, C. (2023). Evaluation of success factors of utilizing AI in digital transformation of health and safety management systems in modern construction projects. Ain Shams Engineering Journal, 14(11), 102551. https://doi.org/10.1016/j.asej.2023.102551
    171. Weichenhain, M., Fengler, W., & Streitferdt, D. (2019). How to Bring Project Management of Embedded Systems to an Appropriate Level of Abstraction for a Discrete Event Model. https://doi.org/10.1109/COMPSAC.2019.10202
    172. Wisdom Ebirim, Danny Jose Portillo Montero, Emmanuel Chigozie Ani, Nwakamma Ninduwezuor-Ehiobu, Favour Oluwadamilare Usman, & Kehinde Andrew Olu-lawal. (2024). the Role of Agile Project Management in Driving Innovation in Energy-Efficient Hvac Solutions. Engineering Science & Technology Journal, 5(3), 662–673. https://doi.org/10.51594/estj.v5i3.864.
    173. Wu, C., Li, X., Jiang, R., Guo, Y., Wang, J., & Yang, Z. (2022). Graph‐based deep learning model for knowledge base completion in constraint management of construction projects. Computer-Aided Civil and Infrastructure Engineering, 38, 702–719. https://doi.org/10.1111/mice.12904
    174. Wu, M., Long, Q., Wang, Y., Chen, Y., & Rahman, M. (2023). Shortest Path Problem for Resource-Constrained Project Scheduling Based on Deep Reinforcement Learning. https://doi.org/10.1109/MLCCIM60412.2023.00019.
    175. Xi, R. (2022). Computer-Aided Design & Applications, 20(S5), 2023, 42-52 © 2023 CAD Solutions, LLC, http://www.cad-journal.net 42 Artificial Intelligence Based Scenario Design of Assembly Building Demonstration Teaching. Computer-Aided Design and Applications, 42–52. https://doi.org/10.14733/cadaps.2023.S5.42-52
    176. Xiao, J., Gao, M. L., & Huang, M. M. (2015). Empirical study of multi-objective ant colony optimization to software project scheduling problems. GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference, 759–766. https://doi.org/10.1145/2739480.2754702
    177. Xiao, X., Li, C., Huang, J., & Yu, T. (2024). Rotating machinery fault diagnosis based on one-dimensional convolutional neural network and modified multi-scale graph convolutional network under limited labeled data. Engineering Applications of Artificial Intelligence, 137, 109129. https://doi.org/10.1016/j.engappai.2024.109129.
    178. Xu, F., & Lin, S. (2015). Theoretical framework of Fuzzy-AI model in quantitative project management. Journal of Intelligent & Fuzzy Systems, 30, 509–521. https://doi.org/10.3233/IFS-151776
    179. Yang, J., Wilde, A., Menzel, K., Sheikh, M. Z., & Kuznetsov, B. (2023). Computer Vision for Construction Progress Monitoring: A Real-Time Object Detection Approach. IFIP Advances in Information and Communication Technology, 688 AICT(October), 660–672. https://doi.org/10.1007/978-3-031-42622-3_47
    180. Younisse, R., & Azzeh, M. (2023). Application of Natural Language Processing Techniques in Agile Software Project Management: A Survey. 2023 14th International Conference on Information and Communication Systems, ICICS 2023, November 2023. https://doi.org/10.1109/ICICS60529.2023.10330468.
    181. Yu, H., Deng, X., & Zhang, N. (2023). To what extent can smart contracts replace traditional contracts in construction project? Engineering, Construction and Architectural Management, 32. https://doi.org/10.1108/ECAM-04-2023-0379
    182. Zahid, T., Agha, M. H., Warsi, S. S., & Ghafoor, U. (2021). Redefining Critical Tasks for Responsive and Resilient Scheduling-an Intelligent Fuzzy Heuristic Approach. IEEE Access, 9, 145513–145521. https://doi.org/10.1109/ACCESS.2021.3123138
    183. Zhang, R., Deng, R., Zhang, Z., & Mao, Y. (2025). Vision-based real-time progress tracking and productivity analysis of the concrete pouring process. Developments in the Built Environment, 21, 100609. https://doi.org/10.1016/j.dibe.2025.100609.
    184. Zhang, S., & Li, X. (2023). A comparative study of machine learning regression models for predicting construction duration. Journal of Asian Architecture and Building Engineering, 23. https://doi.org/10.1080/13467581.2023.2278887
    185. Zhu, L., & Huang, L. (2022). A Resource Scheduling Method for Enterprise Management Based on Artificial Intelligence Deep Learning. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/4277149
    186. Zia, M. T., Nadim, M., Khan, M. A., Akram, N., & Atta, F. (2024). The Role and Impact of Artificial Intelligence on Project Management. The Asian Bulletin of Big Data Management, 4(02). https://doi.org/10.62019/abbdm.v4i02.160.
    187. Ziemba, E. W., Duong, C. D., Ejdys, J., Gonzalez-Perez, M. A., Kazlauskaitė, R., Mazurek, G., Stankevičienė, J., Korzynski, P., Paliszkiewicz, J., & Wach, K. (2024). Leveraging artificial intelligence to meet the sustainable development goals. In Journal of Economics and Management (Poland) (Vol. 46, Issue 1). https://doi.org/10.22367/jem.2024.46.19.

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    Zulkieflimansyah, Khadafie, M. . ., & Furqon , D. A. . (2025). A Systematic Review of The Literature on the TechnologyIts ‎Application and Integration Needs. International Journal of Accounting and Economics Studies, 12(4), 607-626. https://doi.org/10.14419/4t6x0421