1. Project Management Institute (PMI). 2020. The Standard for Project Management. Newtown Square, PA: Author. (Covers foundational PM methodology, governance, and value delivery context for the thesis's Project Management components.)

2. Taboada, I.; Daneshpajouh, A.; Toledo, N.; de Vass, T. (2023). Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Appl. Sci., 13, 5014. (A direct and comprehensive review covering the application of AI techniques (ML, Fuzzy, Heuristics, NLP) within the PM Performance Domains, especially planning, which is highly relevant to your strategic analysis.)

3. Yüksel, N.; Börklü, H.R.; Sezer, H.K. et al. (2023). Review of artificial intelligence applications in engineering design perspective. Engineering Applications of Artificial Intelligence, 118, 105697. (Covers both classical and modern AI/ML techniques used in conceptual product and engineering design, essential for the engineering and manufacturing elements of your scope.)

4. Pan, Y.; Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management A critical review and future trends. Automation in Construction, 122, 103517. (A foundational review focusing specifically on AI adoption in Construction Engineering and Management (CEM), discussing hot research topics like computer vision, NLP, optimization, and future trends (AIoT, Digital Twins, 4D printing).)

5. Fuller, A.; Fan, Z.; Day, C.; Barlow, C. (2020). Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access, 8, 108952–108971. (One of the essential surveys on Digital Twins, covering enabling technologies like ML, IoT, and VR/AR, which are central to your emerging technology focus.)

6. Baik, A. (2025). Three Decades of Innovation: A Critical Bibliometric Analysis of BIM, HBIM, Digital Twins, and IoT in the AEC Industry (1993–2024). Buildings, 15. (A very recent and broad bibliometric analysis covering the convergence of BIM, Digital Twins, and IoT in the AEC sector, identifying AI-enhanced applications and interoperability as key trends.)

7. Yüksel, N.; Çetin, E.; Demirhan, M.A. (2023). Architectural 3D-Printed Structures Created Using Artificial Intelligence A Review of Techniques and Applications. Appl. Sci., 13, 10671. (Provides dedicated analysis of two core technologies—AI tools (ML, ANN, CV) applied specifically to 3D-Printed Structures in the architecture/construction domain.)

8. Almatared, M.; Liu, H.; Tang, S.; Sulaiman, M.; Lei, Z.; Li, H.X. (2022). Digital Twin in the Architecture, Engineering, and Construction Industry: A Bibliometric Review. Constr. Res. Congr., 2022, 670–678. (Specifically reviews DT research within the AEC context, identifying the critical research clusters: information management, AI, and IoT, crucial for defining your MVP area.)

9. Merhi, M.I. (2024). Enablers of artificial intelligence adoption and implementation in production systems. International Journal of Production Research, 62(15), 5457–5471. (Highly relevant for the implementation framework, identifying and ranking critical for AI adoption in production systems (manufacturing) using the Technology, Organization, and Environment (TOE) framework.)

10. Brozovsky, J. et al. (2024). Digital technologies in architecture, engineering, and construction. Automation in Construction, 158, 105212. (A scoping review categorizing a large number of digital technologies in the AEC sector, classifying them into dominant, emerging (like 3D printing, IoT, AI/ML), underdeveloped, and niche categories.)

11. Grieves, M. (2014). Digital twin: Manufacturing excellence through virtual factory replication. White Paper, 1(1), 1–7. (Defines the foundational concept of the Digital Twin as virtual information constructs describing a physical manufactured product, essential for the Manufacturing component.)

12. Borges, A.F.S.; Laurindo, F.J.B.; Spínola, M.M.; Gonçalves, R.F.; Mattos, C.A. (2021). The Strategic Use of Artificial Intelligence in the Digital Era: Systematic Literature Review and Future Research Directions. Int. J. Inf. Manag., 57, 102225. (Systematic literature review on the strategic use of AI, relevant for understanding the organizational capabilities and strategic roadmap needed for adoption/BMI.)

13. Emmert-Streib, F.; Dehmer, M. (2022). Taxonomy of machine learning paradigms: a data-centric perspective. Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 12, 1–24. (Provides a broad taxonomic overview of ML paradigms, necessary for the fundamental understanding of the AI techniques applied across all sectors of the thesis.)

14. Dam, H.K.; Tran, T.; Grundy, J.; Ghose, A.; Kamei, Y. (2019). Towards Effective AI-Powered Agile Project Management. In Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results. (Specific application of AI/ML within an Agile PM context, addressing the broader PM methodology aspect of the thesis.)

15. Al-Radhi, Y.; Roy, K.; Liang, H.; Ghosh, K.; Clifton, G.C.; Lim, J.B. (2023). Thermal performance of different construction materials used in New Zealand dwellings comparatively to international practice—A systematic literature review. J. Build. Eng., 72, 106346. (Provides direct material relevance to the NZ Construction/Housing sector focus, connecting physical performance data to the industry context.)

16. Chiu, N.H. (2011). Combining Techniques for Software Quality Classification: An Integrated Decision Network Approach. Expert. Syst. Appl., 38, 4618–4625. (Represents the application of combined AI techniques (like fuzzy logic/NNs mentioned widely elsewhere) for decision-making and estimation, relevant to the project management functions (forecasting/estimation).)

17. Brecher, C.; Buchsbaum, M.; Storms, S. (2019). Control from the cloud: Edge computing, services and digital shadow for automation technologies. In Proceedings of the IEEE International Conference on Robotics and Automation. (Highlights the "Digital Shadow" concept, a DT predecessor, and enabling technologies like Edge computing and cloud control relevant to manufacturing and automation.)

18. Chowdhury, G.G. (2003). Natural Language Processing. Annu. Rev. Inf. Sci. Technol., 37, 51–89. (Foundational paper on Natural Language Processing (NLP), an AI technique widely used in PM for information extraction (e.g., contracts, maintenance logs) and vital for the discussion on various AI methods.)

19. Ambadekar, P.K.; Gawande, S.H. (2023). Artificial intelligence and its relevance in mechanical engineering from Industry 4.0 perspective. (Provides a clear link between AI/ML and specific Mechanical Engineering applications (ME), including additive manufacturing (AM), supporting the engineering scope.)

20. Tranfield, D.; Denyer, D.; Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag., 14, 207–222. (A seminal paper on the Systematic Literature Review (SLR) methodology, essential for validating the research approach used by almost all comprehensive reviews cited in your sources, including the primary PM reviews.)