Information on the doctoral dissertation of PhD candidate Nguyen Thi Kim Lan: Developing a knowledge management model: a case study at VNU-LIC.

1. Full name of doctoral candidate: Nguyen Thi Kim Lan.    2. Gender: Female

3. Date of birth: April 23, 1987 4. Place of birth: Hanoi

5. Decision to admit doctoral students:

• Decision No. 2755/2020/QD-XHNV dated December 31, 2020, on the recognition of doctoral students of the QH-2020-X cohort by the Rector of the University of Social Sciences and Humanities.

6. Changes to the training process (if any):

• Decision No. 6645/QD-XHNV dated December 5, 2024, on extending the study period of doctoral students of the QH-2020-X program for the second time, issued by the Rector of the University of Social Sciences and Humanities.

• Decision No. 9468/QD-XHNV dated December 31, 2025, on extending the study period of doctoral students of the QH-2020-X cohort for the third time, issued by the Rector of the University of Social Sciences and Humanities.

7. Thesis title: Developing a knowledge management model: A case study at VNU-LIC

8. Major: Information and Library Science. 9. Code: 9320201.01

10. Scientific supervisors: Assoc. Prof. Dr. Nguyen Ngoc Hoa and Dr. Nguyen Hoang Son

11. Summary of the new findings of the thesis:

• This dissertation aims to research, propose, and validate a digital knowledge management framework suitable for the context of multi-campus universities in Vietnam, using the case study of the Vietnam National University, Hanoi (VNU-LIC) as the empirical research subject. The research subjects include technological factors, data/services, and human/digital skills that influence the perceived usefulness and intention to use the digital knowledge management framework within an academic organization.

• In terms of research methodology, the thesis combines qualitative and quantitative research. Specifically, the systematic literature review (SLR) method was used to build the theoretical framework and identify research gaps; quantitative surveys and PLS-SEM analysis were used to test the research model and proposed hypotheses; and expert consultation was applied to assess the suitability and feasibility of the model framework in the practical context.

• The research findings clarified the relationship between technology infrastructure, data/services, people/digital skills, and perceived usefulness, thereby confirming that perceived user value does not come from a single technology investment, but from the synchronized coordination of technology, data standardization, and digital skills development. This is an important empirical finding, contributing to the scientific evidence for technology adoption models in the context of digital knowledge management in academic institutions.

• The thesis's new contributions are evident in both theoretical and practical aspects. Scientifically, the thesis proposes a digital knowledge management framework (vDFKM) based on the Data Fabric architecture, combining Knowledge Graphs and large language models (LLMs), thereby expanding the traditional knowledge management approach towards data-knowledge integration and user-centricity. Practically, the thesis clarifies the operational mechanism of vDFKM at VNU-LIC, illustrated through the uMentor prototype, and proposes feasible management, technical, and human resource solutions for implementation at Vietnam National University, Hanoi.

• This thesis has addressed the scientific and technical issues related to the integration of distributed data, knowledge contextualization, partial automation of the knowledge management cycle, and risk control when applying AI in an academic environment. Although the prototype has only been validated on a few representative datasets, the results show that the vDFKM framework is scalable and adaptable, meeting the requirements for data quality, normalization, and security.

• In terms of practical application, the results of this thesis can be used as a reference for building and implementing a digital knowledge ecosystem at Vietnam National University, Hanoi and similar universities. The thesis contributes to improving the efficiency of knowledge management, supporting research and training activities, promoting innovation, and aiming for sustainable development in the knowledge economy.

12. Directions for further research:

• Expand vDFKM validation to more diverse data types, including administrative and sensitive data, to assess the model's scalability and security.

• Conduct in-depth analysis of differences between user groups (students, faculty, staff) through moderation models or multi-group analysis.

• Research into mechanisms for enhancing trust and protecting privacy, such as Federated Learning and Blockchain, when integrating AI/LLMs into digital knowledge management.

  1. Nguyen LTK, Nguyen HN, Nguyen SH (2023), "From fragmented data to collective intelligence: A data fabric approach for university knowledge management",International Conference on Computational Collective Intelligence, Cham: Springer Nature Switzerland, pp. 16-28.

13. Publications related to the dissertation:

1. Nguyen LTK (2024), "Human resources and management knowledge: The case study of VNU-LIC",Proceedings of the International Conference on Human Resources for Information Industry in the Context of National Digital Transformation in Vietnam, pp. 140-148.

2. Nguyen LTK, Pham LD, Nguyen HN (2024), "uMentor: LLM-powered chatbot for harnessing technology books in digital library",International Conference on Computational Collective Intelligence, Cham: Springer Nature Switzerland, pp. 232-244.

3. Nguyen LTK, Nguyen SH, Nguyen HN (2025), “Interweaving academic insights: advancing university knowledge management through a strategic data fabric framework”,Digital Library PerspectivesVol. 41 (1), pp. 21-44.

4. Nguyen LTK, Connolly J., Nguyen HN (2025), “A systematic review of knowledge improving management with generative AI and large language models”,Journal of Advances in Information TechnologyVol. 16 (4), pp. 594-612, doi: 10.12720/jait.16.4.594-612.

INFORMATION ON DOCTORAL THESIS

1. Full name: Nguyen Thi Kim Lan

2. Sex: Female

3. Date of birth: April 23, 1987

4. Place of birth: Hanoi

5. decision number

• Decision No. 2755/2020/QD-XHNV31^thDecember 2020 on the admission of the doctoral candidate of cohort QH-2020-X from the Rector of The University of Social Sciences and Humanities

1. Changes in the academic process

• Decision No. 6645/QD-XHNV 05^thDecember 2024 on the second extension of the training period for the doctoral candidate of cohort QH-2020-X from the Rector of The University of Social Sciences and Humanities

• Decision No. 9468/QD-XHNV 31^thDecember 2025 on the third extension of the training period for the doctoral candidate of cohort QH-2020-X from the Rector of The University of Social Sciences and Humanities

1. Official thesis title: Developing a Knowledge Management Model: A Case Study at VNU-LIC

2. Major: Library and Information Science

3. Code: 9320201.01

4. Supervisors: Assoc. Prof. Dr. Nguyen Ngoc Hoa and Dr. Nguyen Hoang Son

5. Summary of the new findings of the thesis:

• The thesis aims to investigate, propose, and validate a digital knowledge management framework suitable for the context of multi-campus universities in Vietnam, using the Vietnam National University Library and Information Center (VNU-LIC) as the empirical case study. The research focuses on technological infrastructure, data/services, and human/digital competencies and their impacts on perceived usefulness and intention to use a digital knowledge management framework in academic organizations.

• Methodologically, the study adopts a mixed-methods approach, combining a systematic literature review (SLR) to establish the theoretical foundation and identify research gaps, quantitative survey and PLS-SEM analysis to test the research model and hypotheses, and expert consultation to assess the relevance and suspicion of the proposed framework in practice.

• The findings clarify the relationships between technological infrastructure, data/services, human and digital competencies, and perceived usefulness, demonstrating that user-perceived value does not result from isolated technological investment but from the coordinated integration of technology, data standardization, and digital capacity development. This represents an important empirical contribution to technology acceptance models in the context of digital management knowledge in academic institutions.

• The thesis's novel contributions are reflected in both theoretical and practical dimensions. Academically, it proposes the vDFKM digital knowledge management framework, based on Data Fabric architecture, integrated with Knowledge Graphs and Large Language Models (LLMs), thus extending traditional knowledge management approaches toward data–knowledge integration and user-centered design. Practically, the study elucidates the operational mechanisms of vDFKM at VNU-LIC through the uMentor prototype and proposes feasible governance, technical, and human resource solutions for implementation at Vietnam National University, Hanoi.

• The research addresses key scientific and technical challenges related to distributed data integration, knowledge contextualization, partial automation of the knowledge management lifecycle, and risk control in AI-enabled academic environments. Although the prototype has been validated on selected representative datasets, the results indicate that vDFKM is scalable and adaptable, meeting requirements for data quality, standardization, and security.

• In terms of application significance, the thesis provides a reference framework for developing and deploying digital knowledge ecosystems at Vietnam National University, Hanoi, and comparable universities. The findings contribute to improving knowledge management effectiveness, supporting research and teaching activities, fostering innovation, and advancing sustainable development in the knowledge-based economy.

1. Further research directions

• Expanding the validation of vDFKM across more diverse data types, including administrative and sensitive data, to evaluate the model's scalability and security.

• Conducting in-depth analysis of differences among user groups (students, lecturers, and administrative staff) through moderation models or multi-group analysis.

• Investigating mechanisms to enhance trust and privacy protection, such as Federated Learning and Blockchain, in the integration of AI/LLMs into digital knowledge management.

1. Thesis-related publications

   1. Nguyen LTK, Nguyen HN, Nguyen SH (2023), "From fragmented data to collective intelligence: A data fabric approach for university knowledge management",International Conference on Computational Collective Intelligence, Cham: Springer Nature Switzerland, pp. 16-28.

   2. Nguyen LTK (2024), "Human resources and management knowledge: The case study of VNU-LIC",Proceedings of the International Conference on Human Resources for Information Industry in the Context of National Digital Transformation in Vietnam, pp. 140-148.

   3. Nguyen LTK, Pham LD, Nguyen HN (2024), "uMentor: LLM-powered chatbot for harnessing technology books in digital library",International Conference on Computational Collective Intelligence, Cham: Springer Nature Switzerland, pp. 232-244.

   4. Nguyen LTK, Nguyen SH, Nguyen HN (2025), “Interweaving academic insights: advancing university knowledge management through a strategic data fabric framework”,Digital Library PerspectivesVol. 41 (1), pp. 21-44.

   5. Nguyen LTK, Connolly J., Nguyen HN (2025), “A systematic review of knowledge improving management with generative AI and large language models”,Journal of Advances in Information TechnologyVol. 16 (4), pp. 594-612, doi: 10.12720/jait.16.4.594-612.