Li LeiFollow

Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Computer Information Systems

First Advisor

Dr. Vijay K. Vaishnavi - Chair

Second Advisor

David Washburn

Third Advisor

Mark Keil

Fourth Advisor

Yanqing Zhang

Fifth Advisor

Dr. Richard Baskerville


GENERATING USER-CENTRIC DYNAMIC AND ADAPTABLE KNOWLEDGE MODELS FOR WORLD WIDE WEB By LEI LI JUNE, 2007 Committee Chair: Dr. Vijay Vaishnavi Major Department: Computer Information Systems In the current Internet age, more and more people, organizations, and businesses access the web to share and search for information. A web-based resource is often organized and presented based on its knowledge models (categorization structures). The static and inflexible knowledge models of web-based resources have become a major challenge for web users to successfully use and understand the information on the web. In this dissertation, I propose a research approach to generate user-centric dynamic and adaptable knowledge models for web-based resources. The user-centric feature means that a knowledge model is created based on a web user specified perspective for a web resource and that the user can provide feedback on the model building process. The dynamic feature means the knowledge models are built on the fly. The adaptable feature means the web user can have control of the user adaptation process by specifying his or her perspective for the web resource of interest. In this study, I apply a design science paradigm and follow the General Design Cycle (Vaishnavi and Kuechler 2004) during the course of research. A research prototype, Semantic Facilitator TM SM V2.0, has been implemented based on the proposed approach. A simulation-based experimentation is used to evaluate the research prototype. The experimental results show that the proposed research approach can effectively and efficiently create knowledge models on the fly based on a web user preferred perspective for the web resource. I found that incorporating user feedback into the modeling building process can greatly improve the quality of the knowledge models. At the end of the dissertation, I discuss the limitations and future directions of this research.