Browsing by Author "Hu, KC"

Now showing 1 - 2 of 2

Application of perceived value model to identify factors affecting passengers' repurchase intentions on city bus: A case of the Taipei metropolitan area
(2003-08-01)
Jen, W
;
Hu, KC
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
This paper establishes and tests the perceived value model, which is applied to identify the factors affecting the passengers' repurchase intentions on city bus. In this study, perceived value is a trade-off between perceived benefits and perceived costs. And the main part of perceived benefits is service quality. We ( 1) measured service quality using a multiple-items scale, ( 2) integrated perceived non-monetary price as a part of perceived costs, and ( 3) simultaneously incorporated the attractiveness of alternative modes. Data was collected via a questionnaire survey in the Taipei metropolitan area and the model was tested using path analysis performed by LISREL. The results indicate that all causal relationships are statistical significant. Finally, this study concludes by discussing managerial implications and making suggestions for future research.
Identifying the combination of genetic factors that determine susceptibility to cervical cancer
(2004-03-01)
Horng, JT
;
Hu, KC
;
Wu, LC
;
Huang, HD
;
Lin, FM
Although infection with high-risk types of human papillomavirus (HPV) has been identified as the primary cause of cervical cancer, only some of those infected go on to develop cervical cancer. Obviously, the progression from HPV infection to cancer involves other environmental and host factors. Recent population-based twin and family studies have demonstrated the importance of the hereditary component of cervical cancer, associated with genetic susceptibility. Consequently, single-nucleotide polymorphism (SNP) markers and microsatellites should be considered genetic factors for determining what combinations of genetic factors are involved in precancerous changes to cervical cancer. This study employs a Bayesian network and four different decision tree algorithms, and compares the performance of these learning algorithms. The results of this study raise the possibility of investigations that could identify combinations of genetic factors, such as SNPs and microsatellites, that influence the risk associated with common complex multifactorial diseases, such as cervical cancer.