The Evaluation of User Intention of Cloud Computing Based Context-Aware Diabetes Health Education System - Based on UCF and TTF Integration Model

• Main Authors: Wei-Kai Sung, 宋維凱
• Other Authors: 王淑玲
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/p8qnka

碩士 === 國立臺中科技大學 === 資訊管理系碩士班 === 105 === In recent years, the age of people suffering from diabetes has been declining. Therefore, the diabetes prevention and health care of the young people and middle-aged groups are very important. This study applies near-field wireless communication (NFC) technology to implement a cloud-based context-aware diabetes education system. On the one hand, this study hopes that through this mobile health education system can provide diabetes knowledge for young people in order to early prevent health care. In addition, this study hopes that this system can help the elderly and diabetes patients to improve their personal health care. This study applies NFC technology combine with diabetes ontology classification label to build a cloud-based context-aware diabetes education system in the VMware cloud computing environment. Finally, the system evaluation of research model adopts Task-Technology Fit (TTF) model and the Use-Context Fit (UCF) model, to investigate user''s intention to use the context-aware diabetes education system. This study takes three experimental tests. The first experiment focus on the prevent health care for the young people, this experiment applies SPSS24, SmartPLS3 and Amos24 statistical software for testing the hypotheses. The results show the “task characteristics” have a significant positive impact on “task-technology fit”. The “perceived utilitarian value” have a significant positive impact on “use-context fit”. The “perceived hedonic value” have a significant positive impact on “use-context fit”. The “task-technology fit” and “use-context fit” have a significant positive impact on n system usage intention. The “task characteristics” do not have significant positive impact on “task-technology fit”. The subjects of second experiment are the middle-aged elderly for take health care experiment. This experiment applies SPSS24, SmartPLS3 and Amos24 statistical software for testing the hypotheses. The results show that all hypotheses are all have significant positive effects. The third experiment focus on the community website combine with Diabetes ontology classification label, provide diabetes mellitus information of the acceptance evaluation through mobile devices for the young people. The results of the experiment shows the “task Characteristics” have a significant positive impact on “task-technology fit”. The “perceived utilitarian value” have a significant positive impact on “use-context fit”. The “perceived hedonic value” have a significant positive impact on “use-context fit”. The “task-technology fit” and “use-context fit” have a significant positive impact on system usage intention. The “task characteristics” do not have significant positive impact on “task-technology Fit”.