Browsing by Author "Chih-Young, Hung"

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A Mixed-Methods Study of Users' Journey Mapping Experience and Acceptance of Telehealthcare Technology in Taiwan
(2019-11-01)
Tsung-Yin, Ou
;
Chih-Young, Hung
;
Che-Wei, Lin
;
科技管理研究所
;
Institute of Management of Technology
Background: The integration of telehealthcare technologies into geriatric care has clear advantages, but poses challenges for key stakeholders, including the telehealthcare service industry, smart device developers, marketers and, of course, users. The purpose of this study was to gain insight into user experience and acceptance of telehealthcare technology in Taiwan. Materials and Methods: Taking an integrated mixed-methods approach, 51 participants-33 women (64.71%) and 18 men (35.29%) (mean age: 52.1 years)-were examined before, during, and after using a telehealthcare device. A questionnaire was used to assess users' acceptance of the technology. Interviews were conducted to gather feedback. Results: The quantitative analysis revealed that (1) effort expectancy and relatives significantly influenced users' behavior intentions, (2) error and memorability significantly affected users' satisfaction, and (3) intention and satisfaction significantly affected users' behavior. The qualitative analysis revealed two key implications. First, participants generally were not satisfied with the telehealthcare device. Second, participants suggested the redesign of the telehealthcare device to reduce operating errors and time consumption to meet users' needs. Conclusions: Contemporary telehealthcare devices do not meet users' needs. Participants were made to adapt to the equipment rather than the equipment tailored to meet their needs. Although there were key benefits from home telehealthcare, the systems require enhanced technology, improved usability, and an expanded scale of services backed by government before widespread implementation can successfully proceed.
ENERGY CONSUMPTION FORECASTING IN TAIWAN BASED ON ARIMA AND ARTIFICIAL NEURAL NETWORKS MODELS
(2011)
Feng-Kuang, Chuang
;
Chih-Young, Hung
;
Kuo, Kuo-Cheng
;
Chang, Chi-Ya
;
科技管理研究所
This study compares the prediction performance of Taiwan's energy consumption based on a autoregressive integrated moving average (ARIMA) model and artificial neural networks (ANNs) models for forecasting the short term (I year), the medium term (3 years), the medium-long term (5 years), and the long term (10 years) over the period 1965-2010. On the average of the four time periods, the results indicate that the single-variable ARIMA model shows superior performance than that of ANN1. As to multi-variable-models, the prediction accuracy of different models has advantages in the different time periods. ANN4 model including variables of energy consumption and export shows the most accurate prediction in short term and medium-long term, while ANN6 model including energy consumption, GDP and export has the highest accuracy for medium term prediction. Meanwhile, ANN3 model including energy consumption and population has the best accuracy for the long term prediction. Overall, on the average of the four different time periods of ARIMA model and ANNs models, ANN3 proves the most accurate prediction in comparison to the others. This concludes the contributions of this study.