Browsing by Author "Shih, Po-Hong"

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Applying Artificial Neural Network to Predict Semiconductor Machine Outliers
(2013)
Yang, Keng-Chieh
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Yang, Conna
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Chao, Pei-Yao
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Shih, Po-Hong
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經營管理研究所
Advanced semiconductor processes are produced by very sophisticated and complex machines. The demand of higher precision for the monitoring system is becoming more vital when the devices are shrunk into smaller sizes. The high quality and high solution checking mechanism must rely on the advanced information systems, such as fault detection and classification (FDC). FDC can timely detect the deviations of the machine parameters when the parameters deviate from the original value and exceed the range of the specification. This study adopts backpropagation neural network model and gray relational analysis as tools to analyze the data. This study uses FDC data to detect the semiconductor machine outliers. Data collected for network training are in three different intervals: 6-month period, 3-month period, and one-month period. The results demonstrate that 3-month period has the best result. However, 6-month period has the worst result. The findings indicate that machine deteriorates quickly after continuous use for 6 months. The equipment engineers and managers can take care of this phenomenon and make the production yield better.
The link between state-of-mind and individuals' willingness to adopt and continue using smartphones
(2018-01-01)
Shih, Po-Hong
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Yang, Keng-Chieh
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Yang, Chyan
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Purpose Although numerous studies have examined factors that influence smartphone acceptance and use, few have analyzed cognitive age. This study aims to use the unified theory of acceptance and use of technology (UTAUT) to test two models to analyze the moderating effect of cognitive age. This research offers relevant suggestions among different cognitive age groups. Design/methodology/approach A questionnaire survey was conducted to collect research data in Taiwan and the UTAUT model was used. Model 1 ensures all four antecedent constructs among digital natives (those under 34 years old). Model 2 divides the digital immigrants into two groups to test the influence of cognitive age on the behaviors of smartphone use. This study tests Model 1 using AMOS 20 to examine the measurement and structural model and validates Model 2 using partial least squares (PLSs). Findings In Model 1, the digital natives have sufficient confidence to accept a new technology with ease and little effort owing to most educational resources and the widespread internet. Group 1 in Model 2 reveals that the behavior of digital immigrants is similar to that of digital natives. For Group 2 in Model 2, they tend to infer that skills or tasks they associate with having higher value are more difficult to learn. Originality/value This study provides another dimensional result for different cognitive age groups and it has to consider not only chronological age but also cognitive age in user behavior. The result can enrich the theoretical perspective on technology adoption and use behavior via cognitive age, which is a significant and important self-related factor that can help predict technology adoption and use behavior.