| URN |
etd-0803116-202527 |
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| Author |
Lu-Wen Chen |
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| Author's Email Address |
No Public. |
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| Statistics |
This thesis had been viewed 5632 times. Download 819 times. |
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| Department |
Information Management |
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| Year |
2015 |
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| Semester |
2 |
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| Degree |
Master |
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| Type of Document |
|
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| Language |
zh-TW.Big5 Chinese |
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| Title |
A Neural Science Study on the Effect of Product Recommendation in Electronic Commerce |
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| Date of Defense |
2016-07-26 |
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| Page Count |
87 |
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| Keyword |
Brainwave
Electroencephalogram (EEG)
Eyetracking
Recommendation systems
Neural information systems
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| Abstract |
The rapid development of e-commerce, the problem facing consumers is not insufficient information but information overload. Therefore, recommendation systems have been used to reduce the search cost and they have become an essential function for most websites to enhance consumer’s shopping experience.
Many previous studies have investigated the effect of product recommendation with the goal of finding factors that influence consumer’s attitude and purchase intention. However, most of them used traditional questionnaires to measure collect subjective data, which may be biased and subject to the common method bias. The purpose of this study is to collect objective physiological responses of the subjects using electroencephalogram (EEG) and eye-tracking techniques and compare the result with behavioral data collected from questionnaires.
We further used the neural science data to examine the elimination likelihood data for exploring possible mechanisms in the decision process. Our findings indicate that product type and consumer’s interest will affect the attitude toward the product in the behavioral study. Consumer’s attention levels vary in different product types and consumer’s interest levels based on the EEG and fixation times observed from the eye-tracker. |
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| Advisory Committee |
None - chair
Kai-Lung Hui - co-chair
Ting-Peng Liang - advisor
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| Files |
Indicate in-campus at 2 year and off-campus access at 2 year. |
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| Date of Submission |
2016-09-03 |
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