臺灣博碩士論文加值系統

近年來企業紛紛透過個人化推薦系統(Personalized Recommender Systems)為用戶提供更好的服務，個人化服務提供的好處使用戶喜歡有這些系統的網站，企業為了提高競爭力和顧客忠誠度，期望蒐集更詳細的個人資料來提升服務品質，但隨著大眾對隱私關注的增加，擔心個人資料被盜用、濫用或販售給不知情第三方，進而產生了「個人化與隱私的矛盾」(Personalization-privacy Paradox)。本研究透過隱私計算理論(Privacy Calculus Theory)來了解個人化服務與隱私的關係。用戶對個人資料的隱私擔憂（感知風險）與個人化服務提供的好處（感知利益）對其感知個人化服務的價值為何，以及此價值如何影響他們願意提供的資料類型。為驗證研究之理論架構，本研究透過網路問卷調查及PLS進行模型檢定後發現，個人化服務正向影響消費者的感知利益，資料敏感度與隱私顧慮正向影響消費者的感知風險。然而，消費者並非在所有情況下皆會進行成本效益分析，當被要求提供資料敏感度及隱私顧慮較低程度的資料時，較不會去評估其風險。此外，消費者的感知價值會同時受到感知利益與感知風險的動態交互影響，進而影響他們的個人資料提供意願。本研究結果可作為個人化推薦系統相關研究及廠商建立個人化推薦系統時之參考。

Many websites today use personalized recommender systems to provide more attractive service to their customers. The conveniences provided by personalized services are often able to make the website more attractive to its users. In order to increase their competitiveness and customer loyalty, websites are prompted to collect more and more detailed information from its users. On the other hand, users today are also paying more and more attention on their privacy and personal information. They are worried that the website could steal, misuse or sell their information to a third party while expecting more benefit from personalization services, creating the problem of “The personalization-privacy paradox”. This research utilizes the privacy calculus theory to understand the relationship between personalization and privacy, how the users react when they run into the dilemma between privacy concern (perceived risk) and the benefit of personalization (perceived benefit), and how they perceive the value of personalization and influence their willingness to provide personal information. We use online survey to collect empirical data. The result of PLS analysis indicates that personalized service is positively affects perceived benefit. Information sensitivity and privacy concern both positively affects perceived risk. However, when customers are asked for data with low information sensitivity and low privacy concern, they are less likely to evaluate associated risks by performing a cost-benefit analysis. Perceived value is both influenced by perceived benefit and perceived risk and in term, affects customers’ willingness to provide personal information. We expect the findings from this study to provide some new findings for both researchers and developers of personalized recommender systems.

目 錄
第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 4
第三節 研究流程 5

第貳章 文獻探討 7
第一節 個人化推薦系統 7
第二節 個人資料的類型 10
第三節 隱私 14
第四節 隱私矛盾 17
第五節 隱私計算理論 20

第參章 研究方法 22
第一節 研究架構 22
第二節 假說推論 23
第三節 研究變數操作型定義與衡量 27
第四節 研究設計 32

第肆章 分析與結果 41
第一節 樣本結構分析 41
第二節 實驗操弄分析 41
第三節 敘述性統計分析 47
第四節 衡量模型 52
頁次
第五節 假說與模型檢定 55
第伍章 結論與建議 64
第一節 研究結論 64
第二節 研究貢獻 66
第三節 研究限制與未來發展 68

參考文獻 70

附錄一 83

 
表　目 錄
表3-3-1 個人化服務的價值衡量題項表 27
表3-3-2 感知利益衡量題項表 28
表3-3-3 資料敏感度衡量題項表 29
表3-3-4 隱私顧慮衡量題項表 29
表3-3-5 感知風險衡量題項表 30
表3-3-6 感知價值衡量題項表 31
表3-3-7 提供個人資料的意願衡量題項表 32
表3-4-1 情境設計表 34
表3-4-2 個人化程度敘述性統計資料表 35
表3-4-3 個人化程度變異數分析表 36
表3-4-4 個人化程度事後比較表 36
表3-4-5 資料敏感度敘述性統計資料表 37
表3-4-6 資料敏感度變異數分析表 37
表3-4-7 資料敏感度事後比較表 38
表3-4-8 隱私顧慮敘述性統計資料表 38
表3-4-9 隱私顧慮變異數分析表 39
表3-4-10 隱私顧慮事後比較表 39
表4-2-1個人化程度單因子變異數分析表 44
表4-2-2 個人化程度多重比較表 44
表4-2-3 資料敏感度單因子變異數分析表 45
表4-2-4 資料敏感度多重比較表 45
表4-2-5 隱私顧慮單因子變異數分析表 46
表4-2-6 隱私顧慮多重比較表 46
表4-3-1 問項代碼表 47
表4-3-2 樣本敘述性統計表 49
表4-3-3 情境敘述性統計比較表 51
表4-4-1 研究變數之信度 52
表4-4-2 各構面之AVE值表 53
表4-4-3 各構面之AVE平方根相關矩陣 54
表4-5-1 結構模型分析結果表 56
表4-5-2 全部資料假說檢定結果整理表 57
表4-5-3 情境一結構模型分析結果表 58
表4-5-4 情境一假說檢定結果整理表 59
表4-5-5 情境二結構模型分析結果表 60
表4-5-6 情境二假說檢定結果整理表 61
表4-5-7 情境三結構模型分析結果表 62
表4-5-8 情境三假說檢定結果整理表 63

圖　目 錄
圖1-3-1 研究流程圖 6
圖2-1-1 Youtube推薦影片 9
圖3-1-1 研究架構圖 22
圖4-3-1 敘述性統計比較圖 50
圖4-5-1 研究架構檢測結果圖 55
圖4-5-2 情境一研究架構檢測結果圖 57
圖4-5-3 情境二研究架構檢測結果圖 59
圖4-5-4 情境三研究架構檢測結果圖 61

中文部分
1.電腦處理個人資料保護法(民104修正)。
網路部分
1.Youtube (2017)。Youtube台灣。取自：https://www.youtube.com/?gl=TW&hl=zh-TW (2016/02/03)。
2.Youtube新聞中心(2014)。Youtube台灣。取自： https://www.youtube.com/yt/press/zh-TW/statistics.html (2017/02/01)。

 
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網路部分
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