臺灣博碩士論文加值系統

　　商家在對產品進行銷售時，除了要做到客製化之外，還要能夠進一步做到個人化，而推薦系統即是在做個人化時最有效的工具之一，利用個人化產品推薦系統可以準確的判斷出顧客真正的需求，而系統再針對顧客的需求推薦出最符合的產品。然而現有的推薦系統大多只是單純將產品建議給消費者，並沒有以不同的經驗形式來做推薦，事實上以經驗形式呈現給消費者會更具有說服力，因此我們將會使用以虛擬經驗為基礎之個人化產品推薦系統來進行我們的研究。而在考慮個人化產品推薦時，消費者偏好亦為重要考量因素。因此本研究即是以「個人化產品推薦系統」的四種虛擬經驗類型為基礎，再加上「不推薦」作為自變數，而以「系統接受度」作為依變數，除此之外，再加入「消費者偏好類型」及「產品類型」作為干擾，並以網路問卷的方式來進行我們的實驗。從研究結果可以發現，個人化產品推薦系統確實能提升消費者對於商品之購買意願；除此之外，對偏好一致性低且明確性也低的消費者來說，以娛樂經驗形式來做推薦其接受度最高；而對偏好一致性高但明確性卻低的消費者來說，以美感經驗形式來做推薦其接受度最高，表示不同偏好類型之消費者各有其最適合的虛擬經驗推薦形式。

　　Customers do not want more choices and like to find exactly what they want. Using the recommendation system provides the good solution for users to acquire rich information on Internet. The firms can recommend consumers products via virtual experiences to enhance persuasion effects. However, very little research has explored the impact of this new type of experience especially in combination with personalized recommendation systems. Furthermore, the characteristics of customers’ preferences are the main drivers of the response to marketers’ offers, including individually customized offers. Therefore, we will classify the customers’ preferences into four groups in according to “the degree of stability” and “the degree of clarity”. The research bases on those conceptions. We compare the differences of the degree of acceptance of four virtual experience-based recommendation systems among four consumer preference types. Our research finds that the recommend system of individual production indeed increases consumers’ preference for purchasing production. Moreover, the consumers who are classified into the low stability and high clarity take the advice which was recommended in the sense of entertaining experiences. The consumers belong to high stability and low clarity, and take the advice recommended in sense of beauty experiences. The result shows that consumers with different preferences can get the suitable from of virtual experience.

摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VII
圖目錄 X
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究問題與目的 3
第二章 文獻探討 4
2.1 消費者經驗 4
2.1.1　人際虛擬經驗 6
2.1.2　機械虛擬體驗 8
2.2 個人化推薦系統 11
2.2.1　個人化之定義 11
2.2.2　個人化之技術 12
2.2.3　推薦系統之效果 14
2.2.4　推薦系統之衡量 16
2.3 消費者偏好 18
2.3.1　消費者偏好之定義 18
2.3.2　消費者之偏好類型 19
2.3.3　偏好一致性之影響與衡量 24
2.3.4　偏好明確性之影響與衡量 27
第三章 研究方法 30
3.1 研究架構 30
3.2 研究假說 31
3.2.1　推薦系統之效果 31
3.2.2　產品類型之影響 31
3.2.3　偏好類型之影響 32
3.3 變數的定義與衡量 35
3.3.1　偏好類型 35
3.3.2　系統接受度 37
3.3.3　實驗分組 38
3.3.4　實驗產品 39
3.3.5　信度與效度 41
3.4 實驗設計 42
3.4.1　系統建置 42
3.4.2　網站設計 43
3.4.3　實驗流程 45
第四章 資料分析 46
4.1 前測與操弄檢驗 46
4.2 樣本特性描述 49
4.3 信度、效度分析 50
4.4 隨機性檢驗 51
4.5 假說驗證 53
第五章 結論與建議 68
5.1 研究結論 68
5.1.1　推薦系統之效果 69
5.1.2　產品類型之影響 69
5.1.3　偏好類型之影響 70
5.2 理論與實務意涵 72
5.3 研究限制與未來研究建議 73
參考文獻 74
中文文獻 74
英文文獻 75
附錄一：前測問卷 81
附錄二：實驗問卷 84
附錄三：網站畫面 85

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