線上推薦系統對滿意度與購買意願的影響

Abstract

本研究主要探討協同過濾與內容過濾等推薦系統對使用者滿意度與購買意願的影響。除此之外，本研究也加入了產品認知與推薦接受程度等兩項使用者特性，以進一步探討使用者的個人特質如何影響推薦系統的成效。本研究採取實驗法進行假設驗證，受測者依據個人特性進行分類之後，在一個虛擬線上購物網站上進行購買行為。實驗結束後，則請受測者回報其滿意度與購買意願等衡量指標。實驗結果證實推薦系統確實會增加使用者滿意度與購買意願。除此之外，比較傾向於認知熱門商品的受測者對協同過濾推薦系統較為滿意與存在較高的購買意願，而較傾向於認知利基商品的受測者則是對內容過濾推薦系統表達較高的滿意度與購買意願。最後，擁有高推薦接受程度的受測者，相較於低推薦接受程度的受測者表達了較高的滿意度與購買意願。本研究的結果對於電子商務以及推薦系統影響的相關研究有重要的意涵。

This study investigates the impact popular recommenders such as collaborative-filtering and content-based systems have on user satisfaction and willingness to purchase. Two consumer characteristics, product awareness and recommendation susceptibility, were integrated in this study to form a theoretical model that explains how different consumer characteristics moderate the impacts of recommenders. An experiment was conducted to test the model. Participants were grouped according to their characteristics and were asked to make a purchase at a simulated online retailer. After the experiment, participants were asked to complete a survey to report their satisfactory and willingness to purchase levels. Results indicate that the presence of recommenders increased user satisfaction and willingness to purchase. In addition, consumers that were more aware of hit products preferred collaborative-filtering, whereas ones that were more aware of niche products liked content-based systems more. Finally, recommendation susceptibility was found to have major influence on user satisfaction and willingness to purchase. Our findings have major implications for online retailers and for the ongoing research on the impacts of recommendation systems.