Are You Worried about Personalized Service? An Empirical Study of the Personalization-Privacy Paradox

• Main Authors: LEE, YI-LIN, 李宜霖
• Other Authors: KU, YI-CHEN
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/j7dcw9

碩士 === 輔仁大學 === 企業管理學系管理學碩士班 === 105 === 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.