A trend in the e-commerce business is web-shops giving personalized recommendations to their customers based on past purchasing behaviour or browsing history. Research shows that personalized recommendations can affect online sales with a 12% in average order value for personalized transaction.
A major concern of consumers related to personalized recommendations are the privacy concerns that people have with companies using their private date. The privacy-personalization paradox occurs: People resent that their personal information is used by companies to personalize their services, but also would like to benefit from this personalization. The privacy calculus theory suggests that people will make a calculation in their head if the loss of privacy they perceive will result in more benefits of personalization.
The paper I will discuss today is written by Ting Li and Till Unger in 2012 and explored the relationships between personalization quality and privacy concerns through the privacy calculus theory. They conduct an experiment in combination with a survey in which they show people 8 different scenarios. The scenarios were webpages with different dimensions manipulated. They have chosen for a 2x2x2 between-subject design and manipulated three dimensions namely: privacy, quality of personalization and industry domain.
The findings of this research are very interesting. The authors found that a customer’s intention to use online personalization is negatively influenced by the degree of her privacy concerns. Prior research has argued that privacy concerns play a crucial role in customers’ online purchasing behaviour. The results of their experiment support this hypothesis. They found that the familiarity with online personalization reduces this negative effect of privacy concerns on the intention to use the personalization system.
Also the quality of a recommendation system is researched in this paper. This is the first research on recommendation agents that take quality as a variable. The quality of a recommendation agent seems to be an important factor in the likelihood of using online personalization. When consumers perceive the quality of the recommendation agent as high they are more likely to use the personalized recommendation agent. They also found that higher personalization quality could overcome customers’ privacy concerns.
This research is very important for e-commerce companies. For managers of these companies the information privacy concerns that consumers have are important to notice. Personalization systems that have high quality are very beneficial for their company. They need to build an e-commerce site that creates trust and addresses the information privacy concerns of users. This research shows that using privacy signs can ease the privacy concerns of users. This research also suggests that it is important that a site already established a good relationship with their users before implementing personalization services.