Recommendation agents (RA) are giving online customers recommendations for the past few years. Although the first main function of RAs was to reduce information overload, now it’s also used to increase sales. More and more information is gathered through the internet and especially social media, to improve personalized preference-based recommendations. At the same time, these systems show success measured by online sales and user satisfaction.
Customer loyalty is considered to be a source of competitive advantage and is useful for long-term business success. Research has shown that there is a strong relationship between customer loyalty, firm’s profitability and stock returns. Returning customers are more profitable than new customers and thus good for business. The aim of the study is to identify the effect between various independent variables (e.g. RA Type, Recommendation Quality, Customer Satisfaction, Product Knowledge, and Online Shopping Experience) and on the dependent variable customer loyalty.
Recommendation quality is based on the preferences of the user and the perceived value of the recommended products. This is the outcome of the type of RA, which could be either content-filtering or collaborative-filtering. Also, the impact of the moderating variable Product Knowledge and shopping experience will be measured. When having expertise in a product, this could negatively affect the customer satisfaction when being advised by a recommendation agent. Shopping experience is also hold in account because the more shopping experience a customer has, the more likely the customer is familiar the interaction with RAs, and the more likely the customer is able to use a RA effectively.
The main reasons for the study is that from marketing perspective, the adopted cognitive-affect-conative-action framework of customer loyalty has not been empirically tested in the context of RAs. This framework states that customers become more loyal when going through multiple stages. Every stage represents some sort of loyalty. There has also been done little research assessing the effect of increasingly higher customer expertise on customer loyalty in the presence of RA usage. Thus, central in this research are the moderating effect of product knowledge on the relationship of Recommendation Quality and Customer Satisfaction.
The results showed that the collaborative filtering RA has a higher recommendation quality than a random RA. The recommendation quality has a positive effect on customer satisfaction and customer loyalty. Also, customer satisfaction is positively related to customer loyalty. The results also show that the impact of recommendation quality on customer satisfaction is negatively moderated by customers’ product knowledge. Thus, product expertise negatively affects the perceived value of the outcome of a RA. Shopping expertise does not have an effect the relationship between customer satisfaction and customer loyalty. 70% of the variance in customer loyalty can be explained by customer satisfaction. This research has shown that an effective use of RA positively influences recommendation quality which in turn positively influences customer satisfaction. When users will have increasing levels of product knowledge, it will negatively influence the customer satisfaction with the website.
The increased knowledge about RAs and how it will increase customer loyalty towards your website is interesting for businesses to retain customers. However, retaining customers are likely to get an increased level of product knowledge. Thus, RAs should always be innovated more and more.
Yoon, V. Y., Hostler, R. E., Guo, Z., & Guimaraes, T. (2013). Assessing the moderating effect of consumer product knowledge and online shopping experience on using recommendation agents for customer loyalty. Decision Support Systems, 55(4), 883–893.