Recommendation agents (RAs) are often used in modern applications that expose the user to a large array of items (Shani & Gunawardana, 2011). They provide the user with a set of personalized items, tailored to their preferences (Knijnenburg, Willemsen, Gantner, Soncu & Newell, 2012). Hostler, Yoon, Guo, Guimaraes & Forgionne (2012) tested the impact of RAs on online consumer unplanned purchase behavior. Furthermore, customer satisfaction with the website and other variables were included, namely:
- Product promotion effectiveness: The ability of the RA to recommend products that attract participants’ attention and interest them.
- Product search effectiveness: The ability of the RA to reduce the extent of product search by providing quick access to relevant information.
Hostler et al. (2012) conducted an experiment in which they created a shopping simulation of ‘online purchase of home movies’. Participants were randomly assigned to a control or treatment group and filled out a pre-test questionnaire in which they rated movies and provided information on their online shopping experience. Second, participants listed the movies they considered purchasing. The control group did this without the RA’s assistance, while the treatment group received personalized recommendations from the RA. Hereafter, subjects filled out a survey on impulse buying, the effectiveness of the product promotion and their satisfaction with the website.
Figure 1. Conceptual model of the results
The results (figure 1) show that the effect of product promotion effectiveness on customer satisfaction is the highest, due to effective product promotion through the use of an RA. Furthermore, customer satisfaction and search effectiveness positively impact unplanned purchases.
In the domain of e-commerce, little research has been conducted on how consumer behavior and website satisfaction are affected by RAs. The findings confirm the importance of using RAs to positively influence online consumers’ purchase behavior and fill a gap in the literature. Furthermore, the findings are also relevant for managers, since they indicate that RAs should be designed in ways to allow greater search effectiveness and satisfaction. Letting users answer more questions about their preferences is one way to increase search effectiveness and might also increase product promotion effectiveness.
A business example that effectively uses RAs is Netflix. Approximately 75% of viewer activity is based on recommendations (Vanderbilt, 2013). Netflix’s recommendations are based on i.e. movies users played/ searched for, including the time, date and device. When it comes to giving recommendations, Netflix aims to do this within 90 seconds, because it knows that after 90 seconds users will abandon the service (O’Reilly, 2016). This shows that search effectiveness is important for Netflix.
To improve this study, marketers should identify other sources for generating customer satisfaction and search effectiveness in the use of RAs. The extent to which RAs and websites are user-friendly might affect these variables. The boundary conditions for the positive effect of search effectiveness on unplanned purchases could also be tested, by examining possible moderators such as product knowledge. Lastly, although this study provides insights in the use of RAs, it is limited to the one product category, namely online purchases of home videos. Since this is a relatively simple product, it would be interesting to see whether the results are generalizable to other product categories, such as complex digital products.
Hostler, R. E., Yoon, V. Y., Guo, Z., Guimaraes, T., & Forgionne, G. (2011). Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior. Information & Management, 48(8), 336-343.
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4-5), 441-504.
O’Reilly, L. (2016, February 26). Netflix lifted the lid on how the algorithm that recommends you titles to watch actually works. Retrieved February 14, 2017, from http://www.businessinsider.com/how-the-netflix-recommendation-algorithm-works-2016-2?international=true&r=US&IR=T
Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. In Recommender systems handbook (pp. 257-297). Springer US.
Vanderbilt, T. (2013, July 8). The Science Behind the Netflix Algorithms That Decide What You’ll Watch Next. Retrieved February 14, 2017, from https://www.wired.com/2013/08/qq_netflix-algorithm/