Web personalization can achieve two business goals: increased advertising revenue and increased sales revenue. The realization of the two goals is related to two kinds of user behavior: item sampling and item selection. Prior research does not provide a model of attitude formation toward a personalization agent nor of how attitudes relate to these two behaviors. This limits our understanding of how web personalization can be managed to increase advertising revenues and/or sales revenues. To fill this gap, the current research develops and tests a theoretical model of user attitudes and behaviors toward a personalization agent. The model is based on an integration of two theories: the elaboration likelihood model (ELM) and consumer search theory (CST). In the integrated model, a user’s attitude toward a personalization agent is influenced by both the number of items he/she has sampled so far (from CST) and the degree to which he/she cognitively processes each one (from ELM). In turn, attitude is modeled to influence both behaviors—that is, item selection and any further item sampling. We conducted a lab study and a field study to test six hypotheses. This research extends the theory on web personalization by providing a more complete picture of how sampling and processing of personalized recommendations influence a user’s attitude and behavior toward the personalization agent. For online merchants, this research highlights the trade-off between item sampling and item selection and provides practical guidance on how to steer users toward the attitudes and behaviors that will realize their business goals.
Web personalization allows online merchants to customize Web content to serve the needs of individual customers. Using data mining and clickstream analysis techniques, merchants can now adapt website content in real time to capture the current preferences of online customers. Though the ability to offer adaptive content in real time opens up new business opportunities for online merchants, it also raises questions of timing. One question is when to present personalized content to consumers. Consumers prefer early presentation that eases their selection process, whereas adaptive systems can make better personalized content if they are allowed to collect more consumers' clicks over time. A review of personalization research confirms that little work has been done on these timing issues in the context of personalized services. The current study aims to fill that gap. Drawing on consumer search theory, we develop hypotheses about consumer responses to differences in presentation timing and recommendation type and the interaction between the two. The findings establish that quality improves over the course of an online session but the probability of considering and accepting a given recommendation diminishes over the course of the session. These effects are also shown to interact with consumer expertise, providing insights on the interplay between the different design elements of a personalization strategy.