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.
Personalized information technology services have become a ubiquitous phenomenon. Companies worldwide are using the web to provide personalized offerings and unique experiences to their customers. While there is a lot of hype about delivering personalized services over the web, little is known about the effectiveness of web personalization and the link between the IT artifact (the personalization agent) and the effects it exerts on a user's information processing and decision making. To address the impact of personalized content, this article theoretically develops and empirically tests a model of web personalization. The model is grounded on social cognition and consumer research theories adapted to the peculiar features of web personalization. The influence of a personalization agent is mediated by two variables: content relevance and self reference. Hypotheses generated from the model are empirically tested in a laboratory experiment and a field study. The findings indicate that content relevance, self reference, and goal specificity affect the attention, cognitive processes, and decisions of web users in various ways. Also, users are found to be receptive to personalized content and find it useful as a decision aid. Theoretical and practical implications of the findings are discussed.
With advances in tracking and database technologies, firms are increasingly able to understand their customers and translate this understanding into products and services that appeal to them. Technologies such as collaborative filtering, data mining, and click-stream analysis enable firms to customize their offerings at the individual level. While there has been a lot of hype about web personalization recently, our understanding of its effectiveness is far from conclusive. Drawing on the elaboration likelihood model (ELM) literature, this research takes the view that the interaction between a firm and its customers is one of communicating a persuasive message to the customers driven by business objectives. In particular, we examine three major elements of a web personalization strategy: level of preference matching, recommendation set size, and sorting cue. These elements can be manipulated by a firm in implementing its personalization strategy. This research also investigates a personal disposition, need for cognition, which plays a role in assessing the effectiveness of web personalization. Research hypotheses are tested using 1,000 subjects in three field experiments based on a ring-tone download website. Our findings indicate the saliency of these variables in different stages of the persuasion process. Theoretical and practical implications of the findings are discussed.