Information rules: a strategic guide to the network economy
Information rules: a strategic guide to the network economy
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics
Information Systems Research
Learning and Forgetting: Modeling Optimal Product Sampling Over Time
Management Science
The Role of the Management Sciences in Research on Personalization
Management Science
Consumer Sequential Search: Not Enough or Too Much?
Marketing Science
ACM Transactions on Database Systems (TODS)
A temporal comparison of AltaVista Web searching: Research Articles
Journal of the American Society for Information Science and Technology
IEEE Transactions on Knowledge and Data Engineering
Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites
Information Systems Research
Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective
Information Systems Research
Buyers' Choice of Online Search Strategy and Its Managerial Implications
Journal of Management Information Systems
Eye-tracking product recommenders' usage
Proceedings of the fourth ACM conference on Recommender systems
Reciprocity in effort to personalize: examining perceived effort as a signal for quality
Proceedings of the 14th Annual International Conference on Electronic Commerce
The effects of location personalization on individuals' intention to use mobile services
Decision Support Systems
Timing and basis of online product recommendation: the preference inconsistency paradox
HCI'13 Proceedings of the 15th international conference on Human Interface and the Management of Information: information and interaction for learning, culture, collaboration and business - Volume Part III
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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.