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Abstract:
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Digital technologies allow for tremendous amounts of personal tracking data to be collected, which can serve as input to deliver personalized content to its users (i.e. system-initiated personalization; SIP). Digital information environments also perfectly lends itself for user-customization of in-app features, enabling users to create personalized information environments (i.e., user-initiated customization; UIC). The vast majority of literature on the effectiveness of SIP and UIC in various contexts (e.g., health, news, commerce) have studied these tailoring strategies separately, and have operationalized these concepts inconsistently. This makes it difficult to compare tailoring effects (e.g., attitude, knowledge) and understand which different mechanisms (e.g., attention, control, privacy concerns) are triggered by SIP and UIC, and under which conditions. This project takes on an innovative approach by combining digital trace data with self-report survey measures to explain what makes SIP and UIC effective and for whom. It will show that digital trace data is invaluable in validating self-reported cognitive processes, lending stronger support for the study findings.
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