Due to technological advances, customer surveillance (i.e., the collection, capture, use, or storage of customers’ personal data) is becoming less expensive and more covert. Brands use these personal data that contain needs, preferences, characteristics, behavior, attitudes, or other customer attributes (i.e., market intelligence) to develop more competitive products and services. Customer surveillance also can put stress on customer relationships with brands, thus brands must conduct customer surveillance in a way that is sensitive to customers’ concerns. This dissertation investigates these concerns and proposes attitudes towards customer surveillance based on consumer privacy and value concerns. These attitudes explain differences in both cognitive and automatic reactions to customer surveillance, thus advancing the literature beyond the privacy calculus concept. Through 26 semi-structured interviews, this dissertation explores the implications of individuals having different levels of consumer privacy and value concerns. Next, it focuses on strategies to more efficiently and effectively conduct customer surveillance activities. It does this by proposing the surveillance prompt framework and a method of critically assessing the customer insight value of customer data sources. Using the responses of 1433 participants, four experiments show how different customer data factors predict customer insights (e.g., personality, future purchase behavior) with varying degrees of accuracy and consistency. The dissertation concludes with a summary of the contributions and implications of this research and calls for future customer surveillance research.
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Thesis advisor: Pitt, Leyland
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