Customer data taxonomy: a multidimensional scaling approach

Abstract

Privacy concerns have led customer-centric companies to adopt fair information practices to protect their customers’ private information. This situation forces organizations to understand how their consumers perceive their private data when they are disclosed or are already known by the company. This study aims to establish a classification of private data based on customer perceptions to allow companies to address rising privacy concerns. This study developed a two-dimensional map of the positioning of various pieces of information according to customer perceptions. A total of 157 observations were collected online, and multidimensional scaling was used as an analysis technique. The dimensions that explain the resulting classification or spatial map were the degree of exposure customers feel when their data are known and the likelihood that a specific piece of information can identify a particular person.