A design for a primary personal information market

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As mobile and wearable technologies grow in popularity, ever-increasing volumes of fine-grained personal information are generated as people go about their daily lives. There is a growing consensus that these data are of great value to corporations for a variety of uses and there is a flourishing worldwide secondary market for personal information. From the individual’s, perspective, this personal information may be exchanged for ‘free’ services, but there are currently no widely adopted means by which individuals can benefit financially from their personal information. This research addresses the question as to why this is the case. Although there is a wealth of multi-disciplinary research into related areas such as personal informatics, infomediaries and value co-creation, there is very little research in how a Primary Personal Information Market (PPIM) could operate for the benefit of both individuals and companies. In particular, the application of the integrated Service Innovation Method (iSIM), industry platform theory and digital platform theory to the design of a PPIM has not yet been undertaken in the literature. This presents an opportunity for the development of a novel artefact that would make a contribution to both the theoretical aspects of platform design and also be of practical benefit to those individuals, businesses and entrepreneurs who are looking to monetise personal information. Drawing on design science methodology, market engineering theory, service innovation and digital platform theories, this dissertation presents a novel, detailed design of an innovative digital platform, which would collect and monetise personal information on behalf of the original creators of that information without compromising privacy or security. It presents a justification for the creation of a Primary Personal Information Market (PPIM) as a means of addressing the problem of personal information monetisation and outlines a theoretical model of a PPIM and the IT infrastructure necessary to support it. This dissertation also presents a critical Service Architecture for the PPIM digital platform to ensure scalability and potential commercialisation. In line with Design Science Research Methodology, the structure of this dissertation follows the design evolution of a PPIM digital platform based on academic experts’ iterative feedback in stages over three years. Commencing with an initial design of a two-sided market and the transaction object, which would be traded on such a market, the design of a PPIM then evolves to a four-sided industry platform and culminates in the detailed design for a four-sided digital platform. The PPIM Service Architecture Framework was also developed. A prototype PPIM is described and its viability from the perspective of the digital platform’s four categories of market participants is assessed. The primary theoretical contribution of this dissertation is to develop a theory for design and action (following the classification scheme of Gregor). It applies the integrated Service Innovation Method (iSIM) to the design of a novel digital platform. Following iSIM, this dissertation outlines a schematic description of a novel four-sided PPIM design and the associated PPIM Service Architecture and end-to-end operational models to facilitate a scalable implementation of a PPIM. The schematic representation of the four-sided PPIM design and associated Service Architecture and end-to-end operational models are described at an abstract level without recourse to a specific technology. This dissertation also makes practical (managerial) implications for platform owners and for all four participant types on a four-sided PPIM. Business owners and senior executives in industries which are undergoing “platformisation” will benefit from considering, in the light of this research, how to monetise the personal information they collect. Individuals with a propensity to monetise their personal information will benefit from understanding how this can be facilitated while maintaining privacy and security and model developers and data feed developers will benefit from gaining an insight into how to generate a recurring income stream from personal information.
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