title: Managing Privacy Challenges in Digital Services and Machine Learning
Creators: name: Zöll, Anne
Publisher: name: UNSPECIFIED
Year: 2024

description: Companies‘ data-driven digital services rely on the collection of personal data and its processing by self-learning algorithms. With the help of machine learning, companies can offer personalized services tailored to customer needs. As a result of the intensive collection of personal information by companies, customers have a sense of loss of control over their own personal information. They also have high privacy concerns about data handling. These concerns are amplified by high-profile data breaches such as the Cambridge Analytica scandal. Consequently, customers are increasingly hesitant to share their personal data with these companies, which could pose a risk to data-driven digital services. A smaller amount of data could compromise the performance of algorithms and thus reduce the quality of data-driven digital services. Therefore, the stated goal of this dissertation is to establish the complex balance between protecting customers‘ privacy and improving value creation processes. Thus, the central research question of this dissertation is how companies can mitigate the dilemma between protecting individual privacy and enhancing data-driven digital services.
This dissertation examines the issue from three different perspectives: technological, individual, and organizational. Over the past decades, privacy-enhancing technologies have been developed. These information and communication technologies protect individuals‘ privacy either by removing or minimizing personal information or by preventing unnecessary or unwanted processing of personal information while maintaining the functionality of information systems. Despite the advanced implementation of these privacy-enhancing technologies, they are rarely used in data-driven digital services. Therefore, this dissertation provides an overview of the reasons why these privacy-enhancing technologies are only reluctantly adopted by companies. In particular, it highlights the barriers that arise when integrating these technologies into data-driven digital services. Thus, this dissertation demonstrates that a purely technological solution is not sufficient to fully answer the research question. This is the starting point of this dissertation, which aims to find a solution to mitigate the aforementioned dilemma. As privacy concerns are primarily customer-driven, this dissertation focuses on individuals as a further perspective. This perspective aims to examine how companies should design data-driven digital services to alleviate customer privacy concerns. To achieve this goal, the dissertation draws on theories from privacy research, focusing on individuals‘ control over their personal information and trust in data-driven digital services. Essentially, design principles are developed that are necessary to create data-driven digital services that allow individuals to regain control over their personal data. Furthermore, this dissertation continues to develop design principles to enhance costumers‘ trust in data-driven digital services, especially those based on machine learning. As a third perspective, organizations are included, particularly examining how machine learning can be integrated into companies‘ value creation process to build data-driven digital services. The focus of this research is to identify the factors that either support or hinder the integration of machine learning into companies‘ value creation processes. Although many factors for the adoption of innovations have been examined in previous literature, a re-examination is important because the characteristics of machine learning are significantly different from other technologies. For instance, vast amounts of personal information are processed to generate personalized recommendations for individuals. The ability of machine learning to uncover hidden patterns can lead to the inadvertent disclosure of sensitive personal information, thereby intensifying privacy concerns. Additionally, this dissertation builds on previous research that highlights differences in the acceptance of innovations in different cultures and examines which different factors are important for the adoption of machine learning in data-driven digital services in different cultures. In this regard, this dissertation applies the organizational readiness concept for artificial intelligence within cultural research to gain deeper insights into this intersection.
In summary, this dissertation presents three important perspectives that aim to alleviate the dilemma between the protection of individuals‘ privacy and the use of machine learning for value creation in companies. It deals with privacy-enhancing technologies, prioritizes user-centered approaches, and the strategic design of value creation processes within companies. Particularly driven by the three perspectives, this dissertation motivates the development of a multilevel theory that aim to enable a holistic approach to alleviate the dilemma between privacy protection and value creation by bringing together technology, individuals, and organizations.