Data-driven value creation from businesses to consumers: aligning strategies to inform development and design of data-driven services

Data-driven business models are increasingly changing how businesses generate value, shape customer relationships, and secure competitive advantages. In doing so, businesses face the challenge of integrating data operationally and using it strategically. At the same time, data-driven services increasingly penetrate consumers' lives, influencing and collecting data from their consumption practices. This raises the question of how data-driven services can be designed to create added value for businesses and consumers. Based on the three derived concepts of Business-Data, Consumer-Data, and Business-Consumer Alignment, this thesis addresses this interface by examining the perspectives of businesses and consumers on data-driven services. As a coordination process between business and data strategy, two research papers on Business-Data Alignment examine the challenges businesses face in developing data-driven services. Three papers focus on Consumer-Data Alignment by analyzing the way consumers interact with data-driven services and how data influences their everyday practices. In addition, two research papers develop methodological tools to support Business-Consumer Alignment as a cooperative coordination process between businesses and consumers to achieve common goals and values in designing data-driven services. This thesis bridges theory and practice by deriving action-oriented principles and tools to address strategic and operational challenges in designing data-driven services, contributing to the research domains of Information Systems, Consumer Informatics, Recommender Systems, and Reputation Systems. - Information Systems: This work expands the understanding of data-driven value creation by specifying the strategic link between business and data strategies through Business-Data Alignment. From the perspective of businesses, technical challenges, such as the integration of heterogeneous data sources, and organizational hurdles, such as a lack of data literacy, are addressed. The developed Data Service Canvas and Data Storytelling Process represent practical frameworks that support companies in strategically planning and communicating data-driven business models and services. - Consumer Informatics: This thesis examines the consumer perspective to show how data-driven services shape consumers' everyday consumption practices and how consumers can be involved as active designers of user-centered data-driven services. Concrete design principles based on consumers' needs and practices are also derived for the domains of food and mobility. - Recommender Systems: This work conducts a case study in the food domain in the Recommender Systems research area to show how personalized and value-oriented recommendations can be designed to better support critical consumption practices, such as sustainable or ethical consumption. - Reputation Systems: In the research domain of Reputation Systems, this thesis explores trust-building in the sharing economy, specifically in peer-to-peer carsharing, by examining an algorithm-based trust mechanism to complement user ratings. Evaluating a prototypical algorithm-based reputation system shows how algorithmic ratings can be made transparent and understandable and how such systems can contribute to fair and comprehensible assessments.