The importance of digital platforms and related data-driven business models is ever increasing and poses challenges for the workability of competition in the respective markets (tendencies towards dominant platforms, paying-with-data instead of traditional money, privacy concerns, etc.). Due to such challenges, investigations of such markets are of high interest. One of recent cases is the investigation of Facebook's data collection practices by German competition authorities. Our paper, in contrast to the wide stream of legal studies on this case, aims to analyze whether Facebook's practices regarding data collection could constitute an abuse of market power from an economic perspective, more specifically against the background of modern data economics. In doing so we summarize the state of the advanced theories, including influences from behavioral economics, addressing such markets, and discuss four potential theories of harm.

Personnel scheduling is an important and challenging task that arises in a variety of application areas. Scheduling quality, however, strongly depends on the results of the preceding activities of staffing and shift design. This paper presents a novel approach that integrates staffing, shift design and scheduling into one optimization problem for the first time, providing a holistic view of the workforce planning process. By applying an evolutionary multi-objective bilevel algorithm, the three objectives staffing costs, number of shifts and scheduling quality are optimized simultaneously. The experimental results show that the presented holistic approach is suitable to support decision making regarding staffing as well as shift design by showing the decision maker tradeoffs between different solution alternatives. Furthermore, applying the holistic approach reveals new promising solution areas and valuable individual solutions, which would not have been found when optimizing sequentially.