Previous research has identified customer trust and privacy concerns as key factors for predicting the adoption and continued use of technology, especially for emerging technologies like the smart home. However, there is only limited empirical data on how trust can be restored after privacy violations, which are known to be common in smart home contexts. This study uses a scenario-based policy-capturing design to collect data from long-term smart home users. We examine the effectiveness of verbal (apology, reticence, denial) and substantive (compensation, regulation, third-party involvement) trust repair strategies for two types of privacy violations. Our results indicate that the effectiveness of verbal strategies varies depending on the type of violation, while regulation is the most effective substantive strategy across violation types. Combining verbal and substantive strategies is found to be more successful than isolated approaches. The study concludes with implications and recommendations for future research.

Requirements engineering is often seen as one of the most important phases of a software project. The imposed requirements are the basis for developing a product or software. Despite the existing research on requirements, most techniques and teams focus on the functional requirements, which reflect the functions of the software. Especially when developing software for cyber physical systems e.g. for manufacturing, there are a lot of (technical) constraints, which must be considered. Because of the variety of constraints and their different impact on the software development and the related project, it is needed to classify and differentiate between these. Therefore, the aim of this paper is to present a taxonomy, which can be used to classify constraints in the context of requirements engineering.