Social robots hold potential for supporting children’s well-being in classrooms. However, it is unclear which robot features add to a trustworthy relationship between a child and a robot and whether social robots are just as able to reduce stress as traditional interventions, such as listening to classical music. We set up two experiments wherein children interacted with a robot in a real-life school environment. Our main results show that regardless of the robotic features tested (intonation, male/female voice, and humor) most children tend to trust a robot during their first interaction. Adding humor to the robots’ dialogue seems to have a negative impact on children’s trust, especially for girls and children without prior experience with robots. In comparing a classical music session with a social robot interaction, we found no significant differences. Both interventions were able to lower the stress levels of children, however, not significantly. Our results show the potential for robots to build trustworthy interactions with children and to lower children’s stress levels. Considering these results, we believe that social robots provide a new tool for children to make their feelings explicit, thereby enabling children to share negative experiences (such as bullying) which would otherwise stay unnoticed.

Background: Blockchain technology and accompanying programmed protocols (smart contracts) offer disruptive opportunities for businesses, public institutions, society, and its citizens. However, blockchain is a relatively young research area: the number of publications available regarding blockchain did not begin to rise significantly until 2012, and certain fields of the blockchain domain remain to be explored. A similar situation exists with research into the governance of blockchain solutions focusing on decision rights: the limited number of theoretical and empirical contributions hinders the proper adoption of governance mechanisms in practice. Method: A mixed-method approach was conducted in which 1) a structured literature review, 2) semi-structured interviews, and 3) a focus group discussion were utilized to determine the current situation regarding decision rights in the context of blockchain governance. Results: The structured literature review resulted in a total of 23 relevant contributions. Those contributions were consolidated to serve as input for a total of twelve semi-structured interviews, and for a focus group session with five participants, who were not part of the interviewee pool. Using that approach, an overview of the concepts, relationships and mechanisms pertinent to decision rights was composed. Conclusions: Considered together, the results show that decision rights are often overlooked at the start of a blockchain project, where technical considerations are dominant in the discussion with stakeholders. However, research also points out that the longer it takes to address decision rights in a blockchain consortium, the more complex and costly it becomes to introduce governance mechanisms at a later stage. Another important conclusion is that consensus is currently lacking as to what constitutes blockchain governance and what part decision rights play in governance processes, in both theoretical and practical terms.

Operational decisions significantly influence organizational performance and individual well-being. Decision mining offers a method to discover and analyze decision logic from decision logs, enhancing decision-making processes. However, evaluating the quality of decision discovery algorithms remains a challenge. While precision, fitness, generalization, and simplicity are well-established quality dimensions in process mining, their adaptation to the decision mining domain is underexplored. This study adapts these four dimensions to the necessary characteristics of decision models, providing a framework for evaluating decision discovery algorithms. Using a design science research approach, we develop tailored metrics and functions and demonstrate their application through a practical example of environmental permit management modeled in Decision Model and Notation (DMN). Precision measures how the discovered decision model reproduces the observed fact types and values from the decision log, detecting over-specification in the decision model. Fitness evaluates how completely the decision model covers the behavior in the decision log, identifying missing or under-specified elements in the decision model. Generalization assesses the model’s robustness to unseen decision cases by quantifying how well the discovered decision model performs beyond the training data. Simplicity captures the complexity in the discovered decision model in relation to a human actor-specified threshold. These insights guide decision model improvements, contributing to higher transparency, accountability, and fairness in operational decision-making processes. This research bridges a gap in the body of knowledge by providing a concrete methodology for evaluating decision discovery algorithms. The results support organizations in aligning decision models with regulatory requirements and public values, while also laying a foundation for future research.