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.

Social robots are often ignored in public places. What can such a robot do to attract more people to achieve its goals? In this study, we investigate what a social robot needs to do to achieve better initial human-robot interaction. Two experiments have been conducted at two University of Applied Sciences in the Netherlands (n = 3169) to discover which attraction strategy works best. Four different scenarios have been tested, in which physical movement, lights, and moving images (GIFS) are combined. During the experiments, most people were attracted to scenarios where the robot used a combination of lights, GIFs, and its arms. The active stance of the robot attracted a lot of students, who were trying to interact with the robot. Based on the data analysis we accept both our hypotheses stating that the combination of physical movement, lights, and GIFS decreases the chance that the social robot is ignored and is rated more attractive by the participants (students) compared to social robots that do not possess those properties, in the context of higher education hospitality. Future research should focus on detailing factors that further increase the attractiveness of social robots as this study is limited to only one social robot used.

This study explores the feasibility of using generative AI to enhance spatial and legislative opportunity finding within urban digital twins. Digital Twins (DTs) integrate real-time and historical data to provide comprehensive views of built environments, potentially aiding different disciplines involved in spatial planning practices. However, the complexity of legal frameworks and their visualization in DTs remains a challenge. Leveraging advancements in large language models (LLMs), this research investigates how multimodal AI can interpret complex legislative data to improve spatial planning. The study employs a Design Science Research (DSR) methodology, focusing on tuning existing LLMs with spatial content. Key findings include the successful generation of Geography Markup Language (GML) code, enhancing interoperability with spatial planning tools, and the iterative design process that improved the model's performance. Preliminary results indicate that a multimodal approach, including text, images, and GML code, significantly enhances the model's capability. Future research will focus on improving data quality, expanding multimodal capabilities, and evaluating real-world applications. This study contributes to the development of transparent, contestable, and explainable AI solutions for spatial planning.

In the context of climate change and housing challenges, Digital Twins (DTs) are a promising technology. However, there is little experience regarding good use of this technology. This research proposal explores how DTs can safeguard public values in spatial planning under the Environmental Planning Act (Omgevingswet). Combining a socio-technical perspective with Value-Sensitive Design (VSD), the study focuses on integrating values into DT technology. Expected studies include identifying key values, designing a normative framework, and measuring its impact. This contributes to improved decision-making and spatial planning by regional governments.