This paper presents a design-based research study on the reporting of intellectual capital in firms. It combines the designing of an organizational development (OD) intervention with the testing of the intervention using an action research methodology. A growing gap between theory-based research and practice has been identified as one of the reasons for a lack of renewal in the field of OD. Design-based research (DBR) has been proposed as a methodology that can help bridge the gap between research and practice. The purpose of the paper is to illustrate what a comprehensive methodology for design-based research can look like and to demonstrate the type of OD knowledge this research can produce. The design approach is used to design and test a tool for the reporting of intellectual capital within firms as an OD intervention into the individual and collective sensemaking of managers.
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Background: A patient decision aid (PtDA) can support shared decision making (SDM) in preference-sensitive care, with more than one clinically applicable treatment option. The development of a PtDA is a complex process, involving several steps, such as designing, developing and testing the draft with all the stakeholders, known as alpha testing. This is followed by testing in ‘real life’ situations, known as beta testing, and then finalising the definite version. Our aim was developing and alpha testing a PtDA for primary treatment of early stage breast cancer, ensuring that the tool is considered relevant, valid and feasible by patients and professionals. Methods: Our qualitative descriptive study applied various methods including face-to-face think-aloud interviews, a focus group and semi-structured telephone interviews. The study population consisted of breast cancer patients facing the choice between breast-conserving therapy with or without preceding neo-adjuvant chemotherapy and mastectomy, and professionals involved in breast cancer care in dedicated multidisciplinary breast cancer teams. Results: A PtDA was developed in four iterative test rounds, taking nearly 2 years, involving 26 patients and 26 professionals. While the research group initially opted for simplicity for the sake of implementation, the clinicians objected that the complexity of the decision could not be ignored. Other topics of concern were the conflicting views of professionals and patients regarding side effects, the amount of information and how to present it. Conclusion: The development was an extensive process, because the professionals rejected the simplifications proposed by the research group. This resulted in the development of a completely new draft PtDA, which took double the expected time and resources. The final version of the PtDA appeared to be well-appreciated by professionals and patients, although its acceptability will only be proven in actual practice (beta testing)
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Athlete development depends on many factors that need to be balanced by the coach. The amount of data collected grows with the development of sensor technology. To make data-informed decisions for training prescription of their athletes, coaches could be supported by feedback through a coach dashboard. The aim of this paper is to describe the design of a coach dashboard based on scientific knowledge, user requirements, and (sensor) data to support decision making of coaches for athlete development in cyclic sports. The design process involved collaboration with coaches, embedded scientists, researchers, and IT professionals. A classic design thinking process was used to structure the research activities in five phases: empathise, define, ideate, prototype, and test phases. To understand the user requirements of coaches, a survey (n = 38), interviews (n = 8) and focus-group sessions (n = 4) were held. Design principles were adopted into mock-ups, prototypes, and the final coach dashboard. Designing a coach dashboard using the co-operative research design helped to gain deep insights into the specific user requirements of coaches in their daily training practice. Integrating these requirements, scientific knowledge, and functionalities in the final coach dashboard allows the coach to make data-informed decisions on training prescription and optimise athlete development.
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Post-earthquake structural damage shows that wall collapse is one of the most common failure mechanisms in unreinforced masonry buildings. It is expected to be a critical issue also in Groningen, located in the northern part of the Netherlands, where human-induced seismicity has become an uprising problem in recent years. The majority of the existing buildings in that area are composed of unreinforced masonry; they were not designed to withstand earthquakes since the area has never been affected by tectonic earthquakes. They are characterised by vulnerable structural elements such as slender walls, large openings and cavity walls. Hence, the assessment of unreinforced masonry buildings in the Groningen province has become of high relevance. The abovementioned issue motivates engineering companies in the region to research seismic assessments of the existing structures. One of the biggest challenges is to be able to monitor structures during events in order to provide a quick post-earthquake assessment hence to obtain progressive damage on structures. The research published in the literature shows that crack detection can be a very powerful tool as an assessment technique. In order to ensure an adequate measurement, state-of-art technologies can be used for crack detection, such as special sensors or deep learning techniques for pixel-level crack segmentation on masonry surfaces. In this project, a new experiment will be run on an in-plane test setup to systematically propagate cracks to be able to detect cracks by new crack detection tools, namely digital crack sensor and vision-based crack detection. The validated product of the experiment will be tested on the monument of Fraeylemaborg.
