This paper introduces a new process-oriented visualisation method for risk assessment in groups. Today, in corporate risk assessment there is a lack in visual facilitation methods for collaborative assessments of risks. Existing visualisation methods emphasize analytical purposes. However, they are not useful for the facilitation of risk assessments in a group, such as the board of management. The described risk visualization approach offers a visual dialogue oriented approach to improve the quality of organisational risk-assessment in groups and goes hand in hand with already established risk management processes and systems. Secondly, this paper introduces the “ETH Baugarten Value Lab”, where we tested the tool on touch displays.
Although receiving significant attention in management research, the organizational competence concept still remains difficult to apply, due to the vagueness of the theoretical construct, and due to the lack of pragmatic procedures to make it actionable. According to recent research, knowledge visualization may mitigate the elusiveness of the competence concept by assisting the identification, management, and communication of competences. In this paper, we thus review the academic literature in search for conceptual representations designed to support organizational competence mapping at the intra-, and inter-organizational level. By taking a synoptic overview of the collected representations, we single out the building blocks of competence visualization, and develop a corresponding classification. Drawing on this classification, we position twelve existing competence representation methods in an integrative framework to assist practitioners in selecting the right representation method and to inform scholars about future research and development needs.
Classifying phenomena is a key step to building new knowledge, especially in the early stages of a research process. It can bring about multiple advantages and insights, such as overview and comparison. Yet it also poses several risks and constraints. Thankfully, challenges can be over-come by re-classifying items in a domain with alternative classification principles, which lead to new insights or perspectives, as well as highlight previously neglected considerations. This process can be supported by graphic representations. Visualizing the drawn (and redrawn) distinctions can make a classification accessible and versatile, which makes it easier to compare with other classifications. Visualizing classifications can augment the entire research process, including hypothesis formation, testing, interpretation and result reporting. There is no systematic overview of methods to represent (especially qualitative) classifications graphically. This paper fills that gap in the literature. We distinguish between four types of visual classifications, based on their differing ability to emphasize hierarchies or group relations. We label these four types as compilations, configurations, layers, and trees. We analyze their benefits for the research process and point out potential risks to consider when using visualization for classifications purposes in social science research.
A fundamental problem in processing 3D shapes is insufficient knowledge engineering. On the one hand there are numerous methods to design and manufacture 3D shapes in the real world. On the other hand, numerous digital methods for representing and processing shape have been developed in computer graphics. Most of these methods make certain assumptions about the kind of 3D objects that they will be used for: A surface smoothing algorithm, for instance, is not well suited for assemblies of rectangular blocks or for pipe networks. However, it is currently not possible to formulate the properties of a given shape explicitly in an commonly agreed way. This paper is a first step towards classifying structural descriptions of man-made shape. By listing construction principles and principles for their combination it follows a phenomenological approach. The purpose is to illustrate the inherent complexity of the domain, and to lay out the foundation for subsequent thorough knowledge engineering.
Based on an analysis of existing tools and approaches and literature from the areas of design and cognitive science, we identify a set of functional requirements to be met by diagrammatic knowledge mapping techniques and tools in order to be cognitively adequate for extensive personal knowledge management. This collection of requirements can be used to evaluate existing tools or as a guideline for the design of novel knowledge mapping approaches and tools.