Visualising Knowledge for Decision-Making: A Framework for Selecting Visual Templates

Digital technologies and AI have led to an increase in the automation of work, resulting in computers solving structured problems, while humans are responsible for ill-structured problem-solving, now and especially in the future. Several visual collaboration and knowledge structuring tools, such as Miro, Visio, and Lucidcharts, can help managers and experts to analyse ill-structured problems and co-create solutions. However, selecting the appropriate knowledge visualisation template for a comprehensive description and representation of knowledge remains an open research field. Although well-designed visual representations can improve decision-making, they can also introduce bias if not well conceived. They may constrain the attention to a limited set of decision variables, highlight only less important variables, alter the salience of knowledge, or inspire inappropriate comparisons. The use of well-conceived visual templates can reduce this risk by being easy to use, facilitating pattern recognition, and providing means for knowledge transfer, sharing, codification, and creation. This chapter explores the use of visual templates to support knowledge management activities, problem-solving, and decision-making. It suggests a new approach to the selection of visual templates, which support the decision-making process, and proposes a framework to help scholars and practitioners select the appropriate visual template for representing knowledge associated with a problem, taking into account the level of formality, knowledge type, and other dimensions. It also introduces new criteria for choosing visual knowledge templates, e.g. mental scenarios, knowledge content, and domain dependence. Given the exponential development of AI, the anticipated future steps in knowledge management research and practice are associated with a combination of visual knowledge structuring and AI-driven content recommendations and assistance.