Needs that are shared by members of an organization can trigger an organizational learning process. To a large extent, needs are implicitly anchored in organizations and people can hardly articulate them. In this article, we present Bewextra, a method that allows for identifying hidden needs in organizations. Thereby, we trigger a knowledge conversion process, which is similar to Nonaka's SECI-spiral. In two case studies, we present how our Bewextra-process is applied to projects in educational contexts in Austria. In a first case study, we show that a combination of learning from past and future experiences extend the scope of the overall outcome. Since learning from future experiences requires a distinct environment (enabling spaces), we present a second case study. Here, we conducted a Bewextra-process with a large number of participants (n > 170), focusing on learning from future experiences.

Knowledge is a sustainable advantage and knowledge assets can increase value with use. A snowball effect of knowledge advantage advocates effective knowledge management and fosters its continual growth as it flows. Knowledge, however, flows unevenly throughout an organization and the problem is that the fundamental dynamics of these flows are still not well characterized in theoretical and computational models. This study built on existing work—knowledge-flow theory, need knowledge generation, and the critical success factors for enterprise resource planning implementation—to examine the multidimensional knowledge-flow phenomenon in context, and used the case study methodology for knowledge-flow theory building. The research question was two-pronged: how can need knowledge and its flow across stakeholders within an organization be explained using a multidimensional knowledge-flow model and how can Nissen’s five-dimensional knowledge-flow model be validated using a real-life immersion case? The researcher relied on three sources of evidence for this case study: project-related documentation, archival records, and interviews. Data triangulation yielded three results components: (a) a chronology of key events that obstructed knowledge flow, (b) a logic model depicting themes that contributed to knowledge-flow obstruction, and (c) explanations of the knowledge-flow patterns. This case study suggested enabling need knowledge determinants and obstructing conditions are in play that determine the path of need knowledge flow. These two research artifacts should be considered together to provide a fresh research avenue towards better understanding of knowledge flow dynamics.