Scaling the fractal plain: towards a general view of knowledge management

Abstract

PurposeThe purpose of the paper is to explore coherence across key disciplines of knowledge management (KM) for a general model as a way to address performance dissatisfaction in the field.Design/methodology/approachResearch employed an evidence‐based meta‐analysis (287 aspects of literature), triangulated through an exploratory survey (91 global respondents), to gather data on the drivers for KM. The paper attempts to demonstrate self‐similarity across six key KM disciplines using fractal theory as a data analysis tool.FindingsAppear to demonstrate self‐affinity between key disciplines in the field of KM. This provides a strong signpost for future research in the field when attempting to address practitioner dissatisfaction in performance.Research limitations/implicationsThe paper cannot determine importance, or value of the factors discussed. The meta‐analysis allows us to determine the existence of the identified functions and enablers. Limited representation of literature from outside the Northern Hemisphere will not allow for an assertion as to validity outside of this area. Findings could not determine whether factors were stable through time. While outliers in the data provide signposts for further research, it could be attributed to situated variance.Practical implicationsThis paper could influence future research and practice through support for the development of general models for the field. It signposts affinity between disciplines, which could direct theorists and practitioners to explore solutions outside of their situated discipline through a shared understanding.Originality/valueThe fractal theory data analysis approach appears to be unusual if not unique in the field of KM. The evidence‐based meta‐analysis provides depth and rigour with the results triangulated against an exploratory survey, which offers a richness of findings that speaks directly to the needs of the field.