How to deal with knowledge management misalignment: a taxonomy based on a 3D fuzzy methodology

Purpose: This paper aims to propose a new three-dimensional (3D) fuzzy logic methodology to evaluate the level of misalignment between an enterprise’s knowledge and the knowledge management systems (KMSs) it adopts. Design/methodology/approach: The proposed methodology was implemented by means of a field analysis based on semi-structured face-to-face interviews involving a sample of 61 small and medium enterprises (SMEs) operating in high-tech and/or complex industries. Findings: The paper highlights that while there is generally a high level of misalignment between an enterprise’s knowledge and the KMSs adopted, there are also a broad variety of behaviours. The paper identifies a taxonomy able to bring together the various types of behaviour associated with how an enterprise’s knowledge is related to KMS selection. Specifically, four behaviour patterns were identified, and the enterprises were then categorised accordingly as being guideposts, practice laggards, tool laggards or latecomers. Practical implications: The proposed taxonomy provides an operational tool that can be used by enterprises and policy makers alike. The paper shows how enterprises can use this tool to understand which category they belong to and support decision-making to introduce changes leading to improved levels of alignment. Policy makers, on the other hand, can use the proposed taxonomy to identify measures to support the competitiveness of local systems by improving management processes and knowledge sharing among enterprises. Originality/value: The paper highlights the difficulties that SMEs experience in adopting KMSs that are truly aligned with their knowledge and proposes a methodology to improve alignment.