Corporate Taxonomy Mapping for Performance-Supporting KM

Abstract

Taxonomies are controlled vocabularies and multidimensional frameworks for organizing and classifying content. This study is the first to examine the meanings chief knowledge officers (CKOs) ascribe to corporate taxonomy mapping for enabling sustainable performance-driven knowledge management (KM). Utilizing a qualitative methodology, the research corpus comprised in-depth interviews, focus groups, participant observation, and cyber-ethnography. The findings underscore the essential role of investing resources in systematic taxonomy management as a cornerstone for attaining excellence in KM. Empirical evidence is provided for the critical importance of consistent taxonomies in establishing standardized terminology, facilitating systematic knowledge retrieval, and streamlining access within KM systems. Insight is provided into the constraints of contemporary technological advancements, including the capabilities of auto-tagging and classification through artificial intelligence (AI) and natural language processing (NLP) techniques. We underscore the nuanced interaction between human cognition and automated human-like capabilities in taxonomic classification, stressing the importance of embracing a balanced leadership socio-technical approach to the dynamic development of taxonomies. Furthermore, the study proposes promising avenues for future research to enhance the depth of inquiry into this subject matter.