Purpose The growing number of publications on knowledge management (KM) has addressed heterogeneous topics that lack integration and classification. This article closes the classification gap by presenting a classification scheme, providing an integrated overview of KM publications. Design/methodology/approach The development of the classification scheme follows a multistep approach. By applying a taxonomy development method, the results of a previous content analysis of 4,290 publications were processed to integrate 3,780 keywords into a classification scheme. Findings The classification scheme consists of 13 main categories and subcategories with six levels of detail. The scheme covers not only KM-specific keywords but also keywords from related disciplines, indicating a strong interdependence with related research domains. Research limitations/implications The scheme provides a starting point for ongoing collaboration within the KM community with the aim of improving the classification results and refining the scheme to manifest the core identity. Practical implications The scheme is helpful in understanding whether KM implementation activities in organisations are aligned with overall research activities and topics covered by publications. Originality/value Developing a scheme based on a prior content analysis turns out to be a unique and innovative approach that has never before been done in the KM domain.

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This article reports on the development of a knowledge management (KM) dictionary and its application to automated content analysis to investigate topical foci of KM publications and provide an overview of the current research landscape. While automated content analysis gains importance, a problem prevails concerning the use and analysis of compound concepts (e.g., organizational learning). Using a self-developed dictionary of KM-related compound concepts, a sample of 4,290 publications from ten top-ranked KM journals and one KM conference was analyzed using text-mining software. Based on the dictionary approach, this study investigates core research themes of the KM discipline and compares key research interests throughout the IJKM community and those of other outlets. The investigation provides guidance to identify research opportunities in KM and provides useful implications concerning the application of dictionaries. Practitioners might adapt their organizations' approaches to KM accordingly, with regard to prevailing themes and trends in KM research.

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Aquaculture is one of the fast-growing food-producing agriculture subsectors. However, the digital infrastructures developed in aquaculture are self-organising platforms i.e. they do not rely on a centralized intermediary for monitoring, coordinating activities or for overseeing transactions. Hence, the main objective of this research paper is to identify the challenges farmers face in an entire supply chain for designing a digital platform for the aquaculture domain. The main problems faced by the farmers include water quality issues, disease outbreak, lack of proper information regarding suitable insurance policies etc. We have identified eight such issues that the farmers face in an entire harvest period and also prioritized them. The results from our study could be used for the further advancement of an integrative perspective in the design and implementation of the digital platform for aquaculture.