A Taxonomy of Information System Projects’ Knowledge-sharing Mechanisms

Despite its criticality to the success of information system (IS) projects, knowledge sharing among IS projects is generally ineffective compared to knowledge sharing in IS projects. Although several mechanisms for knowledge sharing exist in the literature, it is difficult to determine which mechanism one should use in a specific context. We lack work that concisely and comprehensively classifies these mechanisms. Based on a literature review, we extracted information from 33 studies and identified twelve mechanisms for sharing knowledge among IS projects. Then, we derived a taxonomy for these mechanisms, which extends previous research by both adapting existing mechanisms and complementing the set of dimensions used for their classification. The results help to systematically structure the fields of knowledge management and IS projects. Both research and practice can use this taxonomy to better understand knowledge in this domain and effectively adopt mechanisms for a particular application

Inspired by Emotions, Guided by Knowledge: Which Emotional Cues Dominate Knowledge Management Research?

Knowledge, being context-specific and bound to individuals, is strongly related to human emotions such as joy or fear. Although emotions play an important role to articulate knowledge in text, KM research only offers insight on emotions from specific angles, neglecting a holistic view. Applying a sentiment analysis, this study closes the aforementioned gap by investigating the occurrence of emotions in KM publications. Based on general sentiment dictionaries, we (1) develop a dictionary aligned with KM, and (2) apply it to KM publications to determine the presence of positive and negative emotions and categorize them according to an emotion scale. Our results reveal that a variety of emotions is expressed in KM studies, both positive and negative, proving its relevance for this domain. We find that there is high term diversity, but also the need for consolidation of terms as well as emotion categories in KM

Topical Research Cluster of BLED Community – A Text Mining Approach

The number of research publications is growing exponentially, also in the discipline of Information Systems (IS). Evidently, we need new automated means for carrying out extensive inquiries into bodies of knowledge to understand the thematic foci of publications. The aim of this study is to apply an automated cluster analysis as a method of text mining and identify thematic foci of 654 BLED conference proceedings obtained from Scopus since 2005. Subsequently, we discuss advantages and challenges associated with the automatic analysis of huge volumes of texts. Our results support scientists and practitioners to focus future research efforts on these topics and thus help to establish and investigate the identity of the IS discipline, particularly against the background of the growing diversity of topics. The results help the conference to align future calls accordingly. In the future, a prototype can be implemented based on the results to suggest suitable search results

Identifying the Opportunities for the Design of Digital Platforms: A Topic Modelling Approach

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

Analyzing the Literature on Knowledge Management Frameworks: Towards a Normative Knowledge Management Classification Schema

Knowledge Management (KM) is a young and interdisciplinary subdomain of the IS discipline and it covers a considerable number of different topics. Due to its interdisciplinary character, a common understanding of KM is still missing. Several studies have already focused their efforts on harmonizing the discipline’s topics by developing KM frameworks. The purpose of this paper is to explore and integrate these frameworks in order to contribute to a common KM understanding. The procedure is twofold: 74 studies are identified through a structured literature review and compared using a concept matrix. The studies discuss already existing KM frameworks from research and practice or present the results of reviewing the KM domain through the development of frameworks. Based on these results, a normative KM classification schema is introduced. This schema comprises seven main KM categories and offers a summary of the common grounds in the domain of KM frameworks. The study provides guidance where to focus future research efforts and helps to identify potentially relevant topics, which, despite their relevance to the KM field, have not been considered up until now. Moreover, businesses can use the schema to get an overview of existing frameworks, which can be adapted in their organization

Knowledge Management in the Era of Artificial Intelligence - Developing an Integrative Framework

Artificial intelligence (AI) and its rapid technological advancement will considerably affect the future of work and the way organizations manage their knowledge management (KM) processes. Typical KM initiatives include the development, transfer, storage, and evaluation of a firm’s knowledge throughout the knowledge lifecycle, but often neglect ongoing advances in the AI area. Thus, organizations struggle to integrate AI into working environments to leverage outcome efficiency. Starting with a descriptive literature review, we draw on two KM strategies–personalization and codification–and introduce an adaptive, AI-specific approach for organizational KM implementation. Our approach supports KM strategy and research, as it outlines how AI affects current working processes. This enables to understand which role AI can take in the human-AI interaction. Knowledge managers are provided with a tool to align organizational KM with the business strategy and current technological progress in AI context. We also outline future research to validate the construct

Impact of Dictionaries on Automated Content Analysis - The Use of Compound Concepts in Analysing Knowledge Management Research

Within the knowledge management (KM) domain, we perceive an increasing number of publications. Considering this increase, content analysis (CA) is a popular and empirically established method to handle context-sensitive information and to achieve an improved understanding. While CA can be applied in an automated way by using software solutions, a problem concerns the analysis of compound concepts (e.g. “intellectual capital”). Whereas dictionaries (i.e. lists of compound concepts) have been suggested to solve this problem, lack of research exists concerning the impact of using such a dictionary. By focusing on the KM domain and using the example of 614 publications within the Journal of Knowledge Management (JoKM), this paper aims to evaluate the impact of dictionaries for automated CA. We perform CA applying the automated approach with and without using a self-developed KM dictionary. The results were compared in terms of result similarity as well as result relevance to the KM discipline. Our findings reveal that using a dictionary for automated CA can lead to an improved context understanding and time savings. However, these benefits are opposed by subjectivity that results from the manual extraction of compound concepts to be used in the dictionary

33rd Bled eConference – Enabling Technology for a Sustainable Society

The number of research publications is growing exponentially, also in the discipline of Information Systems (IS). Evidently, we need new automated means for carrying out extensive inquiries into bodies of knowledge to understand the thematic foci of publications. The aim of this study is to apply an automated cluster analysis as a method of text mining and identify thematic foci of 654 BLED conference proceedings obtained from Scopus since 2005. Subsequently, we discuss advantages and challenges associated with the automatic analysis of huge volumes of texts. Our results support scientists and practitioners to focus future research efforts on these topics and thus help to establish and investigate the identity of the IS discipline, particularly against the background of the growing diversity of topics. The results help the conference to align future calls accordingly. In the future, a prototype can be implemented based on the results to suggest suitable search results.</p

Advancing Automated Content Analysis in Knowledge Management Research: The Use of Compound Concepts

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