An overview of knowledge management research viewed through the web of science (1993-2012)

Abstract

Purpose – The purpose of this paper is to present a bibliometric analysis of scientific output of the knowledge management (KM), the aim being to offer an overview of research activity in this field and characterize its most significant aspects. In addition, this study aims to quantitatively analyze KM research trends, forecasts, and citations from 1993 to 2012 in Web of Science (WOS). Design/methodology/approach – A total of 12,925 documents related to KM research were collected from following databases: Science Citation Index Expanded, Social Sciences Citation Index, Arts & Humanities Citation Index, Conference Proceedings Citation Index-Science, and Conference Proceedings Citation Index-Social Science & Humanities. These documents were carefully reviewed and subjected to bibliometric data analysis techniques. Findings – A number of research questions pertaining to patterns in scientific outputs, subject categories and major journals, author keywords frequencies, characteristics of the international collaboration, most cited papers and significant papers distribution of KM research were proposed and answered. In addition, there are five research sights on KM research are as follows: management science, computer science, information science, business, and engineering. Based on these findings, many implications emerged that improve one's understanding of the identity of KM as a distinct multi-discipline scientific field. Research limitations/implications – Comprehensiveness and inclusiveness of the analyzed KM-related data set in WOS because of some KM-centric journals are not indexed by Thomson Reuters. Originality/value – The paper offers an overview and evaluation of research activity into the KM viewed through the WOS during 1993-2012.

Future research on information technology in knowledge management

“…Qui and Lv conducted a recent review using bibliometric analysis of research indexed in the Web of Science database (Qiu & Lv, ). They identified as the core research areas of the two decades from 1993 to 2012 the following research themes “Knowledge Management,” “Knowledge Sharing,” “Ontology,” “Knowledge,” “Innovation,” “Organizational Learning,” “Knowledge Transfer,” “Tacit Knowledge,” and “Intellectual Capital.” Their analysis indicated that “Knowledge Sharing,” “Ontology,” “Tacit Knowledge,” “Intellectual Capital,” “Knowledge Management Systems,” “Semantic Web,” “Knowledge Creation,” “E‐Learning,” and “Project Management” are increasing topics, whereas research about “Information Technology” and “Information Management” are becoming less important.…”

Editorial: Electronic Commerce Research in seven maps

“…Research in the 1950s by Eugene Garfield on the structure of scientific communities led to the Science Citation Index (SCI) launched in 1964, which was ultimately purchased in 1995 by Thomson Reuters. This marked a significant change in bibliometrics; whereas previous versions of SCI were products of scientific investigation, Thompson Reuters acquisition and subsequent heavy marketing of SCI in the form of Impact Factors, ISI Web of Knowledge and various 'Specialty Citation Indexes' became a major revenue stream [2]. After Thompson Reuters' purchase, SCI related revenues roughly quadrupled every year for the following decade.…”

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

“…Apart from having high practical significance, KM is a well-established discipline with many journals and conference tracks dedicated to investigating and advancing academic KM research [46]. From 1993, when the KM discipline emerged, until 2012, there were 12,925 KM-related publications [42] -a number further increasing to this date.…”

“…Yet, text mining analysis in KM has not necessarily focused on emotions, but on uncovering different KM topics [42]. For instance, Ur-Rahman and Harding investigated the mechanics behind text analysis for organizational KM [54].…”

“…We used the sentiment dictionaries by Hu and Liu [22] and modified them to better suit KM research endeavors. Herewith, we will not only be able to detect which emotions prevail in existing KM research, but also to contribute to future research efforts in this domain by (1) developing a KM-specific sentiment dictionary and (2) applying this dictionary to English KM journals' publications as the majority of publications are written in English [42]. Subsequently, the results of our sentiment analysis are manually categorized and grouped according to emotion scales to classify and structure the results according to appropriate emotional categories, thus providing a preparatory step towards the taxonomy planned for the future.…”