Knowledge Networks of the Information Technology Management Domain: A Social Network Analysis Approach

Abstract

Abstract:Using the social network analysis technique, we decomposed the knowledge networks of the information technology management (ITM) domain. We included a total of 893 papers published during the 1995-2014 period in the network analysis. From this domain, the network and ego level properties-such as, degree centralities, density, components, structural holes, and degree distribution-suggest that, unlike the other information systems communities, the ITM is a community with a unique character and distinct collaboration patterns. The results show that the ITM knowledge networks are fragmented and exhibit a power law distribution in which incoming nodes and links prefer to attach to the nodes that are already well connected. We discuss several implications that arise from the network configuration that could aid the future development of the ITM domain.

Mapping the jungle: A bibliometric analysis of research into construal level theory

“…In addition, the number of coauthorships and components in the entire network similarly increased over the years. Accordingly, from the first to the fourth period, the network's density (i.e., proportion of realized on possible edges) decreased from 1.63% to .64%, .37% and finally .36%, respectively, which is similar to bibliometric analyses in other fields (e.g., Hu et al, 2018; Khan & Wood, 2016). This sparsity is due to the large number of small components that mainly represent single coauthored papers.…”

Methodological research on partial least squares structural equation modeling (PLS-SEM)

“…More specifically, they do not offer any insights into the structure of scholarly networks that have been formed as a result of the collaborative works of researchers and that shape, generate, distribute and preserve the PLS-SEM domain's intellectual knowledge (Khan and Park, 2013) [1]. Understanding the structures of these networks is important, however, as they set the rules for the network's power game, in which authors, editors and topics joust for authority and influence (Khan and Wood, 2016). Thereby, these network structures influence the content, output and performance of those involved in its boundaries (Vidgen et al, 2007).…”

Machine learning in information systems - a bibliographic review and open research issues

“…Based on the assumption that the disciplines influence each other, we argue that additional information about the collaborations and affiliations of researchers to different research areas within our sample would facilitate understanding. In line with Khan and Wood (2016) we argue that collaborative behavior influences the research diversity with all its virtues and thus influences the content, performance, and output of those involved in its boundaries (Vidgen et al 2007).…”

“…At this point, it should be mentioned that this quality assurance can be seen critically in the context of more technical research in IS, which includes the application of more complex ML methods. IS scholars who are rejected within the review process in one of these outlets that are considered as high-quality outlets by senior IS scholars (i.e., they are not accepted within the prevailing IS paradigm) proceed and often get the papers published in Computer Science journals, such as IEEE Transactions or in journals linked to the IEEE Computational Intelligence Society; these journals have much higher impact factors and are from a scientometric perspective (Leydesdorff 2001;Khan and Wood 2016) as relevant as the Basket journals but may (falsely, e.g., Lowry et al 2004, p. 36) not be judged as strong contributions to IS research. Regarding data consistency and relevance across the sample, only publications containing the keywords from Table 2 in their abstract, title, or main text, were retrieved and analyzed.…”

“…This work aims to understand the context of ML (non-) adoption and application as a cutting-edge technology within scientific research in the IS field. Beyond that, this paper also aims to make IS research more aware of the topic of applying ML methods based on our bibliographic review and subsequent scientometric analyses (Leydesdorff 2001;Khan and Wood 2016). By answering our aforementioned research questions, we aim to contribute to IS research in several ways: (1) By analyzing the apparent issue with the application of ML in IS research through a bibliographic bibliographic review in a quantitative manner, we emphasize that a real issue persists.…”