An analysis of the information technology outsourcing domain: A social network and Triple helix approach

Abstract

Information technology (IT) outsourcing has become a widely accepted management strategy. As a consequence, a great deal of research on the IT outsourcing domain, covering a wide range of issues, has been conducted. This study investigates the IT outsourcing knowledge infrastructure from a network point of view. Triple helix indicators and social network analysis techniques are employed on 288 scholarly papers obtained from the Web of Science database using keywords related to IT outsourcing. The results reveal the key players in IT outsourcing research collaborations; their network characteristics, such as degree centrality; and the relationship of academia, industry, and government in terms of IT outsourcing knowledge production. This article also provides results‐based implications.

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

“…Khan and Park (2013) employed the SNA technique to study the hidden institutional, country, and regional network structures and the characteristics (such as degree centralities, components, diameter, and density) of the e-government knowledge network. Likewise, using the triple helix indicators and SNA techniques, Swar and Khan (2013) studied the knowledge networks associated with the IT outsourcing domain and revealed, among other things, the lack of research collaboration between developed and developing countries, which hindered the flow of IT outsourcing related knowledge among the countries. Swar and Khan (2014) applied a similar methodology to investigate and visualize the ICT knowledge infrastructure in South Asia by examining several network parameters including the degree centralities, density, and clusters of the domain.…”

“…For example, Vidgen et al (2007) used the European Conference on Information Systems (ECIS) and measured this community's network properties using a range of centrality measures for individual authors including degree, betweenness, closeness, eigenvector, flow betweenness, and structural holes. Meanwhile, Trier and Molka-Danielsen (2013) examined the IRIS community network and how individual conceptions shape it using a similar range of centrality measures to that of Vidgen et al (2007), Swar and Khan (2013), and Cucchi and Fuhrer (2007). Similarly, Xu and Chau (2006) …”

“…To understand the ways in which knowledge is created in the ITM domain, we need to examine and understand the hidden structures of the ITM research network. Thus, by incorporating the multi-level network analysis concept (i.e., authors, institutions, outlets, and countries) that Khan and Park (2013) use and the core centrality measures that several IS SNA studies (such as Vidgen et al, 2007;Cucchi & Fuhrer, 2013;Swar & Khan, 2013) identify, we examine the ITM domain's network-level (components, diameter, density, the clustering co-efficient, and average degree) and node-level properties (degree, betweenness, eigenvector, centralities, structural holes and hubs) to ascertain how its author-, institution-, journal-, and country-level networks are structured.…”

“…SNA is a structured way of analyzing relationships in groups by providing a rich and systematic means of assessing informal networks by mapping and analyzing relationships among entities, including people, teams, departments, or even entire organizations (Cross, Parker, Prusak, & Borgatti, 2001). Researchers have used the SNA technique to analyze and map knowledge networks formed by information system (IS) and its allied research communities, including electronic government communities (Khan & Park, 2013), information technology (IT) outsourcing communities (Swar & Khan, 2013), the social media-based systems research domain (Khan, 2013), information and communications technology research communities that focus on developing countries (Swar & Khan, 2014), the International Conference on Information Systems (ICIS) community (Xu & Chau, 2006), and the European Conference on Information Systems (ECIS) (Vidgen, Henneberg, & Naude, 2007) and Researchers Seminar in Scandinavia (IRIS) communities (Trier & Molka-Danielsen, 2013). …”

Key Technology Network of BioMEMS through Patent Analysis

“…However, recent studies have adopted the network-centric analytic approach 32,33 to identify key players within a network. In the present study, the latter method was also used to identify the key technology in patent technology network.…”