Academic journal article International Journal of Business

Identify Potential Opportunity for Research Collaboration Using Bibliometrics

Academic journal article International Journal of Business

Identify Potential Opportunity for Research Collaboration Using Bibliometrics

Article excerpt


In recent decades, there has been a notable shift toward R&D that crosses disciplines and organizational boundaries. One reason is because of the complexity and scope of the problems that society is currently facing (e.g., global warming, emerging infectious diseases, and loss of natural resources). These problems require innovative solutions that integrate knowledge from different disciplines. The concept of networking R&D is therefore increasingly important. However, the main challenge in initiating any cross discipline development is how to identify the potential groups of experts for collaboration and which areas of expertise they specialize in. One common expert identification method is based on social connections, i.e., ask people and follow referrals until finding someone with appropriate expertise. However, this could be a time-consuming and biased task. Fortunately with the availability and accessibility of research literature and the advancement in information retrieval, natural language processing, and machine learning, potential experts can be identified automatically from such information sources.

This study aims to apply bibliometric analysis of research publications to discover potential research collaboration among key researchers. To address this challenge, two research questions are needed to be answered: (1) who are the key researchers/practitioners in the specified field? and (2) are there any forms of collaboration or linkages among these experts in the field?

The analysis can identify experts whose relationships have already been established as well as for those who never know each other, yet seem to share similar research interests. The latter case can be considered as a hidden network in which the collaboration among those experts can also be initiated.


A. Bibliometric Analysis

Bibliometrics is the use of statistical and mathematical methods to study publication patterns (McBurney and Novak, 2002). It uses the counts of publications, patents, and citations to develop science and technology performance indicators. These indicators are used to measure research outputs (Narin and Olivastro, 1994). In other words, the number of publications or citations created by a research unit may be used to estimate the output of that research group.

Fundamental work in bibliometrics originated in the 1960's. Derek de Solla Price (Price, 1963) and Eugene Garfield (Garfield, Sher, and Torpie, 1964) were pioneers to develop bibliometric indicators. Later on in the 1970's, Henry Small improved the method with the development of co-citation analysis (Small, 1973). A bibliometric analysis is based on three principles: Activity measurement by counting publications, impact measurement by counting subsequent citations of a publication and linkage measurement involving co-citations and keywords used from paper to paper (Kongthon, 2004). A bibliometric analysis can provide a macroscopic view of the entire field in the global context of related and neighboring fields. The understanding of the bigger picture will allow an individual to rationally choose a specific starting point for more detailed investigations (Kostoff et al., 2001). Bibliometric methods are used to infer knowledge from a body of literature (Porter and Cunningham, 2005; Porter, Kongthon, and Lui, 2002), as well as to communicate the development and evolution of knowledge in a given field (Daim, Rueda, and Martin, 2005), review research and development progress (Daim and Gerdsri, 2009), and explore a research community (Gerdsri, Kongthon, and Vatananan, 2013). Bibliometric analysis has been applied in various areas such as marketing (Baumgartner and Pieters, 2003), technology management (Pilkington, 2004; Porter and Cunningham, 2005), topics in modern science (Saka and Igami, 2007), research profiling (Nerur, Rasheed, and Natarajan, 2008) and mapping the evolution of the intellectual structure in operations management (Porter and Detampel, 1995). …

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