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.
Author(s): Nathasit Gerdsri, Alisa Kongthon
Organization(s): National Electronics and Computer Technology Center, Mahidol University
Source: International Journal of Business
Year: 2018