Abstract
There has been increasing interest in the study of research communities with the goal of optimizing their outcomes and impact. While current methods can predict future trends, they offer little insight about the causes of the trends. However, causal insight is important for strategic decision making to improve a community. This paper presents a new method to predict the possible causes for inefficiencies in a community by relating them to disconnections between trends, like trends in the number of publications, patents, citations, and so on. The method combines traditional scientometric and webometric metrics and metric predictions with a recent model for trend analysis in a community. The proposed method was used to analyze electronic design automation (EDA) domain. The analysis showed intriguing disconnections between the trends of the number of papers, number of granted patents, and impact of its main publications. The analysis suggests a slightly decreasing impact and visibility of EDA, while having less novel, commonly-accepted knowledge in the area. The gained insight suggests three possible strategic decisions to improve EDA community: avoiding to ignore new ideas, reducing the complexity of framed problems, and keeping a minimal gap between real-life needs and academic solutions.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.