Open Access

Measuring Societal Impact Is as Complex as ABC

   | Aug 30, 2019



This paper describes an alternative way of assessing journals considering a broader perspective of its impact. The Area-based connectedness (ABC) to society of journals applied here contributes to the assessment of the dissemination task of journals but with more data it may also contribute to the assessment of other missions.


The ABC approach assesses the performance of research actors, in this case journals, considering the characteristics of the research areas in which they are active. Each paper in a journal inherits the characteristics of its area. These areas are defined by a publication-based classification. The characteristics of areas relate to 5 dimensions of connectedness to society (news, policy, industrial R&D, technology and local interest) and are calculated by bibliometric indicators and social media metrics.


In the paper, I illustrate the approach by showing the results for a few journals. They illustrate the diverse profiles that journals may have. We are able to provide a profile for each journal in the Web of Science database. The profiles we present show an appropriate view on the journals’ societal connectedness.

Research limitations

The classification I apply to perform the analyses is a CWTS in house classification based on Web of Science data. As such the application depends on the (updates of) that system. The classification is available at

Practical implications

The dimensions of connectedness discussed in this paper relate to the dissemination task of journals but further development of this method may provide more options to monitor the tasks/mission of journals.


The ABC approach is a unique way to assess performance or impact of research actors considering the characteristics of the areas in which output is published and as such less prone to manipulation or gaming.

Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology, Project Management, Databases and Data Mining