A large-scale analysis of impact factor biased journal self-citations

Based on three decades of citation data from across scientific fields of science, we study trends in impact factor biased self-citations of scholarly journals, using a purpose-built and easy to use citation based measure. Our measure is given by the ratio between i) the relative share of journal self-citations to papers published in the last two years, and ii) the relative share of journal self-citations to papers published in preceding years. A ratio higher than one suggests that a journal's impact factor is disproportionally affected (inflated) by self-citations. Using recently reported survey data, we show that there is a relation between high values of our proposed measure and coercive journal self-citation malpractices. We use our measure to perform a large-scale analysis of impact factor biased journal self-citations. Our main empirical result is, that the share of journals for which our measure has a (very) high value has remained stable between the 1980s and the early 2000s, but has since risen strongly in all fields of science. This time span corresponds well with the growing obsession with the impact factor as a journal evaluation measure over the last decade. Taken together, this suggests a trend of increasingly pervasive journal self-citation malpractices, with all due unwanted consequences such as inflated perceived importance of journals and biased journal rankings.Transport and Logistic