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Despite their widespread adoption, journal Impact Factors suffer well-knowndrawbacks that limit their usefulness in accurately and fairly assessing scientificquality. Among these is the extreme variance and skewness in the citations toarticles published by a given journal, which results in their sensitivity to a fewhighly cited articles, and enables many infrequently cited articles to “free-ride”on citations to these “skewed few.” To address this problem, I adjust journalImpact Factors according to the relative citedness of the few highly cited articlesin a journal’s h-core (i.e., the h articles that receive at least h citations) and themany infrequently cited articles in its h-tail (i.e., those that receive fewer than hcitations). I gauge the skew of a journal’s citation distribution by e2/ t2, the excess-tail ratio where e2 captures excess citations above the h2 citations received bya few highly cited h-core articles and t2 captures surplus citations received bythe many infrequently cited h-tail articles that fall below the h-core. I employe2/ t2 to adjust raw Impact Factors for 25 selected management and economicsjournals. The adjusted scores, , discriminate Impact Factors based on theshapes of journal citation distributions, leading to more accurate evaluation.I find e2/ t2 < 1 (often << 1) for 23 of these journals to be consistent with anoverstatement of their quality resulting from the sensitivity of Impact Factorsto? a few highly cited articles. Adjusted Impact Factors also yield distinctiveand more consistent journal rankings over standard two-year and five-yeartime horizons. I conclude that the “excess-tail” ratio and are a usefulcomplement to journal Impact Factors, particularly given their increasing usein the evaluation of individual scholarly output.

The excess-tail ratio: correcting journal impact factors for citation distributions