Tenure decisions, key decisions in academic institutions, are primarily based on subjective assessments of candidates. Using a large-scale bibliometric database containing 198,310 papers published 1975-2012 in the field of operations research (OR), we propose prediction models of whether a scholar would perform well on a number of future success metrics using statistical models trained with data from the scholar's first five years of publication, a subset of the information available to tenure committees. These models, which use network centrality of the citation network, coauthorship network, and a dual network combining the two, significantly outperform simple predictive models based on citation counts alone. Using a data set of the 54 scholars who obtained a Ph.D. after 1995 and held an assistant professorship at a top-10 OR program in 2003 or earlier, these statistical models, using data up to five years after the scholar became an assistant professor and constrained to tenure the same number of candidates as tenure committees did, made a different decision than the tenure committees for 16 (30%) of the candidates. This resulted in a set of scholars with significantly better future A-journal paper counts, citation counts, and h-indexes than the scholars actually selected by tenure committees. These results show that analytics can complement the tenure decision-making process in academia and improve the prediction of academic impact.
- Academic impact
- Citation analysis