Once Highly Productive, Forever Highly Productive? Full Professors' Research Productivity from a Longitudinal Perspective

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Y

31

Academic Rank (Professional); Faculty; Research; Productivity; Scientists; Foreign Countries; Persistence; Time Perspective

10.1007/s10734-023-01022-y

0018-1560
1573-174X

This longitudinal study explores persistence in research productivity at the individual level over academic lifetime: can highly productive scientists maintain relatively high levels of productivity. We examined academic careers of 2326 Polish full professors, including their lifetime biographical and publication histories. We studied their promotions and publications between promotions (79,027 articles) over a 40-year period across 14 science, technology, engineering, mathematics, and medicine (STEMM) disciplines. We used prestige-normalized productivity in which more weight is given to articles in high-impact than in low-impact journals, recognizing the highly stratified nature of academic science. Our results show that half of the top productive assistant professors continued as top productive associate professors, and half of the top productive associate professors continued as top productive full professors (52.6% and 50.8%). Top-to-bottom and bottom-to-top transitions in productivity classes occurred only marginally. In logistic regression models, two powerful predictors of belonging to the top productivity class for full professors were being highly productive as assistant professors and as associate professors (increasing the odds, on average, by 179% and 361%). Neither gender nor age (biological or academic) emerged as statistically significant. Our findings have important implications for hiring policies: hiring high- and low-productivity scientists may have long-standing consequences for institutions and national science systems as academic scientists usually remain in the system for decades. The Observatory of Polish Science (100,000 scientists, 380,000 publications) and Scopus metadata on 935,167 Polish articles were used, showing the power of combining biographical registry data with structured Big Data in academic profession studies.

As Provided

2024

EJ1413582