Abstract
This survey study explored various determinants used to predict early-career researchers’ future performance. 50 studies and their relevant references were examined from two main perspectives: (1) what relevant studies expected as outcomes of successful early-career researchers, and (2) which determinants could significantly shape future outcomes. Regarding the first, various performance measures identified as dependent variables in the relevant literature were introduced, as were ways to determine researchers’ success or failure once their performance was measured. Moreover, the criteria used to circumscribe the early career stage were explained. As for the second perspective, the determinants of early-career researchers’ future performance considered in the relevant studies were classified into six categories: research performance; education, supervision, and postdoctoral training; research topics; co-authorship; personal properties; and others. As a result, several studies substantiated that early-career productivity was one salient component of future success, whereas the effect of research impact accrued during the early-career years on future success was less apparent.
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Notes
Obsolescence theory explains the affects that aging has on research productivity. It postulates that older researchers tend to lag behind in knowledge production, and that their decreasing productivity eventually results in obsolete research outcomes (Kyvik, 1990).
Cumulative advantage theory is about the Matthew effect, which explains the increasing divergence of researchers’ productivity as one’s academic career progresses, analogous to “the rich get richer and the poor get poorer.” Thus, success at the early career stages is critical because it can lead to continued professional success (Sabharwal, 2013).
The core articles were those published in the top four most-cited journals in agricultural and resource economics.
Science, technology, engineering, mathematics, and medicine.
These studies described academic rising stars as those who demonstrated less impressive performance at their early-career stage but then showed superb professional progression and eventually became prominent researchers.
Physics, engineering, psychology, history, nursing, and social work.
This study showed that only 25% of Ph.D. candidates from the top-25 universities and 39% of Ph.Ds. from non-top tier universities had co-authored articles with advisors.
The authors collected 4000 records of over 700 awards won by researchers in chemistry, mathematics, medicine/biology, and physics.
Research Papers in Economics, http://repec.org/ (accessed December 2021).
https://academictree.org/ (accessed December 2021).
https://www.genealogy.math.ndsu.nodak.edu/ (accessed August 2022).
Better performance was defined by the sum of their top-10% cited articles in core journals.
The study collected the winning records of 35 prestigious academic awards in seven disciplines: biology, chemistry, computer science and computer engineering, geography, mathematics, physics, and general.
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Acknowledgements
I appreciate the inspiration and valuable advice given by Professor Yannis Manolopoulos of the Open University of Cyprus, in developing this paper’s topic when he asked me to contribute a chapter in his book, Predicting the Dynamics of Research Impact. I also would like to express my deep gratitude to the anonymous reviewer for his/her detailed and insightful comments, which have improved the overall quality of this manuscript.
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Appendices
Appendix 1
Explored disciplines and the sample sizes of relevant studies
Relevant studies | Target disciplines | No. of target researchers | |
|---|---|---|---|
1 | Acuna et al. (2012) | Biology & neuroscience | 3085 neuroscientists and 208 biologists |
2 | Bäker et al. (2020) | Business & economics | 80 researchers who got tenured or were post-doctoral and pre-tenure |
3 | Baruffaldi et al. (2016) | Basic science, computer science, engineering & life science | 4666 doctoral students of two Swiss universities, 2000–2008 |
4 | Batista-Jr. et al. (2021) | Computer science | 8150 researchers |
5 | Billah and Gauch (2015) | Computer science | 26,170 researchers |
6 | Bin-Obaidellah and Al-Fagih (2019) | Computer science | 833 researchers |
7 | Bornmann and Williams (2017) | Unspecified | 13,180 researchers |
8 | Bornmann et al. (2008) | Life sciences | 965 researchers |
9 | Borrego et al. (2010) | All disciplines except humanities | 731 Ph.Ds. graduated from Spanish universities, 1990–2002 |
10 | Broström (2019) | Engineering, mathematics, medicine, science & technology | 734 Ph.Ds. graduated from a Swedish university 2006 |
11 | Chan et al. (2018) | Economics | 6,565 researchers in economics |
12 | Chen et al. (2022) | Economics | 568 Ph.Ds |
13 | Daud et al. (2017) | Computer science | 37,146 researchers |
14 | Daud et al. (2021) | Computer science | 4939 researchers |
15 | Ding et al. (2018) | Physics | Unspecified |
16 | Garcia-Perez (2013) | Psychology | 80 researchers |
17 | Garcia-Suaza et al. (2020) | Economics | 1976 researchers graduated, 2005—2010 |
18 | Havemann and Larsen (2015) | Astrophysics | 27 researchers |
19 | Heinisch and Buenstorf (2018) | Applied physics & electrical engineering | 5373 Ph.D. students |
20 | Hemmings and Kay (2010) | Computing & information sciences, medical sciences, education, humanities & social sciences | 343 researchers |
21 | Hilmer and Hilmer (2007a) | Agricultural & resource economics | 1527 American Ph.Ds. studied, 1987—2000 |
22 | Hilmer and Hilmer (2007b) | Economics | 1900 Ph.Ds. in economics graduated, 1990–1994 |
23 | Horta and Santos (2016) | Engineering, medical & health science, science, social & human sciences | 664 Ph.Ds. working in Portugal, 1968—2009 |
24 | Horta et al. (2018) | Agriculture, engineering & technology, humanities, medical Sciences, natural sciences & social sciences | 4027 Portuguese Ph.Ds |
25 | Hou et al. (2022) | Business | 1933 Ph.Ds. having at least 20 years of publishing careers |
26 | Jung et al. (2022) | Humanities and social sciences | 4846 Korean postdoctoral researchers |
27 | Kahn and Ginther (2017) | Biomedical science | 10,402 Ph.Ds. from American universities |
28 | Laurance et al. (2013) | Biological & environmental sciences | 182 researchers |
29 | Lee (2019a) | Computer science & information science | 4102 researchers |
30 | Li et al. (2009) | Computer science | 64,752 researchers |
31 | Li et al. (2019) | Cell biology, chemistry, neuroscience & physics | 22,601 researchers |
32 | Liénard et al. (2018) | Biomedical science | 18,856 Ph.Ds. undergone both graduate and post-doctoral training |
33 | Lindahl & Danell (2016) | Mathematics (esp. Number Theory) | 451 researchers |
34 | Lindahl (2018) | Mathematics (esp. Number Theory) | 406 researchers |
35 | Lindahl et al. (2020) | Science, medicine & technology | 310 Swedish Ph.Ds |
36 | Ma et al. (2020) | Chemistry, mathematics, biomedicine & physics | 18,265 Ph.Ds |
37 | Milojević et al. (2018) | Astronomy, ecology & robotics | 44,714 researchers |
38 | Nie et al. (2019) | Computer science | 1600 researchers |
39 | Penner et al. (2013) | Biology, mathematics & physics | 762 researchers |
40 | Pinheiro et al. (2014) | Biology, chemistry, computer science, earth & atmospheric sciences, electronic engineering | 780 assistant & associate professors at American research universities |
41 | Prpić (2000) | Biomedical, biotechnological, natural sciences, technology, social sciences & humanities | 840 Croatian researchers, 35 or younger |
42 | Rosenfeld and Maksimov (2022) | Computer science | 3401 Ph.Ds |
43 | Saygitov (2014) | Medicine | 149 PhD candidates and 41 Ph.Ds |
44 | Shang et al. (2022) | Chemistry | 1751 professors at elite Chinese universities |
45 | Stranges and Vouri (2016) | Pharmacy | 152 fellow residents |
46 | Tsugawa et al. (2022) | Humanities and social science, science and technology, and medicine & life science | 10,939 Japanese researchers who won research funding for young scholars (< 40 years old) |
47 | van den Besselaar and Sandström (2015) | Behavioral & educational sciences, economics & psychology | 233 applicants for a national early-career grant |
48 | Yoshioka-Kobayashi & Shbayama (2020) | Bioscience, agriculture, medicine, science & others | 188 mid-career Japanese professors of life sciences |
49 | Zhang et al. (2016) | Physics | Unspecified |
50 | Zhang and Yu (2020) | Climate change, natural earthquake & autism spectrum disorder treatments | 2982 authorships (no detail about the no. of distinct researchers) |
Appendix 2
Performance measures (dependent variables) and the predictive modeling techniques of relevant studies
Relevant studies | Measures of performance (dependent variables) | Predictive model(s) | |
|---|---|---|---|
1 | Acuna et al. (2012) | h-index five years later | Linear regression |
2 | Bäker et al. (2020) | Time from obtaining his or her doctoral degree to getting tenured as a full professor | Cox proportional hazard regression |
3 | Baruffaldi et al. (2016) | No. of publications (both journal articles & conference papers) during their Ph.D. education | Fixed-effects poisson regression with quasi-maximum likelihood estimator |
4 | Batista-Jr. et al. (2021) | Future Q values | Deep neural network and linear regression |
5 | Billah and Gauch (2015) | A certain degree of increase in h-index (i.e., four) for six years | Support Vector Machine (SVM) classifier |
6 | Bin-Obaidellah and Al-Fagih (2019) | Top 30% of the sample ranked by Author Rankings, provided by JCR InCites | Multiple linear regression, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) classifier |
7 | Bornmann and Williams (2017) | Average and sum of field- & time-normalized citation scores for a researcher | ANOVA |
8 | Bornmann et al. (2008) | (1) No. of articles subsequent to funding application, (2) total citation counts prior to application, (3) total citation counts subsequent to application | Zero-truncated negative binomial regression |
9 | Borrego et al. (2010) | (1) No. of post-doctoral publications, (2) citation counts, including self-citations, (3) journal impact factors | Regression, the Mann–Whitney U test, and ANCOVA |
10 | Broström (2019) | Field-normalized citations | Tobit regression |
11 | Chan et al. (2018) | Laureates of the Nobel Prize | Probit regression |
12 | Chen et al. (2022) | (1) Tenure, (2) time to tenure (i.e., the span from Ph.D. graduation to tenure), (3) staying in a tenure-track position | Probit regression for the first and third dependent variables and Cox proportional hazard regression for the second dependent variable |
13 | Daud et al. (2017) | Two sets of performance measures: (1) current affiliations and positions, citation counts of the top-cited papers, list of awards as of the time of research, (2) no. of publications and citation counts | Ranking algorithm combining three explanatory variables |
14 | Daud et al. (2021) | (1) No. of publications, (2) total no. of citations, (3) socialness | Ranking algorithm based on (1) WordNet similarity measure or (2) TF-IDF similarity measure between target researchers’ publications and all sample publications, after applying LDA-based hot topic detection |
15 | Ding et al. (2018) | No. of citations and hit counts of top rising stars among the sample | Classification and Regression Tree (CART) |
16 | Garcia-Perez (2013) | h-index five or ten years later | Linear regression |
17 | Garcia-Suaza et al. (2020) | No. of publications in top-level journals with and without co-authoring with advisors | OLS regression & Poisson Pseudo-maximum likelihood estimator |
18 | Havemann and Larsen (2015) | A certain degree of increase in their citations after five years since their first publication, among the authors of highly-cited articles in astrophysics | Wilcoxon rank-sum test |
19 | Heinisch and Buenstorf (2018) | The binary variable whether a target student eventually became a doctoral advisor in the future | Logit regressions |
20 | Hemmings and Kay (2010) | Scores of peer-reviewed publications including journal articles, conference papers, books, and book chapters | Multiple regression analysis |
21 | Hilmer and Hilmer (2007a) | No. of articles in three categories (all peer-reviewed articles, core articles, and regional articles) | Negative binomial regression |
22 | Hilmer and Hilmer (2007b) | (1) First job placements (dichotomous value indicating whether a job was research-oriented), (2) no. of publications at early career stage | Logit regression and negative binomial regression |
23 | Horta and Santos (2016) | (1) Total no. of publications, (2) average no. of yearly publications, (3) total no. of citations normalized by disciplinary field, (4) average no. of yearly citations, (5) average no. of citations per publication, and (6) collaboration patterns | Multivariable general linear regression |
24 | Horta et al. (2018) | (1) Average no. of publications per year during and after Ph.D., (2) average no. of internationally co-authored publications annually during and after Ph.D., (3) average no. of citations per year, and (4) average no. of citations earned by internationally coauthored publications per year | OLS regression |
25 | Hou et al. (2022) | (1) No. of publications in prestigious journals during the sixth to 20th career years, (2) mean normalized citations in the same career years (i.e., 6–20) | Zero-inflation negative binomial regression and Poisson regression |
26 | Jung et al. (2022) | Occupational type (whether their job at the time of the study was a tenure-track position) | Logistic regression |
27 | Kahn and Ginther (2017) | (1) Five types of positions after 10 years of completing Ph.Ds. (tenure-track academic position, non-tenure track academic position, academic job not conducting research activities, industry and government/non-profit), (2) their salaries | Multinomial logit regression |
28 | Laurance et al. (2013) | No. of journal articles (JCR indexed journals) published a decade after Ph.D. graduation | A generalized linear model |
29 | Lee (2019a) | (1) No. of publications, (2) citation counts from the fifth year of a researcher’s publication tenure up through his or her eighth year | Linear regression |
30 | Li et al. (2009) | (1) A certain degree of relatively high PubRank scores, (2) no. of citations, (3) h-index, (4) current positions and awards | A modified PageRank derived from co-authorship networks |
31 | Li et al. (2019) | (1) Citation counts accrued in the first 20 years, (2) a binary record about whether a junior researcher is a top-level scientist in his or her 20th career year | t-test, linear regress and logistic regression |
32 | Liénard et al. (2018) | (1) The odds of target trainees obtaining an independent research position, (2) the average no. of trainees mentored by target trainees per decade after completing postdoctoral training | Negative binomial regression |
33 | Lindahl & Danell (2016) | Research productivity-based ranks at target researchers’ middle-career phase (i.e., top-10%, 25%, or 50% of the 8th–12th years of researchers’ careers) | Univariate ROC analysis |
34 | Lindahl (2018) | Dichotomous value indicating whether a researcher had published at least one top-10% article in terms of the citations during the second four years career period | Multiple logistic regression and dominance analysis |
35 | Lindahl et al. (2020) | Dichotomous value indicating relative research excellence in terms of top-10% cited publications in their field within two to five years after earning his or her doctorate | Probit regression |
36 | Ma et al. (2020) | (1) Scientific award-winning (of over 700 awards), (2) being elected to the National Academy of Science (NAS), (3) being in the top-25% of citations in their field | Coarsened exact matching regression |
37 | Milojević et al. (2018) | Career survivability (i.e., career longevity) | Cox proportional hazard regression |
38 | Nie et al. (2019) | Dichotomous value indicating whether a given researcher is a rising star based on the study’s incremental impact score | Five classification algorithms: K-Nearest Neighbor, Random Forest, Gradient Boosting Decision Tree, Extreme Gradient Boosting, and Support Vector Machine |
39 | Penner et al. (2013) | h-index in future years | Linear regression |
40 | Pinheiro et al. (2014) | Career-long productivity | Treatment effect model |
41 | Prpić (2000) | No. of publications | Stepwise regression |
42 | Rosenfeld and Maksimov (2022) | h-index, i10-Index, and total no. of citations five or 10 years after Ph.D. graduation and their career longevity | ANOVA with Tukey HSD post-hoc test |
43 | Saygitov (2014) | No. of publications and citations after applying the Russian Foundation President’s Grants for Young Scientists | Negative binomial regression |
44 | Shang et al. (2022) | Winning records of national fundings for young scholars | Multinomial logit regression |
45 | Stranges and Vouri (2016) | Binary variable indicating whether a researcher had a publication within five years after resident training programs | Multivariable and univariate logistic regressions |
46 | Tsugawa et al. (2022) | The total amount of research funding and the number of funded years as a PI | Mann–Whitney U test and descriptions of the longitudinal changes |
47 | van den Besselaar and Sandström (2015) | Academic position nine years after receiving grants | Generalized linear regression |
48 | Yoshioka-Kobayashi & Shbayama (2020) | Organizational independence & cognitive independence | Linear regression and ordered logistic regression |
49 | Zhang et al. (2016) | No. of total citations | Ranking algorithm combining six variables |
50 | Zhang and Yu (2020) | (1) No. of articles, (2) average citations per article, (3) academic age, (4) h-index, (5) no. of articles in top journals, (6) no. of collaborations | Welch’s ANOVA and Games-Howell post-hoc comparison test |
Appendix 3
Factors (independent variables) considered in the relevant studies (the underlined variables in bold were statistically significant and those with asterisks were partially significant for some of the dependent variables)
Relevant studies | Determinants under consideration (these independent variables can be divided into either explanatory variables or control variables.) | ||
|---|---|---|---|
Explanatory variables | Control variables | ||
1 | Acuna et al. (2012) | (1) No. of publications, (2) h-index, (3) years since first publication, (4) no. of distinct journals, (5) no. of publications in prominent nature science journals (e.g., Nature, Science, Nature Neuroscience, Neuron and the Proceedings of the National Academy of Sciences) | |
2 | Bäker et al. (2020) | Mentor’s perceived roles (teacher, sponsor or collaborator)* | (1) Types of mentoring programs, (2) gender of mentees (i.e., target researchers), (3) whether a mentee has a child, (4) field of research, (5) biological age, (6) international mobility, (7) national mobility, (8) duration of Ph.D. program, (9) reputation of Ph.D. program |
3 | Baruffaldi et al. (2016) | Three classifications of students’ master’s programs (the same affiliation with advisors’ affiliation, the external affiliations where advisors’ co-authors worked, and the external affiliations outside of advisors’ co-authorship ties) | (1) characteristics of Ph.D. students and advisors, (2) cultural proximity between a student and advisor, (3) characteristics of university from which students received their master’s degrees |
4 | Batista-Jr. et al. (2021) | (1) Q value of the most prolific senior co-author, (2) h-index of the target researcher, 3) Q value of the target researcher, (4) no. of articles by senior co-author, (5) career age of senior co-author | |
5 | Billah and Gauch (2015) | (1) No. of publications, (2) no. of citations, (3) degree in co-authorship networks, (4) average h-index of all co-authors, (5) average h-index of coauthors’ high-Impact papers, (6) sum of h-index of all co-authors | |
6 | Bin-Obaidellah and Al-Fagih (2019) | (1) No. of publications, (2) total citation counts, (3) the rate of publications cited at least once, (4) citations per publication, (5) contribution impact, (6) international collaboration, (7) research area relevancy, (8) venue reputation | |
7 | Bornmann and Williams (2017) | (1) Ratio of articles published in the first top-quartile level of WoS journals, (2) relative ranks of individual researchers in terms of no. of publications | |
8 | Bornmann et al. (2008) | (1) The success or failure of two European research grant application, (2) no. of pages per paper*, (3) no. of co-authors per paper*, (4) years of publications | |
9 | Borrego et al. (2010) | (1) Gender*, (2) no. of pre-doctoral publications* | |
10 | Broström (2019) | (1) Research group’s human composition about professors, post-doctoral researchers, other Ph.D. students, and research assistants or technical staff, (2) group’s funding situations, (3) scientific strength of groups in terms of supervisors’ citations, (4) the degree of groups’ external collaborations, (5) no. of highly-cited publications published by the university of target students | Personal characteristics of target researchers (i.e., gender, age, current academic/non-academic position, mobility, etc.) |
11 | Chan et al. (2018) | (1) Winning records of the John Bates Clark Medal, (2) no. of publications, (3) citation counts per publications, (4) rankings of Ph.D.-granting institution | |
12 | Chen et al. (2022) | Gender* | (1) Current job characteristics, including the location of jobs, the geographic distance between the current job and graduated university, and job rankings, (2) demographic characteristics, including home country*, master’s degree, (3) academic characteristics, including female ratio in graduating class, the size of graduating class, doctoral program tiers*, teaching award records, publication records in a top-50 journal, manuscript under the revise-and-resubmit review status in a top-50 journal, advisor’s reputation, and advisor’s gender |
13 | Daud et al. (2017) | (1) Co-author citation-based mutual influence, (2) co-author order-based mutual influence, (3) co-author venue’s citations based mutual influence | |
14 | Daud et al. (2021) | Researchers’ contributions to hot research topics | |
15 | Ding et al. (2018) | (1) No. of publications, (2) no. of co-authors, (3) active publication years, (4) citation counts, (5) citations per paper, (6) citations per year, (7) citations of citing references, (8) citations of references, (9) citations of co-authors, (10) the degree of triple closeness | |
16 | Garcia-Perez (2013) | Same determinants with the ones of Acuna et al. (2012) | |
17 | Garcia-Suaza et al. (2020) | (1) Quality-adjusted productivity of advisors*, (2) quality scores of economics departments, (3) advisors’ tenured years, (4) dichotomous variable indicating co-authorship with advisors | (1) Gender match between Advisors and Advisees*, and (2) 17 fields of economic Research |
18 | Havemann and Larsen (2015) | 16 bibliometric indicators (two indicators about ‘productivity’, five about ‘total influence’, four about ‘typical influence’, two about ‘h-index’ and three about ‘fractional h-index’) | Collaborative coefficient |
19 | Heinisch and Buenstorf (2018) | (1) Advisors’ citation counts*, (2) advisors’ three topological properties in their collaboration networks (i.e., principal component*, Bonacich centrality, and degree), (3) no. of advisors’ previous advisees, (4) advisors’ career age, (5) contemporaneous doctoral students trained by same advisors | (1) Students’ no. of publications before graduation, (2) students’ no. of co-authors before graduation* |
20 | Hemmings and Kay (2010) | Self-efficacy scales for four types of research tasks: (1) reporting & supervising research, (2) skills related to conducting & managing research, (3) writing & reviewing articles, (4) having a broad view of one’s research area | |
21 | Hilmer and Hilmer (2007a) | (1) Reputation tiers of Ph.D. program*, (2) advisors’ rankings, according to their research productivity, (3) interaction terms between program reputation tiers and advisors’ productivity rankings, (4) variable indicating whether a first job was an American academic position, (5) several personal properties (including gender, internationality, the research fields of dissertations, years since Ph.D. completion, and the number of other advisees under the same advisors within the same period of target researchers) | |
22 | Hilmer and Hilmer (2007b) | (1) Gender-based configuration between advisors and advisees, (2) academic ranks of doctoral programs*, (3) advisors’ rankings among the top 1,000 economists, provided by an existing study | (1) students’ internationality, (2) dissertation field, (3) years since Ph.D. completion |
23 | Horta and Santos (2016) | No. of articles published in journals indexed in the WoS during Ph.D. study | (1) Gender*, (2) age*, (3) postdoctoral positions, (4) currently being in academia, (5) inter-sectoral mobility*, (6) no. of jobs after Ph.D., (7) disciplinary fields*, (8) time of their Ph.D. graduations*, (9) type of Ph.D. funding, (10) institutional prestige |
24 | Horta et al. (2018) | (1) Records of Ph.D. funding, (2) two types of Ph.D. funding*, (3) productivity during Ph.D.*, (4) impact during Ph.D.* | (1) Age at Ph.D., (2) nationality, (3) gender, (4) field mobility, (5) whether graduated from a prestigious university*, (6) time to Ph.D., (7) more degrees before Ph.D.*, (8) field of science, (9) years after Ph.D., (10) international mobility*, (11) job mobility, (12) whether working in academia*, (13) no. of internationally affiliated co-authorships*, (14) whether research received the Ph.D. in Portugal*, (15) whether a researcher graduated one of the four oldest Portugal universities |
25 | Hou et al. (2022) | A dichotomous variable indicating whether a target researcher had published an article in prestigious business-related journals at early career stage | (1) Career starting year, (2) top institutions, (3) mean normalized citation score in early career, (4) total no. of articles in early career, (5) total no. of coauthors in early career, (6) proportion of papers published as the first author in early career |
26 | Jung et al. (2022) | Winning a government-funded postdoctoral funding* | (1) Age, (2) gender, (3) country in which a Ph.D. was awarded, (4) type of affiliated university at the time of applications, (5) funding application year, (6) career years |
27 | Kahn and Ginther (2017) | Whether a Ph.D. started his or her career as a postdoctoral researcher within three years after completing Ph.D | 1) background (i.e., VISA status in the United States), (2) ability (Ph.D. program rankings, experience as a research assistant), (3) demographic properties (gender, ethnicity, age, marital and family status), (4) academic fields, (5) years spent to complete the Ph.D |
28 | Laurance et al. (2013) | (1) Gender, (2) prestige of university from which they received their doctorates, (3) native language, (4) time difference between Ph.D. graduation and first article, (5) no. of articles during doctoral education, (6) no. of articles published three years after the Ph.D. (as an alternative variable of variable #5) | |
29 | Lee (2019a) | (1) No. of journal articles, (2) no. of conference papers, (3) citations of journal articles*, (4) citations of conference papers*, (5) average citations per journal article, (6) average citations per conference papers*, (7) average no. of journal articles by co-authors, (8) average no. of conference papers by co-authors*, (9) average publishing tenure of co-authors,* (10) degree centrality*, (11) betweenness centrality, (12) modularity, (13) clustering coefficient* | |
30 | Li et al. (2009) | (1) No. of publications, (2) prestige of publication venues, (3) degree of mutual influence between two co-authors | |
31 | Li et al. (2019) | (1) Whether he or she coauthored paper(s) with top-cited scientists, (2) relational institutional prestige, (3) productivity, (4) citations | |
32 | Liénard et al. (2018) | (1) Content-similarities of non-coauthored publications among a trainee, graduate mentor, and post-doctoral mentor, (2) average no. of mentees trained by a target researcher’s advisor, (3) duration of training, (4) ending year of training, (5) mentors’ academic ages, (6) mentor network distance, (7) no. of publications co-authored with mentors | |
33 | Lindahl & Danell (2016) | No. of publications in the first five years of researchers’ careers | (1) No. of articles published in prestigious journals, (2) average no. of co-authors per paper |
34 | Lindahl (2018) | (1) No. of publications, (2) no. of top journal publications, (3) no. of top 10% publications in terms of citations during the first four years of career | The average no. of co-authors per publication |
35 | Lindahl et al. (2020) | (1) No. of publications during doctoral education, (2) relative research excellence in terms of top 10% cited publications during doctoral education, (3) gender | (1) Collaborative coefficient, (2) age at completing doctoral education, (3) discipline |
36 | Ma et al. (2020) | Mentor’s award-winning | (1) mentor’s being elected to NAS, (2) mentor’s citation impact, (3) mentor’s no. of coauthors, (4) mentor’s no. of students, (5) ratio of mentees’ publications coauthored with their mentors, (6) topic dissimilarity between a mentor and mentee, (7) mentee’s citation impact, (8) mentee’s no. of coauthors, (9) whether a mentee has a prizewinning coauthor, (10) mentee’s graduation order, (11) mentee’s discipline, (12) university rank, (13) mentor’s prize year |
37 | Milojević et al. (2018) | The following variables were calculated for the first five years of each researcher’s career: (1) no. of publication, (2) average citations and maximum citations per publication, (3) no. of collaborations, (4) author types (leading or supporting) | |
38 | Nie et al. (2019) | (1) Four types of author-related features, (2) three types of social features, (3) four types of venue features, (4) six types of temporal changes of co-authors and citations | |
39 | Penner et al. (2013) | Same determinants with the ones of Acuna et al. (2012) | |
40 | Pinheiro et al. (2014) | (1) No. of publications co-authored with advisors during Ph.D. education, (2) no. of publications published without co-authoring with advisors during Ph.D., (3) postdoctoral appointments, (4) disciplines*, (5) ethnic groups*, (6) nationality, (7) gender, (8) year of Ph.D | |
41 | Prpić (2000) | (1) Gender*, (2) age*, (3) average grade at university, (4) publications during studies*, (5) research activity during studies, (6) career continuity, (7) academic degree, (8) academic rank*, (9) proficiency level of foreign language, (10) no. of domestic projects, (11) no. of international projects, (12) no. of collaborators*, (13) influence on research task distribution*, (14) local conference attendance*, (15) international conference attendance*, (16) executive position at institution, (17) member of editorial boards, (18) reviewing activity*, (19) member of international scientific societies*, (20) members of domestic scientific societies, (21) academic awards, (22) chances of young researchers | |
42 | Rosenfeld and Maksimov (2022) | Three types of advisor–advisee collaboration patterns (highly, moderately, and weakly interdependent advisees) | |
43 | Saygitov (2014) | Winning or failing records of RF President’s grant | (1) No. of publications and citations before the grant applications and the rankings of current institutions, (2) current institution rank, (3) gender, (4) city, (5) place of employment, (6) specialty |
44 | Shang et al. (2022) | (1) Early-career researchers’ h-index, (2) mentor’s academic ranking*, (3) mentor’s h-index | (1) early-career researcher’s gender*, (2) professorship rankings, (3) time period of doctoral education*, (4) prestige of Ph.D. granting universities* |
45 | Stranges and Vouri (2016) | (1) Publication about residency project, (2) Residency type (university or non-university affiliated residency), (3) no. of total coinvestigators, (4) physician coinvestigators, (5) h-index of nonphysician coauthors, (6) academic degrees of coinvestigators | |
46 | Tsugawa et al. (2022) | (1) Participations in joint research projects in the first three years since their first funding for young researchers, (2) no. of joint research projects in the first three years, (3) degree centrality and between centrality in the collaboration network | Disciplines |
47 | van den Besselaar and Sandström (2015) | (1) Receiving early-career grants or not, (2) gender, (3) citations, (4) no. of publications, (5) disciplines, (6) year of applications, (7) career mobility | |
48 | Yoshioka-Kobayashi & Shbayama (2020) | (1) Five kinds of autonomy functions*, (2) five kinds of cognitive exploration* | (1) Gender, (2) no. of years after graduation*, (3) no. of publications before Ph.D. education |
49 | Zhang et al. (2016) | (1) No. of publications, (2) citation counts, (3) author orders in byline, (4) a paper’s PageRank score in citation network, (5) HITS score of an author in paper-author network, (6) HITS score of a journal in paper-journal network, (7) Coca value computed from authors’ affiliation information | |
50 | Zhang and Yu (2020) | (1) Three article publishing behaviors in early career years*, (2) no. of articles during the first five years of career | |
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Lee, D. Exploring the determinants of research performance for early-career researchers: a literature review. Scientometrics 129, 181–235 (2024). https://doi.org/10.1007/s11192-023-04868-2
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DOI: https://doi.org/10.1007/s11192-023-04868-2