High research productivity in vertically undifferentiated higher education systems: Who are the top performers?
High research productivity in vertically undifferentiated higher education systems: Who are the top performers?
Marek Kwiek 0
0 Center for Public Policy Studies, UNESCO Chair in Institutional Research and Higher Education Policy, University of Poznan , ul. Szamarzewskiego 89, 60-569 Poznan , Poland
The growing scholarly interest in research top performers comes from the growing policy interest in research top performance itself. A question emerges: what makes someone a top performer? In this paper, the upper 10% of Polish academics in terms of research productivity are studied, and predictors of entering this class are sought. In the science system (and Poland follows global patterns), a small number of scholars produce most of the works and attract huge numbers of citations. Performance determines rewards, and small differences in talent translate into a disproportionate level of success, leading to inequalities in resources, research outcomes, and rewards. Top performers are studied here through a bivariate analysis of their working time distribution and their academic role orientation, as well as through a model approach. Odds ratio estimates with logistic regression of being highly productive Polish academics are presented. Consistently across major clusters of academic disciplines, the tiny minority of 10% of academics produces about half (44.7%) of all Polish publications (48.0% of publications in English and 57.2% of internationally coauthored publications). The mean research productivity of top performers across major clusters is on average 7.3 times higher than that of the other academics, and in terms of internationally co-authored publications, 12.07 times higher. High inequality was observed: the average research productivity distribution is highly skewed with a long tail on the right not only for all Polish academics but also for top performers. The class of top performers is as internally stratified as that of their lower-performing colleagues. Separate regression models for all academics, science, technology, engineering and mathematics academics, and social sciences and humanities academics are built based on a large national sample (2525 usable observations), and implications are discussed.
Inequality in science; Publication productivity; Lotka's square law; Stratification in science; Reward structure; Skewed distribution; Stars; Cumulative advantage; Poland
The world of science has always been utterly unequal
(Ruiz-Castillo and Costas 2014;
: the intrinsic property of science has been what de Solla Price (1963) termed
‘‘essential, built-in undemocracy’’ (59). Individual performance in science tends not to
follow a Gaussian (normal) distribution. Instead, it follows a Paretian (power law)
(O’Boyle and Aguinis 2012)
. Distributions of different social phenomena—such
as income, wealth, and prices—show ‘‘strong skewness with long tail on the right,
(Abramo et al. 2017a: 324)
. Academic knowledge production is not
an exception because unproductive scientists work alongside ‘top researchers’ in academic
units, universities, and national systems
(Abramo et al. 2013; Piro et al. 2016)
. In more
internally competitive and vertically differentiated systems (such as Anglo-Saxon
systems), top researchers tend to be concentrated in elite universities and low performers in
less prestigious tiers of the system. In the Polish case of an internally uncompetitive and
vertically undifferentiated higher education system, with a long tradition of equality in
allocating research funding and an only emergent regime of grant-based competitive
research funding from the National Research Council (created in 2011), top researchers are
scattered across the whole system.
The growing scholarly interest in research top performers comes from the growing
policy interest in research top performance itself—and the increasing emphasis on the role
of universities in global competition. Academics are at the center of the global knowledge
production and global academic enterprise
(Cummings and Finkelstein 2012; Leisˇyte and
Dee 2012; Teichler et al. 2013)
. Not surprisingly, a question has emerged: ‘‘What makes
someone a top researcher?’’ (Kelchtermans and Veugelers 2013: 273). In this paper, the
upper 10% of Polish academics in terms of research productivity are studied in relation to
the remaining 90%. The objective of present research is to study specific characteristics of
this unique class of academics: who top performers are, how they work, and what they
think about academic work, and to explore the predictors of entering it, based on a large
sample (2525 usable observations). While bibliometric data from international (or national)
datasets are perfectly suited for research productivity analyses—they can hardly be used in
determining the individual characteristics of top performers, for which large-scale survey
data work better.
The paper is structured as follows: Sect. 2 presents the theoretical framework, and
Sect. 3 presents data and methods. Section 4, focused on the results, includes four
subsections: an overview of top performers, patterns of individual research productivity and
the national research output, bivariate analysis, and logistic regression analysis. The
subsection on bivariate analysis consists of two parts: the first is about research
productivity and working time distribution, and the second about research productivity and
academic role orientation; the logistic regression analysis subsection consists of procedures
and variables in the model and statistically significant individual and institutional
variables. Section 5 presents the discussion and conclusion.
Three quotations from the last half century show roughly the same phenomenon in science:
‘‘the majority of scientific work is performed by a relatively small number of scientists’’
(Crane 1965: 714), ‘‘no matter how it is measured, there is enormous inequality in
scientists’ research productivity’’
(Allison 1980: 163)
; and most recently, ‘‘inequality has
been, and will always be, an intrinsic feature of science’’
(Xie 2014: 809; see MacRoberts
and MacRoberts 1982)
. The skewed distribution of scientific output found first by Lotka
(1926) and shown by Price (1963) was that about 6% of publishing scientists produce half
of all papers
(Lotka’s law, or the inverse square law of productivity, states that the number
of scientists producing n papers is 1/n2 of those producing one paper; see Kyvik 1989;
Bensman and Smolinsky 2017)
. The relative importance of scientists in the right tail of the
output distribution—increasingly termed stars recently—has endured over time
et al. 2017: 1)
. The superstar effect refers to markets
(‘‘relatively small numbers of people
earn enormous amounts of money and dominate the activities in which they engage’’
Rosen 1981: 845)
, and the Matthew effect
(Cole and Cole 1973; Merton 1968)
refers to the
science system: a small number of scholars produce most of the works, attract huge
numbers of citations, hold prestigious academic positions, and form the disciplines’
(Corte´s et al. 2016; Serenko et al. 2011)
. For Robert K. Merton and Sherwin
Rosen, performance determines rewards. In Rosen’s ‘‘economics of superstars,’’ small
differences in talent translate into a disproportionate level of success. However, Rosen
emphasizes innate talent, and Merton emphasizes external resources (DiPrete and Eirich
2006). Resources and the motivation to publish flow to scientists with high esteem in the
scientific community, and that esteem ‘‘flows to those who are highly productive’’
and Stewart 1974: 604)
. Cumulative advantage is a general process by which ‘‘small initial
differences compound to yield large differences’’
(Aguinis and O’Boyle 2014: 5)
Consequently, Merton’s Matthew effect in the system of science inevitably leads to haves and
have-nots, or inequalities in resources, research outcomes, and monetary or non-monetary
(Xie 2014; for a cross-national study of high research productivity and academic
salaries in Europe, see Kwiek 2017a)
In the tradition of the sociology of science, recognition comes from scientific output
(Cole and Cole 1967)
, and the reward system is designed to give recognition and esteem to
the scientists who have best fulfilled their roles. In
: 297) formulation, ‘‘the
institution of science has developed an elaborate system for allocating rewards to those
who variously lived up to its norms’’. The reward system reinforces research activity. Few
scientists will continue to engage in research if they are not rewarded for it
(Cole and Cole
. Academics publish their work in exchange for scientific recognition. As Hagstrom
(1965: 168) stated in his theory of social control in science, ‘‘recognition is given for
information, and the scientist who contributes much information to his colleagues is
rewarded by them with high prestige’’. In this sense, research high performance (as
opposed to low performance) leads to recognition in science.
The accumulative advantage hypothesis
(Cole and Cole 1973)
generalizes the Matthew
effect to include productivity, as well as recognition: the process consists of two feedback
loops in which recognition and resources are intervening variables
(Allison and Stewart
. However, there is also the darker side of the accumulation of rewards: it is ‘‘the
accumulation of failures—the process of ‘accumulative disadvantage’’’
(Cole and Cole
. As scientific productivity is heavily influenced by the recognition of early
work, the skewed distribution of productivity and subsequent rewards also results from the
poor getting poorer. In Merton’s reputation-and-resources model of scientific careers,
resources are not simply a reward for past productivity. They are a mechanism to stimulate
future productivity: ‘‘the scientific community favors those who have been most successful
in the past’’ (DiPrete and Eirich 2006: 282; Hermanowicz 2006).
Scientific productivity is skewed, and its skewness has been widely studied in terms of
two standard measures of individual performance: publication numbers and citations of
(Albarra´n et al. 2011; Carrasco and Ruiz-Castillo 2014; Ruiz-Castillo and
. In a study of 17.2 million authors and 48.2 million publications in Web of
Science, Ruiz-Castillo and Costas (2014) show that 5.9% of authors accounted for about
35% of all publications. The skewness of science implies, as
showed for the
first time, that there will always be authors with huge numbers of publications (attracting
huge numbers of citations) accompanied by a number of academics who do not publish and
a large fraction of uncited publications.
Scholarly interest in the skewness of science and high individual research performance
has been growing exponentially in the last few years. Highly productive academics have
been studied mostly intra-nationally and in single fields of knowledge (particularly in
economics and psychology), sometimes also cross-nationally
(see Kwiek 2016a on top
performers across 11 European systems)
. Recent studies on high research performers—
based either on publication data or citation data—include research on star scientists
(Abramo et al. 2009; Yair et al. 2017)
, star performers (Aguinis and O’Boyle 2014), the
most productive scholars, including rising stars and stars overall
(Copes et al. 2012)
best versus the rest
(O’Boyle and Aguinis 2012)
, academic stars
(Long et al. 2011)
(Aguinis et al. 2016)
, the most prolific female scholars and female
(Weir and Orrick 2013)
, high-performing researchers
(White et al. 2012)
(Agrawal et al. 2017; Serenko et al. 2011)
Methods for determining the characteristics of top performers proliferate, and they are
studied as individual scientists or scientists embedded in organizational contexts, with
reciprocal relationships: how they influence and how they are influenced by their
organizations or collaborative networks. The skyline for star scientists
(Sidiropoulos et al.
is being sought: stars are those scientists whose performance cannot be surpassed by
others with respect to all scientometric indexes selected. Apart from stars, the relevant
studies focus on the scientific elite or the most highly cited scientists
(Parker et al.
, top researchers
(Abramo et al. 2013; Corte´s et al. 2016)
, the academic elite
(Yin and Zhi 2017)
, or prolific professors (Piro et al. 2016). What makes a research star is
an all-pervading question in the current productivity-obsessed and number-based academic
culture. The concept of top research performers in this paper is closer to that of
performance stars rather than universal stars or status stars, to use the recent typology of star
employees (Kehoe et al. 2016). Star performers (‘‘a few individuals who contribute a
disproportionate amount of output’’) occur in all organizations, including universities.
However, a star is a relative position, and identification is possible only by viewing
individuals in relation to others’ productivity
(Aguinis and O’Boyle 2014: 313–315;
DiPrete and Eirich 2006: 282)
Faculty research productivity and its predictors (as opposed to faculty high research
productivity and its predictors) have been thoroughly explored in single-nation academic
(see Allison and Stewart 1974; Cole and Cole 1973; Fox 1983; Ramsden 1994;
Shin and Cummings 2010)
and rarely in cross-national contexts
Drennan et al. 2013; Postiglione and Jung 2013; Teodorescu 2000)
. Although most
productivity studies do not use national samples and focus on faculty from selected academic
fields, especially from natural sciences, the present study uses a national sample and refers
to all academic fields (except for the regression analysis section which includes a science,
technology, engineering and mathematics subsample of academics).
In traditional sociological productivity studies, highly productive academics were
mostly mentioned in passing
(Allison 1980; Cole and Cole 1973; Crane 1965)
include big producers in de Solla Price (1963), Croatian eminent scientists in Prpic´ (1996)
and Golub (1998). More recently,
Abramo et al. (2009)
studied star scientists in the context
of sex differences in research productivity in Italy and
Postiglione and Jung (2013)
top tier researchers in four Asian countries. According to Abramo and colleagues (2009:
143), the (Italian) star scientist ‘‘is typically a male full professor’’. However, as their work
is based on Italian bibliometric data, the authors focus on sex, academic ranks, institutional
types, and academic disciplines rather than predictors of becoming a star scientist. Katarina
Prpic´ compared the scientific productivity of eminent and average scientists in Croatia and
concluded that for this elite group, ‘‘homogeneity is larger and variability is smaller than in
the entire research population’’
(Prpic´ 1996: 199)
. Postiglione and Jung (2013: 164–165)
wanted to understand better ‘‘why some faculty are more prolific in research publications
(Postiglione and Jung 2013: 166)
and studied the 10% most and least
productive academics through descriptive statistics, without referring to predictors of high
research productivity. For the present study, both traditional sociological theories of social
stratification in science and studies of highly productive academics (or stars) provide the
Data and methods
Studying the determinants of individual-level high research productivity
Studying individual-level research performance in which the individual academic is the
unit of analysis differs from studying patterns of research productivity across countries,
institutional types, disciplines, academic ranks or gender (and over time). Two different
methodological approaches in research literature for exploring individual-level high
research productivity and its determinants (which cannot be done through bibliometric
studies) can be distinguished: qualitative and quantitative. The first approach explores
productivity through qualitative material: rankings of highly productive academics in
particular academic disciplines are created, and then the academics in the top ranks are
interviewed with a general research question, such as ‘‘how can they be so productive?’’
(Mayrath 2008: 42)
. Keys to high productivity are drawn from either targeted academic
surveys of productive academics (seeking determinants of high research productivity) or
from interviews with eminent, and prolific academics, or both
(Flanigan et al. 2016;
Kiewra and Creswell 2000; Mart´ınez et al. 2011; Mayrath 2008; Patterson-Hazley and
. Studies on research stars often rely on small-scale faculty surveys and
analyses of selected top peer-reviewed journals, often combined with in-depth interviews.
Qualitative studies based on varying numbers of conversations with highly productive
academics seek to answer a general question: how do scholars become highly productive?
The second approach, in contrast, explores predictors of high research productivity through
quantitative material: academic profession surveys in which academic behavioral and
attitudinal data are combined with publication data. In this paper, the survey-based,
quantitative approach is used.
The paper seeks to contrast Polish top performers with the rest of academics, proceeding
as follows: first, it identifies top performers in the sample; second, it examines their
average research productivity (by several proxies) compared with that of the remaining
90% of academics, and third, it examines their share in the total research output—in all
three steps, by major clusters of academic disciplines. In these introductory procedures
only research productivity data are used. There is a trade off between a disadvantage of
using self-reported data (rather than the Scopus or Web of Science data) and publication
numbers as the only measure of research performance (rather than a combination of
publications, citations, H-index or other measures used in bibliometrics) in introductory
procedures—and an advantage of using individual-level data. Detailed individual-level
data can be collected only through a survey instrument. Therefore, in the next set of
procedures, behavioral and attitudinal data derived from survey questionnaires can be used
as the paper seeks to compare the working time distribution (with average time investments
in teaching, research, service, administration and other academic duties) and academic role
orientation (interests lying primarily in teaching, research or both) of the two classes of
Finally, the paper seeks to find odds ratio estimates by logistic regression for being in
the top 10% in research productivity, with blocks of different individual and institutional
variables. Blocks of individual variables include, for instance, ‘‘socialization to academia’’
(with such variables as intensive faculty guidance and research projects conducted with
faculty), ‘‘internationalization and collaboration’’ (with such variables as research
international in scope or orientation and collaborating domestically), and ‘‘overall research
engagement’’ (with such variables as being a peer reviewer or being an editor of
journals/book chapters). The two blocks of institutional variables are ‘‘institutional policies’’
(for instance, strong performance orientation) and ‘‘institutional support’’ (availability of
research funds and supportive attitude of administration). These variables can be accessed
through survey methodology only, the major drawback being the imprecise nature
(compared with detailed bibliometric datasets) of self-reported productivity data.
Strengths, limitations, and biases of the survey methodology
The analyses are based on self-declared data voluntarily provided by Polish academics. A
crude measure of research productivity was used (the number of peer-reviewed articles and
peer-reviewed article equivalents published during a 3-year reference period). Differences
in reporting publication data can occur between academic disciplines and genders.
Consequently, to different degrees, respondents ‘‘may present an untrue picture to the
researcher, for example answering what they would like a situation to be rather than what
the actual situation is’’
(Cohen et al. 2011: 404)
. Although self-reported publication data
are not perfect, they do not seem to be subject to systematic errors (errors are random) or
(bias occurs when the errors tend to be in one direction more than the other;
Spector 1981: 13)
. The exact formulations of the relevant questions are presented in
Table 15 in Data Appendices. The survey instrument did not distinguish between different
tiers of academic journals or separate top journals from others, and did not allow to study
citation patterns. The impact factor of the journal and the number of citations the author
received could not be analyzed. Individual research productivity could not be linked to
individual institutions due to the data anonymization; it could be linked only to six major
institutional types existing in Poland (such as legally defined universities, universities of
technology, academies etc.). Consequently, it was not possible, for instance, to define the
selectivity level of the employing institution, its geographic location, wealth, size, or
current national and international ranking.
However, to strengthen the robustness of our productivity analyses, apart from
peerreviewed articles (PRA), three additional measures were used: peer-reviewed article
equivalents (PRAE for short), internationally co-authored peer-reviewed article equivalents
(IC-PRAE), and English language peer-reviewed article equivalents (ENG-PRAE).
Publication counts were converted into article equivalents. The PRAE measure is calculated as the
weighted sum of self-reported articles in books or journals (the value of 1 article equivalent),
edited books (the value of 2 article equivalents), and authored books (the value of 5 article
equivalents) published over the 3-year reference period. The same procedure was used in
et al. (2013
: 309), Rørstad and Aksnes (2015: 319), Bentley (2015: 870) and Gorelova and
Lovakov (2016: 11); most survey-based studies equate 4–6 articles to one full monograph. An
individually provided share of peer-reviewed publications is applied to each observation
(following Bentley 2015)
. The advantage of using the PRAE measure in this
multi-disciplinary study is that it captures publishing through various outlets and does not focus on
articles, leaving room for authored books (and edited books), which are still a major outlet in
the social sciences and humanities in Poland. As Bentley (2015: 870) emphasizes, ‘‘using
article equivalents and weighting of books more heavily reflects the relative contribution of
the different publication types’’, minimizing differences across disciplines. The
internationally co-authored PRAE measure applies the individually provided share of publications
co-authored with international colleagues, and the English-language PRAE measure applies
the individually provided share of publications published in a foreign language (the language
in question is predominantly English: 87.1% of Polish academics use English as their major
foreign language in research). The question about the number of scholarly contributions was
thus combined with the question about the percentage of peer-reviewed publications,
English-language publications, and internationally co-authored publications. The conversion of
publication counts into article equivalents is used in research productivity analyses
(especially those focused on productivity correlates) based on survey data in order to make fairer
comparisons of productivity across academic fields with dissimilar publication patterns
(Kyvik and Aksnes 2015)
. So the PRAE measure was used to be able to explore more
comprehensively cross-disciplinary differences in publication patterns between top
performers and the rest of academics, and the IC-PRAE and ENG-PRAE measures were used to
explore internationalization patterns in publishing research results between the two groups.
A substantial proportion of publishing in the humanities and social sciences in Poland
consists of books and edited books, as opposed to publishing in natural sciences. The vast
majority of Polish publications are still outside of major international datasets: for instance,
out of 877,248 publications registered in the PBN (Polish Scientific Bibliography) national
database for the period of 2013–2017, only 18.42% are indexed in the Web of Science
Core Collection, and as many as 60,501 (6.89%) are monographs. Article equivalents were
used specifically in multi-disciplinary studies involving major clusters of academic fields
rather than merely science, technology, engineering and mathematics clusters. Examples
include Ramsden (1994: 213), Guldbrandsen and Smeby (2005: 938), Kyvik and Aksnes
Villanueva-Felez et al. (2013
Piro et al. (2013
: 309), Teichler et al.
(2013: 146–147) and
: 296); article equivalents were also used in
Scientometrics and Journal of Informetrics
(Kyvik 1989: 206; Piro et al. 2016: 945; Bentley
2015: 870; Rørstad and Aksnes 2015: 319)
. In Poland, the notion of article equivalents
have been routinely used in parameterization (a Polish version of a research assessment
exercise) and assessments of individual research output for about a decade: currently, a
conversion system is used in which most Polish articles as well as all book chapters have a
point value of 5 and Polish monographs have a value of 25.
Methods and definitions
In this paper, Teodorescu’s (2000: 206) definition of research productivity is used: the
‘‘self-reported number of journal articles and chapters in academic books that the
respondent had published in the 3 years prior to the survey’’. The data come from the
European Academic Profession: Responses to Societal Challenges (EUROAC) project, a
sister project to the global Changing Academic Profession (CAP) project
2017 for a recent overview of the CAP/EUROAC family of studies)
. The final data set
dated June 17, 2011, created by Rene´ Kooij and Florian Lo¨ wenstein from the International
Centre of Higher Education and Research—INCHER-Kassel, was used. The relatively low
Polish response rate (11.22%) may have been caused by the increasing number of surveys
to which the academic profession is routinely exposed
. The response rate in
Poland has been similar to response rates in several countries studying the academic
profession in the last decade: studies in the Netherlands report 18% (de Weert and van der
Kaap 2014: 121), in Canada 17% (Jones et al. 2014: 348), in the United Kingdom 15%
(Locke and Benion 2011: 178)
, in Hong Kong 13%
(Rostan et al. 2014: 25)
, in the
Republic of Korea 13%
(Shin et al. 2014: 183)
, and in Croatia, Austria, Switzerland, and
Portugal about 10% or less
(Teichler and Ho¨ hle 2013: 8)
. However, the absolute size of the
Polish sample was between two and three times higher compared with other countries
conducting CAP/EUROAC surveys
(Shin and Cummings 2010; Cummings and Finkelstein
2012; Bentley and Kyvik 2013; Teichler et al. 2013; Marquina and Ferreiro 2015; Bentley
: as often argued, the bigger the sample, the more representative it is likely to be,
provided the sample is randomly selected
(Bryman 2012: 198)
No groups of academics were systematically excluded from the sampling frame (so
‘‘sampling bias’’ did not occur). At the time of the survey execution, there were 83,015
academics employed full-time in the public sector (43.8% females and 56.2% males; private
sector academics were excluded, the sector being fully-teaching focused), including 17,683
full and associate professors (21.3%), 36,616 assistant professors (44.1%), 10,784 assistants
(13.0%), and 15,013 senior lecturers and lecturers (18.1%, GUS 2011: 308–309). The sample
of Polish academics was representative of the their population on such strata as gender and
academic rank and included 45.2% of female and 54.8% of male academics, 22.6% of full and
associate professors, 42.1% of assistant professors, 10.9% of assistants, and 24.4% of senior
lecturers and lecturers. Sampling bias did not occur: no members of the sampling frame had
no or limited chances for inclusion in the sample
(Bryman 2012: 187)
. However, it is not
possible to state to what extent the pool of respondents differs from the pool of
non-respondents, and consequently, to state whether ‘‘non-response bias’’ occurs
(Stoop 2012: 122)
‘‘Non-response bias’’ can occur when certain groups of respondents fail to respond or are less
likely than others to participate in the survey or answer certain survey questions (Hibberts
et al. 2012: 72) or when survey participation is correlated with survey variables (Groves
2006). However, non-response biases are only indirectly related to non-response rates: a key
parameter is ‘‘how strongly correlated the survey variable of interest is with response
propensity, the likelihood of responding’’ (Groves 2006: 670). It is conceivable, for instance,
that highly productive academics are prone to refuse to participate in the survey because they
are very busy; however, they may be inclined to participate in the survey because of a sense of
civic (academic) duty, social norms producing a sense of obligation to provide help in the
belief that this serves the common (academic) good, combined with a feeling that their
(Stoop 2012: 126–128)
Stratified random sampling was used to allow the resulting sample to be distributed in
the same way as the population
(Hibberts et al. 2012: 61–62; Bryman 2012: 192–193)
stratified sampling frame was created and two stratifying criteria were used: gender and
academic position. The stratification of the sample mirrored the population stratification on
the stratifying criteria, and mirrored simple random sample in every other way. Random
sampling was used to obtain the elements from each stratum. The identification of
members of the population in terms of the two stratifying criteria was possible due to the
access to a national ministerial database of Polish academics. The survey was performed by
the OPI, or the National Information Processing Institute: an invitation letter to participate
in the web-based survey, with individually coded identifier, was sent in June 2010 to
33,000 academics, or all academics whose e-mail addresses were available at the national
level at the time of the survey execution, two reminders were sent electronically between
June 1, 2010 and July 20, 2010. (The National Information Processing Institute (OPI, see
https://www.opi.org.pl/) is an interdisciplinary research institute which provides access to
complex information concerning Polish science. OPI provides analyses for the two Polish
R&D financing agencies: the National Research Council and the National Centre for
Research and Development. It creates complex IT systems that gather information about
science and higher education architecture in Poland: Polish Science Database, Research
Equipment Database, and Polish Higher Education Information System, POL-on).
However, the paper version of the survey was not mailed to non-respondents.
Due to the survey methodology used, two important methodological issues emerge:
misreporting of self-reported publication data and their misspecification. The publication
number misreporting is predominantly associated with surveys of sensitive topics:
respondents may choose to answer dishonestly ‘‘due to a desire to present themselves in the
best light to the interviewer or to avoid potential repercussions’’
(McNeeley 2012: 382)
The questionnaire used was not viewed as sensitive by Polish academics (and the author
received about 60 e-mails commenting on its content and structure but none about its
sensitive nature). While overreporting socially desirable behavior in academia (for
instance, increasing publication numbers) and underreporting socially undesirable behavior
in academia (for instance, non-publishing) may be an issue (de Vaus 2002), and some level
of misreporting is inevitable, Polish academics seem to have reported publication data and
its proxies accurately: average responses matched expectations based on publicly available
institutional-level and faculty-level productivity data by institutional types. For instance,
average individual publishing rates corresponded to six major institutional types, with the
highest rates for ‘‘universities’’ and ‘‘technical universities’’, and the lowest for
‘‘academies’’ and ‘‘higher vocational institutions’’. Specifically, high percentages of
non-publishers and non-publishers in English (Table 5, ‘‘Rest’’ and Table 7, ‘‘Rest’’ and—for
humanities and social sciences—‘‘Top’’) suggest that the misreporting was not an
The publication type misspecification occurs when, for instance, respondents count their
working papers as peer-reviewed articles or conference papers as book chapters. The exact
formulation of the productivity question was as follows: ‘‘How many of the following
scholarly contributions have you completed in the past 3 years?’’ (Question D4), with the
separate entries for ‘‘scholarly books you authored or co-authored’’ (D4/1), ‘‘scholarly
books you edited or co-edited’’ (D4/2), ‘‘articles published in an academic book or journal’’
(D4/3), ‘‘research report/monograph written for a funded project’’ (D4/4), ‘‘paper presented
at a scholarly conference’’ (D4/5) and ‘‘professional article written for a newspaper or
magazine’’ (D4/6). However, the exact definitions were not provided, assuming their
selfexplanatory nature. The next question was formulated as follows (D5): ‘‘Which percentage
of your publications in the last 3 years were—peer-reviewed’’ (D5/6), ‘‘published in a
language different from the language of instruction at your current institution’’ (D5/1) and
were ‘‘co-authored with colleagues located in other (foreign) countries’’ (D5/3). The
questionnaire was explicit about different types of publications and, importantly, Polish
academics are used to routinely counting different publication types for reporting purposes.
The role of working papers in the Polish academic knowledge production is marginal
because this type cannot be officially reported (or does not count in measuring productivity
at any level, from individual to institutional: a national PBN database which collects all
publications by Polish academics in all languages distinguished between six publication
types (2013–2017): monographs (60,501), book chapters (295,023), and articles in four
categories—List A of journals (161,629; with Impact Factor, listed in the Journal Citation
Report), List B of journals (238,845; without Impact Factor), List C of journals (13,584;
listed in the European Reference Index for the Humanities, ERIH) and articles from not
listed journals (107,666).
Survey respondents marked one of twenty-one disciplines (as officially defined by the
Central Committee for Academic Degrees and Titles in its act of October 24, 2005).
Academics were grouped in eight clusters of academic disciplines, or eight academic fields
in the Polish classification—humanities and arts, social sciences, physical sciences and
mathematics, life sciences, engineering and technical sciences, agriculture, medical
sciences and health-related sciences, and other disciplines (like fine arts)—that best represent
the current structure of the Polish academic profession. The grouping was determined by
the regulation of the Ministry of Science and Higher Education of August 11, 2011 on the
classification of areas, fields, and disciplines: the eight clusters represent eight major
academic fields. The total number of valid responses was 3704; however, in this research,
academics from other disciplines (233 cases), those employed in the postdoctoral position
of docent and teaching-focused lecturers (878 cases), and those whose work contract did
not involve research (68 cases) were excluded. Cases from ‘other disciplines’ were useless
for cross-disciplinary analyses due to their specificity, those from postdoctoral positions of
(before 1990, a position between assistant professor and associate professor)
lecturers were useless for analyses of academic promotions, and teaching-only
observations were useless for research productivity analyses. Finally, 2525 observations from
seven major clusters of academic disciplines (268 top performers and 2257
lower-performing academics) were used for the analyses.
The subsample of academics involved in research from the seven major clusters of
academic disciplines was divided into two subgroups: research top performers (or top
performers henceforth), identified as academics ranked among the top 10% (cut-off points
permitting, from 9.9 to 10.5%) of academics with the highest research performance in each
major cluster of academic disciplines (separately). The second subgroup was the remaining
90% of academics involved in research. The distribution of the sample population by
cluster and the threshold number of publications (the minimum number to be classified as a
top performer) in terms of peer-reviewed article equivalents (PRAE) are presented in
Frequencies of the selected demographic characteristics of the top performers are presented
in Table 2. About two-thirds are men (64%), they are predominantly older (three in four is
at least 40 years old, 75.3%), and almost 60% (59.8%) have at least 10 years of academic
experience (calculated as working full time in the higher education sector beyond teaching
and/or working as a research assistant). The mean age of top performers is 50 (standard
deviation: 11.16, Fig. 1). The dominant age groups of top performers differ by academic
discipline clusters. On average, the top performers are substantially younger in social
sciences and the humanities and older in all other clusters (top performers aged 55 and
more account for about half of the top performers in physical sciences and mathematics,
engineering and technical sciences, and agriculture compared with merely one-third in the
humanities and one-fourth in social sciences).
A good explanation for this cross-disciplinary differentiation by age group is the
deinstitutionalization of the research mission in soft fields (as opposed to hard fields) in the
period of higher education expansion in 1990–2005
(Kwiek 2012, 2017b)
. Young top
performers (an especially acute case is social sciences, with more than half of the top
performers aged less than 40; see Fig. 2) were socialized in their university environment
*Academic experience means the number of years since one’s first full-time job (beyond research and
teaching assistant in the higher education/research sector, Question A6)
when its numerical expansion—ever-increasing enrollments—was already slowing down,
leading to the current system contraction
The divide is also clear in the academic positions which top performers represent. In the
soft fields, the dominant position is assistant professor (or only a PhD degree) as opposed
to hard fields in which the dominant position is full professorship. Again, highly productive
academics in soft fields, on average, are in lower academic positions. In hard sciences, top
performers follow the pattern shown in the traditional cumulative advantage scholarly
(Cole and Cole 1967; Merton 1968; Zuckerman 1970)
: the higher the position,
the higher individual research productivity, or a systematic productivity increase with age
(see Table 3 and Figs. 2, 3). The soft/hard divide in Polish universities is particularly
strong owing to their demand-absorbing growth, turned demographically driven
contraction in the last decade
. The distribution of academics (and consequently top
performers and the rest) across clusters of academic disciplines roughly corresponds to
their distribution in the Polish higher education system (the tiny system of the Polish
Academy of Science was excluded from data collection).
However, the statistically significant differences between top performers and other
academics in terms of the speed of their academic promotion are not at the stage of
studying for their doctorate or in the early academic career stage (see Table 4). The
difference is that top performers receive their Habilitation degree (a second, postdoctoral
degree, required in the Polish system) and then their full professorship, on average, a year
faster for each degree. Answers to Question A1 in the questionnaire provided the dates of
completing studies and receiving a doctoral degree, a Habilitation degree, and the
professorship title, wherever applicable. Thus, the difference between the two groups is not in
terms of academic promotions. The link between publishing a lot and moving up the
academic ladder in Poland is weak. Full professorship is linked not only to publications but
also to what is termed the ‘‘promotion of young academic cadre,’’ that is, the supervision of
doctoral students until they graduate, which prolongs promotion to full professorship
Top performers compared with their lower-performing colleagues share several
common features and represent a common professional profile: top performers tend to be male
Comparisons of column means (t-tests for the equality of means were performed for each academic degree, a
significance level of a = 0.05). For each pair with a mean difference significantly different from zero, the
symbol of the larger category (Top and Rest) appears in the column
academics with a mean age of about 50, are full professors who collaborate more often
nationally and internationally, and publish abroad more often (than the other academics).
The top performers’ research tends to be international in scope or orientation, they work
longer hours and longer research hours, and they are substantially more research-oriented
(see Kwiek 2015a, 2017c)
. They focus on basic and theoretical research, (somewhat
understandably) they sit on national and international committees and boards, and they are
peer reviewers and editors of journals or book series more often than their colleagues (see
Table 22 in the Data Appendices).
Patterns of individual research productivity: top performers and the national research output
Detailed statistics showing average research productivity through the three article
equivalent types (PRAE, IC-PRAE and ENG-PRAE) by academic disciplines cluster and by
group studied (top performers vs. the other academics) is shown in Tables 5, 6 and 7 (and
by peer-reviewed articles (PRA), and IC-PRA and ENG-PRA measures, are presented in
Data Appendices in Tables 16, 17, and 18). By European standards, Polish academics are,
on average, low research performers, and their publication outlets are largely national.
As can be seen in the % non-publishers column in Table 5, between 40 and 57% of
Polish academics who are not top performers who conduct research are non-publishers
(between 38.5% of academics in the humanities and 57.1% of academics in medicine and
health-related fields did not publish a single paper or book during the reference period). As
can be seen from the % not internationally co-authoring (Table 6) and % not publishing in
English (Table 7) columns, their advanced internationalization in research (co-authorship
as a type of collaboration) is marginal. Except for physical sciences and mathematics,
about 85–95% of Polish academics who are not top performers do not co-author
publications internationally; and again, except for physical sciences and mathematics, about
60% do not publish in English (Table 7).
The mean research productivity in terms of all measures for top performers is, on
average, much higher in all clusters of disciplines: about five to eight times higher (see
Fig. 4) than for the other academics. By far the biggest difference in productivity is in
internationally co-authored publications (IC-PRAE)—which shows the determining role of
internationalization in research for productivity: in four clusters, the difference between the
two groups of academics is more than 12 times, and in three about 8 times. Interestingly,
the percentage of IC-PRAE in PRAE is generally similar in all clusters (see Fig. 5): top
performers produce much more, and much more with international colleagues, but there
are significant cross-disciplinary variations rather than intra-disciplinary differences
between the two classes of academics (with PHYSMATH and LIFE clusters with a high
percentage, and HUM and SOC clusters with very low percentages, no matter which class
Consistently across the clusters of academic disciplines, slightly less than half (44.7%)
of all publications (article equivalents: peer-reviewed journal articles, book chapters, and
books) come from about 10% of the most productive academics. Top performers are also
responsible for about half (48.0%) of all publications in English (ENG-PRAE) and almost
60% (57.2%) of all internationally co-authored publications (IC-PRAE); the overall picture
is not much different if only peer-reviewed articles are studied (see Table 8 and, in more
detail, Table 19 in the Data Appendices). Strong cross-disciplinary differences are
observed, however. The top performers in humanities (the upper 10.1%) produce, on
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average, 60.5% of all internationally co-authored publications, and in medicine and
healthrelated fields (the upper 9.9%) about 70.5%.
The average research productivity distribution for all clusters is highly skewed to the
right, not only in the case of all academics (Fig. 6) but also in the case of top performers
(Fig. 7; the details in Tables 20 and 21 in the Data Appendices). Both figures show the
percentage of authors on the vertical axis and the number of papers published on the
horizontal axis. In the upper stratum of academics in terms of their research productivity,
the productivity distribution patterns are as skewed as in the case of the lower-performing
stratum; see the long tail of productivity on the right across all clusters. The upper 10% of
academics is as internally stratified as the lower-performing 90%. However, this is the case
only if an approach of ‘article equivalents’ is used: in the specific Polish case, in which
books and edited books still significantly matter across all disciplines, the rest of academics
is highly skewed but top performers are not (see Figs. 8 and 9 in Data Appendices).
Research productivity and working time distribution
Five dimensions of academic work can be captured in the dataset: teaching, research,
service, administration, and other academic activities. In this paper, the focus is on the
Table 8 Average research output of Polish research top performers as a share of total research output, by
cluster of academic disciplines, by productivity category, for peer-reviewed articles (PRA) and
peer-reviewed article equivalents (PRAE) (in percentage)
disciplines is inconclusive (statistically significant results are obtained for four out of seven
clusters; not reported here due to space limitations).
Logistic regression analysis
Procedures and variables in the model
Differences in individual research productivity can be explained by at least three theories.
The sacred spark theory
(Cole and Cole 1973)
states ‘‘that there are substantial,
predetermined differences among scientists in their ability and motivation to do creative
(Allison and Stewart 1974: 596)
. Highly productive scholars are
motivated by ‘‘an inner drive to do science and by a sheer love of the work’’
Cole 1973: 62)
. Productive scientists are a strongly motivated group of researchers, and
they have the stamina, ‘‘or the capacity to work hard and persists in the pursuit of
(Fox 1983: 287; Zuckerman 1970: 241)
. The accumulative advantage theory
developed by Robert K.
claims that productive scientists are likely to be
even more productive in the future, while the productivity of low performers will be even
lower. The accumulative advantage theory is related to the reinforcement theory
formulated by Cole and Cole (1973: 114) which in its simplest formulation states that ‘‘scientists
who are rewarded are productive, and scientists who are not rewarded become less
productive’’. Finally, according to the utility maximizing theory, all researchers choose to
reduce their research efforts over time because they think other tasks may be more
advantageous. As Kyvik (1990: 40) states, ‘‘eminent researchers may have few incentives
to write a new article or book, as that will not really improve the high professional
reputation that they already have’’ which may mean that ‘‘with each additional year the
rewards for doing research decline’’
(Stephan and Levin 1992: 35)
. Scientists’ engagement
in research can be either investment-motivated (seeking future financial rewards),
consumption-motivated (solving research puzzles), or both
(Thursby et al. 2007)
. Although the
investment motive implies a decline in research productivity over one’s career, the
consumption motive does not imply such a decline
(Levin and Stephan 1991)
. A taste for
(Roach and Sauermann 2010)
—that is, for non-monetary returns—causes scientists
to choose academia over industry. Academics with different abilities and tastes in terms of
non-monetary returns choose different careers: basic or applied research in academia or
(Agarwal and Ohyama 2012)
. Time spent on research reduces current earnings but
increases future earnings, as in investment models of human capital
(see Kwiek 2017a on
European ‘academic top earners’ in 10 countries)
. These three major theories of research
productivity are complementary rather than competitive. To varying degrees, they are all
applicable to the Polish academic profession.
An analytical model for studying high research productivity was prepared based on research
literature, especially Fox (1992: 295–297), Ramsden (1994: 211–212), and Teodorescu (2000:
, it has been assumed that ‘‘any sensible explanation of
research output must take into account personal (individual) and structural (environmental)
factors, and preferably also the interaction between them’’. Independent variables are grouped
as individual and institutional characteristics in eight clusters (Table 13; the exact
formulations of questions are presented in Table 15 in Data Appendices).
All category variables were dichotomized through a recoding procedure. Forty-nine
personal and institutional characteristics grouped in eight clusters were selected. Then
Pearson Rho correlation tests were conducted to find significantly correlated predictors of
the dependent variable. The predictors were entered in a four-stage logistic regression
model. Multicollinearity was tested using an inverse correlation matrix, and no
independent variables strongly correlated with others were found. On the main diagonal of an
inverse correlation matrix, there are values without unequivocal interpretation; however,
they show how strongly a given variable is correlated with all other variables. The
interpretation is performed in such a way that all variables with diagonal values higher than
4 are removed from analysis (see an inverse correlation matrix in Table 23 in the Data
Appendices). In addition, principal component analysis (PCA) was performed to determine
whether any variables, due to their high level of correlation, could be grouped into
homogenous groups. No significant interdependence between any of the variables was
found. Separate models for all academics combined, science, technology, engineering, and
mathematics (STEM) academics, and social sciences and humanities (SSH) academics
were built. The predictive power of the fourth model (as measured by Nagelkerke’s R2)
Results not statistically
significant are not shown in the
***P \ 0.001; **P \ 0.01;
*P \ 0.05
was the highest for STEM academics and was 0.381 (shown in Table 14). In this table, the
results for the final, fourth model are presented.
Statistically significant individual and institutional variables
In the analysis (results shown in Table 14), individual variables emerged as important and
institutional variables emerged as unimportant (in terms of the occurrence and the size of
the regression coefficients). What did not enter the equation? Age, being a female
academic, holding parallel jobs, holding full professorship, and attaching importance to one’s
academic discipline and academic satisfaction. Also in the block of ‘‘socialization to
academia,’’ both variables related to doctoral studies are statistically insignificant. In the
internationalization and collaboration in research block, two variables (international
collaboration and publishing abroad) statistically significantly increase the odds of becoming
a top performer. Domestic collaboration in research does not enter the equation and
‘‘research international in scope or orientation’’ actually decreases the odds of entering the
class of highly productive academics.
In the academic behaviors and working time allocation, annualized mean weekly
research hours emerged as powerful determinative predictors of high research productivity:
a 1-h unit increase (in annualized research hours per week) increases the odds of being a
top performer by 3.8%, on average (ceteris paribus). In the academic attitudes and role
orientation block, research orientation emerges as a powerful predictor, with
Exp(B) = 2.333. In the inferential analyses and in the regression analyses, long research
hours and high research orientation emerge as important characteristics of top performers.
The variables related to the understanding of scholarship (scholarship is best defined as
original research by the respondents) and to the characterization of one’s primary research
as basic or theoretical did not enter the equation.
To strengthen the robustness of the logistic regression analysis, separate models for top
performers from all academic fields (ALL), STEM academics, and SSH academics—as
well as for top performers defined as the upper 5, 10, and 15% of academics in terms of
their research productivity—were constructed (not shown here for space limitations).
Overall, in these models in addition to the core STEM/10% model, new independent
variables entered the equation only exceptionally. In the ALL/5% model, full professorship
increases the odds twice (Exp(B) = 2.211), consistent with the accumulative advantage
theory, and in the SSH/5% models, possessing only a PhD or lower degree decreases the
odds three times (Exp(B) = 0.343). Both findings are consistent with the traditional
seniority-based structure of the Polish academic profession in which research funding
opportunities have been opened to younger academics only within the last few years,
following the creation of the National Research Council (NCN) in 2010
(Kulczycki et al.
2017; Kwiek 2017b)
In the ALL/10% model, intensive faculty guidance increases the odds almost six times
(Exp(B) = 5.837), and being a peer reviewer increases the odds four times
(Exp(B) = 4.192). Two other independent variables emerge as more significant (research
orientation and serving as a journal editor). Interestingly, international research orientation
emerges as a powerful predictor of being a top performer (Exp(B) = 5.511). The only
difference between the core STEM/10% model and the SSH/10% model is the emergence
in the equation of only two variables, albeit with lower intensity. For SSH academics, the
only two predictors are international collaboration in research (Exp(B) = 3.569) and
publishing abroad (Exp(B) = 5.84). In statistical terms, nothing else matters—which is a
good lesson for new entrants in the profession in this cluster on the one hand, and for
national and institutional academic career policies. Finally, in the case of models for top
performers defined more widely, in the ALL/15% model a new variable enters the
equation: sitting in international committees and boards (Exp(B) = 4.759). For STEM and SSH
academics, the predictors are the same, with slightly different intensities.
Discussion and conclusions
orientation among the rest of academics
(following a long line of mostly survey-based
research, Fox 1992; Ramsden 1994; Teodorescu 2000; Cummings and Finkelstein 2012;
. Top performers value research: being interested primarily in teaching virtually
excludes Polish academics from the class of top performers. International collaboration and
publishing abroad significantly increase the odds of becoming a top performer
2017c on ‘international research collaboration’ and ‘international research orientation’
. Annualized mean weekly research hours emerged as a powerful
determinative predictor of high research productivity (in some clusters, for instance in physical
sciences and mathematics, on average, top performers spend an additional 75 full working
days per year, or 13 h per week, on research, which is the entry ticket to the highly
productive class of academics if academic careers are considered). A standard pattern for
Polish top performers is (many) more working hours, and especially (many) more research
hours, than the discipline average. Both in the inferential analyses and the regression
analyses, long research hours and high research orientation emerge as important
characteristics of Polish top performers, consistent with research literature.
Longer working hours, and especially longer research hours, substantially contribute to
(as shown before in Jung 2014; Shin and Cummings 2010; Teichler et al.
. In more competitive Polish disciplines in which competitive funding is more widely
available (such as life sciences or physical sciences and mathematics), top performers work
many more hours compared to the average academics in these disciplines. However, in less
competitive disciplines (such as humanities and social sciences, with marginal access to
competitive research funding), the differences between the two groups are not statistically
significant. Also in the logistic regression analysis, annualized mean weekly research hours
emerged as powerful determinative predictors of high research productivity (consistent
with Cummings and Finkelstein 2012: 58; Drennan et al. 2013: 127; Shin and Cummings
The most instructive example comes from life sciences (with 422 cases and the highest
number of statistically significant differences between the two subpopulations among several
academic activities studied). The top performers in life sciences, on average, seem to follow
all traditional accounts of productive academics in the sociology of science. On average, they
work almost 7 more hours per week, and specifically, they have the traditional working time
distribution attributed to high publishers (Fox 1983; Hagstrom 1974) according to which
research-time allocations compete directly with teaching-time allocations
(Fox 1992; Kyvik
1990; Ramsden 1994)
, or the only relevant difference is in general between research time and
non-research time (Stephan 2012). Their average weekly teaching time is 3.5 h shorter, and
their research time 4 h longer; in addition, they spend 3.7 more hours on administration
(presumably more research involves more research grants which require more administrative
work; alternatively, these academics are more often heads of research groups or
mediumlevel administrators, such as directors and deans).
However, this research has its limitations. Three streams of research studied in literature
could not be followed. First, it was not possible to study differences between top
performers from institutions of lower academic standing and those from the most prestigious
institutions, knowing that minor and major universities
(as in Agrawal et al. 2017; Crane
may provide more and less favorable academic settings and attract more and less
talented students and academics, respectively. Location and affiliation may matter not only
for recognition but also for high research productivity, which could not be verified with the
dataset used. It could not be studied whether top performers gravitate toward institutions
and departments in which research is a priority (as
White et al. (2012)
explored in a sample
of business faculty). Neither within-department (and institution) nor between-department
(or institution) variability could be studied, as in Perianes-Rodrigues and Ruiz-Castillo
(2015) and in
Toutkoushian et al. (2003)
Second, Polish top performers could not be linked to the 963 basic academic units
periodically assessed by KEJN
(Committee for the Evaluation of Scientific Units that uses
national marks to assess the relative research level of each unit, which determines the level
of public research subsidies for a period of 4 years; see Kulczycki 2017)
. For this reason, a
study of the impact of highly productive academics on the general productivity of their
academic units—or of the asymmetry of knowledge production between the within-unit top
performers and the within-unit other academics across different institutions—could not be
(following Piro et al. 2016 who studied Norwegian universities, with the
conclusion that their overall productivity impact on units is modest)
. Top performers may
increase the productivity of those present in the organization, and they may also increase
the productivity of newly hired members due to their reputation (Agrawal et al. 2017).
However, with the instrument used, this could not be explored. And third, only a
crosssectional study could be performed; thus, no changes over time could be analyzed (for
instance, the identification of the persistence of top performance over time as in
Kelchtermans and Veugelers (2013), or the length of periods of the stardom of stars as in
Abramo et al. (2017b) could not be explored).
In logistic regression analysis, surprisingly in the context of much research productivity
literature, in the block of personal and demographic variables, being a female academic did
not enter the equation
(thereby not confirming the results found in Abramo et al. 2009 about
Italian ‘star scientists’)
. Abramo and colleagues (2009: 143) in their study of Italian ‘star
scientists’ conclude that the star scientist ‘‘is typically a male full professor’’ and that female
star scientists are primarily concentrated in the lesser levels of productivity. Holding a parallel
academic job—contrary to expectations in a country with a large, albeit decreasing, private
higher education sector—did not emerge as a predictor of not becoming a top performer.
Surprisingly in the context of previous research on Poland
(Antonowicz 2016; Antonowicz
et al. 2017; Białecki and Da˛browa-Szefler 2009)
, in two complementary models constructed
specifically for social scientists and humanists (the SSH/5% and SSH/15% models), holding a
parallel job (in research institutes or higher education institutions) actually increases the odds
of high research performance. However, these two models do not pertain to the research
productivity of the Polish academic profession in general, only to very high research
productivity of its social sciences and humanities segment. Overall, the combination of all
models shows similar predictors of entering the class of Polish top performers. Also attaching
importance to one’s academic discipline (as opposed to one’s academic institution),
traditionally dividing more productive cosmopolitans from less productive locals
fundamentally different frames of reference in conducting research and publishing research results,
leading them to seeking different sources of recognition and to having different trajectories of
academic careers; across Europe, see Kwiek 2017c; Wagner and Leydesdorff 2005)
satisfaction with one’s current job did not enter the equation
(as in Teichler et al. 2013)
While, similarly to most studies
(Crane 1965; Drennan et al. 2013; Postiglione and Jung
, age did not emerge as a statistically significant variable, also holding full professorship
or having a Habilitation degree in the Polish case (both representing academic seniority) had
no statistical significance. Being a senior-ranking faculty did not increase the odds of
becoming a top performer. This finding does not confirm the conclusions from previous
productivity studies and highlights the specificity of the Polish academic career. A good
explanation can be that Polish academics are not more likely to be promoted to higher ranks if
they are highly productive. High research productivity in Poland does not seem to affect
promotion to full professorship. Also intensive faculty guidance and research projects
conducted with faculty during doctoral studies are statistically insignificant (inconsistent
with findings in Horta and Santos (2016) who focused on the impact of publishing during
doctoral studies on future productivity). Unfortunately, the following could not be tested: a
long line of research in which current affiliation matters (through contacts or halo effects),
whether graduates of major universities are more likely to be highly productive than
graduates of minor universities, and whether the next generation’s most productive scientists
come from a highly selected group of previous top scientists (Crane 1965).
Consistent with previous research
(Bentley 2015; Marquina and Ferreiro 2015; Shin and
Cummings 2010; Kwiek 2016a)
, international collaboration and publishing abroad
statistically significantly increase the odds of becoming a top performer. However, as Ramsden
(1994: 223) argued, ‘‘identifying correlates of high productivity does not mean that we have
identified causal relations’’. Domestic collaboration in research does not enter the equation.
‘‘Research international in scope or orientation’’ (as an academic attitude) actually decreases
the odds of entering the class of Polish highly productive academics, contrary to studies that
tend to suggest a close correlation between internationalization understood as collaboration in
research (as an academic behavior) and research productivity. There are at least two possible
explanations for this finding. First, the international scope or orientation in research does not
have to imply international research collaboration (and does not have to be linked to
international publishing). Second, Polish academics may tend to view international research
orientation through the lenses of European Union (EU) collaborative research projects and
EU structural funds for research, which are often focused on international collaboration rather
than on highly competitive research leading to top-tier publications.
The determinative power of institutional-level predictors emerged as marginal,
consistent with previous research on productivity
(Cummings and Finkelstein 2012: 59;
Ramsden 1994: 220; Shin and Cummings 2010: 588; Teodorescu 2000: 212)
Drennan and colleagues (2013: 128) concluded in a cross-national study that ‘‘institutional
factors were found to have very little impact on research productivity,’’ the present study
results suggest these factors have zero impact. This finding is also consistent with the
conclusion about the American professoriate that intrinsic motivations rather than
institutional incentive structures
(Finkelstein 2006: 97–98; Teodorescu 2000: 217)
research productivity. This might mean that, generally, neither institutional policies nor
institutional support matters substantially in becoming a top performer in Poland, possibly
because top performers and low performers are scattered across the whole system.
Finally, the paper shows that global patterns of stratification in science—found in the
classical sociology of science and in recent bibliometric studies—hold firmly in a heavily
under-resourced and vertically undifferentiated Polish higher education system.1 Polish
academic knowledge production is highly skewed and does not follow a normal
distribution. In this sense, the production is undemocratic and follows a Paretian (power law)
distribution. In a system currently rapidly changing into a much more competition-based
one, inequalities in research productivity are only beginning to lead to inequalities in
resources and rewards, potentially with new haves being recruited from top performers and
new have-nots from low performers.
1 Main Science and Technology Indicators
show that Gross Domestic Expenditure on R&D
(GERD) as a percentage of Gross Domestic Product (GDP) for Poland was the second-lowest in the
European Union in 2015 (1.00, the lowest being for Greece with 0.97; 1.96 for EU-28 countries, 2.10 for
EU-15 countries, and 2.38 for OECD countries). Also Higher Education Expenditure on R&D (HERD) as a
percentage of Gross Domestic Product (GDP) for Poland was the third-lowest in the European Union in
2015 (0.29, higher only to Hungary, 0.17, and Slovenia, 0.23; 0.45 for EU-28 countries, 0.48 for EU-15
countries, and 0.42 for OECD countries).
Acknowledgements The author gratefully acknowledges the support of the National Ministry of Science
and Higher Education through its Dialogue grant 0021/DLG/2016/10 (EXCELLENCE). The support of Dr.
Wojciech Roszka is also gratefully acknowledged. Finally, my gratitude goes to the two anonymous
reviewers for their highly constructive criticism of the original manuscript.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
See Tables 15, 16, 17, 18, 19, 20, 21, 22, 23 and Figs. 8 and 9.
Female (Question F1): ‘‘What is your gender?’’
Mean age (Question F2): Calculated from ‘‘Year of birth’’
Full-time (Question A7): ‘‘How is your employment situation in the current academic year at your higher
education institution/research institute?’’
PhD or lower degree (Question A1): ‘‘What is your academic rank?’’
Habilitation degree (Question A1): ‘‘What is your academic rank?’’
Full professorship (Question A1): ‘‘What is your academic rank?’’
Work at another research institute or HEI (Question A8): ‘‘Do you work for an additional employer or do
additional remunerated work in the current academic year?—In addition to your current employer, you
also work at another research institute or higher education institution’’
Self-employed (Question A8): ‘‘Do you work for an additional employer or do additional remunerated
work in the current academic year?—In addition to your current employer, you are also
My academic discipline/field is important (Question B4): ‘‘Please indicate the degree to which each of
the following affiliations is important to you—My academic discipline/field’’ (answers 1 and 2 on
fivepoint Likert scale from ‘‘very important’’ to ‘‘not important at all’’)
My institution is important (Question B4): ‘‘Please indicate the degree to which each of the following
affiliations is important to you—My institution’’ (answers 1 and 2 on five-point Likert scale from ‘‘very
important’’ to ‘‘not important at all’’)
Satisfaction with current job (Question B6): ‘‘How would you rate your overall satisfaction with the
current job’’? (answers 1 and 2 on five-point Likert scale from ‘‘very high’’ to ‘‘very low’’)
Socialization to academia
Intensive faculty guidance (Question A3): ‘‘How would you characterize the training you received in
your doctoral degree?—You received intensive faculty guidance for your research’’
Research projects with faculty (Question A3): ‘‘How would you characterize the training you received in
your doctoral degree?—You were involved in research projects with faculty or senior researchers’’
Internationalization and collaboration
Collaborating internationally (Question D1): ‘‘How would you characterize your research efforts
undertaken during this (or the previous) academic year?—Do you collaborate with international
Collaborating domestically (Question D1): ‘‘How would you characterize your research efforts
undertaken during this (or the previous) academic year?—Do you collaborate with persons at other
institutions in your country?’’
Publishing in a foreign country (Question D5): ‘‘Which percentage of your publications in the last 3
years were published in a foreign country?’’
Research international in scope or orientation (Question D2): ‘‘How would you characterize the
emphasis of your primary research this (or the previous) academic year?’’
Annualized mean research hours (60% in session and 40% not in session) (Calculated from Questions
B1/1 and B1/2): ‘‘Mean weekly research hours (in session)’’ and ‘‘Mean weekly research hours (not in
session): ‘‘Considering all your professional work, how many hours do you spend in a typical week on
each of the following activities?’’; ‘‘Research (reading literature, writing, conducting experiments,
Academic attitudes and role orientation
Research-oriented (Question B2): ‘‘Regarding your own preferences, do your interests lie primarily in
teaching or in research?’’; answer 4 only: ‘‘Primarily in research’’
Basic/theoretical research (Question D2): ‘‘How would you characterize the emphasis of your primary
research this (or the previous) academic year?—Basic/theoretical’’
Overall research engagement
National/international committees/boards/bodies (Question A13): ‘‘During the current academic year,
have you done any of the following?—Served as a member of national/international scientific
A peer reviewer (Question A13): ‘‘During the current academic year, have you done any of the
following?—Served a peer reviewer (e.g. for journals, research sponsors, institutional evaluations)’’
Editor of journals/book series (Question A13): ‘‘During the current academic year, have you done any of
the following?—Served as an editor of journals/book series’’
Strong performance orientation (Question E4): ‘‘At my institution there is…—A strong performance
Research considered in personnel decisions (Question E6): ‘‘To what extent does your institution
emphasize the following practices?—Considering the research quality when making personnel
Availability of research funds (Question B3): ‘‘At this institution, how would your evaluate each of the
following facilities, resources, or personnel you need to support your work?—Research funding’’
Supportive attitude of administration (Question E4): ‘‘At my institution there is…—A supportive attitude
of administrative staff towards teaching/research activities’’
2 9 0 7 0 9 1
6 2 2 4 6 1 3
3 4 6 6 5 1 7
1 1 1 1 1 1 1
p rd io
c d i
n n v
e ta e
e S D
e - eh
h n s
is o il
l n b
u % p
ev ) d v r 7 7 5 4 7 8 8
) % fi re ep .2 .6 .0 .8 .8 .4 .9
% 01 95% con itn pu 51 61 91 91 81 71 91
rse (ppu ecen ,l adn
MP p o 6 7 7 8 7 6 7
2 7 9 1 2 9 5
4 4 3 5 5 4 5
0 0 2 0 0 0 0
en ,l a
2 3 3 2 2 2 2
c R MP
d v r
fi r e 1 1 4 7 5 5 5
itcv )% 59% cno tien low .12 .42 .62 .02 .71 .71 .51
rpo t(se ean RA .24 .59 .42 .54 .30 .82 .59
2 2 3 2 2 2 1
M C Y E G R D
U O H IF N G E
H S P L E A M
2 9 0 7 0 9 1
6 2 2 4 6 1 3
1 8 6 9 1 4 7
.8 .5 .7 .7 .5 .7 .6
2 1 6 7 4 1 4
t . r
o rn o
n te - th .1 .0 .7 .8 .7 .7 .4
7 7 0 0 4 2 9
% in co au 7 8 1 2 4 5 4
0 0 9 2 0 0 0
T H T L
A C L A
E U E
S IT IC H
M C Y E G R D
U O H IF N G E
H S P L E A M
r c n
a en ,l a
e a b
-y3 )% % dnfi rev rep .54 .21 .4 .61 .87 .28 .52
eh 01 95 co itn pu 1 1 11 8 3 1 4
edh (pu ecen ,l adn
lisub rsem % dnfi traev rebw .030 .080 .075 .593 .451 .150 .830
p ro 95 co in lo
R p n
-P po ae - A 4 2 3 8 1 9 4
C C R .7 .5 .2 .8 .6 .9 .5
I( T MI P 0 0 8 5 2 0 2
1 2 4 0 1 4 2
5 6 7 8 1 6 8
5 2 1 3 5 1 2
4 4 8 6 2 8 1
.4 .3 .4 .8 .9 .9 .1
0 0 2 1 0 0 1
t . r
o rn o
n te - th .2 .8 .2 .6 .3 .8 .4
2 2 3 4 2 3 6
% in co au 9 9 6 7 8 8 8
0 0 0 0 0 0 0
d v r
fi r e
e p 1 5 9 2 8 4
95% con itn pu .1 .1 .5 .7 .3 .3 .3
0 0 1 0 0 0 0
en ,l a
: a b
y d v r
ti fi r e 2 3 1 1 6 8 8
v % n te w
it ) ) 5 o .0 .0 .8 .4 .1 .0 .0
c % 9 c in lo 0 0 0 0 0 0 0
u % 0
d 0 9
ro (9 ( n
p t ts ea - A 6 7 8 4 3 1
h se e C R .0 .0 .1 .6 .2 .2 .2
c r R MI P 0 0 1 0 0 0 0
2 9 0 7 0 9 1
6 2 2 4 6 1 3
.5 .0 .5 .9 .2
1 1 6 3 9 4 8
t sh s
o il l
n b g .8 .6
n 3 5 .3 .8
% pu in E 2 2 0 0 4 0 6
95% con itn pu 4 4 8 6 2 8 2
1 1 1 1
-3y 01% ec dn
1 1 3 3 2 2 2
n b E .2 .9 .3 .3 .7 .1 .6
% pu in 6 6 4 5 5 5 6
3 2 0 3 6 3 0
0 0 1 0 0 0 0
E en ,l a
: a b
y d v r
it fi r e 4 4 5 7 7 5
tiv ) 5% no te w .4 .3 .5 .6 .0 .9 .7
c % 9 c in lo 0 0 2 1 1 0 0
rpo ) t(s an -G A 7 5 9 9 9 2
e e N R .5 .5 .0 .2 .3 .0
rch 0% R ME P 0 0 3 2 1 1 1
5 9 5 2 8
.7 .7 .6 .3 .5 .8 .2
0 0 3 2 1 1 1
T H T L
A C L A
E U E
S IT IC H
M C Y E G R D
U O H IF N G E
H S P L E A M
By top performers
By the rest (90%)
By top performers (n %) HUM PRAE
Table 21 Top performers: the distribution of PRAE published in the 3-year reference period, by clusters of
academic fields and publication number groups (in percent)
Work at another research institute or higher education institution
My academic discipline/field is important
My institution is important
Satisfaction with current job 457
Fig. 9 Top performers: the distribution of peer-reviewed article equivalents (PRA) published during the
3-year reference period, by cluster of academic disciplines and publication number groups (percentage).
Vertical axis: percentage of authors, horizontal axis: number of papers published
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