RESEARCH ARTICLE

Research network propagation: The impact of PhD
students’ temporary international mobility

Keine offenen Zugänge

Tagebuch

1Social Contexts and Policies of Education, Faculty of Education, University of Hong Kong, Hongkong, Hong Kong SAR, China
2Department of Management, Information and Production Engineering, University of Bergamo, via Pasubio 7b,
24044 Dalmine (BG), Italien
3Graduate School of Education, Peking University, Peking, China

Hugo Horta1

, Sebastian Birolini2

, Mattia Cattaneo2

,

Wenqin Shen3

, and Stefano Paleari2

Zitat: Horta, H., Birolini, S.,
Cattaneo, M., Shen, W., & Paleari, S.
(2020). Research network propagation:
The impact of PhD students’ temporary
international mobility. Quantitative
Science Studies, 2(1), 129–154. https://
doi.org/10.1162/qss_a_00096

DOI:
https://doi.org/10.1162/qss_a_00096

Erhalten: 12 Marsch 2020
Akzeptiert: 29 September 2020

Korrespondierender Autor:
Hugo Horta
horta@hku.hk

Handling-Editor:
Ludo Waltman

Urheberrechte ©: © 2020 Hugo Horta,
Sebastian Birolini, Mattia Cattaneo,
Wenqin Shen, and Stefano Paleari.
Veröffentlicht unter Creative Commons
Namensnennung 4.0 International (CC BY 4.0)
Lizenz.

Die MIT-Presse

Schlüsselwörter: PhD students, research collaborations, research network propagation, research productivity,
STEM fields, temporary international mobility

ABSTRAKT

As the global mobility of researchers increases, many of whom are supported by national funding
agencies’ mobility schemes, there is growing interest in understanding the impact of this overseas
mobility on knowledge production and networking. This study addresses a relatively understudied
mobility—the temporary international mobility of PhD students in STEM fields—and its relation to
the establishment of research collaborations between mobile PhD students and researchers at the
host university and with other researchers overseas. Erste, we find that 55% of the participants
established relevant international collaborations (d.h., with hosting supervisors and/or others at the
hosting university), and we explore these collaboration patterns in detail by taking a novel research
propagation approach. Zweite, we identify features of the visiting period that influence the
formation of research collaborations abroad, such as the prestige of the host university, Die
duration of the international mobility period, the cultural distance, and the number of peer PhD
students at the host university. Previous research collaborations between the home and host
supervisors are also found to play a crucial role in research collaboration development. Age at the
time of mobility is not found to be particularly relevant. We find that female PhD students are less
able to benefit from collaborative research efforts than male students. These findings advance the
knowledge of global research networks and provide important insights for research funding
agencies aiming to promote international research mobility at the doctoral level.

1.

EINFÜHRUNG

Scientific collaboration is increasing (Lee, Seo, et al., 2012) and is multilayered, as it encompasses
the geographical (including local, National, and international, and diverse mixes of all of these) Und
the sectoral (within and between academic and nonacademic actors) and is influenced by culture,
Sprache, and the field of research (Schienbein, Lee, & Kim, 2013). Collaboration can occur for various
Gründe dafür, but the increasingly complex challenges faced by researchers are a significant factor, als
they cannot be solved by disciplinary thinking alone and require interdisciplinary problem-solving
strategies (Yates, Woelert, et al., 2016). Collaboration in science is associated not only with issues
of pragmatism and self-organization in the pursuit of complementarities of expertise, competence,
and the sharing of special data and equipment but also with field positioning, socialization,
specialization, knowledge cultures, path dependencies, and trust (see Melin, 2000). Zusätzlich,
the increasing costs of scientific research can only be met through distributing the expenditure

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Research network propagation

and effort, which can be effectively achieved through collaborations (Iglic, Doreian, & Ferligoj,
2017). Several studies have linked research collaborations with research productivity and impact
(z.B., Abbasi, Altmann, & Hossain, 2011; Wagner, Whetsell, & Leydesdorff, 2017), aber die
identified benefits are relatively broad, as several of the potentially advantageous outcomes of
collaboration are not easily measured (Leahey, 2016). The benefits are mainly associated with
the generation of new ideas, but collaboration also develops human and technical expertise,
absorptive capacity, and the transferability of knowledge to both organizations and individuals
(Walsh, Lee, & Nagaoka, 2016). Daher, policymakers and research agencies have developed in-
centives for scientific collaborations to be established, and this development is closely associated
with issues of mobility and migration (Patrício, Santos, et al., 2018).

Scientific collaboration has been extensively researched, as knowledge-exchange processes
have become increasingly important in global, regional, and national systems of innovation. Der
research has mainly focused on collaboration-related determinants and dynamics among
academics, institutions, teams, fields of research, and disciplinary spheres, and between aca-
demic and nonacademic organizations (z.B., Hall, Vogel, et al., 2018). Research collaboration
has also been extensively studied in relation to migration and mobility because these phenom-
ena are closely associated with the establishment of organized science and form part of the
ethos of being an intellectual (Kim, 2017). Globalization processes, economic and geopolitical
competition, fighting for talent, “brain-circulation,” and other factors associated with a knowl-
edge society have increased the importance of mobility and collaboration in terms of knowl-
edge production, competitiveness, and career advancement (Jacob & Meek, 2013). Scientific
mobility has in recent years diversified and extended in terms of countries of origin and desti-
nation, and has become the norm for academics and research students (Czaika & Orazbayev,
2018). The motivations for these various types of mobilities are not necessarily related to better
economic rewards, and may include better research, Ausbildung, and career opportunities, rep-
utational gains, access to sophisticated scientific equipment, and working with prestigious col-
leagues (Fernández-Zubieta, Geuna, & Lawson, 2015). Most studies of research mobility have
focused on the medium to long term, and short-term, temporary mobilities have been neglected
(Teichler, 2015). Research collaborations and the migration and mobility flows of scientists and
PhD students have been steadily increasing (z.B., Gureyev, Mazov, et al., 2020), but the con-
ditions influencing the ability of PhD students to develop research collaborations during tem-
porary international mobility has rarely been considered.

We focus on the temporary mobility of PhD students first because it occurs at a time when the
students are being socialized to become researchers, and their abilities to conduct research
independently are developing (Laudel & Glaser, 2008). Daher, this mobility can offer benefits in
terms of research productivity and network formation, but also in terms of access to leading
research teams and flows of knowledge (z.B., Aksnes, Rorstad, et al., 2013; Franzoni, Scellato,
& Stephan, 2014; Tartari, Lorenzo, & Campbell, 2020). The expected benefits gained through
mobility in this time of social and research skill development can continue after the period of
temporary mobility, and can thus potentially minimize the identified financial, cultural, sozial,
or family constraints, which can occur when mobility continues for longer periods or happens
later in a career (Bauder, 2020; Patrício et al., 2018; Teichler, 2017). Zweite, this type of mobility
tends to involve less expenditure for both the individual and for publicly funded mobility pro-
Gramm, as several types of fees (such as tuition fees) do not need to be covered, while other costs
are minimized, such as those for accommodation if the period abroad is shorter. Die Auswirkungen von
temporary mobility during doctoral education may be similar to doing PhDs abroad in terms of
networking and exposure to new knowledge (Horta & Blasi, 2016). Daher, in this study, we address
two research questions: (A) What characteristics of temporary mobility affect the establishment of

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Research network propagation

research collaborations of PhD students with peers at a hosting institution during a period abroad?
Und (B) What are the determinants associated with international temporary mobility that affect the
ability of PhD students to extend their research networks to include other researchers overseas?

The data used for this analysis were sourced from a survey administered to Chinese students
doing PhDs in a science, Technologie, engineering, or mathematics (STEM) Thema, and who par-
ticipated in the 2014 “National Development High-level University Public-Sponsored
Postgraduate Student Scheme” managed by the China Scholarship Council (CSC). We focus on
the temporary mobility of PhD students funded by the Chinese government, as this is part of an
effort to develop the rapidly growing research system (the number of full-time equivalent
researchers increased from 1,210,840 In 2010 Zu 1,866,108 In 2018)1. The aim is for China to
become more integrated and networked with the global research community and therefore
contribute to its development (Horta & Shen, 2019). This type of mobility is concentrated in the
fields of higher learning and STEM research, as these fields are regarded as critical for developing
knowledge societies that have the potential to become innovative and creative engines and thus
drive economic growth. China awards the second largest number of science and engineering
doctoral degrees in the world after the United States, mit 34,440 awarded in 2015, which repre-
sents 64% of all PhDs awarded in the country (NSB, 2020). The mobility considered in this study
follows a logic of knowledge accumulation and learning. This is intended to create human and
social capital and to stimulate a research system that can meet national and local challenges
through combinations of national and global knowledge (Cao, Baas, et al., 2020). Our study is
also associated with the desire of Chinese researchers to be exposed to new and different knowl-
edge, societies, and cultures. Like most researchers worldwide, they are keen to develop network-
ing opportunities (Yang, Volet, & Mansfield, 2018) and are driven by the complementary
dynamics of competition and collaboration, which involves individuals, Organisationen, and coun-
tries in global science research (Santos & Horta, 2020). The international exposure resulting from
this mobility may also create awareness and trigger change in terms of the institutional and cultural
obstacles that science development can face, which for China involve an excessive focus on short-
term thinking, funding rules, governmental micromanagement and bureaucracy, nonmeritocratic
evaluation systems, and associated guanxi rationales (Han & Appelbaum, 2018). Zusätzlich, Das
government-sponsored mobility program has been implemented by a nation that is growing to
prominence as a scientific superpower, and thus analyzing it can inform both Chinese policy-
makers and those from developing countries that are aiming to catch up. They are likely to require
policies and programs related to talented mobility that can enable them to progress in a faster and
more efficient way (Heitor, Horta, & Mendonça, 2014). Within this context, and bearing in mind
the abovementioned knowledge gap, the analytical setting of this study is aimed at measuring
scientific network propagation, based on the coauthorship of publications, which are then
regressed relative to key factors known to influence research collaboration and relevant to the
mobility of scientists (z.B., Jonkers & Cruz-Castro, 2013).

The remainder of this paper is organized as follows. The next section offers a brief literature
review on issues pertaining to mobility and research collaboration and mobility during doctoral
Ausbildung. This is followed by a research design section that details the data, research collabo-
ration dependent variables, erklärende Variablen, and descriptive statistics. The methodology
section provides details of the models used in the analysis. The results section presents the main

1 Quelle: OECD, Main Science and Technology Indicators (MSTI Database): https://stats.oecd.org/# (zugegriffen

on August 31, 2020).

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Erkenntnisse, which are discussed in the conclusions section, and contributions to knowledge in the
field and policy implications are outlined.

2. LITERATURE REVIEW

Both mobility and research collaborations are of critical importance for the future career oppor-
tunities and progression of doctoral students and early career researchers (Celis & Kim, 2018).
Doctoral students are therefore motivated to be geographically and institutionally mobile
because this mobility is expected to facilitate access not only to different types of learning and
knowledge-sharing experiences that will boost their human and technical capital but also to
increased possibilities for collaboration and integration with international research networks
(Franzoni, Scellato, & Stephan, 2015). The literature suggests that this mobility follows the same
scientific power patterns found in international collaborations. In global terms, PhD students tend
to move toward the scientific powerhouses that occupy a central position in the world of science,
in the same way that scientists try to form research collaborations with colleagues based at these
powerhouses (Gui, Liu, & Von, 2019). Access to better human and technical capital (wie zum Beispiel
working with elite scientists, research funding, and the ability to work in well-equipped labora-
tories and infrastructures, which is key for most research conducted in STEM fields), Und
experiencing greater research autonomy and freedom (Azoulay, Ganguli, & Zivin, 2017) have
led to this trend, but the prestige and signaling gained in national and international scientific
Systeme (Horta, Cattaneo, & Meoli, 2018; Jacob & Meek, 2013) constitute another important
factor.

Although the potential benefits of such mobility are known, the ability to accrue advantages
from it is not straightforward, and some research suggests a darker side to mobility and an inability
to capitalize on the potential benefits (Walsh, 2010). A lack of preparation or interest on the part of
host institutions, or an exploitative interest, may lead to this, but it can also be related to issues
pertaining to the unpreparedness of the students in making the most of this mobile learning
Erfahrung (Netz & Jaksztat, 2017). In diesem Kontext, the role of social-cultural capital and the
doctoral student’s capacity to overcome related shocks and barriers are viewed to be key, as is
previous educational or social experience abroad (Elliot, Baumfield, et al., 2016). The adaptation
of doctoral students to the different cultural and social environments and the flexibility to cope
with it to their benefit can be an important influence on the ability to learn and to engage in
collaborative research activities (Coey, 2018).

The nature of mobility has been changing for PhD students, and while the number of students
doing doctorates abroad is increasing, so is the practice of sending PhD students to host institu-
tions abroad so that they can conduct research in a completely different academic environment
(Horta & Blasi, 2016). Research funding agencies in most countries in the world are interested in
promoting the international mobility of PhD students, and increasingly so on a temporary basis, als
they realize that this mobility is part of a reconfiguration process that has the potential to transform
national scientific systems (with associated expectations of economic transformation), as the
embodied scientific knowledge that international mobility represents is related to integrating
global knowledge networks (Canibano, 2017). Research funding agencies and doctoral students
themselves are increasingly emphasizing temporary international mobility rather than (or in
parallel with) doing a PhD abroad (Canibano, Otamendi, & Solis, 2011). The rationale is that
the temporary mobility of PhD students is expected to generate experiences similar to those that
international PhD students have, in the sense that they will be able to acquire new knowledge
while having the potential to act as knowledge brokers in transnational scientific networks
between their sending and hosting country (Bilecen & Faist, 2015). This can be achieved with

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Research network propagation

lower costs through temporary international mobility (and probably lower administrative barriers)
compared with longer mobility periods or migration (see Orazbayev, 2017).

Personal factors also strongly influence the propensity to be mobile and the potential benefits
that can be gained from mobility (Azoulay et al., 2017). Female PhD students have been found to
be more vulnerable in situations of transnational academic mobility (Mahlck, 2018), und sie sind
known to be traditionally less internationally mobile than men, in both long-term and short-term
mobility spells, which makes them less integrated in international scientific networks (Jons,
2017). This is partly due to socialized gender roles: Female PhD students face a greater strain
in dealing with work–family balance than their male peers, which places them at a disadvantage
in terms of mobility opportunities and benefits (Schaer, Dahinden, & Toader, 2017). Jedoch,
recent research shows that transnationally mobile women publish in better quality journals than
those who are not mobile, underlying the importance of international mobility for women
involved in science (Horta, Jung, & Santos, 2020). Studies have also shown that mobile women
adapt better than men to newer research environments in the hosting institutions and are more
likely to engage in research collaborations (Rhoten & Pfirman, 2007). This adaptation relates to
the cultural dimension of the scientific and technical human capital model that Corley, Bozeman,
et al. (2019) deem critical for intellectual capacity-building and future career development. Das
dimension includes not only gender but also disciplinary culture, ethnische Zugehörigkeit, and socioeconomic
Status. In diesem Kontext, recent research has shown that social class matters in addition to gender in
influencing mobility choices and in dealing with environments that demand previous experience
of cosmopolitanism, which is not available to everyone (Netz & Jaksztat, 2017).

This study is based on a sample of Chinese PhD students. Chinese nationals currently represent
the largest share of international students at all levels of education, and the flow of outbound
Chinese nationals to pursue a PhD abroad or engage in temporary international mobility within
the scope of their PhD studies continues to grow. This is in line with the rapid development of the
Chinese scientific and higher education system (Shen, Wang, & Jin, 2016). In the past 50 Jahre,
Chinese nationals have been internationally mobile for both personal and national reasons. An
the personal level, motives can be associated with the accumulation of symbolic and intellectual
capital, which fosters human and technical capital that can be used both to integrate global
scientific networks and to strengthen career opportunities in China and abroad (Leung, 2013).
This has played a key role in shaping the development of the Chinese scientific system and the
relationships that this system has with its global counterparts, particularly those in North America
and Europe (Jonkers, 2010). At the national level, these personal motivations have been sup-
ported by policies fostering brain-circulation, reverse-brain-drain, and brain-gain, such as the
1000 Talents program, which is aimed at upgrading the research capabilities of Chinese uni-
versities and research institutions (Lu & Zhang, 2015). The major challenges for Chinese PhD
students abroad and those who engage in international mobility during their doctoral studies
are related to the social and cultural adaptation to completely different research and learning
environments and societies, which are likely to influence the benefits gained from the interna-
tional mobility experience (Ye & Edwards, 2015).

3. RESEARCH DESIGN

3.1. Sample and Data

This study relies on a comprehensive data set covering Chinese doctoral students in STEM
fields with international mobility experiences sponsored by the Chinese Scholarship Council
(CSC). In 2007, the CSC launched a publicly sponsored doctoral student mobility program.
This program has two functions: Erste, to fund undergraduates or Master’s graduates to pursue

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doctoral degrees outside mainland China, und zweitens, to fund doctoral students from main-
land China to go overseas for 6–24 months; these are referred to as visiting or exchange
doctoral students. To receive CSC funding, students need to contact a foreign supervisor, obtain
an invitation letter from the host institution, and submit it to the CSC along with a research
proposal. The CSC organizes experts to review the research proposal and decides whether to
grant funding based on criteria including the quality of the proposal, the ranking of the applicant’s
university, and the ranking of the host institution.

Aus 2007 Zu 2014, the CSC supported 41,824 graduate students to pursue PhD degrees or to
study overseas for 6–24 months (Tisch 1). The data for this study are based on a survey of CSC
grantees. In May 2014, using email addresses provided by the CSC, the research team sent online
questionnaires to 16,798 doctoral students who had returned to China. The research team sent
reminders every 7 days to respondents who did not fill out the questionnaire in the first wave.
The implementation of the survey took 1 month in total. A response rate of 35.18% War
achieved, als 5,910 questionnaires were returned. After comparing the sample with the popula-
tion of returned students, their characteristics were found to be very similar in terms of gender,
academic fields, host countries, and university types. Daher, the sample was regarded as represen-
tative. After a selection process, 5,506 valid questionnaires were obtained, of which 5,019 war
from doctoral exchange students who mainly traveled to the United States, das Vereinigte Königreich,
Deutschland, Japan, Australia, Frankreich, and other developed countries.

All of the PhD students in the sample were surveyed in China in May 2014 and provided
sociodemographic information (Alter, Geschlecht, parents’ education) and the STEM subfield of their
doctoral degree, along with past educational experiences and previous international mobility.
Details of supervisors and hosting institutions were also recorded (for details, see Jiang & Shen,
2019).

The information available from the survey was integrated with data related to the PhD students’
scientific productivity. A comprehensive database assembled from Scopus including detailed
Information (at the paper level) on research networks was developed for both the authors (PhD
students) and their coauthors (including supervisors at the home and hosting institutions) to identify
publications resulting from collaborations established during the period abroad. Following the
Literatur (z.B., Aman, 2020; Cattaneo, Malighetti, & Paleari, 2019; Forti, Franzoni, & Sobrero, 2013;

Tisch 1.

CSC grantees, 2007–2014 (China Scholarship Council, 2016)

For doctoral
degrees
394

Exchange and visiting
doctoral students
3,474

Year
2007

2008

2009

2010

2011

2012

2013

2014

Total

2,000

1,879

2,387

2,843

2,726

2,276

2,226

16,731

2,692

2,466

2,458

2,473

3,330

3,586

4,614

25,093

41,824

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Total
3,868

4,692

4,345

4,845

5,316

6,056

5,862

6,840

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Gaule & Piacentini, 2013), matching errors in the bibliometric data set were minimized using a
disambiguation algorithm to retrieve authors’ Scopus numerical identifiers. A specific ID was
associated with a doctoral student if the following criteria were met: (A) The last and the first
name of the doctoral student corresponded to that reported in the survey; (B) the last and the
first name of one coauthor corresponded to the name of the doctoral student’s home supervisor
reported in the survey; (C) the university and departmental (wenn überhaupt) affiliation name was correct; Und
(D) the subject area of the majority of indexed products corresponded to the specific author ID.

By applying our disambiguation algorithm, we were able to match 1,564 PhD students. Wir
then examined in detail the propagation of their international research networks that resulted
from their doctoral visiting experience. The data consisted of 30,029 Papiere (published no later
als 2017) Und 63,407 unique coauthors.

3.2. Quantification of Research Network Propagation

In diesem Abschnitt, we present the identification strategy used to identify the international research
collaborations PhD students established as a direct consequence of their visiting period abroad.
An effective assessment of temporary mobility experiences relies on the accurate identification of
these research collaborations. Daher, after proposing the research network propagation procedure
and providing examples for clarification, the second part of the paper (Abschnitte 4 Und 5) examines
the features of the visiting period (CSC program) to assess what encourages the formation and
development of research collaborations, with the aim of providing valuable insights to funding
agencies and institutions involved in designing such programs.

A network perspective was taken when identifying the international research collaborations
related to the visiting period abroad, (z.B., Dehdarirad & Nasini, 2017). To fully capture the
benefits in terms of research network development over time, the complete history of research
collaborations was considered and not solely those during the author’s time abroad (or a few
years later to account for publication lag). A network propagation approach was taken, in which
the collaborations were categorized as first or second stage based on whether they were initiated
during the period abroad and with authors affiliated with the hosting university (first stage), oder
were established with authors who were not affiliated with the hosting university but who had
previously coauthored with at least one first-stage coauthor (second stage). Second-stage collab-
orations represent the expansion of the scientific collaboration network resulting from the con-
solidation of the first stage collaboration. Figur 1 illustrates the key research network elements
and related terminology for clarification.

The identification process can be generalized in five main steps:

1. Let AU be the set of authors. For each author i, ich 2 AU, determine the set of coauthors

COAUi.

2. For each author–coauthor pair ij, ich 2 AU, J 2 COAUi, determine the set of coauthored papers

(Papij) and compute the first collaboration date as follows:
Firstcolij = {min(datep), P 2 papij}, where datep is the publication date of paper p.
Identify author i’s first-stage coauthors ( J ) based on the following conditions:

3.

(A) J 2 COAUi,
(B) Firstcolij 2 {VisitingTimei, VisitingTimei + k}
(C) AffIdj,Firstcolij = VisitingAffIdi

Author j is identified as a first-stage coauthor for author i if: (A) She is a coauthor of
author i; (B) the first coauthored paper is dated between the visiting period and the

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Research network propagation

Example of a network of research collaborations. Author j (blue dot) is a first-stage coauthor for author i (red dot) as she was
Figur 1.
affiliated (Affid) with author i’s hosting university (KTH—The Royal Institute of Technology, Schweden), and the two collaborated on the first
Papier (2009) during the time author i spent abroad. Author z (green dot) is identified as a second-stage coauthor, as she coauthored a paper
with both authors i and j (In 2012), and she previously collaborated with first-stage coauthor j (2006) before any of author i’s other coauthors.
Grey dots represent general coauthors who are neither first-stage nor second-stage.

considered publication lag (k) taken as 3 years2; Und (C) at the time of the first
collaboration, author j was affiliated to the hosting institution.
Identify author i’s second-stage coauthors (z) based on the following conditions:

4.

(A) z 2 COAUi,
(B) 9 J 2 1stagei | Firstcoljz = min(Firstcolzx), X 2 COAUi,
(C) Firstcoliz = min(datep), P 2 Papij \ Papiz,
(D) Affidz,Firstcoliz

≠ VisitingAffidi

A given coauthor z is considered to be a second-stage coauthor for author i if: (A) She is a
coauthor of author i; (B) there is a first-stage coauthor j who has collaborated with z before
any other coauthor x; (C) the three of them—author i, first-stage coauthor j and x—have
≠ {;}), and that was the first collaboration between
collaborated together (d.h., Pij \ Piz
author i and z; Und (D) x was not affiliated to the hosting university (to avoid overlapping
and misclassification as first-stage coauthors).
Identify research products realized with first- and second-stage coauthors. Insbesondere,
we consider three different quantifications at the first and second stages:
(cid:129) Active collaboration: a dummy variable equal to 1 if the author has published at least

one paper with first-stage and second-stage coauthors, jeweils.

(cid:129) Number of papers: the numbers of papers coauthored with first-stage authors and

5.

second-stage authors, jeweils.

2 Publishing in scholarly peer-reviewed journals usually entails long delays from submission to publication
because of publications’ preparation time and delays resulting from the scholarly review process (Aksnes,
2003; Björk & Solomon, 2013). It is likely that a paper initiated during the visiting period would be published
some years later and this delay also differs across areas and academic journals.

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Research network propagation

Tisch 2.
of papers published by one researcher

Real data demonstrating scientific network propagation based on the cumulative number

Year
2006

2008*

2010

2012

2014

2016

NEIN. Papiere
1

NEIN. papers – first-stage
0

NEIN. papers – second-stage
0

3

9

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15

22

2

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0

1

2

2

2

* Visiting period abroad between 2008 Und 2009 at The Royal Institute of Technology (KTH) in Sweden.

(cid:129) SJR weighted number of papers: the number of papers weighted by the journal

SJR3.

To avoid overlapping and double counting, second-stage research products were cal-
culated as the net of those that also involved first-stage coauthors.

To better demonstrate the data available and the identification procedure, Tisch 2 and Figure 2
represent the evolution of the research collaboration network for an author in our data set who
spent a period abroad between 2008 Und 2009 at the Royal Institute of Technology (KTH) In
Schweden. Figur 2 extends the basic one-to-one relationship and clarifies the evolution of collab-
oration patterns over time and across different types of authors. Twenty-two papers were pub-
lished by the author in 11 years from 2006 Zu 2017. The size of a node is proportional to the
number of cumulative coauthored papers, as is the thickness of the edges. In 2008, the author
published two papers with a first-stage coauthor and three second-stage coauthors (see the green
nodes in Figure 2). In subsequent years, the author strengthened his collaboration with the visiting
institution by collaborating with other first-stage authors (up to nine in 2012) and publishing one
paper each in 2009 Und 2010 with previously met second-stage coauthors.

3.3. Determinants of Individual International Collaborations

In diesem Abschnitt, we examine the contextual factors and the individual characteristics that may
affect PhD students’ research network propagation once enrolled in the CSC program.

3.3.1. Contextual factors

3.3.1.1. University prestige The international standing of a university is measured using the
Academic Ranking of World Universities (ARWU), a metric frequently used in the literature
because of its superiority as a measure of university research orientation (Taylor & Braddock,
2007). This is regarded as a more objective indicator of research outputs than the more
subjective alternative international rankings based on peer review and reputational indicators
(Saisana, d’Hombres, & Saltelli, 2011). The literature suggests that spending a visiting period in
research-oriented universities may increase the probability of doctoral students engaging in
research collaborations (Azoulay et al., 2017). In these contexts, research is a priority that
contributes to attracting standout faculty and dedicated resources to maintain the universities’

3 The SCImago Journal Rank (SJR) indicator is a measure of the scientific influence of scholarly journals that
accounts for both the number of citations received by a journal and the importance or prestige of the jour-
nals where the citations come from.

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Figur 2. Representation of collaborations undertaken by an author in our data set who spent a period abroad between 2008 Und 2009 Bei der
Royal Institute of Technology (KTH) in Sweden. Red nodes indicate general coauthors, while blue and green nodes represent first-stage and
second-stage coauthors, jeweils.

elite status (Jacob & Meek, 2013). In our analysis, a dummy variable equal to 1 for universities
included in the first 50 positions of the yearly ARWU classification is included to measure
university prestige, because it has been reported that the top 50 universities in a ranking define
the common features of a world-class university (see Shehatta & Mahmood, 2016).

3.3.1.2. Visiting duration This is the number of years elapsed between the start of the visiting
program at a cross-border university and the return to China to complete the doctoral program.
Students are assumed to form more international collaborations when they have more time in

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Research network propagation

which to do so, and this is particularly crucial when addressing higher quality collaborations
(Patrício et al., 2018). A longer stay is expected to lead to the emergence of stronger local
networks and personal relationships and the possibility of targeting more complex and
challenging projects (Baruffaldi & Landoni, 2012). Jedoch, the duration of international
mobility may reduce the positive effects, particularly when it is contextualized as part of a
doctoral program that is based at a Chinese university. After a long period, a mobile PhD
student may feel pressed to return to China to finish their program and graduate.

3.3.1.3. PhD students’ “peer crowdedness” Research-oriented doctoral programs aim at main-
taining and boosting their research standing by developing research collaboration networks and
welcoming visiting PhD students from other institutions. This situation is in theory a win-win for
the visiting PhD students and for the hosting PhD supervisor and his or her students. A previous
study found that more than 60% of Chinese exchange doctoral students in a STEM field published
papers with their host supervisors (Shen, 2018). Jedoch, if the hosting university overemphasizes
this practice, one supervisor may be supervising several PhD students from his or her own
university in addition to those visiting. This can lead to overwork situations, which may result
in lower-quality supervision for a PhD student experiencing temporary international mobility at
that university (Grün & Bowden, 2015). Jedoch, larger groups of students at the host institution
have the potential to foster greater exchange of information and research collaboration (Horta &
Lacy, 2011).

3.3.1.4. Cultural similarity The propensity to collaborate across international borders has been
acknowledged to vary globally. The social traits, historical ties, and ethnic specificities of different
countries all influence their cultural proximity and affect the willingness of researchers to
collaborate internationally (Wagner & Leydesdorff, 2005). Daher, PhD students moving to
countries that are more culturally distant from China may find it more difficult to implement
collaborations, as they are hindered by very different methods of establishing social
interrelationships (Ye & Edwards, 2015). In this study, the impact of moving to a different
culture is evaluated by measuring the extent to which Chinese cultural traits are different from
those of the host country across the following Hofstede’s indicators:

(cid:129) Power distance: Perceived power gap between members of an institution.
(cid:129) Individualism: Perceived degree of an individual’s independence from collective actions.
(cid:129) Uncertainty avoidance: Perceived degree of tolerance of the individual for uncertainty

and ambiguity.

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These indicators are entered into our analyses in differences with respect to the baseline value
for China. The larger the difference, either positive or negative, the less alike the two countries
are in terms of Hofstede’s cultural dimensions. In detail:

Cultural difference ¼ ABSvalue IndexChina − Indexhosting country

(cid:1)

(cid:3)

Index ¼ power distance; individualism; uncertainty avoidance
G

F

3.3.2.

Individual factors

Individual dimensions (z.B., Alter, Geschlecht, age at departure) are included as additional explanatory
factors in the model to assess their effect on the ability of students to engage in international
research collaborations. These are detailed in Table 3.

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Research network propagation

Variable
Age at departure

Gender

Past educational

experience abroad

Home supervisor network

Tisch 3. Description of variables

Age of the researcher at the time of departure.

Description

Dummy variable equal to 1 if the researcher is male, Und 0 ansonsten.

Dummy variable equal to 1 for students who have experienced education abroad before

entering the visiting doctoral program, Und 0 ansonsten.

Dummy variable equal to 1 if the home supervisor has coauthored a research article
with the hosting supervisor before the start of the visiting doctoral program, Und
0 ansonsten.

Student’s research autonomy

Dummy variable equal to 1 if the PhD student has published without the home supervisor

before the program, Und 0 ansonsten.

Years from visiting
to graduation

Subfields

(cid:129) Medicine

(cid:129) Maschinenbau

(cid:129) Basic Science

The years elapsed between the year of PhD graduation and the end of the visiting period.

Dummy variable equal to 1 if the doctoral student was enrolled in a PhD program in

Medicine, Und 0 ansonsten.

Dummy variable equal to 1 if the doctoral student was enrolled in a PhD program in

Maschinenbau, Und 0 ansonsten.

Dummy variable equal to 1 if the doctoral student was enrolled in a PhD program in

Basic Science, Und 0 ansonsten.

3.4. Descriptive Analysis

This section reports a set of descriptive statistics related to our sample. Tisch 4 presents the top five
destinations by country for Chinese doctoral students involved in the CSC program. A large
proportion moved to native English-speaking countries, which are also advanced countries in
terms of science positioning worldwide, such as the United States (55.44% der Gesamtbevölkerung
of mobile PhDs), das Vereinigte Königreich (7.10%), und Australien (7.05%). At an institutional level,
students opt to deepen their STEM studies at a doctoral level by moving to technical-oriented
universities, such as the Georgia Institute of Technology in the United States (2.44%) oder der
Karlsruhe Institute of Technology in Germany (0.33%). Others consider prestigious universities
such as UC Berkeley (1.22%), which are known to provide powerful signals in the academic
labor market.

Tisch 5 presents the descriptive statistics for the PhD students’ research network propagation.
Up to 55% of visiting doctoral students were engaged in a research collaboration with authors
affiliated with the hosting university, while 4% of them also collaborated with second-stage
authors who were not affiliated with the hosting university but who had previously coauthored
with at least one first-stage coauthor. This evidence suggests that the CSC program contributes
to boosting the international research networking of Chinese early-career researchers. The inter-
national breadth of the program appears to consolidate previous contacts with authors from the
hosting university and to enable doctoral students to leverage the privileged setting and explore
deeper cross-border collaborations over time. The quality of papers implemented through an

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Tisch 4.

CSC-funded doctoral students’ top five destinations by country

Country
Vereinigte Staaten

Universität

NEIN. students
867

% students
55.44

Georgia Institute of Technology

Purdue-Universität

University of Illinois at Urbana-Champaign

UC Berkeley

Ohio State University

(Other universities: 189)

Großbritannien

University of Cambridge

University of Bristol

University of Southampton

Universität Oxford

University of Nottingham

(Other universities: 36)

Kanada

The University of British Columbia

Universität von Toronto

University of Alberta

University of Waterloo

McGill-Universität

(Other universities: 22)

University of Technology Sydney

University of Queensland

Monash University

University of Western Australia

University of Melbourne

(Other universities: 22)

Karlsruhe Institute of Technology

Technische Universität Munich

Australia

Deutschland

38

25

20

19

19

111

13

11

7

5

5

111

16

15

14

12

8

95

16

16

9

8

6

63

5

5

2.44

1.61

1.28

1.22

1.22

7.10

0.83

0.72

0.44

0.33

0.33

7.10

1.05

0.94

0.89

0.78

0.50

6.05

1.05

1.00

0.55

0.50

0.39

4.05

0.33

0.33

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Country

Universität

NEIN. students

Tisch 4.

(Fortsetzung )

Universitats Klinikum Aachen

Ludwig-Maximilians-Universität Munich

Technische Universität Berlin

(Other universities: 32)

5

4

3

% students
0.33

0.28

0.22

international collaboration is higher compared to the average doctoral students’ research produc-
tivity. On average, the PhD students published papers with an SJR impact factor of 0.62, welche
increased to 1.42 Und 1.18 for papers published with a first- and a second-stage collaboration,
jeweils.

Tisch 6 presents the descriptive statistics for the factors influencing the network propagation
Dynamik. Vor allem, 68% of universities where students spent their visiting research period (An
average 1.5 Jahre) are ranked in the top 50 of the ARWU ranking. Visiting PhD students may
benefit from the chance to collaborate within groups of an average of 6 peers, but some may have
been in groups of more than 20 students, which may have been detrimental to their learning
Erfahrung. The hosting countries were found to be different in terms of individualism when com-
pared to China, but relatively similar in terms of the way people deal with uncertainty. Nach
to Hofstede’s cultural dimension theory, the Chinese population in general can be characterized
as follows: a high power distance, inequalities among people are accepted and hierarchical
power relations are polarized, a relatively low level of individualism, which is to the detriment
of a high degree of interdependence among members of a group (thus a collectivist society), Und
low uncertainty avoidance, meaning that Chinese people can be considered pragmatic, adaptable,

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Total

Variable
nr. (% of PhDs)

Count
1564 (100%)

Mean

Std. dev.

25%

50%

75%

Tisch 5.

Collaboration network variables

NEIN. Papiere

SJR

SJR per paper

First-stage

nr. (% of PhDs)

860 (55%)

NEIN. Papiere

SJR

SJR per paper

Second-stage

nr. (% of PhDs)

64 (4%)

NEIN. Papiere

SJR

SJR per paper

Quantitative Science Studies

19.2

11.9

0.62

6.4

9.1

1.42

3.3

3.9

1.18

20.3

22.8

1.12

12.2

22.7

1.86

3.1

4.6

1.48

7.0

1.7

0.24

2.0

1.3

0.65

1.0

0.6

0.60

13.0

4.9

0.38

3.0

3.2

1.07

2.0

2.6

1.30

25.0

13.4

0.54

6.0

8.1

1.35

4.0

5.5

1.38

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Research network propagation

Tisch 6. Determinants of research network propagation

Variable
University prestige

Visit duration

PhD peers’ crowdedness

Cultural differences

Power distance: diff

Individualism: diff

Uncertainty avoidance: diff

Female

Age at departure

Past educational experience abroad

Home supervisor network

Student’s research autonomy

Years from visiting to graduation

Subfields

Maschinenbau

Medicine

Basic Science

Mean
68.0%

1.4

5.9

40.3

63.4

19.8

33.5%

30.8

50.6%

9.3%

7.5%

1.1

64.7%

9.4%

25.9%

and entrepreneurial4. Women accounted for a third of the mobile population of Chinese doctoral
students, and the average age at the time of departure was 31 Jahre. Half of the students already
had experience abroad before the mobility period. In terms of collaborations, nur 9% were able
to benefit from strong connections that the home supervisor had already established at the hosting
university (at least one coauthored Scopus-indexed article), Und 7.5% had published a paper
without the home institution supervisor before their overseas visit, signaling research autonomy.
The majority of students (65%) were doing their PhD in engineering disciplines and took on
average 1.1 years to graduate after the visiting period.

4. METHODOLOGY

Our analysis examined whether the CSC program generates positive externalities that help ex-
pand the research networks of PhD students who experience temporary international mobility.
Jedoch, as all of the PhD students in the sample were mobile, to address potential selectivity
bias, data were collected from a random sample of 1,200 nonmobile students in 2013 Wer hat
not take part in the program in 2014. Information about each student’s characteristics was obtain-
ed and combined with the matching data on the internationally mobile PhD students. Following
previous studies (z.B., Higgins & Gulati, 2003), a selectivity instrument was created as a control in
our regression analysis based on a two-step Heckman procedure (Heckman, 1979), as the ability

4 Siehe https://www.hofstede-insights.com/country-comparison/china/.

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Research network propagation

to establish connections abroad was observable only for a part of our sample. In the first step, Die
likelihood that a doctoral student would participate in the international program was predicted
(through a probit regression; see the Appendix). The predicted values were then used to create the
selectivity instrument (the Mills ratio), which was included in the models that evaluate the factors
influencing the establishment of international collaborations (Hamilton & Nickerson, 2003;
Higgins & Gulati, 2003; Van de Ven & van Praag, 1981). This provided a better understanding
of how temporary mobility under the CSC program was associated with the establishment of in-
ternational research collaborations by PhD students. In the second stage, the following three
analyses were conducted: (A) the likelihood that at least one international collaboration was
established (the probit regression); (B) the count of the international collaborations (Poisson
regression); Und (C) the quality of the established international collaborations (Tobit regression
based on the weighted sum of the Scimago impact factor of the journals in which the doctoral
students published). These models were first used to establish the broad determinants of international
collaborations, without distinguishing between first- and second-stage collaborations. The focus
then turned to second-stage collaborations, and a Probit model was used to identify the features that
influence the probability of establishing stronger international research links.

5. ERGEBNISSE

We conducted regression analyses to explore how contextual and individual factors can affect
PhD students’ research collaborations with peers at a hosting institution after a mobility period
abroad. Tisch 7 reports the regression results for (A) the likelihood of opening a cross-border
collaboration, whether a first- or second-stage collaboration (Modell 1); (B) the number of such
collaborations (Modell 2); (C) the quality of these international collaborations (Modell 3); Und (D)
the likelihood of opening a second-stage cross-border collaboration (Modell 4).

Four variables are found to be significant. First is the prestige of the hosting university, welche
positively influences the probability of international collaboration with researchers at the hosting
university and influences the intensity of collaborations (d.h., the number of coauthored papers).
Visiting a prestigious university increases the probability of establishing an international collab-
oration by 7 percentage points (S) (Modell 1, Tisch 7) and predicts 0.2 more I stage collabora-
tionen (Modell 2, Tisch 7). This also affects the chance of international collaboration with
researchers who are not affiliated to the hosting university but who had previously coauthored
with at least one first-stage coauthor (d.h., second-stage collaboration), although it does not appear
to affect publication in high-quality journals. These findings suggest that PhD students visiting the
most prestigious universities are better able to engage in first- and second-stage collaborations
than those visiting less prestigious universities. The visitors are likely to benefit from prestigious
universities’ high levels of human and technical capital, and these universities also act as
gatekeepers and facilitators of national and international collaboration dynamics (Jacob &
Meek, 2013). The inability of visiting PhD students to produce high-quality papers compared
to their peers at less prestigious universities may be explained by a multitude of factors, einschließlich
the fact that top scholars in these universities may try to implement a risk-minimizing strategy
when collaborating with visiting Chinese PhD students due to supervisory style differences,
and assumed responsibilities regarding the supervision of visiting PhD students (see Ingleby &
Chung, 2009).

The second variable is the duration of the visiting period, which has a positive impact on most
dependent variables, indicating that for its benefits to be realized, temporary international
mobility must be for a reasonable amount of time. The literature focusing on visiting doctoral
programs has reported that an appropriate time window is required to enable PhD students to

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Research network propagation

Variable
ARWU top 50

Visit duration

Visit duration (kariert)

NEIN. of peer students at destination

Cultural difference

Power distance: diff

Individualism: diff

Uncertainty avoidance: diff

Years from visiting to graduation

Female

Mills ratio

Age at departure

Past international experience

Home supervisor network

Student’s research autonomy

Tisch 7. Determinants of the explanatory variables

(1)
First-stage &
second-stage (0/1)
0.186***

(2)
Count of
collaborations
0.060**

(3)
SJR Weighted
collaborations
0.611

(4)
Second-stage
(0/1)
0.216**

(0.072)

0.537**

−0.230

−0.093

(0.068)

0.004

(0.007)

(0.030)

1.383***

(0.113)

−0.243***

(0.033)

0.016***

(0.003)

(1.591)

18.370***

(5.464)

−3.406**

(1.641)

0.298**

(0.146)

−0.020***

−0.016***

−0.393***

(0.104)

0.095

(0.066)

−0.250***

(0.085)

−2.724**

(1.062)

−2.616*

(1.570)

−8.804

(7.255)

−0.340

(0.414)

1.522

(1.469)

(0.005)

0.003

(0.003)

−0.016***

(0.004)

−0.068

(0.048)

−0.049

(0.071)

0.040

(0.331)

−0.014

(0.019)

0.095

(0.067)

0.813***

(0.129)

0.218

(0.143)

(0.002)

0.000

(0.001)

−0.015***

(0.002)

−0.231***

(0.020)

−0.345***

(0.031)

−0.291**

(0.133)

−0.023***

(0.008)

0.200***

(0.028)

0.948***

(0.034)

0.563***

(0.048)

(0.102)

−0.369***

(0.121)

0.143***

(0.031)

−0.000

(0.011)

−0.002

(0.007)

0.009**

(0.005)

−0.012

(0.007)

0.168***

(0.046)

−0.521***

(0.092)

0.714

(0.733)

−0.042***

(0.015)

0.248

(0.169)

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18.549***

0.552***

(2.338)

6.234**

(3.031)

(0.166)

0.436

(0.426)

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Variable
Subfields

Maschinenbau

Medicine

Constant

Beobachtungen

Tisch 7.

(Fortsetzung )

(1)
First-stage &
second-stage (0/1)

(2)
Count of
collaborations

(3)
SJR Weighted
collaborations

(4)
Second-stage
(0/1)

0.272***

(0.078)

−0.323**

(0.129)

0.734

(0.750)

1,564

0.030

(0.031)

−0.264***

(0.059)

1.806***

(0.307)

1,564

−1.853

(1.716)

−7.475**

(2.982)

8.500

(16.518)

1,564

0.078

(0.079)

−0.016

(0.096)

−1.986***

(0.643)

1,564

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Notiz: ***, **, * Significant at the 1%, 5%, Und 10% levels, jeweils.

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adapt to a new culture and often language, and to a home life abroad, before they are able to start
working effectively. Only after they have adapted can they start developing working relation-
ships and actively participate in the life of the hosting university (Mill, Johnson, et al., 2014). In
detail, our findings show that increasing the visiting period by 1 year improves the probability
of establishing a first-stage cross-border collaboration by 21 S (Modell 1, Tisch 7). Modell 2 fur-
ther suggests that this time horizon would predict four more first-stage collaborations and a boost
in the cumulative SJR indicator equal to 18 (Modell 3, Tisch 7). Jedoch, our results also show
that when the duration of the visiting becomes too long, there are declining marginal gains (ein
inverted U-shaped effect) in terms of the ability to engage in new collaborations and publish in
High-Impact-Zeitschriften. Longer visiting experiences may help ensure research collaborations with
researchers other than their supervisors (second-stage collaborations), Jedoch, the curvilinear
results (a U-shaped effect) suggest that either the PhD students manage to establish a second-
stage collaboration in a short time period or they require a much longer time to embark on this
type of collaboration.

The third finding is that visiting PhD students’ productivity and performance benefit from join-
ing hosting supervisors who manage larger numbers of PhD students. An increase of one super-
vised PhD student predicts 0.1 more first-stage collaborations (Modell 2, Tisch 7) and an increase
in the cumulative SJR indicator of 0.3 (Modell 4, Tisch 7). This may be related to peer-to-peer in-
teractions that complement the supervision of the hosting supervisor through both academic and
emotional support in informal groups (Janson & Howard, 2004), which is acknowledged to transfer
both practical skills and tacit knowledge (Leshem, 2007). A larger research-oriented group does
nicht, Jedoch, influence the establishment of first- and second-stage collaborations, although it is
a determinant of the number of collaborations and the average quality of connections.

The fourth finding concerns the important role of social and cultural similarities and differ-
zen, which are a part of international mobility and important for the personal development
of both those who are mobile and those who host them. The results show that mobile Chinese
PhD students face greater issues in establishing research collaborations in societies where
uncertainty avoidance and power distance are different from Chinese culture. Considering
that most of the mobile Chinese PhD students in the sample moved to English-speaking coun-
tries with almost opposite power distance and uncertainty avoidance, the findings are not

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surprising; they probably reflect the longer time that the students required to adapt to different
societies and cultures and the greater range of obstacles that they must overcome before and
probably during collaborations (see Ye & Edwards, 2015). This is not the case for individual-
ism, possibly because of the positive effects that they may encounter through interacting with
greater numbers of peers at the hosting institution, some of whom may be Chinese nationals
(considering the Chinese diaspora of students doing PhDs abroad; Shen et al., 2016), Und
because the typical adaptive, pragmatic, and entrepreneurial characteristics of Chinese people
help them to overcome barriers and drive them to engage in increasingly autonomous research
collaborations.

The findings for other variables are also relevant. Existing links between the home supervisor
and the hosting supervisor before the PhD student’s international visit takes place represent an
important variable that has been previously identified. Having a home supervisor who has
previous research links with the hosting supervisor has a positive effect on all of the variables
berücksichtigt, including the PhD student being able to publish in high-impact journals. Das ist
the only variable that positively affects all of the dependent variables, which underlines the
important role that previous existing links play in fostering the success of visiting research periods
abroad (as argued by Patrício et al., 2018). This effect is likely to be due to the trust that has already
been built between supervisors, thus helping to establish a trusting relationship between the
visiting PhD student and the hosting supervisor. An existing relationship between supervisors
may also create incentives for the hosting supervisor to pay more attention to the research activ-
ities of the visiting PhD student than if no previous relational rapport exists. In diesem Kontext, Es
should be considered that the hosting supervisor has his or her own PhD students based at the
hosting university, and these are likely to take priority in terms of supervisory time and resources.
Zusätzlich, when supervisors have a previous research relationship or the visiting PhD student is
doing research on a topic that follows or is closely related to research that the supervisors collab-
orated on, the PhD student may then be able to expand his or her network (possibly with other
researchers associated with a project supervised in common).

Women are less likely to set up more collaborations, but also higher quality collaborations
and second-stage collaborations, indicating they are less likely to internationally extend their
collaborations than their male peers. These findings are aligned with the literature suggesting that
female PhD students are not able to take as many benefits from international mobility as their
male counterparts (Jons, 2017; Mahlck, 2018). The visits we investigated were conducted in
the context of STEM fields, in which women are a minority, which may enhance this effect.
The finding does not validate claims by previous researchers that mobile women adapt better
than men to new research environments nor that they are prone to be more engaged in collab-
orative activities (as argued by Rhoten & Pfirman, 2007). Zusätzlich, the ability of PhD students to
publish independently without the supervisor before the mobility period positively influences the
number of collaborations and the ability to publish research from these collaborations in high-
impact journals. This is an expected finding because it underlines that a degree of scientific
maturity on the part of the student is a competitive advantage over other students engaging in
similar mobility experiences for research purposes. The relatively low influence that past inter-
national experience has on most dependent variables, except on the ability to augment the num-
ber of collaborations, is an unexpected finding. The finding concerning time from the visit to
graduation is to the best of our knowledge novel. The longer this time is, the fewer the collabora-
tions and publications in high-impact journals. This may be because during this time PhD students
typically focus on writing up their theses and finishing their PhDs, which is labor intensive and
produces a sense of an end of a cycle (McAlpine, Paulson, et al., 2012). The positive and signif-
icant result concerning second-stage collaborations points to a transition from the conclusion of

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the PhD (end-of-cycle) to the engagement with new collaborators and the establishment of a new
postdoctoral cycle. Endlich, although age during the mobility period does not appear to have much
of an effect, the longer time horizon of younger doctoral students appears to motivate them to
implement second-stage collaborations.

6. CONCLUSIONS

During a period of temporary international mobility, 55% of the mobile PhD students established
first-stage international collaborations (d.h., with hosting supervisors and/or others at the hosting
university), Und 4% of these were able to establish second-stage collaborations (d.h., mit
researchers abroad outside the hosting university). Although the value of temporary international
mobility for early career researchers during their doctoral studies has been recognized (Canibano
et al., 2011), our study identifies specific characteristics that encourage the development of
collaborations for PhD students from STEM fields while on temporary mobility abroad. Wann
designing international mobility programs for PhD students, research funding agencies should
consider previous research collaborations between supervisors at the home and host university,
the prestige of the host university, the duration of the mobility period (in terms of their adaptation
to social and cultural environments that are distant from the culture and society of origin), und das
number of peer PhD students supervised by the hosting supervisor at the host institution. In diesem
Studie, the focus is on the impact of cultural and social distance on research collaborations, aber die
mobility experience also encourages other externalities, which may potentially encourage the
cultural adaptability and integration that are key to developing the global talent that countries
require to foster their scientific and socioeconomic progress (see Corley et al., 2019).

Some studies dispute the benefits of mobility in general. Bernela and Milard (2016), for exam-
Bitte, found little explanatory power for geographical mobility in terms of collaborations based on a
career analysis of two prolific and established chemists. Our study mainly identifies the condi-
tions of temporary mobility that are associated with the creation and development of research
networks based on coauthorship. The effects observed may simply be a reflection of a spatial
translation of existing ties, as in many cases the mobilities (and destinations for those mobilities)
of PhD students and early career researchers are defined, shaped, and developed by supervisors
or senior scholars. Melin (2004) found that contacts made during postdocs abroad often led to
collaborations and copublications (countering the findings of Bernela & Milard, 2016), and also
revealed that postdoctoral mobility is often reliant on the advice, contacts, and networks of senior
colleagues. Our study acknowledges the important positive and significant effect of the home
network supervisor network in first-stage, second-stage, and number of collaborations, und in
publishing in higher impact journals. Jedoch, nur 9.3% of the PhD students that went abroad
had a supervisor at the home university who had collaborated previously with an academic based
at the hosting university, suggesting that for most of the mobile PhD students, network formation
during the time away was not associated with a spatial translation of existing collaborations, Aber
rather with their integration into a new environment, and their agency and effort during the
mobility period.

The extent of the research propagation can also be considered as providing mixed signals in
terms of policy implications. Of the PhD students in the study, 55% were able to publish in
collaboration with scholars affiliated to the hosting university and 4% with authors who were
not affiliated with the hosting university but who had previously coauthored with at least one
first-stage coauthor. This suggests both positive and negative aspects. In terms of negative
outcomes, 45% of the mobile PhD students did not manage to publish collaboratively at all,
but the argument for the positive effects is more persuasive. Erste, the findings are based on

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successful and formalized collaborations, which are measured through coauthorships on
research endeavors that result in publication (Katz & Martin, 1997). Jedoch, informal collabo-
rations may have also been established, and externalities in terms of learning new theories,
Methoden, and exposition to different cultural and knowledge environments may also have been
pursued and achieved. Zweite, the analysis is focused on STEM field PhD students from China
working in different social and cultural settings and within a process of learning and socialization,
and who had probably not yet developed the proficiency to publish, even in collaboration, Und
even if they wished to. In diesem Kontext, it is relevant that only between 75% Zu 90% of students
doing PhDs in US universities in STEM fields are able to publish before concluding their PhDs
(50% Zu 68% of these publications were coauthored with their supervisors; Pinheiro, Melkers, &
Youtie, 2014). Dritte, the average visit period lasts 1.4 Jahre, which is a relatively short time in
which to develop collaborations with scholars at a hosting university. Zusätzlich, nur 9.3% von
those engaged in temporary mobility had supervisors in contact with peers at the hosting institution.
This is possibly due to two reasons. Erste, academics at the host universities are typically busy and
have their own PhD students, whom they are likely to prioritize in collaboration, supervision, Und
coauthorship. Zweite, the network between supervisors in China and international supervisors
is not yet sufficiently consolidated to foster supervisory linkages.

The finding that temporary mobility in prominent universities positively influences all kinds of
networking, including the ability to engage in second-stage collaborations but not the ability to
publish in higher impact journals, is of interest. Students’ motivations to spend time at these
universities may be more closely related to signaling purposes in terms of social capital than to
making use of the superior human and material resources required to develop more ambitious
and frontier research. Network visibility signaling may be more important than the more tradi-
tional scientific signaling (Dalen & Henkens, 2005). Network visibility signaling has been in-
creasingly recognized as a motivation and rationale for those engaged in international mobility
due to the cumulative self-reinforcement of social capital, which in the long run is expected to
enhance international scientific standing, although sometimes at the expense of national social
capital for early-career researchers (Bauder, 2020). Qualitative analyses of Chinese scholar
mobilities suggest that this motivation for international mobility is particularly risky for early
career researchers (Leung, 2013). Zusätzlich, academics at top universities may also be ex-
tremely selective in their collaborations and may want only to collaborate with promising
PhD students, who may well be their own, rather than investing in more ambitious and quality-
driven research with temporarily visiting PhD students.

Endlich, the study offers a new way to explore scientific network propagation. Our approach is
novel because it not only allows identification of the scientific collaborations that are established
with authors affiliated to the hosting university during the period abroad but also captures the
full-scale network benefits that arise from the PhD student’s visit. Unlike other contributions
aimed at developing specific measures of research collaborations or evaluating their determi-
nants and impact (z.B., Abramo, D’Angelo, & Murgia, 2017; Guan, Yan, & Zhang, 2017;
Wang, 2016), our methodology takes a more integrated perspective to explain the evolution of
the scientific profile of a researcher over time by tracking the network expansion that arises from
the cross-border research experience she undertook during her doctoral studies. Given the
complexity of research collaborations (Katz & Martin, 1997), our network analysis considers
the most explicit product of scientific collaborations (coauthorship) to identify such interactions,
depicting as collaborators the scientists who directly contributed to the publication of an article
(Er, Ding, & Ni, 2011). While this restricts the plethora of research collaborations, it considers the
most important channel of scientific transfer and development among individuals and systems
(Sauer, 1988). Daher, our overall approach can be considered as prudential when exploring the

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propagation of visiting PhD students’ research networks. A PhD student could indeed have set
up other collaborations during the visit, beyond those identified by our procedure (Abschnitt 3.2).
We tentatively ignore these dynamics, underestimating the number of research collaborations
established by doctoral students and not introducing potential bias, thus opening an interesting
avenue for future investigation in the area of scientific research network development.

ACKNOWLEDGMENTS

This paper benefited from comments provided during a seminar at the University of Bath, Vereinigtes Königreich,
and from our colleague Michele Meoli.

BEITRÄGE DES AUTORS

Hugo Horta: Konzeptualisierung, Formale Analyse, Akquise von Fördermitteln, Untersuchung, Methodik,
Projektverwaltung, Writing—original draft, Writing—review & Bearbeitung. Sebastian Birolini:
Konzeptualisierung, Datenkuration, Formale Analyse, Methodik, Validierung, Visualisierung,
Writing—original draft, Writing—review & Bearbeitung. Mattia Cattaneo: Konzeptualisierung, Data
Kuration, Formale Analyse, Methodik, Aufsicht, Validierung, Visualisierung, Writing—original
Entwurf, Writing—review & Bearbeitung. Wenqin Shen: Datenkuration, Akquise von Fördermitteln, Project
administration, Ressourcen, Writing—original draft, Writing—review & Bearbeitung. Stefano Paleari:
Aufsicht, Writing—original draft, Validierung.

COMPETING INTERESTS

The authors have no competing interests.

FUNDING INFORMATION
This project was funded by The Research Grants Council’s General Research Fund (Hongkong)
“Factors influencing current and expected career trajectories of PhD students and PhD holders in
China, Hongkong, Macau, and Taiwan” (project number: 17604119), and The Beijing Social
Science Fund Project, “Doctoral Students’ International Experiences and their Benefits” (Projekt
number: 18JYC024).

DATA AVAILABILITY

The data used in this manuscript cannot be made available to third parties as it was obtained in the
scope of a publicly funded project (project number: 18JYC024), which constrained accessibility
to others outside the project. Authors collaborating in the manuscript signed agreements re-
straining them from sharing the micro-data.

VERWEISE

Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects
of co-authorship networks on the performance of scholars: A
correlation and regression analysis of performance measures
and social network analysis measures. Journal of Informetrics, 5(4),
594–607. DOI: https://doi.org/10.1016/j.joi.2011.05.007

Abramo, G., D’Angelo, A. C., & Murgia, G. (2017). The relationship
among research productivity, research collaboration, und ihre
determinants. Journal of Informetrics, 11(4), 1016–1030. DOI:
https://doi.org/10.1016/j.joi.2017.09.007

Aksnes, D. W. (2003). Characteristics of highly cited papers.
Research Evaluation, 12(3), 159–170. DOI: https://doi.org/10
.3152/147154403781776645

Aksnes, D. W., Rorstad, J., Piro, F. N., & Sivertsen, G. (2013). Are
mobile researchers more productive and cited than non-mobile
researchers? A large-scale study of Norwegian scientists. Forschung
Evaluation, 22(4), 215–223. DOI: https://doi.org/10.1093/reseval
/rvt012

Aman, V. (2020). Transfer of knowledge through international sci-
entific mobility—introduction of a network-based bibliometric
approach to study different knowledge types. Quantitative
Science Studies, 1(2), 565–581. DOI: https://doi.org/10.1162
/qss_a_00028

Azoulay, P., Ganguli, ICH., & Zivin, J. G. (2017). The mobility of elite
life scientists: Professional and personal determinants. Forschung

Quantitative Science Studies

150

l

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

P

:
/
/

D
ich
R
e
C
T
.

M

ich
T
.

/

e
D
u
Q
S
S
/
A
R
T
ich
C
e
–
P
D

l

F
/

/

/

/

2
1
1
2
9
1
9
0
6
5
3
3
Q
S
S
_
A
_
0
0
0
9
6
P
D

/

.

F

B
j
G
u
e
S
T

T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Research network propagation

Policy, 46(3), 573–590. DOI: https://doi.org/10.1016/j.respol
.2017.01.002, PMID: 29058845, PMCID: PMC5621650

Baruffaldi, S. H., & Landoni, P. (2012). Return mobility and scientific
productivity of researchers working abroad: The role of home
country linkages. Research Policy, 41(9), 1655–1665. DOI:
https://doi.org/10.1016/j.respol.2012.04.005

Bauder, H. (2020). International mobility and social capital in the
academic field. Minerva, 58(3), 367–387. DOI: https://doi.org/10
.1007/s11024-020-09401-w

Bernela, B., & Milard, B. (2016). Co-authorship network dynamics
and geographical trajectories—what part does mobility play?
Bulletin de Méthodologie Sociologique, 131(1), 5–16. DOI:
https://doi.org/10.1177/0759106316642705

Bilecen, B., & Faist, T. (2015). International doctoral students as
knowledge brokers: Reciprocity, trust and solidarity in transna-
tional networks. Global Networks, 15(2), 217–235. DOI: https://
doi.org/10.1111/glob.12069

Björk, B. C., & Solomon, D. (2013). The publishing delay in scholarly
peer-reviewed journals. Journal of Informetrics, 7(4), 914–923.
DOI: https://doi.org/10.1016/j.joi.2013.09.001

Canibano, C. (2017). Scientific mobility and economic assumptions:
From the allocation of scientists to the socioeconomics of network
transformation. Science as Culture, 26(4), 505–519. DOI: https://
doi.org/10.1080/09505431.2017.1363173

Canibano, C., Otamendi, F. J., & Solis, F. (2011). International tempo-
rary mobility of researchers: A cross-discipline study. Scientometrics,
89, 653–675. DOI: https://doi.org/10.1007/s11192-011-0462-2
Cao, C., Baas, J., Wagner, C.S., & Jonkers, K. (2020). Returning scien-
tists and the emergence of China’s science system. Science and
Public Policy, 47(2), 172–183. DOI: https://doi.org/10.1093/scipol
/scz056

Cattaneo, M., Malighetti, P., & Paleari, S. (2019). The Italian brain
drain: Cream and milk. Higher Education, 77(4), 603–622. DOI:
https://doi.org/10.1007/s10734-018-0292-8

Celis, S., & Kim, J. (2018). The making of homophilic networks in
international research collaborations: A global perspective from
Chilean and Korean engineering. Research Policy, 47(3), 573–582.
DOI: https://doi.org/10.1016/j.respol.2018.01.001

China Scholarship Council. (2016). Statistics of students abroad.

China Scholarship Council.

Coey, C. (2018). International research mobility and knowledge
transfer in the social sciences and humanities. Globalization,
Societies, and Education, 16(2), 208–223. DOI: https://doi.org
/10.1080/14767724.2017.1401918

Corley, E. A., Bozeman, B., Zhang, X., & Tsai, C.-C. (2019). Der
expanded scientific and technical human capital model: Der
addition of a cultural dimension. Journal of Technology Transfer,
44(3), 681–699. DOI: https://doi.org/10.1007/s10961-017-9611-y
Czaika, M., & Orazbayev, S. (2018). The globalisation of scientific
mobility, 1970–2014. Applied Geography, 96, 1–10. DOI:
https://doi.org/10.1016/j.apgeog.2018.04.017

Dalen, H. P. Transporter, & Henkens, K. (2005). Signals in science – on the
importance of signaling in gaining attention in science.
Scientometrics, 64(2), 209–233. DOI: https://doi.org/10.1007
/s11192-005-0248-5

Dehdarirad, T., & Nasini, S. (2017). Research impact in co-authorship
Netzwerke: A two-mode analysis. Journal of Informetrics, 11(2),
371–388. DOI: https://doi.org/10.1016/j.joi.2017.02.002

Elliot, D. L., Baumfield, V., Reid, K., & Makara, K. A. (2016).
Hidden treasure: Successful international doctoral students who
found and harnessed the hidden curriculum. Oxford Review of
Education, 42(6), 733–748. DOI: https://doi.org/10.1080
/03054985.2016.1229664

Fernández-Zubieta, A., Geuna, A., & Lawson, C. (2015). What do we
know of the mobility of research scientists and of its impact on
scientific production. In A. Geuna (Ed.), Global mobility of
research scientists. The economics of who goes where and why
(S. 1–33). London: Sonst. DOI: https://doi.org/10.1016
/B978-0-12-801396-0.00001-6

Forti, E., Franzoni, C., & Sobrero, M. (2013). Bridges or isolates?
Investigating the social networks of academic inventors. Forschung
Policy, 42(8), 1378–1388. DOI: https://doi.org/10.1016/j.respol
.2013.05.003

Franzoni, C., Scellato, G., & Stephan, P. (2014). The mover’s advan-
tage: the superior performance of migrant scientists. Economic
Letters, 122(1), 89–93. DOI: https://doi.org/10.1016/j.econlet
.2013.10.040

Franzoni, C., Scellato, G., & Stephan, P. (2015). International mobil-
ity of research scientists: Lessons from GlobSci. In A. Geuna (Ed.),
Global mobility of research scientists. The economics of who goes
where and why (S. 35–65). London: Sonst. DOI: https://doi.org
/10.1016/B978-0-12-801396-0.00002-8

Gaule, P., & Piacentini, M. (2013). Chinese graduate students and
US scientific productivity. Review of Economics and Statistics, 95(2),
698–701. DOI: https://doi.org/10.1162/REST_a_00283

Grün, P., & Bowden, J. (2015). Moral compass framework:
Individual and collective decision-making in some complex doc-
toral education scenarios. International Journal for Researcher
Development, 6(1), 9–23. DOI: https://doi.org/10.1108/ IJRD
-07-2014-0016

Guan, J., Yan, Y., & Zhang, J. J. (2017). The impact of collaboration
and knowledge networks on citations. Journal of Informetrics, 11(2),
407–422. DOI: https://doi.org/10.1016/j.joi.2017.02.007

Gui, Q., Liu, C., & Von, D. (2019). Globalization of science and
international scientific collaboration: A network perspective.
Geoforum, 105, 1–12. DOI: https://doi.org/10.1016/j.geoforum
.2019.06.017

Gureyev, V. N., Mazov, N. A., Kosyakov, D. V., & Guskov, A. E.
(2020). Review and analysis of publications on scientific mobility:
Assessment of influence, motivation, and trends. Scientometrics,
124, 1599–1630. DOI: https://doi.org/10.1007/s11192-020
-03515-4

Hall, K. L., Vogel, A. L., Huang, G. C., Serrano, K. J., Rice, E. L., …
Fiore, S. M. (2018). The science of team science: A review of the
empirical evidence and research gaps on collaboration in science.
American Psychologist, 73(4), 532–548. DOI: https://doi.org
/10.1037/amp0000319, PMID: 29792466

Hamilton, B. H., & Nickerson, J. A. (2003). Correcting for endo-
geneity in strategic management research. Strategic Organization,
1(1), 51–78. DOI: https://doi.org/10.1177/1476127003001001218
Han, X., & Appelbaum, R. P. (2018). China’s science, Technologie,
engineering, and mathematics (STEM) research environment: A
snapshot. PLOS ONE, 13(4), e0195347. DOI: https://doi.org
/10.1371/zeitschrift.pone.0195347, PMID: 29614123, PMCID:
PMC5882148

Er, B., Ding, Y., & Ni, C. (2011). Mining enriched contextual in-
formation of scientific collaboration: A meso perspective. Zeitschrift
of the American Society for Information Science and Technology,
62(5), 831–845. DOI: https://doi.org/10.1002/asi.21510

Heckman, J. J. (1979). Sample selection bias as a specification error.
Econometrica, 47(1), 153–161. DOI: https://doi.org/10.2307
/1912352

Heitor, M., Horta, H., & Mendonça, J. (2014). Developing human
capital and research capacity. Technological Forecasting & Sozial
Change, 82, 6–22. DOI: https://doi.org/10.1016/j.techfore
.2013.07.008

Quantitative Science Studies

151

l

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

P

:
/
/

D
ich
R
e
C
T
.

M

ich
T
.

/

e
D
u
Q
S
S
/
A
R
T
ich
C
e
–
P
D

l

F
/

/

/

/

2
1
1
2
9
1
9
0
6
5
3
3
Q
S
S
_
A
_
0
0
0
9
6
P
D

/

.

F

B
j
G
u
e
S
T

T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Research network propagation

Higgins, M. C., & Gulati, R. (2003). Getting off to a good start: Der
effects of upper echelon affiliations on underwriter prestige.
Organization Science, 14(3), 244–263. DOI: https://doi.org/10
.1287/orsc.14.2.244.15160

Horta, H., & Blasi, B. (2016). Why public policies fostering knowl-
edge networks in academia matter? Insights from the Portuguese
higher education system until 2010. In D. M. Hoffman and J.
Valimaa (Hrsg.), Re-becoming universities? Higher education insti-
tutions in networked knowledge societies (S. 141–168).
Dordrecht: Springer. DOI: https://doi.org/10.1007/978-94-017
-7369-0_6

Horta, H., Cattaneo, M., & Meoli, M. (2018). PhD funding as a deter-
minant of PhD and career research performance. Studies in Higher
Education, 43(3), 542–570. DOI: https://doi.org/10.1080
/03075079.2016.1185406

Horta, H., Jung, J., & Santos, J. M. (2020). Mobility and research
performance of academics in city-based higher education systems.
Higher Education Policy, 33(3), 437–458. DOI: https://doi.org
/10.1057/s41307-019-00173-x

Horta, H., & Lacy, T. A. (2011). How does size matter for science?
Exploring the effects of research unit size on academics’ scientific
productivity and information exchange behaviors. Science and
Public Policy, 38(6), 449–460. DOI: https://doi.org/10.3152
/030234211X12960315267813

Horta, H., & Shen, W. (2019). Current and future challenges of the
Chinese research system. Journal of Higher Education Policy and
Management, 42(2), 157–177. DOI: https://doi.org/10.1080
/1360080X.2019.1632162

Iglic, H., Doreian, P., & Ferligoj, A. (2017). With whom do re-
searchers collaborate and why? Scientometrics, 112(1), 153–174.
DOI: https://doi.org/10.1007/s11192-017-2386-y, PMID:
28725095, PMCID: PMC5486904

Ingleby, R., & Chung, M. (2009). Cultural issues in commencing the
supervision of Chinese research students. Australian Universities’
Rezension, 51(2), 42–48.

Jacob, M., & Meek, V. L. (2013). Scientific mobility and interna-
tional research networks: Trends and policy tools for promoting
research excellence and capacity building. Studies in Higher
Education, 38(3), 331–344. DOI: https://doi.org/10.1080
/03075079.2013.773789

Janson, A., & Howard, L. (2004). The odyssey of Ph.D. students
becoming a community of practice. Business Communication
Quarterly, 67(2), 168–181. DOI: https://doi.org/10.1177
/1080569904265421

Jiang J., & Shen W. (2019). International mentorship and research
collaboration: Evidence from European-trained Chinese PhD
returnees. Frontiers of Education in China, 14(2), 180–205.
DOI: https://doi.org/10.1007/s11516-019-0010-z

Jonkers, K. (2010). Mobility, migration, and the Chinese research
System. New York: Routledge. DOI: https://doi.org/10.4324
/9780203854952

Jonkers, K., & Cruz-Castro, L. (2013). Research upon-return: Der
effect of international mobility on scientific ties, production and
impact. Research Policy, 42(8), 1366–1377. DOI: https://doi.org
/10.1016/j.respol.2013.05.005

Jons, H. (2017). Feminizing the university: The mobilities, careers,
and contributions of early female academics in the University of
Cambridge, 1926–1955. The Professional Geographer, 69(4),
670–682. DOI: https://doi.org/10.1080/00330124.2017
.1289778

Katz, J. S., & Martin, B. R. (1997). What is research collaboration?
Research Policy, 26(1), 1–18. DOI: https://doi.org/10.1016/
S0048-7333(96)00917-1

Kim, T. (2017). Academic mobility, transnational identity capital,
and stratification under conditions of academic capitalism.
Higher Education, 73(6), 981–997. DOI: https://doi.org/10
.1007/s10734-017-0118-0

Laudel, G., & Glaser, J. (2008). From apprentice to colleague: Der
metamorphosis of early career researchers. Higher Education,
55, 387–406. DOI: https://doi.org/10.1007/s10734-007-9063-7
Leahey, E. (2016). From sole investigator to team scientist: Trends
in the practice and study of research collaboration. Jährlich
Review of Sociology, 42, 81–100. DOI: https://doi.org/10.1146
/annurev-soc-081715-074219

Lee, D. H., Seo, ICH. W., Choe, H. C., & Kim, H. D. (2012).
Collaboration network patterns and research performance: Der
case of Korean public research institutions. Scientometrics, 91(3),
925–942. DOI: https://doi.org/10.1007/s11192-011-0602-8

Leshem, S. (2007). Thinking about conceptual frameworks in a re-
search community of practice: A case of a doctoral programme.
Innovations in Education and Teaching International, 44(3), 287–299.
DOI: https://doi.org/10.1080/14703290701486696

Leung, M. W. H. (2013). “Read ten thousand books, walk ten thou-
sand miles”: Geographical mobility and capital accumulation
among Chinese scholars. Transactions of the Institute of British
Geographers, 38(2), 311–324. DOI: https://doi.org/10.1111/j
.1475-5661.2012.00526.X

Lu, X., & Zhang, W. (2015). The reversed brain drain: A mixed-
method study of the reversed migration of Chinese overseas sci-
Entisten. Wissenschaft, Technology and Society, 20(3), 279–299. DOI:
https://doi.org/10.1177/0971721815597127

Mahlck, P. (2018). Vulnerability, gender and resistance in transna-
tional academic mobility. Tertiary Education and Management,
24(3), 254–265. DOI: https://doi.org/10.1080/13583883
.2018.1453941

McAlpine, L., Paulson, J., Gonsalves, A., & Jazvac-Martek, M.
(2012). “Untold” doctoral stories: Can we move beyond cultural
narratives of neglect? Higher Education Research & Development,
31(4), 511–523. DOI: https://doi.org/10.1080/07294360.2011
.559199

Melin, G. (2000). Pragmatism and self-organization: Research col-
laboration on the individual level. Research Policy, 29(1), 31–40.
DOI: https://doi.org/10.1016/S0048-7333(99)00031-1

Melin, G. (2004). Postdoc abroad: Inherited scientific contacts or
establishment of new networks? Research Evaluation, 13(2),
95–102. DOI: https://doi.org/10.3152/147154404781776455
Mill, J. E., Johnson, L. C., Mendes, ICH. A. C., Ventura, C. A. A., &
Brito, E. S. (2014). A qualitative evaluation of a visiting scholar
program between Brazil and Canada. Journal of Nursing Education
and Practice, 4(3), 252. DOI: https://doi.org/10.5430/jnep
.v4n3p252

Netz, N., & Jaksztat, S. (2017). Explaining scientists’ plans for inter-
national mobility from a life course perspective. Research in
Higher Education, 58, 497–519. DOI: https://doi.org/10.1007
/s11162-016-9438-7

NSB. (2020). Science and Engineering Indicators: The State of US

Science and Engineering. Washington D.C: NSB.

Orazbayev, S. (2017). International knowledge flows and the admin-
istrative barriers to mobility. Research Policy, 46(9), 1655–1665.
DOI: https://doi.org/10.1016/j.respol.2017.08.001

Patrício, M. T., Santos, P., Loureiro, P. M., & Horta, H. (2018).
Faculty-exchange programs promoting change: Motivations, ex-
periences, and influence of participants in the Carnegie Mellon
University-Portugal Faculty Exchange Program. Tertiary
Education and Management, 24(1), 1–18. DOI: https://doi.org
/10.1080/13583883.2017.1305440

Quantitative Science Studies

152

l

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

P

:
/
/

D
ich
R
e
C
T
.

M

ich
T
.

/

e
D
u
Q
S
S
/
A
R
T
ich
C
e
–
P
D

l

F
/

/

/

/

2
1
1
2
9
1
9
0
6
5
3
3
Q
S
S
_
A
_
0
0
0
9
6
P
D

/

.

F

B
j
G
u
e
S
T

T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Research network propagation

Pinheiro, D., Melkers, J., & Youtie, J. (2014). Learning to play the
Spiel: Student publishing as an indicator of future scholarly suc-
Prozess. Technological Forecasting & Social Change, 81, 56–66.
DOI: https://doi.org/10.1016/j.techfore.2012.09.008

Rhoten, D., & Pfirman, S. (2007). Women in interdisciplinary sci-
enz: Exploring preferences and consequences. Research Policy,
36(1), 56–75. DOI: https://doi.org/10.1016/j.respol.2006.08.001
Saisana, M., d’Hombres, B., & Saltelli, A. (2011). Rickety numbers:
Volatility of university rankings and policy implications.
Research Policy, 40(1), 165–177. DOI: https://doi.org/10.1016
/j.respol.2010.09.003

Santos, J. M., & Horta, H. (2020). The association between re-
searchers’ conceptions of research and their strategic research
agendas. Journal of Data and Information Science, 5(4), 56–74.
DOI: https://doi.org/10.2478/jdis-2020-0032

Sauer, R. D. (1988). Estimates of the returns to quality and coau-
thorship in economic academia. Journal of Political Economy,
96(4), 855–866. DOI: https://doi.org/10.1086/261566

Schaer, M., Dahinden, J., & Toader, A. (2017). Transnational mobility
among early-career academics: gendered aspects of negotiations
and arrangements within heterosexual couples, Journal of Ethnic
and Migration Studies, 43(8), 1292–1307. DOI: https://doi.org
/10.1080/1369183X.2017.1300254

Shehatta, ICH., & Mahmood, K. (2016). Correlation among top 100
universities in the major six global rankings: Policy implications.
Scientometrics, 109(2), 1231–1254. DOI: https://doi.org
/10.1007/s11192-016-2065-4

Shen, W. (2018). Transnational research training: Chinese visiting
doctoral students overseas and their host supervisors. Higher
Education Quarterly, 72(3), 224–236. DOI: https://doi.org
/10.1111/hequ.12168

Shen, W., Wang, C., & Jin, W. (2016). International mobility of PhD stu-
dents since the 1990s and its effect on China: A cross-national anal-
ysis. Journal of Higher Education Policy and Management, 38(3),
333–353. DOI: https://doi.org/10.1080/1360080X.2016.1174420
Schienbein, J. C., Lee, S. J., & Kim, Y. (2013). Research collaboration
across higher education systems: Maturity, language use, and re-
gional differences. Studies in Higher Education, 38(3), 425–440.
DOI: https://doi.org/10.1080/03075079.2013.774585

Tartari, V., Lorenzo, F. D., & Campbell, B. A. (2020). “Another roof,
another proof”: The impact of mobility on individual productivity
in science. Journal of Technology Transfer, 45(1), 276–303. DOI:
https://doi.org/10.1007/s10961-018-9681-5

Taylor, P., & Braddock, R. (2007). International university ranking
systems and the idea of university excellence. Journal of Higher

Education Policy and Management, 29(3), 245–260. DOI:
https://doi.org/10.1080/13600800701457855

Teichler, U. (2015). Academic mobility and migration: What we
know and what we do not know. European Review, 23(2), S6–S37.
DOI: https://doi.org/10.1017/S1062798714000787

Teichler, U. (2017). Internationally mobile academics: concepts and
findings in Europe. European Journal of Higher Education, 7(1),
15–28. DOI: https://doi.org/10.1080/21568235.2017.1254921
Van de Ven, W. P., & Van Praag, B. M. (1981). The demand for
deductibles in private health insurance: A probit model with
sample selection. Journal of Econometrics, 17(2), 229–252.
DOI: https://doi.org/10.1016/0304-4076(81)90028-2

Wagner, C. S., & Leydesdorff, L. (2005). Network structure, self-
organization, and the growth of international collaboration in sci-
enz. Research Policy, 34(10), 1608–1618. DOI: https://doi.org
/10.1016/j.respol.2005.08.002

Wagner, C. S., Whetsell, T. A., & Leydesdorff, L. (2017). Growth of
international collaboration in science: Revisiting six specialities.
Scientometrics, 110(3), 1633–1652. DOI: https://doi.org/10.1007
/s11192-016-2230-9

Walsh, E. (2010). A model of research group microclimate:
Environmental and cultural factors affecting the experiences of
overseas research students in the UK. Studies in Higher
Education, 35(5), 545–560. DOI: https://doi.org/10.1080
/03075070903243092

Walsh, J. P., Lee, Y.-N., & Nagaoka, S. (2016). Openness and inno-
vation in the US: Collaboration form, idea generation and imple-
mentation. Research Policy, 45(8), 1660–1671. DOI: https://doi
.org/10.1016/j.respol.2016.04.013

Wang, J. (2016). Knowledge creation in collaboration networks:
Effects of tie configuration. Research Policy, 45(1), 68–80. DOI:
https://doi.org/10.1016/j.respol.2015.09.003

Yang, Y., Volet, S., & Mansfield, C. (2018). Motivations and influ-
ences in Chinese international doctoral students’ decision for
STEM study abroad. Educational Studies, 44(3), 264–278. DOI:
https://doi.org/10.1080/03055698.2017.1347498

Yates, L., Woelert, P., Millar, V., & O’Connor, K. (2016).
Disciplines and interdisciplinarity. In Knowledge at the cross-
Straßen? Physics and history in the changing world of schools
and universities (S. 165–182). Dordrecht: Springer. DOI:
https://doi.org/10.1007/978-981-10-2081-0_10

Ye, L., & Edwards, V. (2015). Chinese overseas doctoral student
narratives of intercultural adaptation. Journal of Research in
International Education, 14(3), 228–241. DOI: https://doi.org
/10.1177/1475240915614934

Quantitative Science Studies

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Research network propagation

APPENDIX

First step of Heckman procedure to predict the likelihood that a doctoral student will partic-
ipate in the international program.

Variable
Female

Age at departure

Age at departure − square

Married

NEIN. peer students at home

College score (baseline: 10%)

10–25%

25–50%

50%

Mother education (baseline: College)

Graduate

Hoch

Mitte

Primary

Constant

Beobachtungen

Probability of being internationally mobile

−0.028

(0.051)

0.907***

(0.093)

−0.014***

(0.001)

0.150***

(0.054)

0.042***

(0.006)

−0.126**

(0.056)

−0.368***

(0.075)

−0.522***

(0.133)

0.120

(0.302)

−0.099

(0.077)

−0.193**

(0.079)

−0.278***

(0.076)

−13.223***

(1.474)

4,071

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