social capital in academia
TRANSCRIPT
Social capital in academia
Claudia N. Gonzalez-Brambila
Received: 4 November 2013� Akademiai Kiado, Budapest, Hungary 2014
Abstract This paper provides useful insights for the design of networks that promote
research productivity. The results suggest that the different dimensions of social capital
affect scientific performance differently depending on the area of knowledge. Overall, dense
networks negatively affect the creation of new knowledge. In addition, the analysis shows
that a division of labor in academia, in the sense of interdisciplinary research, increases the
productivity of researchers. It is also found that the position in a network is critical.
Researchers who are central tend to create more knowledge. Finally, the findings suggest
that the number of ties have a positive impact on future productivity. Related to areas of
knowledge, Exact Sciences is the area in which social capital has a stronger impact on
research performance. On the other side, Social and Humanities, as well as Engineering, are
the ones in which social capital has a lesser effect. The differences found across multiple
domains of science suggest the need to consider this heterogeneity in policy design.
Keywords Social capital � Academia � Scientific productivity � Areas of knowledge �Mexico
Introduction
The increasing importance of knowledge as the pillar of contemporary economies has been
largely recognized over the last decades. The production, dissemination and use of
knowledge, has become a crucial factor in enhancing economic growth and social welfare.
Within this context, science and technology (S&T) is one of the critical means of
knowledge creation. Universities and research institutions in particular, play a vital role in
creating and transmitting scientific knowledge (Etzkowitz and Leydesdorff 1997).
However, we know surprisingly little about how such crucial knowledge is created
(McFadyen and Cannella 2004). The ‘‘ontological’’ dimension of knowledge creation
C. N. Gonzalez-Brambila (&)ITAM, Mexico City, D.F., Mexicoe-mail: [email protected]
123
ScientometricsDOI 10.1007/s11192-014-1424-2
assumes that, although ideas are developed by individuals, the interaction between indi-
viduals typically plays a critical role in articulating and amplifying that knowledge (No-
naka 1994). Thus, it is not surprising to find that a growing attention is given to the role of
collaborative efforts in the process of scientific knowledge generation (Stephan and Levin
1997; Cummings and Kiesler 2005). Most of this research has looked at issues such as size
of scientific teams, institutional collaboration, and the geographic dispersion of team
members (Adams et al. 2005; Stephan and Levin 1997; Wang et al. 2005; Melin 1999;
Melin and Persson 1996; Barnett et al. 1988; Katz 1994). However, these studies have not
analyzed the relational, structural and cognitive dimension of the collaborative networks
involved in scientific knowledge generation.
In this paper, the relationship between social capital and knowledge creation in aca-
demia among different areas of knowledge is examined. For this purpose, knowledge
creation is measured by research papers in internationally peer-reviewed publications, and
social capital is measured through the pattern of connections between actors, where a
connection between two researchers is established through co-authorship.
One of the main contributions of this study is the analysis of the relationship between social
capital and the creation of knowledge across different areas of knowledge. Thus, the results
could provide some guidance for the design of public policies geared to increase scientific
productivity. Besides, it considers all of the following critical aspects of social capital: ties,
strength of ties, density, structural holes, centrality, and external–internal index, in terms of
fields of knowledge. The theories behind these dimensions have been sometimes contra-
dictory (Burt 1992, 2001; Ahuja 2000; Singh 2007), and few empirical studies have been able
to examine the relative contribution of each aspect in an environment like academia
(McFadyen and Cannella 2004; Singh 2007; Gonzalez-Brambila et al. 2013). Another rel-
evant aspect of this study is that it uses a very large sample, since it considers all co-authors
that have published with a Mexican author. Furthermore, it is one of the very few studies that
look at scientific collaboration outside the developed world. The other crucial characteristic
of this research is that it uses recent and extensive panel data that allows controlling for
individual unobserved heterogeneity. This is important because it is reasonable to assume that
individual characteristics that are unobserved by the econometricians, for example the
intrinsic quality of an individual as measured by intellectual ability, might be correlated with
particular network dimensions. In addition, different controls for time varying individual
characteristics that could be confounded with the critical network variables are also included.
Most existing studies in the network literature have not been able to carefully analyze within
differences (Burt 2001) and, as it turns out, controlling for individual specific heterogeneity
has a major impact when looking at the effects of network in the performance of individuals.
This paper is organized in seven sections. The following section presents the theoretical
background of social capital in academia. Section 3 presents the data that are used for the
empirical analysis. The subsequent two sections explain the methods and models of the
study. Section 6 shows the results. Finally, the limitations and conclusions of the study are
presented in Sect. 7.
Social capital in academia
‘‘The social capital metaphor is that people who do better are somehow better connected’’
(Burt 2001 pp. 202). However, there is no compelling evidence of what better connected
means, and what kind of network structure enhances the creation of new knowledge. In the
social network analysis literature, two opposing versions of the theory of network structure
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coexist. According to one view (Coleman 1998) actors in embedded networks have
superior achievements because members obtain more coordination, they trust each other,
and develop better communication skills. An alternative view (Burt 1992) suggests that
actors who are connected to others who are not connected to each other, that is, open social
structures with many structural holes, can take advantages of the ‘‘bridges’’ to connect with
new members in other clusters, and get access to new information.
A different set of views on this issue confers great value to the identification of the
‘‘most important’’ actors (Freeman 1982; Wasserman and Faust 1994; Rotolo and Messeni
Petruzzelli 2013), i.e. those in strategic locations with many close relationships. The idea is
that these actors have advantages because they can get access and transmit new infor-
mation sooner than actors in the periphery. Finally, other scholars (McFadyen and Can-
nella 2004) confer importance to the relational dimension of networks, typically looking at
the number and strength of ties, often disregarding any embeddedness or centrality issues
of the network structure.
Much of the empirical research considering performance-related outcomes has focused on
the structure of networks (Burt 1992), and has paid less attention to the effects of the relational
and cognitive dimensions of networks (Cross and Cummings 2004). Yet it is not clear that the
analysis of network structure alone captures the effects of the relational dimension for the
creation of new knowledge (Cross and Cummings 2004). For example, McFadyen and
Cannella (2004) use a sample of publications of 173 Biomedical scientists from 2 universities
to test the relationship between social capital and knowledge creation. They find that superior
knowledge creation is associated to an early increase in the number and strength of direct
relations, though with diminishing returns, leading to an inverted-U relation between these
variables and performance. Although the evidence offered by this study provides important
insights, the study has some limitations. As acknowledged by the authors, the sample is small
and only considers Biomedical scientists, so it may not be representative. Besides, the study is
limited to direct and strength of ties, but in turn ignores embeddedness or centrality concerns.
In another study, Singh (2007) uses a longitudinal study of worldwide scientists in Bio-
technology and Applied Microbiology to explore the impact of external collaboration across
national, organizational or institutional boundaries. In his study, he explores not only the
impact of the collaborative article itself, but also the effect in the future productivity of
collaborating scientists. He also analyses the influence of the number and intensity of
interpersonal ties, as well as the benefits from structural holes in the creation of new scientific
knowledge. His findings confirm that external collaboration improves future productivity. He
also shows that the three characteristics of a scientists ego-centric network have a positive
effect on productivity. It is argued that to better understand the relationship between the
creation of new knowledge and social capital, it is necessary to analyze these different
dimensions of social capital (Nahapiet and Ghoshal 1998). Gonzalez-Brambila et al. (2013)
also make an analysis of the impact of network embeddedness in knowledge output using a
large sample of scientists in Exact Sciences. The results confirm the need for taking into
account different dimensions of social capital, and show that the network dynamics change
depending on the measure of performance that is used. The various inputs of network em-
beddedness influence quantity and quality of outputs in a diverse way. However, to the best of
our knowledge, there is no published study that explores the dissimilarities of the impact of
social capital across the different domains in science.
Bearing in mind that the access to new information is the most important direct benefit
of social capital (Inkpen and Tsang 2005), in this study we take into account those vari-
ables that can potentially influence the access to new information. In addition, this analysis
isolates the effects of the relational dimension of networks from the structural and
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123
cognitive dimensions. In terms of the relational dimension, first the number of direct
relationships is considered as it can stimulate the combination and exchange of resources
within the relationships (Nahapiet and Ghoshal 1998), and provides researchers with access
not only to new knowledge but also to new experiences. Second, the strength of the
relationships is considered, as it provides the opportunity to develop jointly held resources
(Granovetter 1973; McFadyen and Cannella 2004).
We also look at the two most common measures of network structure: density and
structural holes. As mentioned before, Coleman’s main argument is based on the notion
that without trust and shared norms of behavior, opportunistic behavior could arise, so
sharing knowledge is likely to be difficult. Thus, members in dense networks can secure
the benefits of getting access to information. However, as mentioned by Burt (1992), after
some time this information will become redundant. Therefore, actors embedded in sparsely
connected networks (rich in structural holes) can take advantage of the brokerage oppor-
tunities created to construct an efficient, information-rich network, where the redundancy
between partners is minimized (Burt 1992).
The main intention of including an indicator of centrality as a fifth measure, was the
need to control for the most important and influential actors within a network. It is rec-
ognized that the position in the network affects the opportunities and constraints of an actor
(Hanneman 2001; Rotolo and Messeni Petruzzelli 2013).
Finally, a cognitive dimension is included, considering an external–internal index in
terms of knowledge clustering. As researchers increasingly address more complex research
questions, the participation of scientists from different disciplines is necessary in order to
have access to complementary assets and skills. This index measures the degree of col-
laboration among scientists within the same discipline, versus collaboration of scientists
from different disciplines.
The first proposition of this study is that to better understand the actual impact of social
capital in academia, it is necessary to analyze the effects of all these network variables. The
components of social capital are many and varied, and closely related to each other.
Therefore, the isolated effect of one variable could be very different when the other
components are jointly analyzed. Moreover, because of the highly skewed nature of sci-
ence (Merton 1968 and Stephan 1996), it is imperative to control for the quality of
researchers, since it is likely to have confounded effects between researchers’ motivations
and abilities, and their social capital.
The second proposition is that the impact of social capital affects scientific productivity
differently, depending on the area of knowledge. There are areas of knowledge that tend to
publish more journal papers, like Chemistry, Biology and Medicine; other areas with a
larger number of co-authors, like Astronomy of Physics; and other areas where single-
authored papers are very common (Adams et al. 2005; Gonzalez-Brambila and Veloso
2007); so it would be expected that the impact of social capital will be different depending
on the field of knowledge. Most of the research related to social capital in academia
(McFadyen and Cannella 2004; Tsai and Ghoshal 1998; Rotolo and Messeni Petruzzelli
2013; Gonzalez-Brambila et al. 2013) has used one discipline, and none has explored the
differences among scientific disciplines.
The data
The data on publications consists of all scientific papers that have at least one author from
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123
Mexico, published between 1981 and 2002 in the Science and Social Sciences Citation
Indexes produced by the Institute of Scientific Information (ISI) (ISI 2003). The data base
of Mexican publications (ISI 2003) consists of 69,029 articles and 249,674 authors. An
identification number was assigned to all authors with the same name and similar field of
knowledge,1 resulting in the recognition of 75,515 authors.
In addition, we had access to information on 14,328 researchers, in all fields of
knowledge, who have been part of the Mexican National System of Researchers2 (SNI)
from 1991 to 2002
The data on publications record:
• Date of publication
• Name of authors
• Address information
• Number of co-authors
• Field of knowledge
It is important to note that there is no direct match between author’s name and his/her
address institution; therefore we are not measuring any variable using information on
location.
The data on SNI researchers include:
• Name of researcher
• Gender
• Age
• Country where PhD was earned
• Area of knowledge3
• Number of papers published in ISI4
Methods
As noted before, co-authorship in ISI publications is used to measure relationships. Melin
and Persson (1996) find that a significant proportion of scientific collaboration leads to co-
authored papers, and publications in ISI are the most common measure of scientific pro-
ductivity (Levin and Stephan 1991; Stephan and Levin 1997; Turner and Mairesse 2003).
Yet, an important limitation of using this measure is that not all collaborations end in a
publication on ISI. There are a number of other outputs that can be the result of
1 For this purpose, the ISI subject category of the papers was used. ISI uses 105 different subject category toclassify papers/journals.2 The Mexican National System of Researchers was created in 1984 to enhance the quality and productivityof researchers in Mexico. It gives pecuniary compensation, as a complement of salary, to the most pro-ductive researchers. The selection process is done by peer review committees organized by the same areas ofknowledge that are used in this paper, except that in SNI Humanities and Social Sciences are separate areas.However, both areas have similar ISI publication patterns.3 Researchers in SNI self-select their own area of knowledge.4 The publications were obtained by matching the database of researchers in SNI, with Mexican articlesfrom the ISI database from 1981–2002 (ISI, 2003). This matching was done manually, first checking the lastnames and initials, then verifying the affiliation institution, and finally the field of knowledge. To verify thereliability of the matching process, names of researchers were randomly selected, and their productivitycorroborated against their official CVs (CVU), in a joint effort with the National Council for Science andTechnology (Conacyt).
Scientometrics
123
collaboration among researchers, and those are not reflected in our dataset. For example,
publications in other journals that are not indexed in ISI, books and book chapters are not
included in this study. In addition, there are other forms of collaboration that a bibliometric
study is not able to reveal.
Given that we lack personal information about researchers outside the SNI system, the
productivity analysis is restricted to researchers that have been part of the system at least
one year from 1991 to 2002, and have at least one publication in ISI. The final baseline
sample consists of 7,531 researchers in all disciplines. However, it is important to stress
that all authors in Mexican publications were considered, to establish the network variables
used in the estimation. This implies that the conclusions are only valid for networks in
which SNI researchers participate. In 2002, 30 % of Mexican Researchers were part of SNI
(Conacyt 2003), and those researchers published more than 80 % of the Mexican publi-
cations in ISI. Thus, the sample includes the most productive Mexican researchers,
according to CONACYT.
In addition, because the main purpose of this paper is the analysis of social capital in
academia, publications with single authors and with more than 8 authors were dropped
from the calculation of network variables.5 We believe that publications with more than 8
authors reflect other type of collaboration effort and not necessarily the ‘‘actual’’ social
capital of researchers. A significant number of papers with many authors are the result of
large projects that are conformed from separate contributions that are made in smaller
groups.
Models
To perceive the effects of network structure in the creation of knowledge, it is assumed that
the function determining publishing proficiency Pit is given by:
Pit ¼ F Xit�1; uit�1ð Þ
where i identifies researchers, and t period, Xit-1: variables that vary across time and across
researchers: number of ties, strength of ties, density, structural holes, normalized eigen-
vector, external–internal index, reputation,6 and single author papers. uit-1: is the unob-
served effect that varies in both dimensions.
The number of ties is the number of different co-authors; the strength of ties is the
frequency with which an author i publishes with his/her partners. Density is the number of
ties divided by the number of pairs, times 100, where pairs is the total number of pairs of
alters in the ego network - i.e. potential ties; structural holes is the separation of different
actors who are not connected. This variable was obtained by subtracting 1—Constraint.
The constraint is obtained through Burt’s formula (1992). Centrality was measured by
using the normalized eigenvector proposed by Bonacich (1972). This measure was chosen
over other measures of centrality because the formula recursively implies that the centrality
of an actor is proportional to the centralities of the nodes he is connected to (Borgatti
1995). Finally, the external–internal (E–I) index is the number of ties external to the groups
5 The threshold of no more than 8 authors was chosen to capture around 95 % of the total number ofarticles. According to the ISI database, only 4 % of the articles reported in the Mexican database had morethan 8 authors. Most of these articles are in the Exact Sciences. The number of articles with no more than 7authors is 94 % and with no more than 9 authors is 98 %.6 As a measure of reputation, the number of publications with a foreign address is used.
Scientometrics
123
minus the number of ties that are internal to the group, divided by the total number of ties.
To this purpose, an attribute vector that classifies researchers in fields of knowledge7 was
built. Given that we lack information on the discipline of researchers who have not been
part of SNI, and because a given researcher could have publications in more than one field
of knowledge, it was assumed that the field in which a non-SNI scientist has published the
most of his publications, determined it’s field of knowledge.
We use the negative binomial fixed effects model proposed by Hausman et al. (1984)
because of the panel nature of the data. The fixed effects model allows for both the
possibility of permanent unobserved individual effects, as well as the possibility that some
unobserved effects may be correlated with publications and other explanatory variables.
The Negative Binomial distribution was chosen over the Poisson because the latter
imposes a constant variance. This is not true for the data used in our study, where the
variance of productivity far exceeds the mean. One drawback of the Negative Binomial
distribution is that the conclusions may be less precise, since the estimated standard errors
tend to be larger than in the alternative Poisson model.
The measure of research output that is used is the straight publication counts occurring
over a two-year period. The network variables for researcher i at time t-1 were obtained
from the adjacent matrixes8 considering information on the publications in the previous
3 years. We decided to test productivity by adding 2 years of publications, because there
are many researchers who do not publish every year, so by using this measure we avoid
losing observations from having zeros in the outcomes. The reason behind choosing
publications in the previous 3 years was to balance, in some sense, the research projects’
time frame with having enough observations over time.9
In an effort to isolate the effects of network structure on productivity, a number of
control variables were included. Considering that there has been a significant growth in the
number of publications, time dummies were included to capture time trends. However, it is
important to note that the age of researchers cannot be included along with time dummies,
due to collinearity problems. In addition, we are controlling for reputation of researchers.
We consider that control for reputation is important to isolate the effects of social capital
from personal characteristics and abilities. The international visibility was considered as a
measure of reputation given the evidence that international collaboration tends to receive
more cites than other kind of collaboration (Narin et al. 1991; Pasterkamp et al. 2007; Van
Raan 1998). This variable was calculated by counting the number of articles with a foreign
address in the same three-year window than the rest of the independent variables. Finally,
the number of single-authored papers that researcher i has published in the past three years
was included to take into account the complete productivity of the researcher i and not only
that which is done in collaboration.
Considering that publishing patterns vary significantly across fields of knowledge
(Gonzalez-Brambila and Veloso 2007), the first set included researchers from all areas of
knowledge, and a second set considered those researchers from each specific area of
knowledge. In this paper, six different areas of knowledge were considered: Exact
7 The classification was constructed by considering 11 different fields of knowledge that match the 105 ISIcategories into subject groups. This mapping was based on an analysis of journal usage by researchersworking in different subject departments in UK universities (Adams, 1998). The categories used are the onespublished by ISI in 2003.8 The ‘‘adjacent’’ matrix is a matrix composed of as many rows and columns as there are researchers, andwhere the elements represent the ties between actors, this is, the number of joint publications.9 We also run regressions using a 4 and a 5 year window. However, no significant differences were found inthe coefficients of the network variables.
Scientometrics
123
Sciences, Biology and Chemistry, Health Sciences, Social and Humanities, Agricultural
Sciences, and Engineering.10
Finally, Wooldridge (2002) tests were performed to be certain that the analyses do not
entail autocorrelation problems among variables.
Results
The descriptive statistics of Table 1 highlight the existence of great heterogeneity among
areas of knowledge, and show that they respond to different research regimes. Researchers
in Health Sciences, Exact Sciences, and Biology and Chemistry reach higher productivity,
and tend to have more publications with researchers outside of Mexico than researchers in
Engineering, Agricultural Sciences and Biotechnology, and Social and Humanities.
There is also variation regarding network variables. On average, researchers in the most
productive areas have larger number of ties, and their ego-networks are sparser.
Researchers in Health Sciences have more than twice the number of ties than Social and
Humanities researchers. Also, on average, the formers belong to sparser and less dense
networks than the last ones. Moreover, researchers in Social and Humanities tend to be
more central, and publish more single-authored papers than researchers in any other area.
The richest networks in structural holes are seen among researchers in Health Sciences, and
centrality seems to be less important among researchers in Biology and Chemistry. In
terms of knowledge diversity, the statistics show that researchers in Agricultural Sciences
and Biotechnology tend to publish more with people within the same discipline; the other
extreme is seen in Engineering, where researchers have the smallest external–internal
index.
Table 2 shows the correlation between our variables. As can be seen, there is high
correlation among some variables, mainly between structural holes and density. Thus, three
different specification models were run. The first one considers all network variables; in the
second one the structural holes variable is not considered, and in the third model, the
density variable is not taken into account.
The results of the regressions for all areas of knowledge are shown in Table 3; and
Table 4 shows the result of the regression for each area of knowledge. As can be seen, the
impact of the different dimensions of social capital varies among areas of knowledge.
The results suggest that the characteristics of social capital that positively affects
research productivity are the number of ties, the positional dimension, and publishing with
people from different areas of knowledge. In addition, evidence is found that density
negatively affects future productivity. Also, the structural holes variable becomes positive
and significant when density is dropped from the model. These results suggest that in the
structural dimension of academic networks, the brokerage is more important than cohesion,
supporting Burt’s (2004) proposition than good ideas come from networks rich in structural
holes.
The results also suggest that the different dimensions of social capital affect future
productivity in a distinctive way, depending on the area of knowledge.
The most consistent result is that ties positively affects future productivity, so that it
could be expected that the higher the number of colleagues, the higher the future pro-
ductivity. However, there are some areas in which the number of ties do not necessarily
10 This classification is the same one used by the Mexican System of Researchers (SNI).
Scientometrics
123
Ta
ble
1D
escr
ipti
ve
stat
isti
cs
Des
crit
ive
stat
isti
csA
llar
eas
of
kn
ow
led
ge
Ex
act
scie
nce
sB
iolo
gy
and
chem
istr
yH
ealt
hsc
ien
ces
So
cial
and
hu
man
itie
sA
gri
cult
ura
lsc
ien
ces
and
bio
tech
no
log
y
Engin
eeri
ng
Var
iab
leM
ean
SD
Mea
nS
DM
ean
SD
Mea
nS
DM
ean
SD
Mea
nS
DM
ean
SD
Pu
bs
1.4
89
32
.708
41
.764
23
.00
51
.49
59
2.6
16
32
.166
73
.297
40
.752
61
.68
98
0.7
85
1.5
34
51
.121
12
.41
41
Dir
ectt
ies
7.4
94
57
.987
36
.274
56
.81
84
7.5
00
28
.032
61
0.8
45
89
.788
64
.459
15
.25
74
5.3
80
74
.717
36
.168
56
.92
64
Str
eng
thd
t1
.34
05
0.5
26
1.4
53
40
.65
99
1.3
18
20
.481
31
.339
20
.462
11
.204
60
.53
97
1.2
24
20
.416
31
.311
90
.47
98
Str
uct
ho
les
0.4
42
50
.298
50
.412
0.2
95
20
.45
36
0.2
86
0.5
71
90
.260
40
.236
70
.30
14
0.3
45
10
.291
30
.372
90
.30
75
Den
sity
84
.47
69
28
.32
05
82
.77
19
30
.12
32
83
.17
46
28
.90
67
6.8
52
31
.49
03
93
.64
21
20
.02
14
90
.95
02
22
.26
02
89
.53
59
24
.45
94
Eig
env
ec0
.41
83
4.6
26
70
.343
84
.07
50
.28
34
3.7
89
90
.497
14
.695
1.0
16
98
.15
10
.42
27
5.3
57
30
.551
95
.16
33
E–
IIn
dex
-0
.32
05
0.7
06
4-
0.3
53
80
.72
59
-0
.28
02
0.6
97
-0
.334
90
.670
5-
0.4
46
80
.75
35
-0
.51
04
0.6
51
2-
0.1
30
.73
04
Pas
tp
ub
s2
.21
15
4.2
90
32
.561
94
.61
52
2.3
06
14
.259
3.4
23
65
.586
51
.161
82
.49
22
1.0
79
62
.189
81
.423
23
.34
23
Sin
gau
th0
.09
19
0.4
85
50
.169
40
.67
75
0.0
63
20
.372
60
.073
40
.394
80
.212
80
.85
13
0.0
32
70
.251
90
.055
0.3
42
8
Int
art
0.4
70
11
.446
20
.758
2.0
03
80
.46
73
1.4
48
80
.483
61
.331
90
.200
90
.66
75
0.2
38
60
.760
50
.320
71
.11
Scientometrics
123
have a positive impact, suggesting that there are other aspects of social capital that are
more relevant than having many ties.
The results also suggest that the strength of ties, that is, the frequency of collaboration,
affects in a very distinctive way depending on the area of knowledge. In Social and
Table 2 Correlations
Pubs Directties Strengthdt Structholes Density Eigenvec E-I
Index
Sing
auth
Int
art
Pubs 1
Directties 0.5684 1
Strengthdt 0.2715 0.2108 1
Structholes 0.3802 0.6849 0.1208 1
Density -0.4226 -0.6108 -0.1651 -0.7659 1
Eigenvec 0.2389 0.2841 0.1970 0.1009 -0.1062 1
E–I 0.0619 0.0964 0.0288 0.1292 -0.0895 -0.0098 1
Sing 0.2012 0.0518 0.0311 0.0322 -0.1378 0.0235 -0.0033 1
Int art 0.3984 0.4506 0.2745 0.2731 -0.3398 0.2304 0.0160 0.1394 1
Table 3 Negative binomial fixed effects models for all areas of knowledge
Pubs Model 1 Model 2 Model 3
Coeff. SE Coeff. SE Coeff. SE
Directties 0.0043 0.0009*** 0.0044 0.0009*** 0.0049 0.0009***
Strengthdt -0.0198 0.0111 -0.0199 0.0110 -0.0162 0.0110
Structholes 0.0016 0.0411 0.1313 0.0285***
Density -0.0014 0.0003*** -0.0014 0.0002***
Eigenvec 0.0023 0.0008*** 0.0023 0.0008*** 0.0023 0.0008***
E–I Index 0.0488 0.0128*** 0.0477 0.0128*** 0.0502 0.0128***
Sing auth 0.0221 0.0084** 0.0221 0.0084** 0.0234 0.0084**
Int art -0.0066 0.0029* -0.0066 0.0029* -0.0064 0.0029*
Period 8 -0.0255 0.0139 -0.0255 0.0139 -0.0264 0.0139
Period 7 -0.0845 0.0154*** -0.0846 0.0154*** -0.0861 0.0154***
Period 6 -0.1592 0.0174*** -0.1592 0.0173*** -0.1592 0.0174***
Period 5 -0.3794 0.0203*** -0.3795 0.0202*** -0.3810 0.0203***
Period 4 -0.5149 0.0229*** -0.5150 0.0227*** -0.5166 0.0229***
Period 3 -0.6648 0.0257*** -0.6649 0.0254*** -0.6628 0.0257***
Period 2 -0.5974 0.0278*** -0.5975 0.0276*** -0.5982 0.0278***
Period 1 -0.6548 0.0310*** -0.6550 0.0308*** -0.6550 0.0311***
Cons 1.9916 0.0540*** 1.9931 0.0402*** 1.8327 0.0404***
Wald v2(16) = 2,767.61 Wald v2(15) = 2,767.64 Wald v2(15) = 2,750.32
Log likelihood =-37,852.184
Log likelihood =-37,852.184
Log likelihood =-37,861.736
Number of obs = 28,198
Number of groups = 5,817
*** Significant at 0.1 %, ** significant at 1 %, * significant at 5 %
Scientometrics
123
Ta
ble
4N
egat
ive
bin
om
ial
fixed
effe
cts
model
s
Exac
tsc
ience
sB
iolo
gy
and
chem
istr
y
Model
1M
odel
2M
odel
3M
odel
1M
odel
2M
odel
3
Pubs
Coef
f.S
EC
oef
f.S
EC
oef
f.S
EC
oef
f.S
EC
oef
f.S
EC
oef
f.S
E
Dir
ectt
ies
0.0
027
0.0
021
0.0
015
0.0
021
0.0
033
0.0
020
0.0
049
0.0
016***
0.0
047
0.0
016***
0.0
052
0.0
016***
Str
ength
dt
-0.0
301
0.0
171
-0.0
269
0.0
170
-0.0
282
0.0
170
0.0
048
0.0
227
0.0
055
0.0
226
0.0
097
0.0
226
Str
uct
hole
s-
0.1
601
0.0
694**
0.0
677
0.0
517
-0.0
272
0.0
800
0.1
061
0.0
525*
Den
sity
-0.0
027
0.0
005***
-0.0
018
0.0
004***
-0.0
014
0.0
006*
-0.0
012
0.0
004***
Eig
envec
0.0
058
0.0
018***
0.0
059
0.0
018***
0.0
057
0.0
018***
0.0
040
0.0
015**
0.0
040
0.0
015**
0.0
040
0.0
015**
E–I
Index
0.0
676
0.0
251**
0.0
667
0.0
252**
0.0
723
0.0
250***
0.0
323
0.0
240
0.0
318
0.0
240
0.0
330
0.0
240
Sin
gau
th0.0
294
0.0
124**
0.0
295
0.0
124*
0.0
320
0.0
123**
0.0
372
0.0
194
0.0
372
0.0
194
0.0
375
0.0
194*
Int
art
-0.0
027
0.0
048
-0.0
022
0.0
048
-0.0
014
0.0
048
-0.0
156
0.0
053***
-0.0
156
0.0
053***
-0.0
156
0.0
053***
Per
iod
80.0
115
0.0
262
0.0
139
0.0
262
0.0
103
0.0
262
0.0
290
0.0
269
0.0
292
0.0
269
0.0
290
0.0
269
Per
iod
7-
0.1
235
0.0
298***
-0.1
192
0.0
298***
-0.1
273
0.0
298***
-0.0
038
0.0
291
-0.0
030
0.0
290
-0.0
033
0.0
291
Per
iod
6-
0.1
297
0.0
332***
-0.1
217
0.0
330***
-0.1
265
0.0
332***
-0.1
082
0.0
326***
-0.1
070
0.0
324***
-0.1
071
0.0
326***
Per
iod
5-
0.3
469
0.0
395***
-0.3
352
0.0
392***
-0.3
472
0.0
396***
-0.3
303
0.0
368***
-0.3
291
0.0
367***
-0.3
314
0.0
369***
Per
iod
4-
0.4
936
0.0
446***
-0.4
803
0.0
443***
-0.4
966
0.0
447***
-0.4
431
0.0
410***
-0.4
416
0.0
407***
-0.4
439
0.0
410***
Per
iod
3-
0.7
393
0.0
525***
-0.7
093
0.0
508***
-0.7
202
0.0
521***
-0.6
422
0.0
457***
-0.6
406
0.0
455***
-0.6
425
0.0
458***
Per
iod
2-
0.6
609
0.0
548***
-0.6
449
0.0
544***
-0.6
639
0.0
549***
-0.3
935
0.0
483***
-0.3
911
0.0
478***
-0.3
914
0.0
483***
Per
iod
1-
0.6
809
0.0
592***
-0.6
628
0.0
587***
-0.6
855
0.0
594***
-0.6
153
0.0
566***
-0.6
124
0.0
559***
-0.6
118
0.0
566***
Cons
2.3
150
0.0
959***
2.1
840
0.0
781***
2.0
432
0.0
797***
2.1
663
0.1
098***
2.1
423
0.0
840***
2.0
071
0.0
835***
Wal
dv
2(1
6)
=762.0
9W
ald
v2
(15)
=757.2
9W
ald
v2
(15)
=741.1
2W
ald
v2
(16)
=738.2
7W
ald
v2
(15)
=738.1
0W
ald
v2
(15)
=733.1
8
Log
likel
ihood
=
-9,5
12.8
697
Log
likel
ihood
=
-9,5
15.5
178
Log
likel
ihood
=
-9,5
24.8
329
Log
likel
ihood
=
-10,6
25.0
23
Log
likel
ihood
=
-10,6
25.0
8
Log
likel
ihood
=
-10,6
27.4
53
Num
ber
of
obs
=6,7
90
Num
ber
of
obs
=8,1
88
Num
ber
of
gro
ups
=1,4
07
Num
ber
of
gro
ups
=1,5
91
Scientometrics
123
Ta
ble
4co
nti
nued
Pubs
Hea
lth
scie
nce
sS
oci
alan
dhum
anit
ies
Model
1M
odel
2M
odel
3M
odel
1M
odel
2M
odel
3
Coef
f.S
EC
oef
f.S
EC
oef
f.S
EC
oef
f.S
EC
oef
f.S
EC
oef
f.S
E
Dir
ectt
ies
0.0
042
0.0
017**
0.0
052
0.0
016***
0.0
045
0.0
016**
0.0
087
0.0
092
0.0
056
0.0
088
0.0
088
0.0
092
Str
ength
dt
0.0
620
0.0
251**
0.0
540
0.0
250*
0.0
647
0.0
249**
-0.1
989
0.0
743**
-0.1
887
0.0
733**
-0.1
942
0.0
736**
Str
uct
hole
s0.2
615
0.0
933**
0.3
124
0.0
664***
-0.3
517
0.3
092
-0.1
678
0.1
908
Den
sity
-0.0
006
-0.0
020
0.0
005***
-0.0
018
0.0
024
0.0
003
0.0
015
Eig
envec
0.0
016
0.0
016
0.0
013
0.0
016
0.0
015
0.0
016
0.0
034
0.0
028
0.0
037
0.0
028
0.0
035
0.0
028
E–I
Index
0.0
420
0.0
270
0.0
438
0.0
269
0.0
430
0.0
270
0.0
214
0.0
822
0.0
185
0.0
825
0.0
249
0.0
821
Sin
gau
th0.0
105
0.0
190
0.0
099
0.0
190
0.0
109
0.0
189
0.0
016
0.0
355
0.0
022
0.0
357
0.0
020
0.0
356
Int
art
-0.0
007
0.0
066
-0.0
017
0.0
066
-0.0
008
0.0
066
0.0
185
0.0
317
0.0
206
0.0
316
0.0
197
0.0
317
Per
iod
8-
0.0
543
0.0
288
-0.0
536
0.0
288
-0.0
551
0.0
287
-0.2
486
0.1
052
-0.2
462
0.1
054**
-0.2
447
0.1
053**
Per
iod
7-
0.0
378
0.0
303
-0.0
361
0.0
303
-0.0
395
0.0
302
-0.1
794
0.1
133
-0.1
800
0.1
135
-0.1
760
0.1
134
Per
iod
6-
0.1
058
0.0
334***
-0.1
075
0.0
335***
-0.1
073
0.0
334***
0.0
602
0.1
165
0.0
655
0.1
166
0.0
642
0.1
166
Per
iod
5-
0.3
044
0.0
386***
-0.3
092
0.0
386***
-0.3
059
0.0
386***
-0.2
963
0.1
380*
-0.2
944
0.1
381*
-0.2
947
0.1
381*
Per
iod
4-
0.4
471
0.0
433***
-0.4
545
0.0
432***
-0.4
485
0.0
433***
-0.6
261
0.1
622***
-0.6
137
0.1
621***
-0.6
256
0.1
624***
Per
iod
3-
0.5
113
0.0
469***
-0.5
235
0.0
467***
-0.5
117
0.0
469***
-0.4
744
0.1
741***
-0.4
487
0.1
728***
-0.4
610
0.1
734**
Per
iod
2-
0.5
737
0.0
513***
-0.5
867
0.0
511***
-0.5
749
0.0
513***
-0.9
442
0.2
173***
-0.9
200
0.2
167***
-0.9
408
0.2
176***
Per
iod
1-
0.5
098
0.0
546***
-0.5
242
0.0
544***
-0.5
109
0.0
546***
-0.9
724
0.2
509***
-0.9
505
0.2
504***
-0.9
642
0.2
510***
Cons
1.4
542
0.1
161***
1.6
941
0.0
774***
1.3
869
0.0
774***
2.0
879
0.3
764***
1.7
927
0.2
744***
1.8
788
0.2
567***
Wal
dv
2(1
6)
=752.3
5W
ald
v2
(15)
=748.2
2W
ald
v2
(15)
=751.8
4W
ald
v2
(16)
=67.9
2W
ald
v2
(15)
=66.3
0W
ald
v2
(15)
=67.3
2
Log
likel
ihood
=
-10,2
98.6
29
Log
likel
ihood
=
-10,3
02.5
89
Log
likel
ihood
=
-10,2
98.9
3
Log
likel
ihood
=
-891.4
6,2
29
Log
likel
ihood
=
-892.1
0758
Log
likel
ihood
=
-891.7
4769
Num
ber
of
obs
=6,7
61
Num
ber
of
obs
=805
Num
ber
of
gro
ups
=1,2
23
Num
ber
of
gro
ups
=198
Scientometrics
123
Ta
ble
4co
nti
nued
Agri
cult
ura
lsc
ience
san
dbio
tech
nolo
gy
Engin
eeri
ng
Model
1M
odel
2M
odel
3M
odel
1M
odel
2M
odel
3
Pubs
Coef
f.S
EC
oef
f.S
EC
oef
f.S
EC
oef
f.S
EC
oef
f.S
EC
oef
f.S
E
Dir
ectt
ies
-0.0
026
0.0
061
-0.0
025
0.0
056
-0.0
026
0.0
060
0.0
076
0.0
034*
0.0
090
0.0
033**
0.0
074
0.0
034*
Str
ength
dt
-0.1
898
0.0
555***
-0.1
897
0.0
555***
-0.1
900
0.0
556***
-0.0
953
0.0
394*
-0.1
027
0.0
392**
-0.0
991
0.0
393**
Str
uct
hole
s0.0
075
0.1
765
-0.1
226
0.1
145
0.2
955
0.1
382*
0.1
684
0.0
894
Den
sity
0.0
014
0.0
014
0.0
013
0.0
009
0.0
013
0.0
011
-0.0
005
0.0
007
Eig
envec
0.0
031
0.0
021
0.0
031
0.0
021
0.0
031
0.0
021
-0.0
026
0.0
024
-0.0
028
0.0
024
-0.0
026
0.0
024
E–I
Index
0.0
637
0.0
493
0.0
638
0.0
492
0.0
630
0.0
493
0.0
060
0.0
364
0.0
166
0.0
359
0.0
074
0.0
364
Sin
gau
th0.0
613
0.0
570
0.0
614
0.0
570
0.0
598
0.0
569
0.0
228
0.0
311
0.0
226
0.0
311
0.0
202
0.0
310
Int
art
-0.0
030
0.0
180
-0.0
032
0.0
177
-0.0
046
0.0
180
-0.0
208
0.0
114
-0.0
231
0.0
115*
-0.0
218
0.0
115
Per
iod
8-
0.1
185
0.0
492**
-0.1
186
0.0
492**
-0.1
175
0.0
492**
-0.0
975
0.0
394**
-0.1
007
0.0
393**
-0.0
952
0.0
393**
Per
iod
7-
0.2
567
0.0
563***
-0.2
569
0.0
562***
-0.2
569
0.0
564***
-0.2
225
0.0
485***
-0.2
313
0.0
484***
-0.2
199
0.0
485***
Per
iod
6-
0.4
014
0.0
650***
-0.4
017
0.0
646***
-0.4
028
0.0
650***
-0.4
959
0.0
622***
-0.5
134
0.0
618***
-0.4
946
0.0
622***
Per
iod
5-
0.6
746
0.0
825***
-0.6
749
0.0
823***
-0.6
742
0.0
825***
-0.7
434
0.0
748***
-0.7
584
0.0
745***
-0.7
404
0.0
747***
Per
iod
4-
0.8
754
0.0
954***
-0.8
758
0.0
948***
-0.8
788
0.0
954***
-0.8
014
0.0
844***
-0.8
145
0.0
842***
-0.7
978
0.0
843***
Per
iod
3-
1.0
388
0.1
124***
-1.0
394
0.1
117***
-1.0
407
0.1
125***
-0.9
730
0.0
960***
-0.9
905
0.0
958***
-0.9
701
0.0
960***
Per
iod
2-
0.9
646
0.1
421***
-0.9
653
0.1
414***
-0.9
677
0.1
421***
-0.9
716
0.1
104***
-0.9
890
0.1
101***
-0.9
663
0.1
102***
Per
iod
1-
1.1
060
0.1
752***
-1.1
064
0.1
749***
-1.1
044
0.1
753***
-0.9
933
0.1
313***
-1.0
236
0.1
304***
-0.9
937
0.1
311***
Cons
2.6
533
0.2
556***
2.6
589
0.2
190***
2.7
912
0.2
103***
1.8
016
0.1
794***
2.0
578
0.1
316***
1.9
477
0.1
315***
Wal
dv
2(1
6)
=246.3
9W
ald
v2
(15)
=246.4
0W
ald
v2
(15)
=245.2
0W
ald
v2
(16)
=478.1
6W
ald
v2
(15)
=474.1
1W
ald
v2
(15)
=476.4
8
Log
likel
ihood
=
-2551.9
601
Log
likel
ihood
=
-2551.9
61
Log
likel
ihood
=
-2552.4
287
Log
likel
ihood
=
-3,8
14.1
974
Log
likel
ihood
=
-3,8
16.4
953
Log
likel
ihood
=
-3,8
14.9
281
Num
ber
of
obs
=2,4
93
Num
ber
of
obs
=3,1
61
Num
ber
of
gro
ups
=613
Num
ber
of
gro
ups
=785
***
Sig
nifi
cant
at0.1
%,
**
signifi
cant
at1
%,
*si
gnifi
cant
at5
%
Scientometrics
123
Humanities, Agricultural Sciences and Biotechnology, as well as in Engineering, the
frequency of collaboration affects negatively, so that the costs of organizing and distrib-
uting teamwork, as well as the costs of developing a shared understanding, trust, reci-
procity and the transfer of high quality information and tacit knowledge (Reagans and
McEvily 2003; Inkpen and Tsang 2005), among other costs, do not result in more publi-
cations. On the contrary, in Health Sciences, having frequent collaboration is something
that positively affects future productivity.
Something to stress is that in the relational dimension, those areas of knowledge in
which ties has a positive and significant impact, frequency of ties has a negative effect or is
not significant (Health Sciences is the only area of knowledge in which both relational
dimensions are positive and significant). This could suggest that researchers who have
many ties do not have the time, or other kind of resources, to maintain frequent
collaborations.
To test for potential diminishing returns in the relational dimension, the square terms of
direct and strength of ties were included. No evidence of significant results was found. One
possible explanation is that in our sample both variables have not reached the peak to show
diminishing returns; indeed, the number and strength of ties in the case of Mexico is
smaller than in McFadyen and Cannella (2004) study.
In terms of the structural dimension, the results suggests that overall, density negatively
affects future productivity. That is, researchers who are linked to almost all other
researchers in the network, tend to have less productivity that those who belong to sparse
networks. In small research communities, as the one in Mexico, inbreeding is a common
practice that leans towards dense networks. Surprisingly, in Social and Humanities and
Agricultural Science and Biotechnology, none of the variables of the structural dimension
are significant. Although structural holes has been the most important variable to study
social capital, it seems that, at least in an environment like academia, sign and significance
tend to vary depending on the area of knowledge. The results suggest that the information
benefits that come with networks that span a wide range of contacts is only seen in Health
Sciences, Biology and Chemistry and Engineering. On the contrary, those benefits seem to
affect negatively researchers in Exact Sciences.
The position of the researcher in the network is also an important aspect of the social
capital. Researchers who are connected to central researchers tend to have higher pro-
ductivity. It could be assumed that central researchers are leading professors, who reach
higher productivity because of their position in the network. The lack of significance in
Health Sciences, Social and Humanities, Agricultural Sciences and Biotechnology, and
Engineering, could be interpreted as there are not so many central researchers, because of
the nature of the research that they develop. For example, in Social and Humanities, most
of the work is developed by one author, while in Health Sciences, by relatively large
groups.
Finally, the results of the external–internal index suggest that for all areas of knowledge,
it seems to be a good choice to collaborate with people from other areas of knowledge.
However, when looking at the specific areas of knowledge, this result is only significant for
researchers in Exact Sciences.
Conclusions
Several important results arise from this research. First, the results suggest that with this
strong set of controls, what matters in social capital is having many ties, being in a central
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position, having partners from different areas of knowledge, and being part of a non dense
network. Second, the results provide some insight for having a better understanding of the
characteristics of networks that enhance the creation of new knowledge.
Since a substantial component of research funding comes from government agencies, it
is possible to design funding policies that enhance those networks that promote higher
productivity among scientists, and allocate resources more efficiently. This work shows
that dense networks are associated with inferior performance of researchers in terms of
scientific productivity. This result suggests that for the creation of knowledge, novelty
information coming from a diverse group of individuals is a critical issue, and those groups
that are not exposed to new experiences are not taking the advantages that social capital
brings. It has been internationally noted (European Commission 1995) that inbreeding is an
obstacle to the diffusion of new forms of organization and knowledge, and in relatively
small scientific communities, this problem is even larger. This work offers empirical
support for this concern, suggesting that governments and institutions could design policies
to enhance higher openness and mobility, and discourage inbreeding. Another important
result is the finding that the number of ties enhances productivity, suggesting that science
policies should encourage team collaboration. Moreover, considering that researchers who
collaborate with colleagues from different fields of knowledge are more productive,
interdisciplinary collaborations should be promoted.
Another important result is that the impact of social capital differs importantly
depending on the area of knowledge. Researchers in Exact Sciences have to procure being
central, and ensure collaboration with colleagues from other disciplines, and moderate
frequent collaborations as well as avoid being part of dense networks. Researchers in
Biology and Chemistry have to procure having many ties and being central. In addition,
they also have to procure sparse networks, and for them it is not so important to look for
co-authors from other areas of knowledge. In Health Sciences, what matters for the cre-
ation of knowledge is having many different co-authors, as well as the frequency of
collaborations. Particularly critical is having many structural holes. A dissimilar result was
obtained for Social and Humanities and Agricultural and Biotechnology, in which the
frequency of collaborations seems to negatively affect the production of new knowledge.
Finally, for engineers, the relational dimension of social capital is the most important, in
the sense of having many different colleagues belonging to a network rich in structural
holes, and avoiding frequency in the collaboration. Of course, more research needs to be
done in order to have a deeper understanding of the types of collaborations, and the ways in
which scientists from different disciplines interact to create new knowledge. However, this
research offers evidence of the diversity in the knowledge production process among
domains of science, including social sciences.
However, this study has some limitations. First, co-publication is not an exhaustive
measure of collaboration; there are more products of collaboration than joint publications in
ISI. In addition, it is possible that researchers give co-authorship to some scientists just
because they work at the same laboratory or share some equipment (even if they do not
contribute much to the work), so the analysis of co-authorship might not reflect the actual
relationships in academic networks (Stephan 1996). In addition, publications is not the only
way to measure the creation of knowledge, as there are other types of outputs, like books,
patents or publications not indexed in ISI, that need to be considered in order to have
stronger conclusions. This is particularly true in the case of Social and Humanities, where
researchers tend to publish books or book chapters, and ISI publications do not cover the
entire scientific productivity. Also, the ISI WoS has a natural bias towards English language,
which becomes an intangible barrier for researchers from non-English speaking countries.
Scientometrics
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Moreover, the results must not be considered completely irrefutable, since the sample
includes only the most productive researchers.
No less important is the fact that, while social capital has a ‘‘powerful and intuitive
appeal’’ (Dasgupta 2005), it is very difficult to measure because it has many and varied
components, and some of them are intangible. This study has not considered all the
relationships that someone could have and that could affect, to some degree, the creation of
new knowledge. For example, interaction with students could be a valuable source of novel
information, and that interaction might not always end in a joint publication. Moreover,
only quantitative variables to proxy for social capital are used, without including the wide
range of social phenomena than involves human relationships. Also important is the fact
that we are not taking into account the pathways by which networks are formed, and the
motivations behind their formation. These are important factors that could lead to a better
design of networks that better promote productivity.
Notwithstanding, the contributions of this paper are important not only for the literature
of social networks, but also for the analysis of knowledge creation in academia.
Acknowledgments The author would like to thank the InstitutoTecnologico Autonomo de Mexico(ITAM) and the Asociacion Mexicana de Cultura AC for their support of this work.
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