Measuring Personal Networks And Their Relationship With Scientific Production

Abstract

The analysis of social networks has remained a crucial and yet understudied aspect of the efforts to measure Triple Helix linkages. The Triple Helix model aims toexplain, among other aspects of knowledge-based societies, “the current research system in its social context” (Etzkowitz & Leydes dorff, 2000:109). This paper develops a novel approach to study the re search system from the perspectiv e of the individual, through the analysis of the relationships among resear chers, and between them and other social actors. We develop a new set of techniques and show how they can be applied to the study of a specific case (a group of academic s within a university department). We analyse their informal social networks and show how a relationship exists between the characteristics of an individual’s network of social links and his or her research output.

Full text

Measuring Personal Networks and Their Relationship
with Scientific Production
Africa Villanueva-Felez
•
Jordi Molas-Gallart
•
Alejandro Escriba
´
-Esteve
Published online: 30 October 2013
 Springer Science+Business Media Dordrecht 2013
Abstract The analysis of social networks has remained a crucial and yet under-
studied aspect of the efforts to measure Triple Helix linkages. The Triple Helix
model aims to explain, among other aspects of knowledge-based societies, ‘‘the
current research system in its social context’’ (Etzkowitz and Leydesdorff
2000:109). This paper develops a novel approach to study the research system from
the perspective of the individual, through the analysis of the relationships among
researchers, and between them and other social actors. We develop a new set of
techniques and show how they can be applied to the study of a specific case (a group
of academics within a university department). We analyse their informal social
networks and show how a relationship exists between the characteristics of an
individual’s network of social links and his or her research output.
Keywords Embeddedness  Academic network  Research output
A. Villanueva-Felez  J. Molas-Gallart
Consejo Superior de Investigaciones Cientı
´
ficas (CSIC), Universitat Polite
`
cnica de Vale
`
ncia (UPV),
Valencia, Spain
A. Villanueva-Felez (&)  J. Molas-Gallart
Institute of Innovation and Knowledge Management, INGENIO (CSIC-Universitat Polite
`
cnica de
Vale
`
ncia), Valencia, Spain
e-mail: africa.villanueva@ingenio.upv.es
J. Molas-Gallart
e-mail: jormoga@ingenio.upv.es
A. Escriba
´
-Esteve
Departamento de Direccio
´
n de Empresas, Facultad de Economı
´
a, Universidad de Valencia,
Valencia, Spain
e-mail: alejandro.escriba@uv.es
123
Minerva (2013) 51:465–483
DOI 10.1007/s11024-013-9239-5

Introduction
The Triple Helix model puts forward the notion that innovation is generated
through a complex pattern of interaction among industries, universities and
governments. Etzkowitz et al. (2000) argue that these institutional spheres are
increasingly interwoven with linkages emerging at various stages of the
innovation and policy processes. Social networks are central to Triple Helix
linkages and their development is a frequent policy objective. Consequently, the
establishment of networks can be considered both as one of the processes through
which knowledge flows among actors, and also as an outcome of the policies
oriented to the reinforcement of these flows (Molas-Gallart et al. 2000; Kitagawa
2010).
Yet, despite their importance, social networks are considerably difficult to
analyse and measure. It is understandable that the efforts to define and collect
indicators of university-society relationships (the so-called Third Mission indica-
tors) have focused on clearly identifiable inputs (number of employees in
technology transfer, investments in spin-offs, etc.), and outputs (for instance,
commercialisation indicators like the income from licences) of these processes. The
analysis of social networks may be a crucial yet undervalued method for measuring
Triple Helix linkages and developing innovative indicators.
Some relevant efforts have been made from the Social Network Perspective,
which have studied, among others, the structure of collaborations in research
projects and journal articles (Meyer et al. 2004; Rigby and Edler 2005), academic
research networks that facilitate academic publications (Lowrie and McKnight
2004; Abramo et al. 2009), and the relationship between social networks and
academic career performance (Etzkowitz 2000; Whittington and Smith-Doerr
2008). This paper develops a novel approach to analyse quantitatively the
relationships among researchers, and between them and other social actors, by
measuring their informal social networks. An informal network is formed by those
links among social actors that do not follow prescribed official procedures and that,
therefore, are not necessarily formalised through documents, formal reporting
structures or organisational charts. This type of network includes working relations,
collaborations and exchanges of resources and knowledge that are the result of
personal initiatives among individuals who do not necessarily belong to the same
formal organisational structures (Allen et al. 2007).
Social network studies focus attention essentially on the structural properties of
networks and on the value and consequences a specific position in the network has
for the individual that holds it. In this paper we propose a different approach, which
focuses additionally on the relational features of social networks. From the social
networks perspective, this approach has at times been labelled the ‘‘relational
embeddedness’’ or ‘‘cohesive perspective’’ (Gulati 1998). We expand a method-
ology that has been applied mainly in management studies (Ruef 2002; Uzzi 1997).
For instance, Uzzi (1997) shows the existence of a link between patterns of inter-
firm connections and indicators of industrial performance. In this study we show
that a similar conceptual framework can be used as the basis of a quantitative
466 A. Villanueva-Felez et al.
123

analysis of the relationship between the structure of the social links that academics
establish and research output.
The approach we pilot in this paper offers a quantitative tool for the analysis of
the relationships established by members of academic institutions within and
outside their own organisations, and of the association between the structure of
these social linkages and the performance of academic functions.
The paper first introduces some key concepts derived from social network
analysis and uses them to develop a set of hypotheses relating network patterns with
individual research output. Next, we present our fieldwork and data set and explain
the research techniques used for contrasting the hypotheses. Finally, we discuss the
results and examine the implications of this research for the development of
quantitative approaches to the analysis of Triple Helix relationships.
Theoretical Background and Hypotheses
This study aims to determine whether academic output may be related with the
structure of scholars’ social networks.
1
The networks we are going to focus on are
‘‘first-order ego-centred’’. An individual’s first-order ego-centred social network
consists of those other social actors with whom he or she maintains direct contact,
and has some form of social bond (Adams 1967). Following Nohria (1992), this
network constitutes the most influential part of an actor’s environment (Fig. 1).
Social networks are expected to exert an important influence on individual
scientists’ outcomes because they provide access to key resources for the
development and improvement of their research activities and skills (Villanueva-
Felez 2011). The different access and exposure of individuals to those key
resources, residing and flowing through the network, depends, however, on the
pattern of the social structure in which the actor is embedded (Granovetter 1985).
Therefore, scientists’ social networks differ from each other basically in two aspects
(Burt 1997, 1992; Ibarra 1993; Gnyawali and Madhavan 2001):
1st -order network 2nd -order network
focal actor
1st-order contacts
Fig. 1 Ego-centred network. Source: Uzzi (1997)
1
This study aims to identify which network structures are related to higher research performance.
Causality between network patterns and research output is not assumed.
Measuring Personal Networks 467
123

a. Transactional contents: the quantity, quality and variety of resources that
circulate through the different social structures.
b. The access, determined by the personal network characteristics, that a particular
individual has to these flows of resources to accomplish his or her own
objectives.
Consequently, a researcher’s network will contribute to the enhancement of his
or her own capabilities, and thus to his or her scientific output, when the network’s
structural configuration provides the individual with improved accessibility to a
wider range of resources. On the contrary, the network can have a negative influence
or may constrain the performance of the researcher when it does not provide access
to the required resources. This can be due to a ‘‘negative connectivity’’
2
between the
network’s nodes (Yamagishi et al. 1988), or to the poor quality or redundancy of the
resources provided through the network (Burt 1992; Granovetter 1973).
Embeddedness and Research Output
Analysts have traditionally distinguished between strong and weak ties. Strong ties
are based on trust, reciprocity and frequency of interaction (Brass et al. 1998;
Granovetter 1973; Reagans and Zuckerman 2001; Krackhardt 1992). Trust
facilitates cooperation and support among social actors (Brass et al. 1998), as well
as transactions of resources and information (Krackhardt 1992). When strong ties
exist, individuals acquire detailed knowledge about each other’s capabilities,
attitudes, behaviours and objectives, and detailed and personalised information is
exchanged. The time invested in the relationship generates the necessary experience
that allows participants to predict (a) the contact’s specific information need, and
(b) how the shared information would be used by the partners (Krackhardt 1992;
Uzzi 1997). As a result, strong links provide deeper and specific knowledge in a
particular interest area for the individuals involved (Rowley et al. 2000)
contributing to knowledge creation and dissemination of capabilities.
In contrast, weak ties are defined as casual acquaintances between social actors
(Brass et al. 1998), characterised by infrequency of interaction (Granovetter 1973)
and based neither on trust nor reciprocity. However, these links can act as ‘‘local
bridges’’ to other social circles beyond the individual’s immediate social circle,
providing new information about opportunities and the existence of other resources
(Granovetter 1973; McEvily and Zaheer 1999).
Embeddedness refers to the number of strong ties that an individual maintains in
relation to the total number of links. Our study will analyse embeddedness of first-
order ego-centred networks. Following Uzzi (1997), we will distinguish three
2
Negative connectivity emerges when the relations between one actor and another cause relations
between the same actor and a third one to diminish. Yamagishi, Gillmore and Cook (1988: 835) define it
as follows: ‘‘If two relations, A–B and B–C, are negatively connected at B, exchanges in the A–B relation
diminish or prohibit exchanges in the B–C relation, and vice-versa (e.g., a business meeting with A forces
B to cancel a dinner appointment with C)’’ (1988: 835).
468 A. Villanueva-Felez et al.
123

different types of networks depending on different patterns of embeddedness:
overembedded, integrated and underembedded.
3
A completely overembedded network has no weak ties. Individuals who develop
an overembedded network invest all their time and resources on maintaining strong
ties. This causes two effects in the form and content of the social structure
developed by the individual:
(a) a potentially smaller personal network, as the resources needed to maintain
strong ties are bigger than for weak ties (Boorman 1975), reducing the number of
contacts that the actor can really sustain, and restricting the capacity to reach other
social circles;
(b) an increment of redundant information flow, since as Granovetter (1973)
points out, the strong links tend to connect among themselves reducing connections
with external members who could contribute with innovative ideas (Burt 1992).
Under these circumstances the social network becomes ossified and loses
connection with the surrounding environment (Burt 1992; Gnyawali and Madhavan
2001; Uzzi 1997). Although overembedded networks increase cooperation, support
and joint problem-solving between actors, their members have little contact with
other social circles. For instance, an academic developing an overembedded
network is likely to establish links with members of his or her own department,
without ties with government, industrial, or other societal actors. One can
hypothesise that this situation will narrow the perspective of a researcher and close
him or her to potential developments of theoretical or methodological interest
arising beyond the individual’s immediate academic context.
In contrast, when the network is formed predominantly by weak ties, the network
pattern is underembedded. In this situation network size is likely to be larger than
in overembedded networks, allowing individuals to reach a variety of social circles.
However, individuals with underembedded networks lack the advantages derived
from the trust afforded by strong tie relations (Uzzi 1997). Such networks tend to be
unstable and less durable over time, causing the continuous reshaping of the social
structure (Heracleous and Murry 2001). Nevertheless, Granovetter (1973) famously
stated that weak ties are the bearers of novel and non-redundant information,
indispensable for the discovery of new opportunities. Even so, in the academic
research context, networks that do not foster cooperation and support between
researchers, and consequently the transfer of tacit knowledge, might diminish both
the quantity and the quality of research output.
Finally, an integrated network contains both strong and weak ties. This type of
network combines the benefits generated by embeddedness and trust, like stable
cooperation and support, while still ensuring a flow of novel information through
weak ties (Uzzi 1997). This network pattern is formed by a set of strong ties, which
are stable, lasting and characterised by teamwork and joint problem-solving; and by
a more dynamic, unstable and changing set of social relations (weak ties) providing
3
We are aware that these terms might be normative, in that they are not free of value. We have received
suggestions to change this nomenclature to ‘light’, ‘moderate’ and ‘high’. However, we have decided to
maintain these terms to conform to the sources used (see Uzzi, 1997).
Measuring Personal Networks 469
123

the bridges to new methods, perspectives and ideas made in other sectors and social
environments.
On the basis of the above, we establish the following hypothesis:
Hypothesis 1.1 Researchers with integrated network patterns will have higher
research output than researchers with overembedded network patterns.
Hypothesis 1.2 Researchers with integrated network patterns will have higher
research output than researchers with underembedded network patterns.
Nodal Heterogeneity and Research Output
Embeddedness refers to the strength of links among actors but does not distinguish
among the different types of actors with whom an individual is linked. First-order
nodal heterogeneity refers to the variation in the mix of direct contacts in the social
networks of individuals (McEvily and Zaheer 1999; Reagans and Zuckerman 2001).
A range of nodal heterogeneity patterns can be identified, varying from completely
homogeneous networks to completely heterogeneous structures.
Individuals with a heterogeneous network pattern have a broad variety of
contacts that exposes them to diverse social circles, beyond their immediate circle.
This allows them to reach a wider range of sources of information and opportunities
(McEvily and Zaheer 1999). Thus, the higher the level of heterogeneity in a
network, the larger the quantity, quality and variety of resources the actor can
access. In universities, researchers with heterogeneous networks maintain links with
members of other universities, and industrial and governmental organisations, both
local, national and international.
This approach differs from the embeddedness perspective in that the origin of the
variety of resources is not determined by the strength of the ties, but rather by the
diversity of contacts. McEvily and Zaheer (1999) suggest that the sharing of a
strong tie between two individuals does not necessarily imply the connection of
these two individual’s independent contacts as Granovetter (1973) predicts. In this
Industrial-Governmental Spheres
Academic Sphere
Local-regional location
National location
International
location
Ego
1
st
order contacts
Heterogeneous network
Homogeneous network
Fig. 2 Heterogeneous and homogeneous networks
470 A. Villanueva-Felez et al.
123

perspective, the social circle reached by an actor’s network is independent from the
strength of the link (Fig. 2).
In contrast, a complete homogeneous network is characterised by the absence of
bridging ties to other social circles, i.e. it is formed by nodes from the actor’s most
immediate social environment. McEvily and Zaheer (1999: 1137) argue that
‘‘bridging ties exist when high no redundancy, infrequency of interaction and
geographic dispersion characterize (…)’’ the network. Thus, a homogeneous
network will have a redundancy of contacts, they will be linked between them, will
interact frequently and all of them will be concentrated in a geographic area.
Consequently, this type of network will lack weak ties and will present the same
pattern associated with overembedded networks. The influence of homogeneous
networks on an actor’s actions, behaviour and, in the case of university departments,
on his or her research output, would coincide with the features described for
overembedded networks. Accordingly, we hypothesise:
Hypothesis 2.1 Researchers with the most heterogeneous network patterns will
show the highest research output.
Hypothesis 2.2 Researchers with the most homogeneous network patterns will
show the lowest research output.
Research Techniques
Sample and Data Collection
Our respondents consist of 64 researchers from six departments from the University
of Valencia (Spain), all of them with research interests related to business and
management. The University of Valencia is a research-oriented university that
fosters a policy of support and improvement in research quality and productivity.
The selection of members from the same university and similar disciplines allows us
to neutralise some cultural and institutional aspects that may affect the way
researchers develop their networks patterns (Burt 1997).
Initially, we built a database of 183 academics with the information contained in
the department’s research activity official reports for the years 2003 and 2004. The
population was distributed as follows: 18 professors, 153 lecturers, and 12 teaching
assistants. In order to obtain data about the individual social network of the
researchers, we conducted a survey. The preliminary survey instrument was tested
by three academics in order to identify and correct any difficulties or misunder-
standings in the wording of the questions. The main problems were the length of the
questionnaire and the difficulties for respondents in identifying which of their
contacts belonged to ‘‘industry’’ or ‘‘government’’. They were addressed in
subsequent versions. Second and third versions of the questionnaire design were
tested before the final version was defined. The final questionnaire was sent to the
183 individuals who comprised the identified population. Responses were received
from 75, that is a response rate of 41%. Eleven questionnaires were rejected because
they were incomplete. Consequently, a total of 64 responses are included in the
Measuring Personal Networks 471
123

following analysis, 35% of the initial population. Table 1 shows the final sample
distribution across academic positions.
4
Measures
Research Output
The measurement of research outputs is a complex and controversial area of
research. It is well known, for instance, that most measurements are very sensitive
to contextual conditions: different disciplines display different publication and
citation patterns. In part, these problems are lessened here by the homogeneity of
our group of reference: academics from the same discipline working in the same
university.
Within this context, we develop a composite measure following the approach of
Gulbrandsen & Smeby (2005). They use a unique measure that takes into
consideration both the quality and quantity of an individual’s research output. It
includes (1) papers in scientific and scholarly journals, (2) chapters in academic
books or text books, and papers in conference proceedings, (3) academic books and
textbooks, and (4) ‘‘popular science’’ articles. In order to consider output quality,
publications were recorded to article equivalents. Following Gulbrandsen & Smeby
(2005), we develop a single composite indicator including the following items:
• papers presented at national research conferences (1 point);
• papers presented at international research conferences (2 points);
• articles published in national academic journals as well as chapters in academic
books published in Spain (3 points);
• articles published in international academic journals and chapters in interna-
tional academic books (4 points);
• academic books (5 points)
5
4
Each one of the three types of academic appointment existing in the population (professors, lecturers
and teaching assistants) is represented in the sample. Although the response rate among professors was
lower than for the other two groups, with a 0.01 level of significance the sample is not biased. Further, we
have not used these categories to analyse our data, and our results are therefore not affected by the lower
rate of response among professors.
Table 1 Final sample and response rate by academic rank
Population Final sample Response rate
Professors 18 9.84% 1 1.56% 6%
Lecturers 153 83.61% 56 87.50% 37%
Teaching assistants 12 6.56% 7 10.94% 58%
5
There was a single case of an author who published an academic book in English. If this had been
valued using similar weights to the ones used for international journal articles (i.e. double the ‘‘points’’ of
a domestic publication), the resulting distribution would have become skewed and prevented us from
applying common statistical techniques. We treated this outlier case within a single, broader class of
academic books, without making a distinction between national and international book publications.
472 A. Villanueva-Felez et al.
123

For journal articles we assign double points for those published in indexed
journals (both in Spain and internationally). We used the Thomson’s ISI Journal
Citations Report for the identification of indexed international publications and the
In-Recs index for the Spanish journals.
6
The points assigned to co-authorships are
divided by the total number of authors.
The resulting formula for Research Output (RO) is:
RO=[NatConf?2*IntConf?3*(NatArt?2*NatIndexArt)?4*(IntArt?2*IntIn-
dexArt)?5*Books]/authors
Degree of Embeddedness
The first-order degree of embeddedness is the relationship between strong ties and
the total size of the direct links network. First, to develop an indicator of the degree
of embeddedness it is necessary to identify and measure strong ties. Many
researchers consider that a tie is strong when it is based on trust, is reciprocal, and
the social actors linked interact frequently (Brass et al. 1998; Granovetter 1973;
Krackhardt 1992; Reagans and Zuckerman 2001; Uzzi 1997, 1996). The set of
strong links constitute the durable and stable part of an individual’s network.
The academic research networks we study are composed of two types of
relationships:
1. The research links that an academic maintains with other university academics
and researchers; i.e. person-to-person relations.
2. The ties with firms and institutions from government and industry; i.e. person-
to-organisation relations.
Therefore, the measurement and identification of the strong ties in these two
different contexts must take into account the differential nature of the relationship.
To identify strong ties with other university researchers, we asked the informants to
indicate which of their contacts fulfilled both the following two characteristics:
a. The contact was seen as reliable, competent and would not behave in an
opportunistic manner against the respondent. This condition expresses the features
an individual must have for the actor to trust him/her (Mayer et al. 1995; Escriba
´
-
Esteve 2002).
b. The contact and the respondent were used to working together and would
communicate at least three times per month. This condition reflects strong
interaction and reciprocity between the two actors (Uzzi 1997).
To identify strong ties with industry and government organisations and
institutions, we required that the link be stable and multiplex. We asked the
following closed question: ‘‘With whom would you maintain the link if your main
contact person leaves the organisation?’’ The alternatives given were: a) only with
the organisation, b) only with the person, c) with both and d) with none. Option C
denotes strong links: even when the main contact person leaves the organisation, the
6
In-Recs (Social Science Spanish Journal citation report) has been created by ‘‘Evaluacio
´
n de la ciencia
y de la comunicacio
´
n’’ research group, University of Granada. http://ec3.ugr.es/in-recs/.
Measuring Personal Networks 473
123

relationship is maintained with both the organisation and the person. We take this
view because, first, the relationship with that person is likely to be developed
beyond the organisational limits; and second, because the bond with the
organisation is not held only by one contact person.
Once we identified the strong links, we calculated the degree of embeddedness as
the relation between the sum of total strong ties and the first order network size. The
resulting formula is:
DE ¼
STu þ STorg
TotalTies
where DE equals the degree of embeddedness, STu equals the total number of
strong ties in the university/academia research arena, and STorg equals the total
number of strong ties with organisations or institutions in other non-academic
arenas (i.e. industry and government).
Nodal Heterogeneity
First order nodal heterogeneity refers to the variation in the mix of contacts in the
individuals’ networks of direct links (McEvily and Zaheer 1999; Reagans and
Zuckerman 2001). In order to estimate and measure nodal heterogeneity, we asked
respondents to classify their contacts in relation to the following:
a. geographic location: distinguishing between local, national and international
contacts,
b. institutional sphere: distinguishing between academic and non-academic
contacts
We apply the following entropy measure (Shannon and Weaver 1959) to the two
dimensions above (geographical and institutional) to calculate network
heterogeneity:
D ¼
1
log(n)

X
n
i¼1
y
i
log(y
i
Þ
where D = diversity, n is equal to the social categories considered, y
i
is the
proportion of contacts listed by the respondent within each category i.
This measure varies from 0 for complete homogeneity, i.e. all contacts in the
network belong to the same social category, to 1 for complete heterogeneity, i.e.
each social category considered has the same number of contacts.
Analysis
Our method uses different research techniques for the identification of network
structures. First, we use percentile ranks to identify patterns in relation to the degree
of embededdness. Percentiles are used to describe the characteristics of a
474 A. Villanueva-Felez et al.
123

distribution and indicate the relative position of an individual within a dataset.
Second, we applied cluster analysis to determine nodal heterogeneity patterns of the
researchers’ networks. As Hair et al. (1998: 481) argued, ‘‘the primary goal of
cluster analysis is to partition a set of objects into two or more groups based on the
similarity of the objects for a set of specified characteristics’’. This allows us to
identify underlying structures and to simplify complex sets of data for further
analysis and interpretation.
Once we identified groups of researchers with different network patterns, we used
Mann-Whitney U test for independent populations to compare the research outputs
of the different groups. This test requires no specific assumption regarding the
distribution of research output, allowing us to identify relationships between
network relational structures and our research outputs indicator.
Results and Discussion
Degree of Embeddedness and Research Output
According to Uzzi (1997), three different types of network patterns can be
determined relying on the degree of embeddedness shown - overembedded,
integrated and underembedded networks. The 25
th
percentile scored a value equal to
33.3% of strong links in the network composition and the 75
th
percentile a 71.4% of
strong links. We grouped academics according to those values, given as a result the
three groups shown in Table 2.
We observe that the first group displays an overembedded network pattern as, on
average, 92% of their contacts are maintained through strong ties. Conversely, the
average proportion of strong links for individuals in the third group is only 25%,
thus displaying an underembedded network pattern. Finally, the second group
displays a more even distribution between strong and weak ties, indicating an
integrated network pattern.
To test the hypothesis about the relationship between the different network
relational structures identified above and academic research output, we use Mann-
Whitney U test. Table 2 shows the average research output for the groups displaying
different degrees of embeddedness. The individuals who display a more integrated
network pattern have a higher research performance than those researchers with
either overembedded or underembedded network patterns. Table 2 also shows that
overembedded network patterns are associated with the lowest average research
output.
Table 2 Descriptive—degree of embeddedness
N Degree of embeddedness mean Research output mean
Group 1 – Overembedded network 13 .9214 2.9631
Group 2 - Integrated network 32 .5960 8.0088
Group 3 - Underembedded network 19 .2491 4.3768
Measuring Personal Networks 475
123

It is now necessary to test whether these differences are significant. Table 3
shows the significance values obtained by the application of Mann-Whitney test
procedures. Individuals that maintain a balance between strong and weak ties
(integrated networks) have a significantly higher research performance than
researchers with overembedded networks (p=0.027) and researchers with under-
embedded networks (p=0.063).
The results allow us to confirm hypothesis 1.1 and 1.2 at an acceptable level of
significance. Additionally, although individuals with an underembedded network
show higher research output values than individuals with overembedded network
patterns, the differences obtained are not significant.
7
Nodal Heterogeneity and Research Output
We initially used Two Step Cluster Analysis and Hierarchical Cluster Analysis to
determine network relational structures with regard to nodal heterogeneity. The first
cluster method offered a solution with three clusters, while the hierarchical method
showed one group more. To solve this problem we used a third cluster technique,
K-Means Cluster Analysis, to compare the results of different clustering techniques.
As K-Means Cluster Analysis allows us to specify the number of clusters in
advance, we ran the simulation first with three groups and afterwards with four
groups. We decided to choose the 4 groups K-Means Cluster solution because it
distributed the objects more equitably between the different groups and showed
larger distances between the cluster centres.
Table 4 shows four groups with four network patterns. The values of the entropy
measures range from 0 to 1, where 0 denotes no heterogeneity at all (complete
homogeneity), and 1 indicates complete heterogeneity.
Table 5 presents more detail on the characteristics of the contacts that the
different groups display. Group 1 presents a complete homogeneous pattern
with regard to both geographic diversity (all the contacts are local) and
Table 3 Mann-Whitney U test – Degree of embeddedness
Groups U Sig. (1-tailed)
1–2 131.5 0.027*
1–3 94.0 0.127
2–3 235.5 0.063

* p \ 0.05;

p \ 0.1
7
We have conducted additional analyses to assess whether these results are related to the individuals’
academic position. ‘‘Appendix B1’’ shows a crosstabulation between embedded network pattern and
occupational group. The significance of the chi-square statistic is p = 0.480, which implies that academic
position and network patterns are not related.
476 A. Villanueva-Felez et al.
123

institutional diversity (no links outside academia). Therefore, members of this
group develop a research network consisting of members of their own
university department only. Group 2 shows more geographic diversity than
group 1 (they have university contacts both in their department and in other
departments and they also have more international contacts) but a high degree
of institutional homogeneity (98% of their contacts belong to the University
arena). Members of group 3 concentrate their contacts locally (almost 70% of
their links are local). Nevertheless, this group shows the largest diversity
concerning the institutional distribution of their contacts. They have the highest
percentage of ties with actors from the industrial and governmental spheres
(around 34% of their links). Group 4 represents a high heterogeneity in both
dimensions. It displays the most internationalised network pattern, with around
16% of their links being international, mainly with other academics (11%).
However, as with the rest of the groups, they develop more contacts in the
local academic sphere.
Table 4 Descriptive – nodal heterogeneity
Group N Geographic
diversity
Institutional
diversity
Total
heterogeneity
Research
output means
1 9 .000 .000 0.00 1.5044
2 21 .683 .096 0.39 6.4829
3 19 .517 .882 0.70 6.1674
4 15 .847 .793 0.82 9.1347
Table 5 Contacts means distribution per groups
Means Group 1
N=9
Group 2
N=21
Group 3
N=19
Group 4
N=15
% Local nodes 1.0000 .4395 .6973 .5153
% National nodes .0000 .4124 .2147 .3254
% International nodes .0000 .1481 .0889 .1594
% Academic nodes 1.0000 .9815 .6553 .7269
% Non-academic nodes .0000 .0185 .3447 .2731
% Local nodes ACAD 1.0000 .4238 .3905 .3600
Non-ACAD .0000 .0157 .3068 .1553
% National nodes ACAD .0000 .4110 .1868 .2547
Non-ACAD .0000 .0014 .0279 .0707
% International nodes ACAD .0000 .1467 .0784 .1127
Non-ACAD .0000 .0014 .0105 .0467
Measuring Personal Networks 477
123

Again, to test the hypothesis about the relationship between the different network
relational structures identified above and academic research output, we use Mann-
Whitney U test. Table 3 presents research output means for all groups obtained in
relation to nodal heterogeneity. Individuals from group 2 and from group 3 achieve
similar research outputs. Group 1, with complete homogeneity of network patterns
in both geographic and institutional dimensions, presents the lowest research output
mean. In contrast, group 4 has both the highest research performance and the highest
network pattern heterogeneity. However, not all the differences across groups are
significant.
Table 6 shows the significance values obtained through the application of Mann-
Whitney U test. Group 1 (individuals with homogeneous network patterns) have
significantly lower research output than the rest of the groups; therefore, hypothesis
2.2 is confirmed. However, we cannot confirm hypothesis 2.1 as the group with
highest total heterogeneity (group 4) does not present a significantly higher research
output mean than groups 2 or 3. Although some degree of heterogeneity in the
network structure appears to be associated with a higher research output, we are not
able to determine which kind of network diversity (i.e. based on geographic or on
institutional heterogeneity or both) is more strongly related with higher research
output.
8/9
Conclusions
This paper has shown how social network analysis techniques can be combined with
other statistical tools to explore the networks that academics establish among
8
A possible explanation for this lack of differentiation could lie on similar network transitivity. Network
transitivity occurs when an individual acquires competencies from another to interact independently with
a third individual (Uzzi and Gillespie 2002). In other words, transitivity could act as a measure of the
‘‘social capital’’ available through an individual’s network nodes. However, to measure network
transitivity it would be necessary to analyse second order networks. This falls outside of the scope of this
paper.
9
We conducted additional analyses to test whether the nodal heterogeneity is related to the individuals’
academic position. ‘‘Appendix B2’’ shows a crosstabulation between nodal heterogeneity patterns and
academic rank. The significance of the chi-square statistic is p=0.164, which implies that academic
position and network patterns are not related.
Table 6 Mann-Whitney U test – Nodal heterogeneity
Groups U Sig. (2-tailed)
1–2 51.5 0.048*
1–3 28.5 0.005**
1–4 20.0 0.004**
2–3 184.5 0.684
2–4 126.5 0.319
3–4 119.0 0.415
** p \ 0.01; * p \ 0.05
478 A. Villanueva-Felez et al.
123

themselves and with non-academics. Our approach provides additional insights into
the structure of social networks; in particular, it reveals the internal variation within
groups that, in other studies, have been treated as a unit. Analysis at higher levels of
aggregation (including departmental) would have glossed over the important
differences that emerge at the level of the individual even within the same discipline
and cultural and institutional contexts (Burt 1997). This is not, in itself, a novel
discovery. Qualitative studies have often drawn attention to the importance of the
activities of specific individuals, and there is also substantial quantitative literature
correlating, for instance, the academic performance of individuals with other
individual characteristics. Yet, what the paper shows is that quantitative techniques
can be extended to the analysis of social relationship patterns at the individual level,
and that these techniques can be used as a tool to investigate the nature of the links
within and outside academia, and to relate these links with other variables.
In this paper, we have illustrated the potential of the techniques by exploring the
relationships between the types of social networks that academics establish and their
academic performance. We have shown that the characteristics of a researcher
network are related with his or her academic research output. Specifically, our
results suggest that researchers who are part of an integrated network, with a mix
between strong and weak ties, achieve better research outputs. Overembedded
networks are related with lower academic output. The same can be said of
researchers with completely homogeneous networks: they display the lowest
research output scores. Nodal heterogeneity is positively and significantly related
with research output.
Our results offer further evidence in support of the Triple Helix model and are
consistent with results obtained in previous studies using different techniques.
Etzkowitz (2000) shows that an ‘‘intermediate’’ number of strong ties in the
networks of academics affect scientific productivity positively.
10
Our results
strengthen the view that researchers who establish social networks combining both
strong and weak ties are also more adept at academic knowledge creation. This
outcome is also consistent with the extant social network analysis literature. These
network structures combine the advantages derived from both types of links while
minimising the limitations and threats of underembedded and overembedded social
networks (Uzzi 1997).
Our paper has presented a somewhat narrow and limited application of the
analytical techniques we propose. Replication across different institutional, regional
and academic environments would allow us to determine whether the patterns
identified here are contingent to the specific academic, institutional and cultural
context in which our study is framed, or can be generalised across different
environments.
10
However, Etzkowitz measures the strength of a tie in a different way. See Etzkowitz (2000: 165).
Measuring Personal Networks 479
123

Appendix A: Survey Format
NAME: _______________________________________________________
1. Which year did you start working at the University? _______________year.
RELATIONSHIPS WITHIN ACADEMIA
LOCAL LEVEL
2. Indicate the total number of contacts in your own department with whom you have discussed or commented topics and issues related to
your own research, in the last two years.
TOTAL DEPARTMENT: ___________contacts.
3. Consider the following two possible characteristics about the contacts with whom you discuss aspects related to your research:
a. Your contact is reliable (accomplishes his/her commitments), is competent and would not behave in an opportunistic
manner towards you.
b. Your contact and you are used to working together and communicating at least three times per month.
3.1 How many of your contacts in the department (Question 2) fulfil the TWO characteristics above:
Number: ___________contacts that fulfil the two characteristics.
3.2 How many of your contacts in the department (Question 2) fulfil the just ONE (and only one) characteristic above:
Number: ___________people that fulfil just one characteristic.
3.3 How many of your contacts in the department (Question 2) do not fulfil any of the two characteristics:
Number: ___________people that do not fulfil any characteristic.
NATIONAL LEVEL
4. Indicate the total number of contacts from Spanish academic and research institutions with whom you have discussed or commented topics
and issues related to your own research, in the last two years.
TOTAL NATIONAL: ___________contacts.
5. Consider the following two possible characteristics about the contacts with whom you discuss aspects related to your research:
a. Your contact is reliable (accomplishes his/her commitments), is competent and would not behave in an opportunistic
manner towards you.
b. Your contact and you are used to working together and communicating at least three times per month.
5.1 How many of your contacts in Spanish academic institutions (Question 4) fulfil the TWO characteristics above:
Number: ___________contacts that fulfil the two characteristics.
5.2 How many of your contacts in Spanish academic institutions (Question 4) fulfil the just ONE (and only one) characteristic above:
Number: ___________people that fulfil just one characteristic.
5.3 How many of your contacts in Spanish academic institutions (Question 4) do not fulfil any of the two characteristics:
Number: ___________people that do not fulfil any characteristic.
INTERNATIONAL LEVEL
6. Indicate the total number of contacts from international academic and research institutions with whom you have discussed or commented
topics and issues related to your own research, in the last two years.
TOTAL INTERNATIONAL: ___________contacts.
7. Consider the following two possible characteristics about the contacts with whom you discuss aspects related to your research:
a. Your contact is reliable (accomplishes his/her commitments), is competent and would not behave in an opportunistic
manner towards you.
b. Your contact and you are used to working together and communicating at least three times per month.
7.1 How many of your contacts in international academic institutions (Question 7) fulfil the TWO characteristics above:
Number: ___________contacts that fulfil the two characteristics.
7.2 How many of your contacts in international academic institutions (Question 7) fulfil the just ONE (and only one) characteristic above:
Number: ___________people that fulfil just one characteristic.
7.3 How many of your contacts in international academic institutions (Question 7) do not fulfil any of the two characteristics:
Number: ___________
p
eo
p
le that do not
f
ul
f
il an
y
characteristic.
480 A. Villanueva-Felez et al.
123

Appendix B: Crosstabulation Tables
1. Degree of Embeddedness-Academic Rank
Group 1
Overembedded network
Group 2
Integrated network
Group 3
Underembedded network
Professors 0 (0%) 0 (0%) 1 (100.00%)
Lecturers 13 (23.21%) 26 (46.43%) 17 (30.37%)
Teaching assistants 0 (0%) 6 (85.71%) 1 (14.29%)
N13 3219
Chi-square = 5.516, p = 0.480
8. RELATIONSHIPS WITH PRIVATE SECTOR AND NGO’S
Indicate with initials or names the
firms and non- governmental
organizations with which you
maintain or have maintained an
academic-professional relationship
in the years 2004 and 2005.
In which city or region
do you normally meet or
have met with these
firms or organisations?
With whom would you maintain the relationship if your main
contact person leaves the organisation?
You would maintain:
(Tick the appropriate one)
INITIALS
CITY-REGION
If my main contact person leaves the firm/organization, I
MANTAIN the relationship with….
1.
just the firm/organisation just the person both none
2.
just the firm/organisation just the person both none
3.
just the firm/organisation just the person both none
4.
just the firm/organisation just the person both none
5.
just the firm/organisation just the person both none
6.
just the firm/organisation just the person both none
9. RELATIONSHIPS WITH GOVERMENTAL INSTITUTIONS
Indicate with initials or
name the governmental
organizations or
institutions with which you
maintain or have
maintained an academic-
professional relationship in
the years 2004 and 2005.
Indicate the kind of governmental
institutions with which you maintain or
have maintained a relationship.
(Tick the appropriates ones)
With whom would you maintain the relationship if your
main contact person leaves the organisation?
You would maintain:
(Tick the appropriate one)
INITIALS
SCOPE
If my main contact person leaves the
institution/organization, I MANTAIN the relationship
with….
1.
Local-Regional National International just the institution just the person both none
2.
Local-Regional National International
just the institution just the person both none
3.
Local-Regional National International
just the institution just the person both none
4.
Local-Regional National International
just the institution just the person both none
5.
Local-Regional National International
just the institution just the person both none
6.
Local-Regional National International
just the institution just the person both none
Measuring Personal Networks 481
123

2. Nodal Heterogeneity-Academic Rank
Group 1
Homogeneous
network
Group 2 Geo diversity ?
Inst homogeneity
Group 3 Geo
homogeneity ? Inst
diversity
Group 4
Heterogeneous
network
Professors 0 (0%) 1 (100%) 0 (0%) 0 (0%)
Lecturers 9 (16.07%) 16 (28.57%) 18 (32.14%) 13 (23.21%)
Teaching Assistants 0 (0%) 4 (57.14%) 1 (14.29%)
2 (28.57%)
N 9 21 19 15
Chi-square=6.519, p=0.164
References
Abramo, Giovanni, Ciriaco D’Angelo, and Flavia Di Costa. 2009. Research collaboration and
productivity: Is there correlation? Higher Education 57: 155–171.
Adams, Bert N. 1967. Interaction theory and the social network. Sociometry 30(1): 64–78.
Allen, James, Andrew D. James, and Phil Gamlen. 2007. Formal versus informal knowledge networks in
R&D: A case study using social network analysis. R&D Management 37(3): 179–196.
Boorman, B. 1975. A combinational optimization model for transmission of job information through
contact networks. Bell Journal of Economics 6(1): 216–249.
Brass, Daniel J., Kenneth D. Butterfield, and Bruce C. Skaggs. 1998. Relationships and unethical
behavior: A social network perspective. Academy of Management. The Academy of Management
Review 23(1): 14–31.
Burt, Ronald S. 1992. Structural holes: The social structure of competition. Cambridge: Harvard
University Press.
Burt, Ronald S. 1997. The contingent value of social capital. Administrative Science Quarterly 42(2):
339–365.
Escriba
´
-Esteve, Alejandro. 2002. Procesos de generacio
´
n de confianza en los acuerdos de cooperacio
´
n.
Cuadernos de Economı´a y Direccio´n de la Empresa 13: 469–497.
Etzkowitz, Henry. 2000. Athena unbound: The advancement of women in science. Port Chester, NY,
USA: Cambridge University Press.
Etzkowitz, Henry, and Loet Leydesdorff. 2000. The dynamics of innovation: From national systems and
‘‘Mode 2’’ to a triple helix of university–industry–government relations. Research Policy 29(2):
109–123.
Gnyawali, Devi R., and Ravindranath Madhavan. 2001. Cooperative networks and competitive dynamics:
A structural embeddedness perspective. Academy of Management. The Academy of Management
Review 26(3): 431–445.
Granovetter, Mark. 1973. The strength of weak ties. American Journal of Sociology 78(6): 1360–1380.
Granovetter, Mark. 1985. Economic action and social structure: The problem of embeddedness. American
Journal of Sociology 91(3): 481–510.
Gulati, Ranjay. 1998. Alliances and networks. Strategic Management Journal (1986–1998) 19(4):
293–317.
Gulbrandsen, Magnus, and Jens-Christian Smeby. 2005. Industry funding and university professors’
research performance. Research Policy 34(6): 932–950.
Hair, Joseph F., Rolph E. Anderson, Ronald L. Tatham, and William C. Black. 1998. Multivariate Data
Analysis, 5th ed. Upper Saddle River, NJ, USA: Prentice-Hall International Inc.
Heracleous, Loizos, and John Murry. 2001. Networks, interlocking directors and strategy: Toward a
theoretical framework. Asia Pacific Journal of Management 18(2): 137–160.
Ibarra, Herminia. 1993. Network centrality, power, and innovation involvement: Determinants of
technical and administrative roles. Academy of Management Journal 36(3): 471–501.
482 A. Villanueva-Felez et al.
123

Kitagawa, Fumi. 2010. Pooling resources for excellence and relevance: An evolution of universities as
multi-scalar network organisations. Minerva 48(2): 169–187. doi:10.1007/s11024-010-9147-x.
Krackhardt, D. 1992. The strength of strong ties: The importance of philos in organizations. In Networks
and organizations, eds. Nitin Nohria, and Robert G. Eccles, 216–239. Boston: Harvard Business
School Press.
Lowrie, A., and P.J. McKnight. 2004. Academic research networks: A key to enhancing scholarly
standing. European Management Journal 22(4): 345–360.
Mayer, Roger C., James H. Davis, and F. David Schoorman. 1995. An integration model of organizational
trust. Academy of Management. The Academy of Management Review 20(3): 709–734.
McEvily, Bill, and Akbar Zaheer. 1999. Bridging ties: A source of firm heterogeneity in competitive
capabilities. Strategic Management Journal 20(12): 1133–1156.
Meyer, Martin, Piera Morlacchi, Olle Persson, Eric Archambault, and Ineke Malsch. 2004. Continuous
professional development in emerging technology sectors. In A SPRU Report for the Engineering
and Technology Board: SPRU—University of Sussex.
Molas-Gallart, Jordi, P. Tang, and S. Morrow. 2000. Assessing the non-academic impact of grant-funded
socio-economic research: Research from a pilot study. Research Evaluation 9(3): 171–182.
Nohria, Nitin. 1992. Is a network perspective a useful way of studying organizations? In Networks and
organizations, eds. Nitin Nohria, and Robert G. Eccles, 1–22. Boston: Harvard Business School
Press.
Reagans, Ray, and Ezra W. Zuckerman. 2001. Networks, diversity, and productivity: The social capital of
corporate R&D teams. Organization Science 12(4): 502–517.
Rigby, J., and J. Edler. 2005. Peering inside research networks: Some observations on the effect of the
intensity of collaboration on the variability of research quality. Research Policy 34(6): 784–794.
Rowley, Tim J., D. Behrens, and D. Krackhardt. 2000. Redundant governance structures: Analysis of
structural and relational embeddedness in the steel and semiconductor industries. Strategic
Management Journal 21(3): 369–386.
Ruef, Martin. 2002. Strong ties, weak ties and islands: Structural and cultural predictors of organizational
innovation. Industrial and Corporate Change 11(3): 427–449.
Shannon, Claude E., and Warren Weaver. 1959. The mathematical theory of communication [1949].
Urbana, IL: University of Illinois Press.
Uzzi, Brian. 1996. The sources and consequences of embeddedness for the economic performance of
organizations: The network effect. American Sociological Review 61(4): 674–698.
Uzzi, Brian. 1997. Social structure and competition in interfirm networks: The paradox of embeddedness.
Administrative Science Quarterly 42(1): 35–67.
Uzzi, Brian, and James J. Gillespie. 2002. Knowledge spillover in corporate financing networks:
Embeddedness and the firm’s debt performance. Strategic Management Journal 23(7): 595.
Villanueva-Felez, Africa. 2011. El acceso a recursos desde una perspectiva relacional: Un ana´lisis
contingente de las caracterı´sticas de los vı´nculos sociales. Dissertation, Universidad de Valencia,
Valencia.
Whittington, Kjersten Bunker, and Laurel Smith-Doerr. 2008. Women inventors in context: Disparities in
patenting across academia and industry. Gender Society 22(2): 194–218. doi:10.1177/
0891243207313928.
Yamagishi, Toshio, Mary R. Gillmore, and Karen S. Cook. 1988. Network connections and the
distribution of power in exchange networks. The American Journal of Sociology 93(4): 833–851.
Measuring Personal Networks 483
123