A Cautionary Note on Data Inputs and Visual Outputs in Social Network Analysis

Published date01 January 2014
DOIhttp://doi.org/10.1111/j.1467-8551.2012.00835.x
Date01 January 2014
AuthorSteve Conway
Methodology Corner
A Cautionary Note on Data Inputs and
Visual Outputs in Social Network Analysis
Steve Conway
University of Bath, School of Management, Bath BA2 7AY, UK
Email: s.h.conway@bath.ac.uk
Innovations in network visualization software over the last decade or so have been
important to the popularization of social network analysis (SNA) among academics,
consultants and managers. Indeed, there is a growing literature that seeks to demon-
strate how ‘invisible social networks’ might be revealed and leveraged for ‘visible results’
through management interventions. However, the seductive power of the network
graphic has distracted attention away from a variety of emerging and long recognized
concerns in SNA. For example, weaknesses exist in data collection techniques that often
rely on nominal boundary-setting and respondent recall. Non-response can also be highly
problematic. Increasingly, email data are being employed, yet this represents a poor
proxy for relationships and raises issues of privacy. In displaying relational data, visu-
alizations typically reify and ossify the network. Yet, individual perceptions of a network
can vary greatly from unified visualizations, and their structure is typically fleeting. The
aim of this paper is to draw together the diffuse literature concerning data input and
visual output issues in SNA, in order to raise awareness among management researchers
and practitioners. In doing so, the nature and impact of such weaknesses are discussed,
as are ways in which these might be resolved or mitigated.
Introduction
The network literature has grown exponentially in
recent years across a wide range of fields, including
business and management (Borgatti and Foster,
2003, p. 992). A key approach adopted in this
literature is that of social network analysis (SNA)
(e.g. Ahuja and Carley, 1999; Allen, James and
Gamlen, 2007; Cantner and Graf, 2006; Casper,
2007; Cattani and Ferriani, 2008; Cross, Borgatti
and Parker, 2002; Kijkuit and van den Ende,
2010). It is argued that the emergence over the last
10–15 years of powerful and freely available
network visualization tools (e.g. Krackplot,1
UCINET,2Payek,3Metasight4) has encouraged
the use of SNA techniques by management aca-
demics, and fuelled their popularization among
business consultants and managers.5Indeed, there
is a growing literature that seeks to demonstrate
how ‘invisible social networks’ might be revealed
Early drafts of this paper were presented at a departmen-
tal seminar at the University of Leicester School of Man-
agement and at the 28th SCOS conference in Lille,
France. I am very grateful for the constructive comments
and feedback from colleagues at both of these presenta-
tions as well as from two anonymous reviewers, which
have helped shape the ideas and focus of this paper.
1KrackPlot: http://www.andrew.cmu.edu/user/krack/
krackplot.shtml – well-established SNA software.
2UCINET: http://www.analytictech.com/ucinet/ – well-
established SNA software.
3Pajek: http://pajek.imfm.si/doku.php – specialized soft-
ware for dealing with large networks.
4Metasight: http://www.morphix.com/Pages/MetaSight/
MetaSight.html – uses email data as input.
5See Freeman (2000) for an overview of the history and
diversity of social network visualization tools, and see
wikipedia.org/wiki/Social_network_analysis_software
(accessed 22 November 2011) for a good overview of a
large range of software applications for the visualization
of social network data and links to websites for indi-
vidual applications.
bs_bs_banner
British Journal of Management, Vol. 25, 102–117 (2014)
DOI: 10.1111/j.1467-8551.2012.00835.x
© 2012 The Author(s)
British Journal of Management © 2012 British Academy of Management. Published by John Wiley & Sons Ltd,
9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA, 02148, USA.
and leveraged for ‘visible results’ within organiza-
tions (Cross and Parker, 2004; Cross and Thomas,
2009; Cross, Borgatti and Parker, 2002; Krack-
hardt and Hanson, 1993). Whilst it is recognized
that not all network research employs visualiza-
tion tools to depict the social structure under inves-
tigation, there are nevertheless a rapidly growing
number of examples that can be found within the
academic literature, including the majority of the
studies referenced in this paper, as well as in prac-
titioner texts and on consultancy websites.
However, despite the growing use of SNA by
business and management academics and practi-
tioners, it is contended that too little attention in
the literature has been focused on the nature of
the data being collected, the manner in which it is
being displayed, or the associated ethical issues
in such studies. For example, it is common for
SNA studies in the management field to be silent
or underplay important issues relating to
boundary-setting, informant response rates and
decisions concerning network visualization (e.g.
Allen, James and Gamlen, 2007; Chiffoleau, 2005;
Stephenson and Lewin, 1996). Ethical issues in
relation to SNA research are raised rarely. The
objective of this paper then is to heighten aware-
ness of these concerns within the business and
management community. Issues concerning indi-
vidual techniques for processing and analysing
social network data tend to be highly technical
and, as such, are considered better dealt with in
the specialist social network literature.6
In this paper we start by providing an overview
of the scope of SNA usage across the field of
business and management. We then turn to an
evaluation of the accuracy and completeness of
the data in such network studies, and highlight
possible ways in which weaknesses apparent in
survey methods, for example, might be mitigated.
We consider the nature of the network visualiza-
tion itself, reflecting on the multiple ways in which
a network may be viewed and depicted and how
such depictions may be interpreted. Finally, we
surface the ethical and privacy issues associated
with network research. These are increasingly per-
tinent because of the rise in use of SNA by con-
sultants and managers in relation to decision-
making within organizations (Cross et al., 2001;
Parker, Cross and Walsh, 2001). Indeed, Borgatti
and Molina (2003, p. 338) rightly warn us that
‘consideration of ethical issues [is] increasingly
critical as organizations start basing person-
nel and reorganization decisions on network
analyses’.
The breadth of SNA usage in business
and management
Over the last couple of decades there has been a
rapid growth in the use of SNA techniques to
research a wide range of business and manage-
ment issues and contexts. More recently, such
techniques have been applied to the study of spe-
cialist academic communities within business and
management itself. However, perhaps most inter-
esting is its diffusion into business consultancy
and business practice.
One of the earliest examples of the analysis of a
social network is associated with the classic Haw-
thorne studies of the 1930s, where hand-drawn
‘sociograms’ were produced to map interactions
related to friendship, antagonisms, controversies
and the helping of colleagues (Roethlisberger and
Dickson, 1939, pp. 502–507). Since then, others
have mapped, for example, the informal commu-
nication networks between engineers within the
R&D function of an organization (Allen, 1977, p.
208; Allen, James and Gamlen, 2007, p. 186),
the inter-organizational cooperation networks
between scientists and innovators (Cantner and
Graf, 2006, p. 471; Chiffoleau, 2005, pp. 1200–
1202; Fleming, King and Juda, 2007, pp. 940–941),
cluster formation in biotechnology (Casper, 2007,
pp. 450–452), social networks and knowledge
management in supply chains (Capó-Vicedo,
Mula and Capó, 2011; Kim et al., 2011) and the
connections between the founders of the semicon-
ductor sector (Castilla et al., 2000, p. 228). Studies
have also mapped workplace friendship networks
(Kilduff and Krackhardt, 1994, p. 94), gender and
racial diversity in workplace support and informa-
tion networks (Stephenson and Krebs, 1993, pp.
70–71; Stephenson and Lewin, 1996, pp. 179–180)
and friendship among the French financial elite
(Kadushin, 1995, p. 211).
There are also a growing number of fascinating
SNA studies that have turned the gaze inward,
onto the academic communities within business
and management, such as those mapping the
6Such as Social Networks,Sociometry,Connections, the
Journal of Social Structure and the Journal of Quantita-
tive Anthropology.
Data Inputs and Visual Outputs in Social Network Analysis 103
© 2012 The Author(s)
British Journal of Management © 2012 British Academy of Management.

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