The effects of Facebook use on civic participation attitudes and behaviour: A social network study (DPhil research proposal)

The effects of Facebook use on civic participation attitudes
and behaviour: A social network study
(DPhil research proposal)
Dissertation submitted to University of Sussex in partial fulfilment for the award of
Master of Science in
Cross Cultural Research Methods
BY
Candidate number: 70642
Under the Guidance of
Prof. Gerard Delanty
September 2011

The effects of Facebook use on civic participation attitudes and behaviour: A social network study (DPhil research proposal)
1
Summary
The following proposal suggests a network analysis approach to study the effects of web
communication on civic participation. A three-phase mixed methods research design is proposed to
examine firstly, the effect of supplementary communication via the social networking site Facebook,
on the structure (quantity) and content (quality) of social ties within a network of citizens engaged in
health and social care policymaking. It is proposed that the network variables of tie structure and
content are then tested in an affective capacity against the participatory attitudes and behaviour of
networked individuals. By reframing the study of web use and civic participation under a network
theoretical framework, the proposed study will add to the existing literature in the field through
recognition of the mediative capacity of relational ties in the formation of participatory capital. It is
suggested that it is through their effect on relational tie structure and content within citizen
participation networks, that social networking sites such as Facebook affect participatory attitudes
and behaviour. To set a critical context for the proposed study, a final qualitative phase of research
is suggested to examine the professional power structures impacting upon participant expressions of
agency.

2
CONTENTS
1 PREFACE..........................................................................................................................................3
1.1 Context and rationale .............................................................................................................3
1.2 Research Questions ................................................................................................................5
2 LITERATURE & THEORETICAL OVERVIEW .......................................................................................6
2.1 Social networking sites (SNS) and social relations..................................................................6
2.2 Networked social relations and civic participation...............................................................10
2.3 Critical context: Negotiations of power in civic participation...............................................12
2.4 Bringing it all together: Social relations, civic participation and the web ............................13
3 THEORETICAL FRAMEWORK .........................................................................................................14
3.1 Framework rationale and definition.....................................................................................14
3.2 Network theory and Actor-Network-Theory (ANT)..............................................................15
4 METHODLOGY, RESEARCH DESIGN, AND RESEARCH METHODS..................................................18
4.1 Research context...................................................................................................................18
4.2 A mixed methods approach to network analysis..................................................................19
4.3 Quantitative network analysis ..............................................................................................20
4.3.1 A sociometric approach to data collection ...................................................................20
4.3.2 Quantitative variable definition....................................................................................21
4.3.3 Preliminary hypotheses and regression model responses ...........................................28
4.4 ANT: Narrative theory approach...........................................................................................30
4.5 Data collection, sampling, and final points...........................................................................32
4.5.1 Quantitative data collection and sampling...................................................................32
4.5.2 Qualitative data collection and sampling .....................................................................33
5 BIBLIOGRAPHY ..............................................................................................................................35

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1 PREFACE
1.1 Context and rationale
“Web 1.0 was predominantly a system of cognition. Since the millennium, the character of the
web has been successively changing. With the rise of heavily frequented platforms such as
MySpace, YouTube, Facebook, Wikipedia, Friendster, etc., communication and cooperation have
become more important features of the web”
(Fuchs, 2008: p125)
Web 2.0 represents an evolution of web 1.0, where content was pre-produced, published, and
delivered from one-to-many passive users whose role was one of cognitive interpretation, to an
open-platform environment where many users participate in the production and consumption of
diverse media content (Musser and O’Reilly, 2006). Prolific Internet penetration and evolution of the
web itself have catalysed a change in social relations where “[c]ommunication is the medium in
which belonging is today being expressed in its most important ways” (Delanty, 2010[2003]: p135)
In addition to widespread private and corporate use, as “[d]emocratic governments [come] under
pressure to adopt a new approach to policy-making – one which places greater emphasis on citizen
involvement both upstream and downstream to decision-making” (OECD, 2001: p71), British local
authorities are also beginning to employ web 2.0 social networking sites (SNS) such as Facebook as a
stimulus for citizen participation in the ‘co-planning’ and ‘co-delivery’ (Bovaird, 2007) of public
services: “…social media enables publics to create conversation…and [local authorities to] benefit
from increased participation” (Wakeman, 2008: p26). The (either explicit or implicit) logic behind the
application of SNS communication as a catalyst for civic participation is that Delanty’s (ibid)
‘belonging’ or more specifically in a participatory context, Putnam’s (1995, 1996, 2000) sentiments
of relational ‘trust’ and ‘reciprocity’, which are widely acknowledged as antecedents to collective
civic participation, can be ‘activated' "...through some form of system meta-intervention [i.e.
improved communication between citizens]" (Bovaird et al.; in Gotze, Pederson et al., 2010: p267).
Whilst a limited number of academic studies have established positive causality between additive
web use and participatory attitudes and behaviour, there is endemic failure to treat empirically,
Putnam’s (1995, 1996, 2000) third antecedent of participation: ‘networks’ (see in particular
Blanchard and Horan, 2000; Calhoun, 1998; Kavanaugh et al., 2003; Kotus and Hławka, 2010; Stern

4
and Dillman, 2006; Wellman et al., 2001). Empirical neglect of this antecedent, which mediates input
(SNS use) and output (participation), means that data do not allow for "...strong inferences about
how Internet activity influences…participation" (Wellman et al., ibid: p450; emphasis added).
To refocus the study of web use and civic participation under a network theoretical lens, in the
absence of a comprehensive body of work that has empirically acknowledged the mediative capacity
of measurable network characteristics such as relational connectivity and quality specifically in the
production of participatory capital, it is necessary to merge two bodies of literature that deal with
the endogenous production and exogenous effect of those characteristics in networks more
generally (i.e. outside of a civic participation context). The first body of literature concerns the effect
of web use on the network characteristics of relational connectivity and quality, whilst the second
reviews the idea that such network characteristics have an affective relationship with the attitudes
and behaviour of actors toward network function. In the wider network literature, both quantity and
quality of network relations are found to be positively affected by SNS use and positively affective of
network attitudes and behaviour.
Contextualised within an as yet undecided Local Involvement Network (LINk; see section 4.1 for
definition), the three-phase mixed methods research proposal suggests a quantitative variable
construct based on two sequentially linked stages of empirical research, the first testing for an
affective link between frequency and type of SNS use, and the quantity and quality of relations
within the LINk, and the second seeking to establish a causal relationship between the state of those
relations and participatory attitudes and behaviour. The third, qualitative dimension of the proposal
comes in response to a point made by Jones and Norton (2010: p445) that “[l]ocal people are only
able to be part of local decision making if…the council, and councillors in particular, is willing to
respond positively to the views of the citizen and indeed to change and develop policy accordingly.”
Thus, to ensure a critical account of the extent to which SNS use fosters networked relations
conducive to participatory attitudes and behaviour, the proposal suggests a qualitative
deconstruction of the power relations acting upon those expressions of participatory agency.

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1.2 Research Questions
The following meta-question establishes the direction of the entire study:
To what extent does SNS communication foster networked relations conducive to civic participatory
attitudes and behaviour?
This question is then broken into three related component parts:
1. To what extent does SNS use affect the quantity and quality of social relations within civic
participation networks?
2. To what extent do relational quantity and quality affect the participatory attitudes and
behaviour of individuals within civic participation networks?
3. What is the process of negotiation that occurs between participants and professionals in the
implementation of participant-led public service policy and development ideas?
a. To what extent are participant ideas transformed from their original state before being
applied to public service policy and development?
These questions are further deconstructed in the research hypotheses developed in section 4.3.3.

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2 LITERATURE & THEORETICAL OVERVIEW
2.1 Social networking sites (SNS) and social relations
Zhao (2006) found “…social use of the Internet [rather than ‘antisocial’ independent browsing] is
positively related to interpersonal connectivity”. Zhao finds web chat users to have a significantly
greater number of social ties than non-web users1
, and heavy or frequent web chat users to ‘keep in
touch’ with a significantly greater number of people2
than light web chat users3
. In other words,
those users who make more frequent use of social media have a greater number of social ties than
those who are less frequent users (see also Ellison et al., 2007; Hampton and Wellman, 2003;
Haythornthwaite, 2005).
By contrast, Zhao’s research finds social media to be ineffective4
in maintaining relationships that
are durable across both the online and offline dimensions. This finding may well be attributable to
Zhao’s (ibid) definition of social media, which is limited exclusively to “…chat-rooms, news-groups,
listservs, and bulletin boards” (p849), and excludes SNS such as Facebook, which constitute “… a
newer form of virtual socialising in which connections are initially made offline and then migrated
online, where they can be maintained” (Ellison et al., 2006: p27). In-terms of its social function
Facebook is probably closer, in Zhao’s (ibid) terms at least, to emailing than social media as like
Facebook the former is “…nested within offline social networks” whilst the latter “…largely involves
contact with strangers” (p859; see also; Ellison et al., 2006). Boyd & Ellison (2007) posit that closed
or exclusive SNS such as Facebook are effective in developing social relations that span both the
online and offline dimensions due to their personal contact and community page privacy controls.
This is supported by Dwyer et al. (2007: p10), who found that “…Facebook members were more
trusting of the site and its members [than MySpace members]”5
and more willing to disclose
information” because “…Facebook members use the site to manage relationships initiated offline”
(p8; see also Lampe et al., 2006).
1
17.82 ties for non-users and 27.91 ties for chat users (𝐹(3,894) = 5.268, 𝛼 < .001); where social ties are
defined as ‘friends or relatives contacted at least once a year’.
2
𝛽 = 13.35, 𝑠𝑒 = 5.06, 𝛼 < .01; defined as ‘those who used many-to-many online communications
programs” for more than three hours per week’.
3
𝛽 = 11.91, 𝑠𝑒 = 3.95, 𝛼 < .01.
4
A further conclusion drawn by Zhao (ibid) is that online social media users maintain fewer offline face-to-face
contacts than do email users: 𝐹(2,167) = 3.221, 𝛼 < .05.
5
Measured along two combined seven-point sematic scales: 𝐹𝑎𝑐𝑒𝑏𝑜𝑜𝑘 𝑀 = 8.8382, 𝑀𝑦𝑆𝑝𝑎𝑐𝑒 𝑀 = 7.6875
𝐹 = 4,511, 𝛼 < .05 .

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Thus, whilst the empirical literature finds SNS users may be better connected overall, it also either
implicitly or explicitly differentiates between strong and weaker social relations, the former tending
to be initiated offline (i.e. face-to-face) and the latter initiated online. This distinction is captured in
Wellman et al.’s (ibid; see also Hampton and Wellman, ibid) ‘utopian – dystopian’ debate. On the
basis that social relations require only frequency of communication to emerge and develop, utopians
argue that the web’s capacity to increase relational communication frequency by overcoming spatial
and temporal restrictions (Baym, 1997; Sproull and Kiesler, 1991; Wellman et al., ibid) renders it a
catalyst for an improved era of social relations. By contrast, dystopians argue against the capacity of
web communication to transmit the verbal and nonverbal cues required for the building of
“…complex friendships, [and provision of] intangible resources such as emotional support” (Wellman
ibid: p439).
The utopian interpretation of the web’s influence on social relations draws on Walther’s (1995)
Social Information Processing Theory (SIP), which posits that “[o]ver an extended period, the issue is
not the amount of social information that can be conveyed online; rather; it’s the rate at which that
information mounts up” (Griffin, 2009: p143). In other words an absence of verbal and nonverbal
cues can be compensated for either by an extended period of communication or an increased
frequency of message sending. By contrast, the dystopian perspective draws on Daft et al.’s (1987)
Media Richness Theory (MTR), which suggests that as media can be classified according to its
capacity to transmit verbal and nonverbal cues - its ‘richness’, the web will always be inferior in its
capacity to convey the ‘depth or closeness’ (Marsden and Campbell, 1984) required for the
development of strong social relations relative to face-to-face communication , which conveys an
optimum number and range of cues (see Daft et al., ibid; Fish et al., 1993; Kiesler and Sproull, 1992;
Rice, 1987; Trevino et al., 1990; University of Twente, 2010).
A third argument, from which the present study takes its lead, is that modern day SNS are no
different from any other channel of communication and used in a similar way to earlier
communication technologies “…to keep in touch with old friends and to maintain or intensify
relationships characterised by some form of offline connection” (Ellison et al., 2007: p1162; see also
Flanagan and Metzger, 2001; Koku et al., 2001). As well as supporting the basic idea that SNS use
increases social connectivity in-terms of number of relations (SIP), the centrist argument combines
both utopian SIP and dystopian MRT propositions to suggest that newer SNS such as Facebook also
strengthen existing strong ties, which are necessarily initiated offline (MRT), through increased
frequency of communication (SIP). Farrow and Yuan (2011) find support for this centrist argument

8
empirically within an alumni network, where relations have been initiated offline but are maintained
via Facebook. Facebook group membership is found to be a statistically significant positive predictor
of frequency of communication6
, the latter is then found to be a statistically significant positive
predictor of ‘emotional closeness’ or quality of social relations7
.
Adding a further dimension to the centrist position are a small number of studies that have
combined SIP’s argument for the importance of frequency of interaction with MRT’s idea of cue
transfer, to establish a causal relationship between frequency of richer (versus leaner) media use in
relational communication and the strength of social ties. Thus, an IBM workplace study conducted in
2007 found that when colleagues or business partners chose to communicate more frequently using
richer media - face to face or phone – “…they were perceived as more competent and more
enjoyable, as well as the other person being more aware of their knowledge and skills…[whilst]
[t]here were lower perceptions of all these characteristics when people used email, conference calls
or instant messaging” (Ehrilich and Carboni, 2007: p19-20; see also Cummings et al., 2001)8
.
What Ehrilich and Carboni’s (ibid) study seems to hint at is an affective link between frequency of
richer (versus leaner) media use and the strength of social ties. In the present context, which is
concerned with the influence of the SNS Facebook on social relations, we can refer to Cormode and
Krishnamurthy’s (2008: p18) classification of Facebook applications by type of communications
activity9
:
6
𝛽𝑆𝑁𝑆 𝑀𝐸𝑀𝐵𝐸𝑅𝑆𝐻𝐼𝑃 = .06, 𝑠𝑒 = .02, 𝑡 = 2.71, 𝛼 < .05 i.e. for every one extra alumni group joined on Facebook,
frequency of alumnus communication increases by 0.6 where frequency is measured on a 7-point semantic
scale where 1=never and 7=almost every day.
7
𝛽𝐹𝑅𝐸𝑄𝑈𝐸𝑁𝐶𝑌 𝑂𝐹 𝐶𝑂𝑀𝑀𝑈𝑁𝐼𝐶𝐴𝑇𝐼𝑂𝑁 = .75, 𝑠𝑒 = .02, 𝑡 = 30.95, 𝛼 < .05 i.e. for every one point increase on the 7-
point semantic measure of frequency, emotional closeness increases by 0.75 where emotional closeness is
measured using a composite of 7-point semantic scales (see 2005 PCUAD Alumni Attitude Study [Performance
Enhancement Group, Ltd., 2005]).
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Ehrilich and Carboni (2007) find a significant difference (but not a causal relationship; 𝐹(4,685) = 2.41, 𝛼 <
.05) between weak and strong ties and the preference of each group to communicate more frequently using
face-to-face or via the telephone (𝐹𝑡𝐹 𝑀𝐷 = 1.33, 𝑇𝐸𝐿 𝑀𝐷 = .99). Strong ties were also likely to use email to
communicate less frequently than weak ties (𝐸𝑀𝐴𝐼𝐿 𝑀𝐷 = −.68).
9
Underpinning this functionality is the AJAX (Asynchronous JavaScript and XML) programming technique,
which employs Javascript and XML (EXtensible Markup Language) code alongside the HTML (Hyper Text
Markup Language) that underpinned web 1.0. AJAX harnesses the dynamic functionality of Javascript to allow
for interactive web pages. “Unlike classic web pages, which must load in their entirety if content changes, AJAX
allows web pages to be updated asynchronously by exchanging small amounts of data with the server behind
the scenes” (Google, 2011a). AJAX programming thus allows for the creation of user-led content pages within a
static HTML/CSS (Cascading Style Sheet) template. So, for example, whilst Facebook’s platform is based on a
pre-defined HTML/CSS template, AJAX code allows users to add content to pages to render Facebook what is
known as a ‘mashup website’, “…a website that combines content data from more than one source to create
a new user experience” (Google, 2011b).

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1. Clicks and connections: Simple activities which only require a single click to complete, such as
rating a movie or voting in poll.
2. Comments: Adding a short response, comment or tag to existing content, such as a news story,
blog entry, photo etc.
3. Text communication: Sending a message to another user or group, either via an email-like
system or via instant messaging. These are typically short, a sentence or two per
communication.
4. Content creation: Uploading or entering some entirely new content, such as a webcam movie,
digital photo, or blog posting.
Cormode and Krishnamurthy’s classification can be conceptually linked to Daft et al.’s earlier
proposition that media may be ordered along a continuum in-terms of the complexity of the verbal
and nonverbal cues that it is able to transmit. Simply put, we might say that a Facebook user who
communicates only via clicks or short comments has a lesser capacity to transmit the cues required
for strong social ties than a user who communicates using long-text or video content. By implication
then, after controlling for use/non-use and frequency of use of the Facebook platform itself, we
might assume that more frequent users of richer Facebook applications would have stronger social
ties than users of leaner applications. This assertion is too simplistic however, as it ignores the
fundamental distinction between older and newer SNS, which is the functionality of the latter to
accommodate either purposeful maintenance of pre-existing strong ties or the building of more
general connectivity vis-à-vis, it is not always the intention of Facebook users to strengthen their
social ties, but also to increase their general connectivity or number of non-intimate links to increase
their capacity to transmit and receive information (Granovetter, 1973).
Thus, depending on intent, the applications comprising any one of Cormode and Krishnamurthy’s
categories can be employed by Facebook users either to strengthen intimate social relations or to
increase overall connectivity, for the former through transmission and reception of media via the
Platform’s one-to-one messaging functionality, and for the latter via its many-to-many10
communication channels such as profile and group page ‘walls’. Whilst Comm’s (2010: p3) one
dimensional assertion that “[s]omeone who uses social media successfully…creates conversations…
[, which in turn] create networks” may be too simplistic, it does provide support for the argument
that a further subdivision of the question - ‘do more frequent users of richer Facebook applications
have stronger social ties than users of leaner applications?’ – is required. In short, where the
10
See Smith and Taylor, 2002: p78-80

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intention of a user is to strengthen their stronger ties initiated offline, it may be most pertinent to
ask the question, ‘do users of richer Facebook applications have stronger social ties than users of
leaner applications?’ However, where the intention is not strength but connectivity, irrespective of
tie strength, an entirely different question may need to be asked, ‘do users of richer Facebook
applications have more social ties than users of leaner applications?’
2.2 Networked social relations and civic participation
Wellman et al. (ibid) propose the idea of civic participation - “…involvement in political and voluntary
organisations” (p437; see also Bovaird, ibid: p848), which for Putnam (1995, 1996, 2000) is a
‘tangible expression’ of social capital. Definitions of ‘citizen participation’ tend to vary in the degree
of liberal individual versus consensual behaviour, beneficial for society in the development and
delivery of formal institutions and services (see Isin and Turner, 2002: p18). Citizen participation is
discussed by Putnam and in the present context from a participatory republican perspective (Isin and
Turner, ibid), the premise that participation is expressed and constituted through the engagement of
citizens with formal institutions created by the state and civil society.
Bovaird (ibid) hones the idea of participation to suggest a theory of ‘coproduction’, which he defines
as “…the provision [and development] of services through…relationships between and amongst
professionalised service providers and service users or other members of the community” (p847;
emphasis added). Putnam (1995) also stresses the importance of social relations for catalysing
participatory actions and behaviour, and posits three relationary characteristics - “…networks,
norms, and trust – that enable participants to act together more effectively” (p664-665). Putnam’s
‘norms’, which are extended to read “norms of reciprocity” by Woolcock (1998: p153), and ‘trust’,
are seen as essential characteristics of networks that are successful in the fostering of participation
in civic institutions. These two elements can be easily aligned to Granovetter’s (ibid) concept of
social tie strength, which is determined by a “…combination of…the emotional intensity, the
intimacy, and the reciprocal services which characterise a tie” (p1361).
One obvious line to take is that individuals within a participation oriented network, who
demonstrate greater trust and “…belief that pro-social attitudes and behaviour will be reciprocated”
(Blanchard and Horan, ibid: p6) by others in that network, would also demonstrate more positive
participatory attitudes and behaviour towards the functional activity of that network. This approach
has been empirically tested by, amongst others Farrow and Yuan (ibid), who found that emotional

11
closeness amongst member of an alumni network led to a feeling of emotional closeness towards
the university institution, which in-turn led to an improvement in participatory attitudes and
behaviour within the alumni network11
. This finding is supported in the literature both empirically
and theoretically by a number of studies detailing the positive effect of community or group trust
(see La Porta et al., 199712
; Uslaner and Brown, 200513
; Coleman, 1988; Edmonson, 2003) and
reciprocity (see Lubell and Scholz, 2001; Sugden, 1984; ) on collective civic participation and
contribution.
An extension to the idea of strong ties14
as a catalyst for positive participatory attitudes and
behaviour is Granovetter’s (ibid: p1376) idea that greater connectivity within a network, irrespective
of tie strength, may encourage positive attitudes and behaviour towards network function. From this
perspective, ties may be rendered useful not by “…individual efficiency but by numbers” (Friedkin,
1982: p273). Rationale for this finding comes from the broader organisational literature (see Lee and
Kim, 201115
), which finds central or well-connected actors better able to receive information, and
communicate and organise their own ideas with a greater number of proximate actors. Improved
capacity for communication stimulates a recursive cycle of deliberation and internalisation of
positive, productive attitudes and behaviours (Ibarra and Andrews, 1993; Freeman, 1979).
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𝛽𝐸𝑀𝑂𝑇𝐼𝑂𝑁𝐴𝐿 𝐶𝐿𝑂𝑆𝐸𝑁𝐸𝑆𝑆 𝑇𝑂 𝐴𝐿𝑈𝑀𝑁𝐼 = .69, 𝑠𝑒 = .02 𝛼 < .05 i.e. where feelings of emotional closeness to alumni
increase by one, where emotional closeness to alumni is measured using 10 seven-point semantic scales
adapted from the Sense of Community Index 2 (see Chavis, et al., 2008; Obst and White, 2004), emotional
closeness to the institution increased by .69, where emotional closeness to the institution is measured using
five seven-point semantic scales (see Chavis, et al., ibid). 𝛽𝐸𝑀𝑂𝑇𝐼𝑂𝑁𝐴𝐿 𝐶𝐿𝑂𝑆𝐸𝑁𝐸𝑆𝑆 𝑇𝑂 𝐼𝑁𝑆𝑇𝐼𝑇𝑈𝑇𝐼𝑂𝑁 = .56, 𝑠𝑒 =
.03 𝛼 < .05 i.e. where feeling of emotional closeness to the institution increased by one, attitude towards
volunteerism increased by .56, where attitude towards volunteerism is measured using 4 seven-point semantic
scales adapted from the 2005 PCUAD Alumni Attitude Study (Performance Enhancement Group, Ltd., ibid).
𝛽𝐴𝑇𝑇𝐼𝑇𝑈𝐷𝐸 𝑇𝑂 𝑉𝑂𝐿𝑈𝑁𝑇𝐸𝐸𝑅𝐼𝑆𝑀 = .70, 𝑠𝑒 = .03 𝛼 < .05 i.e. where attitudes towards volunteerism improved by
one, actual voluntary participation increased by .70, where volunteer behaviour was assessed by eight seven-
point semantic scales adapted from the 1990 American Citizen Participation Survey (Verba, Schlozman, Brady,
& Nie, 1990; in Farrow and Yuan, ibid).
12
𝛽𝑇𝑅𝑈𝑆𝑇 𝐼𝑁 𝑃𝐸𝑂𝑃𝐿𝐸 = .1224, 𝑠𝑒 = .0329 𝛼 < .1 i.e. across a 40-country comparative study, for every one per
cent increase in respondents who responded positively to the question ‘would you say that most people can
be trusted?’, civic participation increased by .1244 per cent (percentage of civic activities in which an average
individual participates – drawn from list of civic activities that can be found on La Porta et al., ibid: p314).
13
“[A]ggregate trust is [found to be] the strongest predictor of the share of people in a state who give their
time in volunteering” (see Uslaner and Brown, ibid: p885-886).
14
See Krackhardt, 1992 and Henning and Lieberg, 1996 for further discussion.
15
In an organisational context, Lee and Kim (2011) find actor centrality (connectedness or number of ties) to
be a significant positive predictor of organisational commitment:𝛽 = 0.168, 𝑆𝐸 = 0.058, 𝛼 < .01: i.e. as
centrality increases by 0.01 or 1%, where centrality is measured on a normalised centrality index where 0 = no
direct ties and 1 = direct ties to all network actors, organisational commitment increases by 0.168 units, where
organisational commitment is measured on a five-point Likert scale.

12
For the proposed study two key points can be drawn from the empirical and theoretical literature
pertaining to social relations and civic participation. It is probable firstly, that participatory attitudes
and behaviour are improved by the presence of strong, trusting and reciprocal social relations within
participation networks, and secondly that ‘connectivity’ more generally will foster positive attitudes
and behaviour through a greater capacity to transmit and access relevant information. An implicit
characteristic of these two points is that the participatory attitudes and behaviour of individuals are
influenced, at least partially, by the attitudes or behaviours of actors to which s/he is connected.
2.3 Critical context: Negotiations of power in civic participation
The political context, in which the civic participation that is the focus of the proposed research takes
place, is the on-going public and patient involvement (PPI) in local government and services
policymaking initiative. PPI aims to move policy and decision-making away from the Keynesian
bounded rationality or satisficing model of policy development (Simon, 1957; in Davies et al., 2000),
towards a pluralist or incremental approach, which seeks input from stakeholders involved in and
affected by the policy-making process (Lindblom, 1959; in Davies, 2003).
As neither individual participants nor networked organisations involved in PPI typically command
statutory power, there is a basic decision-making hierarchy to be negotiated by any participant who
proposes a policy idea or initiative. In the case of the proposed study, which suggests a LINk as a
suitable object of study (see below), from inception, a policy idea would need to pass to one or
several elected LINk members, who sit alongside non-executive elected councillors and authority
area health and social care service managers (for example Primary Care Trust representatives) on an
Overview and Scrutiny Committee (OSC; see Department of Health, 2006a, 2006b, 2009). It is the
role of the OSC to review and present the executive council with policy input.
In a study of user participation in the policymaking of two London based mental healthcare trusts,
Rutter et al. (2003) found “[t]rust managers to frequently disparage the views and concerns
of…active, committed users as ‘unrepresentative’” (p1982). Morevover, Rutter et al. (ibid) found
that “…the balance of power remains firmly with provider trusts”, with bureaucratic systems of
participation merely serving to entrench the systems of administration and power that deliver
unsatisfactory services to users and “…poor returns and personal costs in time and effort” (p1982).
These findings are supported by Conklin et al (2004: p26), who suggest that “…public
involvement…will almost inevitably involve trade-offs [between]…what is feasible and what is

13
‘ideal’”. This trade-off, it seems, is one of ‘public representativeness’ and ‘representation’ (Conklin et
al, ibid), with reconciliation sought between the input of individuals at the local level and the
interests of professional policymakers and service managers (see also Bauld et al., 2005; Crawford et
al., 2003; Tait and Lester, 2005; Campbell, 2001; Summers, 2003).
“[I]f public involvement is to be successful, it will require…policy-makers’ genuine willingness to yield
power to the public to ensure the public’s genuine engagement in the health policy process”
(Conklin et al., ibid: px). From this perspective, the idealistic conception of civic participation is
pitched against the reality of bureaucratic and hierarchical public decision-making processes. In light
of this, Bovaird (ibid) suggests a need to “…reconceptualise service provision [and therefore citizen
participation in service provision] as a process of social construction in which actors necessarily
negotiate rules, norms, and institutional frameworks rather than taking the rules of the game as
given” (p858). Social or participatory capital, manifest as public service oriented ideas or action, does
not therefore exist inside a power vacuum, but is subject to a formalised process of negotiation as
initial ideas are transformed or translated from their original form.
2.4 Bringing it all together: Social relations, civic participation and the
web
To recap, the empirical (and theoretical) literature seems to suggest that the frequency with which
an individual uses an SNS such as Facebook, may positively affect that individual’s overall number of
social ties (Zhao, ibid). Moreover, within bi-dimensional social networks (i.e. networks spanning both
the offline and online dimensions), as strong ties tend to be initiated offline (Ellison et al., ibid;
Flanagan and Metzger, ibid; Koku et al., ibid), whilst “…the number of strong ties that a person may
maintain [will] not be significantly increased by online networking technology (Boyd, 2004; in Gross
and Acquisti, 2005: p4), the frequency with which SNS are used in the maintenance of strong ties
initiated offline, will increase the strength of those existing strong ties. We might also extend these
assertions to say that where ties are strong, frequent use of richer Facebook media in the
maintenance of those ties positively predicts tie strength, and moreover that the frequency with
which an individual uses richer Facebook media more generally, is likely to positively predict the
overall connectivity of that individual.
A small body of work has, superficially, tried to establish a causal link between web use and the civic
involvement of individuals (see particularly Welman et al., ibid). The key criticism that can be

14
delivered against this work is a failure to treat empirically, the network component of Putnam’s
(1995, 1996, 2000) theoretically acknowledged antecedents of participation – ‘networks, norms, and
trust’. Empirical neglect of the networked social context of many forms of civic participation, which
mediates input (web use) and output (participation), means that data do not allow for "...strong
inferences about how Internet activity influences…participation" (Wellman et al., ibid: p450;
emphasis added). Responding to this critical point, the second divergent body of literature reviewed
here, suggests that an affective relationship may exist between the characteristics of social relations
(𝑋) within civic participation organisations or networks, particularly relational tie quality and
quantity, and participatory attitudes and behaviour (𝑌; see particularly Putnam, 1995, 1996, 2000;
Farrow and Yuan, ibid; Lee and Kim, ibid).
The following research proposal builds specifically on this methodological critique, reframing the
study of SNS usage and civic involvement within a network theoretical framework.
3 THEORETICAL FRAMEWORK
3.1 Framework rationale and definition
The proposed research draws primarily on network theory. Network theory is chosen here as the
central theory due to the implicit mediative role of networks in the process of building participatory
capital.
Several theories are positioned as inputs and outputs to and from the central network framework.
As is illustrated by Fig 1, the capacity of networked individuals to transmit and process information is
partially determined by use of different types of communication media. Two theories, SIP and MRT
(see previously) are conceived theoretically as antecedent to network theory and practically thus,
media usage is considered to be a determinant of relational tie character and network connectivity.
Theories of social and participatory capital are treated here as subsequent to or rather consequence
of, network theory. In practical terms the inference is that participatory attitudes and behaviour are
at least partially determined by relational tie content and network connectivity. Relational tie
content and network connectivity are linked as determinants of participatory capital via network

15
effects theory, which operationalises exogenously the influence of proximate network actors on
participatory attitudes and behaviour.
Fig 1: Theoretical Framework
The final theory to be employed by the proposed study is the constructivist Actor-Network-Theory
(ANT; not shown in Fig 1), which provides a critical theoretical and methodological framework for
deconstructing the power relations that either restrict or foster civic participatory attitudes and
behaviour.
For economic reasons, the following discussion covers only the central theory – network theory
(including ANT). As such, what follows should be considered alongside the discussions of SIP, MRT,
and social/participatory capital theory, which were offered in the literature and theoretical
overview.
3.2 Network theory and Actor-Network-Theory (ANT)
Rogers and Kincaid (1981: p82) define a social network as “…interconnected individuals who are
linked by patterned communication flows." From this perspective, the social is treated as comprised
of actors connected via communicative associations (Monge and Contractor, 2003). These
communication oriented definitions of social networks are derived from the Castellian (2000a) meta-
theory of the ‘information society’, which emphasises the dominance of information transference
and access in the organisation of modern society:
“Networks constitute the new social morphology of our societies…while the networking
form of social organisation has existed in other times, the new information technology
paradigm provides the material basis for its pervasive expansion throughout the entire social
structure”
Social Information
Processing Theory
Media Richness
Theory
Network Theory Social Capital
Theory
Network Effects Theory

16
(Castells, 2000a: p500).
One way to conceive social networks is therefore in-terms of patterns and types of information
flows. However, this definition may be too narrow. Haythornthwaite (1996) views social networks
more broadly, as “exchanges of resources among actors” (p323; emphasis added). This definition
implies a focus on one or more types of resource, which could be information but, as
Haythornthwaite suggests, could be alternatively be any tangible (e.g. capital or goods) or intangible
(e.g. information, sentiment, authority/power) entity. Following Castells (2000a) and Delanty (ibid)
the proposed research operationalises the idea of networked relations as sentiment expressed via
transitive patterns and flows of communication. Thus, whilst relational ties are conceived as
sentiment-based, the transference of sentiment and building of relations is affected by the available
means of communication (here either face-to-face or Facebook communication).
One implicit criticism that can be levelled against the idea of society as comprised of resource
exchanges, is that in network conceptualisation it is necessary to essentialise both the types of
resources being transferred (where patterns of exchange comprise network structure), and the
boundaries of the network itself (Haythornthwaite, ibid). Treatment of the social in this way is what
Barnes (1954) terms ‘atomisation’, where for example, the study of an organisation or community
that in reality functions on the transfer of many resource types and is also tied to larger networks, is
reduced to the study of the transfer of a single resource and artificially closed network. As society is
not made up of closed networks but endless ties between micro, meso, and macro-levels (Castells,
2000a), which in the process of network research are artificially ‘cut’ by the researcher, all network
research is subject to some degree of atomisation and thus limited in its capacity for interpretation
and generalisability.
Different branches of network theory treat the study of social relations through different theoretical
lenses.
Castells (2000a; in Arsenault, ibid) emphasises the need for network analysts to "…consider the
[macro] network, not the nodes or the association between nodes, as the unit of analysis" (p3).
Whilst he is not a proponent of technological determinism, Castell’s (2000b: p9) emphasis on the
study of macro or entire networks renders his philosophical approach deterministic in nature, with
the agency of relative meso or micro networks, or indeed dyadic relations at any level, ultimately
determined by the structure or functional goals of the parent network(s). Using (quantitative)

17
methods derived from mathematical graph theory or ‘sociometry’ and often discounting formal or
institutional relations in favour of “…informal grapevine communications” (Arsenault, ibid: p4),
network analysts adopting the ‘holistic’ Castellian view of networks are concerned with the patterns
of network ties through which resource flows are either enabled or constrained.
The two inversely related, network analytical concepts proposed here, are network tie strength and
connectivity or centrality. Tie strength is defined by Granovetter (ibid: p1361) as a “…combination
of…the emotional intensity, the intimacy, and the reciprocal services which characterise a tie”, but is
here reconfigured to a civic participation context using Putnam’s (1995, 1996, 2000) sentiments of
‘trust and reciprocity’, which are also antecedent to positive participatory attitudes and behaviour.
Inversely related to the idea of strong ties is Castells’ (2004) proposition that in a society where
social relations are defined by transitive flows of information, power is derived from the capacity to
receive and transmit information. Thus “…the capacity for any communicating subject to act on the
communication network gives people and organisations the possibility of reconfiguring the network
according to their needs, desires, and projects” (p12). Rather than tie strength, what Castells is
arguing for here is the importance of tie ‘connectivity’ more generally: the capacity of well-
connected or ‘central’ individuals to transmit and receive ideas, which in-turn leads to positive,
productive attitudes and behaviour.
Concepts such as tie strength and centrality succumb to a further criticism levelled against
quantitative network analysis, that through focussing ‘non-descriptively’ on informal, macro network
structure, agent-led impositions of power at the micro level are simply ignored. ANT (see Callon and
Latour, 1981; Callon, 1986a, 1986b, 1987; Latour, 1991) is a rejection of the idea that network
research should be oriented towards the whole or ‘macro’ network, and focuses instead on the
negotiations of power that occur between focal actors in the process network construction. As a
constructivist branch of network theory, ANT grants more powerful actors or ‘monads’ the agency to
influence networked reality and structure through their negotiations with less powerful actors.
Central to ANT is Latour’s (ibid: p103) idea that “…in order to understand domination we have to
turn away from an exclusive concern with social relations and weave them into a fabric that includes
non-human actants, actants that offer the possibility of holding society together as a durable
whole.” Uniquely therefore, ANT does not differentiate between humans and non-humans as
prospective agents within networked reality. Non-human actors or ‘actants’ can be either tangible

18
(e.g. a computer) or intangible (e.g. a sentiment or idea), with their agency derived from ideas or
discourses that are ‘frozen’ (Walsham and Sahay, 1999: p42), often immutably in their construction.
In the present context, ANT provides remedy to the otherwise uncritical notion that civic
participation occurs within a power vacuum. As discussed previously, the empirical evidence
indicates that negotiations are likely to occur between the ideas of citizens and the (albeit
structurally imposed) agency of service professionals. ANT (Callon, 1986a) provides a theoretical lens
through which to view this process of negotiation or ‘translation’, which broadly conceived is the
four stage16
process by which an ‘actant’ (for example, a policy idea or initiative) is transformed from
its initial state by the power negotiations that occur during construction of networked reality.
4 METHODLOGY, RESEARCH DESIGN, AND RESEARCH METHODS
4.1 Research context
Bearing in mind the previous discussion, methodological operationalisation of the networked
context of Putnam’s (1995, 1996, 2000) reciprocity and trust antecedents of civic participation,
requires the artificial setting or atomisation of ‘network boundaries’ (Ibarra and Andrews, ibid). That
is, in order to gather network data it is necessary that respondents are connected (or networked)
according to the aim(s) of the research. Thus, where the aim of the proposed study is ‘to establish
the extent to which Facebook communication fosters networked relations conducive to civic
participatory attitudes and behaviour’, it is proposed that network boundaries are defined as
follows:
1. All valid research participants must be registered members of the same PPI public
service initiative.
2. All valid research participants must meet face-to-face on matters relating to the PPI
public service initiative.
3. There must also be an option for participants to communicate on matters relating to
the PPI public service initiative via Facebook.
Bearing in mind these preconditions, an as yet unspecified Local Involvement Network (LINk) is
proposed as the context for the proposed research. LINks were introduced under the Local
16
‘Problematisation’, ‘interessement’, ‘enrollment’, and ‘mobilisation’ (see below also).

19
Government and Public Involvement in Health Act 2007 as a mechanism to allow “…communities to
engage with health and social care organisations” (NHS, 2007: p8), and with the aim of giving
“…citizens a stronger voice in how their health and social care services are delivered” (NHS, 2010).
LINks epitomise a current model of PPI in local authority policy and service development, which
employs web 2.0 communication as a means of growing and strengthening civic participation
networks. Although there is some variation between the local authority areas in which LINks are
established, members typically numbering between 50 and 100 meet once to twice a month in face-
to-face working groups to discuss local health and social care issues, and may also choose to
communicate between meetings using a group Facebook page (see Sheffield LINk (2011) for an
overview of this process and a link to the Sheffield Facebook page).
As for all network studies (including those detailed in the literature review), the capacity of the
proposed study to produce universally generalisable findings is compromised by the specificity of the
LINk. Thus, whilst LINks may provide a useful networked context for research into the effects of web
2.0 communication on civic participation, this does not mean that findings can automatically be
applied to all areas of civic participation. What follows, should be approached with this reflexive
point in mind.
4.2 A mixed methods approach to network analysis
The following section operationalises the proposed theoretical framework, drawing on a mixed
methods quantitative and qualitative approach to research in response to a criticism made of
network studies by Arsenault (ibid), who, referring to quantitative network analysis on one hand and
qualitative ANT on the other comments, “…unfortunately, there is little interaction between these
different bodies of thought. The left hand makes little reference to what the right hand is doing. The
question remains: how can we integrate theories of networks as subjects of analysis with studies of
nodes embedded within those networks?” (p19; emphasis added).
Resolve to Arsenault’s question comes by approaching the proposed research from a pragmatic
ontological perspective, which views both structure and agency as prevalent forces acting within and
upon networked social reality (Cherryholmes, 1992). Once networks are approached pragmatically,
structural network analysis which requires quantitative data, and constructivist ANT which requires

20
qualitative data, become mutually supportive approaches for the examination of structurally
determined and agent-led negotiations of power respectively, which both occur within networks.
4.3 Quantitative network analysis
4.3.1 A sociometric approach to data collection
Whilst following Putnam (1995, 1996, 2000), there is broad acknowledgement that participatory
attitudes and behaviour are formed within a networked context, methodological treatment of this
context is uncommon. The strongest implication of this discrepancy is a discontinuity between the
conceptual and methodological stages of research, where participatory attitudes and behaviour are
conceived of initially as a function of interdependent networked relations, but then treated
methodologically as a product of individual action or sentiment (see particularly Wellman et al.,
ibid17
).
By employing a network theoretical base to the study of civic participation, correction of the
discontinuity of previous literature is provided through implicit methodological operationalisation of
all three of Putnam’s (1995, 1996, 2000) antecedent predictors of civic participation: networks, as
well as reciprocated norms and trust.
Methodologically, the empirical treatment of network ties rather than individuals requires collection
of sociometric data, which “…consist of one (or more) relations measured among a set of actors”
(Wasserman and Faust, 1994: p43). Contrary to typical quantitative data collection that focuses on
individual respondents, sociometric data necessarily treats actor dyads, triads, or subgroups as single
units of observation. The rationale for this is clear if one considers that by definition, a social tie
exists only on the basis of input from two or more actors: even if one actor ‘rejects’ a social tie, this
is still a form of negative input. If, as per the proposed research, one is to treat the network
antecedent of participatory capital empirically, then focus necessarily moves from individual
expressions of trust and reciprocity towards the presence of such sentiments as they exist in ties,
here between LINk participants.
17
Also Blanchard and Horan, ibid; Kavanaugh et al, ibid; Calhoun, ibid; Kotus and Hławka, 2010; Stern and
Dillman, 2006.

21
4.3.2 Quantitative variable definition
4.3.2.1 Communications usage data and variable definition
Operationalisation of SIP and MRT as exogenous determinants of network tie strength and
connectivity requires the collection of sociometric data pertaining to the communication tendencies
of dyadic LINk ties. For the proposed research, three sociometric measures are required to gauge the
communications behaviour of any particular tie: Frequency with which Facebook is used for tie
communication; frequency of face-to-face tie communication; frequency with which richer or leaner
Facebook media is used for tie communication.
The key point to note here is that for each measure, the variable construct must represent a
measure of media usage for both actors in the dyadic tie. The media usage variable will then
represent a network tie rather than individual measure, which can be positioned exogenously
against the sociometric tie measures of reciprocity and trust. By treating variables in this way, we are
implicitly operationalising the ‘network’ component of Putnam’s (1995, 1996, 2000) antecedents of
participatory capital, focussing on interdependent rather than independent expressions of
reciprocity and trust, and correcting the methodological discontinuity of previous empirical work.
Thus for each of the three measures of media usage, each variable construct must incorporate both
a measure of frequency of media use (or frequency of richer or leaner Facebook media use for the
third variable) and account for the difference in scores between dyadically tied actors.
To account for discrepancies in frequency of media usage scores within dyadic ties, we simply divide
the summed scaled scores of both actors and divide by the difference in those scores. We would also
need to +1 to the denominator to avoid dividing by zero when scaled scores are in perfect
agreement. For example, where ‘actor A’ reports a Facebook usage frequency score of ‘5’ in their
communication with ‘B’ , and ‘actor B’ reports a score of ‘3’ in their communication with ‘A’, the
overall score for that tie would be calculated as:
(5+3)
(2+1)
= 2.67; alternatively, where ‘actor C’ reports a
Facebook usage frequency score of ‘2’ in their communication with ‘D’ , and ‘actor D’ reports a score
of ‘1’ in their communication with ‘C’, the overall score for that tie would be calculated as:
(2+1)
(1+1)
=

22
1.5. For any dyadic tie, assuming the use of five-point likert scales, the maximum possible score
would be 10 (
(7+7)
(0+1)
) and the lowest, 1 (
(1+1)
(0+1)
).
The three key measures of tie media use can therefore be calculated as follows:
1. Frequency with which Facebook is used for tie communication:
𝐹𝑐𝑏𝑘(𝑃𝑖 ↔ 𝑃𝑗) =
(∑ 𝐹𝑟𝑞(𝑃𝑖𝑗, 𝑃𝑗𝑖))
(𝐹𝑟𝑞(𝑃𝑖 − 𝑃𝑗) + 1)
𝐹𝑐𝑏𝑘(𝑃𝑖 ↔ 𝑃𝑗) = frequency of Facebook communication between LINk actors 𝑃𝑖 and 𝑃𝑗
∑ 𝐹𝑟𝑞(𝑃𝑖𝑗, 𝑃𝑗𝑖) = sum of the Facebook frequency score that LINk actor 𝑃𝑖 provides referring to LINk
actor 𝑃𝑗 and that LINk actor 𝑃𝑗 provides referring to LINk actor 𝑃𝑖
𝐹𝑟𝑞(𝑃𝑖 − 𝑃𝑗) = difference in Facebook frequency scores between LINk actors 𝑃𝑖 and 𝑃𝑗
2. Frequency of face-to-face communication in tie communication:
𝐹𝑡𝐹(𝑃𝑖 ↔ 𝑃𝑗) =
(∑ 𝐹𝑟𝑞(𝑃𝑖𝑗, 𝑃𝑗𝑖))
(𝐹𝑟𝑞(𝑃𝑖 − 𝑃𝑗) + 1)
𝐹𝑡𝐹(𝑃𝑖 ↔ 𝑃𝑗) = frequency of face-to-face communication between LINk actors 𝑃𝑖 and 𝑃𝑗
∑ 𝐹𝑟𝑞(𝑃𝑖𝑗, 𝑃𝑗𝑖) = sum of the face-to-face communication frequency score that LINk actor 𝑃𝑖 provides
referring to LINk actor 𝑃𝑗 and that LINk actor 𝑃𝑗 provides referring to LINk actor 𝑃𝑖
𝐹𝑟𝑞(𝑃𝑖 − 𝑃𝑗) = difference in face-to-face communication frequency scores between LINk actors 𝑃𝑖
and 𝑃𝑗
3. Frequency with which richer or leaner Facebook media is used for tie communication:
This third variable is more complex in its construction than the former two. Prior to aggregative
treatment as per the previous two variables, a weighted index is constructed to reflect the frequency
with which dyadically tied LINk actors communicate using richer or leaner Facebook media. Here,
Cormode and Krishnamurthy’s (ibid: p18) classification of Facebook applications by communication
activity is combined with Daft et al.’s (ibid) continuum of lean to rich media, the latter taking its
polar opposites from the capacity of media to transmit verbal and non-verbal cues:
Fig 2: Facebook application richness continuum
Clicks & connections Comments Text communication Content creation

23
By assigning each classification of Facebook media a weighting according to its position on the
richness continuum, where ‘actor A’ reports for example that, in their communication with ‘B’, he or
she uses ‘clicks and connection’ very infrequently (1), ‘comments’ somewhat infrequently (2), ‘text
communication’ very frequently (5), and ‘content creation’ very infrequently (1), the sum of
frequency scores multiplied by their respective richness weightings will give an indexed ‘frequency
of richer or leaner Facebook media use’ score for that actor. This process is illustrated in FIG 3:
Fig 3: Frequency with which richer or leaner Facebook media is used for tie communication: Index
construction (example)
Thus, ‘actor A’s’ score is calculated as: (2 ∗ 0.1) + (3 ∗ 0.2) + (4 ∗ 0.3) + (1 ∗ 0.4) = 2.4; and ‘B’s’
score as: (2 ∗ 0.1) + (4 ∗ 0.2) + (4 ∗ 0.3) + (1 ∗ 0.4) = 2.6. To obtain an average frequency of rich
media score for tie 𝐴 ↔ 𝐵, we simply take the mean of the two scores:
(2.4+2.6)
2
= 2.5. Thus, where
the maximum possible index score for the frequency with which richer or leaner Facebook media is
used for tie communication is 5, and the lowest is 1, tie 𝐴 ↔ 𝐵 appears to be a ‘moderate’ user of
rich media.
This variable can be notated as follows:
𝐹𝐹𝑏(𝑃𝑖 ↔ 𝑃𝑗) =
(∑ 𝑃𝑖 → 𝑃𝑗(𝑎𝑖→𝑗𝑏) , 𝑃𝑗 → 𝑃𝑖(𝑎𝑗→𝑖𝑏))
2
𝐹𝐹𝑏(𝑃𝑖 ↔ 𝑃𝑗) = frequency with which richer or leaner Facebook media is used for communication
between actors 𝑃𝑖 and 𝑃𝑗
∑ 𝑃𝑖 → 𝑃𝑗(𝑎𝑖→𝑗𝑏) , 𝑃𝑗 → 𝑃𝑖(𝑎𝑗→𝑖𝑏) = sum of Facebook media use frequency scores multiplied by
respective richness weightings for directed relations 𝑃𝑖 → 𝑃𝑗 and 𝑃𝑗 → 𝑃𝑖
Derivative communications usage variables (individual LINk participants)
LEANEST
APPLICATION
RICHEST
APPLICATION

24
The following variables notate the communication usage scores of individual LINk participants and
are constructed from simple mean averages of summed frequency of use scores per actor. Thus, the
‘frequency with which Facebook is used by individual LINk members to communicate with other
members’, is a simple mean average of the summed frequency of Facebook use scores reported in
tie maintenance by actor 𝑃𝑖:
𝐹𝑐𝑏𝑘(𝑃𝑖) =
(∑ 𝐹𝑟𝑞(𝑃𝑖, 𝑃𝑘)
𝑛
𝑖=1 )
𝑛
𝐹𝑐𝑏𝑘(𝑃𝑖) = frequency of Facebook use for LINk actor 𝑃𝑖
∑ 𝐹𝑟𝑞(𝑃𝑖, 𝑃𝑘)
𝑛
𝑖=1 = sum of the frequency of Facebook use scores reported in tie maintenance by LINk
actor 𝑃𝑖
𝑛 = number of ties reported by LINk actor 𝑃𝑖
‘Frequency with which face-to-face communication is used by individual LINk members for
communication with other members’ is therefore also expressed as follows:
𝐹𝑡𝐹(𝑃𝑖) =
(∑ 𝐹𝑟𝑞(𝑃𝑖, 𝑃𝑘)
𝑛
𝑖=1 )
𝑛
𝐹𝑡𝐹(𝑃𝑖) = frequency of face-to-face communication for LINk actor 𝑃𝑖
∑ 𝐹𝑟𝑞(𝑃𝑖, 𝑃𝑘)
𝑛
𝑖=1 = sum of the frequency of Facebook use scores reported in tie maintenance by LINk
actor 𝑃𝑖
The final derivative communications variable represents a measure of the frequency with which LINk
individuals use richer or leaner Facebook media for tie communication:
𝐹𝐹𝑏(𝑃𝑖) =
(∑ 𝑃𝑖(𝑎𝑖→𝑗𝑏))
𝑛
𝐹𝐹𝑏(𝑃𝑖) = frequency with which richer or leaner Facebook media is used for communication by
actor 𝑃𝑖
∑ 𝑃𝑖(𝑎𝑖→𝑗𝑏) = sum of Facebook media use frequency scores multiplied by respective richness
weightings for actor 𝑃𝑖
4.3.2.2 Reciprocity, trust, and strong tie data and variable definition

25
Hanneman and Riddle (2005: p12) suggest that, in a network context, reciprocity and trust are
assessed by “…asking each actor in a dyad to report their feelings about the other”. In an identical
treatment to the previously detailed communication variables, to establish a measure of trust and
reciprocity under a network theoretical framework, a metric is required for both variables that
accounts firstly, for the ‘total amount’ of sentiment within a tie and secondly, the degree to which
that sentiment is expressed by both rather than just one actor.
Selection of scales with which to measure reciprocity and trust is unproblematic. Harper (2002: p6)
defines reciprocity as the “…willingness [of participatory network actors] to co-operate for mutual
benefit”. ‘Mutually beneficial co-operation’ implies a need for contextual specificity in
measurement, thus in the present context the reciprocity of ties within a LINk would pertain to the
willingness of dyadically tied actors to co-operate with each other in their functional LINk activities.
Such activities might include suggesting a healthcare issue for LINk deliberation, putting forward a
formal proposal to the LINk board, or organising a working group (Durham LINk, 2008; UNISON,
2011). Similarly, trust scales can be adapted from Mishra’s (1996; in Luo, 2005) four-item taxonomy
of trustworthiness, with some fine-tuning to reflect the LINk context: ‘I think that he/she is honest’; ‘I
think that he/she is competent at his/her job’; ‘I think that his/her behaviour is stable’; ‘I think that
he/she is concerned about my interests’. Employing these or similar statements, Hanneman and
Riddle (ibid) suggest that ordinal scales can be used to measure the extent to which both actors
express a degree of trusting or reciprocal sentiment towards the other (i.e. where 1=strongly
disagree and 5=strongly agree).
1. Bearing the previous treatment of the communication variables in mind, mutual trust can
therefore be calculated as follows:
𝑡(𝑃𝑖 ↔ 𝑃𝑗) =
(∑ 𝑡(𝑃𝑖𝑗, 𝑃𝑗𝑖))
(𝑡(𝑃𝑖 − 𝑃𝑗) + 1)
𝑡(𝑃𝑖 ↔ 𝑃𝑗) = trust score of tie between LINk actors 𝑃𝑖 and 𝑃𝑗
∑ 𝑡(𝑃𝑖𝑗, 𝑃𝑗𝑖) = sum of the trust scores that LINk actor 𝑃𝑖 provides referring to LINk actor 𝑃𝑗 and that
LINk actor 𝑃𝑗 provides referring to LINk actor 𝑃𝑖
𝑡(𝑃𝑖 − 𝑃𝑗) = difference in trust scores between LINk actor 𝑃𝑖 and 𝑃𝑗
*+1 is added to 𝑡(𝑃𝑖 − 𝑃𝑗) in order to make zero difference scores divisible
2. Similarly, mutual reciprocity can be calculated as follows:

26
𝑟(𝑃𝑖 ↔ 𝑃𝑗) =
(∑ 𝑟(𝑃𝑖𝑗, 𝑃𝑗𝑖))
(𝑟(𝑃𝑖 − 𝑃𝑗) + 1)
𝑟(𝑃𝑖 ↔ 𝑃𝑗) = reciprocity score of tie between LINk actors 𝑃𝑖 and 𝑃𝑗
∑ 𝑟(𝑃𝑖𝑗, 𝑃𝑗𝑖) = sum of the reciprocity scores that LINk actor 𝑃𝑖 provides referring to LINk actor 𝑃𝑗 and
that LINk actor 𝑃𝑗 provides referring to LINk actor 𝑃𝑖
𝑟(𝑃𝑖 − 𝑃𝑗) = difference in reciprocity scores between LINk actor 𝑃𝑖 and 𝑃𝑗
Summed ‘strong tie’ variable
As has been discussed, trust and reciprocity are treated here as the sentiments that comprise strong
network ties. Thus, combining the trust and reciprocity variable scores on any given LINk tie will
produce a measure of the strength of that tie.
Assuming that both reciprocity and trust are weighted equally, the composite strength of any
particular tie can be notated as follows as:
𝑠(𝑃𝑖 ↔ 𝑃𝑗) = ∑ 𝑃𝑖 ↔ 𝑃𝑗(𝑟𝑖↔𝑗𝑡𝑖↔𝑗)
𝑠(𝑃𝑖 ↔ 𝑃𝑗) = strength of tie between LINk actors 𝑃𝑖 and 𝑃𝑗
∑ 𝑃𝑖 ↔ 𝑃𝑗(𝑟𝑖↔𝑗𝑡𝑖↔𝑗) = summation of the reciprocity and trust scores of tie between LINk actors 𝑃𝑖
and 𝑃𝑗
Derivative strong tie variable (individual LINk participants)
The following variable represents an individual’s number of strong LINk relational ties. Given the
previous ‘summed strong tie variable’, which represents a composite summation of reciprocity and
trust scores that have both been previously aggregated across a series of five-point likert items, the
strongest possible outcome of 𝑠(𝑃𝑖 ↔ 𝑃𝑗) would be 10 and the lowest 1. Making an entirely
subjective judgement, we could state therefore that any tie with a composite strength >5 could be
considered a strong tie. An individual’s number of strong LINk relational ties can therefore be
represented as a summation of the number of dyadic ties to which he or she is party, if and only if
the ‘summed strong tie variable’ score is > 5:
𝑆𝑡𝑟(𝑃𝑖) = ∑𝑃𝑖 ↔ 𝑃𝑗 ⇔
𝑛
𝑖=1
𝑠(𝑃𝑖 ↔ 𝑃𝑗 > 5)
𝑆𝑡𝑟(𝑃𝑖) = number of strong LINk relational ties for LINk actor 𝑃𝑖

27
∑ 𝑃𝑖 ↔ 𝑃𝑗 ⇔
𝑛
𝑖=1 𝑠(𝑃𝑖 ↔ 𝑃𝑗 > 5)= number of LINk relational ties in which actor 𝑃𝑖 is involved ‘if and
only if’ the ‘summed strong tie variable’ score is > 5
4.3.2.3 Centrality data and variable definition
As detailed in both the review of empirical literature and theoretical outline, it was suggested that
relational connectivity as well as tie strength may have a positive effect on participatory attitudes
and behaviour. It is therefore proposed that connectivity, irrespective of tie strength is tested in its
capacity as a predictive variable. Methodologically, this idea can be tested using the network
concept of degree centrality, which is a summation of all direct ties that the focal actor (𝑃𝑖) has to
other actor (𝑃𝑘) (Freeman, ibid):
𝐶𝑑(𝑃𝑖) = ∑ 𝑎(𝑃𝑘, 𝑃𝑖)
𝑛
𝑖=1
𝐶𝑑(𝑃𝑖) = centrality of LINk actor 𝑃𝑖
𝑛 = LINk actors (𝑃𝑘,) to which 𝑃𝑖 is directly connected
4.3.2.4 Network effects data and variable definition
In non-network studies, typically the absence of an exogenous variable from a regression model
simply leads to a less powerful 𝑟2
value for the model, relative to extraneous variance. However,
when conducting research within networks, sampling of participants is inherently non-random due
to the prerequisite that network actors be linked by some form of resource exchange. It follows that
failure to account for the influence of the attitudes and behaviour of proximate actors on that of
‘ego’, will lead to autocorrelation or systematic error upon regression of any networked-derived
vectors (Peters, 1998: p33).
To compensate for autocorrelative error, Ibarra and Andrews (ibid) propose an exogenous network-
effects variable, which is "the only currently available method that directly models the social
influence effect, taking into account the network relationships between individual respondents that
result in non-independent values [autocorrelation] for the dependent variable" (p288). The
exogenous 𝜌𝑊𝑌 is a correlation coefficient (rho) of two vectors: the first, 𝑊, is a product of an
adjacency matrix-vector multiplication, where the network binary adjacency matrix 𝑊is multiplied
by the vector of scores on the dependent variable 𝑌 (in this case the participatory attitudes and

28
behaviour of LINk actors). 𝑊 is a vector of mean averages of the vector product of the matrix-vector
multiplication. An rho coefficient of the average adjacent attitudes vector and the attitudes of 𝑌 will
represent a measure of the weighted degree of influence that the attitudes and behaviour of all
those tied directly to actor 𝑦𝑖 exert on 𝑦𝑖’s participatory attitudes and behaviour.
4.3.2.5 Participatory attitudes and behaviour data and variable definition
The final variable construction pertains to LINk participatory attitudes and behaviour. It is suggested
that this endogenous variable is formed from a composite index of five-point likert scale items
adapted from the ONS (2000) ‘neighbourhood and community involvement survey’, for example: ‘I
am well informed about LINk affairs’; ‘I feel I can influence decisions that affect health and social care
in my area’; ‘I have taken action through the LINk to solve a local problem’. The participatory capital
of any given LINk actor thus becomes a summation of their response to each scale item multiplied a
weighting component:
𝑃𝐶(𝑃𝑖) = ∑ 𝑃𝑖(𝑎𝑖𝑏)
𝑛
𝑖=1
𝑃𝐶(𝑃𝑖) = participatory capital (attitudes and behaviour) of LINk actor 𝑃𝑖
∑ 𝑃𝑖(𝑎𝑖𝑏)
𝑛
𝑖=1 = sum of attitudinal and behavioural scales multiplied by respective weighting
components for LINk actor 𝑃𝑖
4.3.3 Preliminary hypotheses and regression model responses
Having defined the variable constructs, it is now possible to operationalise the theoretical
framework with hypotheses drawn from the literature overview and statistical procedures to test
those hypotheses. As indicated previously by Fig 1, the main body of the proposed project can be
divided into two halves, the first concerns the effect of media usage on LINk relational ties, and the
second tests the effect of those LINk ties on participatory attitudes and behaviour.
Hypotheses pertaining to the effect of Facebook use on LINk relational ties
H1. Frequency with which Facebook is used to engage with LINk members positively predicts an
individual’s number of LINk relational ties.
𝐶𝑑(𝑃𝑖) = 𝐵0 + 𝛽1𝐹𝑐𝑏𝑘(𝑃𝑖) + 𝜀𝑖

29
H2. Frequency with which Facebook is used to engage with LINk members either has no effect or
decreases an individual’s number of strong LINk relational ties.
𝑆𝑡𝑟(𝑃𝑖) = 𝐵0 + 𝛽1𝐹𝑐𝑏𝑘(𝑃𝑖) + 𝜀𝑖
H3. Strong LINk relational ties are more likely to be initiated offline (face-to-face) than via the LINk
Facebook page.
Hypothesis 3 will be tested using a t-test for independent samples and compare the mean tie
strength of ties initiated offline vs. the mean tie strength of ties initiated via Facebook.
H4. Frequency of face-to-face contact positively predicts an individual’s number of strong LINk
relational ties.
𝑆𝑡𝑟(𝑃𝑖) = 𝐵0 + 𝛽1𝐹𝑡𝐹(𝑃𝑖) + 𝜀𝑖
𝐹𝑡𝐹(𝑃𝑖) = frequency of face-to-face communication for LINk actor 𝑃𝑖
H5. Where LINk relational ties are strong, frequency of Facebook use in the maintenance of those ties
positively predicts tie strength.
𝑠(𝑃𝑖 ↔ 𝑃𝑗) = 𝐵0 + 𝛽1𝐹𝐹𝑏(𝑃𝑖 ↔ 𝑃𝑗) ⇔ 𝑠(𝑃𝑖 ↔ 𝑃𝑗 > 5) + 𝜀𝑖
𝐹𝐹𝑏(𝑃𝑖 ↔ 𝑃𝑗) ⇔ 𝑠(𝑃𝑖 ↔ 𝑃𝑗 > 5)= frequency with which richer or leaner Facebook media is used
for communication between actors 𝑃𝑖 and 𝑃𝑗 ‘if and only if’ the ‘summed strong tie variable’ score is
> 5.
H6. Where LINk relational ties are strong, frequency of use of richer Facebook media in the
maintenance of those ties positively predicts tie strength.
𝑠(𝑃𝑖 ↔ 𝑃𝑗) = 𝐵0 + 𝛽1𝐹𝐹𝑏(𝑃𝑖 ↔ 𝑃𝑗) ⇔ 𝑠(𝑃𝑖 ↔ 𝑃𝑗 > 5) + 𝜀𝑖
𝐹𝐹𝑏(𝑃𝑖 ↔ 𝑃𝑗) ⇔ 𝑠(𝑃𝑖 ↔ 𝑃𝑗 > 5) = frequency with which richer or leaner Facebook media is used
for communication between actors 𝑃𝑖 and 𝑃𝑗 ‘if and only if’ the ‘summed strong tie variable’ score is
> 5.

30
H7. Frequency with which richer Facebook media is used to engage with LINk members positively
predicts an individual’s number of LINk relational ties.
𝐶𝑑(𝑃𝑖) = 𝐵0 + 𝛽1𝐹𝐹𝑏(𝑃𝑖) + 𝜀𝑖
𝐹𝐹𝑏(𝑃𝑖) = frequency with which richer or leaner Facebook media is used for communication by
actor 𝑃𝑖
Hypotheses pertaining to the effect of LINk relational ties on participatory attitudes and behaviour
A single, ‘network effects’ regression equation is proposed to respond to the final three
hypotheses18
:
H8. The overall number of LINk relational ties that an individual has is a positive predictor of that
individual’s LINk participatory attitudes and behaviour.
H9. The number of strong LINk relational ties that an individual has is a positive predictor of that
individual’s LINk participatory attitudes and behaviour.
H10. The participatory attitudes and behaviour of LINk members connected to an individual will
positively predict the participatory attitudes and behaviour of that individual.
𝑃𝐶(𝑃𝑖) = 𝐵0 + 𝛽1𝐶𝑑(𝑃𝑖) + 𝛽2𝑆𝑡𝑟(𝑃𝑖) + 𝜌𝑊𝑃𝐶 + 𝜀𝑖
𝑃𝐶(𝑃𝑖) = participatory capital (attitudes and behaviour) of LINk actor 𝑃𝑖
𝜌𝑊𝑃𝐶 = network effects coefficient
4.4 ANT: Narrative theory approach
18
As noted by Ibarra and Andrews (ibid: p289), “…because the endogenous variable (in this case, 𝑃𝐶) appears
as both explanatory variable and outcome, this model cannot be solved numerically. To estimate the model,
iterative maximum likelihood techniques are used.”

31
Employing the ANT theoretical framework, the qualitative stage of the proposed research will
operationalise a narrative based methodological approach. The applicability of this approach is clear
when one considers the transformationary process of ‘translation’, which ANT posits as a necessary
series of actions undertaken by all ‘actants’ (here a LINk policy idea or initiative) in the process of
social construction.
Callon (1986a) suggests that as it moves through the network of membership boards, OSC’s, and
executive councils, the participatory input (i.e. the agency) of any LINk member will, at each
bureaucratic stage, undergo a four-stage negotiation process: ‘Problematisation’ is initial definition
by the focal actant (here, the idea itself) of the ‘problem to be solved’ (Callon, 1986a: p70), to which
it presents itself to a network of actors (i.e. the LINk board, OSC members, and council) as the best
solution or ‘obligatory passage point’ (Callon, 1986a: p70). “Interessement involves convincing other
heterogeneous actors that the interests defined by the focal actor for them are, in fact, consistent
with what their own interests should be” (Sarker, et al., 2006: p55), and is therefore concerned with
the retention or relinquishing of power by (in the present case) the LINk board and OSC members,
and council. ‘Enrollment’ is said to have occurred (Callon, 1986b) to the extent that the focal actant’s
proposal is both altered and accepted. A fourth translation element - ‘mobilisation’ – concerns the
extent to which the interests of all parties are represented throughout translation negotiations.
Immediately obvious is that any methodological approach employed to interpret translation must be
capable of capturing process (Scott and Wagner, 2003): in this case, the temporal change of the LINk
idea as it is negotiated by actors within the bureaucratic network; and also the sense-making
activities of those divergent actors as they negotiate their own positions or identities (Walsham,
1993; Klein and Myers, 1999; in Scott and Wagner, ibid).
In response to these requirements we might consider Bruner’s (2002) etymological deconstruction
of narration: “…‘to narrate’ derives from both ‘telling’ (narrare) and ‘knowing in some particular
way’ (gnarus) - the two tangled beyond sorting” (p27). ‘Narrative’ is thus formed from a composite
of ‘telling’, which infers description, and more specifically the telling of ‘particular knowledge’, which
suggests a knowledge that is personally meaningful to the narrator (Corzatti, 2001).This idea of
subjective meaning is central to constructivist ANT, which “…does not adopt a position of realism
ontologically...[viewing] data not as objective evidence supporting or falsifying an assertion but as
texts and text analogues, whose meanings, when read hermeneutically, can go beyond the original
intentions and meanings attributed by their sources" (Sarker et al., 2006: p53). A narrative based

32
methodological approach will therefore satisfy ANT’s epistemological demand for subjective data or
“…stories of personal experience” (Denzin, 1970: p188).
Furthering Bruner’s definition, Riessman (2008) conceptualises narratives as ‘stories’, which “…have
a sequential and temporal ordering, but also as texts that include some kind of rupture or
disturbance in the normal course of events, some kind of unexpected action that provokes a
reaction and/or adjustment” (p6). This definition not only satisfies ANT’s demand for methodological
subjectivity, but also aligns narrative methodology to Callon’s (1986a, 1986b) translation process,
where the four stages of translation - ‘problematisation’, ‘interessement’, ‘enrollment’, and
‘mobilisation’ – constitute Riessman’s (ibid) ‘sequential and temporal ordering’, and where the
degree to which ‘enrollment’ – a ‘reaction and/or adjustment’, in this case, on behalf of the LINk
board, OSC, and councillors - is considered to have occurred, is indicative of the ‘rupture or
disturbance in the normal course of events’.
4.5 Data collection, sampling, and final points
The proposed research will employ two data collection tools: quantitative sociometric data will be
collected via a survey, whilst qualitative narrative data will be collected through semi-structured
interviews.
4.5.1 Quantitative data collection and sampling
As discussed previously, network analysis requires the collection of sociometric data, where the
same subset of relational and structural data is provided by each participant in response to closed,
scaled questions pertaining to their relationship with every other named network alter. As “[s]urvey
design provides a quantitative or numeric description of trends, attitudes, or opinions of a
population” (Creswell, 2009: p145), it is particularly well suited to the collecting of sociometric data.
Two specific issues arise in the administering of sociometric surveys:
Firstly, responses must be provided for named network alters. That is, respondent 𝑃𝑖 is required to
answer questions pertaining specifically to 𝑃𝑗 and 𝑃𝑘 (and vice versa). Thus, a preparatory stage of

33
survey design will require the agreed19
acquisition of a sampling frame, which could be a LINk
membership list or register of all members who attend the bi-monthly meetings. Once a LINk has
been identified, quantitative data will be collected from all members because “…no generally
accepted techniques have been developed for sampling within a network” (Rogers and Kincaid,
1981; in Ibarra and Andrews, ibid: p286).
Secondly, if one considers that for a LINk of 𝑛 = 50, assuming each participant were required to
respond to 10 likert items about every other member, those members who were more central may
be required to respond to nearly 500 items. With a questionnaire of this length, it is likely that
response rates may suffer (Roszkowski and Bean, 1990). Monge et al. (1983; in Stork and Richards,
1992) find that response rates to lengthy sociometric questionnaires can be improved through group
rather than individual administration, which suggests that (assuming access is agreed) it may be
pertinent to administer the instrument over the course of several LINk meetings.
As a final point pertaining to quantitative data collection: It is important that a significantly large
LINk network is selected for the study in order to satisfy Cohen’s (1988; in Field, 2009: p223)
requirement that statistical tests, as far as possible, meet a 0.8 power benchmark. That is, if as is the
case for several of the predictive models, we have circa three exogenous variables and expect a
small effect size of around 𝑟2
= 0.02 (Cohen, 1988; in Miles and Shevlin, 2001: p120), then we
would need a sample of around 𝑛 = 600 LINk participants to stand an 80% chance of finding a
significant result (𝛼 = 0.05). 𝑛 = 600 is both impractical and impossible, as LINk’s tend to comprise
around 50-100 members. Whilst it is logical to select the largest LINk that is accessible for study, in
order to decrease the required 𝑛 we can also improve the predictive strength of the following
models by adding relevant control variables. Whilst the control variables are not detailed in the
proposed models, education, income, age, race, place of residence, work status, and gender have
been found to be the strongest determinants of both web use and civic participation (see Putnam,
1995, 1996, 2000; Wellman et al., ibid). If, by operationalising these (or similar) variables the
estimated effect size of the following models was raised to around 𝑟2
= 0.26, then a 0.8 power level
could be achieved with a practically realisable LINk size of 𝑛 = 50 (𝛼 = 0.05 ).
4.5.2 Qualitative data collection and sampling
19
This may be subject to ethical clearance and the guarantee of anonymity / changing of actual names in
analysis and reporting.

34
Regarding qualitative data collection, a semi-structured interview approach will accommodate both
the temporally sensitive, processual data that is constitutive of ANT translation narratives, and the
range of responses offered by divergent actants within the LINk (Lindlof and Taylor, 2002: p19).
Unlike a standardised survey the semi-structured interview format is able to fulfil the subjective
epistemological requirements of ANT, allowing for limitless variation in response whilst deriving its
semi-structure (and analytical coding structure) from the four-stage translation framework.
Scott and Wagner (2003) suggest an iterative or ‘snowball’ approach to respondent sampling under
an ANT framework, ‘referring to…narrative accounts [to] set the agenda guiding…the next round of
interviews’ (p294). A snowball approach to respondent sampling makes good sense here, as in the
‘following’ of a LINk policy idea or initiative (the ‘actant’) on its translative journey through the
hierarchical decision-making network, the proposed study will need to identify specific actors with
which the idea enters into negotiation. Whilst the general ‘direction’ along which all LINk policy
ideas pass between inception and implementation will be fairly similar - i.e. from LINk board to OSC
to executive council - the specific actors involved in the negotiation of different ideas may vary. A
snowball approach to sampling will allow for the iterative identification of each subsequent actor in
the decision-making chain “…by someone who knows that a certain person has the necessary
experience or characteristics to be included” (MacNealey, 1999: p157).

35
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