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new media & society

15(1) 5271 The Author(s) 2012

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Social networks in politicalcampaigns: Facebook andthe congressional electionsof 2006 and 2008

Christine B. Williams andGirish J. Jeff GulatiBentley University, USA

Abstract

This study examines the early adoption and dissemination of emerging technology tools incampaigns by analyzing which candidates were the most likely to adopt and use Facebook

in the 2006 and 2008 elections to the US House of Representatives. The researchhypotheses draw primarily from the diffusion of innovation literature. Our analysis of

802 candidates in 2006 and 816 candidates in 2008 indicates that Facebook adoptiondiffused rapidly between 2006 and 2008, with party (Democrats), competition, moneyand the level of education in the district explaining both adoption and implementation.

Challengers and candidates for open seats were more likely to be early adopters, butincumbents used Facebook more extensively. Both higher adoption rates by peers orcompetitors in the candidates own state and a propensity to adopt earlier campaign

technologies are strong positive motivators for early adoption, but irrelevant to usage.

Keywords

Campaign strategy, congressional elections, diffusion of innovations, online campaigns,social media

The 2006 congressional elections marked another milestone in online campaigning. In that

year, the percentage of candidates with a campaign website reached 96 percent of major

party candidates for the US Senate and 86 percent of candidates for the House (Gulati and

Williams, 2007). As the campaign website became a standard communication and

Corresponding author:

Christine B. Williams, Department of Global Studies, Bentley University, 175 Forest Street, Waltham,

MA 02452-4705, USA.

Email: [email protected]

57332NMS15110.1177/1461444812457332newmedia & societyWilliamsand Gulati

Article

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Williams and Gulati 53

fundraising tool, candidates and other online political activists sought to differentiate

themselves through online tools that offered potential electoral benefits. Led by Facebook,

social networking sites emerged as an online tool that could effectively mobilize voters

(Williams and Gulati, 2007). With one cycle of experimentation complete and two crowded

and competitive presidential nominating contests on the horizon, social networking siteswere poised to play a significant role in the 2008 cycle of congressional elections.

This article investigates who adopts new campaign technologies and to what extent.

Its focus is on candidates use of Facebook in the 2006 and 2008 congressional elections.

The diffusion of innovation literature informs our research hypotheses, model specifica-

tion, analysis and interpretation of our data. The study of these emerging technology

tools is important because of their potential to change both the conduct of campaigns and

the relationship between candidates and voters.

Online media accelerate the transmission of content and are accessible to large

numbers of people. Content is disseminated at the initiative of users through theirconnections to social networks or via email exchanges by acquaintances. This viral

property makes it an attractive, inexpensive means of conducting voter outreach, and

given the popularity of online communication with younger voters, a means of targeting

that demographic. Below, we analyze which congressional candidates were more

likely to use Facebook in the 2006 and 2008 election cycles. We supplement our 2008

empirical analysis with interviews of candidates and staffers from over 50 congressional

campaigns to ascertain their perspective on its role.

Profiles of early adopters: diffusion of innovation

literature

Rogers (2003) describes diffusion as the process by which members of a social system

come to know, persuade, decide, implement and confirm their adoption of an innovation.

When a new technology is introduced, innovators are the first to embrace it, followed by

early adopters, early majority, late majority, and finally laggards. The empirical literature

has studied the characteristics of early adopters and also the timing and extent of the

adoptions for both individuals and organizations (Fichman, 1992; Frambach and

Schillewaert, 2002). Adoption decisions are thought to depend not only on the character-istics of the adopter, but also on characteristics of the innovation or technology and of the

environment. For example, Kwon and Zmud (1987) identify five categories of these

contextual factors: characteristics of the adopting organization, the user community, the

innovation or technology, the task, and the environment. Robertson and Gatignon (1986)

describe a large set of factors related to what they call the competitive supply-side and

adopter environments that affect the diffusion of new technologies. The former include

factors such as the degree of competition, standardization of the technology, research and

development resources. The competitive adopter environment includes both structural

attributes of the adopter industry such as its homogeneity and uncertainty of demand(customer needs) and communication attributes such as the professionalism and cosmo-

politanism (external orientation) of the adopter industry.

Political science studies of the diffusion of campaign websites have examined fewer

categories and a more limited number of contextual factors. Most draw upon the same

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54 new media & society15(1)

finite set that is divided between constituency (the user community) and political (the

environment) factors. Constituencies are described demographically by median income

and percentage of urban, white, college-educated, and elderly (Foot and Schneider,

2006). The political environment is described by characteristics of the electoral contest

and candidate: level of office, competitiveness of the race, party identification of thecandidate, party status (major or minor party), status of the seat (incumbent, challenger,

open seat), and amount of campaign funds raised.

The constituency attributes selected to explain campaign website adoption are those

that have been shown to correlate with citizens access to and use of the Internet

(Chadwick, 2006; Klotz, 2004; Mossberger et al., 2003). Higher levels of education

make people more comfortable with and skilled in the use of technology, while higher

levels of income make computers easier to afford. Although whites use the Internet at

higher rates than blacks, racial differences have diminished over time and seem to be a

reflection of disparities in education and income (Marriott, 2006). The age gap persists,however: Internet use declines with each advancing age group. Urban areas have greater

Internet use than rural areas, but the difference has declined substantially. These constitu-

ency demographics in turn influence candidates Internet use (Herrnson et al., 2007).

The diffusion of innovation literature suggests an additional reason why constituency

factors should lead candidates to adopt new technologies. Organizations are mindful of

the degree to which an innovation is compatible and incompatible with expectations

(existing norms and values), as well as the needs and capacities of its users or customers

(Tornatzky and Klein, 1982; Ward and Gibson, 2009). Our interviews with staff and

consultants from the 2008 campaigns seemed to support this view. For example, onecampaign aide told us that Our campaign did not utilize Facebook or other social net-

working sites as part of our campaign. We have an all-volunteer campaign and our out-

reach efforts focused largely on grassroots methods such as door-to-door campaigning

and other personal interaction with voters.1 Another noted that Facebook is more of a

national election tool right now.2

In the early days of web campaigning, incumbents were less likely than challengers to

campaign on the web, but a competitive race increased its use by incumbents and chal-

lengers alike (Foot and Schneider, 2006; Herrnson et al., 2007; Kamarck, 2002; Xenos

and Foot, 2005). Financially disadvantaged candidates also were less likely to have acampaign website in those early days (Gibson et al., 2003), but this has proved less of a

barrier subsequently. Financial resources and major party status still differentiate which

campaigns incorporate the latest technology and features, however (Foot and Schneider,

2006; Strandberg, 2009). Electoral attributes are less important today in differentiating

which campaigns have a website, but remain important determinants of the degree to

which they provide more sophisticated content and use their website to engage and

mobilize supporters (Gulati and Williams, 2007).

Internet diffusion depends on characteristics of the technology, including its relative

advantage, compatibility, versatility and visibility. A fifth characteristic, trialability,

refers to the ease of experimenting with an innovation as it is adopted (see Rogers, 2003).

Social networks in particular benefit from a large number of members or from serving a

specific niche, and they require technical expertise and specialized knowledge to be

designed and leveraged effectively. Campaign staff members recognized some clear



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advantages social networks hold over traditional campaign tools. They often referenced

the importance of Facebooks niche: We are trying to reach younger voters, college

students, and young professionals who have graduated in the last five years Most of

the people who use Facebook are younger students aged 1822.3 Other campaigns were

drawn by low cost: The biggest benefit of Facebook is that it is free. So in terms ofvalue, it is probably one of the best things that we can use to connect to voters. 4

That social networks are experiencing trialability is also evident in staff members

assessments. Right now the campaign is not ready to jump into the Facebook phenom-

enon. Were taking a conservative approach and watching closely what other campaigns

are doing to make changes in the next election.5

New features are going to always come up, new programs will be developed for it. It was six

degrees, then Friendster, MySpace, now Facebook. I think we are going to see this trend

continue to grow, whether or not its with Facebook or something else I cant say, but it willdefinitely be around. I think with more projects and more media attention, campaigns will have

no choice but to use this type of technology in their strategies.6

Media attention has given social networks enormous visibility (see, e.g., Vascellaro,

2006). For example, early press accounts generated significant coverage of MySpace

(e.g. Keen, 2006; Kelly, 2006; Lovley, 2006), YouTube (e.g. Fairbanks, 2006; Wasserman,

2006), and Facebook (e.g. Grynbaum, 2006; Yahr, 2006). And campaigns have taken

note: Nationwide this year social media networking has been huge, and that is becoming

obvious to ignore that is foolish quite frankly.7

Research hypotheses

We hypothesize that the constituency and electoral variables predicting which candidates

updated their Facebook page in 2008 will mirror those that predicted web presence in the

early days of Internet campaigning. Hypothesis 1 reflects the diffusion of innovation

expectation that early adoption will be more common among organizations whose com-

petitive environment offers incentives to innovate, costs not to do so, or is uncertain.

Hypothesis 1 more explicitly recognizes the influence that characteristics of the user

community (i.e. potential voters) have on early technology adoption decisions. In con-

trast with party-centered, proportional representation systems (Anstead and Chadwick,

2009; Carey and Shugart, 1995; Swanson and Mancini, 1996), US campaigns operate

under a candidate-centered (Wattenberg, 1991), winner-take-all system. As a result, the

affinity voters feel for individual candidates and how they relate to those personal char-

acteristics matter in their voting decisions, and thereby become a factor in election cam-

paign strategies and outcomes (e.g. Manzano and Sanchez, 2010; Ragsdale, 1980).

H1: (a) Democrats, (b) challengers, (c) well-financed candidates, and (d) candidates

running in competitive races are more likely to have a Facebook page.H1: Candidates from districts with (e) a low percentage of constituents who reside in

rural areas, (f) a high percentage of constituents who are white, (g) a high percentage

of constituents who have college degrees, and (h) a low percentage of constituents 65

years or older are more likely to have a Facebook page.



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Finally, the diffusion of innovation literature suggests four interrelated hypotheses

based on the profiles of adopter (or adopting organization) categories. The first is that

those who have already demonstrated a propensity to adopt other recent technologies

will be among the early adopters of the latest ones. The second recognizes that infor-

mation, professional networks (Howard, 2006) and other exposure to a new technol-ogy will expedite adoption. In addition to positive peer influence, adoption by

competitors constitutes a potential threat that will create pressure on candidates to

copy what their rivals are doing. For purposes of this analysis, we operationalize such

proximity contagion effects using the state as the geographically relevant peer/

competitor group. Finally, the decision to adopt and the extent of adoption or imple-

mentation differ, with the latter more dependent on internal, organization-specific

factors (Fichman, 2004).

H2: (a) Candidates who used other online technologies in the campaign (i.e. created acampaign website) will be more likely to have a Facebook page.

H2:(b) Candidates whose (geographic) peers and competitors have a Facebook page

will be more likely to have a page themselves.

H2: (c) Previous online technology adoption and geographic network adoption rate

will not increase the extentto which candidates use the new technology.

H2: (d) Organization-specific factors such as relative advantage and compatibility

will influence the extent of implementation and use; but the user community will be

less important.

Online social networking, 2006, 2008

For the 2006 elections, Facebook carved out a special space US Politics on its

network for all US congressional and gubernatorial candidates. Facebook initiated

these profiles by designing a standard template with only the candidates name, office

being sought, and basic contact information. Passwords that allowed the candidates to

assume responsibility for personalizing their profiles were forwarded to the offices of

the Republican and Democratic national committees, who then distributed them to

their candidates.8

After assuming control of their profile, candidates could initiate adiscussion topic, post comments on their wall, and post notes, event information and

videos and photographs. Facebook users who friended a candidate as a way to show

their support on their own profile could also post materials and comments. A candidate

did not need to activate the profile for users to register their support and post

content.

Facebook made some modifications for the 2008 elections. Political candidates were

given pages instead of profiles. These pages were similar to personal profiles but offered

the candidates greater capability to post various kinds of campaign material (e.g.

announcements, links to other pages, YouTube links, notes, photo albums, and event

information) and allowed their supporters to post their own materials (see Figure 1). A

second change was to eliminate the US Politics section and place all the candidates

pages within a Politicians sub-section of fan pages. Thus politicians were clustered near

celebrities and other public figures, sports teams, films, restaurants, bars and clubs,



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products, non-profits, and other organizations. In addition, current elected officials and

candidates for all levels of office in any country were eligible for Politicians pages as

long as an official representative of the politician created the page (see Figure 2).

Figure 1. An example of Facebooks fan pages for politicians.



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While the new format stood to increase the visibility of the candidates on Facebook,

only 8 percent of Americans reported that they had used social networking sites (although

not Facebook specifically) to learn about the campaigns during the 2008 cycle (Smith

and Rainie, 2008). Our analysis in this paper examines how extensively congressional

candidates in 2006 and 2008 used Facebook and offers insights into how this new tool

was integrated into their overall campaign strategy, given their limited, albeit ever-

increasing, reach with the American public.

Data and methods

Identifying candidates pages and activity

We began our examination of how campaigns are using Facebook by first identifying all

the Democratic and Republican nominees for every election for the US House of

Representatives in 2006 and 2008.9 We further differentiated candidates by their status as

an incumbent, a challenger, or contesting an open seat. In 2006, we coded a candidate as

Figure 2. Facebooks Politicians section.



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having a profile only if his or her campaign activated it by making some addition to the

standard template that Facebook provided to every candidate.

To identify the candidates who had their own Facebook page in 2008, we entered the

candidates name into Facebooks internal search engine within the Politicians section

during the final week in October. Second, we determined which of those major partycandidates who established a page updated their profiles as of 28 October, indicating at

least some minimal level of usage. We also conducted a content analysis on a sample of

200 candidate profiles to ascertain the extent to which they were using various features

on their pages. The coded features reported in this paper are: posting of campaign event

information and campaign videos; number of wall posts; and number of fan photos.10

The extent to which candidates employed these various features is indicative of a more

extensive implementation of the innovation.

Lastly, we conducted interviews with representatives from 53 different campaigns

who had first-hand knowledge of the campaigns Internet strategy and operationsbetween 13 October and 10 December 2008. The potential respondents for our inter-

views came from a list of 192 contests that we had selected to monitor closely through-

out the fall campaign season. Although our objective for conducting these interviews

was to understand why (and how) some campaigns chose to use Facebook and comple-

ment the statistical analysis rather than make generalizations, we did strive to obtain as

representative a sample as possible. We selected our sample by first identifying the

races that were deemed competitive in the Cook Political Report on 25 September

2008. This procedure provided us with 96 contests and 192 candidates. We then ran-

domly selected 96 contests out of the remaining non-competitive contests to completeour sample. Of the 384 candidates in our sample, 55 percent were Democrats, 34 percent

were incumbents, 30 percent were competitive, 26 percent were women, and 10 percent

were members of a minority ethnic group. On the whole, therefore, we have a rich set

of data for exploring our diffusion of innovation hypotheses in 2008.

Explanatory models for Facebook activity

To test our remaining research hypotheses and explain why some candidates were more

likely than others to activate their Facebook-created profiles in 2006, and to create,update and extensively use their Facebook pages in 2008, we estimated four multivariate

models. For 2006, the dependent variable Facebook activation was coded 1 if the can-

didate assumed control of and accessed his or her profile, and coded 0 if s/he did not.

We designated a profile as accessed if any additions were made to the standard template

that Facebook provided to every candidate.11 This yielded 802 cases for a multivariate

logistic regression (Model 1). For 2008, we estimated two logistic regression models of

Facebook activation for the major party House candidates. Model 2 analyzes which can-

didates were more likely to assume control of their pages by updating it in some way. The

dependent variablepage creation was coded 1 if the candidate had a profile created in

the Politicians section of Facebook. This yielded 816 cases to analyze. The third model

analyzes only those candidates who had a page in the Politicians section of Facebook,

n = 591), that is, those who were coded 1 forpage creation. The dependent variable in

Model 3 Facebook activation was coded 1 if the candidate updated or simply accessed

his or her page in any way and coded 0 if s/he did not.



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The fourth and final model employs OLS regression to analyze the extent to which

candidates Facebook pages were used in 2008. The dependent variable is an index com-

prised of four activity indicators. Although making the effort to update their Facebook

pages is simple, it may be the most important activity that candidates pursue in utilizing

online social networking sites. Moreover, supporters are more likely to interact withcandidates and leave with a more favorable impression the more candidates seem to be

engaged in their sites. Thus, some candidates may go further than simply updating their

profile with a picture and additional contact and biographical information. And Facebook

pages will have a greater potential impact when visitors are likewise engaged in these

sites. To capture a broader range of Facebook activity by both candidates and supporters,

we constructed our four-item index from the candidates posting of event information

and videos and supporters posting of photographs and wall posts for the 200 candidates

randomly selected for our more detailed analysis of candidate profiles. Each of these

items was normalized and then averaged into a single index ofFacebook activity.For each model, the independent variables are the same. The predictors for which

candidates would update their profiles in 2006 and create, update and evidence more

extensive use of them in 2008 include both electoral and constituency characteristics.

Our four electoral characteristics and three indicators of constituency demand have

been linked both theoretically and empirically to the presence of campaign websites in

previous studies, as noted above (e.g. Gulati and Williams, 2007; Herrnson et al.,

2007; Williams and Gulati, 2007). Dummy variables were constructed for Democrats,

challengers and candidates to open seats, with Republicans and incumbents serving as

the reference categories. Our indicator for the campaigns financial resources is thenatural log of total net receipts collected between 1 January 2007 and 31 December

2008.12 Our fourth electoral variable is the competitiveness of the race. A race was

coded as competitive if it had been designated as a toss-up, leaning toward one party,

or likely for one party by the Cook Political Reporton 3 November 2008.13 The indi-

cators that we used to account for constituency demand were: (1) the percentage of

residents over 24 with a college degree, (2) the percentage of residents classified as

white, (3) the percentage of residents living in rural areas, and (3) the percentage of

residents over age 64.14

The final two independent variables in our predictive models derive from the diffu-sion of innovation literature and represent a refinement over past studies of campaign

website innovation adoption. We operationalized organizational propensity to adopt new

technologies according to whether the campaign has a website presence in the current

election (1) or not (0).15

Proximity contagion effects are measured as the candidates geographic network (in

this case, their state) adoption rate. For each candidate, proximity is calculated as the

ratio of all the other Republican and Democratic candidates running for the House in the

same state who had a Facebook page to the total number of candidates minus one. All

candidates in a state with a Facebook page receive the same score as each other. And all

those without a page also receive the same score. But in both cases their scores differ

from the scores of candidates in other states with the same adoption characteristic who

we are treating in this model as more distant.

Proximity in a contagion effects model refers not simply to physical geography, but

also to a cultural mindset or values in ways analogous to the state political culture



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variables (Johnson, 1976) researchers have examined in state policy innovation studies

(e.g. Gray, 1973). It has also been used to refer to social network proximity (Welch and

Thompson, 1980), which would be difficult to operationalize for congressional candidates

(though easier to do for incumbents because of their co-sponsorship of legislation or

committee assignments, for example). Our purpose here is more limited: to demonstratethe potential utility of these types of diffusion of innovation variables, and thus caution

readers to recognize the limitations of our own and existing datasets for constructing

ideal measures.

Analysis and findings

In 2006, only 16 percent of major party candidates running for the House personalized

their Facebook profile in some way. By 2008, a large majority of both Democratic and

Republican candidates had a presence on Facebook. Among major party House candi-dates, 72 percent (591 out of 816) had a Politicians page, and about half of those candi-

dates updated it (406 of 816, or 49.8 percent), or a threefold increase from the previous

election cycle.

Turning to Hypotheses 1 and 2, Table 1 presents the results of the multivariate logistic

regression analyses of profile activation in 2006 and page creation by House candidates

in 2008 (see Table 1, Model 1, Activation 2006 and Model 2, Page creation 2008).

These results show that Democrats, challengers, better-financed candidates, and candi-

dates running in competitive races were the most likely to update their Facebook profile.

There is also evidence that candidates for open seat contests were more likely than incum-bents to activate their profile in 2006 and create a page in 2008 (the coefficients in Models

1 and 2 have a positive sign), although the coefficients for Model 1 are not significant.

The data presented in the third column of Table 1 include only the 2008 candidates

who created a Facebook page and then updated it in some way (n = 591). Among those

candidates who had a profile present in Facebooks Politicians section, Democrats,

challengers, better-financed candidates and candidates running in competitive races were

the most likely to update their Facebook profile. The effects for all of these variables are

significant.

The Democrats greater enthusiasm for this social network seems to reflect differ-ences in mobilization strategies that find Democrats more eager than Republicans to use

the Internet as a way to communicate with their supporters, and is consistent with their

larger number of supporters in the youth demographic. Although Republicans were early

pioneers in using recent new technologies to identify Republican voters, Republican

strategists and activists typically have worked within a top-down organizational structure

and, as the party in power, tended to rely on communication and mobilization strategies

that they pursued and successfully implemented in the past (see, e.g., Rasiej and Sifry,

2008; Sifry, 2006; Stirland, 2007; Thompson, 2008).

Other campaigns that are the most likely to embrace social networking sites are those

that view this new communication medium as an additional tool for winning votes within

specific blocs or the general electorate that constitute their user community. Challengers

must find a way to overcome the advantages of incumbency, which allows members

of Congress to draw upon an established network of supporters and contacts.



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Our interviews revealed that challengers in 2008 were looking to capitalize on every

resource that could give their campaign an advantage or net them additional votes. Butsince over three-fourths of incumbents already have to maintain both an office and a cam-

paign website, there is even less of a need to dedicate staff time to a third presence online.

When the race is more competitive, however, all candidates, regardless of incum-

bency status, may try to exploit every available technological resource to help them

Table 1. Multivariate logit analysis of Facebook presence in 2006 and 2008 House races.

Independent variables Model 1:Activation2006

Model 2:Page creation2008

Model 3:Page update2008

Party (Republicans = reference category)

Democrats 0.208 0.436*** 0.629**

0.213 0.166 0.245

Incumbency status (incumbents = reference category)

Challengers 0.56** 0.778*** 4.752***

0.261 0.254 0.784

Open seat candidates 0.266 0.599* 3.803***

0.368 0.322 1.108

Contributions received(natural log)

0.133** 0.315*** 0.507***

0.057 0.077 0.177

Competitive seat 0.677** 0.757*** 0.948***

0.289 0.231 0.334

Constituency variables

Percentage rural 0.000 0.003 0.004

0.008 0.006 0.008

Percentage white 0.009 0.018*** 0.018**

0.008 0.006 0.008

Percentage with college

degrees

0.018 0.034*** 0.036**

0.014 0.012 0.017 Percentage age 65 and older

0.012 0.052* 0.034

0.034 0.028 0.039

State average presence 1.713** 0.399 0.697

0.678 0.538 0.76

Campaign website presence 0.753* 0.616** 0.608*

0.449 0.293 0.336

Intercept 5.776*** 6.229 9.013***

0.985 1.165 2.502

n 802 815 591

Percentage correctlypredicted

84.2 69.5 79.2

Mode 84.0 50.1 68.7Pseudo R2 0.127 0.245 0.476

Note: Bold entries are unstandardized logit coefficients; standard errors are in italics. * p < .10, ** p < .05,*** p < .01; two-sided tests of significance.



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expand their electoral base and maximize turnout among their supporters. Since the cost

of updating profiles and posting content is extremely low, we believe that better-financed

candidates were more likely to activate a profile (2006) or create a Politicians page

(2008) on Facebook because campaigns with the most money also tend to have the most

sophisticated and professional organizations, i.e. where professionalism and cosmopoli-tanism allow them to leverage the competitive environment to their advantage. In this

setting, these are the campaigns that first see the Internet as a fundamental component of

any communication and mobilization strategy (cf. Howard, 2006).

The data presented in the lower half of the first two columns of Table 1 are mixed and

therefore only partially support Hypothesis 1(eh). These results show that candidates

running in districts with a higher percentage of college graduates were also the most

likely to have used Facebook in both 2006 and 2008, even though the effect in 2006 was

not statistically significant. Since Facebooks membership was originally open only to

those associated with colleges and universities, and continues to have a strong footholdin that demographic, it is no surprise that candidates running in districts with a high pro-

portion of college graduates would want to experiment with Facebook as a way to com-

municate their message.

We see a similar result with respect to ethnic diversity, which also increased the likeli-

hood of creating or updating a page in 2008, but had no statistically significant effect in

2006. It is not clear whether candidates running in districts with a younger population

were more likely to update their profiles or not. The coefficients for age are statistically

significant in only one of the models. In 2008, the presence of more senior citizens

decreases Facebook page creation (see Model 2) but had no effect on updating (seeModel 3). There was also no relationship between age and adoption in 2006.16 The level

of urbanization in the district had no effect in any of the three models. While there is

evidence linking Internet use and broadband adoption to place of residence (Horrigan,

2009), candidates from rural areas may not have been deterred from activating a page,

since simply viewing basic information on a page does not require the user to have a

high-speed connection.

The last two variables in Table 1, Models 1 and 2, present the tests of two new catego-

ries of predictive variables suggested by the diffusion of innovation literature that politi-

cal scientists who study campaign technology adoption have hitherto ignored. In 2006and 2008, past experience with technology has large, positive and significant coeffi-

cients, and thus provides strong support for Hypotheses 2(a) and 2(b). The coefficients

for geographic network are positive and statistically significant only in 2006, however.

This suggests that while early adopters are making decisions more on peer influence and

probably personal qualities and biases we are unable to measure, later adopters become

more strategic and take other factors into account such as the expectations of voters in

their geographic constituency.

Which campaigns use Facebook most extensively?Turning to Hypotheses 2(c) and 2(d), Model 3 presents the first, lower-effort measure of

the extent to which campaigns are implementing and using Facebook by analyzing those



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who have taken the step of posting some form of additional content to their pages after

initially creating the page (n =591). Table 2 presents the results of our second high-effort

measure of usage, Facebook activity, which counts the combined usage of four candi-date- and supporter-generated features. We collected these data for only a subsample of

House candidates who created pages, which accounts for the much smallern of 152 after

accounting for missing data.

Table 2. OLS regression analysis of Facebook activity in the 2008 House races.

Independent variables Model 4

Party (Republicans = reference category)

Democrats 0.142 * 0.086

Incumbency status (incumbents = reference category)

Challengers 0.086

0.115

Open seat candidates 0.293 **

0.136

Contributions received (natural log) 0.108 ***

0.042

Competitive seat0.152

0.113

Constituency variables

Percentage rural 0.006 *

0.003

Percentage white 0.003

0.003

Percentage with college degrees 0.024 ***

0.007

Percentage age 65 and over 0.014

0.011State average presence 0.273

0.258

Campaign website presence 0.066

0.178

Intercept 0.793

0.618

n 152

R2 0.192

Standard error 0.533Standard deviation of dependentvariable

0.606

Note: Bold entries are unstandardized OLS regression coefficients; standard errors are in italics. * p < .10, **p < .05, *** p < .01; two-sided tests of significance.



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The first finding of note is that the results for Model 3,page update mirror those for

page creation in 2008. This indicates that merely undertaking to add one (or more) type

of content to the page is indeed a very modest effort and low-bar criterion for extent of

usage. If a campaign takes the more important step of creating a Facebook presence, they

almost certainly return at some point to extend its content. Given that this third modelachieves the highest explained variance, .48, it suggests that actually updating the page

is likely a better indicator of what the concept adopting an innovation signifies. We also

believe it confirms that our summated index is a more stringent test of extent of usage

and conclude that it is probably also a more appropriate one for capturing this diffusion

of innovation concept in what we measure.

As a comparison of Table 1 with Table 2 shows, while there are some similarities

across the sets of results, the differences between them strike us as the more important

finding. The amount of contributions received is the only variable that is statistically

significant across all four models. For the three 2008 models, only the coefficients forDemocrats and the percentage of the college graduates living in the district are signifi-

cant and have a positive sign.

Neither the geographic proximity network nor a propensity to adopt other technology

innovations influences the degree to which campaigns implement and use its features.

Consistent with the way the literature differentiates these and Hypothesis 2(c), our data

substantiate that the two are clearly different decisions and subject to different processes.

In retrospect, it suggests an explanation for the low explained variance reported in 2006

by Foot and Schneider (between .09 and .20) and also here in Model 4. Like ours, their

dependent variables are features connoting extent of usage (in campaign websitesthrough informing, connecting, involving and mobilizing). Political science research has

not captured the constructs most related to extent of usage. We suspect that is because

researchers have not hitherto correctly differentiated these two decisions in a way that

would lead us away from simply appropriating the same conventionally employed inde-

pendent variables for both research questions.

The second striking difference across models is the direction of influence of incum-

bency status. In the previous three models, which we now reconceptualize as the technol-

ogy innovation adoption decision, challengers and open seat candidates were the early

adopters. In Model 4, the technology innovation implementation decisions about howextensively it will be used, those coefficients now show incumbents to be the most active

Facebook users in 2008. It is a finding that is consistent with a recent study of YouTube

usage in 2008 (Gulati and Williams, 2010) and another study of campaign websites

(Druckman et al., 2009). The diffusion of innovation literature and empirical studies sug-

gest that organization contingent factors such as compatibility are what matter here.

Incumbents have more capacity (organizational and resource wherewithal) to generate

the content that constitutes (campaign postings) or creates (supporter postings) usage.

As earlier comments of staff members illustrated, among House candidates, non-

incumbents campaigns are heavily dependent on volunteers, have trouble keeping up, or

do not have much happening from day to day about which to post.

The predictive power of the percentage of college-educated residents within House dis-

tricts and the percentage of residents living in rural areas are two more interesting findings.

It is the one instance where our findings are not wholly consistent with Hypothesis 2(d).



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Education could be viewed as corroborating the justifications and interpretations applied

to it in the campaign website studies, which tie it to Internet usage behavior. Yet age and

race/ethnicity continue to demarcate an important, albeit diminishing, digital divide in

social networks more generally, and much more so in Facebook specifically. The diffu-

sion of innovation literature suggests a new way of conceptualizing this result withrespect to the question of usage. College education is an attribute of this technology in the

sense that it was among those with a college education that Facebook originated and who

comprise its most significant user base. Choosing between a diminishing digital divide (a

maturation or developmental explanation) and this technology attribute explanation

would require testing the same model with another differently constituted social network

such as MySpace and across another type of technology innovation such as YouTube or

Twitter.

Discussion and future directions

The diffusion of innovation literature served to inform our research hypotheses, model

specification, analysis and interpretation of our data on who adopts new campaign tech-

nologies and to what extent. Our findings demonstrate the importance of differentiating

between the decision to become an early adopter and the extensive implementation and

use of the technology once it has been adopted. We anticipate that examination of the

next step assessing how well the technology performs, i.e. the quality and/or degree

of its success will likewise benefit from attending to these constructs in refining the

measures, and for the identification of additional or different variables in specifying ourmodels. We should not be using the same conventional variables for all three research

questions.

In addition to the point that the selection of independent variables should be made in

relation to the dependent variable under investigation, diffusion of innovation literature

and our findings underscore that the interrelationships between these two will change

depending on whether we are examining adoption, usage or performance. What competi-

tion means and why it is important or not in the decision to be an early adopter or

extensive implementer may change because our measures are only surrogates or imper-

fect operationalizations of their underlying constructs. In other words, and taking theexample of financial resources, we may be capturing one kind of motivator in the adop-

tion decision (e.g. an external one like norms and expectations surrounding high-visibility,

high level of office campaigns) and the consultants who comingle among them or are

historically associated with that state or district. In the innovations implementation,

however, that same measure of financial resources may be capturing quite a different

driver, namely an internal organization-specific facilitator or constraint in ways analo-

gous to the findings and reasons that large firms (Rogers, 2003) or states (Hage and

Aiken, 1967; Mohr, 1969; Walker, 1969) innovate more and better than smaller ones in

the same industry. Our results also demonstrate that the same independent variable may

be a significant influence on one decision (adoption) and not the other (implementation),

or work in the opposite direction, as we saw with competitiveness of the race.

This study has identified two new independent variables that are important to early

adoption: geographic proximity contagion and propensity to adopt campaign innovation



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technologies. Both measures could be expanded or refined in ways that the limitations of

our data did not allow. For example, geographic proximity defined in terms of state bor-

ders may not be as appropriate or finely measured as a physical proximity distance meas-

ure. And it may be that social interaction networks are additionally or more important.

Furthermore, the low percentage of variance explained in this and similar studies ofcampaign websites underscores the point that we have a long way to go in identifying

variables that appropriately and fully specify our extent of usage models. As Fichman

(2004: 54) observes, Research that goes beyond the dominant paradigm holds more

promise to tell us things about the IT innovation phenomenon that we do not already

know. Part of the reason is that internal organization-specific attributes are difficult for

political scientists to observe directly or publically and to measure quantitatively.

Campaigns are reluctant or unwilling and too busy to reveal this information, and it is

difficult to convert their responses and data into standardized measures comparable

across campaigns. Our interviews of staff members offer tantalizing insights that enrichthe interpretation of the findings in this study, but require more systematic and extensive

replication before they can be employed to adequately test our explanatory models.

Social networking sites like Facebook go beyond simply communicating the cam-

paigns theme and providing information on how to participate in it. Active engagement

by the candidate and a well-maintained site can make the candidate more accessible and

seem more authentic. It also can encourage a more professional discussion among sup-

porters.17 In addition to personalizing the candidate, Facebook puts a face on the other

supporters and facilitates interpersonal connections around activities other than politics.

And because Facebook organizes members by regional and organizational networks andgives greater access to profiles in ones own networks, offline meetings and connections

are possible.

Funding

This research received no specific grant from any funding agency in the public, commercial, or

not-for-profit sectors.

Notes

1. Bob Reilly, campaign of Republican Todd Platts (R-PA), email correspondence, 10 December2008.

2. Melissa Smith, campaign of Christine Jennings (D-FL), telephone interview, 28 October

2008.

3. Ryan, campaign of Senator John Sununu (R-NH), telephone interview, 28 October 2008.

Some of our interview respondents preferred that their last names not be used. In these cases,

we provide only their first names.

4. Ryan Anniston, campaign staff of Senator Roger Wicker (R-MS), telephone interview, 27

October 2008.

5. Peter, campaign staff of Ginny Brown-Waite (R-FL) telephone interview, 27 October 2008.6. Shawn, campaign staff of Parker Griffith (D-AL), telephone interview, 20 October 2008.

7. Ryan Anniston, campaign of Senator Roger Wicker (R-MS), telephone interview, 27 October

2008.

8. Ezra Calahan and Chris Hughes, telephone interview, 24 October 2006.



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9. Candidates were identified initially by monitoring several political websites (i.e. Politics1.

com, The DC Political Report, and Project Vote Smart) that maintained candidate lists. We

later cross-referenced our list with the official list produced by the Clerk of the House.

10. Other coded features not reported in this paper were: posting of personal information, contactinformation; use of mini feeds, external links, and candidate photo albums. Summaries of

these data are available from the authors on request.

11. We also performed a content analysis of profiles for 108 randomly selected candidates. In most

cases (83 percent), campaigns that replaced the profile picture from the default American flag

to a photo of the candidate also posted additional photos and other materials such as messages

to their wall and links to additional content. For 17 percent of the campaigns, therefore, their

only activity on Facebook was to upload a photo of the candidate.

12. Data on campaign contributions were obtained from the Federal Election Commission: www.

fec.gov/finance/disclosure/ftpsum.shtml.

13. See www.cookpolitical.com/charts/house/competitive_2008-11-03_17-12-33.php.

14. These data are from the 2000 Census and were obtained from the US Bureau of the Census.

Because the two candidates in the same congressional district will have the same values on

these four variables, the observations are not fully independent. Thus, a hierarchical linear

model or other multi-level model may have been more appropriate than the regression mod-

els that we estimated. We did not believe that estimating a more complex model and losing

some of the coefficients interpretive value was warranted, given the presence of only one

additional observation with the same district-level variables. Moreover, estimating logistic

and OLS regression models allow for better direct comparisons with previous studies of the

adoption of campaign websites and Facebook.

15. While this seems a low bar, alternative measures (employing mobilization features on their

websites in 2006 and having both a campaign website and a YouTube channel in 2008) only

marginally increased the explained variance and only marginally altered the coefficients and

their significance levels. We did not have campaign website mobilization features data for

2008 and no House candidates had YouTube channels in 2006. To be consistent in how we

operationalized this independent variable across the two election cycles, we used only the

website presence measure in our predictive models.

16. Substituting percentage of residents under 18 in 2000 (hence under 24 or 26 in 2006 and

2008, respectively) for over age 64 in our models makes negligible difference in the coef-ficients or their level of significance, and decreases the variance explained by the models.

17. Ryan Alexander, staff of Senator Evan Bayh, telephone interview, 18 October 2006.

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Christine B. Williams is Professor of Political Science in the Global Studies Departmentat Bentley University, and received her MA and PhD degrees from Indiana University.

She currently serves as North American Managing Editor of the Journal of Political

Marketing, and she is Associate Editor and on the senior editorial board of theJournal of

Information Technology and Politics, among others. Her research area is political com-munication, with emphasis on new and emerging technologies and e-government. Her

work has appeared in academic journals and conference proceedings, trade and profes-

sional association publications, and news media outlets worldwide.

Girish J. Jeff Gulati is an Associate Professor of Political Science at Bentley University

and he earned his PhD from the University of Virginia. His research areas are telecom-

munications policy, e-government, political communication and the news media, cam-

paigns and elections, and representation in theory and practice. He is also a member of

the Regional Working Group on Modern-Day Slavery and Human Trafficking at the CarrCenter for Human Rights at the Harvard Kennedy School of Government and serves on

the editorial boards of theJournal of Information Technology & Politics and theJournal

of Political Marketing.

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