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Journalism Studies
ISSN: 1461-670X (Print) 1469-9699 (Online) Journal homepage: http://www.tandfonline.com/loi/rjos20
Why Web Analytics Click
Edson C. Tandoc Jr.
To cite this article: Edson C. Tandoc Jr. (2015) Why Web Analytics Click, Journalism Studies,
16:6, 782-799, DOI: 10.1080/1461670X.2014.946309
To link to this article: http://dx.doi.org/10.1080/1461670X.2014.946309
Published online: 14 Aug 2014.
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WHY WEB ANALYTICS CLICK
Factors affecting the ways journalists use
audience metrics
Edson C. Tandoc Jr.
This study, based on a survey of 206 online editors, tests a theoretical framework guided by field
theory that seeks to explain the patterns of how journalists use Web analytics in their news work.
Using structural equation modeling, this study finds that journalists’perception of competition in
the field and their conceptions of the audience as a particular form of capital lead them to using
Web analytics in particular ways.
KEYWORDS audience; field theory; gatekeeping; journalism; journalists; online news; Web
analytics
Introduction
The journalistic field is evolving, currently witnessing an unparalleled level of
interaction between journalists and the “people formerly known as the audience”(Rosen
2006). Technology is facilitating a lot of these changes. Feedback to newsrooms used to
come in the form of phone calls, letters to the editor, readership surveys, or television
ratings that either came from a self-selected subset of the actual audience or took a lot of
time to put together (Beam 1995; Gans 1979; Schlesinger 1978). Now, Web analytics
provides journalists with access to a wealth of immediate information about the online
audience. This new audience feedback mechanism allows journalists to know more about
the audience and what the audience does with news (Napoli 2011).
Theories of news construction identify the audience as an important influence
(Domingo et al. 2008; Gans 1979; Herman and Chomsky 2002; Shoemaker and Reese 2013;
Shoemaker and Vos 2009). Since journalism also needs resources to thrive, journalists have
traditionally balanced providing what the public needs and what the audience wants. In
the past, journalists did not really know who the actual audience was. What they thought
the audience wanted was shaped by their own guesses, informed to some extent by the
preferences of their families, friends, and superiors in the newsroom (Gans 1979;
Schlesinger 1978; Wulfemeyer 1984). But the guessing game might soon be over, for
audience preferences are now easily recorded and reported by Web analytics now widely
used in many online newsrooms.
The question then is how journalists are using what they now know about the
audience. Studies have documented how journalists are using audience information from
analytics in various ways, ranging from deciding where to place stories to evaluating job
performance (Anderson 2011; Fischer 2014a; Lowrey and Woo 2010; MacGregor 2007;
Usher 2013;Vu2013; Jenner and Tandoc 2013). This focus on using Web analytics to be
able to cater to audience preferences unfolds against the backdrop of an uncertain
financial future for journalism (Lowrey and Woo 2010;Vu2013). But journalists also follow
Journalism Studies, 2015
Vol. 16, No. 6, 782–799, http://dx.doi.org/10.1080/1461670X.2014.946309
© 2014 Taylor & Francis
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WHY WEB ANALYTICS CLICK
particular norms that frown on compromising editorial autonomy (Christians et al. 2009).
Thus, news judgment in this age of analytics is increasingly becoming a fragile balancing
act between editorial autonomy, on one hand, and the increasing influence of the
audience, on the other (Tandoc 2014).
Through a survey of online editors in the United States, this study seeks to
understand how online journalists are using Web analytics and what factors affect these
usage patterns. Guided by the framework of field theory (Bourdieu 1993,1998), this study
investigates pathways through which the influence of the audience, communicated
through clicks that are tracked, stored, and analyzed using Web analytics, flows into the
news construction process. In doing so, this study seeks to contribute to understanding
the intersection between traditional journalistic norms that have guided news work in the
past and the affordances of new audience information systems that are challenging these
journalistic traditions as journalism ventures into an unsure future.
Literature Review
Field Theory and Journalism
A number of its variants abound in social science but field theory can be considered
as an “approach”with a “sufficiently distinct core”(Martin 2003, 3). From its origins in the
physical sciences, such as in electromagnetism, field theory crossed over to sociology
through the work of numerous scholars, including Kurt Lewin, who argued that behavior is
the function of both the person and the environment (Martin 2003). It reached journalism
studies through the work of French scholar Pierre Bourdieu, who proposed one of the
better known variants of field theory (Benson and Neveu 2005; Neveu 2007; Martin 2003).
Though Bourdieu made a few references to Lewin in his earlier work (Martin 2003),
their approaches to field theory are “not interchangeable”(Shoemaker and Vos 2009, 118).
One of the fundamental differences between the two approaches lies in the role of history.
Bourdieu argued that history was important in understanding the field (Shoemaker and
Vos 2009). In contrast, Lewin argued that “behavior should not be seen as caused by
something in the past (let alone the future), but must be grounded in an understanding of
the totality of the current situation”(Martin 2003, 18). They both agree, however, in a
holistic approach to understanding phenomena, as field theory encompasses micro, meso,
and macro levels (Shoemaker and Vos 2009).
For Bourdieu (1993, 162), a field is a “an independent social universe with its own
laws of functioning, its specific relations of force, its dominants, and its dominated, and so
forth”(Bourdieu 1993, 163). It is marked by eternal struggles for either preservation or
transformation (Bourdieu 2005,1985). The concept of a field has been used in numerous
studies, conceptualizing as fields concepts such as the arts (Bourdieu 2005), organizations
(Emirbayer and Johnson 2008), and even sports (Stempel 2005).
Journalism is an example of a field (Benson 2006; Bourdieu 1998,2005; Handley and
Rutigliano 2012). It is a “microcosm with its own laws, defined by its own position in the
world at large and by the attractions and repulsions to which it is subject from other such
microcosms”(Bourdieu 1998, 39). The field of journalism exists alongside the political and
economic fields, and as such the struggle to transform or preserve it becomes all the more
important (Benson and Neveu 2005). This is why autonomy is central to field theory. Fields
are always confronted with oppositions between heteronomous, or external forces, and
autonomous poles, or resources that differentiate a social space from other fields (Neveu
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2007). For instance, the journalistic field is torn between the pressure of selling
advertisements and increasing circulation, on the one hand, and the influence of the
state, on the other (Bourdieu 2005; Neveu 2007). Thus, Champagne (2005, 50) observed:
“The history of journalism could well be in large part the story of an impossible
autonomy—or, to put it in the least pessimistic way, the unending story of an autonomy
that must be re-won because it is always threatened.”
Those who participate in the struggle are called agents who are expected to play by
the laws of the “social universe”referred to as the doxa,ora“system of presuppositions
inherent in membership in a field”(Bourdieu 2005, 37). The journalistic doxa refers to
norms that have guided news work. It is “the unspoken, unquestioned, taken-for granted,
understanding of the news game and the basic beliefs guiding journalistic practice”(Willig
2013, 374). An example would be the criteria that journalists use to determine what makes
something newsworthy (Willig 2013). How agents understand and apply these rules
depend, in part, on the concept of habitus, which is a “conceptual tool for analyzing how
social agents have different positions in the social space, and how these serve as different
dispositions for social action”(Willig 2013, 378). It refers to a “feel for the game”that shape
practices and perceptions (Johnson 1993, 5). A concept that is difficult to measure and
observe, the habitus is important because of its manifestation in how agents perceive the
field. These perceptions are crucial, for they influence decision-making and day-to-day
actions (Born 2003).
The possession of capital is what allows agents to participate in the struggle
(Handley and Rutigliano 2012). Forms of capital are both “weapons”and “stakes”within a
field (Emirbayer and Johnson 2008, 11). They determine an agent’s location in the struggle,
and as such they are something that agents seek to amass. There are four forms of capital
in the journalistic field: economic, cultural, social, and symbolic (Benson 2006; Benson and
Neveu 2005; Sallaz and Zavisca 2007; Willig 2013). The dominant form in most fields is
economic capital, which refers to anything convertible into money. For example, the
journalistic field depends a lot on advertising revenues and audience size (Benson 2004,
2006; Benson and Neveu 2005; Siapera and Spyridou 2012). Cultural capital refers to the
accumulation of knowledge within the field that elevates an agent’s social position by
virtue of competence (Siapera and Spyridou 2012; Bourdieu 1986). The skills and
educational credentials of online journalists, for example, have been used to assess the
cultural capital of the online journalistic field (Siapera and Spyridou 2012). Cultural capital
refers to “such things as educational credentials, technical expertise, general knowledge,
verbal abilities, and artistic sensibilities”(Benson 2006, 190). Social capital refers to
“durable networks of relationships through which individuals can mobilize power and
resources”(Bourdieu and Wacquant 1992, 119). A journalist’s family and friends form an
example of a journalist’s social capital. Finally, symbolic capital refers to prestige (Benson
and Neveu 2005; Siapera and Spyridou 2012; Johnson 1993), a form of socially
misrecognized economic, cultural, or social capital that leads to recognition of legitimate
competence (Bourdieu 1986). Symbolic capital refers to concepts such as reputation and
image. Since they are the basic currencies that agents use in their struggles within the
field, forms of capital are also used to amass more capital and elevate one’s position in the
field. These forms of capital can also be converted into one another and “organizations
who dominate the field are those successful in converting one form of capital into the
other”(Benson and Neveu 2005, 4).
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WHY WEB ANALYTICS CLICK
These conceptual building blocks of Bourdieu’s field theory demonstrate its holistic
approach to understanding phenomena. Field theory ranges from macro (such as locating
fields alongside one another) to micro (such as understanding perceptions and actions of
individual agents within a field) levels. Since this study attempts to contribute to a
research area generally focused on understanding how Web analytics is influencing, if not
potentially changing, the internal logic of the journalistic field by focusing on the decision-
making of individual agents with regards to Web analytics, it is approaching field theory
from the micro level.
The Audience as Capital
Studying how journalists define the audience is important because how they
imagine the audience influences their news outputs (de Sola Pool and Shulman 1959).
Traditional definitions of the audience that carried an unflattering view of a passive
receiver (McQuail 2010) had given rise to conceptualizations of the audience as mere
recipients, products, or commodities (Loosen and Schmidt 2012; Webster and Phalen
1994). For example, Ang (1991) referred to the conceptualization of the audience as a
market—consistent with the conceptualization of economic capital. But the audience is
also conceived of as a group of citizens (Butsch 2011). Serving the public is central in the
normative functions of journalism (Christians et al. 2009).
The conceptualization of a passive, mass audience might have served traditional
media institutions well, but it no longer accurately reflects how much the audience has
evolved. The public is increasingly taking part in the news construction process through
social media and other new media platforms (Singer and Ashman 2009; Hermida 2011;
Bruns 2005). Thus, the audience is not merely a form of economic capital. The audience is
also important in legitimizing news organizations. A news organization that fulfills its
responsibilities to the audience enhances its reputation. Therefore, the audience is also a
form of symbolic capital. What matters, however, is how journalists conceive of the
audience, for this conception of the audience affects news practices (de Sola Pool and
Shulman 1959; Hinnant, Len-Ríos, and Oh 2011). So how do journalists conceive of their
audience now, especially in this age when knowing what the audience wants is only a
click away?
Web Analytics in Journalism
The internet is not only changing the nature of interaction between audiences and
messages, but it is also a “powerful research tool, one that allows researchers to observe
news reading behavior more reliably and less obtrusively than had been possible before”
(Tewksbury 2003, 695). Keeping up with the changing audience is a task that new forms of
audience measurement, such as Web analytics technology, have immediately caught up
with (Kaushik 2010). Web analytics refers to “the measurement, collection, analysis and
reporting of internet data for the purposes of understanding and optimizing web usage”
(DAA 2008, 3). Web analytics programs enable website owners to monitor how users of
their respective websites behave online by providing metrics, which refer to “any
quantitative measure of passive viewing or consumption of content by internet users”
(Krall 2009, 387). From providing hourly data, Web analytics tools have evolved into being
able to provide real-time data, such as the program Chartbeat, to providing predictions of
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how a story will perform in terms of traffic, such as the program Visual Revenue
(Sonderman 2011).
A growing number of studies have established how newsrooms have institutiona-
lized tracking audience metrics (Anderson 2011; MacGregor 2007; Boczkowski 2010;Vu
2013; Tandoc 2014), prompting other scholars to also warn about the potential ethical
ramifications of a journalism increasingly being dependent on Web analytics (Tandoc and
Thomas 2014). A survey of newspaper editors in the United States found that 84 percent
monitored Web traffic regularly, with 52 percent of them saying they did so every day (Vu
2013). Through Web analytics, journalists can get information about how many views each
story is getting, how much time readers are spending for each story, what pages lead
them to the homepage, and what pages they were viewing when they decided to leave
the website, among others.
While Web analytics provides journalists with a wealth of information about
audience behavior, it does not provide a complete picture (Wiggins 2007). Some confusion
also surrounds the selection of metrics to monitor, of what data constitute key
performance indicators. The Web analytics industry has shifted its standard from hits, to
page views, and to unique monthly visitors—each of which has inherent weaknesses in
terms of accurately capturing what it is that journalists hope to learn from Web analytics
(Krall 2009). Because of these issues, “the possibility of carrying out a census-like
measurement of users’online activities is clearly limited by the lack of a link between
activity and user”(Bermejo 2007, 225). These gaps in Web analytics are filled by other
audience information systems, such as surveys and customer databases (Wiggins 2007),
but they also leave wide spaces for editorial judgment. The need to fill these gaps in
audience research rests on the shoulders of journalists, highlighting the tension between
knowing what the audience wants and imposing one’s news judgment.
Studies found that different factors affected how journalists use Web analytics. For
example, publicly owned, sophisticated, and bigger news websites tend to track online
audience feedback more frequently than smaller websites did (McKenzie et al. 2011). The
degree of uncertainty an editor felt also influenced the extent to which Web traffic was
monitored (Lowrey and Woo 2010). This perception of uncertainty is consistent with the
theorized manifestation of the habitus (Born 2003; Willig 2013). In contrast, a study found
that while Al Jazeera journalists recognized the importance of Web metrics, they “felt
content to ignore metrics in what was perceived as the safe-space of the Al Jazeera
newsroom”(Usher 2013, 343). The journalists of the well-funded newsroom were coming
from a different habitus devoid of financial insecurity. But in an unstable journalistic field
such as the one in the United States, a newspaper editor’s perception of the economic
importance of high readership predicts willingness to change decisions based on audience
metrics (Vu 2013). This perception of the importance of the audience clearly refers to a
conceptualization of the audience as a form of capital.
Research Questions
A theoretical framework developed based on interviews with online journalists and
case studies of three online newsrooms had proposed that “how journalists conceive of
the audience as a form of capital influences the extent to which journalists integrate
audience feedback from web analytics in their news work”(Tandoc 2014, 559). Guided by
the key concepts of field theory and what previous studies had found, this current study
786 EDSON C. TANDOC JR.
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WHY WEB ANALYTICS CLICK
tests this theoretical framework to understand factors that affect how journalists use Web
analytics in their work (see Figure 1).
The proposed theoretical framework argues, consistent with what Boczkowski (2004,
2005) had proposed, that several factors shape the impact of technology on news work.
The use of technology alone is insufficient to understand its effects on news construction.
The proposed framework being tested here looked at the manifestation of the concept of
habitus by accounting for the effect of journalists’perception of uncertainty in the field
(Lowrey 2012; McKenzie et al. 2011; Tandoc 2014). In this current study, this perception of
uncertainty is operationalized by considering journalists’perception of the degree of
competition they face. The framework also looks at the impact of how journalists conceive
of their audience as a form of capital (Vu 2013; Tandoc 2014), which is also a measure of
journalists’representations of the audience (Boczkowski 2005). Thus, based on this
framework and the results of previous studies, the following research questions are
proposed (see Figure 1):
RQ1: What are the patterns with which online editors use Web analytics?
RQ2: How does perception of competition in the field impact (1) frequency of use of
Web analytics and (2) conception of the audience as capital?
RQ3: How does frequency of Web analytics use impact patterns of Web analytics use?
RQ4: How does conception of the audience as a particular form of capital impact
particular patterns of Web analytics use?
Perception of
competition
Patterns of using
Web analytics
on news work
Audience as economic
and symbolic capital Adoption of Web
analytics
FIGURE 1
Theoretical framework. This is a modified version of a theoretical framework proposed
earlier based on case studies of three online newsrooms (see Tandoc 2014). For the
purposes of clarity and theory testing, the original representation of the framework is
modified here to be consistent with the layouts of the operational frameworks
developed and tested here (see Figures 2 and 3)
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Method
This study is based on an online survey of 206 online editors in the United States.
Online surveys themselves are another manifestation of the changing relationship
between message producers and audiences: survey platforms, many of which offer free
access, have allowed practically anyone with internet access to create a survey, collect
data, and report findings (Couper and Miller 2008). Online survey respondents tend to be
internet savvy (Kwak and Radler 2002), which makes this survey format particularly salient
for studies involving populations that can be considered digital natives, such as studies of
online journalists.
Sampling
A sample of 1103 online news editors in the United States was drawn from the
database provided by CisionPoint, a software solution that offers a list of media contacts in
the United States and a few other countries. Only editors that worked for online news-
related desks were included in the sample. Thus, editors working for online newsrooms
not listed under the news category, such as gardening or beauty blogs, were excluded. An
email invitation containing a link to the survey was sent, followed by four reminder emails
in a span of a month, yielding 276 responses, or a 24 percent response rate typical of
online surveys (see Cobanoglu, Warde, and Moreo 2001; Kwak and Radler 2002). A total of
56 editors opted out of the survey, for an active refusal rate of about 5 percent. Incomplete
responses were excluded, leaving the study with at least 206 completed responses.
The average age was 44.5 years (SD = 11.41 years). Some 67 percent were males.
The sample also represented editors from every level of the newsroom hierarchy. Some 32
percent were Web editors while some 25 percent were editors-in-chief. More than 92
percent reported that their newsrooms track metrics, with several newsrooms using
multiple software programs. The most popular program was Google Analytics (78.3
percent) followed by Adobe Site Catalyst (previously known as Omniture; 44.4 percent)
and Chartbeat (34.9 percent). Online newsrooms of different sizes based on traffic were
also represented in the sample. The average number of unique monthly visitors was 4.08
million with a huge standard deviation of 18.7 million. The median value was 3.5 million
unique monthly visitors. Some 44.2 percent reported working for news organizations that
also publish a daily newspaper, 20.4 percent with a weekly paper, and 9.2 percent with a
television station.
Questionnaire and Variables
The survey took about 15 minutes to complete and asked editors to answer a variety
of questions: from demographic information, to information about their organization, to
questions specific to the concepts pertinent to this study. Most of the questions were
developed from previous studies; from interviews with journalists and observations in
three online newsrooms (Tandoc 2014); and from consultations and pretests with five
journalism professors who teach Web analytics and five professional journalists who have
Web analytics experience.
Patterns of Web analytics use. The online editors in the survey rated, using a five-point
scale from “very frequently”(5) to “never”(1), how frequently they engaged in each of 16
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different uses of Web analytics as documented from previous surveys and studies (Fischer
2014a,2014b; Lowrey 2012; MacGregor 2007;Vu2013; Tandoc 2014; Jenner and Tandoc
2013; see Table 1).
The statements were analyzed using exploratory factor analysis to determine
classifications of Web analytics use, the results of which will be reported in the next
section in response to RQ1.
Perception of degree of competition. The online editors were asked to rate, using a five-
point scale from “very competitive”(5) to “very not competitive”(1), the degree of
competition they perceive in their main market in terms of readership, advertising, and
quality of reporting. The participants perceived these areas as competitive, with the area
of advertising as the most competitive (mean = 4.23, SD = 0.91).
Conception of the audience as capital. The online editors were asked to rate, using a
five-point Likert scale from “strongly agree”(5) to “strongly disagree”(1), their level of
agreement with each of six statements about the audience developed from previous
interviews (Tandoc 2014). An exploratory factor analysis (principal axis factoring) using
oblique rotation (oblimin) found two underlying factors of how the editors conceived
TABLE 1
Patterns of Web analytics use
Site-
oriented
Content-
oriented
Traffic-
oriented Mean SD
To determine which stories are doing well −0.039 −0.010 −0.899 4.26 1.00
To determine which stories are not
doing well
0.022 −0.100 −0.706
To decide which stories to cover −0.116 −0.810 −0.146
To determine what stories to do follow-
ups on
0.060 −0.709 −0.114
To determine how to cover a story −0.058 −0.950 0.125
To plan deployment of reporters 0.030 −0.804 0.102 0.97
To help evaluate the performance of
employees
0.085 −0.466 −0.111 0.98
To know which topic areas to increase
coverage in
0.034 −0.666 −0.180
To know where to increase coverage in terms
of geographic location
0.258 −0.392 0.017 0.96
To monitor if the site is working properly 0.530 −0.015 −0.222
To determine where traffic is coming from 0.393 0.023 −0.404
To decide how to write the headline 0.374 −0.210 −0.226
To determine story placement in the
homepage
0.478 −0.147 −0.216
To help design the website 0.835 −0.017 0.074
To improve user experience 0.926 0.002 0.065
To monitor how the story I wrote/edited is
doing
0.186 −0.159 −0.429
Eigenvalues 7.54 1.65 1.10 ––
Variance explained (%) 47.15 10.34 6.84 ––
An exploratory factor analysis (principal components analysis) using oblique rotation (oblimin)
found three underlying patterns of use, KMO = 0.90, Bartlett’s test of sphericity, χ
2
(120) = 1425.89,
p< 0.001, accounting for 67 percent of the variance. Values in bold refer to factor loadings.
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of the audience, explaining 78 percent of the variance, KMO = 0.75, Bartlett’s test of
sphericity, χ
2
(15) = 734.39, p< 0.001. The first factor referred to the conception of the
audience as symbolic capital (eigenvalue = 3.42, variance = 57 percent). This was
measured by statements that described the audience as important because a bigger
audience meant (1) more credibility for the company (mean = 3.51, SD = 0.91), (2) higher
trust (mean = 3.26, SD = 0.82), and (3) better reputation (mean = 3.46, SD = 0.87). The
second factor referred to economic capital (eigenvalue = 1.28, variance = 21 percent)
measured by statements that described the audience as important because a bigger
audience meant (1) more advertisers (mean = 3.91, SD = 0.87) and (2) higher advertising
revenues (mean = 3.85, SD = 0.85). In general, the editors in the survey perceived the
audience more as a form of economic capital (mean = 3.88, SD = 0.84) than as a form of
symbolic capital (mean = 3.42, SD = 0.79).
Frequency of Web analytics use.The online editors reported how frequently they used
each of the following analytics software in their day-to-day work using a six-point scale
from “several times a day”(6) to “not using the program”(0): Chartbeat, Visual Revenue,
Omniture, ComScore, Google Analytics, and another program if they were using one that
was not listed. Since this variable seeks to measure the degree of exposure to metrics data,
a summative index was used.
Data Analysis
Structural equation modeling (SEM) with maximum likelihood estimation was used
to answer the research questions and test the proposed framework (see Figure 1). In
general, SEM is a priori and as such it leads researchers to approach studies based on
theoretical models (Kline 1998). SEM is also particularly appropriate for studying variables
that are not directly observable (Byrne 2010; Schreiber et al. 2006)—the kinds of variables
that researchers deal with most of the time in media sociology. Measurement models were
first tested to confirm the reliability of the concepts used, then an operationalized
framework was tested based on the proposed theoretical framework and the initial results,
as will be discussed in the next section (see Figure 2).
Results
RQ1 asked how journalists use Web analytics in their news work. An exploratory
factor analysis (principal axis factoring) using oblique rotation (oblimin) was conducted to
determine patterns of Web analytics use across the 16 statements that online editors had
to rate using a five-point scale. Three factors emerged from the analysis, accounting for 67
percent of the variance, KMO = 0.90, Bartlett’s test of sphericity, χ
2
(120) = 1425.89, p<
0.001. The first factor includes three items (eigenvalue = 7.54, variance = 47.15 percent).
This factor clearly refers to a site-oriented use, or using Web analytics to make sure the
website is running smoothly. The second factor includes five items (eigenvalue = 1.65,
variance = 10.34 percent) which clearly referred to a content-oriented use. This factor refers
to using metrics to guide editorial decisions, such as using metrics in story selection and
decisions to conduct follow-ups. Finally, the third factor includes two items (eigenvalue =
1.10, variance = 6.84 percent) and refers to traffic-oriented use, or using Web analytics to
monitor traffic (see Table 1).
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SEM is a two-step analytical process: the first component involves a measurement
model while the second component involves the structural model (Byrne 2010; Schreiber
et al. 2006; Kline 1998). The first stage is basically a confirmatory factor analysis (CFA)
which tests the multidimensionality of theoretical constructs involved in a structural
model, while the second stage involves testing of the hypothesized causal model itself
(Byrne 2010; Schreiber et al. 2006; Kline 1998). Thus, in order to proceed with RQ2, RQ3,
and RQ4—essentially testing the theoretical model—the measurement models for the
main constructs had to be evaluated first using the recommended goodness-of-fit
measures (Hu and Bentler 1999; Schreiber et al. 2006).
A measurement model that included the three latent constructs in the model—
perception of competition, audience as economic capital, and audience as symbolic
capital—and their corresponding observed variables was tested to validate the constructs’
multidimensional structure. The CFA model yielded a good fit: Root Mean Square Error of
Approximation (RMSEA) = 0.07, p of Close Fit (PCLOSE) = 0.09; Comparative Fit Index (CFI)
= 0.97; Tucker–Lewis Index (TLI) = 0.95. This confirms the results of the exploratory factor
analysis for conception of the audience and also validates the reliability of the scale for
perception of degree of competition. Based on the results of the exploratory factor
analysis discussed above, a measurement model was also tested that included the three
underlying constructs in using Web analytics: site-oriented use (using Web analytics to
make sure the website is running smoothly); traffic-oriented use (using the site to monitor
traffic); and content-oriented use (using metrics for editorial decisions). The measurement
model to validate these constructs using the observed variables also showed a good fit to
the data: RMSEA = 0.07, PCLOSE = 0.14; CFI = 0.98; TLI = 0.97.
FIGURE 2
Operational framework
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Explaining Web Analytics Use
The relationships explored in RQ2, RQ3, and RQ4 are all nested in the proposed
theoretical framework, which had to be operationalized following the initial results discussed
above (see Figure 2). This hypothesized operational model, grounded in both theory and
previous literature, fits the data well: χ
2
(160) = 246.48, p< 0.001; RMSEA = 0.05, PCLOSE =
0.418; CFI = 0.96; TLI = 0.95; expected cross-validation index (ECVI) = 1.885. However, it still
contained non-significant estimates that had to be cleared for parsimony (Byrne 2010). This
process yielded a simpler model, one that is still consistent with the proposed theoretical
framework but also with unexpected relationship patterns (see Figure 3).
RQ2 asked about the impact of the perception of competition on (1) the frequency
of using Web analytics and (2) the conception of the audience as capital. The results
showed that perception of competition affected the extent of Web analytics use, β= 0.22,
p< 0.01. It did not affect the conception of the audience as capital.
RQ3 asked about the impact of the frequency of using Web analytics on patterns of
Web analytics use. Those who use Web analytics more often tend to engage in site-
oriented (β= 0.46, p< 0.01) and traffic-oriented (β= 0.22, p< 0.001) uses of Web analytics
more than those who use analytics less often. Frequency of Web analytics use, however,
did not predict content-oriented use. This shows that the adoption of Web analytics does
not directly translate into its normalization. The use of Web analytics, at best, directly leads
FIGURE 3
Structural model. The model, grounded in both theory and previous studies, fits the data
well, χ
2
(160) = 246.48, p< 0.001; RMSEA = 0.05, PCLOSE = 0.418; CFI = 0.96; TLI = 0.95;
ECVI = 1.885
792 EDSON C. TANDOC JR.
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WHY WEB ANALYTICS CLICK
to its practical applications of running the site and keeping track of traffic. So what
predicts integration of Web analytics into the editorial decision-making process?
RQ4 asked about the impact of the conception of the audience as a form of capital
on patterns of Web analytics use. The conception of the audience as a form of symbolic
capital predicted content-oriented use of Web analytics, β= 0.18, p< 0.001. Conception of
the audience as a form of symbolic capital directly predicts using Web analytics in content-
related decisions, such as determining stories to write about.
But the final model also showed other significant pathways not included in the
original set of research questions. First, there was a significant link from conceiving of the
audience as a form of economic capital to conceiving of the audience as a form of
symbolic capital, β= 0.43, p< 0.01. Second, an interesting relationship exists between the
three patterns of Web analytics use. Traffic-oriented use leads to content-oriented use (β=
0.60, p< 0.001), which leads to site-oriented use (β= 0.47, p< 0.001). It appears that while
integrating Web metrics in editorial decisions is directly influenced by conception of the
audience as symbolic capital, it is also indirectly affected by an awareness of the changing
field that has made adoption of Web analytics inevitable. Then, exposure to Web traffic
exposes editors to audience preferences, which then leads to a renegotiation of editorial
judgment that now incorporates this awareness of what the audience wants (see Figure 3).
Discussion and Conclusion
Faced with the reality of declining economic capital for traditional journalism still
unmatched by the slow increase in digital revenues, journalists clearly perceive capital
instability within the journalistic field. This has further opened up the gates to audience
influence communicated through clicks, compounded by the omnipresence of audience
feedback in the newsroom. Online editors use Web analytics mainly to keep track of
audience behavior, but metrics are also increasingly being used for editorial decisions such
as story selection, story placement, and even headline writing (Vu 2013; Lowrey and Woo
2010; Anderson 2011; Lee, Lewis, and Powers 2014; Tandoc 2014). This is confirmed by the
findings of this current study (see Table 1). The survey findings showed that online editors
still use Web analytics primarily for monitoring traffic, but this use is also leading them to
use audience information in their decision-making processes.
Editors’perception of the degree of competition in their market leads them to use
Web analytics more often, consistent with what Lowrey and Woo (2010) found about the
impact of perceived uncertainty and with what the theoretical framework tested here had
earlier argued (Tandoc 2014). This frequency of using Web analytics leads to using it
primarily to monitor traffic—in effect taking advantage of the technology’s basic
affordances. But monitoring Web traffic leads editors to use analytics to inform content-
related decisions, thereby allowing audience preferences to influence editorial judgment.
This is, however, only one path to how Web analytics influences news judgment.
Vu (2013) found that newspaper editors who value the economic importance of the
audience were likely to adjust editorial decisions based on Web analytics. This current
study, however, did not find a direct relationship between conception of the audience as
economic capital and using analytics for content-related decisions. Instead, conception of
the audience as economic capital leads to conception of the audience as symbolic capital
which then leads to adjusting editorial decisions based on Web analytics. A crucial
distinction, an important context, needs to be mentioned here. If we focus on this
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particular pathway, from economic capital to symbolic capital to content-oriented use of
Web analytics, we see a move from perception to action. The general conception of the
audience as a form of economic capital leads journalists to conceive of the audience at
present as a form of symbolic capital that then leads them to integrate audience feedback
in the form of Web metrics in their news work.
While a previous study surveyed newspaper editors who also work in online
newsrooms (Vu 2013), this current study surveyed primarily online news editors, some of
whom happen to work in newsrooms that also manage a newspaper. This is an important
distinction. In many news organizations, traditional news outlets still earn revenues much
bigger than what their respective online platforms bring to the organization. Though
revenues are shrinking for traditional news outlets, revenues from online remain small.
For example, a study by the Newspaper Association of America (NAA 2013) found that in
2012, digital advertising accounted for only 11 percent of total revenue for newspaper
companies with digital platforms. The declining print revenue and the increasing online
readership come with the hope that digital revenue will, in the immediate future, be able
to sustain journalism. Thus, while online newsrooms are still not generating a great deal of
revenue, online editors still feel the pressure of growing the audience as a form of
symbolic capital that can be converted into economic capital when online journalism
finally finds a sustainable business model (see Tandoc 2014). This is also clearly reflected in
the findings of this current study. Online editors conceive of the audience as a form of
economic capital. But, in the meantime, the audience is also a form of symbolic capital,
a conceptualization that leads online journalists to accommodate audience preferences
based on Web analytics in their news work. This is reminiscent of how different forms of
capital are convertible as stipulated in field theory, and how an organization successful
at converting one form of capital into another tends to be dominant in the field (Benson
and Neveu 2005).
Building on a theoretical framework developed earlier from a qualitative study
(Tandoc 2014), this current study based on a survey of online journalists offers an empirical
dissection of factors explaining the extent to which journalists incorporate Web analytics
in their journalism. Using quantitative analysis, this current study confirmed earlier
qualitative findings that pointed to the important role of journalists’conception of their
audience as a form of capital in explaining their use of Web analytics in their news work
(Tandoc 2014). This, however, is just one path. This current study found two pathways into
how audience preferences permeate news judgment. First, audience influence flows from
a journalist’s conception of the audience: considering the audience as economic capital
leads to considering the audience as symbolic capital, which then leads to content-
oriented use of Web analytics. Second, a parallel pathway starts from the perception of the
degree of competition faced by one’s organization that leads to adoption of Web analytics
that then leads to using Web analytics for its obvious applications—monitoring traffic and
keeping the website functional—which then leads to content-related use. Thus, while one
pathway to integration of audience feedback into news work is through an individual’s
agency, operating at the individual level, another pathway is through constraining
structures—in this case the influence of perceived competition and the presence of
technology in the newsroom. In other words, while journalists retain individual agency to
mediate the impact of audience feedback on their news work through their conception
of capital, they also face organizational and socio-institutional structures that can make
acts of negotiation unavoidable, leading them to open up the gates, so to speak, to
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accommodate the audience. This is also consistent with Lewin’s original argument when
he introduced field theory to sociology, that behavior is a function of both the person and
the environment (Martin 2003).
This operational framework, it is argued, is a realistic depiction of the context within
which journalists make or unmake decisions that can collectively make or unmake
journalism. This is trying to understand why they do what they do, particularly with their
use of Web analytics, for it is through this understanding that the field itself can look at
how much it has changed and whether or not this trajectory determined by a will to
survive is something that will indeed keep journalism alive.
This study faces several constraints. First, the survey method itself relies on self-
reports, and the responses were therefore at the mercy of the editors’willingness to answer
truthfully and their ability to accurately remember their behavior and preferences. Second,
the study focused only on online editors, but reporters might also be using Web analytics in
different ways. For example, a growing number of media organizations are using Web
analytics information to introduce new pay-per-click schemes (Fischer 2014a). Third, while
measures were undertaken to get a representative sample, the response rate was low
considering the target sample. But driven by a theoretical framework grounded in the
literature and analyzed using SEM, this study provides important findings to understand
how the journalistic field is changing and the factors affecting this evolution. Indeed, the
survey method has its own inherent limitations, and it is therefore important to examine
the results of this current study alongside an earlier qualitative work that relied on
interviews and observations (see Tandoc 2014). This earlier work represented an attempt to
develop a theoretical framework that is subsequently tested in this current study.
Finally, the study found support for the usefulness of the framework of field theory
in understanding how Web analytics is affecting journalism. It highlights how journalists’
perceptions of the field, now rife with uncertainty, and their conceptions of the audience,
now more influential than ever, have repercussions on how journalists do their jobs. Web
analytics now leaves little room for journalists’imaginations of the audience, and yet these
imaginations—their conceptions of the audience—can greatly influence their outputs (de
Sola Pool and Shulman 1959; Ettema and Whitney 1994). In this era marked by constant
change, future studies should continue to investigate how the ways in which journalists
use Web analytics, as well as other new communication technologies, affect the quality of
the journalism that we get.
ACKNOWLEDGEMENTS
This study is part of a dissertation. The author wishes to thank the members of his
dissertation committee (Dr. Tim P. Vos, Dr. Esther Thorson, Dr. Stephanie Craft, Dr.
Amanda Hinnant, Dr. Steven Osterlind, and Dr. David H. Weaver) as well as Prof. Michael
Jenner at the University of Missouri.
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