Article
Individual Differences
in Selective Attention
to Information
Graphics in
Televised Sports
Communication & Sport
2016, Vol 4(1) 102-120
ª The Author(s) 2014
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DOI: 10.1177/2167479513517491
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R. Glenn Cummins1, Zijian Gong1, and Hark-Shin Kim1
Abstract
Despite the ubiquitous presence of information graphics in sport telecasts, little
research has explored how at-home spectators allocate attention to them or individual differences in selective attention. This study demonstrates two viewer characteristics that impact attention to information graphics in two excerpts from the
2012 World Series. Eye-tracking data reveal that although viewers universally attend
to these graphics upon onset, those with greater interest in sports and sports statistics exhibited greater cognitive processing of these elements as indexed by gaze
duration. Differences in selective attention provide a framework for studying attention in new sport media high in visual complexity.
Keywords
attention, information graphics, television, eye tracking, fanship
The use of on-screen graphics in televised sport has become a common means of enhancing telecasts and providing colorful insights into gameplay (Costa, 2013). Although the
introduction of such elements on television broadcasts was initially decried as intrusive
1
College of Media and Communication, Texas Tech University, Lubbock, TX, USA
Corresponding Author:
R. Glenn Cummins, College of Media & Communication, Texas Tech University, Box 43082, Lubbock, TX
79409, USA.
Email: glenn.cummins@ttu.edu
Cummins et al.
103
(Quindt, 2001), they are part and parcel of today’s sport telecasts. Moreover, such informational tools also typify subscription-based and new media sport portals, such as
the National Football League’s RedZone channel or Major League Baseball (MLB)’s
MLB at Bat app for tablet computers or smartphones (Flint, 2012; Snider, 2011). For
example, the MLB at Bat app allows users to customize the interface to include a host
of ancillary graphics or statistics related to the game, as well as monitor other events, all
within a single screen. The visual complexity and viewer agency afforded by such new
media interfaces introduces questions concerning how viewers allocate attention across
these myriad streams of information. Although countless viewers may watch the same
televised competition, they may exhibit marked differences in how they watch the telecast.
Despite the ubiquity of these graphics in both traditional televised sport and interactive portals, examination of individual characteristics that govern selective attention
to such elements has escaped inquiry. However, an abundance of scholarship has
demonstrated how viewer interest in sport impacts other aspects of spectator behavior
such as pre- or postgame information seeking as well as viewing motives (Gantz &
Wenner, 1995; Wenner & Gantz, 1989). Recent studies have also demonstrated differences in sport-viewing motives based on fantasy sport participation and knowledge of
sport statistics (Billings & Ruihley, 2013; Brown, Billings, & Ruihley, 2012; Ruihley
& Billings, 2013). Such findings suggest differences in how viewers might allocate
attention to on-screen visual embellishments. The purpose of this study was to examine how these two characteristics impact selective attention to information graphics in
one marquee sporting event—the 2012 World Series—and suggest a model for the
study of selective processes in information-rich new sport media contexts.
Literature Review
As previously noted, contemporary sport telecasts feature an unprecedented number
of on-screen graphics and visual embellishments to supplement game play (Sandomir, 2004). Surprisingly little research has examined viewer reception of or response
to these embellishments, despite the considerable efforts that content producers
invest in production elements (Krein & Martin, 2006). Much research has explored
how the actual content of mediated sport (i.e., game play, athletic competition) interacts with viewer characteristics to impact viewer response (e.g., domain-specific
interest in sport, team identification, dispositional affiliation with competitors; see
Raney, 2006, 2009; Wann, 2006). However, such elements largely remain outside
the control of content producers. In contrast, production elements governing how
competition is depicted have received considerably less attention, save for a few
exceptions (e.g., Greer, Hardin, & Homan, 2009; Hallmark & Armstrong, 1999).
Selective Attention Versus Selective Exposure
Since the earliest days of media research, preference for various types of content has
been a focus of scholarship exploring selective exposure (e.g., Lazarsfeld, Berelson,
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Communication & Sport 4(1)
& Gaudet, 1948). Mediated sport has had a place in this research due to its potential
to excite understimulated viewers (Bryant & Zillmann, 1984), and research in this
vein has shown selective exposure as a function of information utility (Valentino,
Banks, Hutchings, & Davis, 2009), affect regulation (Christ & Medoff, 1984), and
more. Despite the abundance of research supporting this phenomenon, its applicability in an information-rich media landscape begs an alternate conception of selectivity. As previously noted, sport telecasts are often visually complex and contain
numerous elements that simultaneously compete for attention (i.e., actual competition, supplemental graphics). Moreover, some novel interfaces allow viewers to
monitor multiple competitions simultaneously, further taxing viewers’ ability to
attend to multiple information sources.
Within this context, a more useful framework to explore viewing behavior is
selective attention. Although selective exposure often connotes choice among various messages or channels, selective attention refers to attention allocation to competing elements within one’s environment. Scholars have long asserted that
individuals must employ selective attention as a coping mechanism to effectively
operate in information-rich environments (Treisman, 1969). In the case of media
interfaces or messages, selective attention can be contrasted with selective exposure
by viewing it as intrastimulus selectivity versus interstimulus selectivity. Again,
properties of new media interfaces that present competing streams of information
simultaneously suggest the utility of this concept in examining how media users
allocate attention to message elements (e.g., Josephson & Holmes, 2008; Kallenbach, Närhi, & Oittinen, 2007).
Analogous to the selective exposure literature, the study of individual and message factors that impact selective attention serves as a useful framework. Selective
attention to visual elements of a stimulus can be the result of both automatic (i.e.,
bottom-up) and controlled (i.e., top-down) processes (Bucher & Schumacher,
2006; Pieters & Wedel, 2004; Van der Stigchel et al., 2009). Within communication
research, scholars have characterized bottom-up elements as message elements that
elicit or guide attention, often automatically (Bucher & Schumacher, 2006; Pieters
& Wedel, 2004). For example, onset of a graphic element within a program can capture attention automatically due to novelty (Fox et al., 2004; Lang, Chung, Lee,
Schwartz, & Shin, 2005).
In contrast, the top-down perspective focuses on individual characteristics that govern attention allocation in a more controlled fashion. Scholars have demonstrated selective attention processes as a number of individual characteristics: sensation seeking
(Ball & Zuckerman, 1992); affect regulation (Isaacowitz, 2006); salience or relevance
of an element within a stimulus (Parkhurst, Law, & Niebur, 2002; Sanders-Jackson
et al., 2012); and age (Kirkorian, Anderson, & Keen, 2012). For example, Isaacowitz
and his collaborators (e.g., Ersner-Hershfeld, Carvel, & Isaacowitz, 2009; Isaacowitz,
Toner, Goren, & Wilson, 2008) have used eye tracking to demonstrate differences in
attention as a function of age and mood, which illustrates how individual motivational
forces can shape attention allocation (Isaacowitz, 2006).
Cummins et al.
105
However, to view attention exclusively as a property of the stimulus versus the
viewer is a fallacy; instead, attention results from an interaction between the two
(Bucher & Schumacher, 2006; Duchowski, 2007). Buswell (1935) argued that the
human visual system neither randomly samples visual environment nor simply
reacts to stimuli; instead, attention allocation is the result of an observer’s goals
(i.e., top-down) interacting with the visual stimuli (i.e., bottom-up) to execute a
behavior. Moreover, Van der Stigchel et al. (2009) posit that visual attention works
in a sequential fashion where bottom-up processing precedes top-down or controlled
processing. To apply this to information graphics within mediated sports, onset of a
graphic element should automatically elicit attention; however, viewer identification
with a team—a top-down characteristic—is what determines the motivational relevance of the information and results in controlled attention to the element (Cummins, Tirumala, & Lellis, 2011; Hillman et al., 2000; Potter & Keene, 2012).
Sports Spectators and Selective Attention
Given the myriad sources of information contained in sport telecasts, the applicability of selective attention is obvious as some viewers may choose to attend to information graphics while others elect to allocate less attention or ignore them
altogether. Research demonstrating the importance of stimulus salience and motivational differences in attention informs how at-home spectators could exhibit differences in attention to information graphics (e.g., Isaacowitz, 2006; Sanders-Jackson
et al., 2012). Within the context of mediated sport, three individual characteristics
suggest similar motivational differences: general sport fanship, knowledge of sport
statistics, and interest in fantasy sport.
Although simplification of sport fandom into a dichotomy (e.g., ‘‘die hard’’ vs.
‘‘fair weather’’ fans,’’ ‘‘fans’’ vs. ‘‘spectators,’’ etc.) is misleading (Gantz, 2011),
this bifurcation provides a useful operational framework for differentiating those
with greater interest in sport from those who watch for other motives (Gantz, Wilson, Lee, & Fingerhut, 2008). Compared to nonfans or fans of other genres, sport
fans are more likely to seek out information about a competition prior to a game, are
more affectively involved in viewing, and exhibit greater information seeking after a
competition (Gantz & Wenner, 1995; Gantz, Wang, Paul, & Potter, 2006). Such differences should also be manifested via selective attention while watching, with those
with the greatest interest in sport more closely scrutinizing ancillary graphics that
contain granular information about game play.
Recent research has highlighted additional characteristics beyond mere fanship
that differentiate spectators. Even among ‘‘die-hard fans,’’ individual differences
may yield heterogeneity of motivations and behaviors. With the rapid growth of fantasy sport participation, scholars have examined motives and behaviors that characterize fantasy sport participants (Bowman, McCable, & Isaacson, 2012; McGuire,
Armfield, & Boone, 2012; Nesbit & King, 2010). Raney (2006) observed that sport
fans are often ‘‘walking encyclopedias’’ of sport information (p. 346). This
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Communication & Sport 4(1)
sentiment may be particularly suited for describing the fantasy sport participant.
Billings and Ruihley (2013) reported a constellation of characteristics that differentiate fantasy sport participants, including two variables related to sport knowledge,
Schwabism and mavenism. Schwabism denotes how much a person considers himself or herself as having adequate knowledge when it comes to a particular activity.
The related concept of mavenism posits that an individual not only actively seeks
sport information but also does so to serve as a source of sport information. Both
factors have been useful in distinguishing fantasy sport participants from nonparticipants (Billings & Ruihley, 2013) as well as younger versus older fantasy sport participants (Brown et al., 2012).
Given the information contained within graphics in sport telecasts (i.e., statistical
summaries of play, player performance), such information should be of particular
interest to those high in these distinguishing characteristics. Indeed, scholars have
noted that game consumption, information seeking, and knowledge of sport statistics
are characteristics of the fantasy sport participant (Bowman et al., 2012; Farquhar &
Meeds, 2007; McGuire et al., 2012, Nesbit & King, 2010). Thus, one would expect
to see that those highest in fantasy sport knowledge should allocate greater attention
to information graphics. Although hardly the sole individual characteristics could
impact selective attention, sport fanship along with interest in fantasy sport and sport
statistics suggest potential for differentiating attention allocation among viewers.
Hypotheses and Research Question
In sum, research has demonstrated that although viewers consistently allocate attention to the onset of information graphics in television, selective attention processes
can govern how much attention viewers allocate to these elements. As such, we predict that viewers will universally orient to information graphics upon onset (Hypothesis 1). However, more sustained attention allocation will be greater for more avid
sport fans (Hypothesis 2) as well as for those with greater self-reported knowledge of
sport statistics and fantasy sport (Hypothesis 3). Finally, to illuminate differences
between general sport fans and those with a particular interest in sport statistics and
fantasy sport, we ask which characteristic better predicts attention to information
graphics in sport telecasts (Research question).
Method
To examine the research question and hypotheses, a quasi-experiment was conducted whereby participants were randomly assigned to watch one of two excerpts
from the 2012 World Series that contained four types of information graphics, while
eye-tracking apparatus recorded their gaze. After viewing, they completed posttest
measures of sport fanship, mavenism, and Schwabism, along with demographic
measures. The formal design of the study was a 2 (game segment) 4 (infographic
type) 2 (individual characteristics: fanship, knowledge of sport statistics and
Cummins et al.
107
fantasy sport) mixed measures design, where game segment and the individual characteristics were between-subject variables, and infographic type was a withinsubject variable with four levels. Note that the individual characteristics were not
fully crossed (see Predictor Variables).
Participants
A convenience sample of 62 participants was recruited from a major southwestern
university in the United States to participate for extra course credit or to fulfill course
research requirements. Seven participants’ gaze data were excluded due to data loss
during viewing. Of the remaining 55 participants, their age ranged from 19 to 31
years (M ¼ 21.38, standard deviation [SD] ¼ 2.59). In all, 20 were male and 35
were female. Participants spent 5.21 hours watching actual sport events per week
(SD ¼ 6.21) and 5.85 hours watching sport-related programs per week (SD ¼ 10.93).
Procedure
Participants were recruited to watch a baseball game without knowing the purpose of
this study. Data were collected in individual sessions. Participants were seated
approximately 24 inches from a computer monitor. The researcher first calibrated
the participant’s gaze using a 9-point calibration image to account for individual differences in eye biology. Participants were then asked to watch an approximately 15min baseball game segment as they normally would.
After viewing the stimulus, the researcher led the participant to a different computer to finish an online questionnaire that measured demographics, sport consumption, sport fanship, fantasy sport participation, and fantasy sport involvement.
Predictor Variables
Two individual characteristics served as predictor variables. Sport fanship was conceptually defined as the degree to which one self-identifies as a sport fan, not as a fan of a
particular sport or team. It was operationalized using Wann’s (2002) Sport Fandom
Questionnaire that asks participants to agree with statements such as, ‘‘Being a sports
fan is very important to me.’’ All items were paired with 7-point Likert-type response
scales (1 ¼ Strongly Disagree, 7 ¼ Strongly Agree). Responses were highly consistent
(a ¼ .97) and were averaged to create an overall score. For some analyses, participants
were assigned to low (M ¼ 3.13, SD ¼ 1.23) and high (M ¼ 6.27, SD ¼ .64) sport fanship
groups via median split (Mdn ¼ 5.00). Between-groups differentiation was supported
with independent samples t-tests, t(43.09) ¼ 12.01, p < .001 (Levine’s correction for
degrees of freedom applied for violation of equality of variances).
To demonstrate concurrent validity, hours spent watching televised sport and
hours spent watching sport-related programming were compared between the two
groups. Participants provided open-ended responses to questions asking them to
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estimate the number of hours dedicated to viewing actual sports and watching
sports-related programming (e.g., ‘‘Sports Center,’’ sports talk programs, etc.). For
both measures, results revealed a significant difference, hours watching sport,
t(35.09) ¼ 4.17, p < .001 and hours watching sport-related programming,
t(29.60) ¼ 2.88, p ¼ .006). Avid sport fans reported an average of 8.44 hr per
week watching sport (SD ¼ 7.05) and 10.06 hr watching sport-related programs
(SD ¼ 14.14), which is significantly more than those assigned to the low sport
fanship group (televised sport, M ¼ 2.33, SD ¼ 3.39; sport-related programming,
M ¼ 2.09, SD ¼ 4.53).
Knowledge of sport statistics and fantasy sport was operationalized via measures
employed in past studies of fantasy sport participation, Schwabism and mavenism
(Billings & Ruihley, 2013; Brown et al., 2012; Ruihley & Billings, 2013). Knowledge of sport statistics was operationalized by a Schwabism scale that consisted
of 5 items such as, ‘‘When someone has a question about sport statistics, they ask
me first.’’ Knowledge of fantasy sport was assessed using a measure of mavenism.
As previously noted, mavenism refers not only to knowledge of a topic but also
whether an individual ‘‘enjoys sharing the knowledge and information with others’’
(Billings & Ruihley, 2013, p. 15). For example, participants rated agreement with
the statement, ‘‘I like helping people by providing them with information about fantasy sport.’’
Although conceptually distinct, measures employed to gauge these constructs
suggest some overlap. For example, items measuring Schwabism and mavenism
interchangeably reference both fantasy sport and sport statistics (e.g., Billings &
Ruihley, 2013). With respect to the present data, principal components analysis
using varimax rotation indicated that the measures of Schwabism and mavenism
loaded strongly onto a single factor (Eigenvalue ¼ 1.85, 14.24% variance
explained), with responses to the measure of sport fanship loading onto a separate
factor (Eigenvalue ¼ 9.02, 69.41% variance explained). Rotated item loadings for
measures of Schabism and mavenism were all >.61, and no items cross-loaded onto
the fanship factor. Responses to the Schwabism and mavenism scales were averaged
to yield a single index of knowledge of fantasy sport and sport statistics (a ¼ .97).
For some analyses, participants were categorized as high (M ¼ 4.12, SD ¼ 1.62) or
low (M ¼ 1.31, SD ¼ .36) based on a median split (Mdn ¼ 2.00). Group differentiation was supported via independent samples t-test, t(25.93) ¼ 8.49, p < .001
(Levine’s correction for degrees of freedom applied for violation of equality of
variances).
Although principal components analysis suggested differentiation between sport
fanship and knowledge of fantasy sport and sport statistics, the two variables were
nonetheless highly correlated, r(55) ¼ .67, p < .001. Moreover, a chi-square test
of association found an unequal distribution of frequencies among those high and
low in the two variables (w2 ¼ 19.70, p < .001, F ¼ .60) and cell sizes were largely
imbalanced. As a result, these two variables were examined via separate analysis of
variance (ANOVA) tests.
Cummins et al.
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Dependent Variables
Eye tracking was used to gauge attention. The technique has become an increasingly
popular tool for examining viewer attention to visually complex interfaces such as
television or websites (Josephson & Holmes, 2008; Kallenbach et al. 2007), as it can
provide granular insights into specific message elements that receive attention. Eye
movement is characterized by rapid saccadic movements joined by brief pauses
known as fixations. During these pauses, the eye is generally stationary around a central point, and the object of attention falls within the eye’s fovea where visual acuity is
greatest. A major assumption of eye tracking is that these movements and pauses are
manifest indicators of covert attention allocation (Hoffmann, 1998; Jacob, 1995).
Attention to information graphics was operationalized via three metrics: fixation
frequency, gaze duration, and observation frequency. Fixation frequency is a ratiolevel measure that indicates the number of times gaze is relatively stationary within a
defined area of interest (AOI). Fixations were operationalized here as moments
where the eye was stationary within a 62-pixel radius around a central point
>.100 seconds (Jacob, 1995; Josephson & Holmes, 2008). Gaze duration is a
ratio-level measure of the combined duration of these fixations within the AOI.
Although clearly linked, fixations denote which specific element is receiving attention, and duration corresponds with processing of that element (Just & Carpenter,
1976). Josephson (2005) noted that gaze duration is linked to the assumption that
‘‘the fixation time of an item is directly proportional to the processing time’’ (p.
64). Finally, observation frequency refers to the number of times gaze enters into
a defined AOI or how many times it is observed.
Stimuli
Two innings from game two of the 2012 World Series were recorded in high definition and employed as stimuli. Average length of the segments was 14.63 min.
Commercials were removed from the stimuli. A majority of participants reported
watching some of the World Series (n ¼ 39, 70.9%), although most (n ¼ 38,
69.1%) indicated that they did not watch this particular game.
Both segments contained numerous repetitions of four categories of information
graphics: pitcher statistics, batter statistics, game scores, and on-deck information
(Figure 1). Due to the unequal frequency of the types of graphics within the two innings employed as stimuli, repetitions of the AOIs were averaged for each type within
each inning. As such, a mean value for each gaze metric was computed for each category of information graphic.
Apparatus
Stimuli were presented on a 19-inch widescreen monitor with 1,280 1,024 pixel
resolution. An Applied Science Laboratories EyeTrac 6 control unit with high-speed
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Communication & Sport 4(1)
Figure 1. Game play illustrating two sample areas of interest (AOI; i.e., game score and batter
statistics). AOIs are drawn here for illustration and were not imposed during viewing.
optics was used to record gaze throughout exposure. The system is a bright pupil eye
tracker (Duchowski, 2007) and is noninvasive. A small housing located just below the
participant’s monitor contains a camera along with light-emitting diodes that emit infrared light into the participant’s eye. The light is reflected from the cornea, and the system
continuously monitors the y between this reflection and the pupil center to identify point
of gaze. The accuracy between the true eye position and computed measurement is
better than 0.5 visual angle. Under the viewing condition described earlier, accuracy corresponds to a circle of .21 inch in radius on the monitor. Gaze was sampled
at 120 Hz, and data were recorded by Gazetracker software (V9.0.8000.1000, Eyetellect), which presented the stimuli and synchronized it with gaze data.
Results
To test the hypotheses, a series of mixed-measures ANOVAs examined the differences in mean observation frequency, fixation frequency, and gaze duration as a
function of AOI and individual differences. Participant assignment into low and high
levels of two grouping variables (sport fanship, knowledge of fantasy sport and sport
statistics) were included as between-subject predictor variables to address the
hypotheses. AOI was employed as a within-subject variable with four levels (pitcher
stats, better stats, game scores, and on deck).
Frequency of Observation of Information Graphics
To determine whether onset of graphics would universally elicit attention, the first
hypothesis predicted no differences in observation frequency of the graphics (i.e., the
mean number of times gaze entered into an AOI) as a function of individual differences.
Cummins et al.
111
Table 1. Descriptive Statistics for Gaze Data.
Knowledge of Fantasy Sport
and Sport Statistics
Sport Fanship
Low
Pitcher
stats
Batter
stats
Game
score
Duration
Fixation
frequency
Duration
Fixation
frequency
Duration
Fixation
frequency
On deck Duration
Fixation
frequency
3.54 (SD ¼ 2.19)
High
Low
5.03 (SD ¼ 3.14) 3.62 (SD ¼ 2.13)
High
5.00 (SD ¼ 3.24)
10.29 (SD ¼ .52)
12.83 (SD ¼ .87)
9.93 (SD ¼ 6.18) 13.36 (SD ¼ 8.06)
1.98 (SD ¼ .80)
2.20 (SD ¼ .91)
5.75 (SD ¼ 2.20)
6.07 (SD ¼ 2.37) 5.78 (SD ¼ 1.91)
6.04 (SD ¼ 2.67)
2.54 (SD ¼ 1.66)
3.46 (SD ¼ 3.38) 2.50 (SD ¼ 1.67)
3.55 (SD ¼ 3.40)
6.95 (SD ¼ 4.22)
8.00 (SD ¼ 6.84) 6.64 (SD ¼ 4.28)
8.41 (SD ¼ 6.80)
.61 (SD ¼ .44)
1.86 (SD ¼ 1.36)
.91 (SD ¼ .53)
.65 (SD ¼ .49)
2.51 (SD ¼ 1.39) 1.91 (SD ¼ 1.48)
.88 (SD ¼ .49)
2.48 (SD ¼ 1.26)
2.00 (SD ¼ .70)
2.19 (SD ¼ 1.02)
Note. SD ¼ standard deviation.
Mixed-measures ANOVAs failed to reveal differences for those high and low in the two
individual characteristics, sport fanship, F(1, 53) ¼ 1.92, p ¼ .17, Z2p ¼ .04; knowledge
of fantasy and sport statistics, F(1, 53) ¼ .95, p ¼ .34, Z2p ¼ .02. As predicted, all participants equally observed the graphics.
Sport Fanship and Attention to Information Graphics
Hyothesis 2 predicted that viewers with greater interest in sport would allocate more
attention to information graphics. To test the hypothesis, a pair of mixed-measures
ANOVAs were conducted that included AOI and the two levels of fanship as fixed
factors. Gaze duration and fixation frequency served as dependent variables.
Regarding gaze duration, results showed a difference between high and low sport
fans, F(1, 53) ¼ 5.55, p ¼ .02, Z2p ¼ .10. See Table 1 for descriptive statistics for
all gaze measures. The pattern of results can be seen in Figure 2, which presents time
spent looking at each AOI as a function of sport fanship. Those with greater interest
in sport uniformly spent more time looking at the graphics compared to those with
less interest. Moreover, the analysis failed to yield a significant Fanship AOI
interaction, suggesting that the effect was robust across the various graphics, F(1,
53) ¼ 1.47, p ¼ .22.
However, fixation frequency data failed to reveal a difference between the
groups, F(1, 53) ¼ 2.01, p ¼ .16, Z2p ¼ .04. Thus, results partially support the
hypothesis, as those with the greatest interest in sport spent significantly longer time
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Communication & Sport 4(1)
Figure 2. Time spent viewing information statistics as a function of participants’ general
interest in sport (i.e., sport fanship).
examining information graphics in this sport telecast, although the number of fixations upon the graphics did not differ.
Knowledge of Sport Statistics and Attention to Information Graphics
The final hypothesis (H3) proposed that viewers with greatest knowledge of fantasy
sport and sport statistics would allocate more attention to information graphics. To test
this hypothesis, participant assignment to the two levels of knowledge served as the
grouping variable. Again, gaze duration and fixation frequency served as dependent
variables. The analysis found a difference between the groups, F(1, 53) ¼ 5.28,
p ¼ .02, Z2p ¼ .10. Those with the greatest knowledge of fantasy sport and sport statistics spent significantly longer time looking at on-screen graphics compared to those
with less interest. This effect was not dependent upon a specific AOI, as the test found
no interaction between the grouping variable and the AOI, F(1, 53) ¼ 1.50, p ¼ .22.
Although this difference was not manifested when examining the fixation frequency
data, the test approached statistical significance, F(1, 53) ¼ 3.62, p ¼ .06, Z2 ¼ .06.
Thus, the hypothesis was partially supported via the measure of gaze duration.
Predicting Attention to Information Graphics
The sole research question examined which individual characteristic—sport fanship
or knowledge of fantasy sport and sport statistics—better predicted attention to the
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Table 2. Summary of Regressions Predicting Attention to Information Graphics.
Pitcher
Statistics
B
b
Batter
Statistics
B
b
Game
Score
B
b
On-Deck
Graphic
B
Sport fanship
.51 .35** .05 .10 .24 .17 .09
Knowledge of fantasy sport & sport statistics .44 .29* .13 .28* .36 .24 .06
b
.32*
.20
** p .01. *p < .05.
information graphics. As the previous ANOVA tests failed to yield between-group
differences in attention as indexed by fixation frequency, gaze duration served as the
dependent variable. Furthermore, because graphic type was a repeated measure, a
series of regressions were employed to examine attention to each.
A multiple regression was initially conducted to simultaneously examine the relative contribution of both individual characteristics on attention to pitcher statistics.
Although that test revealed that the overall regression equation was statistically significant, F(2, 52) ¼ 3.73, p ¼ .03, R2 ¼ .13, neither individual characteristic served
as a significant predictor of attention, suggesting a concern with multicollinearity
(Berry & Feldman, 1985). As such, a series of separate regressions were conducted
to independently examine each characteristic. Results of these regressions are
summarized in Table 2. Regression coefficients reveal discrepancies in which characteristic—sport fanship versus knowledge of sport statistics and fantasy sports—
best predicted attention to the various information graphics. For example, sport
fanship better predicted time spent examining pitcher statistics (B ¼ .51, b ¼ .35,
p ¼ .01) and the on-deck graphic (B ¼ .09, b ¼ .32, p ¼ .02). In contrast, knowledge
of sport statistics and fantasy sport was a significant predictor of attention to the
batter statistics (B ¼ .13, b ¼ .28, p ¼ .04) while sport fanship was not (B ¼ .05,
b ¼ .10, p ¼ .73).
Discussion
Despite the ubiquitous use of information graphics in mediated sport, examination of
audience response to these graphics has escaped empirical inquiry. Perennial interest
in sport statistics (Schwarz, 2004) suggests that such features of sport telecasts
should be of particular relevance to spectators who report greatest interest in sport
as well as knowledge of sport statistics and interest in fantasy sport. As such, these
two individual differences represent a useful starting point for exploring selective
attention to visual elements of mediated sport. Results show that sport fans spent significantly more time examining information graphics while viewing portions of the
2012 World Series. Although these findings are admittedly intuitive, this finding
provides a seminal framework for studying differences in spectator behavior in
future sport media portals.
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Communication & Sport 4(1)
Differences in Attention Allocation
As predicted, viewers who reported higher levels of interest in sport and sport
knowledge spent significantly more time examining information graphics that were
employed in the sample sport telecast. As seen in Figure 2, this difference is most
apparent when examining attention to graphics containing information about pitchers in the game. For example, those reporting greater interest in sport spent roughly 2
sec longer examining these graphics. Notably, these differences were primarily manifested when considering the amount of time viewers allocated to these graphics. The
other two measures of attention, the number of times the graphics were merely
observed or the number of fixations concentrated on these areas, failed to yield consistent differences between classes of viewers. Regarding the number of times graphics were merely observed, the failure to find a difference was consistent with the
literature suggesting that humans generally allocate attention to novel elements
within the visual field (Yantis & Jonides, 1984).
The somewhat discrepant findings between the two measures of attention (i.e.,
fixation frequency and gaze duration) resonate somewhat with the argument that the
two index slightly different phenomena. Recall that one key assumption of eye tracking is that although allocation of fixations indicates what is being observed, the
length of these fixations indexes processing of these elements (Josephson, 2005).
As such, the findings suggest that viewers differ more in controlled processing of
these elements than mere attention to on-screen graphics. Different patterns of
results regarding these measures suggest two possibilities that are not mutually
exclusive. One intuitive explanation is that the difference in time spent processing
information graphics results from greater interest in the information contained
therein. Thus, motivational differences among viewers yielded greater processing
of information with the graphic elements (e.g., Hillman et al., 2000; Potter & Keene,
2012). A second possibility is that viewers with greater interest and knowledge of
sport possessed the knowledge needed to understand the information graphics and
therefore spent greater time reviewing the information. However, we should note
that findings related to fixation frequency could also suggest the possibility of type
II error as those tests approached marginal significance. More robust effects could be
exhibited in response to other stimuli or experimental contexts.
Results also demonstrated differences in which individual characteristic—sport
fanship versus knowledge of fantasy sport and sport statistics—impacts attention
to various types of graphics. The analysis painted a curiously inconsistent picture
when examining how these two predictors impacted attention to the four types of
graphics contained within the stimuli. In short, the findings illustrate that ‘‘sport
fans’’ are not a homogenous group. Case in point, sport fanship failed to predict
attention to batter graphics, whereas knowledge of fantasy sport and sport statistics
did. Moreover, the opposite relationship was observed with respect to the appearance of ‘‘on-deck’’ graphics. In sum, even when watching the same event, fans may
exhibit differences in how they watch a competition based on more granular
Cummins et al.
115
distinctions within this population. These demonstrated differences in watching a
sport telecast in a traditional fashion (i.e., television broadcast) suggest a useful
framework for studying sport spectators in new media contexts.
Conclusion
The increased visual complexity of contemporary sports media along with the
increased agency they permit invite questions regarding how viewers will watch
content via interactive technologies. Viewer selectivity—both in terms of selective
exposure and selective attention—is likely if not compulsory. Content providers are
creating new ways for sport fans to watch via interactive portals that represent a new
way to monetize portions of the audience who value the insights that detailed statistical information present (Manly, 2006). To revisit the earlier example, MLB’s MLB
at Bat app can present game play surrounded by a bevy of additional graphic elements such as player statistics, pitch count, or information about other games (Snider, 2011). This increased viewer agency in crafting the viewing experience is
characteristic of the 21st-century entertainment technology (Bryant & Love,
1996) and substantiates predictions of a truly active audience (Ruggiero, 2000).
As Raney (2013) noted, scholars must now work to test extant theory in such new
technological environments, including those where choice is greatly multiplied. As
previously noted, the two characteristics examined here are hardly the only variables
that govern viewer selectivity in new media sports portals. For example, the disposition theory of sport fanship (Raney, 2006) has long provided a useful theoretical
framework for predicting spectator response as a function of individual disposition
toward competitors, with maximum euphoria resulting from seeing a loved team or
athlete defeating a heated foe (Zillmann, Bryant, & Sapolsky, 1989). Moreover, the
arousal and suspense generated by the closeness of competition as well as production
techniques amplifies such affective responses via the theoretical mechanisms within
excitation transfer (Bryant & Raney, 2000; Cummins, Wise, & Nutting, 2012). Likewise, Wann (2006) has amassed considerable body of research demonstrating how
an individual’s identification with a team or athlete governs a host of responses to
a competition.
However, properties of new sports media portals may limit or inhibit theoretical
processes from unfolding. To again turn to time-tested tenets of affective disposition
and team identification, research would suggest selective exposure to competition
where a liked team is featured in hopes of seeing a victory, potentially yielding
enjoyment. However, new media portals also present the opportunity to simultaneously view disliked teams in action, ostensibly in hopes of seeing them defeated,
which should also yield enjoyment. Given such a scenario, it is plausible that events
within the two contests could jointly act to elicit and amplify arousal, which is an
empirical question. However, it is equally plausible that if a liked team was suffering
defeat, viewers could choose to selectively avert gaze (i.e., selective attention) or
even manipulate the viewing portal (i.e., selective exposure) to avoid witnessing the
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defeat as a coping mechanism in order to regulate affect. Alternately, fantasy sport
participants in this scenario could alter their mode of appreciation (as well as literally
alter the viewing interface) to focus on statistical performance of athletes. Even if such
behavior was not manifested, the competing streams of information and visual complexity could inhibit enjoyment (e.g., Cummins, Youngblood, & Milford, 2011).
In conclusion, these scenarios illustrate and demonstrate the need for subsequent
research to examine selective elements of viewer behavior in new sports media contexts, as they can circumvent well-established theories of mediated sport spectatorship. In conclusion, future research could take several forms. The idiosyncratic
nature of the viewing interfaces made possible by new technology make controlled
or quasi-experimental designs challenging. Thus, rich descriptive insights could be
gleaned from mere observation of spectator selectivity in new media portals. Moreover, survey-based research could likewise shed insights into these phenomena by
testing relationships between viewer characteristics and self-reported tendencies to
consume information-rich sports content as well as interactive sports media portals.
Regardless, the findings presented here provide a theoretical and empirical foundation
for examining factors that differentiate sports viewers and impact selective attention.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of
this article.
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