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“I feel the weight of expectations”: how emotions and social norms shape news
choices about superfood diets
Health New Media Res. 2024;8(1):45-52

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“I FEEL THE WEIGHT OF EXPECTATIONS”: HOW EMOTIONS AND SOCIAL NORMS SHAPE NEWS
CHOICES ABOUT SUPERFOOD DIETS


ARTICLE INFORMATION

Health New Media Res. 2024;8(1):45-52
Publication date (electronic) : 2024 June 12
doi : https://doi.org/10.22720/hnmr.2024.00080
Nour Zeid1, Richard L. Street, Jr.1, Dominik J. Leiner2, Sebastian Scherr,1,3,4


1Dept. of Communication & Journalism, Texas A&M University, United States
2Dept. of Communication and Media, LMU Munich, Germany
3Center for Interdisciplinary Health Research, University of Augsburg, Germany
4Dept. of Media, Knowledge, and Communication, University of Augsburg, Germany
Corresponding author Sebastian Scherr Center for Interdisciplinary Health
Research and Department of Media, Knowledge, and Communication, University of
Augsburg, Germany Email:sebastian.scherr@uni-a.de
Received 2024 May 1; Revised 2024 June 6; Accepted 2024 June 9.



ABSTRACT

Focusing on “super diets” and different approaches to integrate superfoods in
one’s diet (i.e., small- vs. large-change approaches), this study examines the
drivers behind healthy eating information management (both seeking and
avoidance). We combine self-reported data (N = 359) about the individual’s
affective states (positive (PA), negative (NA), and mixed (MA)) and the
perceived informational subjective norms (ISN) with unobtrusively measured news
selectivity. The data was analyzed using zero-inflated negative binomial (ZINB)
regression models, which simultaneously accounted for the seeking and avoiding
healthy eating news. Findings revealed that the seeking behavior could neither
be explained by the individual’s affective state (negative or mixed),
informational subjective norms, nor by their interaction. However, contrary to
our predictions, positive affect was not associated with avoiding news about
healthy eating. Regarding specific content features, informational subjective
norms were the only significant predictor of seeking news featuring large-change
approaches to one’s diet. While individuals in negative affective states were
likely to spend less time on news featuring a small-change approach, individuals
with mixed affects were likely to spend more time on news featuring such an
approach. The interaction between mixed affect and negative affect with
informational subjective norms reversed this relationship. Theoretical and
practical implications are discussed.

Keywords: Information Management; Affect; Norms; Healthy Eating; Superfoods


INTRODUCTION

Obesity is a significant threat to global public health (Seidell & Halberstadt,
2015), and despite the increased awareness of the importance of healthy eating,
unhealthy eating habits continue to persist (Sathyamala, 2017; Verplanken &
Orbell, 2022). Media and health campaigns promote dietary changes, leading to
the popularity of “super diets” and different approaches to changing one’s diet.
These approaches range from complete overhauls to replacing certain unhealthy
food choices with healthier ones (large-change vs. small-change; Graham et al.,
2022).

Food choices are about more than information; the individual’s affective state
impacts them - and so does the individual’s media selection (Reinecke, 2016;
Zillmann, 2000). The unprecedented access to information is turning health
information into a highly selective behavior (Kim et al., 2016), managed through
seeking or avoidance (Link, 2021; Wang et al., 2021). Information management can
also be impacted by the perceived pressure to know about healthy eating (e.g.,
informational subjective norms). However, the interplay between the individual’s
affective state, informational subjective norms, and media selectivity about
healthy eating news remains unclear.

This study draws on the selective exposure paradigm and methodology to
simultaneously test the seeking and avoidance behavior of healthy eating
revolving around superfood alternatives in the news. First, we explore how
affective states (positive (PA), negative (NA), and mixed (MA)) and
informational subjective norms (ISN) impact the information management behaviors
of healthy eating news featuring superfoods. Second, the study design tests for
the information management of specific content-related features (large- vs.
small-change approaches) to account for the different ways to integrate
superfoods into one’s diet.


INFORMATION AND THE AFFECTIVE STATE

Affect refers to all feeling-related states (Forgas, 1995), including moods and
emotions (see Russell, 1980; Luong & Knobloch-Westerwick, 2023). To consider the
role of affective states on information management, we turn to the mood
management theory (MMT; Zillmann, 2000) and the risk information-seeking (and
avoidance) models (seeking: RISP and PRISM; Griffin et al., 1999; Kahlor, 2010
and avoidance: PRIA; Deline & Kahlor, 2019;). MMT assumes that mood - an
affective state that is diffused and unfocused (Lischetzke, 2014) - motivates
individuals to arrange their media stimuli to terminate or alleviate negative
affect (NA) and preserve or intensify positive affect (PA) (Reinecke, 2016;
Zillmann, 1988). Media selectivity is one mechanism through which individuals
regulate their affective state (Knobloch-Westerwick, 2006; Zillmann, 2000).

The selectivity of media content and management of information behaviors has
often been examined using the risk information-seeking models (e.g., RISP and
PRISM; Griffin et al., 1999; Kahlor, 2010). These models suggested that affects,
defined as the individual’s emotional valence, directly play a role in
self-regulation by motivating the seeking or avoidance of information (Lu et
al., 2020; Yang & Kahlor, 2013). While NA predicted information seeking using
the RISP model and PRISM (Yang & Kahlor, 2013), such models lacked insight into
information avoidance behaviors. Only recently did the planned risk information
avoidance (PRIA) model address this gap, suggesting that PA predicts information
avoidance (Deline & Kahlor, 2019).

Although these models only consider affective states as a unidimensional
evaluation - either positive or negative (Yang et al., 2014), recent studies
emphasized the role of mixed affect (MA) – the co-occurrence of both negative
and positive affect (Nabi, 2019; Slater et al., 2016). Like NA, which stimulates
cognitive elaboration and careful information processing, MA can heighten the
elaborative processing and reflection of media messages (Bartsch et al., 2014;
Das et al., 2017; Ersner-Hershfield et al., 2008). Given that food choices can
often be emotionally charged and require profound reflective thoughts, MA can
affect an individual’s motivation to learn more about healthier food
alternatives (Schneider & Schwarz, 2017; Shepherd, 2002). We hypothesize:

H1 a-b: a) Negative and b) mixed affective states will be associated with the
seeking of healthy eating news.

H2: Positive affective state will be associated with avoiding healthy eating
news.


ADDED PRESSURE: INFORMATIONAL SUBJECTIVE NORMS

Informational subjective norms (ISN) is a crucial predictor of
information-seeking behavior (Ou & Ho, 2021; Yang et al., 2014). ISN is the
individual’s motivation to conform to the perceived social pressure regarding
what important others think the individual should know about an issue (Griffin
et al., 1999; Kahlor, 2010). Thus, when an individual believes that others
expect them to have some knowledge about an issue, they are likely to act and
seek information more actively (Griffin et al., 2013).

Although affective state and ISN were identified as factors driving health
information management behaviors (Ou & Ho, 2021), gaps in understanding persist
regarding the interaction between affective states and social norms (Deline &
Kahlor, 2019). Recent findings have challenged the notion that affective states
and social norms independently shape information management behaviors,
suggesting a positive correlation between informational subjective norms and
anticipated and experienced positive affects (Lu et al., 2020). This underscores
the need to examine how affective states and social norms (ISN in our case)
jointly predict healthy eating information management behaviors. Thus, we ask:

RQ1: To what extent does the interaction between affective states (positive,
negative, and mixed) and ISN predict information management of healthy eating
news?


APPROACHES TO HEALTHY EATING

The superfoods boom has primarily been driven by the perception that these food
choices are no ordinary food in that they not only fulfill the individual’s
desire for a healthier lifestyle but also contribute to the individual’s eating
identity and social distinction (Graeff-Hönninger & Khajehei, 2019).
Consequently, the ‘super diet’ style has gained popularity in the media (Mintel,
2016; Weitkamp & Eidsvaag, 2014) with calls to use superfoods as part of one’s
diet more often and more effectively (Tacer-Caba, 2019).

Health campaigns typically advocate for either a smallchange or a large-change
approach to integrating healthier food choices (Hayes et al., 2021; Hill, 2009).
A large-change approach requires substantial changes to an individual’s dietary
behavior (Graham et al., 2022), which may lead to remarkable short-term results.
However, individuals often experience a yoyo-effect, regaining most of their
lost weight (Graham et al., 2022). Alternatively, the small-change approach
relies on habit formation by empowering individuals to slowly make small,
achievable changes to their diet (e.g., swapping one food for a healthier
alternative; Hill, 2009; Rodearmel et al., 2007). However, little is known about
how the individual’s affective state and the pressure to know about healthy
eating impacts the selective exposure to a specific healthy eating news
approach. Thus, we ask:

RQ2: To what extent does the affective state (positive, negative, and mixed)
predict information management about specific features of healthy eating news
(i.e., small- vs. largechange)?

RQ3: To what extent does the interaction between affective state (positive,
negative, and mixed) and ISN predict information management about specific
features of healthy eating news (i.e., small- vs. large-change approaches)?


METHOD


PARTICIPANTS

A total of 392 participants completed the study. Nine were excluded for
exceeding the maximum reading time, while 24 were excluded for not passing the
manipulation check or providing implausible responses. The final sample was n =
359 participants. The sample included n = 199 self-identified as female and n =
160 male. Their age ranged from 18 to 77 (M = 29.0, SD = 13.0). Individuals
mostly self-identified as Belgian (92.8%).

The participant’s Body Mass Index (BMI) was based on self-reported weight and
height (M = 23.2, SD = 3.8). The distribution of the BMI was within a healthy
range; 5.6% were underweight (BMI < 18.5), 68.0% had a healthy BMI (BMI 18.5 –
24.9), and 21.7% were overweight (BMI of 25.0 - 29.9). About 4.7% had an obese
BMI (BMI ≥ 30.0) (CDC, 2021).

The sample included 169 (47.1%) with a higher education degree (bachelor and
above), 138 (38.4%) with a high school diploma, 36 (10.0%) with a professional
degree (professional bachelor), and 16 (4.5%) with no high school diploma.


PROCEDURE

An online survey was conducted using snowball sampling. After their consent,
participants answered demographic and healthy eating-related questions and
reported their present affective state. After completing the first part of the
study, they were given a link to a mock-up news web magazine (Foody) and asked
to browse and select news articles they preferred for up to 5 minutes (Zhu et
al., 2024). They were also told that the allocated time was insufficient to read
all articles. Once they received the instructions, they were redirected to the
Foody landing page featuring eight news articles, and their time began. Towards
the end of their time, a pop-up window notified them before they were redirected
to the second part of the survey, which focused on their reading experience and
information processing. After completion, participants were debriefed about the
study’s purpose. The answers from both parts of the survey combined with the
individual’s browsing patterns were merged, thus relating the
information-seeking behavior with the individual’s characteristics (Zhu et al.,
2024).


MATERIALS

News articles were adapted from different news media sources and edited to have
the same length (800 words). To eliminate any presentation-order effects
(Elsenberg & Barry, 1988), the content randomization function in WordPress was
used, which displayed the news articles in a random order each time the landing
page was loaded. Similar images were used to avoid possible visual preferences
(Wells & Windschitl, 1999). All articles focused on superfood alternatives;
however, their content-related features differed - four articles encouraged a
small-change approach, and four encouraged a large-change approach, all
revolving around superfoods). The approach featured in the new article could be
inferred from its title (see Figure 1). The study included two content-related
features: statistical information vs. exemplars and small- vs. large-change
approaches. However, only the small- vs. large-change content factor is relevant
for theorizing this study.

Figure 1.

Screenshot of the Landing Page Highlighting the Two Different Healthy Eating
Content-Related Features

Note: News articles in red represent the small-change (swap) approach, and news
articles in green represent the large-change (new) approach. In large-change
approach news articles, individuals were encouraged to incorporate and eat new
superfoods such as quinoa or pea milk. In small-change approach news articles,
individuals were encouraged to replace certain foods, such as white rice with
whole-grain rice or pasta with whole-grain pasta. The colored frames are for
clarification purposes and were not visible to the individuals who participated
in the study.


MEASURES

INFORMATION MANAGEMENT

Information management was conceptualized as the time, in seconds, a participant
spent on a news article. We employed the selective exposure paradigm (Hastall &
Knobloch-Westerwick, 2013) and measured selectivity using unobtrusive
observations of media exposure. This approach avoids many shortcomings
associated with self-reported measurements (Hastall & Knobloch-Westerwick,
2013).

Participants could choose any article to read and go back and forth between the
landing page and the full article chosen. By clicking on an article, they were
taken to the full article, and the time spent was captured in seconds.
Individuals could only read one article at a time; thus, articles not chosen
were coded as zero, indicative of selective avoidance. The participants’ total
browsing time (M = 86.8, SD = 80.7) as well as the time they spent on specific
content-related features: small-change (M = 43.9, SD = 61.5) and large-change (M
= 42.8, SD = 56.4), was captured automatically and unobtrusively.

AFFECTIVE STATES

Affective states were measured using the Short-Form of the Positive and Negative
Affect scale (I-PANAS-SF; Thompson, 2007). Individuals were asked, “Thinking
about yourself and how you normally feel, to what extent do you feel right now
…?” and their responses were assessed on a 5-point scale ranging from 1 =
strongly disagree to 5 = strongly agree. Responses indicated the current
affective state using five positive (determined, attentive, alert, inspired,
active; Cronbach’s α =.73; M = 3.25, SD = .67) and five negative affects
(afraid, nervous, upset, ashamed, hostile; Cronbach’s α = .80; M = 1.92, SD =
.77).

MIXED AFFECT

Mixed affect was computed following the procedure outlined by Ersner-Hershfield
et al. (2008). The same equation was applied, but the affective states rather
than discrete emotions were used:

MA = MIN [Positive Affect, Negative Affect]

Positive and negative affects needed to be high to obtain a high score on mixed
affect, whereas low positive or negative affect levels resulted in a low mixed
affect score (M = 1.85, SD = .66).

INFORMATIONAL SUBJECTIVE NORMS

ISN was measured using 4-items adapted from Kahlor (2007), which included,
“People whose opinion I value would like me to be informed about healthy eating
and food.” The responses ranged from 1 = strongly disagree to 7 = strongly
agree, and all items were combined into a composite measure (Cronbach’s α =
0.77; M = 4.2, SD = 1.1). The wording of all items can be found in Appendix 1.

COVARIATES

The covariate variables included age, BMI, gender (female; 55.4%), and education
(college degree; 57.1%).


ANALYSIS

We use the zero-inflated binomial (ZINB) regression model to simultaneously
model the seeking (reading times) and avoidance (excess zeros of articles not
chosen) of healthy eating news. Given the nature of the data, including
overdispersion and excess zeros, ZINB models are the most appropriate way to
model selective exposure (Scherr & Leiner, 2021). The model simultaneously
explains the negative binomial distribution of the selective exposure data using
a specific set of predictors and explains the excess zeros capturing information
avoidance as part of a logit model with a different set of predictors. Age,
gender, education, and BMI were included as control variables in the models.


RESULTS

Zero-order correlations revealed that all variables were moderately correlated
(see Table 1).

Table 1.

Zero-Order Correlation between Predictors of Selective Exposure to Healthy
Eating News

The ZINB model revealed that neither the negative affect (B = -.469, SE = .607,
p = .440) nor the mixed affect (B = .671, SE = .710, p = .345) were associated
with the overall healthy eating information seeking (i.e., reading time). Thus,
H1a-b was rejected. Positive affect had a significant but negative effect on
information avoidance (B = -.498, SE = .238, p = .032), suggesting that PA is
not associated with avoidance of healthy eating news, contrary to our
hypothesis. Thus, H2 was also rejected (see Table 2). In addition, ISN was
non-significant for information seeking (B =.180, SE = .114, p = .114).

Table 2.

Zero-Inflated Negative Binomial (ZINB) Regression Model Simultaneously
Predicting Exposure and Avoidance of Healthy Eating News

Addressing RQ1, none of the interactions between affects (negative, positive,
and mixed) and ISN was significant, regardless of whether it was seeking or
avoidance.

Addressing RQ2, none of the affective states, NA (B = .422, SE = .767, p = .583)
and MA (B = .132, SE = .887, p = .882) were associated with large-change
approach news seeking. However, ISN was found to be significantly and positively
associated with the seeking of the large-change approach (B = .287, SE = .129, p
= .027), which indicates that when individuals perceive important others expect
them to know about healthy eating, the large-change approach seems to be more
appealing. Furthermore, PA was significantly but negatively associated with
avoiding a large-change approach (B = -.440, SE = .207, p = .034).

Focusing on the small-change approach, NA was negatively associated with seeking
this approach (B = -2.932, SE = .502, p = <.001). In contrast, MA was positively
associated with seeking the small-change approach (B = 3.025, SE = .779, p =
<.001). In other words, an individual with NA will likely spend less time on
news featuring a small-change approach. However, individuals with MA are likely
to spend more time on news featuring a small-change approach.

Addressing RQ3, none of the interactions between affective states and ISN was
associated with seeking or avoiding the large-change approach. However, the
interaction between NA (B = .455, SE = .095, p = <.001) and MA (B = -.489, SE =
.158, p = .002) with ISN was associated with interest in news featuring a
small-change approach. While the interaction between NA and ISN was associated
with spending more time on news containing information about small changes, the
interaction between MA and ISN was associated with spending less time on such
news. Upon further inspection of both interactions, we found that high NA and
high ISN were associated with more time spent on a small-change approach than
high NA and Low ISN. Furthermore, we also found that high MA and low ISN were
associated with more time on small-change news than high MA and high ISN.


DISCUSSION

The findings suggest that the seeking behavior of healthy eating news could
neither be explained by the main effects of the affective state (whether
negative affect [NA] or mixed affect [MA]), informational subjective norms
(ISN), nor by their interactions. However, positive affect (PA) was not
associated with avoiding healthy eating news. Focusing on the specific
approaches, ISN was the only significant predictor of large-change news seeking.
Additionally, individuals with MA spent more time on news featuring a
small-change approach than those with NA who spent less time on such news
articles. However, when interacting with ISN, these relationships reversed.

Contrary to PRIA predictions (Deline & Kahlor, 2019), PA was not associated with
avoidance of healthy eating news; instead, individuals with PA were less likely
to avoid such news, especially news featuring a large-change approach. One
interpretation is related to social movements such as “body positivity” and
“self-love,” which aim to foster a more inclusive and compassionate society by
promoting self-acceptance and acceptance of all body shape types (Cohen et al.,
2019). Some argue that such movements indirectly contributed to the
normalization of obesity (Pawar et al., 2020; Robinson, 2017). This is reflected
in global web search trends between 2004 and 2019, which show a decline in
searches centered on obesity as a disease and a rise in searches related to
positive acceptance of body image (Pawar et al., 2020). The implications of the
normalization of obesity have been considered an obstacle, hindering the
promotion of healthy environments and the discussion of obesity (Muttarak,
2018). However, our findings suggest that health campaigns can leverage the
positivity fostered by these movements to increase interest in learning about
healthy eating and superfood alternatives, particularly interest in the
large-change approach.

Despite the similarities between NA and MA (Bartsch et al., 2014; Das et al.,
2017), our results indicate that MA is associated with more, while NA is
associated with less time spent on news featuring a small-change approach. One
explanation is that MA - the simultaneous presence of both PA and NA - can lead
to more careful information processing as individuals seek to navigate and
resolve their uncertainty (see Anderson et al., 2019; Tiedens & Linton, 2001).
As for the interest in the small-change approach, an explanation is that NA
increases the salience of immediate and concrete goals without consideration for
health benefits (Gardner et al., 2014), thus leading to less engagement with the
small-change approach, which typically requires more effort to achieve and
sustain (Hill, 2009). Conversely, individuals with MA may be drawn to the
empowerment offered by this approach (Graham et al., 2022; Hayes et al., 2021).

The interplay between social norms and affective state suggested a reciprocal
relationship, wherein emotions can sustain social norms (e.g., comply with
social norms to avoid negative emotions and increase personal satisfaction;
Keltner & Haidt, 1999), and social norms can regulate emotions by shaping their
expression to align with normative expectations (Staller & Petta, 2001; Thoits,
1990). Given that media selectivity is a mechanism to regulate one’s affective
state (Knobloch-Westerwick, 2006), those experiencing high NA tend to gravitate
towards a small-change approach, potentially as a means to terminate or
alleviate their NA. Furthermore, high ISN appears to amplify their interest in
this approach. While these complex interaction effects require further
investigation to fully understand their implications, one practical application
could be their integration into the digital intervention efforts. Such efforts
encourage individuals to swap their initial food choice (make a small-change)
with a healthier alternative using a digital recommendation system (see Jansen
et al., 2021). Thus, such digital interventions can explore the possibility of
strategically emphasizing ISN about healthy eating to potentially lead to a more
permanent dietary change (Hill, 2009).

While this study advances our understanding of the “why” behind the “what”
individuals seek in the context of healthy eating news, the complexity of the
phenomena requires further attention. Information avoidance was measured by the
time not spent on an article (in seconds; Knobloch-Westerwick, 2015). However,
information avoidance can also encompass inattention, biased interpretation of
information, or forgetting (Golman et al., 2017), which, in turn, is embedded in
contextual “cultures of news consumption” (Toff & Kalogeropoulos, 2020). While
the news environment provides unprecedented access to a broad range of dietary
topics and sources (Lioutas, 2014), individuals tend to acquire information
incidentally or through information scanning behavior (Lewis, 2017; Lewis et
al., 2022; Ruppel, 2016; Tian & Robinson, 2009). Thus, understanding attention
to information is as crucial as understanding information retention - the
ability to store and recall information - which is affected by any distractions
that divert attention from a specific task (Barrouillet et al., 2004).
Therefore, the imposed task of the study, the constant back-and-forth between
the landing page and the full articles, and the limited access to various
sources and topics (only diet-related news articles included) may have
influenced the observed information management behavior.


LIMITATION

This study enhances our understanding of how affective states and informational
subjective norms jointly influence health-related information seeking and
avoidance. Insights from this research can inform the design of more effective
health communication strategies and interventions. Specifically, leveraging
informational subjective norms can intensify interest in small dietary changes,
particularly among individuals with high negative affect. Future research should
address some of the limitations inherent in this study. Given the predominant
representation of Belgians in our sample, future studies should include diverse
samples to enhance the cross-cultural applicability of our findings.
Additionally, to better understand selective exposure behaviors, including a
control topic (e.g., sleep or exercise articles) alongside diet-related articles
would enhance the findings’ generalizability to different news items. Further,
employing experimental designs would enable controlled manipulation variables,
such as the self-reported data about the individual’s affective state and
perceived ISN, which would facilitate the observation of causal relationships.
Finaly, despite its overall good reliability and validity (Thompson, 2007), the
I-PANAS-SF ‘s reduced dimensionality, context insensitivity, and the
self-reported bias may limit capturing the full complexity of the emotional
states (Harley, 2016). Future research could leverage physiological responses
associated with emotions, such as heat rate, skin conductance, or brain
activity, for objective measurement (Ba & Hu, 2023; Harley, 2016).




NOTES

Data Availability Statement

The datasets used and/or analyzed during the current study are available from
the corresponding author on reasonable request.

Funding Information

This research received no specific grant from any funding agency in the public,
commercial, or not-for-profit sectors.


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ARTICLE INFORMATION CONTINUED

Copyright © 2024 Health & New Media Research Institute

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the original work is properly cited.


FIGURE 1.



Screenshot of the Landing Page Highlighting the Two Different Healthy Eating
Content-Related Features

Note: News articles in red represent the small-change (swap) approach, and news
articles in green represent the large-change (new) approach. In large-change
approach news articles, individuals were encouraged to incorporate and eat new
superfoods such as quinoa or pea milk. In small-change approach news articles,
individuals were encouraged to replace certain foods, such as white rice with
whole-grain rice or pasta with whole-grain pasta. The colored frames are for
clarification purposes and were not visible to the individuals who participated
in the study.


TABLE 1.

Zero-Order Correlation between Predictors of Selective Exposure to Healthy
Eating News

1 2 3 4 5 6 1 (RT) Small-Change (Swapping) — 2 (RT) Large-Change (New) -.07 — 3
Negative Affect -.08 -.11* — 4 Positive Affect .12* .07 -.11* — 5 Mixed Affect
-.04 -.12* .92*** .04 — 6 Informational Subjective Norm .11* .01 .10 .12* .08 —
M 43.9 42.84 1.92 3.25 1.85 4.16 SD 61.5 56.42 0.77 0.67 0.66 1.08

Note. N = 359; Pearson correlation with two-tailed significance tests.

RT = Reading time in seconds. Both negative and positive affect were assessed on
a 5-point scale ranging from 1 = strongly disagree to 5 = strongly agree. Mixed
affect is defined as the minimum score of positive and negative affect. This
variable ranges from 1 to 3.6. Informational subjective norms were assessed on a
7-point scale ranging from 1 = strongly disagree to 7 = strongly agree.

*

p < .05,

**

p < .01,

***

p < .001


TABLE 2.

Zero-Inflated Negative Binomial (ZINB) Regression Model Simultaneously
Predicting Exposure and Avoidance of Healthy Eating News

Overall Exposure

--------------------------------------------------------------------------------

Large Change (New)

--------------------------------------------------------------------------------

Small Change (Swap)

--------------------------------------------------------------------------------

B SE p B SE p B SE p Covariates Gender (1 = female) .18 .08 .02 .17 .12 .14 .17
.10 .09 Age .00 .00 .90 .00 .00 .89 -.00 .00 .67 BMI .01 .01 .19 -.01 .02 .67
.02 .01 .13 Education (1 = college) -.01 .08 .88 -.07 .11 .51 -.05 .10 .61
Negative Binomial Model Explaining Exposure to Healthy Eating News Features
Negative Affect (NA) -.47 .61 .44 .42 .77 .58 -2.93 .50 .00 Mixed Affect (MA)
.67 .71 .35 .13 .89 .88 3.03 .78 .00 Informational Subjective Norm (ISN) .18 .11
.11 .29 .13 .03 .11 .14 .42 NA x ISN .06 .13 .64 -.09 .16 .56 .46 .10 .00 MA x
ISN -.14 .15 .38 .06 .18 .75 -.49 .16 .00 Logit Model Explaining Avoidance of
Healthy Eating News Features Positive Affect (PA) -.50 .24 .03 -.44 .21 .03 -.38
.22 .08 PA x ISN -.01 .03 .73 .02 .03 .41 -.04 .03 .20 Overall Model Parameter
Log Pseudolikelihood -1713.22 -1307.71 -1180.27 Wald χ2 (9) 14.75 12.10 130.46 p
.098 .208 .000

Note. N = 359; robust standard errors reported; zero-inflated negative binomial
regression model that predicts the information seeking (and avoidance) of the
healthy eating small-change and large-change content-related features. Reading
times were unobtrusively captured, measured, and reported in seconds. Four
articles on the website represented both small-change and large-change content
features.

Dummy variables included gender (1 = female, 0 = male) and education (1 =
college degree, 0 = no college degree); continuous measures included age (range
18 - 77), informational subjective norms (1 = strongly disagree to 7 = strongly
agree), positive affect (1 = strongly disagree to 5 = strongly agree), negative
affect (1 = strongly disagree to 5 = strongly agree), and mixed affect ranges
from 1 to 3.6; high mixed affect suggests that both positive and negative affect
were high.