Critique of Research Article #1 InstructionsCRITIQUE of RESEARCH

Critique of Research Article #1 Instructions
CRITIQUE of RESEARCH ARTICLE #1
Objectives:
· Understand common social science research designs.
· Understand measurement principles.
· Understand the purpose of sampling.
· Critique research findings and effectively communicate
research results to others.
Select an academic/scholarly refereed research article in an area
that fits the topic you want to focus on for the assignments this
semester.
Highlight sentences in the article that you pull your info fr om to
answer the questions. Do not copy and paste answers. Type
in your own words.
After reading and analyzing the article, complete the following
questions. List questions followed by your answers.
1. Specifically, what is the purpose of this research?
2. What is the research design/methodology? Type of data
gathered. Survey, interviews, observations, second-hand data
file, etc. Who are the research participants – population and
sample? How was the sample determined and contacted? Are
they the appropriate population for this study? Do they
accurately represent this study to generalize from the results?
3. What is the “So What?” of this research study? What has
been added to the body of academic and/or professional
knowledge?
4. What are the implications for future research from the
author? What would you add for future research?
5. APA reference of the article.
Format for submission:
Typed, 1” margins, 12-point font. Follow the APA Style
Manual for references.
Attach two files:
PDF of highlighted full-text article

Doc. of your assignment answers. This must be a word doc so
comments can be made when grading
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International Journal of Hospitality Management 60 (2017) 67–
76
Contents lists available at ScienceDirect
International Journal of Hospitality Management
jo u r n al homep age: www.elsev ier .com/ locate / i jhosman
nderstanding responses to posted restaurant food safety
scores: An
nformation processing and regulatory focus perspective
imberly J. Harris (Ed. D.) (Professor, The Bessie Morgan
Marshall Professor of Hospitality Management)
Lydia Hanks (Ph. D.) (Assistant Professor), Nathaniel D.

Line (Ph. D.) (Assistant Professor)
Sean McGinley (Ph. D.) (Assistant Professor)
edman School of Hospitality Management, Florida State
University, 288 Champions Way, UCB 4112, P. O. Box
30306541, Tallahassee, FL 32306-2541, USA
r t i c l e i n f o
rticle history:
eceived 17 May 2016
eceived in revised form 11 August 2016
ccepted 11 September 2016
vailable online 18 October 2016
a b s t r a c t
Ensuring the safety of food served in restaurants continues
to be an essential issue in the hospitality
industry. An important part of the efforts to stem the
outbreak of foodborne illnesses are the mandatory
inspections of any entity that serves food to the public.
Unfortunately, while posting food safety scores is
intended to help consumers make better dining choices,
interpreting these scores can often be difficult
and confusing. The purpose of this study is to use
information processing theory as a framework to
eywords:
ood safety
nformation processing theory
egulatory focus
estaurants
ood safety inspection scores
investigate how consumers evaluate food safety inspection
scores. To achieve this goal, this research
provides an account of the effect of food safety concern

on consumers’ attitudes toward restaurants under
conditions of both positive and negative health inspection
results. The results identify a moderating effect
of health score in the formation of consumers’ attitudes
toward restaurants. The downstream effects on
expected satisfaction and behaviors are also established.
Published by Elsevier Ltd.
. Introduction
Incidents of foodborne illness outbreaks in the United States
re climbing despite the increase in food safety management pro-
rams and the implementation of the Food Safety Modernization
ct in 2011 (FDA, 2011). The recent outbreaks of foodborne
illness
t Chipotle restaurants and on commercial cruise lines have
served
o highlight the fact that food safety is an ongoing issue in the
hos-
itality industry (CDC, 2016; Food Safety News, 2016). In fact,
in
013 (the latest year for which data are available), the U.S.
Centers
or Disease Control (CDC) reported 13,360 cases of foodborne
ill-
ess, a 38% increase from the year before, and projections
suggest
hat this number will continue to grow (CDC, 2013).
As outlined in the Food and Drug Administration’s Food Code
2013), an integral part of preventing such outbreaks is the food
stablishment inspection process wherein health inspectors regu-
arly inspect restaurant food handling practices and subsequently
ate these establishments on adherence to food safety guidelines.
esults of these inspections are public information, and many

states
∗ Corresponding author.
E-mail addresses: [email protected] (K.J. Harris),
[email protected]
L. Hanks), [email protected] (N.D. Line), [email protected]
S. McGinley).
ttp://dx.doi.org/10.1016/j.ijhm.2016.09.002
278-4319/Published by Elsevier Ltd.
require the public posting of inspection results in the form of an
alphabetic letter, usually an A, B, or C (McVicar, 2011).
Accordingly,
the issue of food safety is an important one for restaurants, as
the
negative publicity associated with a poor health rating can
result
in loss of consumer trust, public relations problems, and legal
costs
(DiPietro et al., 2011).
Food safety is also of importance to a restaurant’s customer
base (Fatimah et al., 2011) as consumers often look to posted
food
safety scores to make dining decisions (Henson et al., 2006).
How-
ever, while consumers can benefit from the disclosure of
restaurant
inspection results, interpreting posted food safety scores can
often
be difficult and confusing (Filion and Powell, 2011). To better
under-
stand this phenomenon, previous studies have investigated the
delivery of food safety rating information, such as format,

location,
and information source (e.g., Choi et al., 2013; Filion and
Powell,
2001). While such research has advanced the knowledge of con-
sumer reactions to restaurant food safety in a general way, very
few studies have explored (1) the cognitive traits consumers use
to
interpret food safety scores and (2) the ways in which food
safety
information is used to make assessments about the
establishment.
Accordingly, very little is known about how individual
personal-
ity traits affect consumer’s perceptions of food safety
inspection
results and the interpretation of posted scores.
The purpose of this study is to bridge this gap in the literatur e
by using information processing theory as a framework to inves-
dx.doi.org/10.1016/j.ijhm.2016.09.002
http://www.sciencedirect.com/science/journal/02784319
http://www.elsevier.com/locate/ijhosman
http://crossmark.crossref.org/dialog/?doi=10.1016/j.ijhm.2016.0
9.002&domain=pdf
mailto:[email protected]
dx.doi.org/10.1016/j.ijhm.2016.09.002
lrk0003
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8 K.J. Harris et al. / International Journal
igate the how consumers evaluate posted restaurant food safety
nspection scores. Understanding the extent to which
individually

arying traits affect food safety perceptions is important because
he evaluation of food safety information can affect beliefs about
restaurant’s food, service expectations, and consumer behavior
Henson et al., 2006). To achieve this goal, this research inves -
igates the effect of food safety concern on consumers’ attitudes
oward restaurants under conditions of both positive and
negative
ealth inspection results. The hypotheses inherent to the
proposed
ramework are tested using data from a sample of restaurant con-
umers in the United States. Upon establishing the moderating
ffect of health score in determining consumers’ attitudes toward
estaurants, the effects on expected satisfaction and behaviors
are
xamined. The implications for restaurant operators and those
nvolved in developing food service policy are discussed.
. Literature review
Food safety inspection programs in the U.S. vary according
o state, regional and city food safety management programs.
odified by the Food and Drug Administration (FDA), authorized
ealth agencies are approved to execute inspection programs and
ave flexibility in evaluation methods and reporting nomencla-
ure. Inspections are conducted by trained sanitarians, otherwise
nown as health inspectors, and methods of inspection are devel -
ped by the local agency. Adopted methods and evaluation
criteria
re used to guide the evaluation as sanitarians visit restaurants
for
nspections, which are usually conducted twice per year;
however,
ith the recent implementation of the Food Safety Modernization

ct, those entities qualifying as high risk can be inspected more
ften (FDA, 2011, 2016). Inspections can be based on the 2009
or
013 adopted FDA Food Code or on the guidelines established by
uthorized health agencies (FDA, 2009, 2013).
Regardless of the food safety inspection protocol, inspections
re conducted and results are reported and summarized. After the
nspection has been conducted, the food establishment is
informed
f any violations and the criticality of identified issues. Based on
he level of violation(s) received, restaurant operators are
notified
f specific areas of concern and the actions that should be imple -
ented to correct the noted violations. If a violation is considered
o pose a significant safety risk to the consumer, the
establishment
an be closed for a number of days until the situation is
corrected.
f correction is not accomplished in the timeline designated by
the
nspector, the establishment can be permanently closed (Waters
t al., 2013).
The practice of posting food safety scores is important for a
num-
er of reasons. Posted inspection notices impact consumer
choice,
eighten the motivation of food service establishments to receive
igh scores on food safety inspections, and serve as a marketing
dvantage, whether overtly or covertly, for food establishments
Choi et al., 2013; Tarca and Murphy, 2014). Unfortunately, the
ethods of communicating such information to the public are
nconsistent in the U.S., and the results are often confusing for

ustomers (Baker-White, 2014; Fatimah et al., 2011). As follows,
nformation processing theory and regulatory focus theory are
used
o provide a framework for consumer interpretation of posted
food
afety inspection reports (see Fig. 1; H = Hypothesis).
.1. Information processing theory
Information processing theory (Chaiken, 1987; Petty and
acioppo, 1986) suggests that individuals process new pieces of
nformation either heuristically or systematically. Heuristic pro-
essing is based on shortcuts, clues, proxies, or stereotypes to
valuate a situation and can be advantageous as it is more
efficient
pitality Management 60 (2017) 67–76
and requires less cognitive energy than deeper processing
(Chaiken,
1987; Petty and Cacioppo, 1986). The disadvantage of using
heuris-
tics is that this process is less detailed and can result in less
accurate
conclusions, resulting in potentially incomplete gathering of
infor-
mation.
Conversely, systematic processing is a deeper and more cogni -
tive processing of information (Chaiken, 1987; Petty and
Cacioppo,
1986). Systematic processing entails gathering a plethora of
infor-

mation, analyzing it, and using it to arrive at a conclusion. This
process is much more time-intensive, effortful, and cognitively
demanding than heuristic processing, but the result often leads
to
a more accurate conclusion or evaluation.
The information processing model suggests that when deter-
mining whether an individual will process a new piece of
information heuristically or systematically, people tend to be
inher-
ently predisposed to the conservation of cognitive resources
(Taylor
and Fiske, 1978). This conservative approach leads to heuristic
pro-
cessing whenever possible, unless the individuals are otherwise
motivated to expend the extra time, energy, and cognitive effort
on systematic processing. Previous studies have found that
moti-
vations to engage in systematic information processing can
include
importance, relevance, outcome dependency, mood, need for
cog-
nition, and desire for control (Neuberg and Fiske, 1987; Petty et
al.,
1981; Pittman and D’Agostino, 1989).
Positive cues result in heuristic processing, while negative cues
often lead to systematic processing. For example, a good mood
or
positive environment can result in evaluations that are creative,
simplified, and characterized by less attention to detail (Mackie
and
Worth, 1989; Schwarz et al., 1991). Positive cues send a
message
that “all is as it should be,” and there is no need to further
evaluate

the situation; thus, the heuristic-based conclusion will be
adequate.
Contrariwise, a bad mood, a negative environment, or an
adverse
occurrence often leads to more systematic processing (Bohner et
al.,
1994; Schwarz, 1990). Some researchers suggest that such
negative
cues signal threats or danger in the environment, prompting a
per-
son to look carefully, gather detailed information, and analyze it
critically to solve problems (Frijda, 1988; Schwarz, 1990;
Schwarz
et al., 1991). Negative cues send a message that “something is
not
right,” or “possibly dangerous,” and in such cases, systematic
pro-
cessing is a way of minimizing the possibility that one might
arrive
at a wrong, and possibly dangerous, conclusion.
The present research uses information processing theory as
framework to understand consumers’ reactions to posted health
inspection scores in a restaurant environment. However, equally
important to the proposed framework are the constructs of pro-
motion and prevention focus. According to regulatory focus
theory,
these constructs are proposed to affect consumer atti tudes and
behaviors when positive or negative visual cues are presented.
Thus, the premise of the present research is that individually
vary-
ing traits affect consumers’ perceptions of food safety
inspection
scores and subsequently determine whether they use heuristic or
systematic processing to form attitudes about the restaurant.
These

specific traits are discussed as a part of the following account
of
regulatory focus theory.
2.2. Regulatory focus theory
The concept of regulatory focus suggests that people are moti-
vated by two different kinds of goals: promotion goals and
prevention goals (Higgins, 1998; Lockwood et al., 2002).
Promotion
goals are oriented toward maximizing positive outcomes and
pre-
vention goals are oriented toward minimizing negative
outcomes.
Regulatory focus is an inherent, individual-level trait, but can
also
be primed by the situation, as in the presentation of a health
inspec-
tion score.
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K.J. Harris et al. / International Journal of Hospitality
Management 60 (2017) 67–76 69
H2

H1
H3
H7 (+)
Attitude toward
Restaurant
Word of Mouth
Expected
Satisfaction
H5
H6 (+)
H8 (+)
Health Score
H4
Interpersonal
Influence
Food Safety
Concern
Prevention
Focus
Promotion
Focus

Self Efficacy
Intent to
Patronize
ptual
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H1-H5: (a, b)
Fig. 1. Conce
The promotion-focused individual is aspirational and seeks
leasure. These individuals are focused on being the best they
can
e, achieving the most success possible, and gaining the highest
evel of rewards. If a promotion-focused individual is dining
with
riends or co-workers at a restaurant, this person is looking
forward
o tasty food, fun with friends, and a pleasant experience.
Accord-
ng to regulatory focus theory, such individuals are predisposed
o process information regarding food safety heuristically, as
their
egulatory focus causes them to perceive the landscape as gener -
lly safe, negating the need to gather information and process too
eeply. Thus, it is expected that when a promotion-focused
individ-
al encounters a good health safety score, they will have a
positive
ttitude toward the restaurant. Conversely, if the rating is poor,
hey will have a negative attitude toward the restaurant. Thus, it

is
ypothesized that:
ypothesis 1. A restaurant’s posted health score will moder -
te the relationship between an individual’s promotion focus and
is/her attitude toward the restaurant such that the relationship
ill be (a) significantly positive when the score is good and (b)
ignificantly negative when the score is poor.
An individual with a prevention-based regulatory focus is ori-
nted toward minimizing negative outcomes. Issues such as
safety,
ecurity, and responsibility are of concern to the prevention-
ocused individual (Chitturi et al., 2008). When faced with a
choice
r decision, prevention-focused individuals are likely to think,
How can I minimize loss or negative outcomes?” It is expected
hat a person highly concerned with prevention will be heavily
ocused on preventing foodborne illness, even when the restau-
ant has received a positive inspection grade. However, when the
estaurant receives a negative inspection score, it is expected
that
he prevention-focused consumer would be extremely sensitive
to
his threat and would thus exhibit a negative attitude toward the
estaurant. Thus, it is hypothesized that:
ypothesis 2. A restaurant’s posted health score will moder -
te the relationship between an individual’s prevention focus and
ill be (a) insignificant when the score is good and (b)
significantly
egative when the score is poor.

framework.
Furthermore, a prevention-focused consumer, according to
information processing theory, is concerned with minimizing or
avoiding negative outcomes, such as foodborne illness. This
theory
suggests that a poor health inspection score acts as a negative
cue,
signaling that danger or threat is present, leading to deeper,
more
systematic processing. When systematic processing is engaged,
the
person seeks additional detailed information to make an
evaluation
about the restaurant and uses it to decide whether or not to eat
there. In this study, three additional pieces of information that
the
consumer may assess are examined: susceptibility to
interpersonal
influence, level of food safety concern, and sense of self-
efficacy.
2.3. Interpersonal influence
If a customer is seeking additional information in order to make
an evaluation about restaurant choice, consideration of others
din-
ing with the individual might influence the decision. This may
or may not influence their own decision, subject to their level
of
susceptibility to interpersonal influence. Susceptibility to inter-
personal influence is a general personality trait that exists
across
conditions (McGuire, 1968) and has been described as a confor -
mation to expectation (Bearden et al., 1989). People may

conform
to influence from others in order to gain acceptance, raise
status,
gain rewards from others, or avoid punishments (Burnkrant and
Cousineau, 1975; Park and Lessig, 1977).
The consumer behavior literature has examined the effect of
this trait on consumer decision-making when others are present
in
the environment (Ford and Ellis, 1980; Ratner and Khan, 2002)
and
found that interpersonal influence plays a major role in the
forma-
tion of consumer attitudes and behaviors (Chaplin and John,
2010;
Mourali et al., 2005). Thus, when a customer is deciding
whether
or not to eat at a restaurant with which they are not entirely
com-
fortable, they may seek the opinion of others in the dining
party. It
is expected that the importance of this information to their deci -
sion will depend on the individual’s susceptibility to
interpersonal
influence, such that:
Hypothesis 3. A restaurant’s posted health score will moderate
the relationship between an individual’s interpersonal influence
and his/her attitude toward the restaurant such that the rela-
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ionship will be (a) insignificant when the score is good and (b)
ignificantly positive when the score is poor.
.4. Food safety concern
Food safety concern is a measure of the concern an individual
as regarding food safety issues including food to be consumed,
leanliness, foodborne illness possibilities, and the overall
restau-

ant environment. Due to the proliferation of the Internet and the
4-h news cycle, outbreaks of foodborne illness and violations of
roper food safety protocols are now widely publicized. This has
reated a sense of concern on the part of some consumers as to
the
isks of eating at restaurants where the origins of the food and
the
rocedures by which the food is stored and handled are unknown
Ortega et al., 2011; Yeung and Yee, 2012). However, even in
the face
f such publicity, some consumers do not have a great concern
for
ood safety issues. When a customer is engaging in systematic
pro-
essing due to a prevention focus, their level of food safety
concern
hould be relevant such that:
ypothesis 4. A restaurant’s posted health score will moderate
he relationship between an individual’s food safety concern and
ill be (a) insignificant when the score is good and (b)
significantly
egative when the score is poor.
.5. Perceived self-efficacy
The construct of self-efficacy is an outgrowth of Bandura’s
2001) social-cognitive theory and encompasses the idea that an
ndividual can determine or affect outcomes in his/her own life
or
heir environment (Scholz et al., 2002). Self-efficacy can be con-
eptualized as an individual-level generalized personality trait
that
s stable across domains (Scholz et al., 2002; Sherer and
Maddux,

982). General self-efficacy indicates that the individual holds a
road set of optimistic self-beliefs about an ability to affect
change
r impact personal life outcomes. General self-efficacy is a
deter-
inant and motivator of attitudes and actions (Bandura, 2001)
and
person’s level of self-efficacy determines how they perceive a
ituation and respond to it. As it relates to foodborne illness, a
erson’s sense of self-efficacy speaks to their belief that they can
void contracting foodborne illness by avoiding restaurants with
ubstandard food handling practices. As such, it is expected that:
ypothesis 5. A restaurant’s posted health score will moderate
he relationship between an individual’s self-efficacy and his/her
ttitude toward the restaurant such that the relationship will be
(a)
nsignificant when the score is good and (b) significantly
positive
hen the score is poor.
.6. Attitude and behavior
Attitude can be defined as a function of one’s beliefs about an
ssue in combination with an evaluation of that belief (Fishbein,
963). Attitude encompasses the degree of an individual’s eval -
ation of an object or situation, either positive or negative;
onsequently, the resulting evaluation impacts and predicts con-
umer behaviors that may include loyalty, purchase, and word of
outh (Ajzen and Fishbein, 1980).
A consumer’s attitude about an organization can be impacted
y any number of factors. This study is concerned specifically
with

he extent to which regulatory focus, perceived self-efficacy,
inter-
ersonal influence, food safety concern, and the inspection rating
f the restaurant influence restaurant consumers’ attitudes toward
he restaurant and the subsequent behavioral effects of these atti -
udes. Based on the established relationships between attitudes
and
ehavior, it is predicted that:
pitality Management 60 (2017) 67–76
Hypothesis 6. An individual’s attitude toward a restaurant will
positively affect the level of satisfaction he/she expects to
experi-
ence as a result of patronizing that restaurant.
Hypothesis 7. An individual’s expected satisfaction will affect
his/her intentions to patronize the restaurant.
Hypothesis 8. An individual’s expected satisfaction will affect
his/her intentions to spread positive word of mouth about the
restaurant.
3. Methodology
3.1. Measured variables
To test hypotheses developed in the preceding section, the con-
structs in the proposed model were operationalized and
formatted
into an electronic questionnaire. The variables were largely
oper-
ationalized using the seminal conceptualizations of pre-existing
scales. The two regulatory focus constructs (�prevention =

0.85 and
�promotion = 0.86) were operationalized according to the
approach
developed by Lockwood et al. (2002). Likewise, the
susceptibility
to interpersonal influence construct (� = 0.90) was
operationalized
using the normative items created by Bearden et al. (1989), and
the self-efficacy construct (� = 0.92) came from Sherer and
Maddux
(1982).
Regarding the dependent variables, two different scaling tech-
niques were used. Respondents’ attitude toward the restaurant
was
measured on three 7-point semantic differential scales (� =
0.99).
Expected satisfaction was also measured using a semantic
differ-
ential approach (� = 0.99).
The behavioral constructs were measured on 7-point likelihood
scales (highly unlikely/highly likely). Word of mouth intention
(� = 0.99) and patronage intention (� = 0.98) were both opera-
tionalized using scales previously employed by Zeithaml et al.
(1996). Analyses of skewness (range: −1.16:1.20) and kurtosis
(range: −1.79:2.27) across all the measurement items indicated
that the data were distributed appropriately for the subsequent
use of structural equation modeling (Curran et al., 1996).
Throughout the data collection process, a number of steps were
taken to minimize the potential effects of common method
biases
(Podsakoff et al., 2003). For example, multiple scaling
techniques
were used to eliminate item characteristic effects and attention

checks were used to eliminate fatigue effects. In order to
confirm
that the bias reduction methods were successful, two tests were
conducted to assess the amount of shared variance potentially
attributable to the measurement process. First, Harmon’s single
factor test demonstrated that less than 50% of the model
variance
(31.5%) was explained by all measurement items modeled as a
single factor. Second, the common latent factor method ( ̌ =
0.51)
indicated that no more than 26.01% of the variance in the mod-
els was attributable to the methods. Together, these tests
indicate
that common method biases were likely not a significant source
of
variance in the measurement model (Podsakoff et al., 2003).
3.2. Manipulations
Because the purpose of this research was to identify the effect
that a restaurant’s health score exerts on consumers’ attitudes
toward the restaurant, it was necessary to create a manipulation
of the health score of the referent restaurant. The health score
vari-
able was manipulated by presenting participants with a scenario
about a hypothetical restaurant selection experience. In the sce -
nario, respondents were told to imagine a situation regarding
the
decision to eat lunch at a restaurant. The scenario read as
follows:
“You are on your lunch break from work, and you decide to try
a
new place to eat for lunch. The place you have chosen is located

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K.J. Harris et al. / International Journal
n a strip mall shopping area near a collection of shops, stores,

and
estaurants. You have shopped in the area and you have eaten at
ome of the other establishments in the strip mall, but you have
ever been to this one before. You park and walk up to the
restau-
ant. As you get to the front door, you see the following sign
posted
n the window:”
Following the scenario, respondents were shown an image of
formal health score posting document. Half of the respondents
ere shown an image of an ‘A’ rating for meeting the overall san-
tary requirements while the other half of the respondents were
hown the image of a ‘C’ rating. In order to ensure that the sce-
ario and the manipulated health score did not bias participants’
esponses to the individual trait variables, the scenario was pre -
ented following the measurement of the independent variables.
ikewise, to ensure that sample demographics would not
confound
ny potential moderation effects, a series of ANOVAs were con-
ucted. The results of these analyses suggest that there were no
ignificant differences among the measured demographic
variables
gender, ethnicity, education, age, income) across the high and
low
ealth score conditions.
Following the measurement of the behavioral variables, a series
f manipulation checks were conducted to ensure a successful
anipulation of the health score variable in terms of content and
ealism. Regarding the scores, both a subjective and an objec-
ive manipulation check were included. In the objective check,
articipants were asked to respond to the statement: “This restau-
ant complies with state mandated sanitary regulations.” Results

f an ANOVA (F = 3,365.7, p < 0.001) indicated that respondents
n the A score condition had significantly higher mean responses
MAScore = 6.22) to this statement than respondents in the C
score
ondition (MBScore = 2.40). In the subjective manipulation
check,
espondents were asked to indicate the extent to which they
greed with the statement: “This restaurant got a good score on
anitation.” Again, respondents in the A score condition reported
ignificantly higher mean responses (MAScore = 6.27) to this
state-
ent than respondents in the C score condition (MBScore = 2.13)
F = 3,786.2, p < 0.001). Finally, responses to realism checks
indi-
ated that sll respondents found the scenarios to be realistic
M = 5.41). The results of the manipula tion checks indicated a
suc-
essful manipulation of the health score variable.
.3. Data collection
The data used to test the proposed framework were collected
rom a sample of restaurant consumers in the United States.
Ama-
on Mechanical Turk was used as the sampling frame for data
ollection. The use of Mechanical Turk as a sampling frame was
urposeful, as users of this service have been demonstrated as
rel-
tively representative of consumer demographics in the United
tates (Mason and Suri, 2011). Additionally, Mechanical Turk
has
een found to be an effective sampling tool when demographic
iversity is desirable (Buhrmester et al., 2011).
As an incentive to participate, respondents were given a $.50

redit to their Mechanical Turk account. A total of 1547
responses
ere recorded in this manner. To eliminate potentially spurious
ata resulting from poor attention, three attention check items
ere embedded within the survey (e.g., “please choose somewhat
isagree. . .”). A total of 203 responses failed at least one of
these
hecks and were subsequently omitted from further analysis.
After
eleting these data, a total of 1324 usable responses remained.
iven the number of estimated parameters in the measurement
odel (n = 113), the non-nested structural model (n = 96), and the
oderated structural model (n = 192), the final sample size meets
he required threshold of free parameters to observations as rec -
mmended by Kline (2005).
pitality Management 60 (2017) 67–76 71
Regarding the characteristics of the sample, respondents were
relatively evenly distributed across several important
demographic
variables. In terms of age, 18.6% of the respondents were
between
18 and 25 years old, while another 38.9% were between 26 and
35 years old. A further 18.6% were between 36 and 45 years
old with the remainder of the sample (23.9%) reporting an age
of 45 or older. Additionally, while a majority of the respondents
were Caucasian (n = 1044, 78.9%), minority groups were rela-
tively well represented (e.g., African American (n = 88);
Hispanic
(n = 66); and Asian (n = 90)). Regarding gender, there were
slightly
more female respondents (n = 737, 55.7%) than male

respondents
(n = 582, 44.0%).
Concerning respondents’ education, 40.6% reported having a
high school degree or attending at least some college. Another
59.5%
reported the completion of either an undergraduate or graduate
degree. Finally, the respondents were also relatively evenly dis -
tributed in terms of their reported household income. Forty one
percent of the sample reported a household income of $40,000
or less. Another 36.4% reported a household income of between
$40,000 and $80,000. The remaining respondents (22.1%)
reported
an annual household income of more than $80,000.
Finally, regarding dining behavior, respondents showed a rela-
tively wide range of responses in answer to the question “How
often
do you eat outside of your home?” This information was
captured
on a per-week scaling basis with 29.2% of respondents reporting
that they eat out less than once per week. The largest category
of
respondents (57.9%) indicated they eat out at least one to three
times per week, while a further 10.8% indicated eating meals
out
four to eight times per week. These results suggest that the sam-
ple had sufficient experience with the phenomenon in question
to
accurately respond to the survey content.
4. Results
4.1. Measurement model
To assess the validity of the constructs in the hypothesized

framework, a measurement model was specified using struc-
tural AMOS equation modeling software. The resultant fit
indices
indicated that the measurement model was a good fit to the
data (�2 = 1,752.97, df = 553; RMSEA = 0.04; CFI = 0.98; NFI
= 0.97;
TLI = 0.98). Table 1 provides a full psychometric account of
each
construct. Means and composite reliabilities are reported along
with the standardized loadings for each reflective indicator and
the
associated error variances.
The average variance extracted (AVE) of each latent variable
was calculated to assess the validity of each of the constructs in
the measurement model (Fornell and Larcker, 1981). The AVE
is
presented in Table 2 along with the critical ratio (CR),
maximum
shared squared variance (MSV), and average shared squared
vari-
ance (ASV) for each construct. As seen in the table, the critical
ratio
is greater than the average variance extracted for all constructs
in
the model. Additionally, all instances of AVE are greater than
0.50.
These results provide support for the convergent validity of the
constructs (Hair et al., 2006).
To establish discriminant validity, the square root of the AVE
was calculated for each operational construct. This value was
then
compared to the correlations between the construct under
consid-
eration and all other constructs in the measurement model

(Fornell
and Larcker, 1981). Table 2 shows the correlations among all
con-
struct pairs in the measurement model. Additionally, the square
root of the AVE is presented on the diagonal. As seen in the
table,
the shared variance between each construct pair is less than the
AVE, providing strong evidence of discriminant validity among
the
constructs (Hair et al., 2006).
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72 K.J. Harris et al. / International Journal of Hospitality
Management 60 (2017) 67–76
Table 1
Scale properties.
Constructs and Indicators Mean: A Mean: C Std. Est. Std. Err.
Promotion Focus: Likert (� = 0.85) 5.37a 5.37a 0.59b
I typically focus on the success I hope to achieve in
the future.
5.50 5.44 0.83* 0.029
I often think about how I will achieve success. 5.28 5.31 0.83*
0.030
My major goal right now is to achieve my ambitions. 5.22 5.25
0.73* 0.033
I often imagine myself experiencing good things that
I hope will happen to me.
5.47 5.48 0.68* 0.032
Prevention Focus: Likert (� = 0.86) 3.95a 4.16a 0.55b
I am anxious that I will fall short of my
responsibilities and obligations.
4.12 4.36 0.70* 0.044
I often think about the person I am afraid I might

become in the future.
3.48 3.73 0.75* 0.045
I often worry that I will fail to accomplish my goals. 4.06 4.29
0.81* 0.043
I often imagine myself experiencing bad things that I
fear might happen to me.
3.60 3.83 0.81* 0.044
I frequently think about how I can prevent failures in
my life.
4.51 4.60 0.63* 0.041
Interpersonal Influence: Likert (� = 0.92) 2.44a 2.52a 0.69b
I rarely purchase the newest products until I am sure
my friends will approve of them.
2.50 2.56 0.64* 0.038
It is important that others like the products and
brands I buy.
2.35 2.36 0.85* 0.033
When buying products, I generally purchase those
brands that I think others will approve of.
2.33 2.44 0.95* 0.031
If other people can see me using a product, I often
purchase the brand they expect me to buy.

2.24 2.36 0.92* 0.031
I like to know what brands and products make good
impressions on others.
2.78 2.88 0.76* 0.040
Food Safety Concern: Likert (� = 0.95) 4.28a 4.52a 0.84b
I am concerned that the food served to me in
restaurants is not safe.
4.16 4.39 0.91* 0.034
restaurants has a good chance of making me sick.
4.02 4.26 0.96* 0.035
restaurants might be contaminated.
4.18 4.45 0.94* 0.035
restaurants might not be handled safely by
employees.
4.75 4.97 0.86* 0.035
Self-Efficacy: Likert (� = 0.90) 5.32a 5.20a 0.59b
It is easy for me to stick to my aims and accomplish
my goals.
5.08 4.94 0.66* 0.033

Thanks to my resourcefulness, I know how to handle
unforeseen situations.
5.30 5.22 0.83* 0.029
I can solve most problems if I invest the necessary
effort.
5.62 5.50 0.75* 0.026
I can remain calm when facing difficulties because I
can rely on my coping abilities.
5.26 5.13 0.80* 0.031
When I am confronted with a problem, I can usually
find several solutions.
5.21 5.12 0.74* 0.027
I can usually handle whatever comes my way. 5.42 5.30 0.83*
0.027
Attitude: Semantic Differential (� = 0.99) 6.49a 1.76a 0.99b
Negative: Positive 6.49 1.80 0.99* 0.050
Bad: Good 6.49 1.79 0.99* 0.050
Unfavorable: Favorable 6.50 1.69 0.99* 0.051
Expected Satisfaction: Semantic Differential (� = 0.99) 6.33a
1.93a 0.96b
Displeased: Pleased 6.39 1.84 0.99* 0.049
Disgusted: Contented 6.32 1.97 0.98* 0.048
Dissatisfied: Satisfied 6.33 1.91 0.98* 0.049

Unhappy: Happy 6.28 2.01 0.98* 0.048
Patronage Intention: Likelihood (� = 0.98) 6.15a 1.64a 0.96b
How likely is it that you would eat at this restaurant
on your lunch break?
6.30 1.62 0.99* 0.050
How likely is it that you would choose this
restaurant for a future dining experience?
5.99 1.66 0.97* 0.049
Word of Mouth: Likelihood (� = 0.99) 5.35a 1.73a 0.97b
How likely is it that you would say positive things
about this restaurant to other people.
5.36 1.85 0.98* 0.041
K.J. Harris et al. / International Journal of Hospitality
Management 60 (2017) 67–76 73
Table 1 (Continued)
Constructs and Indicators Mean: A Mean: C Std. Est. Std. Err.
How likely is it that you would recommend this
restaurant to someone who seeks your advice.
5.42 1.68 0.99* 0.042
How likely is it that you would encourage friends

and relatives to patronize this restaurant.
5.27 1.67 0.98* 0.042
Fit: �2 = 1752.97, df = 553; RMSEA = 0.04; CFI = 0.98; NFI =
0.97; TLI = 0.98.
a = Construct mean.
b = AVE.
Table 2
Validity assessment criteria and correlation matrix.
CR AVE MSV ASV PRE PRO II FSC SE ATT SAT INT WOM
PRE 0.86 0.55 0.20 0.04 0.74a
PRO 0.85 0.59 0.36 0.05 −0.09 0.77a
II 0.92 0.69 0.04 0.01 0.21 −0.02 0.83a
FSC 0.95 0.84 0.03 0.01 0.18 0.09 0.05 0.92a
SE 0.90 0.59 0.36 0.07 −0.44 0.60 −0.10 −0.01 0.77a
ATT 0.99 0.99 0.96 0.35 −0.06 0.02 0.00 −0.08 0.07 0.99a
SAT 0.99 0.96 0.96 0.35 −0.05 0.05 0.01 −0.09 0.07 0.98 0.98a
INT 0.98 0.96 0.95 0.35 −0.06 0.04 0.02 −0.08 0.08 0.97 0.98
0.98a
WOM 0.99 0.97 0.90 0.33 −0.05 0.06 0.05 −0.07 0.08 0.92 0.93
0.95 0.98a
C al infl
r mouth

4
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s
H
a
i
A
d
s
i
m
t
t
w
t
t
(
a
f
s
t
w
R
i
h
p
s

r
H
a
c
R = critical ratio; PRE = prevention focus; PRO = promotion
focus; II = interperson
estaurant; SAT = expected satisfaction; INT = patronage
intention; WOM = word of
a = square root of AVE.
.2. Nested structural model
After confirming the validity of the constructs in the measure-
ent model, a structural model was specified according to the
elationships hypothesized in Fig. 1. The fit of the structural
model
o the data was relatively good (�2 = 2,064.7, df = 570;
RMSEA = 0.05;
FI = 0.98; NFI = 0.97; TLI = 0.97). However, as this research is
con-
erned with the extent to which the hypothesized relationships
old true across both high and low health scores, a nested model
pecification was necessary to test for the moderation specified
in
ypotheses 1–5. To test for the moderating effect of health score,
two group model was specified split between the respondents
n the A score condition and respondents in the C score
condition.
s expected, the fit of the two-group specification (�2 = 2,815.5,
f = 1140; RMSEA = 0.03; CFI = 0.96; NFI = 0.93; TLI = 0.96)
was a
ignificantly better fit to the data than the non-nested model
spec-

fication (��2 = 750.8; �df = 570; p < 0.001). As such, the
nested
odel specification was accepted over the non-nested specifica-
ion.
After determining that the nested model was a significantly bet-
er fit to the data, the parameter estimates of the specified paths
ere compared between the two groups to determine the extent
o which the direction and significance of the estimates were
struc-
urally analogous across the manipulated health score conditions
Table 3). As expected, the estimates were significantly different
cross the parameters reflecting each of the first five hypotheses.
The results of the nested model analysis indicated full support
or Hypothesis 1 and for Hypotheses 3–5. Hypothesis 2 was
partially
upported. Broadly speaking, the results confirm the hypothesis
hat health scores identified as good are processed heuristically
hile health scores identified a poor are processed systematically.
egarding Hypothesis 1, a promotion focus was found to be a
signif-
cant predictor of participants’ attitude toward the restaurant. As
ypothesized, when the score was good, a promotion focus was a
ositive predictor of attitude (� = 0.24, p < 0.001) while when
the
core was poor, a promotion focus was a negative predictor of
estaurant attitude (� = −0.19, p < 0.001). Likewise, in support
of
ypothesis 2a, a prevention focus was not a significant predictor
of
ttitude in the good score condition (� = −0.07, p = 0.19).

However,
ontrary to what was hypothesized for Hypothesis 2b, a
prevention
uence congruence; FSC = food safety concern; SE = self-
efficacy; ATT = attitude to
.
focus was identified as a positive predictor of restaurant
attitude
in the poor health score condition (� = 0.13, p = 0.01). As this
path
was hypothesized to be negative based on the tenets of
information
processing theory, only partial support accrues to Hypothesis 2.
Similar moderated effects were identified for Hypotheses 3–5.
Susceptibility to interpersonal influence was not a significant
pre-
dictor of attitude in the good score condition (� = −0.07, p =
0.11)
but was significantly positive in the poor score condition (� =
0.24,
p < 0.001). Likewise, food safety concern was not a significant
pre-
dictor of attitude in the good score condition (� = 0.06, p =
0.16) but
was a significantly negative predictor in the poor score
condition
(� = −0.17, p < 0.001). Finally, self-efficacy was not a
significant pre-
dictor of attitude in the good score condition (� = −0.01, p =
0.84)
but was significantly positive in the poor score condition ( � =
0.19,

p = 0.004). These results provide full support for Hypotheses 3–
5.
Regarding the behavioral constructs, no moderation was estab-
lished. Regardless of the health score, there was a positive
effect of attitude toward the restaurant on expected satisfac-
tion (�AScore = 0.86, p < 0.001; �BScore = 0.87, p < 0.001).
These results
support Hypothesis 6. Likewise there was a positive effect
of expected satisfaction on patronage intention (�AScore =
0.79,
p < 0.001; �BScore = 0.90, p < 0.001) and on word of mouth
inten-
tion (�AScore = 0.60, p < 0.001; �BScore = 0.79, p < 0.001).
Again, these
results were consistent regardless of the reported health score.
Thus, the results provide full support for Hypotheses 7 and 8 as
well. The implications of these results are discussed in detail in
the
following section.
5. Discussion
Food safety is of paramount concern in the hospitality indus-
try, and an integral part of preventing outbreaks of foodborne
illness is the food establishment inspection process. While,
many
states require the posting of inspection results within the restau-
rant (McVicar, 2011), few studies have explored the
mechanisms
through which the consumer interprets this information. Accord-
ingly, the ways in which consumers use this information to
make assessments about the establishment is not well
understood
(Filion and Powell, 2011). To address these issues, this study

used
information processing theory as a framework to investigate the
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74 K.J. Harris et al. / International Journal of Hospitality
Management 60 (2017) 67–76
Table 3
Results of nested model analysis.
Parameter Group: A Group: C
̌ S.E. ̌ S.E.
Promotion Focus-Attitude to Restaurant 0.242** 0.044
−0.193** 0.060
Prevention Focus-Attitude to Restaurant 0.069 0.041 0.133*
0.054
Interpersonal Influence-Attitude to Restaurant −0.066 0.033
0.240** 0.041
Food Safety Concern-Attitude to Restaurant 0.056 0.031
−0.166** 0.041
Self Efficacy-Attitude to Restaurant −0.013 0.049 0.189**
0.068
Attitude to Restaurant-Expected Satisfaction 0.863** 0.031
0.874** 0.027
Expected Satisfaction-Patronage Intention 0.794** 0.043

0.896** 0.032
Expected Satisfaction-Word of Mouth 0.602** 0.046 0.788**
0.030
F
m
s
c
t
c
a
p
e
f
p
b
a
c
e
r
r
t
w
q
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r
a
s
w
a
e
t
r

a
t
p
p
h
t
m
A
e
s
b
t
i
o
c
r
i
r
s
it: �2 = 2815.53, df = 1140; RMSEA = 0.033; CFI = 0.96; NFI
= 0.93; TLI = 0.96.
** p < 0.001.
* p = 0.01
oderating impact of restaurant inspection scores on the relation-
hips among regulatory focus, interpersonal influence, food
safety
oncern, and self-efficacy. Upon establishing the nomological
rela-
ionships between these constructs, the downstream effects on
onsumer attitudes and behaviors were established.
Based on information processing theory (Chaiken, 1987; Petty
nd Cacioppo, 1986), this research suggested that customers
would

rocess the information contained in the food safety rating score
ither heuristically or systematically, based on their regulatory
ocus. Individuals with a promotion focus were proposed to be
redisposed to processing food safety information heuristically,
ecause a promotion focus causes them to perceive the landscape
s generally safe, negating the need to gather information and
pro-
ess too deeply. Thus, it was hypothesized that (1) when a person
ncounters a good health safety score, indicating that the restau-
ant is a safe place to eat, he will have a positive attitude toward
the
estaurant, and (2) if the rating is poor, he will have a negative
atti-
ude toward the restaurant. The results supported this prediction.
It
ould appear that promotion-focused consumers do indeed make
uick, heuristic judgments without thinking too deeply. When the
ating was an A, the participants had a good attitude toward the
estaurant, and when the rating was a C, they exhibited a
negative
ttitude toward the restaurant.
Conversely, it was expected that for prevention-focused con-
umers, the predisposition to prevent or avoid negative outcomes
ould cause them to process information about food safety
system-
tically, in an effort to prevent foodborne illness. In this case, it
was
xpected that the relationship between prevention focus and atti -
ude toward the restaurant would be insignificant when the
safety
ating was an A, but negative and significant when the rating
was

C. While the former proposition was supported (Hypothesis
2a),
he latter proposition (Hypothesis 2b) was not. Hypothesis 2b
pro-
osed that when a prevention-focused person, who was already
redisposed to preventing negative outcomes, was faced with
poor
ealth inspection for the restaurant, this would cause them to
hink more critically and arrive at the conclusion that they were
ore likely to contract a foodborne illness at this establishment.
ccordingly, this was expected to lead to a negative attitudinal
valuation of the restaurant. However, this hypothesis was not
upported. When the food safety score was a C, the relationship
etween prevention focus and attitude was significant and posi -
ive. The explanation for this unexpected outcome potentially
lies
n the additional information the customer may have gathered in
rder to make an evaluation (i.e., Hypotheses 3–5).
The relationships between interpersonal influence, food safety
oncern, self-efficacy and attitude were significant only in the
poor
ating condition (when the restaurant received a grade of C). It
s notable that these relationships were insignificant when the
estaurant received a rating of A, but when the C rating
prompted
ystematic processing, these relationships all became significant.
These results lend support to the idea that information
processing
is an important factor in how consumers assimilate information
about restaurant inspection ratings.

Regarding the lack of support for Hypothesis 2b, it is possi -
ble that when the customer evaluated the additional information
regarding interpersonal influence, food safety concern, and self-
efficacy, the integration of this information shifted his
attitudinal
evaluations in a way that was not expected. For example, per -
haps the prevention-focused customer did note the C rating, but
was influenced by his friends and co-workers’ attitudes about
the
restaurant. It is also possible that this consumer was well aware
that
a rating of C was not good, but upon further examination,
decided
that he was really not all that concerned about food safety
issues
in general. Waters et al. (2013) found that food safety
inspection
results have little predictive value in whether or not a foodborne
illness will occur, and Henson et al. (2006) reported that
consumers
use much more than inspection results to determine whether a
restaurant is a safe choice, including visible cues of food, staff
and
their overall personal inspection once inside. Thus, while this is
not
a definitive explanation, the results do suggest such a
possibility,
and provide an interesting area for future research.
The above results have important theoretical implications for
the hospitality literature, as this study represents a novel
approach
to understanding the ways in which customers integrate
informa-
tion about a restaurant’s safety inspections. The findings
suggest

that consumer reaction to the information contained in food
safety
ratings is not as simple as it may appear at first glance. This
study
adds to the extant research of information processing and
regula-
tory focus theory to demonstrate how these two frameworks
work
together to influence consumers’ attitudes and behaviors in
reac-
tion to new pieces of information. The results also extend the
body
of literature on food safety and restaurant inspection scores by
indicating that individual-level traits have a profound impact on
customer reactions to posted food safety ratings.
5.1. Practical applications
The results of this study can serve to inform public policy. Not
all states have legislated requirements that foodservice
establish-
ments post their food safety scores on site in a clear manner for
their patrons. However, the results of this study clearly demon-
strate that food safety signs can influence customer perceptions
of a restaurant. Strengthening consumer protections by
requiring
clearly visible signs for restaurants can help promote food
safety in
states due to the countervailing economic force of consumer
choice.
Additionally, regulating where information is posted should be
important to those involved food safety inspection programs and
restaurant companies. In states where signs are posted
physically at

a restaurant, many consumers may order food online or even
make
their choice of where to dine by scanning information on
websites
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K.J. Harris et al. / International Journal
uch as GrubHub, Penny Deliveries, and Yelp, and may not be
aware
f the inspection score. In 2014, $600 million was invested in
online
ood ordering companies (Kim, 2015), suggesting that online
food
rdering is quickly gaining popularity. Forcing food safety scores
o be displayed prominently on food ordering websites may be
the
ext generation of regulations.
Finally, restaurateurs may use the results of this study for their
wn benefit. Not only do the results of the study suggest that
t is important to have a good food safety score, but that posi-
ive food safety scores stimulate consumers to process
information
euristically. Consumers may not systematically evaluate menu
hoices, seating choices, or other decisions when dining due to a
ositive food safety score. Thus, offering easy to access
inspection
cores, presenting customers with staff who practice high
personal
ygiene, and maintaining a clean environment could effectively

elp consumers efficiently and positively process the various ele -
ents of their restaurant experience.
.2. Limitations and future research
While this research makes several important contributions, a
umber of questions remain unanswered. First, it is important to
ote that this study was conducted in the United States where
ood safety norms are often quite different from other places in
the
orld. As such, the results of the research should not be prema-
urely generalized outside of this cultural context. Future
research
hould continue to examine the relationships proposed in this
esearch in other cultural and national contexts.
Second, it is also important to acknowledge that the posting
f health scores in an alphabetical format is not the only method
sed by inspection agencies to communicate food safety
inspection
esults. Reporting norms vary by state and may feature numeri-
al scores or color-coded cards to communicate inspection
results.
hile the use of an A versus C score in the present research was
urposeful to establish two-group moderation, the findings do not
ecessarily correspond to all forms of inspection reporting.
Future
esearch should expand the framework to other forms of
reporting.
Finally, the boundary conditions imposed by the scenario should
e mentioned. For example, in the methods, respondents were
told

hat they would be making a lunch decision with a group of col-
eagues. The decision to include relevant others was purposeful
to
llow for the exploration of susceptibility to interpersonal influ-
nce and dining out is, more often than not, a shared experience.
owever, because an “others” group was included in the methods,
he results do not necessarily reflect how individuals would
process
nformation if they were dining alone.
eferences
jzen, I., Fishbein, M., 1980. Understanding Attitudes and
Predicting Social
Behavior. Prentice-Hall, Englewood Cliffs, NJ.
aker-White, A., 2014. The ABCs of Restaurant Inspections:
Online Disclosure and
Grading Systems. The Network for Public Health Law,
https://www.
networkforphl.org/the network blog/2014/04/04/435/the abcs of
restaurant
inspections online disclosure and grading systems. accessed
22.04.16.
andura, A., 2001. Social cognitive theory: an agentic
perspective. Annu. Rev.
Psychol. 52, 1–26.
earden, W.O., Netemeyer, R.G., Teel, J.E., 1989. Measurement
of consumer
susceptibility to interpersonal influence. J. Consum. Res. 15
(4), 473–481.
ohner, G., Chaiken, S., Hunyadi, P., 1994. The role of mood and

message ambiguity
in the interplay of heuristic and systematic processing. Eur. J.
Social Psychol. 24
(1), 207–221.
uhrmester, M., Kwang, T., Gosling, S.D., 2011. Amazon’s
Mechanical Turk a new
source of inexpensive, yet high-quality, data? Perspect. Psychol.
Sci. 6 (1), 3–5.
urnkrant, R.E., Cousineau, A., 1975. Informational and
normative social influence
in buyer behavior. J. Consum. Res. 2, 206–215.
enters for Disease Control, 2013. Surveillance for Foodborne
Disease Outbreaks
United States, 2013: Annual Report, Retrieved from
http://www.cdc.gov/
foodsafety/pdfs/foodborne-disease-outbreaks-annual-report-
2013-508c.pdf.
accessed 09.04.16.
pitality Management 60 (2017) 67–76 75
Centers for Disease Control, 2016. Centers of Disease Control
Vessel Sanitation
Program Report (2016), Retrieved from
http://www.cdc.gov/nceh/vsp/surv/
gilist.htm#2016. accessed 09.04.16.
Chaiken, S., 1987. The heuristic model of persuasion. In:
Zanna, M.P., Olson, J.M.,
Herman, C.P. (Eds.), Social Influence: The Ontario Symposium,
vol. 5. Lawrence

Erlbaum Associates, Inc, Hillsdale, NJ, pp. 3–39.
Chaplin, L.N., John, D.R., 2010. Interpersonal influences on
adolescent materialism:
a new look at the role of parents and peers. J. Consum.
Psychol. 20 (2), 176–184.
Chitturi, R., Raghunathan, R., Mahajan, V., 2008. Delight by
design: the role of
hedonic versus utilitarian benefits. J. Mark. 72, 48–63.
Choi, J., Miao, L., Almanza, B., Nelson, C.D., 2013.
Consumers’ responses to
restaurant inspection reports: the effects of information source
and message
style. J. Foodserv. Bus. Res. 16 (3), 255–275.
Curran, P.J., West, S.G., Finch, J.F., 1996. The robustness of
test statistics to
nonnormality and specification error in confirmatory factor
analysis. Psychol.
Methods 1 (3), 16–29.
DiPietro, D.B., Parsa, H.G., Gregory, A., 2011. Restaurant QSC
inspections and
financial performance: an empirical investigation. Int. J.
Contemp. Hosp.
Manage. 23 (7), 982–999.
Food and Drug Administration, 2009. Food Code 2009,
http://www.fda.gov/Food/
GuidanceRegulation/RetailFoodProtection/FoodCode/ucm20193
96.htm.
accessed 22.04.16.
Food and Drug Administration, 2011. FDA Food Safety

Modernization Act, http://
www.fda.gov/Food/GuidanceRegulation/FSMA/. accessed
22.04.16.
Food and Drug Administration, 2013. Food Code 2013,
http://www.fda.gov/Food/
GuidanceRegulation/RetailFoodProtection/FoodCode/ucm37427
5.htm.
accessed 22.04.16.
Fatimah, U.Z.A.U., Boo, H.C., Sambasivan, M., Salleh, R.,
2011. Foodservice hygiene
factors-the consumer perspective. Int. J. Hosp. Manage. 30 (1),
38–45.
Filion, K., Powell, D., 2011. Designing a national restaurant
inspection disclosure
system for New Zealand. J. Food Prot. 74 (11), 1869–1874.
Fishbein, M., 1963. An investigation of the relationship
between beliefs about an
object and the attitude toward that object. Hum. Relat. 16, 233–
240.
Food Safety News, 2016. Retrieved from
http://www.foodsafetynews.com/2016/
02/cdc-declares-chipotle-e-coli-outbreaks-over-cause-
unknown/#.
VwlHsPkrJD8. accessed 09.04.16.
Ford, J.D., Ellis, E., 1980. A re-examination of group influence
on member brand
preference. J. Mark. Res. 17, 125–132.
Fornell, C., Larcker, D.F., 1981. Evaluation of structural
equation models with

unobservable variables and measurement error. J. Mark. Res.
28, 39–50.
Frijda, N.H., 1988. The laws of emotion. Am. Psychol. 43, 349–
358.
Hair Jr., J.F., Black, W.C., Babin, B.J., Anderson, R.E.,
Tatham, R.L., 2006. Multivariate
Data Analysis, 6th ed. Pearson Prentice Hall, Upper Saddle
River, NJ.
Henson, S., Majowicz, S., Masakure, O., Sockett, P., Jones, A.,
Hart, R., Carr, D.,
Knowles, L., 2006. Consumer assessment of the safety of
restaurants: the role
of inspection notices and other information cues. J. Food Saf.
26, 275–301.
Higgins, E., 1998. Promotion and prevention: regulatory focus
as a motivational
principle. Adv. Exp. Social Psychol. 30, 1–46.
Kim, E., 2015. A Secular Shift to Online Food Ordering. Crunch
Network, Retrieved
from http://techcrunch.com/2015/05/07/a-secular-shift-to-
online-food-
ordering/ accessed 02.04.16.
Kline, R.B., 2005. Principles and Practice of Structural
Equation Modeling, 2nd ed.
The Guilford Press, New York, NY.
Lockwood, P., Jordan, C.H., Kunda, Z., 2002. Motivation by
positive or negative role
models: regulatory focus determines who will best inspire us. J.
Pers. Soc.

Psychol. 83, 854–864.
Mackie, D.M., Worth, L.T., 1989. Processing deficits and the
mediation of positive
affect in persuasion. J. Pers. Soc. Psychol. 57, 27–40.
Mason, W., Suri, S., 2011. Conducting behavioral research on
Amazon’s Mechanical
Turk. Behav. Res. Methods 44 (1), 1–23.
McGuire, W.J., 1968. Personality and susceptibility to social
influence. In: Borgatta,
E.F., Lambert, W.W. (Eds.), Handbook of Personality Theory
and Research. Rand
McNally, Chicago, pp. 1130–1187.
McVicar, B., 2011. Some States Grade Restaurant Based on
Health Inspections,
Retrieved from
http://www.mlive.com/news/muskegon/index.ssf/2011/08/
some states grade restaurants.html. accessed 02.04.16.
Mourali, M., Laroche, M., Pons, F., 2005. Individualistic
orientation and consumer
susceptibility to interpersonal influence. J. Serv. Mark. 19 (3),
164–173.
Neuberg, S.L., Fiske, S.T., 1987. Motivational influences on
impression formation:
outcome dependency, accuracy-driven attention, and
individuating processes.
J. Pers. Soc. Psychol. 53 (3), 431–444.
Ortega, D.L., Wang, H.H., Wu, L., Olynk, N.J., 2011. Modeling
heterogeneity in
consumer preferences for select food safety attributes in China.

Food Policy 36
(2), 318–324.
Park, C.W., Lessig, P., 1977. Students and housewives:
differences in susceptibility
to reference group influence. J. Consum. Res. 4, 102–110.
Petty, R.E., Cacioppo, J.T., 1986. Communication and
Persuasion: Central and
Peripheral Routes to Attitude Change. Springer, New York, NY.
Petty, R.E., Cacioppo, J.T., Goldman, R., 1981. Personal
involvement as a
determinant of argument-based persuasion. J. Pers. Soc.
Psychol. 41 (5),
847–855.
Pittman, T.S., D’Agostino, P.R., 1989. Motivation and
cognition: control deprivation
and the nature of subsequent information processing. J. Exp.
Soc. Psychol. 25
(6), 465–480.
http://refhub.elsevier.com/S0278-4319(16)30200-6/sbref0005
http://https://www.networkforphl.org/the_network_blog/2014/04
/04/435/the_abcs_of_restaurant_inspections_online_disclosure_
and_grading_systems

and_grading_systems
and_grading_systems
http://https://www.networkforphl.org/the_network_blo g/2014/04
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems

and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems
and_grading_systems

http://refhub.elsevi er.com/S0278-4319(16)30200-6/sbref0025

http://www.cdc.gov/foodsafety/pdfs/foodborne-disease-
outbreaks-annual-report-2013-508c.pdf
http://www.cdc.gov/nceh/vsp/surv/gilist.htm#2016

http://refhub.elsevier.com/ S0278-4319(16)30200-6/sbref0065

http://www.fda.gov/Food/GuidanceRegulation/RetailFoodProtec
tion/FoodCode/ucm2019396.htm

http://www.fda.gov/Food/GuidanceRegulation/FSMA/

http://www.foodsafetynews.com/2016/02/cdc-declares-chipotle-
e-coli-outbreaks-over-cause-unknown/#.VwlHsPkrJD8

http://refhub.elsevier.com/ S0278-4319(16)30200-6/sbref0135

http://techcrunch.com/2015/05/07/a-secular-shift-to-online-
food-ordering/
food-ordering/
food-ordering/
food-ordering/
food-ordering/
food-ordering/
food-ordering/
food-ordering/
food-ordering/
food-ordering/
food-ordering/
food-ordering/
food-ordering/
food-ordering/

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