The Impact of Customer Loyalty and Restaurant Sanitation Grades on Revisit Intention and the Importance of Narrative Information: The Case of New York Restaurant Sanitation Grading System

Abstract

New York City launched a restaurant sanitation letter grade system in 2010. We evaluate the impact of customer loyalty on restaurant revisit intentions after exposure to a sanitation grade alone, and after exposure to a sanitation grade plus narrative information about sanitation violations (e.g., presence of rats). We use a 2 (loyalty: high or low) × 4 (sanitation grade: A, B, C, or pending) between-subjects full factorial design to test the hypotheses using data from 547 participants recruited from Amazon MTurk who reside in the New York City area. Our study yields three findings. First, loyal customers exhibit higher intentions to revisit restaurants than non-loyal customers, regardless of sanitation letter grades. Second, the difference in revisit intentions between loyal and non-loyal customers is higher when sanitation grades are poorer. Finally, loyal customers are less sensitive to narrative information about sanitation violations.

Keywords

NYC restaurant sanitation grading system customer loyalty information processing

Introduction

The US Department of Agriculture (USDA) reports that almost half of U.S. food expenditures occur in restaurants (USDA Economic Research Service [ERS], 2016), and approximately one third of calories are consumed away from home (Lin & Guthrie, 2012; McKelvey, Wong, & Matis, 2015). Moreover, approximately two thirds of foodborne illnesses between 1998 and 2008 involved restaurants (Gould et al., 2013; McKelvey et al., 2015). Consequently, consumers who are increasingly focused on health are concerned about food safety while dining out (Choi, Miao, Almanza, & Nelson, 2013; Michaelidou & Hassan, 2008).

However, it is not easy to obtain information on how safely restaurant food is prepared (Choi et al., 2013; Mazzocchi, Lobb, Traill, & Cavicchi, 2008). In an effort to provide food safety information to consumers, several local health departments (e.g., Los Angeles and New York) now issue restaurant inspection reports for public review. New York City (henceforth, NYC), for example, uses letter grades (A, B, C, and pending) to indicate a restaurant’s level of cleanliness, and these grades must be conspicuously displayed (Bloomberg & Farley, 2016) so consumers can evaluate a restaurant’s sanitation status. About 88% of customers in NYC consider a restaurant’s sanitation grade when making dining decisions (McKelvey et al., 2015), and the system certainly has helped to increase the awareness of potentially poor sanitation conditions and enhanced the cleanness of NYC restaurants: 85% of the restaurants were evaluated as Grade A in 2014 vs. 72% in 2011 (New York City Department of Health and Mental Hygiene, 2015).

Despite the successful implementation of the New York restaurant sanitation grading system, no academic research has been performed to investigate how customers utilize and think about the system and how these behaviors affect related concepts such as customer loyalty. Customer loyalty is a fundamental component of any company’s long-term competitiveness (Kotler, Bowen, & Makens, 2006). This study examines different revisit intentions between loyal and non-loyal customers perceiving various restaurant sanitation grades. In other words, are highly loyal customers more forgiving of poor sanitation grades, or do they react in the opposite manner? This study also tests the impact of providing information about the sanitation grades for both loyal and non-loyal customers on their revisit intentions. Does specific information on why a restaurant received a particular grade influence consumers’ revisit intentions? How would loyal and non-loyal customers respond differently? The findings of this study have important implications for restaurants operating in areas with stringent food sanitation regulations, such as NYC.

Conceptual Background

NYC Restaurant Sanitation Grading System

The NYC Department of Health adopted its restaurant sanitation grading system in July 2010. Most food service establishments including restaurants, bars, cafeterias, coffee shops, nightclubs, bakeries, and fixed-site food stands are required to participate in this effort (Bloomberg & Farley, 2016). The letter grades (i.e., A, B, or C) are based on violation points. Violations are accrued as inspectors check for a variety of sanitation issues such as personal hygiene, food handling and temperature of food, vermin control, and maintenance of the facility and equipment (Bloomberg & Farley, 2016). The inspection cycle is comprised of two stages: an initial inspection and a revisit (McKelvey et al., 2015).

If a food service establishment does not receive an A grade during the initial inspection, its violation points are scored and an inspector makes a second unannounced visit approximately 7 to 30 days later. At that point, the restaurant must post the grade or grade Pending card. This system of grades and violation points is shown in Figure 1.

Figure 1.

NYC Restaurant Grade Program.

Customer Loyalty

Customer loyalty can be defined as “a deeply held commitment to re-buy or re-patronize a preferred product or service consistently in the future, despite situational influences and marketing efforts having the potential to cause switching behavior” (Oliver, 2010, p. 432). Moreover, loyalty is a mix of attitudes and behaviors that benefit a service provider (Dick & Basu, 1994; Watson, Beck, Henderson, & Palmatier, 2015). In other words, both attitudinal loyalty and behavioral loyalty matter (Chaudhuri & Holbrook, 2001; Watson et al., 2015). Attitudinal loyalty is defined as “a cognition or pleasurable fulfillment” favoring one entity such as a firm, its brand, its salesperson, or its offerings (Oliver, 1999, p. 35), whereas behavioral loyalty is defined as “repeated purchases that stem from a conation¹ or action orientation involving a ‘readiness to act’ favoring one entity” (Watson et al., 2015, p. 791).

While the attitudinal element focuses on psychological commitment toward the store/brand, the behavioral element refers to the concepts of repeat patronage, purchase frequency, and referral (Dick & Basu, 1994; Jin, Line, & Goh, 2013). Loyal customers tend to have a strong emotional bond with the service provider (Gounaris & Stathakopoulos, 2004) and are inclined to focus on positive previous experiences and interactions with staff (Mittal, Huppertz, & Khare, 2008). Loyal customers also are less likely to switch to competitors (Petrick, 2004; Yoo & Bai, 2013). In sum, loyal customers are described as “customers who hold favorable attitudes toward the company, commit to repurchase the product/service and recommend the product to others” (Bowen & Chen, 2001). We expect that due to their affective commitment, loyal customers exhibit higher levels of behavioral loyalty than their less loyal counterparts. Thus, we hypothesize the following:

Hypothesis 1 (H1): In general, loyal customers have higher restaurant revisit intentions than non-loyal customers, regardless of the restaurant’s sanitation grade.

Grades B, C, and pending indicate that a restaurant has many sanitation violations that could result in potential harm to the customer. Such grades thus are akin to service failures. Service failure occurs when service does not meet customer expectations (Agarwal, Mehrotra, & Barger, 2016). Typically, service failure leads to customer dissatisfaction and negative behavioral consequences such as spreading negative word-of-mouth and switching (Balaji, Roy, & Quazi, 2017; Reynolds & Harris, 2009; Tsarenko & Strizhakova, 2013). According to Hirschman (1970), there are three basic responses to a service failure: voice (complain to the service provider), exit (leave and do not re-patronize the service provider), and loyalty (remain in the relationship with the service provider). Day and Landon (1977) proposed a hierarchical model Appendix of consumer complaint behavior (CCB), the first stage being action or no-action. The second stage is comprised of two types of action responses: public actions and private actions. When faced with a service failure, loyal customers tend to act based on Hirschman’s “loyalty” category or Day and Landon’s “no-action” category. In the CCB literature, customer loyalty is defined as taking no action and remaining with the service provider, trusting that the situation will improve (Hirschman, 1970; Ro & Mattila, 2015). Because loyal customers are emotionally bonded to the service provider, they are hesitant to jeopardize the relationship with the provider by complaining (Dewitt & Brady, 2003; Mittal et al., 2008; Ro & Mattila, 2015).

In this article, we argue that loyal customers with high levels of affective commitment to a restaurant react less negatively to a poor sanitation grade than non-loyal customers. Compared with non-loyal customers, loyal customers might continue to patronize a restaurant because they want to protect it (Dewitt & Brady, 2003; Mittal et al., 2008; Ro & Mattila, 2015). Based on their cumulative positive experiences, they might want to give the restaurant another chance to recover from the poor grade (Ro & Mattila, 2015).

We thus propose that when the failure magnitude increases (e.g., sanitation grade B to grade C or pending), non-loyal customers’ revisit intentions drop dramatically. However, due to their established relationships, loyal customers’ revisit intentions remain less affected. In other words, emotional bonding might prevent negative behaviors such switching to another service provider (e.g., Priluck & Wisenblit, 2009; Ro & Mattila, 2015). Accordingly, we hypothesize the following:

Hypothesis 2 (H2): Revisit intentions decrease more among non-loyal customers than among loyal customers when the sanitation grade is lower.

Impact of Information and Information Processing

The current grading system does not give customers any detailed information on what types of violations the restaurant has committed on-site unless customers go online and visit the NY Health Department’s website or download a mobile application called ABCEats to seek further information. As shown in Figure 1, even a restaurant with the highest sanitation grade A can have some severe violations such as evidence of rats or roaches on the premises. We thus argue that a grade without any further information about why the grade was given is not that useful. If customers are provided with detailed information about the sanitation grade, does it change their behavioral intentions?

The information processing literature suggests that there are two distinct strategies of information processing: heuristic processing and systematic processing (Chaiken, 1980). In the heuristic–systematic model, Chaiken, Liberman, and Eagly (1989) explained that heuristic processing uses “minimal information input in conjunction with simple (declarative or procedural) knowledge structures to determine message validity quickly and efficiently” (Chaiken et al., 1989, p. 216), while systematic processing indicates a “comprehensive, analytic orientation in which perceivers access all informational input for its relevance and importance to their judgment task” (Chaiken et al., 1989, p. 212). Heuristic information processing has the economic advantage of utilizing less cognitive effort. Based on their past experiences and observations, individuals may use relatively general rules called heuristics to activate judgment shortcuts and make everyday decisions using salient and easily comprehended cues. Often, individuals use this information processing strategy when they lack either the motivation or the ability to process information (Chaiken, 1980; Chaiken & Ledgerwood, 2012). In contrast, individuals generally engage in systematic processing when they have sufficient motivation and ability to access and process information. Requiring a considerable amount of cognitive effort, systematic processing requires deep engagement in analysis and reasoning to make judgments and decisions (Chaiken, 1980; Chaiken & Ledgerwood, 2012; Sparks, Perkins, & Buckley, 2013). These two styles of information processing are not mutually exclusive; they interact and individuals attenuate strategies depending on the situation (Chaiken & Ledgerwood, 2012). In certain circumstances, individuals engage in heuristic processing first and utilize systematic processing later, as needed (Chaiken & Ledgerwood, 2012).

A restaurant sanitation letter grade provides a cue or a symbol without detailed information; hence, customers are likely to engage in heuristic information processing to make judgments about the sanitation condition of a restaurant. However, when given detailed explanations for the grade, customers are more likely to process information in a more systematic manner. Systematic processing of detailed information induces both loyal and non-loyal customers to investigate negative information related to an A grade. Because a company with severe sanitation violations can still receive an A grade, revisit intentions could decrease for both customer groups.

Do loyal customers continue to exhibit high revisit intentions despite knowledge of damaging details (i.e., the assimilation effect, whereby information has an impact on interpretations or judgments in the direction of its implications; Strack, Schwarz, Bless, Kübler, & Wänke, 1993)? Or does the detailed information lead to strong feelings of disappointment, resulting in significantly decreased return intentions (i.e., the contrast effect, whereby assessed information has an impact on interpretations or judgments in the opposite direction; Strack et al., 1993)? In this article, we argue that a positive overall evaluation biases loyal customers’ information processing. More specifically, we propose that assimilation effects can influence loyal customers’ revisit intentions after exposure to damaging, detailed violation information (Herr, Sherman, & Fazio, 1983; Kan, Lichtenstein, Grant, & Janiszewski, 2014). This indicates that the affective commitment of loyal customers buffers consequences of exposure to negative information, such as decreased revisit intentions. When customers have strong affective commitment, including emotional attachment to a firm, friendship with employees of the firm, and strong commitment to the relationship with the firm, customers are less likely to damage the relationship in service failure situations (Liu & Mattila, 2015; Mattila, 2004; Mittal et al., 2008). Consequently, loyal customers are likely to continue to exhibit higher revisit intentions than their less loyal counterparts. Conversely, we argue that detailed violation information has a highly negative impact on non-loyal customers, making them less likely to return to the establishment. Thus, we hypothesize the following:

Hypothesis 3 (H3): Exposure to negative detailed information influences revisit intentions less among loyal customers than among non-loyal customers.

Method

Study Design and Instrument

We used a 2 (loyalty: high or low) × 4 (sanitation grade: A, B, C or pending) between-subjects full factorial design to test the hypotheses. We manipulated customer loyalty via written scenarios. The scenario for the high-loyalty condition involved a restaurant that the participant had frequented for a long time, whereas the scenario for the low-loyalty group involved a visit to a new restaurant. Sample scenarios are provided in Online Appendix A. After reading the scenario, participants answered a survey regarding their reactions to the scenario. Four grades (A, B, C, and P) reflecting the scale used in NYC were randomly assigned to the two types of scenarios. To test H3, we asked participants who were assigned to the Grade A condition additional questions to measure reactions to the specific violations cited at the restaurant. After indicating their revisit intentions based on exposure to sanitation grades alone (see Online Appendix B), they re-indicated their revisit intentions after exposure to detailed information about specific violations. The violation information included the restaurant name, violation points, current grade, inspection date, and the list of violations, based on the real form used by the NYC Health Department (see Online Appendix C). At the end of the survey experiment, participants provided demographic information and were encouraged to respond to an open-ended question about their real-life experiences with the grading system in NYC and recommendations for its improvement.

Participants

We used Amazon Mechanical Turk (MTurk) to recruit participants who live in the NYC area and, therefore, are likely to be aware of the sanitation grading system. We used three layers of filtering to identify participants who reside in the NYC area. First, the study instructions on MTurk included a clear statement indicating that only NYC and New Jersey (henceforth, NJ) residents were eligible to participate in the study. We included NJ residents in the study because they potentially work and dine in NYC and might know about the system. Second, using the address information from survey participants stored in their MTurk account information, we blocked participants whose addresses were outside of NYC and NJ. Finally, post-data collection, we identified the physical locations of participants’ IP addresses provided by the survey program and excluded any participants with IP addresses outside of NYC and NJ. In exchange of taking survey, every participant was rewarded $0.50, and participants who were assigned with Grade A condition were paid additional $0.20 (total of $0.70) as the survey is longer than other seven conditions. A total of 547 participants were randomly assigned to one of the eight experimental conditions. We summarize participants’ demographic information in Table 1.

Table 1.

Participant Profile (n = 547).

Variable	n	%
Gender
Female	289	52.8
Male	257	47
Other	1	.2
Age
Less than 20 years	140	25.6
20 to 29 years	247	45.2
30 to 39 years	92	16.8
40 to 49 years	34	6.2
50 to 59 years	25	4.6
60 to 69 years	0	0
70 years and above	9	1.6
Income
$150,000 and over	25	4.6
$100,000-$149,999	84	15.4
$85,000-$99,999	61	11.2
$70,000-$84,999	88	16.1
$55,000-$69,999	100	18.3
$40,000-$54,999	165	30.2
$20,000-$39,999	17	3.1
Under $20,000	7	1.3
Race
Caucasian	353	64.5
Hispanic	69	12.6
African American	54	9.9
Asian	54	9.9
Other	17	3.1
Educational level
Less than high school	2	.4
High school diploma	114	20.8
Associate’s degree	96	17.6
Bachelor’s degree	235	43
Graduate degree	100	18.3
Frequency dining out (per month)
0-2 times	85	15.5
3-5 times	263	48.1
6-12 times	121	22.1
Over 12 times	78	14.3

Measures

Dependent variable

We measured the dependent variable using three items from the revisit intentions scale adapted from Blodgett, Hill, and Tax (1997) and Maxham and Netemeyer (2002): “I am willing to revisit this restaurant”; “I will probably revisit this restaurant”; and “It is likely that I will revisit this restaurant” (α = .98 for initial revisit intentions for those in all conditions, and α = .98 for revisit intentions after exposure to narrative information for those in the Grade A condition). All questions were answered on a 7-point Likert-type scale (1 = very unlikely, 7 = very likely).

Manipulation checks

We measured customer loyalty using a five-item scale: “I consider this restaurant my first choice when I choose a restaurant”; “I would say positive things about this restaurant to other people”; “I intend to go to this restaurant more within the next few years”; “I intend to switch to another restaurant in the near future (reverse coded)”; and “I would be dedicated to doing business with this restaurant” (α = .93). We checked scenario realism with the following item: “This situation could happen, or has happened, to me or to someone I know.” All questions were answered in on a 7-point Likert-type scale (1 = strongly disagree, 7 = strongly agree).

Results

Manipulation checks

The manipulation check for customer loyalty indicates that the scenarios were successful, high loyalty: M = 4.95, low loyalty: M = 3.58; t(545) = 10.74, p < .001. The scenarios were perceived as realistic (overall: M = 5.12; conditions with further violation information: M = 4.91; all other conditions: M = 5.2).

Hypotheses testing

The effect of loyalty and sanitation grades on revisit intentions

To test H1 and H2, we ran a two-way ANOVA with revisit intentions as the dependent variable and loyalty and sanitation grades as the independent variables. First, the main effect of loyalty on revisit intentions is significant, such that loyal customers exhibit higher revisit intentions than non-loyal customers across all four grade levels, F(1, 539) = 139.251, p < .001; see Table 2 and Figure 2. Therefore, H1 is supported.

Table 2.

ANOVA Results of Effects of Loyalty and Sanitation Grade on Revisit Intention (All Four Conditions).

Source	Type III SS	df	MS	F	Significance
Corrected model	878.555	7	125.508	69.766	.000
Intercept	12,660.455	1	12,660.455	7,037.593	.000
Loyalty	250.508	1	250.508	139.251	.000
Sanitation grade	624.457	3	208.152	115.706	.000
Loyalty × Sanitation Grade	11.641	3	3.880	2.157	.092
Error	969.648	539	1.799
Total	14,699.333	547
Corrected total	1,848.203	546

Note. R² = .475 (adjusted R² = .469).

Figure 2.

Interaction Effect of Loyalty and Sanitation Grade on Revisit Intention.

When considering all sanitation grades (A, B, C, and P), the interaction effect on revisit intentions is not significant, F(3, 539) = 2.16, p = .092; see Table 2 and Figure 2. However, the interaction effect between loyalty and sanitation grade is significant, F(1, 280) = 4.59, p < .05, when limiting the analyses to Grades A and B (see Table 3). Therefore, H2 is partially supported. With letter grades A and B, loyalty has a significant effect on revisit intentions, F(1, 280) = 45.31, p < .001, ω = .37. The effect of letter grade on revisit intentions is significant as well, F(1, 280) = 111.92, p < .001, ω = .53.

Table 3.

ANOVA Results of Effects of Loyalty and Sanitation Grade on Revisit Intention (Only With Grade A and Grade B Conditions).

Source	Type III SS	df	MS	F	Significance
Corrected model	306.237	3	102.079	71.905	.000
Intercept	8,640.723	1	8,640.713	6,086.576	.000
Loyalty	103.626	1	103.626	72.995	.000
Sanitation grade	200.323	1	200.323	141.109	.000
Loyalty × Sanitation grade	6.522	1	6.522	4.594	.033
Error	397.498	280	1.420
Total	9,408.778	284
Corrected total	703.735	283

Note. R² = .435 (adjusted R² = .429)

The effect of loyalty and details about sanitation violations on revisit intentions

To test H3, we re-measured revisit intentions after exposing participants to detailed information about violations incurred by a Grade A restaurant. We limited the sample to respondents who had been assigned to the Grade A scenario. We assumed that people are not likely to expect an A grade restaurant to have severe sanitation issues (i.e., presence of mice and roaches). We thus created an individual-level panel data set such that we recorded two observations for revisit intentions for each participant in the Grade A scenario (i.e., pre- and post-exposure to violation details).

We found significant main effects of loyalty, F(1, 288) = 74.66, p < .001, ω = .31, and exposure to detailed information about violations, F(1, 288) = 362.07, p < .001, ω = .7. Moreover, the interaction effect between loyalty and violation details on revisit intentions is significant, F(1, 288) = 8.71, p < .05, as shown in Table 4.

Table 4.

ANOVA Results of Effects of Provision of Violation Details and Loyalty on Revisit Intention (Time 1 and 2—Without and With Violation Details—on Grade A).

Source	Type III SS	df	MS	F	Significance
Corrected model	829.135	3	276.378	149.598	.000
Intercept	6,850.177	1	6,850.177	3,707.855	.000
Provision of violation details	668.921	1	668.921	362.073	.000
Loyalty	137.929	1	137.929	74.658	.000
Provision of Violation Details × Loyalty	16.082	1	16.082	8.705	.003
Error	532.073	288	1.847
Total	8,163.333	292
Corrected total	1,361.209	291

Note. R² = .609 (adjusted R² = .605).

To probe the interaction effect, we examined the differences between high-loyalty and low-loyalty customers (see Figure 3). Participants in the high-loyalty condition show a significant decrease in revisit intentions (2.56 points, on average) between Time 1 and Time 2 (after exposure to detailed violation information), t(11.031) = 11.03, p < .001. Similarly, participants in the low loyalty condition show a significant decrease in revisit intentions (3.5 points, on average) between Time 1 and Time 2, t(16.016) = 16.02, p < .001. Consistent with our predictions, the decrease in revisit intentions is steeper for the low-loyalty group.

Figure 3.

Change of Revisit Intention Base on Share of Violation Details in Grade A Condition.

Discussion

Our findings have important implications for research on the role of restaurant sanitation grades on revisit intentions. First, regardless of the sanitation grade, loyal customers exhibit a higher tendency to revisit a restaurant than their non-loyal counterparts. Customer loyalty plays a critical role in relationship building; thus, creating and maintaining strong customer relationships is essential (Kandampully, Zhang, & Bilgihan, 2015; Pan, Sheng, & Xie, 2012).

Our findings further demonstrate that loyalty might buffer the negative effects of poor sanitation grades. Loyal customers show commitment and emotional attachment toward a company and are less attracted to offerings from competitors (Kandampully et al., 2015; So, King, Sparks, & Wang, 2013). In addition to their intentions to pay more and purchase more frequently, loyal customers are less prone to switching behaviors (Evanschitzky et al., 2012; Kandampully et al., 2015). In this study, loyal customers who were shown sanitation grades of A and B exhibited higher revisit intentions than their less loyal counterparts. In other words, loyalty buffers the effect of sanitation violations on revisit intentions, again emphasizing customer loyalty as an important source of competitive advantage.

An important finding of this study is the role of detailed information about sanitation violations. Currently, customers who dine out in NYC do not have access to such detailed information by visiting a restaurant. Our findings indicate that when exposed to violation details, revisit intentions fall drastically for both loyal and non-loyal customers. In Table 5, we present some of the comments participants voluntarily provided about how detailed information affected their interpretations of the grading scheme. Their feedback indicates that detailed information is needed so that customers can have a more realistic picture of the sanitation conditions in NYC restaurants.

Table 5.

Participants’ Comments Showing the Importance of Providing Sanitation Violation Details.

Content Categories	Actual Comments
Surprised that a restaurant with Grade A can have such serious problems	“I had no idea a restaurant could have so many violations and still have an A rating. I thought an A rating was No Violations at all.”
Never recognized such problems can happen	“Although I’ve lived in the metro area my entire life, and am aware of the grading system, I was unaware that there are a certain amount of violation points allotted to still receive a grade of ‘A.’”
Inferring from the violations of Grade A, even some participants worry further about the sanitation condition of restaurants with a grade lower than A	“If grade A has those types of violations I’m imagining what in the world the other grades have, must be much much worse. Yuck!”
Hoping that they could access to those violation details along with the letter grade, and they believe that the system can be misleading otherwise	“It should be a requirement that the sanitation grade is posted on the wall of the restaurant (visible to all patrons) and list why they got the grade they got or what improvements they need to make. This way people would trust the sanitation grade, the government and the restaurant to be honest about what they need to work on and what they already do well.”

Note. 84.1% of participants (n = 460 of 547) answered that they know about the NYC restaurant sanitation grading system.

In addition, the buffering effect of loyalty remained even when participants were exposed to detailed information regarding sanitation violations (e.g., rodents and roaches on the premises). Specifically, the decrease in revisit intentions was less steep for loyal customers than for their less loyal counterparts. This is consistent with previous literature suggesting that loyal customers react to service failures less severely than non-loyal customers due to their tendency to be more forgiving (e.g., Ro & Mattila, 2015). This finding is interesting, as it shows that the buffering effect of loyalty remains even when customers use systematic information processing.Theoretically, this implies that even in contexts involving exposure to detailed violation information which might require customers to engage in systematic information processing, loyalty has a positive effect on customers’ behaviors in severe service failure situations. In other words, loyalty can affect customers’ behaviors regardless of which information processing style they employ; thus, it is extremely important for businesses to actively cultivate customer loyalty.

Last, Grade P (pending) has interesting implications. Participants assigned to the Grade P condition expressed higher intentions to revisit the restaurant than those assigned to the Grade C condition. The difference in revisit intentions is significant among both the high-loyalty group, t(115.495) = −6.109, p < .001, average difference: −1.68, and the low-loyalty group, t(133) = −5.517, p < .001, average difference: –1.34. It is reported that 85% of the New York City restaurant had Grade A in 2014, and restaurants with Grade A has been gradually increasing (New York City Department of Health and Mental Hygiene, 2015). Customers can easily find Grade A restaurants, meaning they do not have to compromise food quality or taste to ensure satisfactory sanitation. Therefore, it is possible that customers might not discern differences among restaurants with lower grades. Some participants mentioned that they were confused about the meanings of Grades B, C, and pending and that they found it difficult to make judgment calls when exposed to a pending grade. This suggests that customers probably feel more comfortable visiting restaurants that receive Grade P (vs. Grades B or C), because not many customers immediately associate a “Pending” status with low grades. Based on these results, if a restaurant receives a sanitation grade of C, it might be better to post a grade of pending until the next inspection.

Many participants in this study stated that sanitation information has a strong influence on their restaurant choices, particularly for establishments they have never patronized. Accordingly, an increasing number of states, counties, and cities in North America have begun to publish restaurant sanitation reports (McKelvey et al., 2015). In sum, this study highlights the importance of customer loyalty in buffering the negative impact of sanitation violations and the need to provide detailed information regarding sanitation violations.

Limitations

As with any study, this research has some limitations. First, while a scenario-based experiment has a high level of internal validity and effectiveness, it has low external validity (Barling & Phillips, 1993; Sparks & Fredline, 2007). Therefore, future research in a field setting is warranted. Second, we collected data online via MTurk. As a result, the age level of the participants of this study is quite young (71% below 30 years). Collecting data from participants in various age groups would help improve the generalizability of our results. Finally, our research setting was limited to NYC. Additional research should be conducted in different regions to test for region-specific implications for restaurant sanitation policies.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, or publication of this article: This work was supported by the Hongik University new faculty research support fund.

Notes

Author Biographies

Min Gyung (Min) Kim, PhD, is an assistant professor in the College of Business Management at Hongik University, Republic of Korea. Min holds a PhD in Hospitality Management at the Pennsylvania State University. Her research interests are consumer emotions, sensory marketing, and branding for enhancing consumer experiences in the service setting.

Hyunjoo Yang, PhD, is an assistant professor in School of Northeast Asian Studies at the Incheon National University. He holds a PhD in economics from Brown University. His research interests are applied microeconomics and development economics.

Anna S. Mattila, PhD, is a Marriott Professor of lodging management at The Pennsylvania State University. She holds a PhD in services marketing from Cornell University. Her research interests focus on service encounters with a particular interest in service recovery, corporate social responsibility, social media, and crosscultural research.

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