research articles on fast food restaurants

Fast Food Restaurants Have Expanded More Than Their Menus

Portion sizes, calories, and sodium levels have steadily crept up over the last 30 years

Photo courtesy of Sergey Nazarov/iStock

Sarah Wells (COM’18)

Since they first started popping up across America in the 1950s, fast food restaurants have drastically changed food culture. A meal that was once made slowly has now been pushed to the peak of efficiency and can be eaten on-demand almost 24/7. While this easy access to food certainly has its benefits—cost effectiveness and time saving, to name a few—its popularity has been instrumental in upping our nation’s consumption of low-nutrient and high-calorie meals.

Today, fast food makes up 11 percent of adult energy intake in the United States and has been implicated in rising rates of obesity, diabetes, and heart disease. A new study looking back at fast food’s evolution over the last 30 years could help explain why these health issues have become more common. In a paper published in the Journal of the Academy of Nutrition and Dietetics , researchers from Boston University and Tufts University report that meals have packed on more calories and salt over the years.

It’s the longest-spanning and most in-depth look at fast food’s caloric energy and nutrient makeup, decade over decade, that has ever been conducted, according to Megan McCrory, research associate professor at BU’s College of Health & Rehabilitation Sciences: Sargent College and the lead author on the paper.

The researchers looked at menu items from ten different fast food restaurants, including crowd favorites like McDonald’s, Dairy Queen, and KFC. Using three specific years (1986, 1991, and 2016) as snapshots, they calculated how portion size, energy content, and nutrient profiles have changed over the three decades.

To do so, they first had to collect, categorize, and analyze 1,787 menu items, information they gathered from the restaurants’ websites or from analog copies of The Fast Food Guide .

Once they had standardized their data, which McCrory says had to account for the fact that not all item names or energy descriptions were consistent across restaurants and time periods, they grouped the items into three categories: entrées, sides, and desserts.

Between 1986 and 2016, McCrory says the researchers calculated that the number of items offered on restaurant menus grew a staggering 226 percent, an average of 22.9 items per year. Along with the menu expansions, they found that portion sizes and calories had increased as well.

Desserts grew in size by an average of 62 calories per decade—just under 200 calories over the 30-year span. Meanwhile, entrées gained an average of 30 calories per decade, nearly 100 calories overall. Sides did not increase by much in terms of calories, but like entrées and desserts, became noticeably saltier.

McCrory says that fast food’s sodium content—too much of which can increase blood pressure and risk of heart disease—has consistently grown higher over the years. Based on a 2,000-calorie-per-day diet, fast food has steadily undergone an increase in the percentage of recommended daily values of sodium, creeping up 4.6 percent for entrées, 3.9 percent for sides, and 1.2 percent for desserts on average each decade.

Calcium and iron, which McCrory says can increase bone density and reduce anemia, have also increased in fast food items over time, primarily in desserts. On average per decade, the daily value of calcium increased 3.9 percent in desserts and the daily value of iron increased 1.4 percent. Yet McCrory says that news shouldn’t be viewed as a green light to splurge on more fast food.

“Although these increases seem desirable, people should not be consuming fast food to get more calcium and iron in their diet because of the high calories and sodium that come along with it,” McCrory says.

While this research, funded in part by the U.S. Department of Agriculture, provides a much-needed perspective on the incremental changes that have accumulated in fast food over the past 30 years, McCrory says that new research should focus on the changes that still need to be made. According to the National Center for Health Statistics, 36.6 percent of US adults consume fast food on any given day, and McCrory says it’s time they have healthier paths to choose from when eating at those restaurants.

“It doesn’t seem like fast food is going away anytime soon,” says McCrory. “I think we need more research into what kinds of solutions are going to help people make better choices at fast food restaurants” and to find “alternatives to eating fast food in the first place.”

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Consider that the average McDonald’s drive-through order from speaker box to food pickup takes a little less than three minutes, Senatore said. And McDonald’s already cut 15 to 20 seconds off order times in 2019 because of smaller menu reductions and internal productivity incentives, added Peter Saleh, managing director and senior restaurant analyst at BTIG.

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• Open access
• Published: 04 June 2020

Satisfaction and revisit intentions at fast food restaurants

• Amer Rajput 1 &
• Raja Zohaib Gahfoor 2  

Future Business Journal volume  6 , Article number:  13 ( 2020 ) Cite this article

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This study is to identify the positive association of food quality, restaurant service quality, physical environment quality, and customer satisfaction with revisit intention of customers at fast food restaurants. Additionally, word of mouth is investigated as moderator on the relationship of customer satisfaction with revisit intentions of customers at fast food restaurants. Data were collected through a questionnaire survey from 433 customers of fast food restaurants through convenience sampling. Hypotheses of proposed model were tested using structural equation modeling with partial least squares SEM-PLS in SMART PLS 3. The results confirmed the positive association of food quality, restaurant service quality, physical environment quality, and customer satisfaction with revisit intentions of customers at fast food restaurants. However, word of mouth does not positively moderate the relationship of customer satisfaction with revisit intentions of customers at fast food restaurants. This study emphasizes the importance of revisit intention as a vital behavioral reaction in fast food restaurants. This study reveals revisit intention’s positive association with food quality, restaurant service quality, physical environment quality, and customer satisfaction based on stimulus-organism-response (S-O-R) theory. Furthermore, it is identified that social conformity theory does not hold its assumption when consumers experience quality and they are satisfied because word of mouth does not moderate the relationship of customer satisfaction with revisit intention of customer.

Introduction

Background of the study.

Hospitality industry is observing diversified changes in highly competitive environment for restaurants [ 1 ]. Consumers are becoming conscious of food quality (FQ), restaurant service quality (RSQ), and physical environment quality (PEQ) of the fast food restaurants. Consumers switch easily in case of just one evasive experience [ 2 , 3 ]. Fast food restaurants must attract new customers and retain the existing customers. There is a growing trend in Pakistani culture to dine out at fast food restaurants with family, friends, and colleagues [ 4 ]. Restaurants focus to provide a dining experience by combining tangible and intangible essentials [ 5 ]. Decisive objective is to achieve customer satisfaction (CS), word of mouth (WOM), and future revisit intention (RVI) at fast food restaurant.

Restaurants differ in offerings, appearance, service models, and cuisines; this classifies restaurants as downscale and upscale [ 6 , 7 ]. Revisit intention is the willingness of a consumer to revisit a place due to satisfactory experience. Customer satisfaction generates a probability to revisit in presence or absence of an affirmative attitude toward the restaurant [ 8 ]. Revisit intention is a substantial topic in hospitality research [ 8 , 9 , 10 ]. To date there has been little agreement on that word of mouth can affect revisit intention after experience of customer satisfaction. For instance, when a customer is satisfied at a fast food restaurant experience, however, the customer’s family and friends do not share the same satisfying experience. Will this word of mouth affect the customer’s revisit intention? Food quality is acknowledged as a basic component of the restaurant’s overall experience to affect consumer revisit intention. Fast food quality is substantially associated with customer satisfaction and it is an important predictor of behavioral intention [ 11 ]. Service quality is an essential factor to produce consumers’ revisit intentions [ 12 ]. Furthermore, physical environment quality affects behavior of consumers at restaurants, hotels, hospitals, retail stores, and banks [ 13 ]. Physical environment quality is a precursor of customer satisfaction [ 9 ]. This suggests that customer satisfaction is associated with fast food quality, restaurant service quality, physical environment quality, and revisit intention.

Aims of the study

This study is to investigate the association of fast food quality, restaurant service quality, physical environment quality with customer’s revisit intention through mediation of customer satisfaction using S-O-R theory and moderation of word of mouth on the relationship of customer satisfaction with revisit intention based on social conformity theory. This study empirically tests a conceptual research framework based on S-O-R and social conformity theory adding value to the knowledge. Objectives of the study are given below.

To investigate the association of fast food quality, restaurant service quality, and physical environment quality with revisit intention through customer satisfaction based on S-O-R theory in the context of Pakistani fast food restaurants.

To investigate moderation of WOM on relationship of customer satisfaction with revisit intention based on social conformity theory in the context of Pakistani fast food restaurants.

Furthermore, little empirical evidence is present about customer satisfaction with respect to fast food restaurant service quality [ 14 ]. Customer satisfaction is a post-consumption assessment in service industry. Customer satisfaction acts as the feedback mechanism to boost consumer experience [ 15 ]. Customer satisfaction brings competitive advantage to the firm and produces positive behavioral revisit intention [ 16 ]. Marketing literature emphasizes customer satisfaction in anticipation of positive word of mouth, revisit intention, and revisit behavior [ 5 ]. Behavioral intention is assessed through positive WOM, and it is important in service industry [ 15 ], whereas social influence in shape of WOM affects the behavior of individuals toward conformity leading to a driving effect based on social conformity theory [ 17 ].

• Food quality

Food quality plays a central role in the restaurant industry. Food quality is essential to satisfy consumer needs. Food quality is a substantial condition to fulfill the needs and expectations of the consumer [ 18 ]. Food quality is acknowledged as a basic component of the restaurant’s overall experience. Food quality is a restaurant selection’s most important factor, and it is considerably related to customer satisfaction [ 11 ]. Food quality affects customer loyalty, and customer assesses the restaurant on the basis of food quality [ 19 ]. Food quality entails food taste, presentation, temperature, freshness, nutrition, and menu variety. Food quality influences customers’ decisions to revisit the restaurant [ 20 ]. Academic curiosity is increasing in the restaurant’s menus, as variety of menu items is considered the critical characteristic of food quality [ 11 ]. Taste is sensual characteristic of food. Taste is assessed after consumption. Nonetheless, customers foresee taste before consumption through price, quality, food labels, and brand name. Taste of food is important to accomplish customer satisfaction. Presentation of food enhances dining customer satisfaction [ 21 , 22 ]. Customer’s concerns of healthy food substantially affect customer’s expectations and choice of a restaurant [ 23 ]. Freshness is assessed with the aroma, juiciness, crispness, and fresh posture of the food. Food quality enhances customer satisfaction [ 24 ].

• Restaurant service quality

Quality as a construct is projected by Juran and Deming [ 25 , 26 ]. Service quality is comparatively a contemporary concept. Service quality assesses the excellence of brands in industry of travel, retail, hotel, airline, and restaurant [ 27 ]. Restaurant service quality affects dining experiences of customers. Service quality creates first impression on consumers and affects consumers’ perception of quality [ 28 ]. Service industry provides good service quality to the customers to attain sustainable competitive advantage. Customer satisfaction depends on quality of service at the restaurant [ 29 ]. Service quality entails price, friendliness, cleanliness, care, diversity, speed of service, and food consistency according to menu. Customer satisfaction also depends on communication between restaurant’s personnel and the customers [ 30 ]. Consumer’s evaluation of service quality is affected by level of friendliness and care. Service quality leads to positive word of mouth, customer satisfaction, better corporate image, attraction for the new customers, increase revisits, and amplified business performance. Service quality increases revisits and behavioral intentions of customers in hospitality industry [ 12 ].

• Physical environment quality

PEQ is a setting to provide products and services in a restaurant. Physical environment quality contains artifacts, decor, spatial layout, and ambient conditions in a restaurant. Customers desire dining experience to be pleasing; thus, they look for a physical environment quality [ 31 ]. Physical environment quality satisfies and attracts new customers. PEQ increases financial performance, and it creates memorable experience for the customers [ 9 ]. Consumers perceive the quality of a restaurant based on cleanliness, quirky, comfortable welcoming, physical environment quality, and other amenities that create the ambiance [ 32 ]. Effect of physical environment quality on behaviors is visible in service businesses such as restaurants, hotels, hospitals, retail stores, and banks [ 33 ]. Physical environment quality is an antecedent of customer satisfaction [ 34 ]. Thus, restaurants need to create attractive and distinctive physical environment quality.

• Customer satisfaction

Customer satisfaction contains the feelings of pleasure and well-being. Customer satisfaction develops from gaining what customer expects from the service. Customer satisfaction is broadly investigated in consumer behavior and social psychology. Customer satisfaction is described “as the customer’s subjective assessment of the consumption experience, grounded on certain associations between the perceptions of customer and objective characteristics of the product” [ 35 ]. Customer satisfaction is the extent to which an experience of consumption brings good feelings. Customer satisfaction is stated as “a comparison of the level of product or service performance, quality, or other outcomes perceived by the consumer with an evaluative standard” [ 36 ]. Customer satisfaction constructs as a customer’s wholesome evaluation of an experience. Customer satisfaction is a reaction of fulfilling customer’s needs.

Customer satisfaction brings escalated repeat purchase behavior and intention to refer [ 37 ]. Dissatisfied consumers are uncertain to return to the place [ 38 ]. Satisfactory restaurant experience can enhance revisit intention of the consumer. Positive WOM is generated when customers are not only satisfied with the brand but they demand superior core offering and high level of service [ 15 ].

• Word of mouth

Word of mouth is described as “person-to-person, oral communication between a communicator and receiver which is perceived as a non-commercial message” [ 39 ]. WOM is also defined as “the informal positive or negative communication by customers on the objectively existing and/or subjectively perceived characteristics of the products or services” [ 40 ]. Moreover, [ 41 ] defines it as “an informal person to person communication between a perceived non-commercial communicator and a receiver regarding a brand, a product, an organization or a service”. WOM is described as a positive or negative statement made by probable, actual or former customers about a product or a company, which is made available through offline or online channels [ 42 , 43 ]. WOM is an important and frequent sensation; it is known for long time that people habitually exchange their experiences of consumptions with others. Consumers complain about bad hotel stays, talk about new shoes, share info about the finest way of getting out tough stains, spread word about experience of products, services, companies, restaurants, and stores. Social talks made more than 3.3 billion of brand impressions per day [ 44 ].

WOM has substantial impact on consumer’s purchasing decision; therefore, a vital marketing strategy is to initiate positive WOM [ 45 ]. However, negative WOM is more informative and diagnostic where customers express their dissatisfaction [ 38 ]. Word of mouth communications are more informative than traditional marketing communications in service sector. WOM is more credible than advertisement when it is from friends and family [ 46 ]. WOM is a vital influencer in purchase intention. WOM escalates affection that enhances commitment of consumer purchase intention. WOM is generated before or after the purchase. WOM helps the consumers to acquire more knowledge for the product and to reduce the perceived risk [ 47 ]. WOM in the dining experience is very important. People tend to follow their peers’ opinions when they are to dine out.

• Revisit intention

To predicting and to explain human behavior is the key determination of consumer behavior research. Consumer needs differ and emerge frequently with diverse outlooks. Revisit intention is to endorse “visitors being willing to revisit the similar place, for satisfactory experiences, and suggest the place to friends to develop the loyalty” [ 48 ]. Consumer forms an attitude toward the service provider based on the experience of service. This attitude can be steady dislike or like of the service. This is linked to the consumer’s intention to re-patronize the service and to start WOM. Repurchase intention is at the core of customer loyalty and commitment. Repurchase intention is a significant part of behavioral and attitudinal constructs. Revisit intention is described as optimistic probability to revisit the restaurant. Revisit intention is the willingness of a consumer to visit the restaurant again. Furthermore, the ease of visitors, transportation in destination, entertainment, hospitability, and service satisfaction influence visitor’s revisit intention.

Consumer behavior encircles the upcoming behavioral intention and post-visit evaluation. Post-visit evaluation covers perceived quality, experience, value, and the satisfaction. Restaurant managers are interested to understand the factors of consumer revisit intention, as it is cost effective to retain the existing customers in comparison with attract new customers [ 49 ]. Substantial consideration is prevailing in literature for the relationship among quality attributes, customer satisfaction, and revisit intention. There is a positive association between customer satisfaction and revisit intention. Indifferent consumer, accessibility of competitive alternatives and low switching cost can end up in a state where satisfied consumers defect to other options [ 2 ]. Consumer behavior varies for choice of place to visit, assessments, and behavioral intentions [ 50 ]. The assessments are about the significance perceived by regular customers’ satisfactions. Whereas, future behavioral intentions point to the consumer’s willingness to revisit the similar place and suggest it to the others [ 51 ].

S-O-R model is primarily established on the traditional stimulus–response theory. This theory explicates individual’s behavior as learned response to external stimuli. The theory is questioned for oversimplifying ancestries of the behaviors and ignoring one’s mental state. [ 52 ] extended the S-O-R model through integrating the notion of organism between stimulus and response. S-O-R concept is embraced to reveal individual’s affective and cognitive conditions before the response behavior [ 53 ]. S-O-R framework considers that environment comprises stimuli (S) leading changes to the individual’s internal conditions called organism (O), further leading to responses (R) [ 52 ]. In S-O-R model, the stimuli comprise of various components of physical environment quality, organism indicates to internal structures and processes bridging between stimuli and final responses or actions of a consumer [ 9 ]. Behavioral responses of an individual in a physical environment quality are directly influenced by the physical environment quality stimulus [ 54 ]. S-O-R framework is implemented in diverse service contexts to examine how physical environment quality affects customer’s emotion and behavior [ 55 ]. The effect of stimulation in an online shopping environment on impulsive purchase is investigated through S-O-R framework [ 56 ]. The effects of background music, on consumers’ affect and cognition, and psychological responses influence behavioral intentions [ 57 ]. Perceived flow and website quality toward customer satisfaction affect purchase intention in hotel website based on S-O-R framework [ 58 ]. Therefore, this study conceptualizes food quality, restaurant service quality, and physical environment quality as stimuli; customer satisfaction as organism; and revisit intention as response.

Moreover, social conformity theory (SCT) is to support the logical presence of WOM in the conceptual framework as a moderator on the relationship of customer satisfaction and revisit intention. Social conformity influences individual’s attitudes, beliefs and behaviors leading to a herding effect [ 17 , 59 ]. Thus, social influence (WOM) moderates the relationship of customer satisfaction and revisit intention. Following hypotheses are postulated, see Fig.  1 .

Conceptual research framework

Food quality is positively associated with customer satisfaction in fast food restaurant.

Restaurant service quality is positively associated with customer satisfaction in fast food restaurant.

Physical environment quality is positively associated with customer satisfaction in fast food restaurant.

Customer satisfaction is positively associated with revisit intention of customer in fast food restaurant.

Customer satisfaction mediates between food quality and revisit intention of customer in fast food restaurant.

Customer satisfaction mediates between restaurant service quality and revisit intention of customer in fast food restaurant.

Customer satisfaction mediates between physical environment quality and revisit intention of customer in fast food restaurant.

WOM positively moderates the relationship between customer satisfaction and revisit intention of customer in fast food restaurant.

There are two research approaches such as deductive (quantitative) and inductive (qualitative). This study utilized the quantitative research approach as it aligns with the research design and philosophy. Quantitative research approach mostly relies on deductive logic. Researcher begins with hypotheses development and then collects data. Data are used to determine whether empirical evidence supports the hypotheses [ 60 ]. The questionnaires survey is used. This study chose the mono-method with cross-sectional time horizon of 6 months. Deductive approach is utilized in this study. Cross-sectional time horizon also known as “snapshot” is used when investigation is related with the study of a specific phenomenon at a particular time [ 61 ]. Questionnaire survey is mostly used technique for data collection in marketing research due to its effectiveness and low cost [ 62 ]. Data are collected through self-administered questionnaires. Following the footsteps of Lai and Chen [ 63 ] and Widianti et al. [ 64 ] convenience sampling is applied. Famous fast food restaurants in twin cities (Rawalpindi and Islamabad) of Pakistan were chosen randomly. Furthermore, 650 questionnaires (with consideration of low response rate) were distributed to the customers at famous fast food restaurants. Moreover, researchers faced difficulty in obtaining fast food restaurant’s consumers data.

It yielded a response rate of 68.92% with 448 returned questionnaires. Fifteen incomplete questionnaires are not included; thus, 433 responses are employed for data analysis from fast food restaurant customers. The obtained number of usable responses was suitable to apply structural equation modeling [ 65 , 66 , 67 , 68 ].

Sample characteristics describe that there are 39.7% females and 60.3% males. There are 31.4% respondents of age group 15–25 years, 48.3% of age group 26–35, 12.2% of age ranges between 36 and 45, 6.7% of age ranges between 46 and 55, and 1.4% of age group is above 56 years. The educational level of the respondents indicates that mostly respondents are undergraduate and graduate. Occupation of respondents reflects that 28.6% work in private organizations and 24.9% belong to student category. Monthly income of 29.3% respondents ranges between Rupees 20,000 and 30,000 and 25.6% have monthly income of Rupees 41,000–50,000. Average monthly spending in fast food restaurants is about Rupees 3000–6000, see Table  1 .

Measures of the constructs

Food quality is adopted from measures developed by [ 69 ]. Food quality contains six items such as: food presentation is visually attractive, the restaurant offers a variety of menu items, and the restaurant offers healthy options. Restaurant service quality is adopted with six items [ 70 ]. This construct contains items such as: efficient and effective process in the welcoming and ushering of the customers, efficient and effective explanation of the menu, efficient and effective process in delivery of food. Physical environment quality is adopted with four items [ 71 ], and one item is adopted from measures developed by [ 70 ]. The items are such as: the restaurant has visually striking building exteriors and parking space, the restaurant has visually eye-catching dining space that is comfortable and easy to move around and within, and the restaurant has suitable music and/or illumination in accordance with its ambience. Revisit intention is measured through four adapted items [ 8 ]; such as: I would visit again in the near future and I am interested in revisiting again. Customer satisfaction is measured by three adopted items [ 29 ]; such as: I am satisfied with the service at this restaurant, and the restaurant always comes up to my expectations. Word of mouth is measured with four adopted items such as: my family/friends mentioned positive things I had not considered about this restaurant, my family/friends provided me with positive ideas about this restaurant [ 72 ]. Each item is measured on 5-point Likert scale, where 1 = strongly disagree, 3 = uncertain, and 5 = strongly agree.

Results and discussion

Validity and reliability.

Validity taps the ability of the scale to measure the construct; in other words, it means that the representative items measure the concept adequately [ 73 ]. The content validity is executed in two steps; firstly, the items are presented to the experts for further modifications; secondly, the constructive feedback about understanding of it was acquired by few respondents who filled the questionnaires. Each set of items is a valid indicator of the construct as within-scale factor analysis is conducted.

The factor analyses allotted the items to their respective factor. Fornell and Lacker’s [ 74 ] composite reliability p is calculated for each construct using partial least squares (PLS) structural equation modeling and Cronbach’s coefficient α [ 75 ]. Cronbach’s α is used to evaluate the reliability of all items that indicates how well the items in a set are positively related to one another. Each Cronbach’s α of the instrument is higher than .7 (ranging from .74 to .91); see Table  2 .

Common method bias

Same measures are used to collect data for all respondents; thus, there can be common method bias [ 76 ]. Firstly, questionnaire is systematically constructed with consideration of study design. Secondly, respondents were assured for the responses to be kept anonymous [ 77 ]. Common method bias possibility is assessed through Harman’s single factor test [ 78 , 79 , 80 , 81 , 82 , 83 ]. Principal axis factor analysis on measurement items is exercised. The single factor did not account for most of the bias and it accounted for 43.82% variance that is less than 50%. Thus, common method bias is not an issue [ 80 , 81 ].

SEM-PLS model assessment

Survey research faces a challenge to select an appropriate statistical model to analyze data. Partial least squares grounded structural equation modeling (SEM-PLS) and covariance-based structural equation modeling (CB-SEM) are generally used multivariate data analysis methods. CB-SEM is based on factor analysis that uses maximum likelihood estimation. PLS-SEM is based on the principal component concept; it uses the partial least squares estimator [ 84 ]. PLS-SEM is considered appropriate to examine complex cause–effect relationship models. PLS-SEM is a nonparametric approach with low reservations on data distribution and sample size [ 84 ].

Measurement model assessment

To evaluate convergent validity measurement model (outer model) is assessed that includes composite reliability (CR) to evaluate internal consistency, individual indicator reliability, and average variance extracted (AVE) [ 85 ]. Indicator reliability explains the variation in the items by a variable. Outer loadings assess indicator reliability; a higher value (an item with a loading of .70) on a variable indicates that the associated measure has considerable mutual commonality [ 85 ]. Two items RSQ 14 and PEQ 24 are dropped due to lower value less than .60 [ 86 ]. Composite reliability is assessed through internal consistency reliability. CR values of all the latent variables have higher values than .80 to establish internal consistency [ 85 ]; see Table  2 .

Convergent validity is the extent to which a measure correlates positively with alternative measures of the same variable. Convergent validity is ensured through higher values than .50 of AVE [ 74 ], see Table  2 . Discriminant validity is the degree to which a variable is truly distinct from other variables. Square root of AVE is higher than the inter-construct correlations except customer satisfaction to hold discriminant validity [ 74 ]. Additional evidence for discriminant validity is that indicators’ individual loadings are found to be higher than the respective cross-loadings, see Table  3 .

Structural model assessment

Structural model is assessed after establishing the validity and reliability of the variables. Structural model assessment includes path coefficients to calculate the importance and relevance of structural model associations. Model’s predictive accuracy is calculated through R 2 value. Model’s predictive relevance is assessed with Q 2 , and value of f 2 indicates substantial impact of the exogenous variable on an endogenous variable in PLS-SEM [ 85 ]. SEM is rigueur in validating instruments and testing linkages between constructs [ 87 ]. SMART-PLS produces reports of latent constructs correlations, path coefficients with t test values. The relationships between six constructs of food quality, restaurant service quality, physical environment quality, customer satisfaction, word-of-mouth, and revisit intention are displayed in Fig.  2 after bootstrapping. Bootstrapping is a re-sampling approach that draws random samples (with replacements) from the data and uses these samples to estimate the path model multiple times under slightly changed data constellations [ 88 ]. Purpose of bootstrapping is to compute the standard error of coefficient estimates in order to examine the coefficient’s statistical significance [ 89 ].

Bootstrapping and path coefficients

Food quality is positively associated to customer satisfaction in fast food restaurant; H 1 is supported as path coefficient = .487, T value = 8.349, P value = .000. Restaurant service quality is positively associated with customer satisfaction; H 2 is supported as path coefficient = .253, T value = 4.521, P value = .000. Physical environment quality is positively associated with customer satisfaction in fast food restaurant; H 3 is supported as path coefficient = .149, T value = 3.518, P value = .000. Customer satisfaction is positively associated with revisit intention of customer in fast food restaurant; H 4 is supported as path coefficient = .528, T value = 11.966, P value = .000. WOM positively moderates the relationship between customer satisfaction and revisit intention of customer in fast food restaurant; H 8 is not supported as path coefficient = − .060, T value = 2.972, P value = .003; see Table  4 .

Assessing R 2 and Q 2

Coefficient of determination R 2 value is used to evaluate the structural model. This coefficient estimates the predictive precision of the model and is deliberated as the squared correlation between actual and predictive values of the endogenous construct. R 2 values represent the exogenous variables’ mutual effects on the endogenous variables. This signifies the amount of variance in endogenous constructs explained by total number of exogenous constructs associated to it [ 88 ]. The endogenous variables customer satisfaction and revisit intention have R 2  = .645 and .671, respectively, that assures the predictive relevance of structural model. Further the examination of the endogenous variables’ predictive power has good R 2 values.

Blindfolding is to cross-validate the model’s predictive relevance for each of the individual endogenous variables with value of Stone–Geisser Q 2 [ 90 , 91 ]. By performing the blindfolding test with an omission distance of 7 yielded cross-validated redundancy Q 2 values of all the endogenous variables [ 88 ]. Customer satisfaction’s Q 2  = .457 and RVI’s Q 2  = .501; this indicates large effect sizes. PLS structural model has predictive relevance because values of Q 2 are greater than 0, see Table  5 .

Assessing f 2

Effect size f 2 is the measure to estimate the change in R 2 value when an exogenous variable is omitted from the model. f 2 size effect illustrates the influence of a specific predictor latent variable on an endogenous variable. Effect size f 2 varies from small to medium for all the exogenous variables in explaining CS and RVI as shown Table  6 .

Additionally, H 5 : CS mediates between food quality and RVI is supported as CS partially mediates between FQ and RVI. Variation accounted for (VAF) value indicates that 70% of the total effect of an exogenous variable FQ on RVI is explained by indirect effect. Therefore, the effect of FQ on RVI is partially mediated through CS. Similarly, the VAF value indicates that 70% of the total effect of an exogenous variable RSQ and 35% VAF of PEQ on RVI is explained by indirect effect. Therefore, the effects of RSQ and PEQ on RVI are also partially mediated through CS. H 6 is supported as the effect of CS is partially mediated between RSQ and RVI of customer in fast food restaurant. H 7 is supported as the effect of CS is partially mediated between PEQ and RVI of customer in fast food restaurant, see Table  7 . This clearly indicates that customer satisfaction mediates between all of our exogenous variables (food quality, restaurant service quality and physical environment quality) and dependent variable revisit intention of customer in fast food restaurant [ 88 , 92 ] (Additional files 1 , 2 and 3 ).

This is interesting to note that food quality, restaurant service quality, physical environment quality, and customer satisfaction are important triggers of revisit intention at fast food restaurants. However, surprisingly, word of mouth does not moderate the relationship of customer satisfaction with revisit intention of customer at fast food restaurant. The results of the study correspond with some previous findings [ 15 , 29 , 32 , 69 , 93 ]. Positive relationship between customer satisfaction and revisit intention is consistent with the findings of the previous studies [ 5 , 8 , 94 , 95 , 96 ]. Food quality is positively associated with revisit intention; this result as well corresponds to a previous study [ 24 ]. Furthermore, interior and amusing physical environment is an important antecedent of revisit intention at a fast food restaurant; this finding is congruent with previous findings [ 29 , 70 , 97 , 98 ] and contrary to some previous studies [ 9 , 15 ].

Intensified competition, industry’s volatile nature, and maturity of the business are some challenges that fast food restaurants face [ 5 ]. Amid economic crunch, competition becomes even more evident, driving fast food restaurants to look for unconventional ways to appeal the customers. In fact, these findings somehow show that significance of physical environment quality in creating revisit intention is probably lower in comparison with food quality and restaurant service quality. Nonetheless, fast food restaurant’s management should not underrate the fact that physical environment quality considerably affects the revisit intention. Due to this, the importance of physical environment quality must not be overlooked when formulating strategies for improving customer satisfaction, revisit intention and creating long-term relationships with customers.

Managerial implications

The results imply that restaurant management should pay attention to customer satisfaction because it directly affects revisit intention. Assessing customer satisfaction has become vital to successfully contest in the modern fast food restaurant business. From a managerial point of view, the results of this study will help restaurant managers to better understand the important role of food quality, restaurant service quality and physical environment quality as marketing tool to retain and satisfy customers.

Limitations

There are certain limitations with this study. This study is cross sectional, and it can be generalized to only two cities of Pakistan. Scope of research was limited as the data were collected from two cities of Pakistan (Islamabad and Rawalpindi) using convenience sampling.

Future research

A longitudinal study with probability sampling will help the researchers to comprehensively investigate the relationships among the constructs. Moreover, it would be useful for future research models to add information overload as an explanatory variable and brand image as moderating variable in the research framework. Additionally, moderation of WOM can be investigated in other relationships of conceptual model.

The study encircles the key triggers of customer satisfaction and revisit intention in fast food restaurants. It also offers a model that defines relationships between three factors of restaurant offer (food quality, restaurant service quality, and physical environment quality), customer satisfaction, word of mouth, and revisit intention at fast food restaurants. The model specially focuses the revisit intention as dependent variable of conceptual model despite behavior intentions. The findings suggest the revisit intention is positively associated with customer satisfaction, food quality, restaurant service quality, and physical environment quality in a fast food restaurant.

However, contrary to the findings of a previous study [ 99 ], WOM do not positively moderate between the relationship of customer satisfaction and revisit intention. The empirical findings confirm the significant impact of food quality, restaurant service quality, physical environment quality, and customer satisfaction which are important antecedents of revisit intention at fast food restaurant through mediation of customer satisfaction. Moreover, findings of the research support the assumptions of SOR theory strengthening our conceptual model which states the external stimuli (FQ, RSQ, PEQ) produced internal organism (CS) which led to the response (RVI). However; assumption of social conformity theory failed to influence the satisfied customer. In other words, customer satisfaction plays dominating role over social influence (i.e. WOM) in making revisit intention. Therefore, WOM was not able to influence the strength of relationship of CS and RVI.

Availability of data and materials

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

Abbreviations

Social conformity theory

Stimulus-organism-response

Structural equation modeling with partial least squares

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Rajput, A., Gahfoor, R.Z. Satisfaction and revisit intentions at fast food restaurants. Futur Bus J 6 , 13 (2020). https://doi.org/10.1186/s43093-020-00021-0

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• Published: 27 October 2020

Food and Health

Trends in the healthiness of U.S. fast food meals, 2008–2017

• Eleanore Alexander   ORCID: orcid.org/0000-0002-8998-4186 1 ,
• Lainie Rutkow 1 ,
• Kimberly A. Gudzune 2 , 3 ,
• Joanna E. Cohen 4 , 5 &
• Emma E. McGinty 1  

European Journal of Clinical Nutrition volume  75 ,  pages 775–781 ( 2021 ) Cite this article

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• Cardiovascular diseases
• Risk factors

This study aimed to examine trends in the healthiness of U.S. fast food restaurant meals from 2008 to 2017, using the American Heart Association’s Heart-Check meal certification criteria.

Data were obtained from MenuStat, an online database of the leading 100 U.S. restaurant chains menu items, for the years 2008 and 2012 through 2017. All possible meal combinations (entrées + sides) were created at the 20 fast food restaurants that reported entrée and side calories, total fat, saturated fat, trans fat, cholesterol, sodium, protein, and fiber. Chi-square tests compared the percent of meals meeting each American Heart Association (AHA) nutrient criterion; and the number of AHA criteria met for each year, by menu focus type.

Compared with 2008, significantly fewer fast food meals met the AHA calorie criterion in 2015, 2016, and 2017, and significantly fewer met the AHA total fat criterion in 2015 and 2016. Significantly more meals met the AHA trans fat criterion from 2012 to 2017, compared to 2008. There were no significant changes over time in the percent of meals meeting AHA criteria for saturated fat, cholesterol, or sodium.

Conclusions

Efforts to improve the healthiness of fast food meals should focus on reducing calories, total fat, saturated fat, and sodium.

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Alexander, E., Rutkow, L., Gudzune, K.A. et al. Trends in the healthiness of U.S. fast food meals, 2008–2017. Eur J Clin Nutr 75 , 775–781 (2021). https://doi.org/10.1038/s41430-020-00788-z

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• Published: 08 July 2011

Neighborhood fast food restaurants and fast food consumption: A national study

• Andrea S Richardson 1 ,
• Janne Boone-Heinonen 2 ,
• Barry M Popkin 1 &
• Penny Gordon-Larsen 1  

BMC Public Health volume  11 , Article number:  543 ( 2011 ) Cite this article

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Recent studies suggest that neighborhood fast food restaurant availability is related to greater obesity, yet few studies have investigated whether neighborhood fast food restaurant availability promotes fast food consumption. Our aim was to estimate the effect of neighborhood fast food availability on frequency of fast food consumption in a national sample of young adults, a population at high risk for obesity.

We used national data from U.S. young adults enrolled in wave III (2001-02; ages 18-28) of the National Longitudinal Study of Adolescent Health (n = 13,150). Urbanicity-stratified multivariate negative binomial regression models were used to examine cross-sectional associations between neighborhood fast food availability and individual-level self-reported fast food consumption frequency, controlling for individual and neighborhood characteristics.

In adjusted analysis, fast food availability was not associated with weekly frequency of fast food consumption in non-urban or low- or high-density urban areas.

Conclusions

Policies aiming to reduce neighborhood availability as a means to reduce fast food consumption among young adults may be unsuccessful. Consideration of fast food outlets near school or workplace locations, factors specific to more or less urban settings, and the role of individual lifestyle attitudes and preferences are needed in future research.

Peer Review reports

Neighborhood availability of fast food restaurants has recently received considerable attention as a target to prevent obesity [ 1 – 6 ]. It is intuitive that fast food restaurants contribute to obesity by promoting fast food consumption. However, few studies have tested the relationship between access to fast food and diet behavior and those that have rely on measures of fruit and vegetable intake [ 7 – 9 ]. Findings from an even smaller literature that investigates direct relationships with fast food consumption are mixed [ 10 – 12 ].

Furthermore, most evidence focuses on urban populations, with little research in suburban or rural populations. One of the difficulties is that urbanicity is often classified according to population density [ 13 ], which may correlate with cultural or social influences on diet and thus obscure important heterogeneity across urban, suburban and rural areas. Because the nature of accessibility in suburban or rural environments differ from urban environments, and other social, environmental, and individual influences of diet behavior may differ according to urbanicity, residential fast food restaurant availability may influence fast food consumption differently in rural, suburban, and urban contexts. Therefore, we hypothesize that the relationship between chain fast food availability with fast food consumption varies by urbanicity. In most existing research, generalizability across multiple contexts and comparisons by urbanicity are not possible due to small sample sizes and constrained geographic areas.

Additionally, variation in and limitations of neighborhood fast food availability measures may contribute to inconsistent and sometimes counterintuitive findings. Measures capturing fast food restaurants within a straight line (Euclidean) distance may obscure realistic proximity through usual routes of travel along roadways [ 6 ]. In contrast, network buffers, the polygon shaped by the street network up to a given distance, represent access to resources relative to the street network [ 14 ]. Whereas the count (or number) of fast food restaurants within a given area does not take into account population density and development, which are correlated with neighborhood food resource availability and independently related to dietary behavior [ 15 ]. Conceptually, greater density of food resources per capita may represent greater quantity and diversity of restaurant choices that may, in turn, influence decision for where and when to eat outside of the home. Fast food restaurant density per population [ 16 , 17 ] may therefore capture population density or serve as a proxy for physical development density. Alternatively, a roadway-scaled measure that represents the concentration of fast food outlets along access routes may help account for differences in fast food restaurant counts according to the amount of commercial activity.

We used national data from 13,150 sociodemographically diverse young adults living throughout the U.S to test the hypothesis that individuals living in neighborhoods with greater chain fast food availability report more frequent fast food consumption. We improve on prior fast food availability measures by examining fast food restaurants per roadway mile within 3 km street network distance from each respondent home, with sensitivity analysis comparing to measures used in published research. Capitalizing on the size and geographic scope of our study, we investigated how the association between fast food availability and consumption varies by urbanicity.

Study population and data sources

Our study sample is derived from respondents aged 18 to 28 years who participated in Wave III (2001-02) of the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative, prospective cohort study of adolescents representative of the U.S. school-based population in grades 7 to 12 (11-22 years of age) in 1994-95 followed into adulthood. The Wave I Add Health study population (n = 20,745) was obtained through a systematic random sample of 80 high schools and 52 middle schools in the United States, stratified to ensure that the schools were representative of US schools with respect to region, urbanicity, school type, percentage of white students, and school size [ 18 ]. Respondents were followed through Wave II (n = 14,738, 1996) and Wave III (n = 15,197). Add Health included a core sample plus subsamples of selected minority and other groupings collected under protocols approved by the Institutional Review Board at the University of North Carolina at Chapel Hill. The survey design and sampling frame have been discussed elsewhere [ 18 , 19 ].

We used the Add Health Obesity and Neighborhood Environment database (ONEdata), a Geographic Information System that linked time-varying, community-level data to Add Health respondent Wave III home addresses geocoded with street-segment matches (n = 13,039), global positioning system (GPS) measurements (n = 1,204), and ZIP/ZIP+4/ZIP+2 centroid match (n = 685) among 14,322 Wave III respondents with sample weights. Residential locations were linked to attributes of areas within 1,3,5, and 8 km straight line distance (Euclidean neighborhood buffer) and through the street network (street network neighborhood buffer) surrounding each wave-specific respondent residence; and block group, tract, and county attributes from time-matched U.S. Census and other data, which were merged with individual-level Add Health interview responses [ 20 ]. The number of census block groups (n = 7,588) represents 3.6% of block groups included in the 2000 U.S. Census.

Of 14,322 Wave III respondents with sample weights, we excluded those reporting disability or pregnancy and Native Americans (due to sparse data) (n = 582). Remaining respondents missing individual and geographic data were also excluded (n = 590), leaving an analytic sample of 13,150. The analytic sample had higher parental income, had fewer children, were more likely to own a vehicle, and lived in more highly educated and lower poverty neighborhoods compared to those excluded for missing data.

Study variables

Fast food intake.

Weekly frequency of fast food consumption was ascertained from the question: "On how many of the past seven days did you eat food from a fast food place McDonalds, Kentucky Fried Chicken, Pizza Hut, Taco Bell, or a local fast food restaurant?"

GIS-derived neighborhood fast food availability data

Neighborhood fast food restaurant data were obtained from a commercial dataset of U.S. businesses corresponding to the Wave III interview period (2001). Fast food restaurants include a wide range of quick service establishments providing generally premade food and little table service; they include traditional burger outlets as well as delicatessens and coffee shops. To capture a more homogenous, well-defined category, we examined only chain fast food restaurants (e.g., McDonald's or Pizza Hut), classified according to the 8-digit Standard Industrial Classification (SIC) code 58120307 (fast-food restaurant, chain).

Kilometers of secondary/connecting and local, neighborhood and rural roads were obtained from StreetMap Pro (July 2003, v.5.2) data from Environmental Systems Research Institute (ESRI, http://www.esri.com ) in Redlands, CA. We defined fast food availability as the number of chain fast food restaurants per 100 kilometer of roadway within a 3 km network buffer to account for differences in fast food restaurant counts according to the amount of commercial activity in an area. Our research with Add Health and a similar large population study suggests that restaurant proximity within a 3 km buffer is the most appropriate distance for examining associations between neighborhood fast food restaurant availability and individual level behavior [ 21 , 22 ].

U.S. Census-defined urbanized areas (UA) were used to classify residential locations as non-urban (outside UA) or urban (inside UA). Urban locations were further distinguished as 1) low density [≤95% (75th percentile) developed land cover] and 2) high density [> 95% developed land cover] urban areas based on the area of developed land as a proportion of total area within 3 km after excluding water and ice (calculated using Fragstats software with U.S. Geologic Survey National Landcover Data). Our measure of developed land cover provides an indicator of urban development that is independent of population density, which may correlate with cultural or social influences on diet, and correctly classifies areas as within or outside of a UA (Receiver Operating Characteristic curve area = 0.937). Region was defined as Midwest, West, South, or Northeast.

GIS-derived neighborhood sociodemographics

We examined neighborhood sociodemographic characteristics within 2000 U.S. Census block groups because they more likely adhere to individually perceived neighborhood boundaries [ 23 , 24 ] and are more sociodemographically homogeneous than larger units. Using the federal definition of "poverty area" [ 25 , 26 ], we dichotomized neighborhood poverty into > 20% or ≤20% of population below the federal poverty level. We defined neighborhood-level education as percent of persons ≥25 years with a college degree. We obtained population density per squared kilometer by using Census block-group population count, weighted according to the proportion of block-group area within the 3 km neighborhood buffer, after excluding water and ice, and divided by the area.

Individual characteristics

We adjusted for the following individual characteristics: race, age, parental income (> $36,000), employment status, has any children, vehicle ownership ("has a car/motorcycle/van"). We included parental rather than the young adults' own education (> high school) because parental SES is shown to be a strong predictor of obesity and obesity-related behaviors [ 27 – 29 ] during the complex transitional stage of young adulthood [ 30 , 31 ]

Statistical analysis

All statistical analyses were carried out using Stata version 10.1 (Stata Corp, College Station, TX). Descriptive characteristics were calculated within each urban strata. In multivariable analysis, we used negative binomial regression to model individual-level, self-reported fast food consumption (days eating fast food in last week) as a function of neighborhood-level fast food restaurants per 100 kilometer roadway within 3 km network buffers, controlling for neighborhood sociodemographics, individual characteristics, and region. We hypothesized a priori that the association between fast food availability and fast food consumption is different across non-urban, low density urban, and high density urban areas. Because fast food outlets and neighborhood sociodemographics varied dramatically across urbanicity and thus limited comparability across sociodemographic and geographic subpopulations, we stratified by urbanicity to avoid structural confounding [ 32 ].

Our preference was to use consistent restaurant availability measures for all strata, but the alternative specifications yielded invalid estimates because they violated model assumptions and/or were highly unstable. For example, nearly 100% of the high density urban respondents had at least one fast food restaurant within 3 km of their home, so the dichotomous measure was unstable and relied on 60 observations. In contrast, approximately 70% of non-urban respondents had no fast food restaurants within 3 km, resulting in a highly skewed distribution of fast food availability and heteroskedasticity in our models. Thus, these distributions necessitated different measures across strata of urbanicity. We analyzed fast food availability as a continuous variable for low and high density urban strata and as a dichotomous measure (any versus none) for the non-urban stratum. Statistical interactions between fast food availability and vehicle ownership, poverty, and sex were not significant (p > 0.10) and therefore excluded from all models. We tested higher order relationships for all continuous variables and included where statistically significant (p < 0.05).

Parental selection of a neighborhood may be influenced by preferences or constraints related directly or indirectly to the area's food resources and diet behaviors, which may cluster within school populations. Thus, to address our concern that unobserved factors associated with fast food consumption might be associated with neighborhood choice and school at baseline (Wave I), we tested baseline school indicator variables in the models using a Hausman-like test [ 33 ], which indicated that the school indicator variables were necessary (p < 0.05). Thus, we include school indicator variables in our models to capture long lasting unobservable influences on individual decisions even after the individual leaves their place of residence at wave I.

Buffer-based fast food availability measures were individual-level variables. While block groups used for neighborhood-level control variables could comprise a third level in multilevel analysis, we did not perform multilevel modeling because census unit boundaries did not correspond with school catchment areas, so schools and census units were not hierarchically related. Therefore, analyzing census units as higher levels in multi-level models was not possible while controlling for school indicators, the primary sampling unit for Add Health. Furthermore, census block groups contained sparse, unbalanced numbers of respondents (mean 1.9, range 1-57 respondents), which can lead to bias in non-linear multi-level models, and clustering within census block groups was minimal (0.08 intraclass correlation for reported fast food consumption). We do, however, include school indicator variables in the models, which correct for school-level clustering and account for survey design.

Sensitivity Analysis

We evaluated the sensitivity of our findings to the following aspects of our fast food availability measure (count of chain fast food restaurants per 100 kilometer roadway within 3 km street network buffers): (1) the variable used for scaling (roadway km vs. population), (2) network vs. Euclidean buffers, and (3) 1 km vs. 3 km buffers, and (4) total (chain and non-chain) vs. chain fast food restaurants. Corresponding alternative variables included (1) chain fast food restaurant counts per 10,000 population within 3 km Euclidean (straight-line) buffers (population counts were derived from 2000 US Census block-group population count weighted according to the proportion of block-group area within the neighborhood buffer); chain fast food restaurant counts per 100 kilometer roadway within (2) 3 km Euclidean buffers and (3) 1 km network buffers; and (4) total fast food (chain and non-chain) restaurant counts per 100 kilometer roadway within 3 km street network buffers.

We repeated our multivariable analysis using each of the four alternative fast food availability measures. Few non-urban and low density urban areas had any fast food restaurants within the 1 km network buffer (8% and 25%, respectively); therefore, we analyzed fast food availability within 1 km in high density urban areas only.

Our diverse national sample of 13,150 young adults reflects a variety of individual sociodemographic and neighborhood differences by urbanicity. In general, respondents of racial/ethnic minority lived in greater proportion in high density urban areas (Table 1 ). Joblessness and car ownership among respondents was highest in non-urban areas (Table 1 ). All neighborhood-level characteristics differed across urban strata (Table 2 ).

Fast food availability was not associated with reported fast food consumption in non-urban, low density urban, or high density urban areas after controlling for individual and neighborhood characteristics (Table 3 ). In general, estimated associations were not sensitive to alternative definitions of fast food availability: the total fast food measure and chain fast food measures scaled by population and within different neighborhood buffers yielded similarly small and precise estimates. However, findings suggest that estimates may be sensitive to measure differences within the context of high density urban areas. A few estimates approached marginal significance but in inconsistent directions.

In contrast with our hypothesis, we found that neighborhood fast food availability was not related to fast food consumption in our large, national sample of young adults residing in neighborhoods throughout the U.S. Our findings suggest that targeting neighborhood fast food availability may not reduce consumption or obesity among young U.S. adults.

Our findings, of no relationship between fast food availability on fast food consumption among adults, are consistent with prior research [ 11 , 34 ]. One study reports a positive association between fast food availability and fast food consumption, but only among a subset of 404 adults living in Montreal who were "reward sensitive" [ 10 ].

Null results may reflect: (1) that young adults more often purchase and consume fast food in settings other than their residential neighborhoods, such as school or workplace locations, (2) that lifestyle factors such as family structure or employment status are stronger determinants of fast food consumption [ 35 – 37 ] or (3) the possibility that unmeasured neighborhood and social preferences, such as location selection factors, more strongly influence dietary behaviors. That is, the social, economic, cultural factors that affect where a person is able or wishes to live may also influence dietary behaviors [ 38 ]. Much would be gained by broadening research to focus on environmental contexts beyond the residential location, such as college and workplace neighborhoods as well as commuting routes. In addition, future research should incorporate other neighborhood- and individual-level factors to determine which settings and individual and neighborhood characteristics are most salient for dietary behaviors.

The majority of published research has focused on the indirect relationship between fast food availability and obesity, rather than investigating direct effects on fast food consumption. Greater neighborhood fast food availability is consistently related to higher obesity [ 1 , 3 , 39 , 40 ] yet it is possible that this relationship reflects processes other than higher fast food consumption. In particular, fast food restaurants may cluster with other environmental characteristics that influence obesity [ 20 , 41 ]. For example, automobile access may be important for fast food restaurants because of their dependence on drive-thru business. Therefore, neighborhoods with more fast food restaurants may promote obesity as a result of dominating road structures that hinder active transportation. Our findings that neighborhood fast food availability is unrelated to fast food consumption suggest that associations between fast food availability and obesity may reflect this or similar processes.

Our study expands current literature by comparing fast food restaurants scaled by roadway kilometers within a street network buffer - a measure of availability that improves upon widely used restaurant count measures by accounting for urban development and street access - across multiple geographic and sociodemographic characteristics. We present data from a large sample of U.S. young adults that uses a more refined urban/rural classification than the traditional urban/rural dichotomy. Furthermore, we observed vast differences in the availability of fast food for non-urban compared to urban respondents. While these differences precluded the use of comparable availability measures across urbanicity levels, they suggest that the built environment may operate quite differently in rural, suburban, and urban areas, and thus may necessitate different measurement approaches. This might also underlie some of the mixed findings in the literature [ 10 – 12 ].

Strengths and limitations

We present data from a unique and large national cohort that includes a variety of detailed environmental data combined with individual-level diet data in a cohort of young adults from around the U.S. To our knowledge ours is the only large analysis of fast food availability and consumption that accounts for multiple environmental features and individual characteristics simultaneously.

Yet, our study has some limitations. Our measure of fast food consumption is based on self-report and, like all self-report diet measures, has inherent recall and reporting error. Respondents were not instructed how to report meals versus snacks; that is, snacking at a fast food restaurant may be undercounted if the respondent did not consider it as a visit. Further, because the question asks about the number of days that respondents ate fast food in the last week, fast food consumption may be under-represented if the respondent ate more than one fast food meal in a day. Yet, this is a measure that is commonly used to assess fast food consumption in large population-based studies such as the Panel Study of Income Dynamics and Coronary Artery Risk Development in Young Adults [ 39 , 42 ]. Moreover, we are unable to ascertain what foods were available and consumed at each fast food visit. This is increasingly an issue as fast food restaurants are including healthier options in response to consumer health concerns [ 43 ]. In addition, our study is cross-sectional and thus does not capture changes in the food environment or consumption over time. We were also unable to control for factors related to selection of residential neighborhoods, however by including Wave I school indicator variables, we attempted to address unmeasured characteristics associated with baseline neighborhood. Our 3 km network neighborhood buffer may not accurately reflect food-purchasing areas for different urban settings and sociodemographic subgroups, however estimates for the 1 km network buffer were very similar. Significant differences in characteristics of respondents included versus excluded in our sample may have biased our results.

There may be error in our roadway (StreetMap Pro) and food resource data that we are unable to investigate in our national sample. In addition, the relatively narrow range of fast food availability may limit our ability to detect effects of fast food availability in relation to fast food consumption. We were not able apply spatial interaction models to our national sample, but our models account for spatial clustering of respondents. The trade-off is our large, national sample and the ability to compare individuals living in non-urban, low density urban, and high density urban environments, within the context of the same cohort. There are no other datasets in which such a study is possible at the small, geographic unit used in our study.

Despite these limitations, our study is an essential step in understanding the allocation and consumption of fast food restaurants across geographic space over the entire U.S. and within urbanicity levels, and our findings can inform measurement and design in future individual-level and longitudinal studies.

Implications

Our findings are significant in light of the recent efforts to reduce obesity though policies targeting the fast food environment. For example, a one-year ban on fast food restaurants was unanimously put forth in South Los Angeles (LA) in an effort to reduce obesity in this low SES area, despite lower fast food restaurant per capita relative to more affluent West LA [ 44 ]. Given evidence that eating fast food increases BMI and obesity risk [ 39 , 45 ], reducing fast food consumption may be a valuable aim. However, specific environmental factors that influence fast food consumption among young adults are not well understood. Our findings suggest that greater residential neighborhood fast food availability may not be an important driver of fast food consumption. Greater understanding of how lifestyle factors and neighborhood food resources interact to influence fast food consumption is needed to inform effective policy.

Findings from our large, national sample of U.S. young adults do not support the hypothesis that neighborhood fast food availability increases the likelihood of fast food consumption among young adults. To understand the complex relationship between fast food availability and individual dietary behaviors, future research should investigate the settings in which young men and women consume fast food, other individual lifestyle and contextual influences on dietary choices, and how these processes differ across urbanicity and sociodemographic contexts.

Add Health data

The more extensive restricted-use data, available by contractual agreement, will be distributed only to certified researchers who commit themselves to maintaining limited access. To be eligible to enter into a contract, researchers must have an IRB-approved security plan for handling and storing sensitive data and sign a data-use contract agreeing to keep the data confidential.

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Acknowledgements

The authors would like to thank Brian Frizzelle, Marc Peterson, Chris Mankoff, James D. Stewart, Phil Bardsley, and Diane Kaczor of the University of North Carolina, Carolina Population Center (CPC) and the CPC Spatial Analysis Unit for creation of the environmental variables. The authors also thank Ms. Frances Dancy for her helpful administrative assistance. There were no potential or real conflicts of financial or personal interest with the financial sponsors of the scientific project.

This work was funded by National Institutes of Health grants R01HD057194 and R01 HD041375, R01 HD39183, a cooperative agreement with the Centers for Disease Control and Prevention (CDC SIP No. 5-00), and the Interdisciplinary Obesity Training Program (T32MH075854-04). The authors received support from grant, 5 R24 HD050924, Carolina Population Center, awarded to the Carolina Population Center at The University of North Carolina at Chapel Hill by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. This research uses data from Add Health, a program project directed by Kathleen Mullan Harris PhD and designed by J. Richard Udry PhD, Peter S. Bearman PhD, and Kathleen Mullan Harris PhD at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss PhD and Barbara Entwisle PhD both from the University of North Carolina at Chapel Hill for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website http://www.cpc.unc.edu/addhealth ). No direct support was received from grant P01-HD31921 for this analysis. None of the acknowledged individuals received compensation for their assistance.

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Richardson, A.S., Boone-Heinonen, J., Popkin, B.M. et al. Neighborhood fast food restaurants and fast food consumption: A national study. BMC Public Health 11 , 543 (2011). https://doi.org/10.1186/1471-2458-11-543

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California Dreaming: The Effects of California's "Fast Food" Minimum Wage

By richard b. mckenzie.

By Richard B. McKenzie, May 6 2024

The governor and the legislative backers of the wage hike might have reasoned that the limited mandate would force other, higher-level restaurants (fast casual and casual dining, like Applebee’s, Islands, and Panera’s) and even stores with low-wage workers (such as Dollar General) to match the mandated fast-food minimum. Why? Because if they didn’t match the new minimum, their workers making less than $20 an hour could be enticed to move to covered fast-food restaurants for higher pay.

But the backers would be wrong.

Prior Measured Effects of Minimum-Wage Increases

As a multitude of academic studies have shown, 1 a minimum wage increase for fast food (especially as high as this one with unprecedented narrow market coverage) will far more likely cause covered restaurants to cut their workforces, either by laying off workers or reducing their hours. This means that even workers who keep their low-wage jobs but get fewer hours could be induced to move to non-covered jobs with more hours in higher-level restaurants. (A wage of $16 an hour on a forty-hour workweek would generate $640 in gross pay; a $20 hourly rate with only 25 hours a week would yield a weekly pay of $500, a 22 percent reduction.)

Of course, fast-food restaurants would cherry-pick among their workers, laying off a disproportionate number of their lower-skilled/low-productivity workers. However, note that any reduction in fast-food employment could transmute into an increase in worker supply for uncovered, higher-level restaurants, with the supply increase putting downward pressure on their workers’ wages and benefits—a market effect that wage-hike backers likely haven’t considered.

Again, those restaurants will also cherry-pick among new applicants, choosing a disproportionate count of the relatively lower-skilled/low-productivity workers they interview. This means that the lowest of the low-skill/low-productivity workers will suffer most in terms of ending up on the unemployment lines, perhaps not exactly what backers expected.

Lost Fringes and Geater Work Demands

The political supporters, who surely know the findings of the minimum-wage studies, might object vociferously: “Most past statistical studies on minimum-wage hikes have found meager percentage reductions in employment in covered worker groups (generally, lower than 3 percent of covered workers) and hours worked.” They would be right, for the literature they’ve reviewed . Yet they overlook how employers are not fools, unable to recognize and use other ways of legally responding to government mandates, with the intent of offsetting partially, if not totally, the labor-cost increases from money wage-rate hikes.

Employers know very well that the mandated money-wage increase is hardly the only way workers are compensated, and may not even be the most important form of compensation (on the margin) for some, or even a few, covered workers (especially those with children who need flexible schedules).

Employers also face competitive market pressures to control their labor costs and advance their profits in financial markets. Employers who don’t respond to minimum-wage mandates by cutting their labor costs (perhaps because they want to be “nice” to their workers) can be left behind with relatively higher production costs, and with higher prices and lower sales than those who do make the cuts. The extant competition can force all competitors to respond even when they would prefer not to do so.

Faced with an above-market minimum wage, employers will be pressed to offset the money-wage hike with savings in labor costs that can come with replacement of covered workers by uncovered “non-human workers”—kiosk order takers and “burger bots.” These “tech workers” have an enviable market-wage advantage over their human competitors: Their legal California minimum wage is hard to beat: $0.00!

Employers can also reduce or eliminate whatever (minimal) fringe benefits they offer their workers, such as flexible scheduling, hours off for taking college classes, and even limited health benefits. Employers who have no fringes to trim can always increase work demands. Employers can do that because of work opportunities reduced by the minimum-wage hikes. Indeed, when fast-food restaurants cut their workforces, many will be pressed to transfer some or all of the lost workers’ tasks to the remaining workers. And many of the lost benefits will never be recognized by government minimum-wage monitors.

Why Job Losses to Minimum-Wage Hikes Have Been “Small”

The lost worker benefits unseen by minimum-wage backers is one reason reformers perennially claim that fast-food workers are poorly compensated and why minimum-wage hikes seem to be an ineffective policy for raising covered workers out of “poverty,” as one of my UC-Irvine economist colleagues David Neumark has argued in the Wall Street Journal . 2 Another unheralded reason is that a wage hike for poor workers who are on several welfare programs can mean a loss of more in welfare benefits than they can gain from a higher minimum wage.

Why? For a simple, but obscure reason. Most welfare program benefits are reduced as covered workers’ earnings rise, as economist Craig Richardson and I have shown, 3 leaving covered minimum-wage workers facing higher marginal tax rates that are higher than the marginal income tax rates paid by the rich—even higher than 100 percent (which means that some covered minimum-wage California workers on welfare will lose more in benefits than the money they gain from the $4 increase in their minimum wage), an unseen consequence that hardly their incentives to continue working.

Why Past Minimum-Wage Hikes Have Been So “Small”

Apparently, hike backers also haven’t realized that the reported “small” employment effects of past wage hikes have been largely attributable to how small the hikes have been (10 percent or so, sometimes spread over years) and how easy it has been for employers to offset their low increase in wage costs with reductions in fringes and increases in work demands—as well as with price increases and replacement of unskilled workers with more skilled (productive) workers.

Because California’s minimum-wage hike is so large this time (and has not been gradually increased), employers could quickly run out of ways to develop offsets for the $4 wage increase, which means that the 25 percent minimum-wage increase can be expected to disproportionately magnify its employment effect (far beyond the “small” percentage effects reported in almost all previous statistical studies). California could be conducting a social experiment in poverty relief that could very well spark backer-remorse and reduce backers in other states enthusiasm for minimum-wage hikes.

Why Covered Workers Who Keep their Jobs Can Be Made Worse Off Than the Workers Who Leave

Typically, economists’ arguments for and against minimum-wage hikes lead to an often-touted conclusion, that workers who keep their jobs are made better off by hikes, while those who are let go are made worse off, because they must accept unemployment or lower-paying jobs uncovered by the hikes. That facile deduction, adamantly supported for decades, is also likely wrongheaded. My explanation is straightforward: When employers offer workers fringe benefits, they likely expect their costs to be covered either by an increase in worker productivity and/or lower wages (brought on by an increase in the number of potential workers). This means that when benefits are reduced because of wage hikes, the value of workers’ lost fringes (say, $5, only as an illustration) will tend to be worth more than their minimum-wage increase ($4 an hour, in the California case). It also means that firm profits will be higher than without the non-wage benefit adjustments.

Doubt that covered workers will suffer non-money-wage effects, often in unseen ways? A bartender at a local casual dining restaurant recently reported his restaurant just hired two employees whose hours were cut at a Chipotle (after Apri1) in the same shopping center, giving his restaurant a chance to expand business in the tight Orange County labor market without having to increase its starting wage. A manager at a local Chick-fil-A has reported that her company has matched the mandated $4 wage increase, keeping its starting wage $1 above the state’s required $20 minimum, but has changed its standards for annual raises; instead of basing raises on both tenure and responsibilities, it now offers higher wages only for increased responsibilities.

The result is that a forced minimum-wage hike will negate many mutually beneficial market trades, making many workers worse off on net: The workers may receive a higher money wage, but will tend to lose benefits and must meet greater work demands. The very workers this legislation was supposed to help will, with time, tend to be net losers.

Concluding Comments

Members of Congress seem to have gotten economists’ message. The federal minimum-wage of $7.25 has not been raised since 2009. The federal real minimum has, as a consequence, deteriorated by 40 percent since 2009.

Yet that real-wage reality doesn’t mean that workers covered only by the federal minimum are worse off today. The market minimum wage in most states has continued to rise. Even if California had no state minimum today, no firm in the state could get by with paying the federal minimum, as evident by the fact that many firms, including fast-food restaurants, were paying above the state’s $16 minimum before April 1, and some restaurants were even paying more than $20 an hour, and with more worker benefits than years ago.

• Minimum Wages, by Linda Gorman. Concise Encyclopedia of Economics .
• “Progressives’ Desires to Help the Poor Will End Up Hurting Them Instead,” by Craig J. Richardson and Richard B. McKenzie. Library of Economics and Liberty, Sep. 6, 2021.
• “Large Increases in the Minimum Wage Are Likely to Destroy Jobs,” by Robert P. Murphy. Library of Economics and Liberty, Oct. 5, 2015.

Nevertheless, many state politicians continue to favor minimum-wage increases. The reasons are not as clear as may be thought: “Politicians want to help poor workers” or “They have simply not appreciated the economics of the minimum wage in competitive markets.” These are not inconsequential points, but they seem too obvious and facile for my academic proclivities. I am inclined to believe that both proponents and opponents of minimum-wage hikes find the political forces behind minimum-wage hikes more powerful than the economic forces. But that is hardly a comfortable admission.

[1] See for example “On the Minimum Wage, Both Sides Have Their Economics Wrong,” by Richard B. McKenzie in Regulation. Summer, 2021.

[2] David Neumark, “ California’s Crazy ‘Fast Food’ Minimum Wage Takes Effect,” Wall Street Journal , March 31, 2024. Paywalled.

[3] Craig J. Richardson and Richard B. McKenzie, “Progressives’ Desire to Help the Poor Will End Up Hurting Them Instead.” Library of Economics and Liberty, September 6, 2021.

* Richard McKenzie is a professor of economics emeritus in the Merage Business School at the University of California, Irvine. He is also author most recently of Reality Is Tricky: Contrarian Arguments on Contested Economic Issue and a soon-to-be-released book (June 2024) on Rationality Evolved! Why We Have No Choice Over Having Choices.

For more articles by Richard B. McKenzie, see the Archive .

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Americans are choking on surging fast-food prices. "I can't justify the expense," one customer says

By Khristopher J. Brooks

Edited By Anne Marie Lee

Updated on: May 9, 2024 / 2:03 PM EDT / CBS News

Kevin Roberts remembers when he could get a bacon cheeseburger, fries and a drink from Five Guys for $10. But that was years ago. When the Virginia high school teacher recently visited the fast-food chain, the food alone without a beverage cost double that amount.

Roberts, 38, now only gets fast food "as a rare treat," he told CBS MoneyWatch. "Nothing has made me cook at home more than fast-food prices."

Roberts is hardly alone. Many consumers are expressing frustration at the surge in fast-food prices, which are starting to scare off budget-conscious customers.

A January  poll  by consulting firm Revenue Management Solutions found that about 25% of people who make under $50,000 were cutting back on fast food, pointing to cost as a concern.

For some of the nation's best-known restaurant chains, losing lower-income customers means weaker sales, and potentially a dent to profits, said restaurant analyst Mark Kalinowski, president of Kalinowski Equity Research.

"When you look at McDonald's, they're not getting a majority of high-income customers — the middle- and lower-income class are the bulk of their business," he said. "They need to be cautious with their spending, and that's what you're seeing right now."

"Forget about it — I'm going home"

As fast-food prices have risen, recent earnings reports from industry leaders such as McDonald's and Taco Bell parent Yum Brands show that same-store sales have slipped over the last year. 

"The whole conceit was that you were getting some OK-level of food for a low price and you could get it quick," Roberts said. "Now I can't justify the expense. If I'm paying $15 for a burger and fry and drink and it's McDonalds quality, forget about it — I'm going home."

Casual dining restaurants are also feeling the absence of low-income Americans. The CEO of Dine Brands, which owns Applebee's and IHOP,  told CNBC this week that the casual restaurants are seeing a decline in low-income customers. 

How much have fast-food chains raised prices?

Fast-food prices have shot up over the last decade,  according  to FinanceBuzz. The personal finance site found that the price of a McDonald's Quarter Pounder with Cheese meal from McDonald's more than doubled in price from $5.39 in 2014 to $11.99 this year.

Other restaurant chains also have jacked up their prices, FinanceBuzz said. Between 2014 and 2024, Popeye's, Jimmy John's and Subway hiked their food prices 86%, 62% and 39%, respectively. The price of a two-piece chicken combo at Popeyes jumped from $6.49 to $11.39 over that period, while an eight-inch club tuna from Jimmy Johns rose from $5.75 to $9.10, according to FinanceBuzz. 

FinanceBuzz derived its data by selecting 10 menu items from each fast-food chain, using third-party websites like fastfoodmenuprices.com and menuwithprice.com to check the menu prices in 2014, 2019 and 2024. 

To be sure, menu item prices at fast-food restaurants can vary wildly by state. While prices are set at the corporate level for some fast-food restaurants, they are determined by individual franchise owners at others. 

Why are fast-food prices rising?

Restaurant chains point to rising labor costs as a  key factor driving up prices . Across the U.S., 22 states raised their  minimum wages in January , although the federal baseline pay remains stuck at $7.25 an hour. In California, fast-food chains with 60 or more locations nationwide are now required to pay their workers a minimum wage of  $20 an hour  following passage of a new law last fall. 

Labor advocates dispute that rising employee wages are to blame for higher fast-food costs. A March analysis of California fast-food restaurants by the Roosevelt Institute, a liberal think tank, noted the industry's record profit margins.

"Our analysis of financial data for the past decade finds increases in fast-food industry operating profits and rising markups, suggesting that affected employers can absorb the increased operating costs associated with a higher industry minimum wage without increasing consumer prices or reducing employment," the report states.

Jack in the Box, Jimmy Johns, McDonald's, Popeyes, Subway and Yum did not respond to requests for comment from CBS MoneyWatch. 

For now, companies appear to be looking toward rewards points programs,  discounts  and mobile  apps  in an effort to keep customers loyal. But McDonald's CEO Chris Kempczinski acknowledged the impact of rising prices last month in an earnings call. 

"[A]cross almost all major markets, industry traffic is slowing," Chris Kempczinski told Wall Street Analysis. "McDonald's has a long history of being the go-to destination for value, and it's imperative that we continue to keep affordability at the forefront for our customers."

Khristopher J. Brooks is a reporter for CBS MoneyWatch. He previously worked as a reporter for the Omaha World-Herald, Newsday and the Florida Times-Union. His reporting primarily focuses on the U.S. housing market, the business of sports and bankruptcy.

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Checking in on fast food workers and franchise owners after a month of wage increase

Farida Jhabvala Romero / KQED

A month after fast food workers in California started earning at least $20 an hour, how is the financial picture for them and franchise owners shaping up?

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Impact of California Fast Food Worker Wage Increase Still Too Early to Gauge

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When Karina Ceballos received her first paycheck reflecting California’s new minimum wage for fast-food workers, she felt a big wave of relief. The single mom said she earned about $400 extra last month, which made it much easier to pay bills and rent for her family’s apartment in Castro Valley.

Ceballos’ fridge is now packed with green vegetables, fresh mangoes and other fruits — healthier foods for her kids that she couldn’t buy much of before, she said, even as she worked more than 60 hours weekly holding two fast food jobs at a Jack in the Box and a TOGO’s.

“I can really feel the change,” said Ceballos, 43, one of the many workers who has marched and advocated for the wage increase in recent years as state lawmakers weighed the issue. “I feel less stressed out. Before, it was really tight financially. Now, I might be able to even save some money.”

About a month after California began requiring most fast food employers to pay their workers at least $20 an hour , as compared to the state’s $16 general minimum wage, economists said it’s still too early to determine the wage hike’s broader impact on the industry, particularly in light of changing inflation rates and other economic trends.

While some fast food workers, like Ceballos, have reported quality-of-life improvements because of the increase, others said they’ve had their hours cut and actually lost income as quick-service restaurants adjust to more expensive payrolls.

Alejandra Aguilar Perez said her employer at a Taco Bell in downtown Los Angeles cut her hours from about full-time to half-time last month, a drastic drop in overall earnings that left her scrambling to support her 7-year-old daughter.

“I’ve been struggling,” said Aguilar Perez, 28, who is now looking for a second job. “It’s hard to pay bills. It’s hard to pay rent.”

Still, Aguilar Perez notes the long lines at the Taco Bell where she works and said she believes the industry will stabilize in the coming months, prompting employers to restore staffing hours.

“The bosses right now are mad,” she said. “But eventually, they are going to have to give in, and they are going to have to give us our hours.”

The majority of California’s roughly half a million fast food workers are women of color, most of whom previously made close to the state’s $16 an hour minimum wage, according to the UC Berkeley Labor Center .

The state law that instituted the $20 hourly minimum wage, which went into effect last month, also created a first-in-the-nation Fast Food Council , made up of worker and employer representatives, that can keep raising the minimum wage by about 3.5% each year through 2029. The law resulted from a compromise between the industry and labor groups and applies only to large chains with more than 60 establishments nationwide.

Most fast food restaurants in the state are franchises, meaning that small business owners pay fees to corporations like McDonald’s to represent their brand. Almost none of the franchisees at the handful of Bay Area fast food restaurants that KQED visited responded to requests for comment.

Brian Hom, who owns two San José Vitality Bowls franchise restaurants that sell acai bowls and salads — and the one franchisee who did respond to KQED — said he does not foresee increasing staff hours any time soon.

To address higher labor costs, Hom, 66, reduced the morning and late afternoon shifts at one of his restaurants from three to two employees and increased menu prices. He sees the new wage requirement as another challenge for his business, which is navigating higher food costs and expensive Silicon Valley rents.

“I’m happy for my employees getting the $20 minimum wage. But they know that if we can’t continue having good sales because of price increases, they may not have a job,” said Hom, a former IBM employee who opened his first restaurant seven years ago. “I’ve talked to my wife. … If things get really bad, we’ll just close the business.”

He won’t know if his sales have dropped until a bookkeeper’s report comes later this month, he said. But some customers have told him that the new prices — including $13.99 for a popular acai, strawberry and banana bowl — are getting too high.

A spokeswoman for the California Restaurant Association, which represents some franchise owners, pointed to recent headlines chronicling higher fast food prices and reduced working hours, which she attributed to the minimum wage increase.

“Feedback from our members suggests this has become a breaking point for many small restaurant businesses,” Megan Gamble, a spokesperson for the association, said in a statement.

However, economist Michael Reich said it’s still too soon to tell how the wage hike will impact employment or menu prices. Reliable data from the U.S. Bureau of Labor Statistics and other sources will start becoming available in about a month, offering more evidence, he said.

More data — as opposed to anecdotal reports — is also needed to determine the cause of fast food industry shifts, Reich said, particularly amid rising inflation rates and as more restaurants use self-order kiosks and other technologies to save on labor costs.

“The cost of food has gone up by 20-something percent in the last three years, so that’s another reason that prices have been going up. It doesn’t mean the minimum wage has caused the price increase,” said Reich, who chairs UC Berkeley’s Center on Wage and Employment Dynamics.

The fast food industry has absorbed past state and local minimum wage hikes by moderately raising prices at levels that didn’t scare customers away, he said. Reich’s research on the impact of California’s previous minimum wage increase found no statistically significant cutbacks in hours or jobs in the fast food industry. But a study he’s conducting on the new fast-food minimum wage could yield different results, he added.

“Fast Food is the biggest user of low-wage workers, and a minimum wage does what it’s intended to do — to raise their living standards. And it does so, at least at the levels we’ve been studying, without causing job loss,” Reich said. “I’ll be very interested to see what happens with $20.”

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Russia Becomes a Magnet for U.S. Fast-Food Chains

By Andrew E. Kramer

• Aug. 3, 2011

MOSCOW — Earlier in his career, Christopher Wynne put his Russian expertise to work researching arms proliferation for the American government. Now he’s engaged in geopolitics of another sort: deploying American fast food for the emerging Russian middle class.

Mr. Wynne is the top franchisee in Russia for the Papa John’s Pizza chain. His competitors include the American chains Sbarro and Domino’s, and a Russian upstart, Pizza Fabrika. But so far, compared with the largely saturated United States market for fast food, Mr. Wynne says he is finding plenty of demand.

“I could succeed in my sleep there is so much opportunity here,” said Mr. Wynne, who has just opened his 25th Papa John’s outlet in Russia, doubling the number in the last year.

American fast food has been going global for years, of course. And China and India continue to be big expansion markets. But lately, the industry is finding a growing appetite for its fare in Russia — not only pizza, but Burger King’s Whoppers, Cinnabon’s Classic Rolls and Subway’s barbecue pulled pork sandwiches, among others.

“As consumers have more disposable income they will spend it on fast food,” Jack Russo, a fast-food industry analyst at Edward Jones, said in a telephone interview. He compares the market here to the United States half a century ago.

For years, McDonald’s, which opened its first restaurant on Pushkin Square in 1990 and generated gigantic lines, was the only American fast-food chain in Russia. McDonald’s now operates 279 restaurants in Russia.

But other chains are flocking in. Burger King has opened 22 restaurants, mostly in mall food courts, in two years. Carl’s Jr. has 17 restaurants in St. Petersburg and Novosibirsk. Wendy’s has opened two restaurants including a flagship on Arbat Street in Moscow, and plans 180 throughout Russia by 2020. The Subway sandwich chain has opened about 200 shops in Russia, working through several franchisees. Yum Brands, which owns KFC, Pizza Hut and Taco Bell, operates a co-branded chicken restaurant chain in Russia, called Rostik’s-KFC, and Il Patio in the Italian food segment. Yum now has about 350 restaurants in Russia.

Paving the way has been Russia’s development in many cities of the modern infrastructure needed for fast food to flourish — including malls with food courts, highways with drive-through locations, and specialty suppliers of frozen foods and packaging.

Moreover, Russian consumers are increasingly affluent, partly because of the trickle down from the nation’s lucrative oil exports. And though they still trail far behind the average household income of Americans — $43,539 in the United States versus $7,276 here — Russian consumers tend to have a large portion of their money for discretionary spending.

They are unburdened by the hangover of consumer debt that has curbed American purchasing power. Nor do Russians have high medical bills because the health care system, if flawed, is largely socialized. The income tax is a flat 13 percent. And a majority of Russians own property mortgage-free, as a legacy of the mass privatization of apartments in the 1990s.

As a result, the fast-food chains find they can charge higher prices in Russia than in America. The average check at a Russian fast-food outlet — $8.92 according to research by a Wendy’s franchisee here — is significantly higher than the United States average of $6.50.

A large “the works” pizza at Papa John’s in the company’s home base of Louisville, Ky., for example, costs $14, compared with $21.62 for the same pizza in Moscow.

Ready buyers include Valery V. Mamayev, a man who reached his 30s without ever ordering a pizza. But he has been a steady Papa John’s customer since a shop opened in the spring in his neighborhood, the Maryino district, an hour’s drive from central Moscow. Maryino is a cityscape of concrete apartment blocks, tangled skeins of traffic-clogged thoroughfares and, these days, an ever growing array of food chain outlets.

On a recent Sunday, Mr. Mamayev padded into the hallway of his apartment building in boxer shorts to take delivery of a pie topped with chorizo, salami, ham, Italian sausage and pepperoni.

“All I have in the refrigerator is a jar of lightly salted pickles,” said Mr. Mamayev, a 32-year-old diesel mechanic. “I thought, that’s not really something to eat. It’s easy and fast to order pizza. And pizza is tasty.”

By opening 19 restaurants in Moscow — besides the six in other cities — Mr. Wynne’s Papa John’s franchise has become the third-largest takeout pizza company in the city.

“The bottom line is the opportunity is here,” said Mr. Wynne, who in a presentation to prospective investors earlier this year said the Russian operation had 21 percent annual revenue growth in stores open more than a year. The franchise does not disclose its average sales per restaurant but says it is the highest figure among 35 countries where Papa John’s operates.

Mr. Wynne, who is 34, speaks fluent Russian and holds a master’s degree in international affairs from George Washington University. He was formerly in the United States National Nuclear Security Administration, entrenched enough that he had a top security clearance. But in 2007, sensing the time had come to beat swords into pizza pans, he acquired 51 percent of the Papa John’s Russian franchise.

Mr. Wynne says it costs about $400,000 to set up a store in Moscow, which can turn an operating profit in three months. The enterprise is well financed, with a $10 million loan at 7 percent interest from the United States Overseas Private Investment Corporation, an agency that encourages American exports.

Moscow, a city of 13 million, so far has only about 300 pizza restaurants — compared with 4,000 in Manhattan, which has a population of about 1.6 million. That, for Mr. Wynne, is a market begging to be mined, which Papa John’s is doing in part with advertising focused on developing pizza delivery customers among men ages 25 to 35. Because pizza can be delivered with beer here, a free bottle is sometimes part of the promotion.

Not everything has gone smoothly for Mr. Wynne. Russia’s weak courts and poor protection of intellectual property rights have posed problems for all American chains.

Papa John’s settled out of court with a pizza restaurant in Moscow that called itself Papa John’s, by persuading the owner to rename his location Papa’s Place.

Starbucks had more severe run-ins with Russian trademark squatter. For years, a Russian man, Sergei A. Zuykov, claimed to own the brand here and was trying to sell it for $600,000. Starbucks never paid, but the dispute delayed its entering the market until 2007. It now has 47 Starbucks outlets in Russia.

And not all Western food forays have succeeded here, as some companies have stumbled over cultural differences difficult to anticipate.

Campbell Soup, for example, left Russia this year because of soft sales on four flavors of soup stock sold in pouches. It had seemed a sure bet because the varieties included a broth that could cut the labor time for making borscht. But as it turned out, Russians prefer to build their borscht from scratch.

On the other hand, Papa John’s hottest-selling pizza this spring was a recipe made especially for the local market but found in no Russian cookbook: a topping of blue cheese, chicken, celery and Tabasco sauce.

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McDonald's says it's listening to penny-pinching customers and focusing on value

• Consumers are "price weary," and McDonald's is paying attention.
• McDonald's will be "thoughtful" about any further price increases in 2024, CFO Ian Borden said.
• People are getting less fast food. "Everybody is fighting for fewer consumers," Borden said.

McDonald's says it's doubling down on value as customers increasingly feel the strain.

"The consumer is price weary," McDonald's CFO Ian Borden told analysts at the company's earnings call on Tuesday. "And I think we certainly are going to be prudent and thoughtful about any further price increases that we're looking at for the rest of 2024."

"Consumers continue to be even more discriminating with every dollar that they spend as they faced elevated prices in their day-to-day spending which is putting pressure on the QSR [quick service restaurant] industry," CEO Chris Kempczinski said.

He said that diners from all income cohorts are looking for value, though he noted that it "may be more pronounced with the lower income consumer ."

"I think all consumers are looking for good value, for good affordability," Kempczinski said.

Prices spiked during the pandemic when restaurants' costs went up because of labor shortages and supply-chain woes. While grocery inflation has moderated, fast food prices are still rising at higher rates than pre-pandemic.

Some diners say fast food no longer represents value for money and are cutting down in favor of cooking at home or dining at sit-down restaurants.

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Other restaurant chains, including Starbucks and Burger King parent company, Restaurant Brands International, have said this week that customers are being cautious with their spending.

"Clearly, everybody is fighting for fewer consumers or consumers that are certainly visiting less frequently," CFO Borden said, reiterating comments he'd made in March that higher prices were deterring some diners from eating out .

But during Tuesday's call, McDonald's execs highlighted the chain's work around affordability. "We literally wrote the playbook on value," Kempczinski said.

Regarding McDonald's prices, Kempczinski said: "I feel like we are in a decent shape from an overall menu standpoint."

Kempczinski said that 90% of McDonald's US franchisees were offering meal bundles that cost $4 or less. Internationally, it's also been offering value bundles at "various price points" to provide "smaller, more affordable meals," he said. In Germany, for example, its McSmart menu sold record units in the first quarter, he said.

Kempczinski also highlighted that diners could get discounts by ordering on its app .

But McDonald's needs to do more work to promote its value offerings nationally and drive customer awareness, Kempczinski said.

"We're doing it in 50 different ways with local value," he said. "And what we don't have in the US right now is a national value platform at the same time that our competitors are out there with the national value platform."

McDonald's posted a 2.5% increase in comparable US sales for the quarter to March 31, down massively from 12.6% in the same quarter the previous year. Total revenue for the quarter rose 5% year-over-year to $6.17 billion.

Is fast food too expensive? Email this reporter at [email protected].

Watch: How McDonald's is using data and its loyalty program to make new promotions like the adult Happy Meal

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Reporting by Granth Vanaik in Bengaluru; Editing by Anil D'Silva, Pooja Desai and Shounak Dasgupta

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Do we still need spaces to support women in restaurants? Certainly, this L.A. group says

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When chef Dominique Crenn won the World’s 50 Best Restaurants’ 2016 best female chef award, she famously called it “stupid. A chef is a chef.”

“I agree with Dominique: A chef is a chef,” says Mary Sue Milliken, chef and co-founder of Regarding Her, a nonprofit organization for women in the food and beverage industry. “I agree we don’t need to talk about ‘best’ women anything. But the barriers for women in this field, specifically, and many others, need to be eliminated in order for women to wield half the power and to create an industry that’s more hospitable and sustainable.”

It’s a lonely, isolated business, especially for women.

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So on a Wednesday night in March, applause breaks out when an apron-clad Stephanie Izard stands at the end of a long table set up inside Guerrilla Taco’s adjacent Guerrilla Cafecito, which normally serves coffee, pastries and breakfast burritos during the day in downtown L.A.’s Arts District.

“How is everybody?!” the chef of the nearby restaurant Girl & the Goat asks the group of cheering guests. Izard has just come out of the kitchen alongside chefs Crystal Espinoza of Guerrilla Tacos and Kat Hong of Yangban. The three of them made buttery Peruvian empanadas, hamachi tostadas and golden Hokkaido scallop toast as part of the Women’s History Month festival put on by Regarding Her.

“I’m really excited,” Izard tells the diners. “I think as much as we can celebrate women in the industry, the more the better.”

In an industry that has never been easy for women and is itself struggling, Regarding Her provides educational and financial programming to help female chefs, leaders and entrepreneurs.

A reckoning of the restaurant industry due to the pandemic has given restaurant workers a chance to step back and see that the system was broken. “We’re focused on women because it’s been so much harder for women for so many different reasons and we want for this organization to be able to help accelerate gender parity [and] eliminate the barriers for women,” says Milliken.

The Chefs of the Arts District dinner is just one of dozens of events organized by Regarding Her, born in 2020 as a pandemic crisis response from nine L.A. women restaurant professionals on a Zoom call. The group has since ballooned to over 1,000 members in Los Angeles and Washington, D.C., and has awarded hundreds of thousands of dollars in grants to business owners and launched the Academy, a 10-week career accelerator program for female entrepreneurs in the food industry.

“I remember exactly where I was,” says founding member and Guerrilla Tacos owner Brittney Valles of the moment that Regarding Her (or RE:Her) began. “I was in the catering van driving. I was on the phone to pick up a heater from some trailer park because we had to move our restaurant outside,” she says, recalling being in the throes of the pandemic. That’s when she joined forces with the other eight chef and restaurateur founders: Milliken of Socalo, Border Grill and Alice B; Dina Samson of Rossoblu and Superfine Pizza; Lien Ta of All Day Baby and Here’s Looking at You; Bricia Lopez of Guelaguetza; Kim Prince of Hotville Chicken and Dulanville; Love & Salt’s Sylvie Gabriele; Gasolina Cafe’s Sandra Cordero; and Botanica’s Heather Sperling.

“We quickly realized that we really had struck upon something that needed a lot more of our energy,” Milliken adds. That “something” was support of all kinds: practical, emotional and financial for women in the food industry, and Milliken and RE:Her have no problem focusing specifically on women.

And though this is the crux of what Regarding Her sets out to do, Milliken also makes a point to shed light on the unsustainable financial model of restaurants.

“The idea was, at that time, to not only drive business to women-owned restaurants, but to try to raise money to help those who were really struggling,” says Samson of their initial COVID grant program. Samson herself raised $150,000 through DoorDash and OpenTable partnerships, which they then distributed to 15 female applicants.

Now, despite partnerships and donations from large corporations, individual donors and grants, funding remains Regarding Her’s greatest challenge.

Membership to join Regarding Her is free and the qualifications have expanded from only female business owners to allowing women in other leadership positions like CEOs and general managers to apply. It also offers access to an online network called Circle where women can ask each other questions such as how to negotiate a lease, where to find a good plumber or how to choose a good point-of-sale system. According to Samson, members are quick to share resources and RE:Her has even offered advice and moral support to restaurants during the process of closing.

In 2022, Regarding Her launched its biggest program yet, the Academy, the female entrepreneurs program that also offered each participant a $20,000 grant.

Chef Rashida Holmes in part credits her participation in the Academy to her ability to transition her acclaimed pop-up Bridgetown Roti into a bricks-and-mortar business set to open this summer.

“I know I can reach out to them for anything,” says Holmes. “Someone like me who has spent 15 years in kitchens, nobody taught me how to do HR. Nobody taught me different strategies of management.”

The consensus among women who participate is that the community and practical support has been the most invaluable and life-changing.

“I think a lot of us sacrifice our own mental health so that other people in our restaurants, in our communities, can be better,” says Valles. “But, ultimately, the fish rots at the top, so if you’re not taking care of you, people are going to sense it.”

Pointing chefs toward resources like therapy sessions and the 3 Chefs 3 Moms program from the Chicago-based nonprofit Abundance Setting is one of the ways Regarding Her has aimed to encourage and support women.

“I think Regarding Her, if we are successful, will help move that needle to heal the industry in certain ways, to make it easier and more sustainable and more attractive to women who want to have families and more attractive to people who love it, but don’t want to work for dirt wages,” says Milliken.

“Nonprofits are not going to save the restaurant industry,” she says when asked if organizations like Regarding Her could become the norm. “The nonprofit business will not be how the industry rights itself and gets on a better course. That’s going to happen through legislature and tax credits.”

The Academy will soon be accepting applications for its summer program and community programming like the dinner at Guerrilla Cafecito where chefs Izard, Hong and Espinoza collaborate.

“It’s just fun to be back in the kitchen talking about our kids and talking about just being a female chef,” says Izard. “I would say we’re talking about balancing life, but that doesn’t exist.”

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