The E ﬀ ects of Fast Food Restaurant Attributes on Customer Satisfaction, Revisit Intention, and Recommendation Using DINESERV Scale

: The fast food restaurant business is one of the fastest-growing industries in the world. International and local restaurant chains are trying to satisfy the demands of customers for a variety of products and services. Along with changing market trends, customers are now becoming more sophisticated and demanding. Customer satisfaction is an essential business issue, as entrepreneurs have realized that favorable customer feedback is key for a long-term sustainable operation. Customers who have an excellent experience at a restaurant may recommend the restaurant to others, spread positive information, or become a loyal customer. The fast food industry has only recently developed in Mongolia and an increasing number of global fast food chains are now entering the market every year. The purpose of this paper is to examine and evaluate the factors a ﬀ ecting customer satisfaction, revisit intention, and likelihood of recommendation for Mongolian fast food restaurants, as well as a global fast food restaurant in Mongolia using the DINESERV scale. This study focuses on comparing directly competing food chains; only two brands were studied because of the limited fast food presence in Ulaanbaatar. Then, it aims to analyze how satisfaction levels inﬂuence a customer’s revisit intention and likelihood of recommending a restaurant. Furthermore, an in-depth analysis of the di ﬀ erence between local and global fast food brands is a key element that this paper analyzes. Moreover, this paper investigates how results can be di ﬀ erent according to whether the respondent resides in Mongolia or Korea and discusses business implications. The results of this paper show that four factors (food quality, service quality, price, and atmosphere of a restaurant) positively inﬂuence customer satisfaction, revisit intention, and likelihood of recommendation for Mongolian and global fast food restaurants, and customer satisfaction has a positive inﬂuence on customer revisit intention and likelihood of recommendation for both types of restaurants. However, depending on whether it is a Mongolian fast food restaurant or a global fast food restaurant, the factors a ﬀ ecting customer satisfaction, revisit intention, and recommendation are di ﬀ erent.


Introduction
The fast food restaurant business is one of the fastest-growing industries in the world and the global fast food market is expected to grow at a compound annual growth rate (CAGR) of 5.1% from 2020 to 2027 [1]. International and local restaurant chains are trying to satisfy the demand of customers for a variety of products and services. People prefer to use fast food restaurants for their convenience and to save time, and there have been certain changes in consumer trends that have increased the popularity of eating out; therefore, the fast food restaurant industry is growing rapidly.
The DINESERV is well known to be a reliable, relatively simple measurement tool for determining how consumers view a restaurant's quality [10]. Similar to SERVQUAL [11], DINESERV was developed by Stevens et al. [12] to assess customers' perceptions of restaurant service quality. In many previous studies, the DINESERV instrument has been used to investigate ways to improve a restaurant's quality and increase customer satisfaction, which, in turn, determines revisit intentions and likelihood of recommendation [10,[12][13][14].

Service Quality
Service quality is a measure of how well a service conforms to the customer's expectations and the success factor of a fast food restaurant [11]. Service quality is the main component of a fast food restaurant that can be measured and improved continuously. When there is a close interaction between a service employee and a customer, the perception of what is being delivered is as important as what is actually delivered [15]. In other words, it is the result of the comparison between expectations about service and perceptions of the way the service has been performed that customers make. Therefore, the employees' behaviors and attitudes can influence a customer's perception of quality for the service offered [16].

Food Quality
Food quality is an important component and it has been constantly shown to be a core value that a customer considers in deciding which fast food restaurant to eat at [17]. Many studies have investigated food quality characteristics, such as the freshness of food, food presentation, food taste, a variety of menu options, and food temperature. Food quality is considered to be a key foundation for customer satisfaction and customers' revisit intention [18][19][20]. According to Peri [21], food quality is an absolute requirement to satisfy the needs and expectations of restaurant customers. Youth-aged customers who mostly prefer eating delicious food require good quality food and beverages to satisfy them. Analysts have stated that the quality of menu items affects customers' revisit intention [22].

Atmosphere
Nowadays, people prefer to eat a lot more often. Customers are more aware of the atmosphere in which they are dining in than they were before. This requires restaurant owners to put more effort into designing and providing more comfortable surroundings for their customers. The atmosphere of a restaurant can be as important as the food itself [23]. The restaurant atmosphere is influenced by several factors, such as the interior design, temperature, cleanliness, music, and table arrangement.

Price
The price of a product or service can affect the level of satisfaction among customers because it has an associated sense of fairness. A customer's perceptions of unfair pricing lead to negative outcomes, such as a lower level of revisit intention, dissatisfaction, and negative word of mouth. The pricing of restaurant items also varies according to the type of restaurant. If the price is high, customers are likely to expect high quality, otherwise, it can induce a sense of being "ripped off." Likewise, if the price is low, customers may question the ability of the restaurant to deliver product and service quality. Moreover, due to the competitiveness of the restaurant industry, customers are able to establish internal reference prices. When establishing prices for a restaurant, an internal reference price is defined as a price in a buyer's memory that serves as a basis for judging or comparing actual prices [24].

Customer Satisfaction
Customer satisfaction is becoming a common goal for businesses. Customer satisfaction, as defined by Oliver [25], is the after-purchase judgment or evaluation of a product or service. It is also frequently described as the extent to which the chosen product meets or exceeds consumer expectations. It is, hence, a comprehensive domain that is the result of several inter-related variables impacting each other on an ongoing basis, rather than a single variable [23]. Customer satisfaction is an overall evaluation that compares post-purchase perceived performance with purchase expectations [26].
Taking the past as an example, when consumers decide to have a meal in a specific restaurant, they will have an expectation of how they will be served. After the meal, they will compare the serving experience with their level of anticipation. If the service quality the restaurant offered is equal to or higher than expected, they will be satisfied with this restaurant and likely come to the same restaurant again [27]. Based on this theory, customer satisfaction is the measure of the gap between a customer's expectations and perceived performance. Therefore, to enlarge the market segments in the restaurant industry, customer satisfaction is a powerful predictor of customer intent to repurchase [28].
In Qu's study, by analyzing data from Chinese restaurants in Indiana, it was found that the higher a customer's satisfaction with food and environment, service and courtesy, price and value, location, and advertising and promotion, the greater the likelihood of the customer returning [13]. Different from Qu's conclusion, Weiss et al. [29] found that customer revisit intention is only influenced by satisfaction with the restaurant food quality and atmosphere. Although dimensions used to estimate customer satisfaction in different studies have not been identical, the use of satisfaction as a determinant factor of customer revisit intention has been consistent across different studies. Many studies have identified factors that influence customer satisfaction, including service quality, variety of the menu, price, food quality, food presentation, ambience, and convenience.

Revisit Intention and Recommendation
When a company offers a product or service, it is possible that there are many similar products or services on the market provided by their competitors. Customers usually have many alternative choices. Therefore, it is important for companies to improve the value experienced by existing customers and take effective steps to encourage their repurchase behaviors, as well as attract new customers. Repeat customers are more profitable than new customers. Chen and Hu [30] described customer revisit intention as a customer's intention to revisit the same restaurant and recommend it to members of their circle. Customers that have an excellent experience at the restaurant will recommend the restaurant to others, spread positive information, or become a loyal customer. Customer revisit intention has been studied in many domains, such as tourism services, catering services, hospital services, retail business, bank services, and telecom businesses. A number of models of factors driving revisit intention have been constructed by means of structural equation modeling or logistic regression. The factors considered in these models include satisfaction, number of previous visits, cost, and customer value. Among the factors influencing revisits is customer satisfaction [27].

Research Model and Methodology
Like Kim and Choi [31], we investigated which factors-such as food quality, service quality, atmosphere and interior, and price and value-affect customer satisfaction, which, in turn, determines revisit intentions and likelihood of recommendation, using a factor analysis and a regression analysis.
There is a variety of measurement tools and techniques for assessing service quality. One of the most popular and widely used is the SERVQUAL (service quality) instrument [32]. In restaurant settings, service quality is usually measured with an adapted version of SERVQUAL, called DINESERV [12]. A modified version of DINESERV is applied in this study as well. The DINESERV is considered a reliable and relatively simple tool for determining how customers view a restaurant's quality [33] and has been used in many restaurant settings, such as fine dining [34], casual dining [35], fast food [36,37], food courts [38], and chain restaurants [39]. During the last two decades, SERVQUAL and DINESERV have been widely used to measure service quality in the hospitality industry [40]. The DINESERV tool was applied to assess managers' perceptions of service quality, and firms' financial reports were used to analyze operational efficiency and profitability [41].

Research Model
The DINESERV, constructed by Stevens et al. [12], consists of service-quality standards that fall into DINESERV factors: assurance, empathy, reliability, responsiveness, and tangibles. Kim et al. [42] found that the five restaurant factors-food quality, service quality, price and value, atmosphere, and convenience-had a significant impact on the customer's satisfaction with dining facilities [43]. Kim and Choi [31] used four factors-food quality, service quality, interior, and price and value-to investigate the perception gap of service attributes between operators and customers. Like Kim and Choi [31], we investigated how four factors-food quality, service quality, atmosphere and interior, and price and value-affect customer satisfaction, which, in turn, determines revisit intentions and likelihood of recommendation, using a factor analysis and a regression analysis. Table 1 shows the four service factors of restaurants used in this study and related results in previous studies. Overall satisfaction [10] Revisit Revisit [27,29] Recommendation Recommendation [30] Figure 1 shows the conceptual research model proposed to investigate how four service factors of restaurants, such as food quality, service quality, atmosphere, and price, affect the customer's satisfaction, revisit intention, and likelihood of recommendation.

Recommendation
Recommendation [30] Figure 1 shows the conceptual research model proposed to investigate how four service factors of restaurants, such as food quality, service quality, atmosphere, and price, affect the customer's satisfaction, revisit intention, and likelihood of recommendation.

Data Collection and Methods
Two of the most popular fast food restaurants in Ulaanbaatar were selected for the study: namely, Burger King and Berlin Burger. This study focused on comparing directly competing food chains; only two brands were studied. The limited fast food presence in Ulaanbaatar inevitably led to this limitation, but further research may take a more comprehensive approach of studying the entire fast food environment in Mongolia. Data were collected over a two-week period in October, 2018. The data analysis was based on 151 valid questionnaire responses collected through Google surveys. The period of data collection was short, and for this reason, only 151 questionnaires were returned.
Data were analyzed using the statistical package SPSS. A structured questionnaire using a five-point Likert scale was used to collect the data. The content of the questionnaire was divided into four sections. The first section, related to the respondent's demographic profile, included their age, gender, marital status, occupation, and income. The second section focused on how often the respondent eats out and what influences him/her to visit that restaurant. The third section measured the respondent's perceptions of the independent variables in the fast food restaurant: atmosphere, service quality, price, and food quality. A five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) was used to measure the customer's perceptions of the factors. This was intended to help respondents make their choice for each question. The fourth section measured the respondent's willingness to revisit and recommend the restaurant. The questionnaire consisted of 19 questions.

Demographic Profile of Respondents
A descriptive statistical analysis was run on respondents' demographic profiles. To gain a better understanding of the customer's level of satisfaction from fast food restaurants, the demographic characteristics of the respondents were analyzed. The results are shown in Table 2. The demographic data of respondents, including gender, age, marital status, occupation, and income, are shown in Table 2. The sample contained more females (53.6%) than males (46.4%). Almost 52 percent of them were aged between 26 and 40 years old. Ninety of the respondents (59.6%) lived in Mongolia and 61 of them (40.4%) lived in South Korea. In terms of occupation, 40.4% of them were students and 40.4% of respondents worked in the private sector. A total of 38.4% of the respondents had an annual income of more than 1,500,000 tugruk, and 27.2% of them had an income between 1,000,000 and 1,500,000 tugruk.  Table 3 shows the responses to how often the participants reported eating at the fast food restaurant. A total of 22.5% of Burger King customers reported eating out once a week, and 13.2% of them reported eating out once every two weeks. A total of 37.1% reported eating out once a month. The results also show that 7.3% of Mongolian fast food restaurant respondents reported eating out once a week, 10.6% reported eating out once every two weeks, and 30.5% reported eating out once a month. The results of the t-tests on food quality, service quality, atmosphere, customer satisfaction, revisit intention, and likelihood of recommendation for Mongolian and global fast food restaurants are shown in Table 4.  Table 4 shows that the mean scores were higher for Burger King than Berlin Burger in all groups-that is, whether the respondent resided in Mongolia or Korea, they preferred Burger King, a global fast food chain. This trend shows consistency over all items. The restaurant temperature (3.5828) and food taste (3.5629) had the highest mean scores for the global fast food restaurant, while the variety of menu options (3.0331) and portions (3.0265) had the highest mean scores for the Mongolian fast food restaurant. Discount had the lowest mean score for both restaurants: 3.0530 for the global fast food restaurant and 2.5099 for the Mongolian fast food restaurant. Burger King outperformed Berlin Burger most in the "atmosphere" factor. Moreover, for the "good portions" and "valuable price" items, the score between the two fast food chains was smallest. Berlin Burger seemed to have an advantage in price relative to other items; although the difference was statistically significant, it was also the smallest.
In addition, Table 4 exhibits a difference in perception scores depending on whether the respondent was living in Korea or Mongolia. Overall, respondents residing in Korea gave higher scores for all items. Moreover, they regarded Burger King's relative superiority over Berlin Burger less significant for most items; the score difference for the two fast food chains were smaller for this group. Such perception difference depending on the respondent's residence may be due to several factors, including experience of other global fast food chains and long term perception. The score difference between Burger King and Berlin Burger was relatively smaller for Korea-residing respondents, especially for freshness and taste of food, and less so for items such as music and feeling, temperature, clean dining areas, and well-trained employees. These findings suggest that the positive reputation of Burger King in atmosphere-related areas had a lasting effect in comparison to food-related items.
Furthermore, one can also infer that such differences may be due to the fact that Mongolians residing in Korea have greater accessibility to fast food and are more familiar with it; therefore, these respondents may generally have more lenient standards for judging fast food options. Such findings imply the following: first, the preference for Burger King over Berlin Burger owes greatly to perception differences for the "atmosphere" factor. This suggests that Berlin Burger should focus on enhancing customer experiences regarding service and ambience. This is especially important as these items seemed to have a greater lasting effect on customer perception. Furthermore, as Burger King's superiority in food quality was least significant, especially in the long-term perspective, it would be prudent for the brand to invest more resources in related areas.

Factor Analysis
The principal component analysis started with 16 items. After performing the principal component analysis with varimax rotation, the results revealed that the 16-item scale fell into four factors. All items had loadings of greater than 0.5, and there were no items that needed to be removed. Tables 5  and 6 show the results of the four factors for Burger King and Berlin Burger.  A KMO test is a measure of how suited the data are for factor analysis. The test measures the sampling adequacy for each variable in the model and for the complete model. If the value for the KMO test is greater than 0.50, then a factor analysis can be done for the same data set. It should also be significant at the 5% level and the p-value should be less than 0.05. Based on Table 5, it can be observed that the KMO measure was 0.942, which meant that the variables were suitable for factor analysis.
Cronbach's alpha was used to examine the internal reliability of the 16 items used to measure the four factors. The Cronbach's alpha varies from 0 to 1, and a value of 0.6 or less indicates unsatisfactory reliability. Table 5 shows that all factors had values exceeding 0.6. The service quality was measured by five items and had the highest alpha coefficient of 0.977. Atmosphere was measured by five items and had an alpha coefficient of 0.969. Price was measured by two items and showed the lowest alpha coefficient of 0.897. Table 6 shows that the KMO measure for Berlin Burger was 0.941, which meant that the variables used were suitable for factor analysis. It was also significant at the 5% level because the p-value was 0.000, which is less than 0.05. Table 6 also shows that the Cronbach's alpha exceeded 0.6 for all factors. Service quality was measured by five items and showed the highest alpha coefficient of 0.983. Price was measured by two items and showed the lowest alpha coefficient of 0.871.

Customer Satisfaction
A regression analysis was conducted to investigate the influence of the institutional DINESERV factors on customer satisfaction. Table 7 shows the results of the regression analysis with the four factors as independent variables and customer satisfaction as the dependent variable.  Table 7 shows the regression results indicating that four factors had significant and positive effects on customer satisfaction. The adjusted R square value of this model, which was a more conservative estimate of the variance by considering error variance, was found to be 0.824, indicating that 82.4% of customer satisfaction could be explained by the four independent factors. Thus, the explanatory power of the model was satisfactory. Moreover, the coefficients of the four factors were significant at the 1% level, suggesting a positive relationship between customer satisfaction and food quality, service quality, atmosphere, and price. In other words, all factors (food quality, service quality, price and value, and atmosphere) were found to be significant predictors affecting customer satisfaction. Meanwhile, the "atmosphere" variable was shown to have the highest standardized coefficient (β = 0.550, p < 0.01) with regards to customer satisfaction for the global fast food restaurant, which implied that atmosphere was the most influential factor for predicting customer satisfaction in the case of Burger King. Next was food quality (β = 0.457, p < 0.01), followed by atmosphere (β = 0.454, p < 0.01) and service quality (β = 0.332, p < 0.01). Table 8 indicates that the coefficients of the four factors were significant at the 1% level, suggesting a positive relationship between customer satisfaction and food quality, service quality, atmosphere, and price. The adjusted R square value of 0.768 indicated that 76.8% of the customer satisfaction could be explained by the four independent variables. The results showed that service quality was the highest standardized coefficient (β = 0.502, p < 0.01) of customer satisfaction for the Mongolian fast food restaurant, which implied that service quality was the most influential factor for Berlin Burger, followed by food quality (β = 0.441, p < 0.01), atmosphere (β = 0.415, p < 0.01), and service quality (β = 0.395, p < 0.01). All factors (food quality, service quality, price and value, and atmosphere) were found to be significant predictors affecting customer satisfaction for Berlin Burger.  Table 9 shows that the coefficients of the four factors were significant at the 1% level, suggesting a positive relationship between customer revisit intention and food quality, service quality, atmosphere, and price. The results indicated that price attained the highest beta coefficient, which implied that price was the most influential factor (β = 0.528, p < 0.01) in predicting customer revisit intention for the global fast food restaurant. The second highest was atmosphere (β = 0.475, p < 0.01), followed by food quality (β = 0.407, p < 0.01) and service quality (β = 0.275, p < 0.01). The adjusted R square value of 0.739 indicated that 73.9% of customer revisit intention was explained by the four factors. The results indicated that these factors had a positive impact on customer revisit intention.  Table 10 shows that the coefficients of the four factors for Berlin Burger were significant at the 1% level, suggesting a positive relationship between customer revisit intention and food quality, service quality, atmosphere, and price. The results indicate that service quality attained the highest standardized coefficient, which implies that service quality was the most influential factor (β = 0.517, p < 0.01) of customer revisit intention for the Mongolian fast food restaurant. The next was food quality (β = 0.416, p < 0.01), followed by price (β = 0.385, p < 0.01) and atmosphere (β = 0.377, p < 0.01). The adjusted R square value of 0.723 indicated that 72.3% of the customer revisit intention could be explained by the four independent variables. Research Question 2 asked whether food quality, service quality, atmosphere, and price have positive influences on the customer revisit intention. The results indicated that these factors had positive impacts on customer revisit intentions, supporting a positive answer to Research Question 2.  Table 11 shows that the coefficients of the four factors were significant at the 1% level, suggesting a positive relationship between customer likelihood of recommendation and food quality, service quality, atmosphere, and price. The regression results in Table 11 indicate that price attained the highest standardized coefficient, which implies that price was the most influential factor (β = 0.552, p < 0.01) affecting the likelihood of a customer recommending the global fast food restaurant. The next highest was atmosphere (β = 0.464, p < 0.01), followed by food quality (β = 0.406, p < 0.01) and service quality (β = 0.302, p < 0.01). The adjusted R square value of 0.770 indicates that 77% of the customer likelihood of recommendation could be explained by the four factors.  Table 12 indicates that the coefficients of the four factors were significant at the 1% level, suggesting a positive relationship between customer revisit intention and food quality, service quality, atmosphere, and price. The regression results indicate that service quality attained the highest standardized coefficient, which implies that service quality was the most influential factor (β = 0.464, p < 0.01) affecting the likelihood of a customer recommending the Mongolian fast food restaurant. The next highest was the price (β = 0.427, p < 0.01), followed by food quality (β = 0.396, p< 0.01) and atmosphere (β = 0.356, p < 0.01).  Table 13 indicates that all four factors (food quality, service quality, atmosphere, and price) had significant effects on customer satisfaction, revisit intention, and customer likelihood of recommendation. However, differences between the global fast food restaurant and Mongolian fast food restaurant were found in terms of t-values. The t-values of the food quality, atmosphere, and price for the global fast food restaurant were higher than those of the Mongolian fast food restaurant, and the t-value of service quality was lower than the t-value of the Mongolian fast food restaurant. The results answered research question 4, showing that the DINESERV factors and the customers' overall satisfaction level differed between the global and Mongolian restaurants.

The Relationship between Customer Satisfaction, Revisit Intention, and Recommendation
Research Question 5 asked whether there are any significant relationships among customer satisfaction, revisit intention, and likelihood of recommendation. The question can be interpreted through the following hypotheses. Hypothesis 1. Customer satisfaction will have a positive influence on customer revisit intention and customer likelihood of recommendation.

Hypothesis 2.
Customer revisit intention will have a positive influence on customer likelihood of recommendation.
In order to answer the research question about the association between customer satisfaction and revisit intention and likelihood of recommendation, the results of Pearson correlations for three variables are presented in Table 14. Customer satisfaction was found to have a strong, positive relationship (r1 = 0.907 for Burger King and 0.919 for Berlin Burger, p < 0.01) with revisit intention. This result indicates a strong relationship between the satisfaction level of the respondents with their revisit intention. Customer satisfaction was also strongly positively correlated (r1 = 0.909 for Burger King and 0.913 for Berlin Burger, p < 0.01) with likelihood of recommendation. This high correlation shows that high customer satisfaction leads to a high likelihood of recommendation. In addition, likelihood of recommendation was strongly correlated (r1 = 0.884 for Burger King and 0.915 for Berlin Burger, p < 0.01) with revisit intention. The finding implies that likelihood of recommendation helped to enhance the revisit intention of fast food restaurant customers. Thus, Hypothesis 1, customer satisfaction will have a positive influence on revisit intention, was supported (p < 0.000). The results show that customer satisfaction had a significant effect on customer revisit intention for both restaurants. Furthermore, the hypothesis that customer satisfaction will have a positive influence on customer likelihood of recommendation was supported (p < 0.000) for both restaurants. Table 14 shows that the level of satisfaction had an effect on the global fast food restaurant and the Mongolian fast food restaurant. However, the t-values of revisit intention and likelihood of recommendation for the Mongolian fast food restaurant were higher than those for the global fast food restaurants. This means that customer satisfaction influenced customer revisit intention and likelihood of recommendation for the Mongolian fast food restaurant to a greater extent. Moreover, Hypothesis 2, customer revisit intention will have a positive influence on customer likelihood of recommendation, was supported (p < 0.000) for both restaurants.

Discussion and Implications
This research delves into the competitive advantages of global and local food chains for the sustainability of the Mongolian fast food industry, including suggestions regarding which factors are relatively more essential from the management perspective. Both the t-test and regression analysis show that the "atmosphere" factor, which includes music, comfortable seats, cleanliness, interior, and temperature, is a key asset of global franchises. Although brand perceptions of the global food chain were more positive than the local brand for all categories, both score differences and regression coefficients imply that atmosphere is the global food chain's prime advantage over local ones. Furthermore, the results of this study show that this factor is especially important, since it seems to have an enduring effect on positive customer perceptions. These findings relay significant business implications; in order for local food chains to thrive, they must invest more resources into enhancing customer experiences related to the "atmosphere" category, and should continue to achieve comparative advantages in "service quality," which was found to be the key element in predicting customer satisfaction for the local food chain.
This study also provides evidence regarding perception differences between customer groups that have greater accessibility to various franchises and those that have not. Compared to South Korea, residents of Mongolia have less experience in global food chains as the introduction of such franchises is very recent, and the simple presence of stores in Seoul and Ulaanbaatar is incomparable. Such environmental differences lead to different customer perceptions; customers residing in Korea appear to harbor more positive attitudes towards fast food in general, as scores for both franchises were higher than for respondents residing in Mongolia. Furthermore, the results show a difference in perception scores depending on whether the respondent was living in Korea or Mongolia. The score difference between Burger King and Berlin Burger was relatively smaller for Korea-residing respondents, especially for freshness and taste of food, and less so for items such as music and feeling, temperature, clean dining areas, and well-trained employees. These findings suggest that the positive reputation of Burger King in atmosphere-related areas has a lasting effect in comparison to food-related items. This suggests that Berlin Burger should focus on enhancing customer experiences regarding service and ambience. Furthermore, as Burger King's superiority in food quality is least significant, especially in the long-term perspective, it would be prudent for the brand to invest more resources in related areas.

Conclusions
This study was conducted to gain a better understanding of customer satisfaction in restaurants by studying the factors of food quality, service quality, atmospherics, and price. It fulfilled its aims of identifying the relationships among the four variables with customer satisfaction, which leads to revisit intention and likelihood of recommendation. The results show that all four factors (food quality, service quality, price, and atmosphere of a restaurant) positively affect customer satisfaction, revisit intention and likelihood of recommendation for the global fast food and Mongolian restaurants. Moreover, the results show that customer satisfaction will have a positive influence on revisit intention and likelihood of customer recommendation for both restaurants. However, depending on whether it is a Mongolian fast food restaurant or a global fast food restaurant, the level of factors affecting customer satisfaction were different. For a global fast food chain, the restaurant atmosphere was considered the most important factor influencing customer satisfaction. Price was also considered the most important factor for customer revisit intention and likelihood of recommendation. This shows that customers of global fast food restaurants in Mongolia consider atmosphere to be the most important factor for customer satisfaction, revisit intention, and likelihood of customer recommendation. Thus, managers of global fast food restaurants in Mongolia should pay attention to the polite behavior of staff and whether there is a comfortable atmosphere, which, in turn, enhances customer satisfaction. For the Mongolian fast food restaurant, service quality was considered the most important factor influencing customer satisfaction, revisit intention, and likelihood of customer recommendation. Food quality was the second most important factor affecting customer satisfaction and revisit intention, and price was the second most important factor affecting customer recommendation. This means that customers of the Mongolian fast food restaurant put more focus on service quality and food quality. Thus, restaurant owners need to make a constant effort to improve service quality and offer delicious meals at valuable prices to their customers.
This study also showed a difference in perception scores depending on whether the respondent was living in Korea or Mongolia. Overall, respondents residing in Korea gave higher scores for all items. They regarded Burger King's relative superiority over Berlin Burger less significant for most items. The score difference between Burger King and Berlin Burger was relatively smaller for Korea-residing respondents, especially for freshness and taste of food, and less so for items such as music and feeling, temperature, clean dining areas, and well-trained employees. These findings suggest that the positive reputation of Burger King in atmosphere-related areas has a lasting effect in comparison to food-related items. One can also infer that such differences may be due to the fact that Mongolians residing in Korea have greater accessibility to fast food and are more familiar with it; therefore, these respondents may generally have more lenient standards for judging fast food options.
This study aimed to investigate factors affecting customer satisfaction, revisit intention, and recommendations for fast food restaurants in Mongolia. As the industry has only recently developed, research is currently lacking in the field. This paper provides a starting point in research in the Mongolian fast food market, as well as customer perceptions. Furthermore, this study conducts an in-depth analysis of the difference between local and global fast food chains, especially depending on whether the customer resides in a fast food-friendly environment or not. The findings in this study also lead to important managerial implications regarding competitive advantages.
There are some limitations associated with this study. First, the sample size could have been larger. The period of data collection was short, and for this reason, only 151 questionnaires were returned. This suggests that the research should take place over a couple of months. Secondly, as our study focused on comparing directly competing food chains, only two brands were studied. The limited fast food presence in Ulaanbaatar inevitably led to this limitation, but further research may take a more comprehensive approach of studying the entire fast food environment in Mongolia. Further studies may also examine the restaurant service quality for a particular type of restaurant using a larger sample size or using different sets of factors for each type of restaurant. An extension of the study range and inclusion of other study methodologies to analyze the restaurant service quality may also develop the implications of this study.