Proposing a Restaurant Preference Behavior Model ...

Interdisciplinary approach to quality, ed. P. Nowicki & T. Sikora , Towarzystwo Naukowe PTTŻ & Cracow University of Economics, Cracow 2015, pp. 51-65.

Dziadkowiec Joanna1, Rood A. Scott2 1 Cracow University of Economics, Poland 2 Grand Valley State University, USA

PROPOSING A RESTAURANT PREFERENCE BEHAVIOR MODEL (RPB) Key words: restaurant preferences, restaurant related lifestyles, cross cultural analysis, RPB model Abstract The purpose of this paper is to build a model to support the research of consumer preferences, behaviors, and feelings regarding restaurants. Based on synthesizing existing methods a new tool is proposed: “Restaurant Preference Behaviors” (RPB). A preliminary model was built and tested on two populations. Findings indicate the model successfully grouped questions from a variety of previously used and verified methods. The model was able to identify one universal group of preferences regarding casual dining restaurants. The model also showed applicability across two different national populations, indicating the similarities and differences between them. Hence, the tool can be applied in practice to investigate customer preferences regarding casual dining restaurants.

Introduction The restaurant purchase experience can be thought of as a unique bundle of tangible and intangible products and services provided to the consumer1. The study of consumer behavior illuminates this process. In order to understand consumer behavior constructs such as personality, attitude and lifestyle must be investigated 2. Consumers’ opinions and interactions with service environments are vital and directly relate to one’s willingness to spend3. Cullen suggests that consumer’s respond effectively to alternative ways of satisfying wants; establishing positive or negative attitudes toward the new product and their

1

Harrington R., Ottenbacher C., Kendall, K., Fine-Dining Restaurant Selection: Direct and Moderating Effects of Customer Attributes, Journal of Foodservice Business Research, 2011, Vol. 14(3), 272-289. 2 Foxall G.R., Goldsmith R.E., Consumer psychology for marketing. New York: Routledge 1994. 3 Kim D., Park S., Customers' Responses to Crowded Restaurant Environments: Cross-Cultural Differences Between American and Chinese, Journal of Hospitality & Leisure Marketing, 2008, Vol. 16 (1-2), 137-157.

Interdisciplinary approach to quality edited by P. Nowicki and T. Sikora.

decision to consume. Values constitute the most abstract level of cognition, influencing perception and evaluation4. Sholderer et al. suggests values are universal in the sense that the same values are pursued around the world5. The consumer is cognitively engaged in responding to information received and modifying their beliefs6. The Food Related Lifestyle instrument7 uses a means-end theoretical approach wherein distinct parts of consumers’ cognitive structures reflect the following life domains: ways of shopping, cooking methods, quality aspects, consumption situations and purchasing motives8. This paper reports the findings of a study that was conducted to identify consumer preferences towards the casual dining experience. The purpose of this study is to build a model to support research of the consumer preferences, behaviors and feelings regarding these restaurants. The new model is derived from several existing food-related lifestyle models and tools of data collection concerning consumer behavior with respect to casual dining restaurants. Principal component analysis (PCA) was used to identify correlation and reduce the number of variables. To control its usefulness and applicability the model was tested on two different populations. Based on synthesizing existing methods a new tool is proposed: “Restaurant Preference Behaviors” (RPB). 1. Literature Review Consumer preferences have been considered a major factor when designing marketing strategies aimed at developing global appeal for consumers, or life-style products9. Studies in product development include identification of

4

Cullen F. (2004). Factors influencing restaurant selection in Dublin. Journal of Foodservice Business Research, 7(2), 53-85. 5 Scholderer J., Brunsø K., Brendahl L., Grunert, K., Cross-cultural validity of the food-related…, 197–211. 6 Cullen F., Kingston H., Analysis of Rural and Urban Consumer Behavior Toward New Food Products Using a Food-Related Lifestyle Instrument. Journal of Foodservice Business Research, 2009, Vol. 12(1), 18-41. 7 Brunsø K., Grunert K., Development and Testing of a Cross-Culturally Valid Instrument: Food – Related Lifestyle. Advances in Consumer Research, 1995, Vol. 22, 475-480. 8 O’Sullivan C., Scholderer J., Cowan C., Measurement equivalence of the food related lifestyle instrument (FRL) in Ireland and Great Britain, Food Quality and Preference, 2005, Vol. 16 (1), 112. 9 Buzzell R., Can you standardize multinational marketing? Harvard Business Review, 1968, November-December, 98-104. 52

Dziadkowiec J., Rood A.S., Proposing a restaurant…

consumers segments and the evaluation of their liking patterns10. Sahmer et al. used cluster analysis to segment consumers according to their scores of liking11. To be able to perceive trends regarding consumer preferences is an important input into restaurant decision-making. However, understanding preferences with respect to restaurants is difficult because of the complexity of the service experience. For example, which of the following complex functions are more important: the provision of food, social interactions, or a variety of other lifestyle measures? Information about consumers’ preferences is relevant both for improvement and for the development of new products. June and Smith developed a model of consumer choice behavior and illustrate the effectiveness of their model through decisions made about a restaurant meal12. Chen and Hu found that the restaurant experience is determined by various attributes and the importance of these attributes for customers13. Many studies confirmed that the most important attributes for customers are food-related such as taste, nutritional content, healthy choice, or sensory properties14. But prior studies also showed that non-food attributes like atmospheric elements, service quality, and price and value also influence customer preferences and behaviors15 analyzed preferences towards restaurant environment consisting 10

Piccolo D., D’ Elia A., A new approach for modeling consumers’ preferences, Food Quality and Preference, 2008, Vol. 19, 247–259. 11 Sahmer K., Vigneau E., Quannari E., A cluster approach to analyze preference data: Choice of the number of clusters, Food Quality and Preference, 2006, Vol. 17, 257–265. 12 June L., Smith S., Service Attributes And Situational Effects On Customer Preferences For Restaurant Dining , Journal of Travel Research, 1987, Vol. (26), 20-27. 13 Chen P., Hu H., How determinant attributes of service quality influence customer-perceived value: an empirical investigation of the Australian coffee outlet industry, International Journal of Contemporary Hospitality Management, 2010, Vol. 22, 535–551. 14 Rozin P., Tuorila H., Simultaneous and temporal contextual influences on food acceptance, Food Quality and Preference, 1993, Vol. 4 (1), 11–20; Namkung Y., Jang S., Does food quality really matter in restaurants? Its impact on customer satisfaction and behavioral intentions, Journal of Hospitality & Tourism Research, 2007, Vol. 31 (3), 387–409; Howlett E.A., Burton S., Bates K., Huggins K., Coming to a restaurant near you? Potential consumer responses to nutrition information disclosure on menus, Journal of Consumer Research, 2009, Vol. 36 (3), 494– 50; 15 Sulek J.M., Hensley R.L., The relative importance of food, atmosphere, and fairness of wait: the case of a full-service restaurant, Cornell Hotel and Restaurant Administration Quarterly, 2004, Vol. 45 (3), 235–247; Kim W.G., Ng C.Y.N., Kim Y., Influence of institutional DINESERV on customer satisfaction, return intention, and word-of-mouth, International Journal of Hospitality Management, 2009, Vol. 28 (1), 10–17; Ha J., Jang S.S., Effects of service quality and food quality: the moderating role of atmospherics in an ethnic restaurant segment, International Journal of Hospitality Management, 1010, Vol. 29 (3), 520–529; Dziadkowiec J., Rood A.S., 53

Interdisciplinary approach to quality edited by P. Nowicki and T. Sikora.

of physical, social, and ambient dimensions16. Liu and Jang (2009) found that environmental elements such as interior design, ambience, and spatial layout influence customer emotions17. There are other important non-food attributes related to service. One such attribute is price, which is a major marketing element and a common strategy to increase market share (Mohammed et al., 2005). The inclusion of price in customer’s evaluations of service leads to a cognitive judgment of perceived value which may have a significant influence on satisfaction, and in turn, affect post-purchase behavior (Tarn, 1999). Han & Ryu (2008) found that overall service quality is an antecedent of customer service, and customer service is a significant predictor of repeat visit intention and word of mouth intention. In addition, they found that the strength of these relationships is strongly influenced by personal characteristics. Preferences are not always based on specific attribute-by-attribute comparisons. In some cases an overall evaluation or attitude-based strategy is used involving impressions and intuitions. To investigate food preferences Brunsø and Grunert developed the food related lifestyle (FRL) instrument18. It comprises a 69-item questionnaire measuring 23 lifestyle dimensions in five major life domains, including ways of shopping, cooking methods, quality aspects, consumption situations and purchasing motives. The FRL is one of the most elaborate segmentation tools in the field of food research as it measures how people link food to the attainment of life-values19. The FRL instrument was the first lifestyle survey constructed with a theoretical foundation consistent with the means-end approach to consumer behavior20. The instrument uses a multi-attribute approach where consumers base decisions upon the assumption that quality is a multidimensional phenomenon21. Wykorzystanie metody Mystery shopping do badań porównawczych usług świadczonych przez restauracje (na przykładzie Polski i USA), Problemy Jakości, 2010, Vol. 11, 37-42. 16 Baker J., The role of the environment in marketing services: the consumer perspective, in: Czepeil J.A., Congrarn C.A., Shanahan J., (Eds.), The Service Challenge: Integrating for Competitive Advantage, American Marketing Association, Chicago, IL, 1986, 79–84. 17 Liu Y., Jang S.S., Perceptions of Chinese restaurants in the US: what affects customer satisfaction and behavioral intentions?, International Journal of Hospitality Management, 2009, Vol. 28 (3), 338–348. 18 Brunsø K., Grunert K., Development and Testing of a Cross-Culturally…475-480. 19 Wycherley A., McCarthy M., Cowan C., Speciality food orientation of food related lifestyle (FRL) segments in Great Britain, Food Quality and Preference, 2008, Vol. 19, 498-510. 20 Olson J. C., Reynolds T. J., Understanding consumers ‘cognitive structures: Implications for advertising strategy, in L. Percy & A. G. Woodside (Eds.), Advertising and consumer psychology. Lexington, MA: Lexington Books, 1983. 21 Grunert K. G., What’s in a steak? A cross-cultural study on the quality perception of beef. Food Quality and Preference, 1997, Vol. 8, 157–174. 54

Dziadkowiec J., Rood A.S., Proposing a restaurant…

The FRL was constructed with the explicit aim of being applicable to a broad range of (western) food cultures22, defined as ‘‘a culinary order whose traits are prevalent among a certain group of people’’23. A limited number of dimensions can be replicated across nations, enabling a comparison of cultures on the basis of same dimensions, and allowing for different positions of culture on those dimensions24. Despite the focus of prior research on consumer behavior preferences and the FRL, the specific nature of the relationship between FRL and restaurant preferences has not been examined, remains unclear and is one goal of the present study. A number of studies have looked at the influence of brand on consumer preferences. Research by Aaker empirically identified five dimensions of brand personality: competence, sincerity, excitement, sophistication, and ruggedness25. Studies have shown that well-established brands attract increased preference and usage26. If a meaningful and consistent brand personality that fits with the right target market can be created and established, it can result in increased preference and usage. Kim et al. perceived brand heterogeneity was found to enhance the effect of brand preference27. Nam et al. investigated attitudinal loyalty and posit brand loyalty as the consumer’s intention to visit or willingness to recommend the hotel or restaurant brand28. Murase and Bojanic examined the differences in restaurant brand personality across cultures. They found little cultural differences in the perception of brand personalities29. 2. Methodology Stage 1 – Selection of questions

22

O’Sullivan C., Scholderer J., Cowan C., Measurement equivalence…, 1-12. Askegaard S., Madsen T. K., The local and the global: Exploring traits of homogeneity and heterogeneity in European food cultures. International Business Review, 1998, Vol. 7, 549–568. 24 O’Sullivan C., Scholderer J., Cowan C., Measurement equivalence…1-12. 25 Aaker, J. L., Dimensions of brand personality, Journal of Marketing Research, 1997, Vol. 34, 347–356. 26 Sirgy J. M., Self-concept in consumer behavior, Journal of Consumer Research, 1982, Vol. 9, 287-300; East R., Consumer behavior: Advances and applications in marketing. Hemel Hempstead, England: Prentice Hall, 1997. 27 Kim W., Ok C., Canter D., Contingency variables for customer share of visits to full-service restaurant, International Journal of Hospitality Management, 2010 Vol. 29, 136-147. 28 Nam J., Ekinci Y.,Whyatt, G., Brand Equity, Brand Loyalty and Consumer Satisfaction, Annals of Tourism Research, 2011, Vol. 38 (3), 1009–1030. 29 Murase H., Bojanic D., An Examination of the Differences in Restaurant Brand Personality Across Cultures, Journal of Hospitality & Leisure Marketing, 2004, Vol. 11, (2-3), 97-113. 23

55

Interdisciplinary approach to quality edited by P. Nowicki and T. Sikora.

After a systematic critical literature review, a preliminary model for testing restaurant’s consumer preferences and behaviors was built. Based on a review of the extant literature, 50 questions were extracted regarding restaurants and certain elements regarding food consumption habits. This was done by using an expert method. The questions selected were: 16 from Scholderer et al.30, 16 from Kim et al.31, 7 from Wycherley et al.32,, and 11 from Rood & Dziadkowiec33 (Fig. 1). All construct measures were obtained from scale items used in these 4 previous empirical studies. All items used a five-point Likert scale that was placed predominantly on the survey instrument as “strongly agree” (5), “agree” (4), “neither agree nor disagree” (3), “disagree” (2), and “strongly disagree” (1).

Fig. 1. The sources of questions in Restaurant Preference Behavior Model Source: own research Stage 2 - question grouping and renumbering The objective of this stage was to verify whether the questions are not repeated. As a result, 43 questions have been divided into 7 constructs. These 30

Scholderer J., Brunsø K., Brendahl L., Grunert, K., Cross-cultural validity of the foodrelated…197–211. 31 Kim W., Ok C., Canter D., Contingency variables for… 32 Wycherley A., McCarthy M., Cowan C., Speciality food orientation of food related…, 498-510. 33 Rood A.S., Dziadkowiec J., Why use Importance Performance Analysis in Mystery Shopping? A USA - Poland comparative answer, Journal of Quality Assurance in Hospitality and Tourism, 2010, Vol. 11(1): 1-17.

56

Dziadkowiec J., Rood A.S., Proposing a restaurant…

questions include relevant aspects regarding preferences and behaviors identified based on the literature review34. The identified aspects have been randomly placed in the questionnaire, avoiding thematic grouping in order to increase the credibility of the results. The purpose of this exercise was to achieve the maximum independent answers for each question; without suggesting which group of attributes (constructs) should be associated with a particular question. Stage 3 – testing the research model A unified demographic segment was selected from American hospitality and tourism management students and Polish hospitality and tourism management and quality management students. These segments were selected because they have already shaped their preferences, and are shaping their consumer behaviors. The next step was to conduct the research in practice. The model was tested via a survey which was completed by over 900 students. These samples have been selected also out of convenience, since the researchers come from these countries and therefore they understand better the specificity of their cultures. In addition, Rood and Dziadkowiec demonstrated that populations from these two countries have different expectations regarding certain aspects of services provided by casual dining restaurants. It has also been assumed that there are major differences between these two populations regarding preferences and behaviors with respect to the restaurants, because the populations represent different continents35. Stage 4 – creating the final version of research model Statistical analyses were applied to identify relationships between variables and to determine the optimal number of questions within the RPB model. The major goal of this stage was to reveal any hidden structures in the collected data sets and to show that the final model is suitable in cross-cultural comparisons.

34

The full set of questions is presented in: Dziadkowiec J., Rood A.S., Casual-Dining Restaurant Preferences: A Cross-Cultural Comparison, Journal of Foodservice Business Research, 2015, Vol. 18 (1), 73-91. 35 Rood A.S., Dziadkowiec J., Why use Importance Performance Analysis… 1-17.

57

Interdisciplinary approach to quality edited by P. Nowicki and T. Sikora.

3. Results and discussion The first step was verifying the survey’s internal consistency and reliability. Because of the relatively low internal consistency, especially with the American respondents, 7 variables were eliminated. Therefore the applied measurement tool consisted of 43 questions. Cronbach’s Alpha co-efficient for the Polish population was 0.78; for the American population is 0.65. Univariate analysis was performed in order to compare the two populations with respect to 43 questions independently. The groups of respondents were compared in terms of the average value of their responses. Probability value (p-value) was computed for the null hypothesis which assumes that the means of two populations are equal. For this purpose, a t-test was performed for two independent samples. Standard deviations were calculated. To test the null hypothesis with regard to equality variance in the two populations, the F-test was employed. The p-value was calculated as the level of statistical significance at which a tested hypothesis can be rejected. Typically the null hypothesis is rejected if the p-value is lower than 0.05. However, at the same time 43 null hypotheses are tested, so the probability of rejection of one of them is greater than 0.05. In order to eliminate such a situation, a multiple comparison correction has been applied, also called the Bonferroni correction, which divides the limit of test probability (0.05) by the number of tests (50). It indicates that the null hypothesis should be rejected if the p-value is lower than 0.001. A similar procedure was employed for testing variance equality. The values fulfilling the conditions are shown. For these 24 there is a statistically significant difference between two populations. A similar procedure was employed for testing variance equality. The next stage of descriptive analysis was to calculate correlations between the selected pairs of variables. Figure 2 shows variables with high level of correlation (the absolute value of the correlation coefficient is higher than 0.5). Pairs of variables connected by lines represent such correlations.

58

Dziadkowiec J., Rood A.S., Proposing a restaurant…

Poland

United States

Fig. 2. Correlation analysis for Polish and American samples Source: own research In both diagrams questions 9, 15, 17, 20, 23, 29, 31, 32, 44, 45 and 46 have significant connection with at least one or more question. It can be further analyzed whether the relationship between the questions are similar for the two countries. The populations differ with respect to preferences and attitudes towards casual restaurants. However, it is worthy to note that 44% of the questions are answered very similarly. The fewest differences between populations are observed in group “Brand Preference” as more than 50% of the answers are the same. The highest number of differences is within group “Attitudes towards advertising”, and group “Branded vs. Independent”. Question groups were compared between populations. Analysis shows that there are significant correlations between questions in different groups and that these correlations are different in both populations. With respect to the United States, the network relationship is denser than that of Poland (Fig. 2). The US population is characterized by more questions with a high number of correlations: 7 questions have been identified as correlated with at least 5 other questions; in Poland, there are only 2 questions fulfilling this condition. This indicates that the initial grouping of the questions, based on the literature review, changed after data analysis.

59

Interdisciplinary approach to quality edited by P. Nowicki and T. Sikora.

4. Cross cultural comparisons Polish respondents go to restaurants less often than the American ones. The majority of American respondents (µUS(Q17)=3.19) reported that “going out for dinner is a regular part of their eating habits”, while not many Polish respondents accepted this statement (µPL(Q17)=2.21). Similarly, fewer respondents from Poland (µPL(Q8)=2.19, µUS(Q8)=3.23) confirmed that “dining out is their routine behaviour”. Standard deviation did not show any significant difference, which means that both populations were relatively homogenous. Americans strongly agreed with the statement that they “enjoy going to restaurants with my family and friends” (µUS(Q50)=3.19), while Polish respondents expressed lower preference towards spending their free time this way (µPL(Q50)=3.97). Polish respondents “meet with their friends to eat dinner together” less often compared with their American counterparts (µPL(Q21)=2.86, µUS(Q21)=3.36 ). Fewer respondents in Poland than in America think that “dining out is an important part of their social life” (µUS(Q21)=3.40, µPL(Q21)=3.79 ). Polish respondents have less experience dining out in restaurants, which may explain the differences when looking for a restaurant that meets their requirements. Despite the fact that more Americans than Polish respondents agreed with the statement that “service quality varies a lot among different casual dining restaurants” (µPL(Q14)=3.51, µUS(Q14)=3.88 ), Polish respondents seem to have more problems than Americans identifying a suitable restaurant. “It is too much trouble to find an acceptable casual dining restaurant” (µPL(Q6)=3.01, µUS(Q6)=2.11 ); “It is hard to find a good casual dining restaurant that meets my expectations” (µPL(Q10)=3.10, µUS(Q10)=2.49 ); and “Searching for an acceptable casual dining restaurant is too much trouble in terms of time and efforts” (µPL(Q45)=3.04, µUS(Q45)=2.37 ). Polish and American consumers have different attitudes towards the advertisement of products and services as Americans agree that “the advertisement helped to make better buying decisions” (Q1) (µUS(Q1)=3.45 ) and “have more trust in the advertised products” (µUS(Q19)=3.37). Conversely, Polish respondents displayed much lower level of acceptance with these statements (µPL(Q1)=2.88, µPL(Q19)=3.15 ). There are also large differences within the Polish sample with question (σPL(Q19) = 3.37 ), suggesting that they are consumers with different attitudes towards advertisements of products. With behaviors related to “trying new foods / dishes” (Q9), both groups declared similar and high levels of interest (µPL(3.66, µUS=3.66). However, within the populations statistically significant differences are observed as the American group exhibits much more internal homogeneity (σUS (Q19) = 0.87) and the Polish 60

Dziadkowiec J., Rood A.S., Proposing a restaurant…

group exhibiting the biggest dispersion (σPL(Q19)=1.06). At the same time, Americans expressed much stronger agreement than Polish respondents with the statement that they prefer “doing new things” than “doing familiar things” (µUS(Q20) = 3.26, µPL(Q21)=3.0), while Polish respondents expressed much stronger agreement than American respondents that they “dislike everything that might change their eating habits” (µPL(49)=2.39, µUS(Q06)=2.06). This suggests that the US respondents are generally more open towards new culinary experiences. Yet at the same time they “prefer consistency more than change”, and fewer Polish respondents agreed with the statement that they “love to try recipes from foreign countries” (µPL(32)=3.74, µUS(Q32)=3.46). Both groups of respondents “are much eager to try new dishes in their favourite restaurants”, but with Polish respondents the mean value was significantly higher than the American score. Respondents in both groups confirmed that taste of food is very important for them. They strongly agreed with the statements: “I enjoy a good meal” (Q3) (µPL(Q3)=4.53, µUS(Q3)=4.79), and “Enjoying the taste of food is important to me when I am eating” (Q12) (µPL(Q12)=4.65, µUS(Q12)=4.59). Statistically significant differences are demonstrated between the populations, however the level of acceptance of these statements is very high in both populations. In the Polish population, high diversity can be noticed in the Q3 (σPL(Q3)=0.9)), while in American group it is very low (σPL(Q3)=0.48). The results of the preference survey concerning attitudes towards branded versus independent restaurants confirmed that respondents in both countries differ in terms of attitudes and preferences towards branded vs. independent restaurants. Polish respondents rather agreed with the statement “that food in branded restaurants is usually cheaper that in the independent ones”, while American participants did not have preferences in this respect (µPL(Q26)=3.49, µUS(Q26)=3.09). However, the responses to the opposite of this statement showed inconsistency in both populations (µPL(Q39)=3.46, µUS(Q39)=2.76), a finding that will require further analysis. A significant difference was observed with respect to the acceptance of the statement “I usually prefer to go to branded restaurant than to the independent one”. Although both populations rather disagreed with this statement the lack of acceptance was much higher with American respondents (µPL(Q11)=2.84, µUS(Q11)=2.59). In order to confirm the reliability of the proposed tool additional analysis were conducted. Cross-cultural comparisons showed that American and Polish consumers have different preferences in five of the seven construct measures: preferences toward chain and independent restaurants, attitudes to advertising, quality aspects of food perception, perceptions of procedural costs and social

61

Interdisciplinary approach to quality edited by P. Nowicki and T. Sikora.

event relationships36. Culture, gender and individual differences analysis confirmed that preferences toward restaurants are culture specific and vary between two surveyed cultures37. However, the most important distinction seems to be differences among customers in preferences that are individualistic, rather than explained by culture or gender based. 5. Developing the RPB model The next step of analysis was to detect the structure of variables and find out if it is possible to reduce the number of questions. For this purpose principal component analysis (factor analysis) was implemented. Initially the Kaiser– Meyer–Olkin (KMO) measure was used to verify the adequacy of the sample for analysis. The results of that measure were satisfactory – 0,75 for Poland and 0,78 for the US. The criteria for the number of factors to be extracted were based on eigenvalues, percentage of variance, significance of factor loading, and assessment of the structure. Factors with an eigenvalue greater than 1 were considered significant. A variable was considered to be of practical significance and included in a factor when its factor loading was equal to or greater than 0,50. The final RPB model consists of 30 variables. Five factors influencing preferences toward casual dining were extracted: “Trying new food in restaurant”, “Dining out as social events”, “Dining out behaviors”, “Attitudes toward branded/chain restaurants” and “Procedural costs” (Fig. 3). Conclusions There are useful existing tools to investigate satisfaction from services provided by restaurants, which indirectly include the research of preferences. There are also tools for studying consumer behaviors, taking into account restaurant preferences at different forms and levels. However, the proposed RPB model provides a new ability to study restaurant preferences specifically. The multi-stage procedure of creating the RPB model determined an optimal number of variables and determined the structure of variables. Questions from a variety of previously used and verified models were successfully grouped. 36

Dziadkowiec J., Rood A.S., Casual-Dining Restaurant Preferences…, 73-91, Rood A.S., Dziadkowiec, J., Examining the importance of Culture, Gender and Individual Differences in Customers, European Journal of Tourism, Hospitality and Recreation, 2014, Vol. 5(2): 137-152. 37

62

Dziadkowiec J., Rood A.S., Proposing a restaurant…

Fig. 3. Restaurant Preferences Behavior Model (RPB) Source: own research

This study shows the compilation of the selected models makes sense; it was possible to identify a wide range of preferences regarding casual dining

63

Interdisciplinary approach to quality edited by P. Nowicki and T. Sikora.

restaurants. Factor analysis identified low correlated questions that were removed and the variables were appropriately regrouped. The RPB model can aid restaurant managers by determining the preferences of existing and potential customers. The model also shows applicability across two different national populations, showing how the populations differ. The tool can now be applied in practice to investigate customer preferences regarding casual dining restaurants. The model’s statistical output provides a correlation analysis of particular factors influencing customer preferences, in order to better meet customer expectations. It can identify client groups with different expectations and examine various factors that influence preferences within these sub-groups. Finally, detailed analysis of selected areas of customer preferences, enabling optimization of the service can be pursued by using conjoint analysis. References [1] Aaker, J. L., Dimensions of brand personality, Journal of Marketing Research, 1997, Vol. 34, 347–356. [2] Askegaard S., Madsen T. K., The local and the global: Exploring traits of homogeneity and heterogeneity in European food cultures. International Business Review, 1998, Vol. 7, 549– 568. [3] Baker J., The role of the environment in marketing services: the consumer perspective, in: Czepeil J.A., Congrarn C.A., Shanahan J., (Eds.), The Service Challenge: Integrating for Competitive Advantage, American Marketing Association, Chicago, IL, 1986, 79–84. [4] Brunsø K., Grunert K., Development and Testing of a Cross-Culturally Valid Instrument: Food – Related Lifestyle. Advances in Consumer Research, 1995, Vol. 22, 475-480. [5] Buzzell R., Can you standardize multinational marketing? Harvard Business Review, 1968, November-December, 98-104. [6] Chen P., Hu H., How determinant attributes of service quality influence customer-perceived value: an empirical investigation of the Australian coffee outlet industry, International Journal of Contemporary Hospitality Management, 2010, Vol. 22, 535–551. [7] Cullen F. (2004). Factors influencing restaurant selection in Dublin. Journal of Foodservice Business Research, 7(2), 53-85. [8] Cullen F., Kingston H., Analysis of Rural and Urban Consumer Behavior Toward New Food Products Using a Food-Related Lifestyle Instrument. Journal of Foodservice Business Research, 2009, Vol. 12(1), 18-41. [9] Dziadkowiec J., Rood A.S., Casual-Dining Restaurant Preferences: A Cross-Cultural Comparison, Journal of Foodservice Business Research, 2015, Vol. 18 (1), 73-91.

64

Dziadkowiec J., Rood A.S., Proposing a restaurant… [10] Dziadkowiec J., Rood A.S., Wykorzystanie metody Mystery shopping do badań porównawczych usług świadczonych przez restauracje (na przykładzie Polski i USA), Problemy Jakości, 2010, Vol. 11, 37-42. [11] East R., Consumer behavior: Advances and applications in marketing. Hemel Hempstead, England: Prentice Hall, 1997. [12] Foxall G.R., Goldsmith R.E., Consumer psychology for marketing. New York: Routledge 1994. [13] Grunert K. G., What’s in a steak? A cross-cultural study on the quality perception of beef. Food Quality and Preference, 1997, Vol. 8, 157–174. [14] Ha J., Jang S.S., Effects of service quality and food quality: the moderating role of atmospherics in an ethnic restaurant segment, International Journal of Hospitality Management, 1010, Vol. 29 (3), 520–529. [15] Harrington R., Ottenbacher C., Kendall, K., Fine-Dining Restaurant Selection: Direct and Moderating Effects of Customer Attributes, Journal of Foodservice Business Research, 2011, Vol. 14(3), 272-289. [16] Howlett E.A., Burton S., Bates K., Huggins K., Coming to a restaurant near you? Potential consumer responses to nutrition information disclosure on menus, Journal of Consumer Research, 2009, Vol. 36 (3), 494–50. [17] June L., Smith S., Service Attributes And Situational Effects On Customer Preferences For Restaurant Dining , Journal of Travel Research, 1987, Vol. (26), 20-27. [18] Kim D., Park S., Customers' Responses to Crowded Restaurant Environments: CrossCultural Differences Between American and Chinese, Journal of Hospitality & Leisure Marketing, 2008, Vol. 16 (1-2), 137-157. [19] Kim W., Ok C., Canter D., Contingency variables for customer share of visits to full-service restaurant, International Journal of Hospitality Management, 2010 Vol. 29, 136-147. [20] Kim W.G., Ng C.Y.N., Kim Y., Influence of institutional DINESERV on customer satisfaction, return intention, and word-of-mouth, International Journal of Hospitality Management, 2009, Vol. 28 (1), 10–17. [21] Liu Y., Jang S.S., Perceptions of Chinese restaurants in the US: what affects customer satisfaction and behavioral intentions?, International Journal of Hospitality Management, 2009, Vol. 28 (3), 338–348. [22] Murase H., Bojanic D., An Examination of the Differences in Restaurant Brand Personality Across Cultures, Journal of Hospitality & Leisure Marketing, 2004, Vol. 11, (2-3), 97-113. [23] Nam J., Ekinci Y.,Whyatt, G., Brand Equity, Brand Loyalty and Consumer Satisfaction, Annals of Tourism Research, 2011, Vol. 38 (3), 1009–1030. [24] Namkung Y., Jang S., Does food quality really matter in restaurants? Its impact on customer satisfaction and behavioral intentions, Journal of Hospitality & Tourism Research, 2007, Vol. 31 (3), 387–409.

65