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Predicting Tourists' Intention to Try Local Cuisine Using a Modified Theory of Reasoned Action: The Case of New Orleans

Kisang Ryua; Heesup Hanb a Lester E. Kabacoff School of Hotel, Restaurant and Tourism Administration, The University of New Orleans, New Orleans, LA, USA b Department of Tourism Management, College of Business Administration, Dong-A University, Seo-gu, Busan, Korea Online publication date: 30 July 2010

To cite this Article Ryu, Kisang and Han, Heesup(2010) 'Predicting Tourists' Intention to Try Local Cuisine Using a

Modified Theory of Reasoned Action: The Case of New Orleans', Journal of Travel & Tourism Marketing, 27: 5, 491 — 506 To link to this Article: DOI: 10.1080/10548408.2010.499061 URL: http://dx.doi.org/10.1080/10548408.2010.499061

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Journal of Travel & Tourism Marketing, 27:491–506, 2010 Copyright © Taylor & Francis Group, LLC ISSN: 1054-8408 print / 1540-7306 online DOI: 10.1080/10548408.2010.499061

PREDICTING TOURISTS’ INTENTION TO TRY LOCAL CUISINE USING A MODIFIED THEORY OF REASONED ACTION: THE CASE OF NEW ORLEANS Kisang Ryu Heesup Han

Downloaded By: [Ryu, Kisang] At: 03:15 2 August 2010

ABSTRACT. The purpose of the present study was to examine the utility of the modified theory of reasoned action (TRA) in predicting tourists’ behavioral intention to try the local cuisine in New Orleans. The results indicated that the proposed model had strong predictive ability regarding tourists’ intention to try the local cuisine. Attitude and past behavior were significant predictors of tourists’ behavioral intention. In addition, the interdependence between attitudinal and normative components was partly supported. Gender had a significant moderating role in the relationships between attitude/past behavior and behavioral intention to experience the local cuisine. Theoretical and practical implications of the findings are discussed.

KEYWORDS. Modified theory of reasoned action, local cuisine, attitude, subjective norm, gender, behavioral intention, culinary tourism

INTRODUCTION Culinary tourism has received increasing attention in both the restaurant and tourism industries. Links between restaurants and destinations have played important roles in the overall success of regional tourism, and vice versa—that is, the tourism industry contributes to the restaurant business as well. As consumers become increasingly interested in cuisine and travel for culinary experiences, the tourism and restaurant industries have become more aware of the need to market, develop, and promote the restaurant industry as part

of the tourism product (Sparks, Bowen, & Klag, 2003). According to the 2004 Restaurant and Foodservice Market Research Handbook, travelers’ expenditures accounted for approximately 50% of revenues in the restaurant industry (Graziani, 2003). Tourists’ growing interest in cuisine has motivated the restaurant and tourism industries to pay more attention to culinary tourists’ dining expectations (Cohen & Avieli, 2004; Kivela & Crotts, 2006; Nield, Kozak, & LeGrys, 2000; Okumus, Okumus, & McKercher, 2007; Quan & Wang, 2004; Ryu & Jang, 2006; Smith & Costello, 2009; Sparks et al., 2003).

Kisang Ryu, PhD, is Assistant Professor in the Lester E. Kabacoff School of Hotel, Restaurant and Tourism Administration at The University of New Orleans in New Orleans, LA 70148, USA (E-mail: [email protected]). Heesup Han, PhD, is Assistant Professor in the Department of Tourism Management, College of Business Administration at Dong-A University, 2-1 Bumin-dong, Seo-gu, Busan, Korea 602-760. This study was supported by research funds from Dong-A University. Address correspondence to: Heesup Han, PhD, at the above address.

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The restaurant sector’s contributions to the attractiveness of tourism destinations (e.g., New Orleans) must also be noted. New Orleans is acknowledged annually by the top culinary and travel media as one of the best food destinations in the world. Even after the devastating Hurricane Katrina, the number of restaurants increased from 809 restaurants (preKatrina number) to 1,069 restaurants (postKatrina number) in the city (Fitzmorris, 2010). One of its food festivals, the New Orleans Wine and Food Experience, is recognized as one of the most prestigious festivals of its kind. The 5-day event features more than 175 wineries and 1,000 wines from around the world; 75 of New Orleans’ best restaurants showcase their offerings. According to Ruth Reichl, Gourmet magazine editor, “people who are interested in food are curious about the rest of the world too” (Reichl, as cited in Sparks et al., 2003, p. 6). Kivela and Crotts (2006) also found that gastronomy plays a major role in tourists’ experience of a destination and indicated that some travelers return to the same destination to savor its unique gastronomy. Tourists’ increasing expectations for top service and food quality have created a strong demand for a wide variety of dining venues and menu options, with a growing emphasis on regional specialties and fresh ingredients (Fox, 2007). Based on the aforementioned discussion, an analysis of tourists’ behavioral intentions to experience local food or local cuisine will provide useful insights into their culinary experiences at a particular travel destination. The theoretical framework for this study was based on the theory of reasoned action (TRA; Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). The TRA has emerged as one of the most influential and popular conceptual frameworks for the study of human behavior, and strong support has been shown for the efficacy of the theory as a predictor of behavioral intentions and behaviors in a variety of experimental and naturalistic settings over the last three decades (Ajzen, 1991; Ajzen & Fishbein; Fishbein & Ajzen; Hee, 2000; Omar, 2007). Ajzen (1991) indicated that in a certain context, the theoretical mechanism of the TRA could be better comprehended by altering the model to ensure adequacy in a specific context. That is, the theory can

be deepened through the process of modifying the model, capturing a greater proportion of the variance in behavioral intention/behavior (Perugini & Bagozzi, 2001). Since travelers’ decision-making process can be very complicated and involve an intricate process that can differ from decision-making processes followed in other contexts, the TRA must be modified to improve the prediction of intention. The importance of culinary tourism has been largely ignored by academicians. In particular, little attention has been paid to research on travelers’ local cuisine experiences at a travel destination. Although the efficacy of the theory has received much support as a predictor of behavioral intentions and behaviors, relatively few interventions are based on the TRA within the hospitality and tourism literature. Additionally, while numerous studies in various settings have extensively tested and verified the impact of personal characteristics such as gender on buying behaviors (Evanschitzky & Wunderlich, 2006; Homburg & Giering, 2001; Im, Bayus, & Mason, 2003), the specific role of gender in tourists’ decision-making process has rarely been examined. Thus, this study aimed to fill the research gap by applying a modified TRA model to tourists’ decisions to try local cuisine at a tourism destination. The purpose of this study was to examine the validity of a modified version of the TRA model and to predict tourists’ behavioral intentions to try the local cuisine. The article begins with a review of the TRA model and associated concepts to propose specific research hypotheses within the revised theoretical framework. Next, the research method used to test these hypotheses is described before the results are presented. Finally, theoretical and practical implications of findings as well as the limitation of the study and future directions for further research are discussed.

THEORETICAL BACKGROUND Theory of Reasoned Action (TRA) During the last three decades, the TRA has been applied in diverse research areas to identify the determinants of an individual’s


Kisang Ryu and Heesup Han

specific behavior in volitional control (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). The theory works on the premise that a person’s intention to perform a behavior is the sole predictor of behavior; in turn, behavioral intention is a function of an attitudinal (personal) component and a normative (social) component. “The attitudinal component refers to the person’s attitude toward performing the behavior under consideration” (Ajzen & Fishbein, p. 54), while the normative component refers to “the person’s subjective norm, that is, his/her perception that most people who are important to him/her think he/she should or should not perform the behavior in question” (Ajzen & Fishbein, p. 57). Thus, the basic paradigm of the TRA model posits that a person’s behavior (B) is likely to be influenced by behavioral intentions (BI) and that the behavioral intentions are likely to be determined by attitudes toward the behavior (Aact) and subjective norms (SN). Furthermore, attitude is represented as a function of behavioral beliefs (BB) and outcome evaluation (E; Bagozzi, 1992). An attitude toward a behavior is defined as an individual’s overall positive or negative judgment of a particular behavior after the evaluation of the perceived consequences of an act (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975; Lee & Back, 2007). The BB is the perceived consequences of performing a particular behavior in a given situation and the E refers to the evaluation of those consequences. The subjective norm is represented as a function of normative beliefs (NB) and the individual’s motivation to comply (MC) with important others (Bagozzi). The subjective norm refers to the individual’s perception of whether other important people believe that he/she should perform the behavior (Ajzen & Fishbein; Fishbein & Ajzen). Normative beliefs are those experienced by a person who believes that others have formed opinions about what he or she should do in a situation; the motivation to comply reflects what others think should be done. Ajzen and Fishbein (1980) suggested that the relative importance of attitudinal and normative components in predicting behavioral intentions would vary by the behavior, situation, and individual differences. For behaviors that involve strong attitudinal or personal influences

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(e.g., selecting a restaurant for myself), Aact is the dominant predictor of behavioral intentions. Subjective norms are of less importance than attitude. For behaviors that hold strong normative implications (e.g., selecting a restaurant to celebrate Mother’s Day), subjective norms should be the dominant determinant of behavioral intentions. Based on the previous discussion, the following hypotheses were developed. H1: BBi Ei has a positive influence on attitude toward the behavior. H2: NBj MCj has a positive influence on subjective norms. H3: Attitude toward the behavior has a positive influence on behavioral intentions. H4: Subjective norms have a positive influence on behavioral intentions.

Interdependence Between Attitude and Subjective Norm Components Several researchers have argued that a possible relationship exists between the attitudinal and normative components of the TRA model (Chang, 1998; Oliver & Bearden, 1985; Vallerand, Deshaies, Cuerrier, Pelletier, & Mongeau, 1992). Oliver and Bearden found that a crossover effect could exist between the attitudinal and the normative structures of the model, indicating that the NBj MCj multiplicative term might influence attitudes. Vallerand et al. (1992) also claimed that a causal link from the NBj MCj multiplicative structure to attitudes should be added to the model. In addition, Chang showed that the subjective norm does not significantly affect behavioral intentions, but the indirect effect through attitude is highly significant. In sum, an increasing number of studies have criticized the lack of interdependency between attitudinal and normative components. Thus, a direct causal path linking the subjective norm component to the attitude component was added to the modified TRA model in this study. Based on these study findings, the following hypotheses were developed. H5: NBj MCj has a positive influence on attitude toward the behavior. H6: Subjective norm has a positive influence on attitude toward the behavior.

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Past Behavior A few previous studies have tested the specific role of past behavior (PB) in the context of the TRA (Ajzen, 1991; Bagozzi, 1992; Bagozzi, Wong, Abe, & Bergami, 2000; Lam & Hsu, 2004, 2006; Ouellette & Wood, 1998; Ryu & Jang, 2006). The findings from these studies showed that inclusion of the past behavior as a predictor significantly enhanced the predictive ability of the TRA model in intentions and/or actual behaviors. Ajzen claimed that past behavior might be the best predictor of future behavior and a measure of past behavior could be used to test the sufficiency of any model designed to predict future behavior. Bagozzi et al. (1992) found that past behavior is a determinant of intention to use condoms in the TRA. Ouellette and Wood conducted metaanalysis by examining 64 studies. Their findings revealed robust evidence for the influence of past behavior structure on behavioral intentions and future behavior. Bagozzi et al. (2000) examined the usefulness of the TRA for fastfood restaurant patronage decisions. The study revealed that the impact of adding past behavior to the TRA increased considerable amount of variance accounted for in behavioral intentions. Lam and Hsu (2004) discovered that past behavior had a positive causal relationship with travel intention in the theory of planned behavior model. Ryu and Jang found the direct impact of past behavior on behavioral intentions using a hypothetical scenario for tourists’ local cuisine experience. Lam and Hsu (2006) discovered that past behavior was significant predictor of behavioral intention of choosing a travel destination in the theory of planned behavior model. Overall, based on the findings from the previous literature, it was assumed that past behavior would have a direct influence on behavioral intentions in this study. Based on these study findings, the following hypothesis was developed. H7: Past behavior has a positive influence on behavioral intentions.

Gender Difference The impact of personal characteristics on customers’ buying behavior has gained a great

deal of research attention (Evanschitzky & Wunderlich, 2006; Homburg & Giering, 2001; Im et al., 2003). Findings from a substantial body of research have shown that personal characteristics (e.g., gender) are the key moderating variables generally influencing consumer behaviors (Homburg & Giering; Im et al.; Mittal & Kamakura, 2001). In this study, gender was employed as a moderator variable that affects each link in illustrating the formation of behavioral intentions. Gender differences have been investigated quite extensively in the literature related to consumer behavior. While little research has considered the influence of gender, particularly in previous TRA studies, there has been both theoretical and empirical support for moderating the role of gender in forming behavioral intentions. A proposition that is frequently employed in explaining gender-related differences in many fields is social role theory (Saad & Gill, 2000). According to this theory, women and men play different roles and exhibit dissimilar behaviors in society because they are differently socialized (Saad & Gill). Specifically, the early socialization of females tends to be passive and restrained, while the socialization of males is likely to make them more proactive and/or selfreliant. In particular, males are more willing to take a risk than females (Powell & Ansic, 1997) and this can influence consumption-related behavior (Jianakoplos & Bernasek, 1998). For instance, exploring new flavors/authentic ingredients in a local cuisine creates the perceived risk of undesirable outcomes. While male travelers are more likely to take such a risk and try an adventurous/interesting food that they have never eaten before, female travelers are less likely to experience something new, avoiding the perceived risk (e.g., uncertainty or undesirable consequences). Gender differences in consumer behaviors also have been extensively empirically investigated in marketing. Mittal and Kamakura’s (2001) findings showed that the link between repurchase behavior and its antecedent variable was significantly influenced by gender, with the link being stronger for men than for women (Mittal & Kamakura). Wolin and Korgaonkar (2003) investigated gender differences in beliefs, attitudes, and behaviors related



to web advertising. Their findings indicated that men and women exhibited different beliefs, attitudes, and purchasing behaviors. Specifically, men showed more positive attitudes and beliefs toward web advertising than women, and were more likely to purchase a product from the web. While findings about the strength of the links across genders were inconsistent, the results of previous empirical investigations in the consumer literature strongly supported the presence of gender differences in consumer behaviors. Thus, it can be assumed that the relationship between behavioral intentions and its antecedent variables (e.g., attitude, subjective norm, and past behavior) in the modified TRA model is likely to be influenced by gender. Based on the previous discussion, the following hypotheses concerning the moderating role of gender on the relationships between behavioral intentions and their predictors are proposed. H8: Gender has a significant moderating role in the relationship between attitude and behavioral intentions. H9: Gender has a significant moderating role in the relationship between subjective norms and behavioral intentions. H10: Gender has a significant moderating role in the relationship between past behavior and behavioral intentions. In sum, this study was designed to: (a) examine the construct validity of a modified TRA model in explaining tourists’ intentions to try local cuisines on vacation; (b) test the effects of the antecedents on attitude and subjective norms; (c) investigate the effects of attitude and subjective norms on behavioral intentions; (d) check the influence of past behavior on behavioral intentions; (e) test the interdependence between attitudinal and normative structures in the modified TRA model; and (f) discover the relationships among attitude and behavioral intention, subjective norm and behavioral intention, and past behavior and behavioral intention by examining the moderating effects of gender on these relationships. Figure 1 presents the proposed model for the modified TRA. The hypothesized moderating effects of gender on Aact-BI, SN-BI, and PB-BI

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links were also integrated into the proposed model. The dotted lines in Figure 1 signify modification to the original TRA model.

METHODOLOGY Measurements Items for Beliefs Behavioral Beliefs. The measurement items for each construct with the means and standard deviations are presented in the Appendix. For behavioral beliefs (BB), participants were asked to indicate on a 7-point scale (1 = very unlikely, 7 = very likely) the likelihood that each of the four outcomes (e.g., a new meal I have never eaten before) would occur if he or she experienced local cuisine. For example, “experiencing the local cuisine during this vacation in New Orleans means that I will explore new flavors that I have never tasted.” The evaluative component (E) was measured by asking respondents to evaluate the consequences of four belief items on a 7-point scale ranging from very good (7) to very bad (1). For example, “Exploring new flavors that I have never tasted is . . .”. Normative Beliefs. Normative Beliefs (NB) was measured by asking subjects to indicate on a 7-point very true–very false scale each referent’s likelihood of experiencing local cuisines while they travel. Normative belief was assessed by three items—e.g., “my close friends think I should experience local cuisine during this vacation in New Orleans.” To measure motivation to comply (MC), subjects were asked to rank on a 7-point very much–not at all scale how much they wanted to comply with what the referents thought they should do. Motivation to comply was measured using three items—e.g., “generally speaking, how much do you want to do what your close friends think you should do?”

Items for Other Study Variables Attitudes Toward the Behavior. Attitudes toward the behavior (Aact) were operationalized using semantic differential scales

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FIGURE 1. Proposed Model for the Modified TRA.

Gender

BBiEi

H1

Attitude H9 H10

H3


NBjMCj

H2

Subjective Norm

H4

Behavioral Intention

H7 Past Behavior

(e.g., −3 = very bad, 3 = very good) with four items (e.g., good–bad) in response to the statement, “for me, experiencing local cuisine during this vacation in New Orleans will be . . .” (Ajzen & Fishbein, 1980; Bagozzi et al., 2000). Subjective Norms. Subjective norms (SN) were measured by three 7-point very unlikely– very likely scale items: for example, “Most of people who are important to me think it is good for me to experience local cuisine during this vacation in New Orleans.” Past Behavior. Past behavior (PB) was measured with two items. For instance, “I experience local cuisine every time I am on vacation at my travel destination.” A 7-point not at all (1)–very much (7) scale was used for each item. Behavioral Intentions. Behavioral intentions (BI) were assessed by asking respondents to comment on three statements. For example, “I intend to experience local cuisine during this trip in New Orleans.” Participants responded to these items on a 7-point scale (e.g., 1 = very unlikely, 7 = very likely).

Data Collection A questionnaire was developed to gather tourists’ perceptions of their local cuisine experiences while on vacation. It was based on surveys used in previous studies (Ajzen & Fishbein, 1980; Bagozzi et al., 2000; Cheng, Lam, & Hsu, 2006; Fishbein & Ajzen, 1975; Hee, 2000; Ryu & Jang, 2006). Then, a pilot test was conducted to evaluate the wording, phrasing, and layout of the questionnaire. A self-administered questionnaire was used to collect the data from tourists waiting for their luggage at an airport baggage carousel in New Orleans. New Orleans was selected as a travel destination because it is recognized as one of the top destinations for culinary tourism in the United States. Although its image was negatively influenced by Hurricane Katrina in 2005, the city is still famous for its food, music, culture, events, and festivals among domestic as well as international travelers. Tourists who agreed to participate in the survey were given a questionnaire while they were waiting for their luggage. Using a convenience sampling approach, a total of 300 responses were collected. After removing incomplete and


unusable responses, 294 responses were finally used in data analysis.


Data Analysis Procedure A measurement model was estimated before the structural model through Confirmatory Factor Analysis (CFA) with the maximum likelihood estimation method by using AMOS 5 (SmallWaters Corp., Chicogo, IL), following Anderson and Gerbing’s (1988) two-step approach. Researchers agree that assessing a measurement model using the CFA allows us to test measurement quality, such as reliability and construct validity (Anderson & Gerbing; Fornell & Larcker, 1981; Hair, Anderson, Tatham, & Black, 1998). After testing the sufficiency of the measurement model, a structural analysis was conducted to assess the proposed model. Finally, the moderating effect of gender was tested by using tests for metric invariances. In particular, to test a measurement invariance, a non-restricted model without constraining any parameters was compared to the full metric invariance model where all underlying factors were constrained to be equivalent. Once the full metric invariance was supported, which indicates the patterns of factor loadings across gender groups were invariant, the structural invariance was tested by comparing a baseline model (full metric invariance of the structural model) to the constrained model (full path invariance model) to identify equivalence of the structural paths. Lastly, invariance tests for hypothesized paths were conducted to test path differences across gender groups.

RESULTS Measurement Model The chi-square value (χ 2 ) of the measurement model was 396.36 (df = 174, p < .001). The χ 2 /df value of 2.28 fell within a range of acceptable values from 2 to 5 (Hair et al., 1998). Other practical fit indices demonstrated that the measurement model fit the data reasonably well (RMSEA = .066; CFI = .98; NFI = .97; TLI = .92; IFI = .93). All standardized factor loadings emerged fairly high,

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ranging from .71 to .94, which met the minimum criterion of .40 (Hair et al.). A construct reliability test was used to assess the consistency of the result measurements—that is, it assessed the internal homogeneity among the items scale in this study. The alpha values for the multiitem scales are presented in Table 1. Values for study constructs ranged from .81 to .95. These values were above the suggested cut-off of .70, thus indicating internal consistency (Nunnally, 1978). The measurement model was assessed for construct validity, using the factor loadings within the constructs, average variance extracted (AVE), and the correlation between constructs. As shown in Table 1, AVE values for all constructs exceeded .50, demonstrating convergent validity (Hair et al., 1998). In addition, all factors met Fornell and Larcker’s (1981) criterion for discriminant validity since the AVE in each construct exceeded the variance explained (squared correlations in Table 1) between constructs. In sum, an assessment of the TRA model measurement showed strong evidence of reliability and validity for the operationalization of the latent constructs.

Structural Model A structural analysis was performed to validate the modified TRA in predicting behavioral intentions using the maximum likelihood estimation method. The causal model was evaluated using two criteria: fit indices and path significance. Table 2 shows the structural model results. The chi-square (χ 2 ) value of the proposed model was 483.03 (df = 181, p < .001). Other goodness-of-fit indices also revealed that the measurement model fit the data reasonably well (RMSEA = .075; CFI = .99; NFI = .98; TLI = .93; IFI = .94). The amount of variance explained by BBi Ei , NBj MCj , and subjective norm in attitude was .36, and the variance-explained estimate for subjective norm by NBj MCj was .55. The total coefficient for determination of behavioral intentions was .63. This proposed model was compared with the original TRA model, which also had a good fit to the data [χ 2 = 452.50 (df = 130, p < .001), RMSEA = .079; CFI = .92; NFI = .92;

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TABLE 1. Measure Correlations, Squared Correlations, and AVE Correlations among latent constructs (squared)a

Measure

1 BBi Ei 2 NBj MCj 3 PB 4 Aact 5 SN 6 BI

BBi Ei

NBj MCj

1.00 .44 (.19) .46 (.21) .58 (.34) .43 (.19) .57 (.33)

1.00 .27 (.07) .35 (.12) .73 (.53) .25 (.06)

PB

Aact

1.00 .46 (.21) .32 (.10) .55 (.30)

1.00 .38 (.14) .80 (.64)

SN

1.00 .34 (.12)

BI

AVE

Alpha

1.00

.77 .61 .70 .73 .71 .67

.95 .81 .81 .91 .87 .83


Note. BB: behavioral beliefs; E: evaluations of outcome; NB: normative beliefs; MC: motivation to comply; PB: past behavior; Aact: attitude toward the behavior; SN: subjective norm; BI: behavioral intention. a All correlations were significant at the .05 level. Model measurement fit: χ 2 = 396.36 (df = 174, p < .001), RMSEA = .066, CFI = .98, NFI = .97, TLI = .92, IFI = .93.

TLI = .91; IFI = .93]. As expected, the proposed modified model (R2 = .63) had a slightly better predictive ability than the original model (R2 = .61). Thus, this finding indicated that the modified TRA model can better predict and explain tourist intentions to experience local cuisine than the original TRA model. As shown in Table 2, the relationship between BBi Ei and attitude was significant (β = .53, t = 8.23, p < .01), and the linkage between NBj MCj and subjective norm was

also significant (β = .74; t = 10.56, p < .01), supporting hypotheses 1 and 2. These findings indicated that BBi Ei is a significant predictor of attitude, and NBj MCj is also a significant predictor of subjective norm. The subjective norm and attitude linkage was significant (β = .14; t = 2.45, p < .05), supporting hypothesis 6. This result is consistent with previous studies, which implies the interdependence of attitudinal and normative structures (Chang, 1998). However, the NBj MCj and attitude link was not significant

TABLE 2. Structural Parameter Estimates Hypothesized path H1: BBi Ei → Aact H2: NBj MCj → SN H3: NBj MCj → Aact H4: Aact → BI H5: SN → BI H6: SN → Aact H7: PB → BI R 2 (Aact) R 2 (SN) R 2 (BI) Goodness-of-fit statistics:

Coefficient

t value

Result

.53 .74 .01 .76 −.01 .14 .24

8.23∗∗ 10.56∗∗ .13 12.14∗∗ −.16 2.45∗ 4.85∗∗

Supported Supported Not supported Supported Not supported Supported Supported

.36 .55 .63 χ 2 (181) = 483.03, p < .001 χ 2 /df = 2.67 RMSEA = .075 CFI = .99 NFI = .98 TLI = .93 IFI = .94

Note. BB: behavioral beliefs; E: evaluations of outcome; NB: normative beliefs; MC: motivation to comply; PB: past behavior; Aact: attitude toward the behavior; SN: subjective norm; BI: behavioral intention. ∗ p < .05, ∗∗ p < .01.



(β = .01; t = .13, p > .05). Thus, hypothesis 5 was not supported, indicating that NBj MCj did not have a direct influence on attitude. This finding is inconsistent with some previous studies (e.g., Oliver & Bearden, 1985), implying the independence of attitudinal and normative components. The effects of attitude and subjective norm on behavioral intentions were tested. The regression path from attitude to behavioral intentions (β = .76, t = 12.14, p < .01) was significant, thus supporting hypothesis 3. Attitude had a positive influence on behavioral intentions to experience local cuisine on vacation. However, the non-significant estimate of subjective norm on behavioral intentions (β = −.01; t = −.16, p > .05) suggested that subjective norm was not a direct predictor of the behavioral intentions in this modified TRA model, and thus did not support hypothesis 4. This finding, which was consistent with some previous studies (e.g., Bansal & Taylor, 1999; Lam & Hsu, 2004) implied that a traveler’s perceived social pressure does not directly drive his/her intention to try local cuisine. The negative and insignificant estimate for this path coefficient could be attributed to suppressor effects (Bollen, 1989). A negative coefficient could also be caused by multicollinearity and/or redundancy of estimation. Indeed, the results of three simple regression models without other predictor variables (subjective norm → BI; Aact → BI; PB → BI) showed that the regression coefficient of each model was positive and significant at an alpha level of .01. Further, the relationship between past behavior and behavioral intentions was tested. The direct effect of past behavior on behavioral intentions was significant (β = .24, t = 4.85, p < .01). This result implies that travelers’ behavioral intentions to experience local cuisine became stronger as they gained in prior experience. The coefficient and t value of the path from attitude to behavioral intentions was greater than those for the path from subjective norm to behavioral intentions and the path from past behavior to behavioral intentions. The result of the Fisher test also showed that the coefficients of these paths had statistically different strengths (p < .01). This indicated that attitude slightly better predicts behavioral intentions than subjective norm and past behavior in the proposed model.

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The mediating role of subjective norm/ attitude in the study model was tested by investigating the direct/indirect effect of NBj MCj /Aact on Aact/BI. The use of the mediating framework provides a better understanding of the relationships among NBj MCj , attitude, subjective norm, and behavioral intentions within the modified TRA model. While the direct effect of NBj MCj on attitude was not significant, the indirect effect of NBj MCj on attitude through subjective norms was significant (β NBjMCj-SN-Aact = .11) at an alpha level of .05. This finding indicated that the NBj MCj only indirectly influenced attitude through subjective norms. That is, subjective norms acted as a mediator in the relationship between NBj MCj and attitude. Furthermore, the indirect effect of subjective norms on behavioral intentions through attitude (β SN-Aact-BI = .11) was significant at an alpha level of .05, and the estimate for the path linking subjective norms to behavioral intentions was not significant. This result indicated that attitude significantly mediated the relationship between subjective norms and behavioral intentions.

Empirical Testing of Moderating Effects of Gender An empirical examination was conducted of the hypothesized moderating effect of gender on Aact-BI, SN-BI, and PB-BI links. The respondents were divided into male and female groups. The number of cases in each group was 155 (male group) and 139 (female group), respectively. Before comparing key paths between gender groups, a measurement invariance test was conducted to assess whether a measurement model is invariant across groups. Table 3 shows the results of the measurement invariance for gender groups. A non-restricted CFA model was first assessed, and then the model was compared to the full metric invariance model. Since the chi-square difference between the non-restricted model and the full metric invariance model was not significant [χ 2 (15) = 12.53, p >.05], full metric invariance was supported. This result indicated that factor loadings were equivalent across male and female groups. Accordingly, this full metric invariance model for gender was

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TABLE 3. Measurement and Structural Invariance χ2

df

Nonrestricted model

591.42

Full metric invariance of CFA model [L(X)Y = IN∗ ]a Full metric invariance of structural model [L(X)Y = IN∗ ] Full path invarianceb [L(X)Y = IN, GA = IN, BE = IN]

Models Measurement invariance for gender groups

Structural invariance for gender groups

RMSEA CFI

NFI

TLI

IFI

348

.049

.95

.89

.94

.95

603.95

363

.048

.95

.88

.94

.95

465.86

266

.051

.99

.98

.94

.95

479.99

273

.051

.99

.98

.94

.95


Note. ∗ IN = invariance. a Chi-square difference test: χ 2 (15) = 12.53, p > .05 (insignificant), thus, full metric invariance is supported. b Chi-square difference test: χ 2 (7) = 14.13, p < .05 (significant), thus, full structural invariance is not supported.

used to run the structural equation modeling and in subsequent analyses. The next step was to test structural invariance to assess whether groups were significantly different. After adding paths among constructs rooted in the full metric invariance model, the entire structural model was run to generate the baseline model for structural invariance. Table 3 shows the results of the structural invariance test for gender groups. The chi-square difference between the baseline model and constrained model was significant [χ 2 (7) = 14.13, p < .05]—-thus, paths across male and female groups were not equivalent or at least some of the coefficients differed across gender groups. For more rigorous investigation, the invariance of a specific path was tested by constraining the particular parameter of interest in the nested model to be equal in sequence (the AactBI, SN-BI, and PB-BI links), and all paths in the baseline were allowed to be freely estimated. Bagozzi and Yi (1989) indicated that the chi-square difference test, by comparing the baseline model with the nested model computed for 1 degree of freedom, allows invariance to be tested for the specific parameter of interest across two groups. Table 4 shows the findings for the invariance tests of key paths. For the path from attitude to behavioral intentions, the estimate across groups was different, and a significant chi-square difference was found (χ 2 = 8.65, df = 1, p < .01)—thus, supporting the moderating role of gender (Hypothesis 8).

Specifically, the strength was higher in the male group than in the female group (male: β = .89, t = 11.06, p < .01; female: β = .61, t = 7.02, p < .01). Regarding the path from subjective norm to behavioral intentions, there was no significant difference between groups (χ 2 = 1.17, df = 1, p > .05)—thus, hypothesis 9 was not supported. The path across gender groups was not statistically significant (male: β = −.09, t = −1.38, p > .05; female: β = .01, t = .18, p > .05), indicating that gender did not have a moderating role in the relationship between subjective norm and behavioral intentions in the proposed framework. Since regression coefficients obtained from three simple regression models (SN → BI; Aact → BI; PB → BI) in the male group and three models (SN → BI; Aact → BI; PB → BI) in the female group were positive and significant (p < .01), the insignificant path from subjective norm to behavioral intentions in both groups was caused by suppressor effects or multicollinearity (Bollen, 1989). Finally, the estimates for path coefficients from past behavior to behavioral intentions across groups were compared. The path was found to be significantly different across gender groups (χ 2 = 4.93, df = 1, p < .05). This result was consistent with hypothesis 10. The estimate was higher in the female group than in the male group (male: β = .13, t = 2.46, p < .05; female: β = .36, t = 4.00, p < .01). This finding implies that female tourists are more willing to


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TABLE 4. Invariance Tests of Hypothesized Paths Paths

Gender

Aact→BI SN→BI PB→BI

Fit of the model with the path

Test of invariance

Baseline model (Freely estimated)

Nested model (Constrained to be equal)

Chi-square difference test

χ 2 (266) = 465.86 χ 2 (266) = 465.86 χ 2 (266) = 465.86

χ 2 (267) = 474.51 χ 2 (267) = 467.03 χ 2 (267) = 470.79

χ 2 (1) = 8.65, p < .01 χ 2 (1) = 1.17, p > .05 χ 2 (1) = 4.93, p < .05

Note. PB: past behavior; Aact: attitude toward the behavior; SN: subjective norm; BI: behavioral intention.

experience local cuisine than male tourists once they have prior experience.


CONCLUSION The overriding aim of this study was to examine the validity of a modified version of the Theory of Reasoned Action (TRA) model and to predict tourists’ behavioral intentions to experience the local cuisine while on vacation. In this respect, the study appeared to work well. The results supported the belief that the TRA model could well predict tourists’ intentions toward the local cuisine (R2 = .63), indicating the model’s applicability in the hospitality and tourism industries. Study findings partially supported the overall structure of the TRA model in understanding tourists’ specific behavior. The hypotheses relating to the structural parameters of BBi Ei to Aact and NBj MCj to subjective norms were supported. Although the subjective norm was not a significant predictor of behavioral intentions, attitude was found to be the dominant significant antecedent of behavioral intentions. Since tourists’ intentions to experience a local cuisine were highly dependent on their own personal attitudinal factors rather than social norms, the main focus should be pointed out enhancing tourists’ attitude toward the local cuisine. That is, to ensure tourists’ positive behavioral intentions, managers should foster positive beliefs through marketing efforts (e.g., developing and promoting adventurous, interesting authentic food). As Johns and Kivela (2001) suggested, more attention should be paid to the messages (e.g., pictures showing interesting dishes) emitted by restaurant exteriors and interiors so that tourists feel less threatened

and more secure or comfortable with unfamiliar local menus. The hypothesized dependence between the attitude component and the subjective norm component was partly supported. While the causal path linking NBj MCj to Attitude (NBj MCj -Aact) was not supported, the causal path from subjective norm to Attitude (SNAact) was supported. The hypotheses about the structural parameters of BBi Ei to Attitude (BBi Ei -Aact) and NBj MCj to subjective norm (NBj MCj -SN) were also supported. The significant causal paths of BBi Ei -Aact and SN-Aact showed that not only did behavioral beliefs influence Aact but subjective norm also affected Aact. The results indicate that individual beliefs might be influenced by significant others (e.g., family, close relatives, and close friends). Thus, important referents might influence tourists’ specific behaviors (e.g., experiencing local cuisine) both via individual attitudes and social pressures. The findings showed a positive causal relationship from past behavior to behavioral intentions, indicating that past experience of local cuisine could significantly strengthen tourist intentions to experience local cuisine. This indicates that local cuisine could serve to attract tourists who already have experienced local cuisine at the same and/or different travel destinations. It might carefully imply that local cuisine might play an important role in the way tourists experience a destination, suggesting that culinary experiences can be powerful tools in marketing a destination’s unique food to tourists. For instance, marketers in New Orleans can utilize its resources, such as unique cuisine, creative chefs, unique marine and agricultural products, unique culinary heritage, and variety


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of food festivals. Stressing the important role of culinary tourism, marketers can develop further distinguishing strategies, such as an increased number of culinary tours, more food-oriented travel guide books, more focus on travel in epicure magazines, and more events that celebrate food and drink as an integral part of the entire travel experience. Marketers also need to understand the importance of tourists’ positive word-of-mouth in experiencing local cuisine at a travel destination. Thus, information gathering and assessment on tourists’ satisfaction with their culinary experiences, particularly relating to local cuisine, must be emphasized and also enhanced via provision of unique foods with exotic flavors and authentic ingredients. The hypothesized moderating effect of gender on the relationships among Aact-BI, SNBI, and PB-BI links was partly supported. The findings suggested that gender had a significant moderating role in the relationship between attitude and behavioral intentions. More specifically, the result of the test for metric invariance showed that the relationship between attitude and behavioral intentions was stronger for male travelers than for female travelers. This finding implies that male tourists are more willing to try local cuisine than female tourists. According to the social role theory and evolutionary psychology paradigm, men are more willing to take risks than women (Powell & Ansic, 1997) because men are expected to behave in such a manner in society, and such behavior has given men a benefit in the process of natural selection. This could have direct implications for consumption-related behavior (Jianakoplos & Bernasek, 1998). Exploring new flavors/authentic ingredients in a local cuisine carries the risk of uncertainty or undesirable consequences. This study found male travelers are more willing to take such a risk than female travelers in regard to trying local cuisine in a travel destination. This result suggests that restaurateurs in tourist destinations should more aggressively promote their authentic local cuisines to male tourists, particularly targeting male groups. One common approach is to recommend experiencing the local cuisine through personal selling by waiting staffs when tourists ask their suggestions with regard to menu order.

The restaurant management can train employees to promote the local cuisine particularly targeting male tourists. However, gender was not a significant moderator in the relationship between subjective norms and behavioral intentions. Findings also showed that gender had a significant moderating role in the relationship between past behavior and behavioral intentions. The strength of the link between past behavior and behavioral intentions differed significantly. Interestingly, the relationship was stronger for female travelers than for male travelers. This finding indicates that female tourists are more willing to experience local cuisine than male tourists once they have (positive) prior experience with a local cuisine at any travel destination. Some of the previous literature on consumer behavior would explain such a phenomenon (Han & Ryu, 2006; Homburg & Giering, 2001). Homburg and Giering indicated that female customers’ intention to repurchase a product is stronger than men’s when they are satisfied with the sales process and their experience with the product. In addition, Han and Ryu’s findings revealed that female customers showed a stronger intention to revisit the restaurant when satisfied with an experience at a specific restaurant. Similarly, in the present study, once female tourists were satisfied with past experiences, they regarded sampling the local cuisine at a new destination to no longer be risky. They were more willing to try quality local foods again rather than familiar food because experiencing these new items might enhance the enjoyment of their vacation. Overall, past experience could significantly strengthen female travelers’ intention to try local cuisine. This finding highlights the importance of the first experience. To ensure that it is favorable and positive, restaurateurs should pay more attention to satisfying female tourists as they first try the authentic local cuisine. When they have had a positive experience, they are not only more likely to try the local cuisine again, but they are also more likely to recommend the local cuisine to others. This study’s results should be interpreted with certain limitations in mind. The data



were collected using a convenience sampling approach in New Orleans. Thus, the generalizability of the findings might be arguable. The caution should be used when extrapolating the results of this study to practitioners and academic scholars. Additionally, the questionnaire did not particularly control for participants’ personal experiences with the local cuisine in New Orleans. Positive or negative personal experiences might influence their behavioral intention to experience the local cuisine. There are several directions for future research. First, the TRA hypothesizes that behavioral intentions are the only direct antecedent of behavior. Behavioral intention is expected to predict behavior accurately if the behavior is under people’s volitional control. When this condition does not hold, explained variance estimates in behavior might be typically lower. The theory of planned behavior (TPB) was developed to strengthen this weakness. Many researchers have compared the sufficiency of both TRA and TPB (Madden, Ellen, & Ajzen, 1992). Some studies have shown that the TPB has more power than the TRA regardless of the degree of volitional control, while some studies have reported that the TRA is enough to explain the relationship between attitude and behavior. The inconsistency may be attributable to differences in situations. Thus, future research should examine the degree of volitional control (e.g., problem drinker vs. non-problem drinkers) with the TRA and/or the TPB within the field of hospitality and tourism. Nevertheless, the results show that not only the TRA could well-predict tourists’ intentions toward the local cuisine on vacation, but also gender is an important variable in the attitude-behavior intention and past behavior-behavior intention relationship in the TRA. Second, while the TRA model has gained considerable interest in the United States, scholars have paid little attention to the empirical evidence to support its validity in other cultural settings. The relative strengths of attitudes and subjective norms on behavior may vary cross-culturally in the TRA (Hee, 2000). Understanding consumer behavior in diverse cultures has been increasingly important, especially in the hospitality and tourism industries, due to globalization. Therefore, future studies should investigate the cross-cultural validation

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of the TRA. Lastly, while the sample size in the present study (294 cases) was adequate, exceeding the absolute minimum sample size (Hair et al., 1998), the sample size was relatively small to conduct a group comparison. Thus, to enhance generalizability of the study findings, it is recommended to test the proposed relationships among study constructs by using a greater sample size.

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SUBMITTED: October 14, 2009 FINAL REVISION SUBMITTED: March 1, 2010 ACCEPTED: March 5, 2010 REFEREED ANONYMOUSLY


APPENDIX Measures of Variables for the Theory of Reasoned Action Model Variables BBi (Behavioral beliefs)

Measures (Mean, SD) Very unlikely–Very likely

Sources Ajzen & Fishbein (1980); Ryu & Jang (2006)

BB1 : Experiencing local cuisine during this vacation in New Orleans means that I will try a new meal I have never eaten before. (5.91, 1.14) BB2 : _____ eat adventurous food. (5.53, 1.20) BB3 : _____ try interesting food. (5.76, 1.10) BB4 : _____ explore new flavors that I have never tasted. (5.78, 1.12) Ei (Outcome evaluations)

Very bad–Very good

Ajzen & Fishbein (1980); Ryu & Jang (2006)

E1 : Trying a new meal I have never eaten before is . . . (5.87, 1.00) E2 : Eating adventurous food is . . . (5.72, 1.05) E3 : Trying interesting food is . . . (5.83, .99) E4 : Exploring new flavors that I have never tasted is . . . (5.92, 1.01) NBj (Normative beliefs)

Very false–Very true

Ajzen & Fishbein (1980); Hee (2000)

NB1 : My parents think I should experience local cuisine during this vacation in New Orleans. (5.58, 1.21) NB2 : My close relatives _____. (5.20, 1.18) NB3 : My close friends _____. (5.27, 1.15) MCj (Motivations to comply)

Not at all–Very much

Ajzen & Fishbein (1980); Hee (2000)

MC1 : Generally speaking, how much do you want to do what your parents think you should do? (5.04, 1.15) MC2 : _____ close relatives _____? (4.52, 1.00) MC3 : _____ close friends _____? (4.86, 1.09) (Continued)

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APPENDIX (Continued)


Variables

Measures (Mean, SD)

Sources

Aact (Attitudes)

For me, experiencing local cuisine during this vacation in New Orleans will be . . . Aact1 : Good–Bad (5.99, .99) Aact2 : Desirable–Undesirable (5.99, 1.05) Aact3 : Valuable–Worthless (5.83, 1.10) Aact4 : Enjoyable–Unenjoyable (5.94, 1.05)

Ajzen & Fishbein (1980); Bagozzi et al. (2000)

SN (Subjective norms)

Very unlikely–Very likely SN1 : Most people who are important to me think I should experience local cuisine during this vacation in New Orleans. (5.63, 1.07) SN2 : It is expected of me that I experience local cuisine during this vacation in New Orleans. (5.41, 1.27) SN3 : Most of people who are important to me think it is good for me to experience local cuisine during this vacation in New Orleans. (5.61, 1.12)

Ajzen & Fishbein (1980); Bagozzi et al. (2000); Cheng et al. (2006)

PB (Past behavior)

Not at all–Very much PB1 : I experience local cuisine every time I am on vacation at my travel destination. (5.83, 1.31) PB2 : I never experience local cuisine when I am on vacation at my travel destination. (5.68, 1.22)

Ryu & Jang (2006)

BI (Behavioral intention)

BI1 : I intend to experience local cuisine during this trip in New Orleans. (Very unlikely–Very likely) (6.09, 1.03) BI2 : I plan to experience local cuisine during this trip in New Orleans. (Strongly disagree–Strongly agree) (5.61, 1.27) BI3 : I will try to experience local cuisine during this trip in New Orleans. (Very unlikely–Very likely) (6.25, .94)

Ajzen & Fishbein (1980); Bagozzi et al. (2000); Ryu & Jang (2006)