The effects of cognitive food image and tourists’ attitude toward local food on tourists’ intention associated with destination food: A meta-analysis

The effects of cognitive food image and tourists’ attitude toward local food on tourists’ intention associated with destination food: A meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The effects of cognitive food image and tourists’ attitude toward local food on tourists’ intention associated with destination food: A meta-analysis Jianlun Zhang, Yun Rao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8683550/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Previous literature that investigates the determinants of various aspects of tourists’ intention related to local food has not been reviewed in a comprehensive manner. By employing the meta-analysis method, the study examines the antecedents of tourists’ intention related to local food. The objectives of the study are firstly, to review previous literature about tourists’ various aspects of intention, cognitive food image and tourists’ attitude constructs. Secondly, we examine the magnitude of the relationships of cognitive food image-intention and attitude-intention by the combined effect size index. A total of 57 studies that investigate the effects of external (i.e. cognitive food image) and internal (tourists’ attitude toward local food) factors on tourists’ intention related to destination food are selected for meta-analysis. The results show that cognitive food image has large effect size on tourists’ intention to recommend local food. Medium level of effect size is found in the cognitive food image-behavioral intention, cognitive food image-intention to visit the destination for food tourism and attitude-intention to consume local food relationships. The effect sizes of cognitive food image-intention to consume, cognitive food image-revisit intention, attitude-behavioral intention, attitude-intention to visit and attitude-intention to recommend relationships are low. The study provides implications on the management of local food offerings to destination managers. Earth and environmental sciences/Environmental social sciences Biological sciences/Psychology Social science/Psychology Figures Figure 1 1. Introduction Local food is one of the key factors that contributes to the success of a tourism destination (Seo et al., 2017 ). According to a survey by World Food Travel Association ( 2020 ), 70% of people select destination based on its food and beverage offerings. Since the global food tourism industry becomes increasingly competitive, investigating how tourists’ intention related to destination food is formed is an important subject in tourism development and marketing. The investigation of tourists’ intention can inform destination manager of the overall success of destination food as a tourism offering (Ling et al., 2010 ). Existing research shows that tourists’ intention can be affected by external cues such as cognitive food image (Yasami et al., 2020 ; Ibrahimet al., 2025 ) and internal factors such as tourists’ attitude toward local food (Choi et al., 2013 ). However, in destination food research, there is no attempt to integrate findings of past empirical studies regarding the effects of cognitive food image and tourists’ attitude on various aspects of tourists’ intention. It has been acknowledged that cognitive food image is an external stimulus of tourists’ response such as behavioral intention (Yasami et al., 2020 ). Previous research also supports that tourists’ perceived image of destination food can significantly affect their destination choice (Björk & Kauppinen-Räisänen, 2016 ), evaluation of travel experience (Karim & Chi, 2010 ) and post-trip behaviors such as intention to recommend local food to others (Choe & Kim, 2018 ). Presumably, a favorable cognitive food image can trigger tourists’ intention related to destination food. However, previous studies generate inconclusive findings of cognitive food image-intention relationship. A number of studies (e.g. Yasami et al., 2020 ; Soltani et al., 2020 ) contend that cognitive food image has a positive and statistically significant effect on tourists’ intention, while some (e.g. Wu & Liang, 2020 ; Aydin et al., 2021 ) point to a negative and statistically non-significant cognitive food image-intention relationship. The conflicting research findings may be attributed to the different measures used in cognitive food image construct, research settings and sampling methods employed (Lai et al., 2020 ). These differences make it difficult to precisely compare the research findings of studies investigating the same relationship between cognitive food image and intention. This is a common problem in social science research, as “the findings will typically vary across studies in bizarre ways” (Hunter et al., 1982 , p.129). In addition, although many studies (e.g. Tsai & Wang, 2011; Kim et al., 2014 ; Ding et al., 2022 ) support the effect of cognitive food image on tourists’ intention, the magnitude of such effect remains understudied. Therefore, an integrative approach is necessary to synthesize the quantitative results of empirical studies of cognitive food image-intention relationship, which is expected to generate conclusive findings of the magnitude and direction of the relationship. Existing studies support that tourists’ attitude toward local food is a psychological factor internal to tourists, which can influence tourists’ intention related to destination food (Hanafiah & Hamdan, 2020; Issariyakulkarn, 2020 ; Bui et al., 2025 ). Previous studies of destination food research generate mixed findings with respect to the attitude-intention relationship. Although a number of studies (e.g. Phillips et al., 2013 ; Gupta & Sajnani, 2019 ) support a positive and statistically significant attitude-intention relationship, several studies (e.g. Rousta & Jamshidi, 2020 ; Horng et al., 2013 ) find that the effect of attitude on intention is not statistically significant. Moreover, the magnitude of the effect of attitude on intention remains unknown. A scientific and rigorous meta-analytic approach that integrates the conflicting findings of attitude-intention relationship from various destination food studies may yield some generalizations. However, to our knowledge, no studies in destination food research review comprehensively the effect of attitude on intention. The purpose of this research is to draw informative conclusions on the magnitude of the effects of cognitive food image and tourists’ attitude on intention based on the results of previous quantitative studies. We firstly review previous literature about intention, cognitive food image and tourists’ attitude constructs. Secondly, we examine the magnitude of the relationships of cognitive food image-intention and attitude-intention by the combined effect size index. We conduct a literature search in major academic databases for published articles that investigate cognitive food image-intention and attitude-intention relationships. A total of 57 quantitative studies published between 2000 and 2025 are identified, which include 54 articles published in academic journals and 3 doctoral dissertations. The meta-analysis is therefore based on the findings of the 57 studies and the focus is on the magnitude and the direction of the effects of cognitive food image and attitude on intention. This study is divided into several sections. Section 2 provides definitions and measures of the dependent variable (i.e. tourists’ intention associated with destination food). The independent variables, namely cognitive food image and tourists’ attitude toward local food, are discussed separately in section 3 and 4. Section 5 reports the literature search, coding procedures and the statistical analysis method. Section 6 provides the findings of the meta-analysis, and Section 7 provides discussion, practical implications and limitations of the research. 2. Dependent variable-tourists’ intention associated with local food Since intention is an important metric to assess the overall attractiveness of tourism offerings in a destination, research on intention is of great importance to destination marketing (Afshardoost & Eshaghi, 2020 ). According to Lee et al., ( 2007 ), intention is defined as people’s anticipation of performing a desirable behavior in the future. White ( 2014 ) contends that intention expresses one’s personal judgement about whether he/she is likely to perform an actual behavior in the future. Intention has been frequently used as an outcome that corresponds to one’s behavioral response in tourist-image studies (Yasami et al., 2020 ). In the context of destination food research, intention is an important dependent variable that associates with the outcome of one’s decision making and captures tourists’ behavioral response to local food offerings in a destination (Seo et al., 2017 ). Previous research identifies 5 aspects of tourists’ intention related to destination food. Several studies (e.g. Chi et al., 2013 ; Choi et al., 2013 ; Wang, 2015 ) use behavioral intention as an outcome variable, which is defined as the degree to which an individual has made conscious plans to engage in some future behaviors (Warshaw & Davis, 1985 ). Indicators used to measure behavioral intention capture tourists’ intention to perform different behaviors, such as intention to consume and recommend local food (Choi et al., 2013 ). However, most studies only examine one aspect of intention, such as tourists’ intention to visit the destination for food tourism (Kim et al., 2012 ), intention to consume destination food (Seo et al., 2017 ), intention to revisit the destination for local food (Li et al., 2021 ) and intention to recommend local food to friends and/or relatives (Rousta & Jamshidi, 2020 ). All five aspects of intention are reflectively measured by different indicators. Based on the extant research on destination food, the antecedents of tourists’ intention can be classified into external and internal factors. Research finds that cognitive food image is an external factor that acts as the stimulus of tourists’ intention (Seo et al., 2017 ; Yasami et al., 2020 ). According to Yasami et al., ( 2020 ), external factor is not under the control of tourists but can have an impact on their intention. In contrast to external factor, internal factor corresponds to the predisposition of intention, which includes psychological constructs such as tourists’ attitude. Internal factor generally resides in individual’s mind and expresses chronic characteristics of influence on one’s intention (Choi et al., 2013 ). Although previous studies of destination food have investigated the determinants of tourists’ intention, most of them only focus on one or two aspects of intention and the entire body of literature has not been examined in a comprehensive manner. 3. Independent variable-cognitive food image 3.1 Definitions and measures Studies of the image of tourism destinations/products have received growing interest since Hunt ( 1975 ) introduced the word ‘image’ in tourism planning and development research. According to Gartner ( 1993 ), image is defined, in the marketing study, as the combination of people’s emotional and factual input of a specific object in the external environment. In tourism studies, Agapito et al., ( 2013 ) define image of a destination as a set of attributes that stimulate tourists’ cognition and emotion associated with the destination, which can affect tourists’ intention in 3 stages (i.e. pre-visit, visit and post-visit stages). Long (2004) claims that destination food image is derived from destination image research, which aims to understand the impact of local food on destination image. With respect to the conceptualization of image, Gartner ( 1993 ) argues that image is a higher-order construct (HOC) which can include its cognitive, affective and conative components. In destination food research, only one study (i.e. Lai et al., 2020 ) examines 3 components of food image mentioned above. Cognitive food image is defined as a set of intangible attributes of local food which can affect individual’s perception of destination food and represents the sum of his/her understanding on destination cuisine. The affective component of food image captures the emotional content of food stimulus that can affect individual’s feelings towards local food. As illustrated in Appendix 1, 31 studies have investigated the relationship between cognitive food image and intention. Existing studies on the relationship between affective food image and intention are limited, and only 7 articles have investigated affective food image (see Appendix 1). In addition, previous destination food research generates consistent findings that affective food image is positively related to intention. The study by Lai et al., ( 2020 ) is the only one that investigates conative food image and they use conative image of destination food interchangeably with tourists’ intention to visit the destination for food tourism. However, in psychology research, conation is defined as a psychological process that drives an individual to perform a certain behavior, which is not exactly the same as intention (Perugini & Bagozzi, 2004). White ( 2014 ) contends that in tourist-image studies, conative image is defined as the possibility or tendency that one will behave in a particular way toward a tourism product and is used as a predictor of intention. The above discussion seems to suggest that both affective image and conative image of destination food have received little attention. Moreover, previous research has not distinguished conative food image from intention, and no studies have investigated the effect of conation food image on intention. Therefore, this study will only focus on analyzing the effect of cognitive image of local food on tourists’ intention. Research supports that tourists’ perception of local food can be determined by a set of food consumption values, such as health, price and experiential values (Choe & Kim, 2018 ; Soltani et al., 2020 ; Ibrahim et al., 2025 ). In addition, tourists’ perceived image of local food can be affected by a set of food attributes such as perceived food quality and culinary culture in the destination (Seo et al., 2017 ). Tourists’ cognitive perception of local food originates from various attributes of destination food, which aptly supports that cognitive image of destination food is measured as a multi-dimensional construct (Karim & Chi, 2010 ; Chi et al., 2013 ). Several studies (e.g. Ling et al., 2010 ; Lertputtarak, 2012 ) measure cognitive image of destination food as a uni-dimensional construct which generally captures sensory attributes of local food. Appendix 2 demonstrates studies that measure cognitive image of destination food as a multi- or uni-dimensional construct. In the 31 studies that investigate cognitive food image, a total of 18 studies measure cognitive food image as a multi-dimensional construct and the rest of the studies measure it as a uni-dimensional construct. Existing studies measure cognitive food image construct using both formative (e.g. Lai et al., 2020 ) and reflective (e.g. Ding et al., 2022 ) measurement methods. Using formative/reflective measurement methods, cognitive food image construct is formatively/reflectively measured by different dimensions (e.g. food culture, dining environment and food quality). These dimensions are also formatively/reflectively measured by various indicators. According to Coltman et al., ( 2008 ), formative measurement model assumes that indicators define the construct and the direction of causality flows from the indicators to the construct. In contrast, reflective measurement models denote that indicators are correlated and the direction of causality flows from the construct to the indicators. In addition, formative indicators are not necessarily interchangeable, while reflective indicators are usually interchangeable. Figure 1 depicts 2 types of measurement models for cognitive food image construct. These measurement models are developed based on the review of previous studies of cognitive food image. We follow previous literature to measure cognitive food image as a multi-dimensional construct using both formative and reflective measurement methods. It should be noted that the relations shown in Fig. 1 do not represent the path/structural relationship between constructs. It shows how different dimensions of cognitive food image are formatively/reflectively measured. Insert Fig. 1 here 3.2 Cognitive food image and intention Previous tourist-image studies are generally conducted from the perspectives of different stakeholders, such as the host and tourist perspectives (Sims, 2009 ). The majority of existing studies investigate cognitive food image from the tourists’ perspective, focusing on the research on the relationship between cognitive food image as external stimulus and tourists’ behavioral response such as intention (Chatterjee & Suklabaidya, 2020; Yasami et al., 2020 ). Research on the relationship between local food image and tourists’ behavioral response is important since it sheds light on tourists’ expectation of local food experience in the destination (Badu-Baiden et al., 2022 ; Lokman & Buyruk, 2025 ). The investigation of cognitive food stimulus that affects tourists’ intention associated with local food can provide insights into the image projection strategies (Yasami et al., 2020 ). Several studies in destination food research support the positive relationship between cognitive image of destination food and tourists’ behavioral intention (Ling et al., 2010 ; Lertputtarak, 2012 ; Tu et al., 2017 ). The study by Seo et al., ( 2017 ) supports that cognitive image of destination food can have significant impact on tourists’ intention to consume local food. Choe and Kim ( 2018 ) contend that the formation of tourists’ cognitive food image can affect tourists’ intention to visit the destination for food tourism. Toudert and Bringas-Rábago ( 2019 ) find a statistically significant relationship between cognitive food image and international tourists’ intention to revisit USA for local food. Yasami et al., ( 2020 ) employ the stimulus-organism-response (SOR) framework to explain why tourists want to recommend local food to friends/relatives. They argue that local food image is a source of external stimulus (S) that influences tourists’ perception of local food. Such perception of destination food affects the processing of information related to food image in tourists’ mind and their sense of satisfaction on local food is formed (O). Such sense of satisfaction can ultimately affect tourists’ behavioral response (R), such as tourists’ intention to recommend local food. Although previous research supports the positive effects of cognitive food image on 5 aspects of intention, the magnitude of these effects remains unknown. In addition, Fam et al., ( 2019 ) find that the effect of cognitive food image on tourists’ intention to consume local food is not statistically significant. Research by Chi et al., ( 2013 ) suggests that the effect of cognitive food image on tourists’ behavioral intention in general is negative and non-significant. Aydin et al., ( 2021 ) reveal a negative and statistically non-significant cognitive food image-intention to visit destination for food tourism relationship. Since there are some inconsistencies in the findings reported in past studies, it is necessary to conduct a meta-analysis to draw conclusion on the direction and magnitude of the cognitive food image-intention relationship. Table 1 summarizes previous research on destination food image-intention relationship. Table 1 Summary of studies of destination food image Author (year) Purpose of the study Method and sample Measures of destination food image (DFI) Data analysis method Contributions/limitations Cognitive image (CI) Affective image (AI) Seo et al., ( 2017 ) To examine how destination food image affects tourists' preference of destination cuisine and intention to eat local food. An on-site survey was conducted in the Incheon International Airport. Respondents include Japanese, Chinese and Americans (N = 357). Multi-dimension with 28 indicators. 5 dimensions : -Quality & safety -Food attractiveness -Health benefits -Food culture -Cooking methods Uni-dimension with 5 indicators : -Contentment -Fulfillment -Pleasantness -Enjoyment -Excitement Structural Equation Modeling (SEM) Contributions : -Propose the determinants of DFI and its measures. -Model CI as a multi-dimensional construct. -Include AI in the conceptual model and test its effect on intention. -Identify the effect of culture difference on DFI. Limitations : -Only Korean food is investigated. Results might not be generalizable. -Lacks investigation on the change of DFI at the post-visit stage. Lai et al., ( 2020 ) 1) To model CI as a formatively measured construct. 2) To test the cognitive-affective-conative model in the context of destination food research. 3) To examine the effect of food personality traits. Phase 1 : Conduct a survey among food tourism industry stakeholders to identify food destination attributes. Phase 2 : Conduct a survey among Chinese tourists (N = 520). Multi-dimension with 40 indicators. 6 dimensions : -Destination environment -Food culture -Food & People -Food quality -Dining places -Food activities Uni-dimension with 8 indicators : -Excitement -Satisfaction -Arousal -Enjoyment -Fun -Pleasantness -Relaxation -Good Partial least square-structural equation modeling (PLS-SEM). Contributions : -Propose and test the cognitive-affective-conative model in the context of destination food research. -Identify the moderating effect of food personality traits. -Model CI using formative measures. Limitations : -Only Chinese tourists are involved in the survey. -Lack theoretical justification on the use of conative food image. Table 1 (Continued) Author (year) Purpose of the study Method and sample Measures of destination food image (DFI) Data analysis method Contributions/limitations Cognitive image (CI) Affective image (AI) Fam et al., ( 2019 ) 1) To examine how destination food image affect tourists’ expectation, satisfaction and behavioral intention in the U.S. 2) To investigate the behavioral patterns of local food consumption among tourists and excursionists. An on-site survey was conducted in the U.S. among international tourists (N = 518). Multi-dimension with 11 indicators. 4 dimensions : -Food quality -Product quality -Food value -Food diversity Not tested PLS-SEM Contributions : -Fill in the gap of research of destination food image and behavioral responses to local food. -Model CI as a multi-dimensional construct using hierarchical component method. -Identify different perceptions of food image based on tourists’ profile (i.e. tourists and excursionists). Limitations : -Lack investigation on culinary culture dimension in CI. -Affective image was not investigated in the study. Choe & Kim ( 2018 ) To investigate the relationship among food consumption values, tourists’ attitude toward local food, destination food image and tourists’ behavioral intention. -Interviews with 10 experts in the food tourism industry -An on-site survey was conducted among tourists in Hong Kong International airport (N = 875). Uni-dimension with 5 indicators which include: -Rich food culture -Food diversity -Food quality -Traditional food culture -Food uniqueness Not tested SEM Contributions : -Model food consumption values as the antecedents of destination food image. -Identify the culture difference among Asians and Westerners in terms of food consumption values. Limitations : -Scales developed to measure food consumption values have not been tested in different contexts. -Lack theoretical support on food consumption values-food image relationship. Insert Table 1 here 4. Independent variable-tourists’ attitude toward local food 4.1 Definitions and measures Consumer’s attitude toward a product/service has been a vital aspect in marketing and psychology research. Although the definition of attitude can vary slightly across different research disciplines, attitude is generally defined as a predisposed state of mind which is triggered by a responsive expression (Eagly & Chaiken, 1993). Such responsive expression is internally driven by one’s evaluation of a person, an object and a behavior, which impacts his/her intention (Fishbein & Ajzen, 1974 ). In destination food research, individual’s attitude toward destination food is defined as the degree of one’s favor or disfavor of local food (Choe & Kim, 2018 ). According to Rousta and Jamshidi ( 2020 ), tourists’ attitude toward destination food is measured by an item which can be worded as “destination food is valuable to me”. Existing research measures tourists’ attitude toward local food as a uni-dimensional construct by reflective measurement method. Previous studies find that tourists’ positive attitude toward local food can be driven by a pleasant memory of local food tasting experience (Badu-Baiden et al., 2022 ), food consumption values (Choe & Kim, 2018 ; Ibrahim et al., 2025 ), emotions linked with local food (Li et al., 2021 ), destination foodscape (Su et al., 2020 ) and satisfaction of tasting destination cuisine (Jaeheng & Han, 2020). Tourists’ attitude toward local food is an antecedent of tourists’ intention, such as behavioral intention (Gupta et al., 2018 ; Bui et al., 2025 ), intention to consume destination food (Su et al., 2020 ), intention to visit the destination for food tourism (Pratt & Sparks, 2014 ) and intention to recommend local food (Choe & Kim, 2018 ). Studies that examine the effect of tourists’ attitude toward local food on tourists’ intention to revisit the destination for local food are not available. The investigation of how tourists’ intention is influenced by tourists’ attitude toward local food is important because it shows the overall success of local food offerings (Badu-Baiden et al., 2022 ). 4.2 Tourists’ attitude toward local food and intention Tourists’ attitude toward local food is an important construct in tourism research, since it can explain the variation of tourists’ food consumption intention (Choe & Kim, 2018 ). Choi et al., ( 2013 ) support that tourists’ perceived risks/benefits of consuming street food in South Korea affect the formation of tourists’ attitude toward South Korea street food, which consequently influences their behavioral intention. Choe and Kim ( 2018 ) contend that tourists’ positive attitude toward local food stimulated by food sensory attributes, perceived food taste and the perception of food service environment can affect tourists’ intention to visit the destination for food tourism. Phillips et al., ( 2013 ) find that the country image of South Korea can influence tourists’ attitude toward local food, which in turns affects tourists’ intention to visit South Korea as a culinary destination. Several studies (e.g. Zhang et al., 2018 ; Ahmad et al., 2019 ; Ting et al., 2019 ) employ the theory of planned behavior (TPB) proposed by Ajzen ( 1991 ) as the theoretical foundation to explain how tourists’ intention to consume local food is triggered by their attitude. The TPB suggests that individual’s attitude toward performing a certain behavior is formed by a set of evaluative appraisals and is one of the factors that influences his/her local food consumption intention (Hsu et al., 2018 ). Su et al., ( 2020 ) argue that tourists’ positive attitude triggered by a favorable destination foodscape is a strong predictor of tourists’ intention to consume local food. The study by Jaeheng and Han (2020) identifies a set of Thai street food attributes and they contend that tourists’ attitude toward Thai street food can influence their intention to recommend the food to friends and/or relatives. However, Rousta and Jamshidi ( 2020 ) reveal that the relationship between attitude toward local food and intention to recommend destination food to friends and/or relatives is negative and statistically non-significant. In addition, Horng et al., ( 2013 ) study factors that influence tourists’ behavioral intention but they find a statistically non-significant attitude-behavioral intention relationship. It can be seen that previous studies on tourists’ attitude toward local food-behavioral intention and attitude-intention to recommend destination food relationships generate mixed findings. A meta-analysis that integrates the findings of extant research on how attitude toward local food affects tourists’ intention is necessary. Table 2 summarizes past studies of the relationship between tourists’ attitude and tourists’ intention. Table 2 Summary of studies of tourists’ attitude toward local food Author (year) Purpose of the study Method and sample Theoretical underpinning Data analysis method Contributions/limitations Ryu & Jang ( 2006 ) To predict tourists’ behavioral intention toward consuming destination food and test the validity of the Theory of Reasoned Action (TRA). A survey was conducted among undergraduate and postgraduate students at the Midwestern University in the U.S (N = 366). TRA SEM Contributions : -Empirically test the TRA in the context tourists’ food-related behavioral intention. -Provide evidence that supports the relationship between tourists’ attitude toward destination food and behavioral intention on local food. Limitations : - Respondents are all university students. Results might not be generalizable. -The study does not validate the model by respondents with different cultural background. Ting et al., (2016) To investigate the determinants of tourists’ local food consumption behavior and test the Theory of Planned Behavior (TPB). A survey was conducted among college and university students who are not from Malaysia (N = 211). TPB PLS-SEM Contributions : - Empirically test the TPB in the context tourists’ food-related behavioral intention. -Identify the moderating effect of food-related personality on the attitude-intention relationship. Limitations : - Respondents are college and university students. Results might not be generalizable. -Respondents’ belief of Malaysia ethnic food which can be the antecedent of behavioral intention is not investigated. Table 2 (Continued) Author (year) Purpose of the study Method and sample Theoretical underpinning Data analysis method Contributions/limitations Jeaheng & Han ( 2020 ) -To identify the food attributes that contribute to the formation of tourists’ attitude toward local food. -To investigate the moderating effect of tourists’ perceived risk of food consumption on the attitude-intention relationship An on-site survey was conducted among international tourists who have tasted Thai street food (N = 475). Not specified SEM Contributions : -Identify a set of food attributes that contribute to the formation of tourists’ attitude toward local food. -Support the moderating effect of perceived risks of street food consumption on the attitude-intention relationship. Limitations : - Effect of cofounding variable such as cultural difference among respondents is not included in the model. -Theoretical underpinning of the study is not specified. Wu et al., (2016) To investigate the determinants of Chinese tourists’ local food consumption behavior in the U.S. and test the Theory of Planned Behavior (TPB). Snowball sampling method was employed to collect data from friends and relatives of the authors (N = 278). TPB PLS-SEM Contributions : - Empirically test the TPB in the context tourists’ food-related behavioral intention and identify the antecedents of tourists’ attitude toward local food. -Incorporate the moderating effect of food-related personality in the TPB. Limitations : - The study only investigates Chinese tourists’ local food consumption intention. Results might not be generalizable. -Sample of the study is collected using a nonrandomized data collection technique, which might affect the credibility of the results. Insert Table 2 here 5. Methodology Meta-analysis has been used as a statistical method for synthesizing data from a set of empirical studies that address the same research question. In social science research, such method was first proposed by researchers in psychology, which focuses on finding a general answer to a research question and examining the strength of relationship between two constructs (Lim, 1999 ; Field & Gillett, 2010 ). In meta-analysis, effect size is used to quantify the strength of relationship and is defined as the measure of association between two constructs (Field & Gillett, 2010 ). In the medical field, researchers are interested not only if a treatment is effective but also how much it affects the patients (Borenstein, 2009 ). Therefore, researchers conduct a meta-analysis to compute the effect size and draw conclusion on the magnitude of the treatment effect. Meta-analysis has been widely used in various fields of scientific research (e.g. medical and pharmaceutical research) and social science research (e.g. psychology and economics), but is rarely used in tourism and hospitality research (Afshardoost & Eshaghi, 2020 ). To conduct a meta-analysis, researchers review and select existing studies that investigate the relationship between two constructs (Lim, 1999 ). Data used to indicate the strength and statistical significance of the relationship between two constructs are collected from selected studies. The effect size is calculated to assess the direction and magnitude of the relationship. The effect sizes of the paired constructs in different studies will be standardized and combined to determine the overall magnitude of the effect (Field & Gillett, 2010 ). The comprehensive meta-analysis (CMA) program (version 3.0) is used to conduct the meta-analysis in this study. 5.1 Literature search and coding procedures We conduct a thorough search on databases that include academic journal articles to select studies which investigate the relationship between cognitive food image, tourists’ attitude toward local food and tourists’ intention related to destination food. The search of these databases is a continuous process starting from January 2022 to December 2025, and we keep reviewing and adding articles for analysis during this period. These databases include ScienceDirect, Sage Journals Online, Taylor & Francis Online, Wiley Online Library and Emerald Insight. We use key words such as cognitive food image, destination food image, cuisine image, tourists’ attitude and intention, to search for related articles. To conduct a more comprehensive search, we also use Google Scholar to identify related journal articles. The key words mentioned above are transformed to Boolean phases (i.e. cognitive food image and intention OR destination food image and intention OR cuisine image and intention OR tourist food attitude and intention) to increase the scope of search on articles. A total of 179 articles are screened. We conduct a review of these articles and 54 studies are selected for meta-analysis. Then we conduct ancestral searches by screening the reference lists of the selected articles to identify studies that are omitted in previous searches. We found 3 more studies which are doctoral dissertations. Thus, a total of 57 studies are selected for meta-analysis. We employ the following criteria to select qualified studies from the 179 articles found by searches in journal article databases. First, the study should be written in English. Second, the study is expected to be published between 2000 and 2025. We select the year of 2000 as the starting point because several studies (e.g. Okumus, 2021 ) indicate that research on food tourism has gained increasing attention since the early 2000s. Third, the sample of the study should be based on tourists and the study is expected to investigate the effects of cognitive food image or tourists’ attitude on intention. Fourth, the study is expected to employ quantitative research methods and report the correlation coefficients, path coefficients and/or regression coefficients. A total of three professors (two professors from the University of Macau and one professor from Macau City University) who have been conducting research and teaching in tourism field kindly provide their help to read the 57 studies. They work independently and review all articles. All of them confirm that the selected studies meet the criteria of the articles selection. Appendix 1 shows the 57 studies selected for the meta-analysis. A total of 31 studies investigate the cognitive food image-intention paired constructs and 30 studies examine the effect of attitude on intention. We code the selected articles based on the publication and the article details. Publication details include authors, source of the article (the database we use to retrieve the article) and the name of the journal that publishes the article. Article details are sample size, country/region where the sample was collected, respondents’ profile, correlation/regression/path coefficients, t-values, p-values, independent and dependent variables used in the study. The above data is synthesized by the fist author in an excel file for further data analysis. 5.2 Statistical analysis In meta-analysis, measures of effect size include correlation coefficient, standardized group mean difference (Cohen’s d and Hedges’s g ) and log risk/odds ratios (Harrer et al., 2021 ). Since all studies selected for the meta-analysis employ regression-based method, we use correlation coefficients extracted from the selected studies as measures of effect size. The correlation coefficients will be converted to an effect size index, which corresponds to the observed effect size. The combined effect size is computed based on the weighted mean of the observed effect size value of every study included in meta-analysis to determine the strength of the relationship among constructs (Borenstein et al., 2009 ). In order to compute a proper combined effect size, we check and make sure that the relationship of each pair of constructs (i.e. cognitive food image-intention and attitude-intention) is analyzed by previous studies at least 3 times (Afshardoost & Eshaghi, 2020 ). According to Borenstein et al., ( 2009 ), the correlation coefficient of each study is converted to a Fisher’s Z-scale, which is calculated as: Z i = 0.5ln[(1 + r i )/(1- r i )] (1) The Fisher’s Z-scale is transformed to a weighted-average ( \(\:\stackrel{-}{{\text{Z}}_{\text{r}}}\) ) score using the following equation: $$\:\stackrel{-}{{\text{Z}}_{\text{r}}}=\:\frac{\sum\:_{\text{i}=1}^{\text{g}}{(\text{n}}_{\text{i}}-3)\ast\:{\text{Z}}_{\text{i}}}{\sum\:_{\text{i}=1}^{\text{g}}{\text{n}}_{\text{i}}}$$ 2 The weighted average score is converted to the combined effect size ( \(\:\stackrel{\text{-}}{\text{r}}\) ) using the following equation: $$\:\stackrel{\text{-}}{\text{r}}=\:\frac{{e}^{2\stackrel{-}{{\text{Z}}_{\text{r}}}}-1}{{e}^{2\stackrel{-}{{\text{Z}}_{\text{r}}}}+1}$$ 3 where Z i is the Fisher’ Z-scale of study i and r i is the correlation coefficient of study i; g is the number of effect size; \(\:{\text{n}}_{\text{i}}\) is the sample size of study i; e stands for the exponential constant. It can be seen that \(\:\stackrel{\text{-}}{\text{r}}\) is a transformed effect size index based on the correlation coefficients we extract from various studies that investigate the relationship of paired constructs. In this study, we distinguish between the fixed-effect and random-effects models to estimate the combined effect size. One can employ a fixed-effect model for meta-analysis if the studies selected are identical to each other, which means all studies sample subjects from the same group of people with same research setting. The word ‘fixed’ implies that a designated group of people in the population is under study (Borenstein et al., 2010 ). Some studies included in our meta-analysis may be similar in terms of research context, but it is very unlikely that these studies are identical to each other. In most cases, random-effects model is a more appropriate choice because it allows for variations in research setting and sampling methods across studies. Since there may be multiple true effect sizes under study and thus the plural term (‘effects’) is used in the random-effects model. In addition, the random-effects model can produce generalized conclusions to population (Harrer et al., 2021 ). Therefore, the fixed-effect model is ruled out at the primary stage and we document the combined effect size based on the random-effects model. Furthermore, we make the decision to adopt the random-effects model based on two tests in relation to the heterogeneity in true effect sizes across different studies. We follow Afshardoost and Eshaghi ( 2020 ) and conduct the Q and I 2 tests to examine the variation in true effect sizes. The Q test has a null hypothesis that all studies share a common true effect size and a statistically significant Q statistic (at the 5% significance level) indicates the heterogeneity in effect size. The I 2 test calculates the ratio of between-studies variance to total variance of the observed effect sizes. According to Higgins et al., ( 2003 ), an I 2 value of 0% means the between-studies variance is equal to 0 and all studies share a common true effect size. The values of 25%, 50% and 75% for the I 2 statistic demonstrate low, moderate and high heterogeneity in true effect sizes, respectively. The value of the I 2 statistic that is equal to or greater than 75% supports the use of random-effects model for estimating the combined effect size (Afshardoost & Eshaghi, 2020 ). 6. Findings 6.1 Verification of the use of random-effects model We have to conduct a series of true effect size heterogeneity tests to verify the appropriateness of using the random-effects model to compute the combined effect size. As shown in Table 3 , the Q test statistics reject the null hypothesis of true effect size homogeneity across all studies. The I 2 statistics demonstrate a high level of heterogeneity of true effect size for studies that investigate the relationship of paired constructs. Therefore, the use of the random-effects model to compute the combined effect size is supported in the current study. Table 3 Heterogeneity tests results Variables Behavioral outcomes Q-values DF (Q) P-values I 2 Cognitive image of destination food Behavioral intention 97.848 4 0.000 95.912 Intention to consume destination food 54.553 6 0.000 90.835 Intention to visit the destination 348.567 12 0.000 96.557 Intention to recommend destination food 235.365 6 0.000 98.301 Intention to revisit the destination 120.094 9 0.000 95.004 Attitude toward destination food Behavioral intention 106.735 5 0.000 94.379 Intention to consume destination food 266.741 18 0.000 93.252 Intention to visit the destination 143.931 5 0.000 97.221 Intention to recommend destination food 42.168 3 0.000 92.886 Note: DF stands for the degree of freedom. The column of p-values shows the statistical significance of the Q statistics at the 5% significance level. Insert Table 3 here 6.2 Examinations of the combined effect size This study investigates the direct path relationships of cognitive food image-intention and tourists’ attitude toward local food-intention. As shown in Table 4 , the number of effects corresponds to the number of correlation coefficients included to compute the combined effect size index. Some scholars (e.g. Pratt & Spark, 2014; Choe & Kim, 2018 ; Li et al., 2021 ) include both cognitive food image and attitude as independent variables and 2 behavioral outcomes in a single study. In addition, some studies (e.g. Kim et al., 2014 ; Ting et al., 2019 ; Fam et al., 2019 ) compare findings using 2 to 4 subsamples in a single study. Since the correlation coefficients of the paired constructs of the subsamples are different, they will be considered as separate effects. Therefore, the total for the number of effects (see column 3 in Table 4 ) is higher than the number of studies selected for meta-analysis. Sample size shows the aggregated number of sample in the selected studies. The lower and upper limits correspond to the confidence interval of the combined effect size. The z-statistics and p-values show the statistical significance of the combined effect size. According to Borenstein ( 2009 ), combined effect size values of 0.1, 0.3, 0.5 and 0.7 demonstrate a low, medium, large and very large effect, respectively. Table 4 Effect sizes of cognitive food image and tourists’ attitude toward destination food on intention Independent variables Behavioral outcomes Number of effects Sample size Combined effect size ( \(\:\stackrel{\text{-}}{\text{r}}\) ) Lower limit Upper limit Z-values P-values Cognitive image of destination food Behavioral intention 5 1848 0.330 0.114 0.516 2.947 0.003 Intention to consume destination food 7 1877 0.286 0.067 0.410 2.667 0.008 Intention to visit the destination for food tourism 13 6634 0.301 0.157 0.397 4.323 0.000 Intention to recommend destination food to friends and/or relatives 7 3118 0.514 0.246 0.709 3.507 0.000 Intention to revisit the destination for local food 10 3262 0.281 0.131 0.419 3.596 0.000 Attitude toward destination food Behavioral intention 6 3765 0.289 0.135 0.426 3.612 0.000 Intention to consume destination food 19 6461 0.342 0.311 0.472 8.525 0.000 Intention to visit the destination for food tourism 6 2874 0.234 0.043 0.458 2.235 0.020 Intention to recommend destination food to friends and/or relatives 4 2543 0.245 0.021 0.312 2.236 0.025 Note: Some studies include both cognitive food image and attitude as independent variables and two behavioral outcomes in a single study. In addition, several studies use different independent subsamples to estimate the correlations of a paired construct in a single study. Since the correlations of the paired constructs in these studies are different, they will be treated as separate effects. Therefore, the sum of the number of effects column is more than 56 (the number of selected studies in the meta-analysis). Past literature has not investigated the attitude-revisit intention relationship. Table 4 shows that cognitive food image has a positive influence on different aspects of intention since all effect size estimates are positive. Cognitive food image has a greater effect on tourists’ intention to visit the destination for food tourism ( \(\:\stackrel{\text{-}}{\text{r}}\) = 0.301, 95% [CI] = 0.157 to 0.397, p < 0.01) than tourists’ revisit intention ( \(\:\stackrel{\text{-}}{\text{r}}\) = 0.281, 95% [CI] = 0.131 to 0.419, p < 0.01). The effect of cognitive food image on tourists’ intention to recommend local food to friends and/or relatives is the largest ( \(\:\stackrel{\text{-}}{\text{r}}\) = 0.514, 95% [CI] = 0.246 to 0.709, p < 0.01), compared to the other 4 behavioral outcomes. The lowest value of effect size is found in the relationship between cognitive food image and intention to consume local food ( \(\:\stackrel{\text{-}}{\text{r}}\) = 0.286, 95% [CI] = 0.067 to 0.410, p < 0.01). Table 4 also shows that tourists’ attitude is positively related to various aspects of intention as evidenced by the positive effect size estimates. The greatest effect size is found in the attitude-intention to consume local food relationship ( \(\:\stackrel{\text{-}}{\text{r}}\) = 0.342, 95% [CI] = 0.311 to 0.472, p < 0.01). The effect of attitude on intention to visit the destination for food tourism ( \(\:\stackrel{\text{-}}{\text{r}}\) = 0.234, 95% [CI] = 0.043 to 0.458, p < 0.05) is the lowest among the 4 behavioral outcomes. To compare the effect of cognitive food image with attitude on intention, cognitive food image has larger effect on tourists’ behavioral intention, intention to visit and intention to recommend. The value of the effect size of attitude-intention to consume local food relationship is relatively higher than that of the relationship between cognitive food image and intention to consume local food. Overall, the effect size of cognitive food image-intention is higher than that of attitude-intention relationship. Insert Table 4 here 6.3 Publication bias Publication bias was firstly proposed by researchers in psychology in the 1950s (Thorton & Lee, 2000). Publication bias is a term that associates with the concern about the representativeness of a single study or a set of studies. In meta-analysis, publication bias may arise when researchers only focus on published studies in a given period, which may lead to misleading conclusions (Thorton & Lee, 2000). Since we do not include articles that are published before 2000, there may be articles omitted in our study. We conduct 2 statistical tests to detect if there is publication bias in our study. First, we conduct the Egger’s regression to regress the observed effect size of study i divided by its standard error on the inverse of standard error of study i. The Egger’s regression tests the null hypothesis that the intercept of the regression is equal to zero in population. A statistically significant intercept (at the 5% significance level) indicates the existence of publication bias in the meta-analysis. Second, we conduct a fail-safe-number (FSN) test to estimate the number of missing studies required to bring down the p-values of the combined effect size to a non-significant level. At the 5% significance level, a FSN value exceeding 5 times of the number of studies plus 10 (5N + 10) rejects the null hypothesis of publication bias (Carson et al., 1990 ). As demonstrated in Table 5 , the Egger’s regression intercept shows that the null hypothesis of no publication bias cannot be rejected at the 5% significance level. The critical values of FSN all exceed the suggested threshold of 5 times of the number of studies plus 10 at the 5% significance level. To interpret the meaning of the FSN, we exemplify the FSN value for studies that investigate the cognitive food image-behavioral intention relationship in Table 5 . It is very unlikely that we miss 298 studies during the search of articles investigating such relationship, and the number of 298 exceed 5 times of the number of studies plus 10 (5N + 10). Thus, the issue of publication bias is not a concern for this study and the meta-analysis generates reliable results. Table 5 Publication bias test results Variables Behavioral outcomes Number of effects Egger’s regression intercept P-values FSN Cognitive image of destination food Behavioral intention 5 1.315 0.285 298 Intention to consume destination food 7 1.077 0.342 251 Intention to visit the destination 13 0.734 0.478 1723 Intention to recommend destination food 7 0.687 0.541 1221 Intention to revisit the destination 10 0.310 0.769 560 Attitude toward local food Behavioral intention 6 0.854 0.427 652 Intention to consume destination food 19 0.968 0.347 5007 Intention to visit the destination 6 1.550 0.219 197 Intention to recommend destination food 4 0.570 0.626 63 Insert Table 5 here 7. Discussion and practical implications 7.1 Discussion In spite of the increase of research on destination food in the past two decades, very few studies conduct a review on the determinants of various aspects of tourists’ intention related to local food. This research is the first meta-analysis that investigates the antecedents of various aspects of tourists’ intention in the context of destination food research. To our knowledge, none of the existing studies integrate cognitive food image and attitude constructs to investigate their effects on different aspects of intention in a single study. A thorough literature search yields 179 published journal articles in major academic databases and a total of 57 studies with a sample size of 32,382 are synthesized into a meta-analysis. A comprehensive review of the 57 selected studies finds that various aspects of tourists’ intention related to local food can be affected by external (i.e. cognitive food image) and internal factors (i.e. tourists’ attitude toward local food). Although several studies in previous destination food research find non-significant cognitive food image-intention (e.g. Aydin et al., 2021 ; Hashemi et al., 2023 ) and attitude-intention relationships (e.g. Rousta & Jamshidi, 2020 ; Horng et al., 2013 ), results of our study generally support that both cognitive food image and tourists’ attitude have positive and significant impact on tourists’ intention. In the research on destination food image, a number of studies empirically support the positive relationship between cognitive food image and tourists’ intention, such as tourists’ intention to visit the destination for food tourism and intention to consume local food (Seo et al., 2017 ; Choe & Kim, 2018 ; Li et al., 2021 ). Based on the empirical results of previous research, our research examines the effect sizes of cognitive food image on various aspects of intention. The largest effect size is found in the relationship between cognitive food image and intention to recommend destination food, while the smallest effect size is found in the cognitive food image-intention to consume local food relationship. Compared to cognitive food image, affective food image receives less attention in destination food research. Studies that examine the influence of affective food image on tourists’ behavioral intention, intention to consume local food, intention to recommend destination food and revisit intention are not available. Additionally, little is known in terms of the measure of the affective food image construct. Therefore, it is necessary to conduct more research on affective food image. Research on the conative food image component has received very little attention. Only one study in destination food research (i.e. Lai et al., 2020 ) investigates the conative food image construct. In addition, the relationship between conative food image and intention has not been examined. Thus, more future research needs to be conducted to validate the conative food image component and its effect on intention. 7.2 Practical implications The findings of the study support that various aspects of tourists’ intention associated with local food can be affected by external (i.e. cognitive food image) and internal (i.e. tourists’ attitude toward local food) factors. The understanding of local food offerings and the antecedents that can be used to predict tourists’ intention related to destination food are important to destination marketers. Instead of providing a narrative review of relevant studies that investigate the determinants of intention, this study conducts a review of previous literature through a meta-analysis. Destination managers can refer directly to the findings of this study with regard to the internal (i.e. tourists’ attitude toward local food) and external factors (i.e. cognitive food image) that affect various aspects of tourists’ intention. An enhanced knowledge of cognitive food image-intention and attitude-intention relationships provides better understanding of tourists’ decision-making and behavioral process associated with local food. Such knowledge is important to the formation of destination managers’ strategic vision and initiatives of food tourism destination promotion. If destinations’ promotion using local food is effective, tourists’ positive perception of destination food should have large effect on both tourists’ local food consumption intention and revisit intention. However, the study finds that cognitive food image has low effect on both tourists’ intention to consume local food and revisit intention. The findings seem to suggest that the use local food is not sufficiently attractive for destination promotion and to entice tourist to revisit. Destination managers need to consider other tourism offerings (e.g. festival and heritage) in conjunction with local food to promote the destination. The results of this research show that tourists’ attitude toward local food has a positive influence on intention. Since tourists’ attitude can act as an internal driver of intention, destination managers can consider ways to create and affirm tourists’ positive attitude in relation to destination food. For example, destination managers can promote and emphasize the memorable experience with local food, which may help to create a positive evaluation of destination food. This can be done by showing videos and narratives to tell stories about local food in destination promotional media and materials, which might include interaction with local people in food preparation and tasting, themed-based learning of making local food, and promotion of food souvenirs. 7.3 Limitations and future research There are several limitations in this study. First, according to Lim ( 1999 ), since the meta-analysis is based on the aggregated data of individual studies, the quality of the meta-analysis may be dependent on the quality of the selected studies. This is one of the inherent limitations of a meta-analysis, which indicates the need for advancement of conceptualization and research framework for primary studies (Afshardoost & Eshaghi, 2020 ). Second, although meta-analysis is a powerful statistical method that synthesizes previous research findings, the method has been criticized that it fails to incorporate contextual information such as sample characteristics and variation in research design (Borenstein et al., 2010 ). Similar to other meta-analysis research, our study is unable to report such contextual information. Future research may use other effect size measures to investigate indirect paths such as the effect of mediating variables in the cognitive food image-intention relationship. Ethical approval statement The study does not involve human participants or their data. Declarations Competing interests The author(s) declare no competing interests. Informed consent The study does not involve human participants or their data and therefore the informed consent was not obtained. Author Contribution 1. J. Z. comes up with the research idea, reads the relevant articles in the article databases, selects the qualified articles, performs data collection and coding of the articles for meta-analyse, conducts the statistical analyses and writes the paper. Y.R. writes the discussion and implication section and revises the whole paper. Acknowledgement J. Z. discloses support for the research of this work from Shunde Polytechnic University doctoral startup research grant [KYQD035]. Y. R. discloses support for the research of this work from Shunde Polytechnic University doctoral startup research grant [KYQD075] and Shunde Polytechnic University 2025 Annual Key Research Achievement Cultivation Project [2025-KJXJ039]. Data Availability The synthesized table of data for analysis supporting the findings of this study is available from the corresponding author upon reasonable request. References Afshardoost M, Eshaghi MS (2020) Destination image and tourist behavioral intentions: A meta-analysis. 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J Bus Res 69(10):4706–4713 Yasami M, Promsivapallop P, Kannaovakun P (2020) Food image and loyalty intentions: Chinese tourists’ destination food satisfaction. J China Tourism Res 17(4):592–612 Zhang H, Li L, Yang Y, Zhang J (2018) Why do domestic tourists choose to consume local food? The differential and non-monotonic moderating effects of subjective knowledge. J Destination Mark Manage 10:68–77 Zhang J, Choe JYJ, Lim C (2022) The influence of cognitive image on tourists’ desire and intention to try Macanese food: Moderating roles of perceived difficulty and gender. J China Tourism Res 19(3):489–516 Zhu Y, Zhu L, Weng L (2024) How do tourists’ value perceptions of food experiences influence their perceived destination image and revisit intention? moderated mediation model Foods 13(3):412–435 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8683550","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":624922580,"identity":"29288c2c-285f-4cd8-ac58-f4f679292cd3","order_by":0,"name":"Jianlun Zhang","email":"","orcid":"","institution":"Shunde Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Jianlun","middleName":"","lastName":"Zhang","suffix":""},{"id":624922582,"identity":"580752d5-76e7-40dd-9830-ca48c1719902","order_by":1,"name":"Yun Rao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACfoaDjY9/VDAwNoB4PMRokWw8fNiY4QxQCxuxWgwOH0uTZmwjRQvDsTPGxoXz6mS3yzcwPnjbxiBvTkgHY88Zw8cztx023tnGwGw4t43BcGcDAS3MEmeMDXi3HUjccIyBTZq3jSHB4AABLWzyb8wkeOfUgbSw/yZKCw8D0Pu8DcxgW5iJ0iLBcPiw4YxjIL8kNkvOOSdhuIGQFvsDBxsffKgBhhjz4YMf3pTZyBO0BQ4MIAlAglj1YC2jYBSMglEwCnAAAFdQRJo/i57aAAAAAElFTkSuQmCC","orcid":"","institution":"Shunde Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Yun","middleName":"","lastName":"Rao","suffix":""}],"badges":[],"createdAt":"2026-01-24 03:53:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8683550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8683550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107377379,"identity":"6226a321-a33a-4b9c-9bd8-1b1f41775832","added_by":"auto","created_at":"2026-04-21 01:24:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100056,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8683550/v1/aa60df2c5ce0cc148145ee59.jpg"},{"id":107377408,"identity":"b5b56742-247b-412d-8a70-ce897c8d1567","added_by":"auto","created_at":"2026-04-21 01:24:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1035410,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8683550/v1/f2868e40-603c-4681-8e7d-60bdd03a1734.pdf"},{"id":107377377,"identity":"e3be556d-13a2-4714-bca6-027a76048edb","added_by":"auto","created_at":"2026-04-21 01:24:21","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":43938,"visible":true,"origin":"","legend":"","description":"","filename":"Tablesandappendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8683550/v1/48f88af51d4df2f2a8638afc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effects of cognitive food image and tourists’ attitude toward local food on tourists’ intention associated with destination food: A meta-analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLocal food is one of the key factors that contributes to the success of a tourism destination (Seo et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to a survey by World Food Travel Association (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), 70% of people select destination based on its food and beverage offerings. Since the global food tourism industry becomes increasingly competitive, investigating how tourists\u0026rsquo; intention related to destination food is formed is an important subject in tourism development and marketing. The investigation of tourists\u0026rsquo; intention can inform destination manager of the overall success of destination food as a tourism offering (Ling et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Existing research shows that tourists\u0026rsquo; intention can be affected by external cues such as cognitive food image (Yasami et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ibrahimet al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and internal factors such as tourists\u0026rsquo; attitude toward local food (Choi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, in destination food research, there is no attempt to integrate findings of past empirical studies regarding the effects of cognitive food image and tourists\u0026rsquo; attitude on various aspects of tourists\u0026rsquo; intention.\u003c/p\u003e \u003cp\u003eIt has been acknowledged that cognitive food image is an external stimulus of tourists\u0026rsquo; response such as behavioral intention (Yasami et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Previous research also supports that tourists\u0026rsquo; perceived image of destination food can significantly affect their destination choice (Bj\u0026ouml;rk \u0026amp; Kauppinen-R\u0026auml;is\u0026auml;nen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), evaluation of travel experience (Karim \u0026amp; Chi, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and post-trip behaviors such as intention to recommend local food to others (Choe \u0026amp; Kim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Presumably, a favorable cognitive food image can trigger tourists\u0026rsquo; intention related to destination food. However, previous studies generate inconclusive findings of cognitive food image-intention relationship. A number of studies (e.g. Yasami et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Soltani et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) contend that cognitive food image has a positive and statistically significant effect on tourists\u0026rsquo; intention, while some (e.g. Wu \u0026amp; Liang, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Aydin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) point to a negative and statistically non-significant cognitive food image-intention relationship. The conflicting research findings may be attributed to the different measures used in cognitive food image construct, research settings and sampling methods employed (Lai et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These differences make it difficult to precisely compare the research findings of studies investigating the same relationship between cognitive food image and intention. This is a common problem in social science research, as \u0026ldquo;the findings will typically vary across studies in bizarre ways\u0026rdquo; (Hunter et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1982\u003c/span\u003e, p.129). In addition, although many studies (e.g. Tsai \u0026amp; Wang, 2011; Kim et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ding et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) support the effect of cognitive food image on tourists\u0026rsquo; intention, the magnitude of such effect remains understudied. Therefore, an integrative approach is necessary to synthesize the quantitative results of empirical studies of cognitive food image-intention relationship, which is expected to generate conclusive findings of the magnitude and direction of the relationship.\u003c/p\u003e \u003cp\u003eExisting studies support that tourists\u0026rsquo; attitude toward local food is a psychological factor internal to tourists, which can influence tourists\u0026rsquo; intention related to destination food (Hanafiah \u0026amp; Hamdan, 2020; Issariyakulkarn, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bui et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Previous studies of destination food research generate mixed findings with respect to the attitude-intention relationship. Although a number of studies (e.g. Phillips et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gupta \u0026amp; Sajnani, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) support a positive and statistically significant attitude-intention relationship, several studies (e.g. Rousta \u0026amp; Jamshidi, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Horng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) find that the effect of attitude on intention is not statistically significant. Moreover, the magnitude of the effect of attitude on intention remains unknown. A scientific and rigorous meta-analytic approach that integrates the conflicting findings of attitude-intention relationship from various destination food studies may yield some generalizations. However, to our knowledge, no studies in destination food research review comprehensively the effect of attitude on intention.\u003c/p\u003e \u003cp\u003eThe purpose of this research is to draw informative conclusions on the magnitude of the effects of cognitive food image and tourists\u0026rsquo; attitude on intention based on the results of previous quantitative studies. We firstly review previous literature about intention, cognitive food image and tourists\u0026rsquo; attitude constructs. Secondly, we examine the magnitude of the relationships of cognitive food image-intention and attitude-intention by the combined effect size index.\u003c/p\u003e \u003cp\u003eWe conduct a literature search in major academic databases for published articles that investigate cognitive food image-intention and attitude-intention relationships. A total of 57 quantitative studies published between 2000 and 2025 are identified, which include 54 articles published in academic journals and 3 doctoral dissertations. The meta-analysis is therefore based on the findings of the 57 studies and the focus is on the magnitude and the direction of the effects of cognitive food image and attitude on intention.\u003c/p\u003e \u003cp\u003eThis study is divided into several sections. Section 2 provides definitions and measures of the dependent variable (i.e. tourists\u0026rsquo; intention associated with destination food). The independent variables, namely cognitive food image and tourists\u0026rsquo; attitude toward local food, are discussed separately in section 3 and 4. Section 5 reports the literature search, coding procedures and the statistical analysis method. Section 6 provides the findings of the meta-analysis, and Section 7 provides discussion, practical implications and limitations of the research.\u003c/p\u003e"},{"header":"2. Dependent variable-tourists’ intention associated with local food","content":"\u003cp\u003eSince intention is an important metric to assess the overall attractiveness of tourism offerings in a destination, research on intention is of great importance to destination marketing (Afshardoost \u0026amp; Eshaghi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to Lee et al., (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), intention is defined as people\u0026rsquo;s anticipation of performing a desirable behavior in the future. White (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) contends that intention expresses one\u0026rsquo;s personal judgement about whether he/she is likely to perform an actual behavior in the future. Intention has been frequently used as an outcome that corresponds to one\u0026rsquo;s behavioral response in tourist-image studies (Yasami et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the context of destination food research, intention is an important dependent variable that associates with the outcome of one\u0026rsquo;s decision making and captures tourists\u0026rsquo; behavioral response to local food offerings in a destination (Seo et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Previous research identifies 5 aspects of tourists\u0026rsquo; intention related to destination food. Several studies (e.g. Chi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Choi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) use behavioral intention as an outcome variable, which is defined as the degree to which an individual has made conscious plans to engage in some future behaviors (Warshaw \u0026amp; Davis, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Indicators used to measure behavioral intention capture tourists\u0026rsquo; intention to perform different behaviors, such as intention to consume and recommend local food (Choi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, most studies only examine one aspect of intention, such as tourists\u0026rsquo; intention to visit the destination for food tourism (Kim et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), intention to consume destination food (Seo et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), intention to revisit the destination for local food (Li et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and intention to recommend local food to friends and/or relatives (Rousta \u0026amp; Jamshidi, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). All five aspects of intention are reflectively measured by different indicators.\u003c/p\u003e \u003cp\u003eBased on the extant research on destination food, the antecedents of tourists\u0026rsquo; intention can be classified into external and internal factors. Research finds that cognitive food image is an external factor that acts as the stimulus of tourists\u0026rsquo; intention (Seo et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yasami et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to Yasami et al., (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), external factor is not under the control of tourists but can have an impact on their intention. In contrast to external factor, internal factor corresponds to the predisposition of intention, which includes psychological constructs such as tourists\u0026rsquo; attitude. Internal factor generally resides in individual\u0026rsquo;s mind and expresses chronic characteristics of influence on one\u0026rsquo;s intention (Choi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although previous studies of destination food have investigated the determinants of tourists\u0026rsquo; intention, most of them only focus on one or two aspects of intention and the entire body of literature has not been examined in a comprehensive manner.\u003c/p\u003e"},{"header":"3. Independent variable-cognitive food image","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Definitions and measures\u003c/h2\u003e \u003cp\u003eStudies of the image of tourism destinations/products have received growing interest since Hunt (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) introduced the word \u0026lsquo;image\u0026rsquo; in tourism planning and development research. According to Gartner (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), image is defined, in the marketing study, as the combination of people\u0026rsquo;s emotional and factual input of a specific object in the external environment. In tourism studies, Agapito et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) define image of a destination as a set of attributes that stimulate tourists\u0026rsquo; cognition and emotion associated with the destination, which can affect tourists\u0026rsquo; intention in 3 stages (i.e. pre-visit, visit and post-visit stages). Long (2004) claims that destination food image is derived from destination image research, which aims to understand the impact of local food on destination image.\u003c/p\u003e \u003cp\u003eWith respect to the conceptualization of image, Gartner (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) argues that image is a higher-order construct (HOC) which can include its cognitive, affective and conative components. In destination food research, only one study (i.e. Lai et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) examines 3 components of food image mentioned above. Cognitive food image is defined as a set of intangible attributes of local food which can affect individual\u0026rsquo;s perception of destination food and represents the sum of his/her understanding on destination cuisine. The affective component of food image captures the emotional content of food stimulus that can affect individual\u0026rsquo;s feelings towards local food. As illustrated in Appendix 1, 31 studies have investigated the relationship between cognitive food image and intention. Existing studies on the relationship between affective food image and intention are limited, and only 7 articles have investigated affective food image (see Appendix 1). In addition, previous destination food research generates consistent findings that affective food image is positively related to intention. The study by Lai et al., (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) is the only one that investigates conative food image and they use conative image of destination food interchangeably with tourists\u0026rsquo; intention to visit the destination for food tourism. However, in psychology research, conation is defined as a psychological process that drives an individual to perform a certain behavior, which is not exactly the same as intention (Perugini \u0026amp; Bagozzi, 2004). White (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) contends that in tourist-image studies, conative image is defined as the possibility or tendency that one will behave in a particular way toward a tourism product and is used as a predictor of intention. The above discussion seems to suggest that both affective image and conative image of destination food have received little attention. Moreover, previous research has not distinguished conative food image from intention, and no studies have investigated the effect of conation food image on intention. Therefore, this study will only focus on analyzing the effect of cognitive image of local food on tourists\u0026rsquo; intention.\u003c/p\u003e \u003cp\u003eResearch supports that tourists\u0026rsquo; perception of local food can be determined by a set of food consumption values, such as health, price and experiential values (Choe \u0026amp; Kim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Soltani et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ibrahim et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, tourists\u0026rsquo; perceived image of local food can be affected by a set of food attributes such as perceived food quality and culinary culture in the destination (Seo et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Tourists\u0026rsquo; cognitive perception of local food originates from various attributes of destination food, which aptly supports that cognitive image of destination food is measured as a multi-dimensional construct (Karim \u0026amp; Chi, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Several studies (e.g. Ling et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lertputtarak, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) measure cognitive image of destination food as a uni-dimensional construct which generally captures sensory attributes of local food. Appendix 2 demonstrates studies that measure cognitive image of destination food as a multi- or uni-dimensional construct. In the 31 studies that investigate cognitive food image, a total of 18 studies measure cognitive food image as a multi-dimensional construct and the rest of the studies measure it as a uni-dimensional construct.\u003c/p\u003e \u003cp\u003eExisting studies measure cognitive food image construct using both formative (e.g. Lai et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and reflective (e.g. Ding et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) measurement methods. Using formative/reflective measurement methods, cognitive food image construct is formatively/reflectively measured by different dimensions (e.g. food culture, dining environment and food quality). These dimensions are also formatively/reflectively measured by various indicators. According to Coltman et al., (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), formative measurement model assumes that indicators define the construct and the direction of causality flows from the indicators to the construct. In contrast, reflective measurement models denote that indicators are correlated and the direction of causality flows from the construct to the indicators. In addition, formative indicators are not necessarily interchangeable, while reflective indicators are usually interchangeable. Figure\u0026nbsp;1 depicts 2 types of measurement models for cognitive food image construct. These measurement models are developed based on the review of previous studies of cognitive food image. We follow previous literature to measure cognitive food image as a multi-dimensional construct using both formative and reflective measurement methods. It should be noted that the relations shown in Fig.\u0026nbsp;1 do not represent the path/structural relationship between constructs. It shows how different dimensions of cognitive food image are formatively/reflectively measured.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInsert Fig.\u0026nbsp;1 here\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Cognitive food image and intention\u003c/h2\u003e \u003cp\u003ePrevious tourist-image studies are generally conducted from the perspectives of different stakeholders, such as the host and tourist perspectives (Sims, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The majority of existing studies investigate cognitive food image from the tourists\u0026rsquo; perspective, focusing on the research on the relationship between cognitive food image as external stimulus and tourists\u0026rsquo; behavioral response such as intention (Chatterjee \u0026amp; Suklabaidya, 2020; Yasami et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research on the relationship between local food image and tourists\u0026rsquo; behavioral response is important since it sheds light on tourists\u0026rsquo; expectation of local food experience in the destination (Badu-Baiden et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lokman \u0026amp; Buyruk, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The investigation of cognitive food stimulus that affects tourists\u0026rsquo; intention associated with local food can provide insights into the image projection strategies (Yasami et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies in destination food research support the positive relationship between cognitive image of destination food and tourists\u0026rsquo; behavioral intention (Ling et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lertputtarak, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tu et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The study by Seo et al., (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) supports that cognitive image of destination food can have significant impact on tourists\u0026rsquo; intention to consume local food. Choe and Kim (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) contend that the formation of tourists\u0026rsquo; cognitive food image can affect tourists\u0026rsquo; intention to visit the destination for food tourism. Toudert and Bringas-R\u0026aacute;bago (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) find a statistically significant relationship between cognitive food image and international tourists\u0026rsquo; intention to revisit USA for local food. Yasami et al., (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) employ the stimulus-organism-response (SOR) framework to explain why tourists want to recommend local food to friends/relatives. They argue that local food image is a source of external stimulus (S) that influences tourists\u0026rsquo; perception of local food. Such perception of destination food affects the processing of information related to food image in tourists\u0026rsquo; mind and their sense of satisfaction on local food is formed (O). Such sense of satisfaction can ultimately affect tourists\u0026rsquo; behavioral response (R), such as tourists\u0026rsquo; intention to recommend local food.\u003c/p\u003e \u003cp\u003eAlthough previous research supports the positive effects of cognitive food image on 5 aspects of intention, the magnitude of these effects remains unknown. In addition, Fam et al., (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) find that the effect of cognitive food image on tourists\u0026rsquo; intention to consume local food is not statistically significant. Research by Chi et al., (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) suggests that the effect of cognitive food image on tourists\u0026rsquo; behavioral intention in general is negative and non-significant. Aydin et al., (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reveal a negative and statistically non-significant cognitive food image-intention to visit destination for food tourism relationship. Since there are some inconsistencies in the findings reported in past studies, it is necessary to conduct a meta-analysis to draw conclusion on the direction and magnitude of the cognitive food image-intention relationship. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes previous research on destination food image-intention relationship.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of studies of destination food image\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAuthor (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePurpose of the study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMethod and sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMeasures of destination food image (DFI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eData analysis method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eContributions/limitations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive image (CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAffective image (AI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeo et al., (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo examine how destination food image affects tourists' preference of destination cuisine and intention to eat local food.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAn on-site survey was conducted in the Incheon International Airport. Respondents include Japanese, Chinese and Americans (N\u0026thinsp;=\u0026thinsp;357).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMulti-dimension\u003c/b\u003e with 28 indicators.\u003c/p\u003e \u003cp\u003e\u003cb\u003e5 dimensions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Quality \u0026amp; safety\u003c/p\u003e \u003cp\u003e-Food attractiveness\u003c/p\u003e \u003cp\u003e-Health benefits\u003c/p\u003e \u003cp\u003e-Food culture\u003c/p\u003e \u003cp\u003e-Cooking methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eUni-dimension\u003c/b\u003e with \u003cb\u003e5 indicators\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Contentment\u003c/p\u003e \u003cp\u003e-Fulfillment\u003c/p\u003e \u003cp\u003e-Pleasantness\u003c/p\u003e \u003cp\u003e-Enjoyment\u003c/p\u003e \u003cp\u003e-Excitement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStructural Equation Modeling (SEM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eContributions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Propose the determinants of DFI and its measures.\u003c/p\u003e \u003cp\u003e-Model CI as a multi-dimensional construct.\u003c/p\u003e \u003cp\u003e-Include AI in the conceptual model and test its effect on intention.\u003c/p\u003e \u003cp\u003e-Identify the effect of culture difference on DFI.\u003c/p\u003e \u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Only Korean food is investigated. Results might not be generalizable.\u003c/p\u003e \u003cp\u003e-Lacks investigation on the change of DFI at the post-visit stage.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLai et al., (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1) To model CI as a formatively measured construct.\u003c/p\u003e \u003cp\u003e2) To test the cognitive-affective-conative model in the context of destination food research.\u003c/p\u003e \u003cp\u003e3) To examine the effect of food personality traits.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePhase 1\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eConduct a survey among food tourism industry stakeholders to identify food destination attributes.\u003c/p\u003e \u003cp\u003e\u003cb\u003ePhase 2\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eConduct a survey among Chinese tourists (N\u0026thinsp;=\u0026thinsp;520).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMulti-dimension\u003c/b\u003e with 40 indicators.\u003c/p\u003e \u003cp\u003e\u003cb\u003e6 dimensions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Destination environment\u003c/p\u003e \u003cp\u003e-Food culture\u003c/p\u003e \u003cp\u003e-Food \u0026amp; People\u003c/p\u003e \u003cp\u003e-Food quality\u003c/p\u003e \u003cp\u003e-Dining places\u003c/p\u003e \u003cp\u003e-Food activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eUni-dimension\u003c/b\u003e with \u003cb\u003e8 indicators\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Excitement\u003c/p\u003e \u003cp\u003e-Satisfaction\u003c/p\u003e \u003cp\u003e-Arousal\u003c/p\u003e \u003cp\u003e-Enjoyment\u003c/p\u003e \u003cp\u003e-Fun\u003c/p\u003e \u003cp\u003e-Pleasantness\u003c/p\u003e \u003cp\u003e-Relaxation\u003c/p\u003e \u003cp\u003e-Good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePartial least square-structural equation modeling (PLS-SEM).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eContributions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Propose and test the cognitive-affective-conative model in the context of destination food research.\u003c/p\u003e \u003cp\u003e-Identify the moderating effect of food personality traits.\u003c/p\u003e \u003cp\u003e-Model CI using formative measures.\u003c/p\u003e \u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Only Chinese tourists are involved in the survey.\u003c/p\u003e \u003cp\u003e-Lack theoretical justification on the use of conative food image.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e(Continued)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAuthor (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePurpose of the study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMethod and sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMeasures of destination food image (DFI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eData analysis method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eContributions/limitations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive image (CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAffective image (AI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFam et al., (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1) To examine how destination food image affect tourists\u0026rsquo; expectation, satisfaction and behavioral intention in the U.S.\u003c/p\u003e \u003cp\u003e2) To investigate the behavioral patterns of local food consumption among tourists and excursionists.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAn on-site survey was conducted in the U.S. among international tourists (N\u0026thinsp;=\u0026thinsp;518).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMulti-dimension\u003c/b\u003e with 11 indicators.\u003c/p\u003e \u003cp\u003e\u003cb\u003e4 dimensions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Food quality\u003c/p\u003e \u003cp\u003e-Product quality\u003c/p\u003e \u003cp\u003e-Food value\u003c/p\u003e \u003cp\u003e-Food diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePLS-SEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eContributions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Fill in the gap of research of destination food image and behavioral responses to local food.\u003c/p\u003e \u003cp\u003e-Model CI as a multi-dimensional construct using hierarchical component method.\u003c/p\u003e \u003cp\u003e-Identify different perceptions of food image based on tourists\u0026rsquo; profile (i.e. tourists and excursionists).\u003c/p\u003e \u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Lack investigation on culinary culture dimension in CI.\u003c/p\u003e \u003cp\u003e-Affective image was not investigated in the study.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChoe \u0026amp; Kim (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo investigate the relationship among food consumption values, tourists\u0026rsquo; attitude toward local food, destination food image and tourists\u0026rsquo; behavioral intention.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-Interviews with 10 experts in the food tourism industry\u003c/p\u003e \u003cp\u003e-An on-site survey was conducted among tourists in Hong Kong International airport (N\u0026thinsp;=\u0026thinsp;875).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eUni-dimension\u003c/b\u003e with 5 indicators which include:\u003c/p\u003e \u003cp\u003e-Rich food culture\u003c/p\u003e \u003cp\u003e-Food diversity\u003c/p\u003e \u003cp\u003e-Food quality\u003c/p\u003e \u003cp\u003e-Traditional food culture\u003c/p\u003e \u003cp\u003e-Food uniqueness\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eContributions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Model food consumption values as the antecedents of destination food image.\u003c/p\u003e \u003cp\u003e-Identify the culture difference among Asians and Westerners in terms of food consumption values.\u003c/p\u003e \u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Scales developed to measure food consumption values have not been tested in different contexts.\u003c/p\u003e \u003cp\u003e-Lack theoretical support on food consumption values-food image relationship.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Independent variable-tourists’ attitude toward local food","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Definitions and measures\u003c/h2\u003e \u003cp\u003eConsumer\u0026rsquo;s attitude toward a product/service has been a vital aspect in marketing and psychology research. Although the definition of attitude can vary slightly across different research disciplines, attitude is generally defined as a predisposed state of mind which is triggered by a responsive expression (Eagly \u0026amp; Chaiken, 1993). Such responsive expression is internally driven by one\u0026rsquo;s evaluation of a person, an object and a behavior, which impacts his/her intention (Fishbein \u0026amp; Ajzen, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1974\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn destination food research, individual\u0026rsquo;s attitude toward destination food is defined as the degree of one\u0026rsquo;s favor or disfavor of local food (Choe \u0026amp; Kim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to Rousta and Jamshidi (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), tourists\u0026rsquo; attitude toward destination food is measured by an item which can be worded as \u0026ldquo;destination food is valuable to me\u0026rdquo;. Existing research measures tourists\u0026rsquo; attitude toward local food as a uni-dimensional construct by reflective measurement method. Previous studies find that tourists\u0026rsquo; positive attitude toward local food can be driven by a pleasant memory of local food tasting experience (Badu-Baiden et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), food consumption values (Choe \u0026amp; Kim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ibrahim et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), emotions linked with local food (Li et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), destination foodscape (Su et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and satisfaction of tasting destination cuisine (Jaeheng \u0026amp; Han, 2020). Tourists\u0026rsquo; attitude toward local food is an antecedent of tourists\u0026rsquo; intention, such as behavioral intention (Gupta et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bui et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), intention to consume destination food (Su et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), intention to visit the destination for food tourism (Pratt \u0026amp; Sparks, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and intention to recommend local food (Choe \u0026amp; Kim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Studies that examine the effect of tourists\u0026rsquo; attitude toward local food on tourists\u0026rsquo; intention to revisit the destination for local food are not available. The investigation of how tourists\u0026rsquo; intention is influenced by tourists\u0026rsquo; attitude toward local food is important because it shows the overall success of local food offerings (Badu-Baiden et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Tourists\u0026rsquo; attitude toward local food and intention\u003c/h2\u003e \u003cp\u003eTourists\u0026rsquo; attitude toward local food is an important construct in tourism research, since it can explain the variation of tourists\u0026rsquo; food consumption intention (Choe \u0026amp; Kim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Choi et al., (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) support that tourists\u0026rsquo; perceived risks/benefits of consuming street food in South Korea affect the formation of tourists\u0026rsquo; attitude toward South Korea street food, which consequently influences their behavioral intention. Choe and Kim (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) contend that tourists\u0026rsquo; positive attitude toward local food stimulated by food sensory attributes, perceived food taste and the perception of food service environment can affect tourists\u0026rsquo; intention to visit the destination for food tourism. Phillips et al., (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) find that the country image of South Korea can influence tourists\u0026rsquo; attitude toward local food, which in turns affects tourists\u0026rsquo; intention to visit South Korea as a culinary destination.\u003c/p\u003e \u003cp\u003eSeveral studies (e.g. Zhang et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ting et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) employ the theory of planned behavior (TPB) proposed by Ajzen (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) as the theoretical foundation to explain how tourists\u0026rsquo; intention to consume local food is triggered by their attitude. The TPB suggests that individual\u0026rsquo;s attitude toward performing a certain behavior is formed by a set of evaluative appraisals and is one of the factors that influences his/her local food consumption intention (Hsu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Su et al., (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argue that tourists\u0026rsquo; positive attitude triggered by a favorable destination foodscape is a strong predictor of tourists\u0026rsquo; intention to consume local food. The study by Jaeheng and Han (2020) identifies a set of Thai street food attributes and they contend that tourists\u0026rsquo; attitude toward Thai street food can influence their intention to recommend the food to friends and/or relatives. However, Rousta and Jamshidi (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reveal that the relationship between attitude toward local food and intention to recommend destination food to friends and/or relatives is negative and statistically non-significant. In addition, Horng et al., (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) study factors that influence tourists\u0026rsquo; behavioral intention but they find a statistically non-significant attitude-behavioral intention relationship. It can be seen that previous studies on tourists\u0026rsquo; attitude toward local food-behavioral intention and attitude-intention to recommend destination food relationships generate mixed findings. A meta-analysis that integrates the findings of extant research on how attitude toward local food affects tourists\u0026rsquo; intention is necessary. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes past studies of the relationship between tourists\u0026rsquo; attitude and tourists\u0026rsquo; intention.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of studies of tourists\u0026rsquo; attitude toward local food\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurpose of the study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod and sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTheoretical underpinning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eData analysis method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eContributions/limitations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRyu \u0026amp; Jang (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo predict tourists\u0026rsquo; behavioral intention toward consuming destination food and test the validity of the Theory of Reasoned Action (TRA).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA survey was conducted among undergraduate and postgraduate students at the Midwestern University in the U.S (N\u0026thinsp;=\u0026thinsp;366).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eContributions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Empirically test the TRA in the context tourists\u0026rsquo; food-related behavioral intention.\u003c/p\u003e \u003cp\u003e-Provide evidence that supports the relationship between tourists\u0026rsquo; attitude toward destination food and behavioral intention on local food.\u003c/p\u003e \u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e- Respondents are all university students. Results might not be generalizable.\u003c/p\u003e \u003cp\u003e-The study does not validate the model by respondents with different cultural background.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTing et al., (2016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo investigate the determinants of tourists\u0026rsquo; local food consumption behavior and test the Theory of Planned Behavior (TPB).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA survey was conducted among college and university students who are not from Malaysia (N\u0026thinsp;=\u0026thinsp;211).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePLS-SEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eContributions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e- Empirically test the TPB in the context tourists\u0026rsquo; food-related behavioral intention.\u003c/p\u003e \u003cp\u003e-Identify the moderating effect of food-related personality on the attitude-intention relationship.\u003c/p\u003e \u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e- Respondents are college and university students. Results might not be generalizable.\u003c/p\u003e\u003cp\u003e-Respondents\u0026rsquo; belief of Malaysia ethnic food which can be the antecedent of behavioral intention is not investigated.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e(Continued)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurpose of the study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod and sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTheoretical underpinning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eData analysis method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eContributions/limitations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJeaheng \u0026amp; Han (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-To identify the food attributes that contribute to the formation of tourists\u0026rsquo; attitude toward local food.\u003c/p\u003e \u003cp\u003e-To investigate the moderating effect of tourists\u0026rsquo; perceived risk of food consumption on the attitude-intention relationship\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAn on-site survey was conducted among international tourists who have tasted Thai street food (N\u0026thinsp;=\u0026thinsp;475).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eContributions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e-Identify a set of food attributes that contribute to the formation of tourists\u0026rsquo; attitude toward local food.\u003c/p\u003e \u003cp\u003e-Support the moderating effect of perceived risks of street food consumption on the attitude-intention relationship.\u003c/p\u003e \u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e- Effect of cofounding variable such as cultural difference among respondents is not included in the model.\u003c/p\u003e \u003cp\u003e-Theoretical underpinning of the study is not specified.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWu et al., (2016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo investigate the determinants of Chinese tourists\u0026rsquo; local food consumption behavior in the U.S. and test the Theory of Planned Behavior (TPB).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSnowball sampling method was employed to collect data from friends and relatives of the authors (N\u0026thinsp;=\u0026thinsp;278).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePLS-SEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eContributions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e- Empirically test the TPB in the context tourists\u0026rsquo; food-related behavioral intention and identify the antecedents of tourists\u0026rsquo; attitude toward local food.\u003c/p\u003e \u003cp\u003e-Incorporate the moderating effect of food-related personality in the TPB.\u003c/p\u003e \u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e- The study only investigates Chinese tourists\u0026rsquo; local food consumption intention. Results might not be generalizable.\u003c/p\u003e\u003cp\u003e-Sample of the study is collected using a nonrandomized data collection technique, which might affect the credibility of the results.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Methodology","content":"\u003cp\u003eMeta-analysis has been used as a statistical method for synthesizing data from a set of empirical studies that address the same research question. In social science research, such method was first proposed by researchers in psychology, which focuses on finding a general answer to a research question and examining the strength of relationship between two constructs (Lim, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Field \u0026amp; Gillett, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In meta-analysis, effect size is used to quantify the strength of relationship and is defined as the measure of association between two constructs (Field \u0026amp; Gillett, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In the medical field, researchers are interested not only if a treatment is effective but also how much it affects the patients (Borenstein, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, researchers conduct a meta-analysis to compute the effect size and draw conclusion on the magnitude of the treatment effect. Meta-analysis has been widely used in various fields of scientific research (e.g. medical and pharmaceutical research) and social science research (e.g. psychology and economics), but is rarely used in tourism and hospitality research (Afshardoost \u0026amp; Eshaghi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo conduct a meta-analysis, researchers review and select existing studies that investigate the relationship between two constructs (Lim, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Data used to indicate the strength and statistical significance of the relationship between two constructs are collected from selected studies. The effect size is calculated to assess the direction and magnitude of the relationship. The effect sizes of the paired constructs in different studies will be standardized and combined to determine the overall magnitude of the effect (Field \u0026amp; Gillett, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The comprehensive meta-analysis (CMA) program (version 3.0) is used to conduct the meta-analysis in this study.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Literature search and coding procedures\u003c/h2\u003e \u003cp\u003e We conduct a thorough search on databases that include academic journal articles to select studies which investigate the relationship between cognitive food image, tourists\u0026rsquo; attitude toward local food and tourists\u0026rsquo; intention related to destination food. The search of these databases is a continuous process starting from January 2022 to December 2025, and we keep reviewing and adding articles for analysis during this period. These databases include ScienceDirect, Sage Journals Online, Taylor \u0026amp; Francis Online, Wiley Online Library and Emerald Insight. We use key words such as cognitive food image, destination food image, cuisine image, tourists\u0026rsquo; attitude and intention, to search for related articles. To conduct a more comprehensive search, we also use Google Scholar to identify related journal articles. The key words mentioned above are transformed to Boolean phases (i.e. cognitive food image and intention OR destination food image and intention OR cuisine image and intention OR tourist food attitude and intention) to increase the scope of search on articles. A total of 179 articles are screened. We conduct a review of these articles and 54 studies are selected for meta-analysis. Then we conduct ancestral searches by screening the reference lists of the selected articles to identify studies that are omitted in previous searches. We found 3 more studies which are doctoral dissertations. Thus, a total of 57 studies are selected for meta-analysis.\u003c/p\u003e \u003cp\u003eWe employ the following criteria to select qualified studies from the 179 articles found by searches in journal article databases. First, the study should be written in English. Second, the study is expected to be published between 2000 and 2025. We select the year of 2000 as the starting point because several studies (e.g. Okumus, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) indicate that research on food tourism has gained increasing attention since the early 2000s. Third, the sample of the study should be based on tourists and the study is expected to investigate the effects of cognitive food image or tourists\u0026rsquo; attitude on intention. Fourth, the study is expected to employ quantitative research methods and report the correlation coefficients, path coefficients and/or regression coefficients.\u003c/p\u003e \u003cp\u003eA total of three professors (two professors from the University of Macau and one professor from Macau City University) who have been conducting research and teaching in tourism field kindly provide their help to read the 57 studies. They work independently and review all articles. All of them confirm that the selected studies meet the criteria of the articles selection. Appendix 1 shows the 57 studies selected for the meta-analysis. A total of 31 studies investigate the cognitive food image-intention paired constructs and 30 studies examine the effect of attitude on intention.\u003c/p\u003e \u003cp\u003eWe code the selected articles based on the publication and the article details. Publication details include authors, source of the article (the database we use to retrieve the article) and the name of the journal that publishes the article. Article details are sample size, country/region where the sample was collected, respondents\u0026rsquo; profile, correlation/regression/path coefficients, t-values, p-values, independent and dependent variables used in the study. The above data is synthesized by the fist author in an excel file for further data analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Statistical analysis\u003c/h2\u003e \u003cp\u003eIn meta-analysis, measures of effect size include correlation coefficient, standardized group mean difference (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e and Hedges\u0026rsquo;s \u003cem\u003eg\u003c/em\u003e) and log risk/odds ratios (Harrer et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since all studies selected for the meta-analysis employ regression-based method, we use correlation coefficients extracted from the selected studies as measures of effect size. The correlation coefficients will be converted to an effect size index, which corresponds to the observed effect size. The combined effect size is computed based on the weighted mean of the observed effect size value of every study included in meta-analysis to determine the strength of the relationship among constructs (Borenstein et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In order to compute a proper combined effect size, we check and make sure that the relationship of each pair of constructs (i.e. cognitive food image-intention and attitude-intention) is analyzed by previous studies at least 3 times (Afshardoost \u0026amp; Eshaghi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to Borenstein et al., (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the correlation coefficient of each study is converted to a Fisher\u0026rsquo;s Z-scale, which is calculated as:\u003c/p\u003e \u003cp\u003eZ\u003csub\u003ei\u003c/sub\u003e= 0.5ln[(1\u0026thinsp;+\u0026thinsp;r\u003csub\u003ei\u003c/sub\u003e)/(1- r\u003csub\u003ei\u003c/sub\u003e)] (1)\u003c/p\u003e \u003cp\u003eThe Fisher\u0026rsquo;s Z-scale is transformed to a weighted-average (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{\\text{Z}}_{\\text{r}}}\\)\u003c/span\u003e\u003c/span\u003e) score using the following equation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\stackrel{-}{{\\text{Z}}_{\\text{r}}}=\\:\\frac{\\sum\\:_{\\text{i}=1}^{\\text{g}}{(\\text{n}}_{\\text{i}}-3)\\ast\\:{\\text{Z}}_{\\text{i}}}{\\sum\\:_{\\text{i}=1}^{\\text{g}}{\\text{n}}_{\\text{i}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe weighted average score is converted to the combined effect size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e) using the following equation:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\stackrel{\\text{-}}{\\text{r}}=\\:\\frac{{e}^{2\\stackrel{-}{{\\text{Z}}_{\\text{r}}}}-1}{{e}^{2\\stackrel{-}{{\\text{Z}}_{\\text{r}}}}+1}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Z\u003csub\u003ei\u003c/sub\u003e is the Fisher\u0026rsquo; Z-scale of study i and r\u003csub\u003ei\u003c/sub\u003e is the correlation coefficient of study i; g is the number of effect size; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{n}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the sample size of study i; e stands for the exponential constant. It can be seen that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e is a transformed effect size index based on the correlation coefficients we extract from various studies that investigate the relationship of paired constructs.\u003c/p\u003e \u003cp\u003eIn this study, we distinguish between the fixed-effect and random-effects models to estimate the combined effect size. One can employ a fixed-effect model for meta-analysis if the studies selected are identical to each other, which means all studies sample subjects from the same group of people with same research setting. The word \u0026lsquo;fixed\u0026rsquo; implies that a designated group of people in the population is under study (Borenstein et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Some studies included in our meta-analysis may be similar in terms of research context, but it is very unlikely that these studies are identical to each other. In most cases, random-effects model is a more appropriate choice because it allows for variations in research setting and sampling methods across studies. Since there may be multiple true effect sizes under study and thus the plural term (\u0026lsquo;effects\u0026rsquo;) is used in the random-effects model. In addition, the random-effects model can produce generalized conclusions to population (Harrer et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the fixed-effect model is ruled out at the primary stage and we document the combined effect size based on the random-effects model.\u003c/p\u003e \u003cp\u003eFurthermore, we make the decision to adopt the random-effects model based on two tests in relation to the heterogeneity in true effect sizes across different studies. We follow Afshardoost and Eshaghi (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and conduct the Q and I\u003csup\u003e2\u003c/sup\u003e tests to examine the variation in true effect sizes. The Q test has a null hypothesis that all studies share a common true effect size and a statistically significant Q statistic (at the 5% significance level) indicates the heterogeneity in effect size. The I\u003csup\u003e2\u003c/sup\u003e test calculates the ratio of between-studies variance to total variance of the observed effect sizes. According to Higgins et al., (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), an I\u003csup\u003e2\u003c/sup\u003e value of 0% means the between-studies variance is equal to 0 and all studies share a common true effect size. The values of 25%, 50% and 75% for the I\u003csup\u003e2\u003c/sup\u003e statistic demonstrate low, moderate and high heterogeneity in true effect sizes, respectively. The value of the I\u003csup\u003e2\u003c/sup\u003e statistic that is equal to or greater than 75% supports the use of random-effects model for estimating the combined effect size (Afshardoost \u0026amp; Eshaghi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Findings","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Verification of the use of random-effects model\u003c/h2\u003e \u003cp\u003eWe have to conduct a series of true effect size heterogeneity tests to verify the appropriateness of using the random-effects model to compute the combined effect size. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the Q test statistics reject the null hypothesis of true effect size homogeneity across all studies. The I\u003csup\u003e2\u003c/sup\u003e statistics demonstrate a high level of heterogeneity of true effect size for studies that investigate the relationship of paired constructs. Therefore, the use of the random-effects model to compute the combined effect size is supported in the current study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity tests results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDF (Q)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCognitive image of destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to consume destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to visit the destination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e348.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to recommend destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to revisit the destination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAttitude toward destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to consume destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e266.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to visit the destination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to recommend destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: DF stands for the degree of freedom. The column of p-values shows the statistical significance of the Q statistics at the 5% significance level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Examinations of the combined effect size\u003c/h2\u003e \u003cp\u003eThis study investigates the direct path relationships of cognitive food image-intention and tourists\u0026rsquo; attitude toward local food-intention. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the number of effects corresponds to the number of correlation coefficients included to compute the combined effect size index. Some scholars (e.g. Pratt \u0026amp; Spark, 2014; Choe \u0026amp; Kim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) include both cognitive food image and attitude as independent variables and 2 behavioral outcomes in a single study. In addition, some studies (e.g. Kim et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ting et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fam et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) compare findings using 2 to 4 subsamples in a single study. Since the correlation coefficients of the paired constructs of the subsamples are different, they will be considered as separate effects. Therefore, the total for the number of effects (see column 3 in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e) is higher than the number of studies selected for meta-analysis. Sample size shows the aggregated number of sample in the selected studies. The lower and upper limits correspond to the confidence interval of the combined effect size. The z-statistics and p-values show the statistical significance of the combined effect size. According to Borenstein (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), combined effect size values of 0.1, 0.3, 0.5 and 0.7 demonstrate a low, medium, large and very large effect, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect sizes of cognitive food image and tourists\u0026rsquo; attitude toward destination food on intention\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCombined effect size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZ-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCognitive image of destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to consume destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to visit the destination for food tourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to recommend destination food to friends and/or relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to revisit the destination for local food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAttitude toward destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to consume destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to visit the destination for food tourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to recommend destination food to friends and/or relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Some studies include both cognitive food image and attitude as independent variables and two behavioral outcomes in a single study. In addition, several studies use different independent subsamples to estimate the correlations of a paired construct in a single study. Since the correlations of the paired constructs in these studies are different, they will be treated as separate effects. Therefore, the sum of the number of effects column is more than 56 (the number of selected studies in the meta-analysis). Past literature has not investigated the attitude-revisit intention relationship.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that cognitive food image has a positive influence on different aspects of intention since all effect size estimates are positive. Cognitive food image has a greater effect on tourists\u0026rsquo; intention to visit the destination for food tourism (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e = 0.301, 95% [CI]\u0026thinsp;=\u0026thinsp;0.157 to 0.397, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) than tourists\u0026rsquo; revisit intention (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e = 0.281, 95% [CI]\u0026thinsp;=\u0026thinsp;0.131 to 0.419, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The effect of cognitive food image on tourists\u0026rsquo; intention to recommend local food to friends and/or relatives is the largest (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e = 0.514, 95% [CI]\u0026thinsp;=\u0026thinsp;0.246 to 0.709, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), compared to the other 4 behavioral outcomes. The lowest value of effect size is found in the relationship between cognitive food image and intention to consume local food (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e = 0.286, 95% [CI]\u0026thinsp;=\u0026thinsp;0.067 to 0.410, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e also shows that tourists\u0026rsquo; attitude is positively related to various aspects of intention as evidenced by the positive effect size estimates. The greatest effect size is found in the attitude-intention to consume local food relationship (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e = 0.342, 95% [CI]\u0026thinsp;=\u0026thinsp;0.311 to 0.472, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The effect of attitude on intention to visit the destination for food tourism (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e = 0.234, 95% [CI]\u0026thinsp;=\u0026thinsp;0.043 to 0.458, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) is the lowest among the 4 behavioral outcomes.\u003c/p\u003e \u003cp\u003eTo compare the effect of cognitive food image with attitude on intention, cognitive food image has larger effect on tourists\u0026rsquo; behavioral intention, intention to visit and intention to recommend. The value of the effect size of attitude-intention to consume local food relationship is relatively higher than that of the relationship between cognitive food image and intention to consume local food. Overall, the effect size of cognitive food image-intention is higher than that of attitude-intention relationship.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Publication bias\u003c/h2\u003e \u003cp\u003ePublication bias was firstly proposed by researchers in psychology in the 1950s (Thorton \u0026amp; Lee, 2000). Publication bias is a term that associates with the concern about the representativeness of a single study or a set of studies. In meta-analysis, publication bias may arise when researchers only focus on published studies in a given period, which may lead to misleading conclusions (Thorton \u0026amp; Lee, 2000). Since we do not include articles that are published before 2000, there may be articles omitted in our study. We conduct 2 statistical tests to detect if there is publication bias in our study. First, we conduct the Egger\u0026rsquo;s regression to regress the observed effect size of study i divided by its standard error on the inverse of standard error of study i. The Egger\u0026rsquo;s regression tests the null hypothesis that the intercept of the regression is equal to zero in population. A statistically significant intercept (at the 5% significance level) indicates the existence of publication bias in the meta-analysis. Second, we conduct a fail-safe-number (FSN) test to estimate the number of missing studies required to bring down the p-values of the combined effect size to a non-significant level. At the 5% significance level, a FSN value exceeding 5 times of the number of studies plus 10 (5N\u0026thinsp;+\u0026thinsp;10) rejects the null hypothesis of publication bias (Carson et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the Egger\u0026rsquo;s regression intercept shows that the null hypothesis of no publication bias cannot be rejected at the 5% significance level. The critical values of FSN all exceed the suggested threshold of 5 times of the number of studies plus 10 at the 5% significance level. To interpret the meaning of the FSN, we exemplify the FSN value for studies that investigate the cognitive food image-behavioral intention relationship in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5\u003c/span\u003e. It is very unlikely that we miss 298 studies during the search of articles investigating such relationship, and the number of 298 exceed 5 times of the number of studies plus 10 (5N\u0026thinsp;+\u0026thinsp;10). Thus, the issue of publication bias is not a concern for this study and the meta-analysis generates reliable results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePublication bias test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEgger\u0026rsquo;s regression intercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFSN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCognitive image of destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to consume destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to visit the destination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to recommend destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to revisit the destination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAttitude toward local food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to consume destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to visit the destination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntention to recommend destination food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Discussion and practical implications","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Discussion\u003c/h2\u003e \u003cp\u003eIn spite of the increase of research on destination food in the past two decades, very few studies conduct a review on the determinants of various aspects of tourists\u0026rsquo; intention related to local food. This research is the first meta-analysis that investigates the antecedents of various aspects of tourists\u0026rsquo; intention in the context of destination food research. To our knowledge, none of the existing studies integrate cognitive food image and attitude constructs to investigate their effects on different aspects of intention in a single study. A thorough literature search yields 179 published journal articles in major academic databases and a total of 57 studies with a sample size of 32,382 are synthesized into a meta-analysis. A comprehensive review of the 57 selected studies finds that various aspects of tourists\u0026rsquo; intention related to local food can be affected by external (i.e. cognitive food image) and internal factors (i.e. tourists\u0026rsquo; attitude toward local food). Although several studies in previous destination food research find non-significant cognitive food image-intention (e.g. Aydin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hashemi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and attitude-intention relationships (e.g. Rousta \u0026amp; Jamshidi, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Horng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), results of our study generally support that both cognitive food image and tourists\u0026rsquo; attitude have positive and significant impact on tourists\u0026rsquo; intention.\u003c/p\u003e \u003cp\u003eIn the research on destination food image, a number of studies empirically support the positive relationship between cognitive food image and tourists\u0026rsquo; intention, such as tourists\u0026rsquo; intention to visit the destination for food tourism and intention to consume local food (Seo et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Choe \u0026amp; Kim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on the empirical results of previous research, our research examines the effect sizes of cognitive food image on various aspects of intention. The largest effect size is found in the relationship between cognitive food image and intention to recommend destination food, while the smallest effect size is found in the cognitive food image-intention to consume local food relationship. Compared to cognitive food image, affective food image receives less attention in destination food research. Studies that examine the influence of affective food image on tourists\u0026rsquo; behavioral intention, intention to consume local food, intention to recommend destination food and revisit intention are not available. Additionally, little is known in terms of the measure of the affective food image construct. Therefore, it is necessary to conduct more research on affective food image. Research on the conative food image component has received very little attention. Only one study in destination food research (i.e. Lai et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) investigates the conative food image construct. In addition, the relationship between conative food image and intention has not been examined. Thus, more future research needs to be conducted to validate the conative food image component and its effect on intention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Practical implications\u003c/h2\u003e \u003cp\u003eThe findings of the study support that various aspects of tourists\u0026rsquo; intention associated with local food can be affected by external (i.e. cognitive food image) and internal (i.e. tourists\u0026rsquo; attitude toward local food) factors. The understanding of local food offerings and the antecedents that can be used to predict tourists\u0026rsquo; intention related to destination food are important to destination marketers. Instead of providing a narrative review of relevant studies that investigate the determinants of intention, this study conducts a review of previous literature through a meta-analysis. Destination managers can refer directly to the findings of this study with regard to the internal (i.e. tourists\u0026rsquo; attitude toward local food) and external factors (i.e. cognitive food image) that affect various aspects of tourists\u0026rsquo; intention. An enhanced knowledge of cognitive food image-intention and attitude-intention relationships provides better understanding of tourists\u0026rsquo; decision-making and behavioral process associated with local food. Such knowledge is important to the formation of destination managers\u0026rsquo; strategic vision and initiatives of food tourism destination promotion.\u003c/p\u003e \u003cp\u003eIf destinations\u0026rsquo; promotion using local food is effective, tourists\u0026rsquo; positive perception of destination food should have large effect on both tourists\u0026rsquo; local food consumption intention and revisit intention. However, the study finds that cognitive food image has low effect on both tourists\u0026rsquo; intention to consume local food and revisit intention. The findings seem to suggest that the use local food is not sufficiently attractive for destination promotion and to entice tourist to revisit. Destination managers need to consider other tourism offerings (e.g. festival and heritage) in conjunction with local food to promote the destination.\u003c/p\u003e \u003cp\u003eThe results of this research show that tourists\u0026rsquo; attitude toward local food has a positive influence on intention. Since tourists\u0026rsquo; attitude can act as an internal driver of intention, destination managers can consider ways to create and affirm tourists\u0026rsquo; positive attitude in relation to destination food. For example, destination managers can promote and emphasize the memorable experience with local food, which may help to create a positive evaluation of destination food. This can be done by showing videos and narratives to tell stories about local food in destination promotional media and materials, which might include interaction with local people in food preparation and tasting, themed-based learning of making local food, and promotion of food souvenirs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Limitations and future research\u003c/h2\u003e \u003cp\u003eThere are several limitations in this study. First, according to Lim (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), since the meta-analysis is based on the aggregated data of individual studies, the quality of the meta-analysis may be dependent on the quality of the selected studies. This is one of the inherent limitations of a meta-analysis, which indicates the need for advancement of conceptualization and research framework for primary studies (Afshardoost \u0026amp; Eshaghi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Second, although meta-analysis is a powerful statistical method that synthesizes previous research findings, the method has been criticized that it fails to incorporate contextual information such as sample characteristics and variation in research design (Borenstein et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Similar to other meta-analysis research, our study is unable to report such contextual information. Future research may use other effect size measures to investigate indirect paths such as the effect of mediating variables in the cognitive food image-intention relationship.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e \u003cb\u003estatement\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe study does not involve human participants or their data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eInformed consent\u003c/h2\u003e \u003cp\u003eThe study does not involve human participants or their data and therefore the informed consent was not obtained.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1. J. Z. comes up with the research idea, reads the relevant articles in the article databases, selects the qualified articles, performs data collection and coding of the articles for meta-analyse, conducts the statistical analyses and writes the paper. Y.R. writes the discussion and implication section and revises the whole paper.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eJ. Z. discloses support for the research of this work from Shunde Polytechnic University doctoral startup research grant [KYQD035]. Y. R. discloses support for the research of this work from Shunde Polytechnic University doctoral startup research grant [KYQD075] and Shunde Polytechnic University 2025 Annual Key Research Achievement Cultivation Project [2025-KJXJ039].\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe synthesized table of data for analysis supporting the findings of this study is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfshardoost M, Eshaghi MS (2020) Destination image and tourist behavioral intentions: A meta-analysis. Tour Manag 81:104154\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgapito D, Valle P, Mendes J (2013) The cognitive-affective-conative model of destination image: A confirmatory analysis. J Travel Tourism Mark 30(5):471\u0026ndash;481\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad MS, Jamil A, Latif KF, Ramayah T, Leen JYA, Memon M, Ullah R (2019) Using food choice motives to model Pakistani ethnic food purchase intention among tourists. Br Food J 122(6):1731\u0026ndash;1753\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179\u0026ndash;211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAydin B, Erdogan BZ, Baloglu S (2021) Examining the role of country image in the relationship between cuisine image and intention to visit a country. Int J Tourism Res 23(4):555\u0026ndash;568\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBadu-Baiden F, Kim SS, Xiao H, Kim J (2022) Understanding tourists' memorable local food experiences and their consequences: the moderating role of food destination, neophobia and previous tasting experience. Int J Contemp Hospitality Manage 34(4):1515\u0026ndash;1542\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBj\u0026ouml;rk P, Kauppinen-R\u0026auml;is\u0026auml;nen H (2016) Local food: a source for destination attraction. Int J Contemp Hospitality Manage 28(1):177\u0026ndash;194\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorenstein M (2009) Effect sizes for continuous data. In: Cooper H, Hodges LV, Valentine JC (eds) The handbook of research synthesis and meta-analysis. Russel Sage Foundation, pp 221\u0026ndash;235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorenstein M, Hedges LV, Higgins JP, Rothstein HR (2009) Introduction to meta-analysis. Wiley\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorenstein M, Hedges LV, Higgins JP, Rothstein HR (2010) A basic introduction to fixed-effect and random‐effects models for meta‐analysis. Res synthesis methods 1(2):97\u0026ndash;111\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBui TP, Nguyen TQ, Lee HC (2025) Destination choice intentions based on attitudes toward local cuisine: The empirical study from an emerging country. Int J Gastronomy Food Sci, 101194\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarson KP, Schriesheim CA, Kinicki AJ (1990) The usefulness of the failsafe statistic in meta-analysis. Educ Psychol Meas 50(2):233\u0026ndash;243\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee S, Suklabaidya P Food Image and Travel Intention: From New Dehli to New York. J Gastronomy Hospitality Travel, 3(1), 3\u0026ndash;19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChi CGQ, Chua BL, Othman M, Karim SA (2013) Investigating the structural relationships between food image, food satisfaction, culinary quality, and behavioral intentions: The case of Malaysia. Int J Hospitality Tourism Adm 14(2):99\u0026ndash;120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChi HH, Huang KC, Nguyen BDT (2019) A perception into food image and revisit intention for local cuisine from foreign tourist perspective-The case of Ho Chi Minh City-Vietnam. 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J China Tourism Res 19(3):489\u0026ndash;516\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Y, Zhu L, Weng L (2024) How do tourists\u0026rsquo; value perceptions of food experiences influence their perceived destination image and revisit intention? moderated mediation model Foods 13(3):412\u0026ndash;435\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8683550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8683550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e Previous literature that investigates the determinants of various aspects of tourists\u0026rsquo; intention related to local food has not been reviewed in a comprehensive manner. By employing the meta-analysis method, the study examines the antecedents of tourists\u0026rsquo; intention related to local food. The objectives of the study are firstly, to review previous literature about tourists\u0026rsquo; various aspects of intention, cognitive food image and tourists\u0026rsquo; attitude constructs. Secondly, we examine the magnitude of the relationships of cognitive food image-intention and attitude-intention by the combined effect size index. A total of 57 studies that investigate the effects of external (i.e. cognitive food image) and internal (tourists\u0026rsquo; attitude toward local food) factors on tourists\u0026rsquo; intention related to destination food are selected for meta-analysis. The results show that cognitive food image has large effect size on tourists\u0026rsquo; intention to recommend local food. Medium level of effect size is found in the cognitive food image-behavioral intention, cognitive food image-intention to visit the destination for food tourism and attitude-intention to consume local food relationships. The effect sizes of cognitive food image-intention to consume, cognitive food image-revisit intention, attitude-behavioral intention, attitude-intention to visit and attitude-intention to recommend relationships are low. The study provides implications on the management of local food offerings to destination managers.\u003c/p\u003e","manuscriptTitle":"The effects of cognitive food image and tourists’ attitude toward local food on tourists’ intention associated with destination food: A meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 01:23:27","doi":"10.21203/rs.3.rs-8683550/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-30T10:06:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T10:38:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T11:13:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232361956408800071043300404955308407858","date":"2026-04-13T04:40:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266370773771589336708315511785488213166","date":"2026-04-11T04:44:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250521682883949464298098437607331929706","date":"2026-04-10T10:11:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-10T09:03:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T08:56:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-12T08:40:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T01:09:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-02-10T01:04:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6dd1dcb5-1b2c-4796-be99-6faa7968e6c0","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-30T10:06:32+00:00","index":72,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66517893,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":66517894,"name":"Biological sciences/Psychology"},{"id":66517895,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-21T01:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 01:23:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8683550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8683550","identity":"rs-8683550","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}