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The role of traditional restaurants in tourist destination loyalty.

ABSTRACT: The aim of this study is to examine the effect that visitor satisfaction with traditional restaurants has on perceptions of the local gastronomy, the overall image of a city and loyalty to that destination. Fieldwork has been carried out in Córdoba, a city in southern Spain famous for being a UNESCO World Heritage city and for its traditional gastronomy. The methodology used is based on structural equation modeling (PLS-SEM). This paper makes a novel contribution in that no previous studies to date have explored satisfaction with traditional restaurants, with respect to the food, the service and the atmosphere. To achieve the proposed objective, a structured questionnaire has been used to find out the opinions of diners in renowned restaurants that base their cuisine on traditional dishes made with quality local ingredients. The results obtained confirm that a satisfactory experience with the food of a traditional restaurant has a positive effect on the image of the destination and the gastronomy of the place, as well as on visitors' intentions to recommend and repeat the visit to said destination. Based on the analysis carried out, effective strategies are suggested to help manage these types of restaurants. The study provides theoretical and practical implications from a gastronomic perspective, which can enable tourism managers to employ new strategies to retain tourists visiting a city, based on increasing their post-experience satisfaction with restaurants featuring local cuisine.

SUBMITTER: Hernandez-Rojas RD

PROVIDER: S-EPMC8211172 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background The factors influencing tourist loyalty are widely highlighted in the literature. However, we find that the relationship between some influencing factors and loyalty is still inconsistent, and we don’t yet know the strength and magnitude of the relationships. To address this issue, this study examined a meta-analysis of the five factors (satisfaction, motivation, perceived value, perceived quality, and experience quality) influencing tourist loyalty and its sub-dimensions. Methods The samples included articles from major academic databases, including Web of Science, Wiley Online, EBSCO, SAGE, Taylor and Francis, and Elsevier. Studies written in Chinese were retrieved from CNKI.com. We used the following keywords for retrieval: loyalty, behavioral intention, recommendation intention, word-of-mouth, revisit intentions, intention to revisit, willingness to recommend, and similar related terms. Conceptual and empirical studies published between January 1989 and September 2021 were extracted. To test whether there was publication bias, we used Fail-Safe-Number (FSN) to verify the stability of the results. The homogeneity test of the selected statistical model was based on the Q test and I2. The results were obtained by combining multiple single effect values into the combined effect value. Results We developed 21 hypotheses and proposed a theoretical framework and analyzed 114650 accumulated sample sizes from 242 independent empirical studies. Among the 21 hypotheses proposed in this paper, the remaining 20 hypotheses have been proved except for hypothesis H6. Conclusions The findings showed that the five factors had varying degrees of positive and significant relationships with tourist loyalty and its sub-dimensions. In the descending order of effects, the five factors are degree of satisfaction, quality of experience, perceived value, perceived quality and motivation. We discussed the significance of the meta-analysis, theoretical and practical implications for destination marketing.

Project description:User-generated content (UGC) is an important data source for tourism GIScience research. However, no effective approach exists for identifying hidden spatiotemporal patterns within multi-scale unstructured UGC. Therefore, we developed an algorithm to measure the tourist destination popularity (TDP) based on a multi-spatiotemporal text granular computing model, called TDPMTGC. To accurately granulate the spatial and temporal information of tourism text, tourism text data granules are used to represent landscape objects. These granules are unified objects that possess multiple attributes, such as spatial and temporal dimensions. The multi-spatiotemporal scales are characterized by the multi-hierarchical structure of granular computing, and transformations of granular layers and data granule size are achieved by scale selection in the spatial and temporal dimensions. Therefore, all scales between the spatial and temporal dimension are related, which allows for the comparability of the data granules of all spatial-spatial, temporal-temporal and spatial-temporal layers. This approach achieves a quantitative description and comparison of the popularity value of granules between adjacent scales and cross-scales. Therefore, the TDP with multi-spatiotemporal scales can be deduced and calculated in a systematic framework. We first introduce the conceptual framework of TDPMTGC to construct a quantitative measurement model of TDP at multi-spatiotemporal scales. Then, we present a dataset construction approach to support multi-spatiotemporal scale granular reorganization. Finally, TDPMTGC is derived to describe both the TDP at a single spatial or temporal scale and the patterns and processes of the TDP at multi-spatiotemporal scales. A case study from Jiuzhaigou shows that the TDP derived using TDPMTGC is consistent with the conclusions of existing studies. More importantly, TDPMTGC provides additional detailed characteristics, such as the contributions of different scenic spots in a tourist route or scenic area, the monthly anomalies and daily contributions of TDP in a specific year, the distinct weakening of tourist route scale in tourist cognition, and the daily variations of TDP during in-season and off-season times. This is the first time that a granular computing model has been introduced to tourism GIScience that provides a feasible scheme for reorganizing large-scale unstructured text and constructing public spatiotemporal UGC tourism datasets. TDPMTGC constitutes a new approach for exploring tourist behaviors and the driving mechanisms of tourism patterns and processes.

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The role of traditional restaurants in tourist destination loyalty.

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