Tourist Satisfaction Analysis Through Structural Equation Models

Tourist Satisfaction Analysis Through Structural Equation Models

José Carlos Casas-Rosal (University of Cordoba, Spain), Juan Antonio Jimber del Río (University of Cordoba, Spain), Ricardo David Hernández Rojas (University of Cordoba, Spain) and Amalia Hidalgo-Fernández (University of Cordoba, Spain)
DOI: 10.4018/978-1-7998-2603-3.ch006
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Abstract

This chapter analyses the tourist behaviour through the estimation of a statistical model of structural equations, observing to what extent the motivation, interest, and value given to the destination of the trip are determining factors. A study of the socio-demographic profiles, the level of satisfaction, and the loyalty of the tourist with the destination is carried out in this context and in response to the need to analyse and understand the reasons why tourists have a greater or lesser degree of satisfaction with the destination, which is truly necessary to improve the public administrations and private entrepreneur management in this sector. This has been applied to a case study: tourists visiting the city of Cordoba, designing a survey for those who visit the city for cultural reasons.
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Background

The economic impact of tourism on the world economy is an established fact. On the one hand, the World Tourism Organization (UNWTO, 2016) reports that tourism creates 1.1% of worldwide employment and, on the other hand, that the related aspects of cultural offerings, nature and gastronomy are – in that order – the main motives that lead tourists to visit a destination. (Rosales, Salas, & Palacios-Rangel, 2019).

However, in certain countries the sector’s economic impact is of much greater relevance. One example of this is Spain, where according to annual studies by Figuerola (2018), business activity related to tourism accounted for an estimated 11.8% of gross domestic product (GDP) in 2018, making tourism a strategic sector for the national economy. As a consequence, it is useful for both public and private entities to study and measure those factors linked to improving the tourist experience in general, so that measures can be taken to create loyalty and reinforce a brand image.

In this way, measuring tourist satisfaction is a key factor in helping improve tourism management. This improvement is reflected in both quality and quantity, as decisions can be made according to concrete opinions voiced by tourists visiting a place, and because those decisions are applicable to future visits, they will influence the satisfaction of future tourists and directly affect the destination in terms of repeated visits and recommendations that people visit the destination. (Yuksel, 2001; Broncano & Andrés, 2009; Yuksel, Yuksel, & Bilim, 2010; Dodds & Holmes, 2019; Kim & Nurhidayati, 2019).

Key Terms in this Chapter

Motivation: Emotional response of tourists to receiving a product or service that determines future actions.

Tourist Destination: Place a tourist chooses to visit, for which he/she feels some degree of interest and motivation.

Interest: Attitude of a person – in this case, a future tourist – that demonstrates a desire to direct his/her attention to a set of products or services that attract him/her and inspire curiosity.

Latent Variable: Complex variable that cannot be directly measured, either because the respondent lacks in-depth knowledge of the concept due to its complexity, or because the variable is impossible to assess without considering its determining factors.

Perceived Value: Measure by which tourists evaluate the characteristics of a product or service and its ability to satisfy their needs and expectations, especially when compared to other similar products or services.

Satisfaction: Emotional response of tourists to a received product or service, which also involves the degree to which the prior expectations have been met.

Structural Equation Model: Parametric statistical technique that allows one to test, within a given collection of data, whether a theoretical model related to a complex set of measurable and latent can be verified at the population level.

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