The main purpose of this research was to develop a universal model to evaluate the perceived value of tourism services and satisfaction with, and loyalty to, destinations from the consumers’ perspective and demonstrated the model’s applicability in this context.
Using the structural equation model, cause and effect relationships were identified between the proposed model’s constructs, and indices of quality, satisfaction and loyalty among tourists were estimated. This system was applied to a large set of data collected with a structured questionnaire distributed to tourists visiting the city of Seville through a non-probabilistic sampling by intentional quotas method. In total, 922 valid surveys were obtained.
The indices show that tourists who visit Seville report a high level of loyalty to, and satisfaction with, this place because of the perceived quality of a variety of services. It is observed that the perceived quality index is much higher (17.95 per cent) than the expected quality index, so the quality of the service received by the tourist during his/her visit to Seville is described as excellent.
Regarding this study’s limitations, other variables could have been included that influence tourist satisfaction, such as the climate, the effect of advertising medium, the prices and the emotional components. In addition, surveying tourists’ expectations before their visit is virtually impossible, as is surveying the same tourists again about their perceived value and satisfaction after their visit. Future lines of research could focus on the intersection of information between tourism offer and demand, providing information about an appropriate balance in specific markets. The proposed model can also be applied to other tourism places that are similar to Seville’s tourism offer, allowing useful comparisons and identification of critical points and ways to improve customer satisfaction continuously.
By establishing indices of expected and perceived quality and satisfaction and loyalty among tourists, tourism authorities and different economic agents involved in this sector can receive objective information about the results and quality of tourism services. Tourism managers, thus, can set objectives for improvements and competitiveness, as well as building and maintaining customer loyalty. At the same time, these indices allow comparisons with other organisations and places. By facilitating greater transparency in the measurement of quality and satisfaction, service providers connected to tourism can create a platform on which to articulate clearly their contributions to interested parties and local communities.
These results constitute strategies and findings that any tourism place has to consider in the planning and development of its products. Therefore the model can help to encourage a long-term market perspective among tourism sector regulators, investors and agencies. With the information obtained with this model, areas needing improvement can be identified and the appropriate procedures can be put into practice to improve the tourism offer, adjusting it to meet travellers’ needs according to their motivations to travel to the destination. Residents also can benefit from these measures, as their quality of life will improve through upgrades of the city’s tourism facilities.
The unique contribution of the present study lies in how the indices or indicators of quality of, satisfaction with and loyalty to destinations among tourists are easily measured by applying structural equation modelling. A new approach to measure satisfaction, loyalty and quality is used based on a scale from 0 to 100, and the index results are very useful for comparing different tourist places.
Castillo Canalejo, A. and Jimber del Río, J. (2018), "Quality, satisfaction and loyalty indices", Journal of Place Management and Development, Vol. 11 No. 4, pp. 428-446. https://doi.org/10.1108/JPMD-05-2017-0040Download as .RIS
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Innovation, improvement and creative commercialisation of tourist experiences determine the continued success of destinations and constitute tourism authorities’ main tasks. While other indices can help identify tourists’ perspectives on tourism projects, tourist satisfaction has become increasingly important in recent years. Customer satisfaction measurement is now considered a crucial factor in the survival, development and success of service industries such as tourism (Sirakaya et al., 2004). According to Song et al. (2012), one possible definition of success for tourism products is “satisfied tourists”. A more precise definition of customer satisfaction does not yet exist, but the present study accepted Kotler’s (2005) definition of customer satisfaction as an individual’s state of mind that results from a comparison of the perceived results of a product or service with that person’s expectations. Tourism managers’ objective is, consequently, to completely satisfy tourists, thereby increasing their loyalty to tourist destinations.
Therefore, a system that measures the quality of tourism products or services from customers’ viewpoints – indicated by their satisfaction with, and loyalty to, destinations – is extremely important to tourist place management. This system needs to be able to identify the cause and effect relationships associated with customer satisfaction. If satisfaction levels fall, this system has to identify possible causes (e.g. perceived quality, expected quality and other expectations) and consequences (e.g. less consumer loyalty and no recommendations to visit) to find appropriate solutions.
By establishing indices of expected and perceived quality, satisfaction and loyalty among tourists, tourism authorities and different economic agents involved in this sector can receive objective information about the results and quality of tourism services. In this way, tourism managers can set objectives for improvements and competitiveness, as well as for building and maintaining customers’ loyalty. At the same time, these indices allow comparisons with other organisations and destinations. By facilitating greater transparency in quality and satisfaction measurement, service providers linked to tourism can create a platform to clearly articulate their contributions to interested parties and local communities.
The main objective of this research was to develop a universal model to evaluate the perceived value of tourism services and satisfaction with, and loyalty to, destinations from the viewpoint of consumers. This model was applied to a large set of data collected through a survey of tourists visiting the city of Seville. The unique contribution of the present study lies in how the indices or indicators of the quality of, satisfaction with and loyalty to destinations among tourists can be measured by applying structural equation modelling to incorporate the above latent variables into the analyses.
2. Literature review
Understanding the factors that influence tourist satisfaction is one of the most important topics in tourism sector research because of the impact that tourist satisfaction has on the success of any tourism product or service. In an increasingly competitive market such as tourism, it is important to establish destination brands (Swanson, 2017) where the ultimate objective of any marketing strategy is to obtain satisfied and loyal customers (Pato and Kastenholz, 2017). A more positive evaluation of a place leads to a higher general satisfaction (Zenker and Braun, 2017). Most tourists have had experiences with multiple places, so their perceptions are influenced by comparisons of services, attractions and other features (Laws, 1995). Tourists’ high level of satisfaction leads to positive future behaviours, such as intentions to revisit destinations, improved reputations, reduced price elasticity, current market shares insulated from competitors, lower transaction costs, increased loyalty and positive word of mouth (Song et al., 2012; Sarra et al., 2015). Augustyn and Ho (1998) estimated that, if a client likes a tourism service, he or she will transmit this to three people; but, if a client dislikes the service, he or she will communicate this to 11 people. In other words, dissatisfied clients are much more likely to report their discontent to others. For this reason, creating customer loyalty is more cost-effective than finding and persuading new customers to buy (Song et al., 2012). Many studies have found evidence for the relationship between tourist satisfaction and intention to return, as well as positive word of mouth and customer loyalty (Pizam, 1994; Hallowell, 1996; Beeho and Prentice, 1997; Baker and Crompton, 2000; Caneen, 2003; Dimitriades, 2006; Sung et al., 2016). A similar relationship has been found between tourist dissatisfaction, willingness to look for other destinations and negative word of mouth (Pizam, 1994; Kozac and Rimmington, 2000; Alegre and Garau, 2010).
Another consistent relationship within the theoretical literature is the positive affect that the perceived quality of the destination has on overall satisfaction (Jalilvand, et al., 2014; Campon-Cerro et al., 2016) and the positive relationship between expectations and consumer satisfaction. The latter implies that raising tourist expectations may increase perceived performance, which further enhances tourist satisfaction (Bosque et al., 2006; Fornell et al., 2006; Song et al., 2012). Expectations and satisfaction can be influenced by adverts (i.e. advertising and sales promotion methods) and social environments (i.e. interactions with friends, relatives and reference groups). The literature shows that experts widely accept that advertising affects tourist satisfaction (Kirmani, 1990; Moorthy and Hawkins, 2005; Chen et al., 2016).
Cultural differences in attitude, behaviour and social class also influence consumer expectations and perceptions. Less sophisticated tourists are often less demanding and will be more satisfied as a result. At the same time, levels of satisfaction can be influenced by tourists’ country of origin because of cultural differences (Kozac and Rimmington, 2000; Kozak, 2002; Moital et al., 2013). Another variable that can influence tourists’ satisfaction is the number of times they have visited a destination (Kozac and Rimmington, 2000; Correia et al., 2008). Each component of tourism products or services must be identified and measured to calculate overall satisfaction; thus, tourists’ evaluations of the supply area (i.e. attractions and levels of service quality) are regarded as crucial in determining tourist satisfaction (i.e. the demand side) (Whipple and Thach, 1988).
A review of the literature reveals an increase in articles that focus on explaining – at both empirical and theoretical levels – different aspects of tourist satisfaction. For example, Reisinger and Waryszak (1995) analysed satisfaction with tourism guides. Furthermore, Sung et al. (2016) investigated visitors’ satisfaction with festivals, while Cole and Scott (2004) focused on zoo visitors; Hughes (1991) focused on cultural tours; Moital et al. (2013) examined golf tourists’ satisfaction; and Ross and Iso-Ahola (1991) looked at daily tours. Meanwhile, Saleh and Ryan (1992) studied satisfaction with hotels, and Fick and Ritchie (1991) focused on restaurants. Finally, one of the main objectives of research in recent decades has been to measure tourists’ satisfaction levels with destinations based on different models, although no consensus has yet been reached about the best approach (Kozac and Rimmington, 2000).
Most of the literature shows two ways of measuring satisfaction. On the one hand, the American school led by Parasuraman et al. (1985) considers satisfaction as a positive or negative result of a process of comparison between the initial expectations and the received service. On the other hand, the Nordic school led by Gronroos (1990) believes that satisfaction is only the result of the actual quality of tourism products and its consumers’ perceptions.
This last model contends that a consumer’s perceived service quality is determined by a gap between the expected and the perceived service. Therefore, there exists a meaningful relation between the expected and the perceived quality among tourists, as set out in H1 of our model (Figure 1). Earlier, Oliver (1980) created an expectancy–disconfirmation paradigm, and Pizam and Milman (1993) applied this approach in their research, which tries to measure satisfaction with the trip experience of first-time travellers from the USA to Spain. The results demonstrated that specific expectations before a journey takes place are relatively good predictors of overall satisfaction with a destination (H5 of our model). While Pizam et al. (1978) created a performance-only model, which suggests that the factors influencing tourist satisfaction depend on each tourist destination, Chon and Olsen (1991) opted to use Sirgy’s congruity model, which examined the role of the image of a destination in tourism with regard to consumer satisfaction. More recently, Song et al. (2012) developed a tourist satisfaction index based on Oliver’s (1980) expectancy–disconfirmation framework. The most important findings of this research confirm H1, H3, H4, 6 and H7 of our model. Sarra et al. (2015) developed the item response theory, in which customers’ satisfaction is evaluated by only examining the perceived performance, disregarding customers’ expectations. Thus, the hypotheses of this study relate the level of satisfaction to demographic and travel behaviour characteristics.
One of the most widely accepted causal models of tourist satisfaction is the expectancy–disconfirmation paradigm (Oliver, 1980). This paradigm suggests that satisfaction is the result of the interaction between tourists’ experiences in destinations and the expectations they had about those destinations. Tourists are, in turn, satisfied when the performance exceeds their expectations. Alternatively, they are dissatisfied if the actual result is worse than their expectations (Pizam et al., 1978; Sarra et al., 2015). The development of several consumer satisfaction indices, such as the Swedish customer satisfaction barometer (Fornell, 1992) and the American customer satisfaction barometer (Fornell et al., 1996), is also based, at a theoretical level, on the expectancy–disconfirmation framework (Oliver, 1980).
The model developed in the present study shares a similar theoretical underpinning to the American Customer Satisfaction Index (ACSI). The ACSI uses periodic surveys to measure domestic consumer satisfaction with American products and services. Satisfaction is measured on a scale of 0 to 100, and the resulting index is extremely useful for comparing businesses in the same sector. However, the present model has some new and different features. Although ACSI focused on the tourism sector, it was restricted to domestic tourists and it excluded the international market, even though this constitutes an important percentage of many destinations, including Seville. Another new feature of the present model is the type of variables included to calculate the expected quality. For instance, tourist expectations include travel motivations, as these have a significant influence on this latent variable.
The proposed model uses four variables to measure satisfaction. Perceived quality represents the quality arising from the attributes of products or services from the customer’s point of view. This measure implies a recent use of the product or service in question. The product or service’s quality is related to how it satisfies customers’ needs, as well as the availability of the product or service (ACSI, 2016).
The Expected quality is the quality that customers think they will get when they acquire a particular product or service. This measure represents previous experiences with – or references of any kind about – the good (ACSI, 2016). In contrast with the ACSI, however, the present study also includes travel motivations as a variable that influences expected quality.
The Perceived value concerns the consumer’s understanding of the product’s benefits (Nilson, 1992). According to Goyhenetche (1999), this involves the customer’s assessment of how the product corresponds to their expectations. Once they go on a trip, for example, tourists’ experiences are an assessment of what they expected to find versus what they received.
Finally, customer satisfaction depends on customer loyalty, which is a measure that takes into account the percentage of people who indicate that they would not change their provider, who intend to replace the provider with another or who are willing to pay more for the required good. Loyalty is an important factor because it is directly related to the profitability of products and services (ACSI, 2016). It includes variables that have an impact on tourists’ loyalties and intentions to revisit destinations and recommend them to others.
Based on the above literature review, the following hypotheses were formulated:
A significant relationship exists between expected quality and perceived quality among tourists.
A significant relationship exists between expected quality and perceived value among tourists.
A significant relationship exists between perceived quality and perceived value among tourists.
A significant relationship exists between perceived quality and satisfaction among tourists.
A significant relationship exists between expected quality and satisfaction among tourists.
A significant relationship exists between perceived value and satisfaction among tourists.
A significant relationship exists between tourist satisfaction and tourist loyalty.
3. Geographical description of the studied area
Seville is the capital of Andalusia, Spain, and is the largest and most densely populated province of this autonomous community. It shares borders with the provinces of Malaga and Cadiz to the south, Huelva to the west, Badajoz to the north and Cordoba to the east. The province of Seville has an area of 14,036.09 km2 and 1,941,355 inhabitants (Municipal Register, 2014) in 105 municipalities. The Guadalquivir River passes through Seville City, 70 km from the sea, and the riverside villages and cities are a living testimony to the rich historical and cultural past of the province. The province of Seville has a tapestry of cultures that are rooted in antiquity, dating as far back as the first millennium bce, the Phoenician colonisation and the Tartessian culture. The location of cities at the river’s confluence and main thoroughfares facilitated the economic growth of the surrounding valley and lands.
Julius Caesar founded the Julia Romula Hispalis colony, which soon developed an impressive trade network. Important settlements appeared throughout the region, which were marked by monuments that have lasted to the present day. The Moors also left an indelible cultural and monumental imprint on these lands. Seville experienced its most splendid period in the sixteenth century. Merchandise from all over Europe and precious ore from the New World arrived in Seville’s port, contributing to the development of all Western Europe. After a period of decline, Seville’s commerce, agriculture and industry revived during the Enlightenment era. More recently, Expo 1992 spread and expanded Seville’s reputation even further.
Tourism is one of the strategic drivers of economic development in this city and is thus considered an essential tool for generating wealth, employment and new business. Seville’s resilience as a first-order urban tourist destination on an international level is based on the competitiveness of its various types of tourism, including, among others, cultural, religious, recreation/festival, gastronomic, health, sports and business tourism.
Table I shows Seville’s tourism growth in 2016. In the first six months, 1.3 million travellers stayed in the city, which is an increase of 13.46 per cent over the same period in 2015. This resulted in 2.5 million overnight stays, or 14.74 per cent, more than the previous year. These increases applied to both domestic and international tourists. Some years ago, the average stay in tourist apartments was longer than that in hotels, but these figures have been converging progressively to two days.
According to the annual tourism report of the Institute of Economic and Business Analysis of Andalusia (2006), the socio-demographic profile of a Seville tourist is a person between 18 and 29 years old, with a special incidence of older visitors, highlighting especially people up to 65 years. Tourists in Seville are divided between Spanish and foreigners, with German, English and French visitors being the main foreigners. Hotels are the preferred accommodation of Sevillian tourists; apartments and flats of relatives and friends are the second option, followed by hostels.
4. Materials and methods
The selected method for collecting data was a structured questionnaire. In this first phase of the study, an appropriate data collection tool was developed to gather the information needed to meet the research objectives. A self-administered closed questionnaire was selected from the available options to collect information. The items were formulated based on those selected from previous studies (Kozak, 2002; Anderson, 2012; Özdemir et al., 2012) to guarantee the questionnaire’s validity. The items selected in the questionnaire were evaluated by two experts: a researcher in the tourism sector and a professional employed in the tourist activity of the region.
The validity of the items that formed the constructs of the proposed model was confirmed twice this way (Figure 1).
The survey was conducted in person with tourists visiting the main touristic areas of the city: the Alcazar and Cathedral, Torre del Oro (Tower of Gold), Parque de María Luisa (María Luisa Park), Avenida de la Constitución (Constitution Avenue), Plaza de España (Spain Square) and Puerta de Jerez (Jerez Gate). The research team informed tourists about the research objective and asked them to participate in the study before distributing the survey. The tourists then filled out the questionnaire entirely by themselves.
The questionnaire had three separate sections. The first dealt with the tourists’ socio-demographic profile and details of their trip. The second contained items related to indices of satisfaction based on the tourists’ assessment of perceived and expected quality, their main travel motivations, the value they assigned to the destination and their general experience and expectations. The third section analysed the tourists’ loyalties, based on their intention to revisit the destination and recommend it to others.
After a process of refinement based on the Cronbach’s alpha coefficient calculated for each construct, the final number of items was 15. The survey was conducted from January to December 2015, through a non-probabilistic sampling by intentional quotas. The number of valid questionnaires was 922 in total; four questionnaires filled out by non-tourists were eliminated, obtaining a confidence level of 95 per cent and a sampling error of 3.23 per cent.
The sample data collection was carried out through a questionnaire individually applied and in morning, afternoon, and evening hours. The sample was selected in the most touristic and representative areas of Seville and targeted tourists of different ages.
Questions in the first part of the questionnaire (i.e. socio-demographic profile and trip details) were close-ended. Responses to items in the second and third sections used a five-point Likert-type scale with one equalling “not likely”, “very unsatisfied” or “never”, and five equalling “very likely”, “very satisfied” and “definitely” – depending on each question’s wording. Cronbach’s alpha value calculated for each of the constructs exceeds 0.75 and is therefore acceptable, as Nunnally and Bernstein (1994) consider a scale acceptable if its Cronbach alpha value is above 0.7.
The elaborated questionnaires had 20 items in the initial proposal, but 5 items were eliminated after sending them to two independent experts for evaluation. They based their elimination on the following reasons:
expert opinion; and
supported by alpha variation (α), i.e. by eliminating the proposed items, the alpha of the construct increases.
In the present study, the methodology used to construct the indicators of expected and perceived quality of tourism destinations, as indices of tourist satisfaction and loyalty, was based on structural equation modelling. In tourism research, the variables under study, such as tourism product quality and tourist satisfaction and loyalty, often cannot be directly measured. Hence, these have to be measured through other, more easily observable variables. Structural equation modelling enables researchers to estimate multiple dependent relationships and represent these relationships between unobservable or latent variables, while taking into account measurement error in the estimation process (Reisinger and Turner, 1999). The present research data were tabulated and analysed using IBM SPSS 23 statistical software, and the estimations and structural equations were produced using IBM SPSS Amos 23. The initial model (Figure 1) was designed based on the ACSI (2016), which includes the observed and latent variables – both exogenous and endogenous.
The model also incorporates disruptive expressions, including the omitted variables effects, measurement errors, uncertainty in the specified process and regression coefficients. The latter represent the relationships between exogenous latent variables and those that are endogenous, and relationships between endogenous latent variables themselves, as well as covariation between exogenous latent variables.
The model was estimated using the unweighted least squares (ULS) method, which is a means of estimating parameters. This is the most appropriate method, as the studied variables are of ordinal type, and therefore, they do not follow a normal distribution. This prevents the model coefficients’ calculation of the t statistics.
Previous research has not been able to establish whether observed variables should follow a specified distribution, which is recommended for categorical variables and based on the matrix of polychoric correlations (Bollen, 1989; Schumacher and Lomax, 1996; Batista Foguet and Coenders Gallart, 2000). In addition to the ULS method, other alternatives can be considered when dealing with ordinal variables. Among these, the robust ULS (RULS) method is recommended by Yang-Wallentin et al. (2010). Like the ULS method, RULS also works with a polychoric correlations matrix, but these correlations are the starting point for subsequently obtaining the matrix of asymptotic covariances that can interfere with the W matrix of distribution-free estimators. After eliminating the non-significant variables one by one, a new structural coefficients estimation was carried out, included in the causal or path diagram in Figure 2. Table IV shows the structural coefficients estimated using the ULS method.
The descriptive statistical analysis of the data collected through the first section of the questionnaire supports the conclusion that most tourists who travelled to Seville in 2015 were young (78 per cent were younger than 44 years old). In addition, most have a higher education degree. The sample of respondents was also quite evenly divided in terms of sex (53.9 per cent female and 46.1 per cent male). A little more than half are Spanish, and 31.3 per cent are other Europeans. A full 65.5 per cent were visiting the city for the first time, 14 per cent had visited it once before and 20.5 per cent had visited Seville twice or more. Tourists stayed mostly in hotels and tourist apartments (70.7 per cent) for an average of 4.48 days. Practically, no visitors stayed only one day, which reflects the wide array of attractive tourism products offered by this provincial capital. Whilst 78.7 per cent of the respondents used the internet to organise their trip, and their main form of transport was the airplane, followed by their own car and the train.
It is observed that the variables that measure tourist loyalty (recommend visiting the city of Seville and intend to return) are significantly correlated, p < 0.01, with the number of times they have visited the city, showing that the more often they visited, the greater their loyalty. The tourists’ country of origin, however, does not influence loyalty.
According to the proposed model, the relationship between the different items and the proposed constructs (Table II) is observed in the calculated theoretical model. We have calculated the structural coefficients of the standardized model, as well as the average variance extracted, calculated as the sum of the loading factor divided by n items of the construct. The average variances extracted exceed 0.5, indicating that measurement items for each construct capture more variance in the underlying construct than measurement error. This criterion assured convergent validity of constructs in our study.
The expected quality is related to the various motivational items, such as attending social and sporting events as a reason for visiting, or business, family vacations, cultural enrichment, knowing and enjoying the local gastronomy and the expectations of the trip to Seville.
The perceived quality concept has shifted towards an approach based on the visitor tourist, according to which “quality resides in the eyes of the recipient”. Quality is what customers perceive it to be; they are the ones who observe and determine if a service is of quality or not. Many of the definitions that are used today revolve around the idea that the quality of a service (as perceived by the client) is a global consumer judgement, relative to the service superiority that results from the comparison made by clients between their service expectations and the perceptions of the actual service organization’s performance (Gronroos, 1990; Parasuraman et al., 1985). This implies that, in the moment of the provision of the service or the moment of the meeting between buyer and seller, three situations can occur:
If the expected quality is less than the perceived quality, the perceived value will be rated as excellent quality.
If the expected quality is equal to the quality perceived, the perceived value will be qualified as a correct or acceptable quality.
If the expected quality is greater than the perceived quality, the perceived value will be classified as poor or low quality.
The perceived quality is determined by perceptions of service quality, the accommodation qualification, the catering qualification, the qualification of leisure and entertainment and the qualification of the experience in general.
The measurement of expected quality indices and perceived quality will allow us to confirm the direct relationship between both constructs, and the quality of the tourism service received during the visit to Seville city.
Visitors’ satisfaction is important for a variety of reasons, especially highlighting the fact that it is one of the most important antecedents of future visitors’ behaviour or loyalty, as demonstrated by numerous studies (Kozac and Rimmington, 2000; Bigné et al., 2001; Yoon and Uysal, 2005; Chi and Qu, 2008; Yuksel et al., 2009). Indeed, satisfaction constitutes an important compound of the overall rating of the journey.
A large number of variables intervene in the concept of customer loyalty (Oliver, 1980; Westbrook, 1987; Oliver and Swan, 1989; Bitner, 1990; Anderson et al., 1994; Anderson and Sullivan, 1993; Fornell and Lehmann, 1994; Baker and Crompton, 2000; Bigné et al., 2001; Brady et al., 2002; Setó, 2003; Casado et al.,2004; Ryzin et al., 2004), and it is intended to conduct a serious investigation of all variables that precede customer loyalty. Fidelity is composed of the items: “would recommend visiting Seville” and “would visit Seville again”.
In relation to the constructs, in Table III, we can observe intensity measured by the structural coefficients. The values between the variables “client satisfaction (tourist)” and “client’s loyalty,” as well as the one between “quality perceived” and “client’s satisfaction” are the main ones. On the other hand, the values referred to the relation between “quality expected” and “client satisfaction”, in addition to “value perceived” and “quality expected” should be pointed out.
Subsequently, the client’s satisfaction has an impact on his/her loyalty. This loyalty is very strongly linked to the client’s perception of the quality. Although H2 and H5 do not present intensity in their coefficients, we could say that as the structural coefficients obtained for the verification of H1 and H4 are significant, in practice, the relationship between the expected quality and satisfaction could be mediated by the perceived quality. In other words, there could be an indirect relation between the expected quality and the satisfaction of the client through the perceived quality.
To confirm the proposed model’s goodness of fit and support for the formulated hypotheses, absolute, incremental and parsimonious goodness of fit measures were calculated (Table IV). The model was evaluated using various indices:
The normed fit index (NFI) is a measure of discrepancies between the adjusted model and the base model.
The goodness-of-fit index (GFI), like the NFI, compares discrepancies between the adjusted model and the model before adjustment.
The adjusted goodness-of-fit index (AGFI) is the same as the GFI, except that it is weighted by the freedom ratio degrees of the base and the adjusted models.
The parsimonious normed fit index (PNFI) is obtained through the NFI and weighted by the quotient of freedom degrees of the adjusted and based models.
The root mean square error approximation (RMSEA) index is the square root of the ratio of the non-centrality parameter adjusted by the degrees of freedom.
Regarding the absolute measures of fit, the RMSEA index is 0.061, which is adequate, as is the GFI value of 0.994. Some incremental measures of fit, such as the AGFI, are sensitive to the number of indicators and closely related to the GFI – with the present scores indicating adequate fit values. With regards to the parsimonious measures of fit, PNFI and PGFI are significant above 0.06, which is also the case for the calculated method. Based on the above results, the model can be said to present adequate goodness of fit indices.
The results of this study highlight the direct and positive relationship between – and influence and impact of – expected and perceived quality and perceived value and customer satisfaction regarding customer loyalty to Seville City. The estimated structural coefficients of the model provided the basis for a comparison of the hypotheses formulated at the beginning of this study.
H1 postulates that a significant relationship exists between the expected quality and the perceived quality among tourists, which was confirmed, as this relationship has a statistically significant structural coefficient. A higher level of expected quality has a significant positive effect on perceived quality for tourists.
H2 According to the results obtained from the standardized structural coefficients, a significant relation between the perceived value and the quality expected cannot be affirmed; so H2 is not confirmed.
H3 According to the results obtained from the standardized structural coefficients, a significant relation between the perceived value and the quality perceived cannot be affirmed; so H3 is not confirmed.
H4 postulates that a significant relationship exists between the perceived quality and satisfaction among tourists, which was confirmed by a statistically significant structural coefficient. As found in other research, such as Ekinci (2004), the quality of services is an important antecedent of satisfaction, because the first has a clear and significant influence on the second. Some authors specifically maintain that quality of service has a non-linear effect on satisfaction.
H5 According to the results obtained from the standardized structural coefficients, a significant relation between clients’ satisfaction and quality expected cannot be affirmed. However, as mentioned in the results obtained, this relationship could occur indirectly.
H6 suggests that a significant relationship exists between the perceived value and satisfaction among tourists, which was confirmed by a statistically significant structural coefficient. Therefore, higher levels of perceived value have a significant positive effect on customer satisfaction. The concept of satisfaction is closely linked to the user-perceived value (Dorai and Varshney, 2012). Gil et al. (2005) contend that there is a relationship between the interactions that take place in a meeting of the service and the customer satisfaction. For Woodruff and Gardial (1996), the nexus between value and satisfaction is critical, due to the natural affinity between both concepts, as both are formed from evaluative judgements (Woodruff, 1997).
H7 postulates that a significant relationship exists between tourist satisfaction and tourist loyalty. A statistically significant structural coefficient confirms that higher levels of customer satisfaction have a significant positive effect on customer loyalty. The existing literature also provides evidence that a positive relationship exists between consumer satisfaction and loyalty (Dick and Basu, 1994), with the first leading to the second, which is normally measured through post-purchase behavioural intentions (Henard and Szymanski, 2001). Therefore, measuring the variable of satisfaction is important, because of its relationship with customer loyalty (Hallowell, 1996).
The present study’s results imply that improving services offered to tourists will increase their expected quality and satisfaction. The results also reveal that raising tourist expectations may increase perceived performance of tourism products through perceived quality, which further enhances tourist satisfaction (Bosque et al., 2006; Fornell et al., 2006; Song et al., 2012).
5.1 Indices of quality, satisfaction and loyalty among tourists
Based on the proposed model, the indices of latent variables (ILV) – perceived and expected quality, perceived value, customer satisfaction and customer loyalty – were calculated using the following formula:
Expected quality index = 2.8185 or 56.37 per cent.
Perceived quality index = 3.7158 or 74,32 per cent.
Received value index = 2.9979 or 59.96 per cent.
Tourist satisfaction index = 3.8731 or 77.46 per cent.
Tourist loyalty index = 4.6136 or 92.27 per cent
As can be seen from these indicators, tourists who visit Seville are extremely loyal (92.97 per cent) and quite satisfied (77.46 per cent) with this destination because of the perceived quality of a variety of services (74.32 per cent): accommodations, restaurants, entertainment and services in general. The index with the lowest results is expected quality, which depends, according to the proposed model, on tourists’ travel motivations and expectations. Thus, this indicates that Seville’s tourism offer could be better managed based on different types of tourism already present, to offer each tourist what he or she wants according to his or her travel motivation (i.e. cultural, gastronomic, recreation/festival, religious, business and sports tourism). It is observed that the perceived quality index is much higher (17.95 per cent) than the expected quality index; thus, the quality of the service received by the tourist during his/her visit to Seville is described as excellent.
6. Conclusions and implications
As satisfied tourists have an important impact through their intentions to revisit and recommend, the management of tourist satisfaction is a vitally important aspect of destinations’ success. Tourists have an image and expectations of their destinations, and the experiences they have can modify the original perceived image of the place. However, only positive results can enhance tourist loyalty. In this context, the role played by agencies and managers of places in developing tourist loyalty is extremely important. Therefore, through diverse initiatives, these agencies and managers need to improve the information on – and quality and image of – various tourist destination attractions. Only an adequate coordination and cooperation between these agencies and managers can ensure tourist satisfaction.
In a model that includes quality, satisfaction and loyalty indices among tourists, it is important to analyse structural relationships to identify the most critically important aspects. The model developed for this study reveals those variables that directly influence tourist satisfaction and loyalty, and their relation. The results show that the expected quality among tourists who visit Seville has a direct positive influence on their perceived quality. And at the same time, the perceived quality and perceived value among tourists has a direct positive influence on their satisfaction. In addition, evidence for a direct positive relationship between tourist satisfaction and loyalty was found, as shown by the tourists’ intention to return to Seville and recommend it to others. The indices also illustrate that the tourists surveyed are loyal and satisfied with the quality of services received, but the expected quality can be improved by adjusting Seville’s tourism offer to match the tourists’ travel motivations (i.e. cultural, gastronomic, recreation/festival, religious, business, and sports tourism).
The findings of this study provide valuable guidelines for city administrators that will help them understand and measure tourist satisfaction and loyalty in any place through a model and indices that are easy to understand and calculate. A new approach to measure satisfaction, loyalty and quality is used based on a scale from 0 to 100, and the index results are very useful for comparing different tourist destinations.
This research confirms that the perceived quality (in a direct way) and the expected quality (in an indirect way) are important for a tourist destination that seeks visitor satisfaction and loyalty. Therefore, a tourist who experiences a high level of quality and satisfaction will intend to recommend that place. A novel feature of the present model is that travel motivations significantly influence the expected quality. In this sense, this study prompts organisations that manage those places to develop effective marketing strategies depending on the purpose of the trip, to increase the expected quality and thus satisfaction.
These results constitute strategies and findings that any tourism location has to consider in the planning and development of its products. Therefore, the proposed model can help encourage a long-term market perspective among tourism sector regulators, investors and agencies. With the information obtained through this model, areas needing improvement can be identified. Accordingly, appropriate procedures can be put into practice to improve quality and satisfaction. Such strategies should be focused on a market orientation and bear in mind that loyalty constitutes a competitive advantage that helps a tourism location make more profits based on the value that loyal customers offer over time. Residents can also benefit from these measures, as their quality of life will improve through upgrades of the city’s tourism facilities.
6.2 Limitations and future research
Regarding this study’s limitations, other variables could have been included that influence tourist satisfaction, such as climate, effect of advertising medium, prices and emotional components. In addition, surveying tourists’ expectations before their visit is virtually impossible, as is surveying those same tourists about their perceived value and satisfaction after their visit. Future lines of research could focus on the intersection of information between tourism supply and demand, providing information about an appropriate balance in specific markets. Finally, the proposed model can also be applied to other tourist destinations that are similar to Seville’s tourism offer, allowing useful comparisons and the identification of critical points and ways to continuously improve customer satisfaction.
Travellers, overnight stays and average stay, first half of 2016
|Residentin Spain||Resident inother countries||Total||% Changeover 2015*|
For average stay, figures have differences in points
Source: Tourism Observatory of Sevilla
Structural coefficients of the model items and average variance extracted (AVE)
Structural coefficients between constructs by the method
Goodness of fit of the proposed model
|Absolute measures of fit|
|Incremental measures of fit|
|Parsimonious measures of fit|
Notes: RMR = root mean square residual;
PGFI = parsimonious goodness of fit index
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About the authors
Ana Ma Castillo Canalejo is a Senior Lecturer at the Department of Applied Economics at the University of Cordoba, Spain. She has a European Doctoral Degree in Economics (PhD). She has written more than 30 papers that have been published in different academic journals related to Tourism. Her main lines of research are tourism economics and environment. Currently, her research focuses on wine and gastronomy tourism, community-based tourism in developing countries, tourism demand, tourist satisfaction and loyalty. She has participated in research projects financed by public funds and performed research stays in different European and Africa countries. She has worked as a consultant for Arthur Andersen.
Juan Antonio Jimber del Río is a Lecturer at the Department of Statistics at the University of Cordoba (Spain). He has a European Doctoral Degree in Business (PhD). He has written more than 20 papers that have been published in different academic journals related to Tourism. His main lines of research are tourism economics. Currently, his research focuses on border tourism, community-based tourism in developing countries, tourism demand, tourist satisfaction and loyalty and structural equations. He has participated in research projects financed by public funds related to tourism and performed research stays in different European countries.