Removing Uncontrollable Factors in Benchmarking Tourism Destination Satisfaction

Abstract

High level of visitor satisfaction is an important signal of sustained success for tourism destinations. The primary goal of this research study is to identify differences in reported visitor satisfaction that do not accurately reflect differences in the delivery of satisfaction by destinations. Our aim is to reveal the influence of factors, such as weather conditions, that may distort comparisons of tourism destinations when measuring visitors’ satisfaction with their stays. We used a generalized linear model (GLM) to estimate reported satisfaction as a function of various factors, with weather included as a factor. The analysis shows that weather as well as the other extraneous factors play an important role in measuring visitors’ satisfaction. The results suggest that when comparing the relative success of various tourism destinations, adjustments in destination benchmarking are necessary to avoid arbitrary bias caused by differences in the timing and conditions of visitor data collection.

Keywords

tourism destination weather satisfaction visitor benchmarking

Introduction

Tourism destinations comprise a highly competitive market where government funding and support for destinations is often contingent on third-party evaluations of the effectiveness of the destination in attracting visitors and successfully satisfying visitors’ needs and desires. Visitor satisfaction responses are the link between the qualitative performance of the tourism destination and the evaluation of that performance. Thus, achieving high levels of visitor satisfaction is one of the most common goals of successful tourism destinations.

The benchmarking of destinations by funding agencies places increasing importance on the methods used to evaluate destinations, not only from the viewpoint of primary attractions offered but also from the viewpoint of service quality and other aspects that affect overall satisfaction of visitors.

The objective of this study is to identify biases in the comparison of tourism destinations from the visitor satisfaction point of view. Controlling for these biases may then improve future accuracy in benchmarking tourism destination visitor satisfaction. The source of the potential bias is related to visitor satisfaction data collection in various destinations within a larger tourism destination. The data for larger destinations, such as regions or countries, are often collected on different days during the season, where the actual weather can have a significant influence on the survey results (Hewer, Scott, and Gough 2015; Jeuring 2017; Steiger, Abegg, and Jänicke 2016).

On one hand, the weather is an integral part of a visitor experience; its effects on tourism spending were indicated by Wilkins et al. (2017). On the other hand, if we want to compare the relative satisfaction performance of smaller destination units within the region or country, it is necessary to get a clear view of the individual unit performance based on controllable factors. However, when we collect the data on different days during the tourist season, the smaller destination unit results are susceptible to the influence of uncontrollable factors. For example, we gather the data in two neighboring destinations within one region during two different days. The weather in one destination on the first day is visibly worse than in the neighboring one on the second day. This causes differences in survey results that distort our view of relative satisfaction performance, and we are not able to compare them properly. We must also recognize that weather may influence the visitor’s real satisfaction with the stay.

The overall satisfaction during a stay in a destination is influenced by many endogenous and exogenous factors. Weather is not explicitly assessed as an attribute of satisfaction (if we do not take into account climate at sun and sea destinations), and its effect on overall satisfaction has been demonstrated in a number of studies, but only some of them work with objective hydrometeorological data (Baylis et al. 2018; Hewer and Gough 2016). However, an examination of the objective effect of weather on the overall satisfaction of visitors when comparing destinations was not researched in scientific studies. The divergent results of studies concerning the weather and visitors’ satisfaction, also discussed in the following text, imply the need for further research.

To address the research gap, this study demonstrates the effect of objective weather conditions on the overall satisfaction of the destination’s visitors with regard to visitors’ origin and seasonality of visits. At the same time, a methodology is presented to remove from the visitor satisfaction the influence of these uncontrollable factors in order to improve comparison of tourism destinations from the visitor satisfaction point of view.

This study contributes to the existing literature because the previous studies offer no practical method for how to deal with the weather effect when comparing multiple destinations from the visitors’ satisfaction viewpoint. Our study represents the first research that measures the effect of weather on the overall visitors’ satisfaction in the destination benchmarking context using real data from meteorological stations. Previous studies, which used this type of data about objective weather conditions, were aimed only at specific tourism attractions (Hewer and Gough 2016) or services (Baylis et al. 2018), not at the tourism destination. Moreover, the effect of objective weather conditions on visitors’ satisfaction has not yet been proven in city destinations and landlocked countries. We may consider this perspective fairly unique where, to the best of our knowledge, there seems to be a gap in the current literature.

Therefore, we made the statistical tests and, based on the results, we have proven that weather influences the overall visitors’ satisfaction in a tourism destination. We have proven the influence of weather conditions to visitors’ satisfaction in two specific city tourism destinations. There are statistically significant differences in overall satisfaction between residents and nonresidents, and visitors’ satisfaction in the high season also differs from the satisfaction in the shoulder season. Thus, not only can weather distort the destination benchmarking when measuring visitors’ satisfaction, but data collection in high season and shoulder season, and differences in the origin of respondents can play important roles as well.

The methodological contribution of this work is the improvement in accuracy of destination benchmarking by means of visitors’ satisfaction. In practice, the results can be used, for instance, for improved distribution of financial support among tourism areas. The results of the study can also be used to improve accuracy of predicting visitor turnout in tourism destinations and for simplifying decision making within destination quality management.

Literature Review

Over time we have witnessed, year on year, an increasingly demanding tourism customer. This is largely attributed to a rapidly changing tourism marketing environment, especially through the development of new, constantly updating information-communication and distribution technologies. The increasing connectedness of society to these technologies allows consumers to more efficiently compare and choose among far more destinations than in the past. Along with increasing standards of living and an accelerated pace of life that affect tourism customers’ preferences, this growth in access to information drives up the expectations regarding the quality level of services provided.

Customer satisfaction is one of the most important elements of destination performance and, thus, one of the greatest sources of competitive advantage. The concepts of performance and satisfaction (gap analysis) can be used to benchmark strengths and weaknesses of different destinations by considering actual tourist experiences (Kozak 2002). Satisfaction with a tourism product is also considered one of the essential objectives and indicators of success in tourism destination management (Mazanec, Wober, and Zins 2007; Ryglova and Vajcnerova 2014; Štumpf, Vojtko, and Valtrová 2018) and as one of the most important factors of destination sustainability and competitiveness (Iniesta-Bonillo, Sánchez-Fernández, and Jiménez-Castillo 2016; Jarvis, Stoeckl, and Liu 2016; Sukiman et al. 2013; Ryglová et al. 2018).

Destination Benchmarking

Destination benchmarking is a conceptual approach of evaluating, measuring, and comparing the sustainability of performance of destinations in a competitive environment. Several approaches can be used for tourism destination benchmarking, such as the ongoing process of benchmarking the individual destination, benchmarking each destination against the best, benchmarking destinations against average values, performance improvement benchmarking, and gaining new information (Kozak 2004a, 2004b, Kozak 2002; Kozak and Rimmington 1999).

There are also various factors of visitors’ satisfaction that can be benchmarked from the tourism destination’s point of view, including accommodation services, facilities and activities, local transport services, hospitality and customer care, destination airport facilities and services, hygiene, sanitation and cleanliness, price, or language communication (Kozak 2002).

In this context, Cernat and Gourdon (2012) provided Sustainable Tourism Benchmarking Tool (STBT) based on a number of benchmarks against which the sustainability of tourism activities in various countries or destinations can be assessed. Fuchs (2004) presents the Data Envelopment Analysis (DEA) benchmarking approach and claims that efficiency benchmarking offers a first guide of measuring the well-being of tourism destination units and may need to be carefully addressed and measured for (1) strategic reasons, in order to compare the performance of a destination organization with its competitors or with its strategic (i.e., coproducing) partners; (2) tactical reasons, to enable performance control of a destination; (3) planning reasons, to compare the benefits accruing from the use of different resource inputs or from varying proportions of the same inputs (i.e., destination resource configuration structure).

Both qualitative and quantitative methods can be used for destination benchmarking, but it is extremely complicated to gather the data for benchmarking on destination level, especially on the national or higher level. That is why the recent studies are more aimed at the benchmarking of very few countries (George Assaf 2012) or regions (Blancas et al. 2017; Khazai, Mahdavian, and Platt 2017; Kozak 2002).

Satisfaction and Destination Quality

Determining the destination quality, in the context of tourism development, is not an easy target, as confirmed by different approaches to their measurement in scientific studies, such as those of Hudson (2006); Ryglová, Rašovská, and Šácha (2017); and Woods and Deegan (2003). The difficulty in measuring destination quality is caused by the complexity of the destination’s offerings, the high levels of subjectivity in the process of destination quality evaluation, and by the specific character of tourism services, which exhibit intangibility, transience, and variability.

Some authors have treated service quality and consumer satisfaction as being synonymous (Crompton and Love 1995; Otto and Ritchie 1996), or have narrowed the distinction between the two concepts (Spreng, MacKenzie, and Olshavsky 1996). The majority of researchers conclude that quality is a predictor of satisfaction (Fornell et al. 1996; Kozak and Rimmington 1999; Moital, Dias, and Machado 2013; Wu et al. 2017). Empirical studies carried out on a sample of destination visitors confirm that perceived quality is a direct determinant of satisfaction (Campo-Martínez and Garau-Vadell 2010; de Rojas and Camarero 2008). Furthermore, Zeithaml, Bitner, and Gremler (2006) confirm that concepts of service quality and consumer satisfaction have to be considered separately. These authors define service quality as a component of customer satisfaction, with other components being the quality of products and their prices. Customer satisfaction is generally perceived as a broad concept, whereas the quality of services concentrates especially on the dimensions of services. Zeithaml, Bitner, and Gremler (2006) include not only key dimensions of service quality and relations to customer satisfaction in their model, but they also approach customer satisfaction as an indispensable presumption for reaching the required loyalty of a client. At the same time, they do not disregard the significant influence of situational and personal factors of a client.

The relationships between satisfaction, perceived quality, and loyalty are constantly attracting the attention of the researchers, such as Baker and Crompton (2000), Boo, Busser, and Baloglu (2009), Chi and Qu (2008), or Žabkar, Makovec Brenčič, and Dmitrović (2010). Furthermore, Chekalina, Fuchs, and Lexhagen (2018) propose a customer-based destination brand equity model (CBDBE) that consists of awareness as an exogenous variable, loyalty as an endogenous variable, and three mediator variables: (1) destination resources, (2) value in use, and (3) value for money.

From a marketing perspective, a tourism destination is a complex product that requires the necessary level of quality and strategic management. From this point of view, managing the destination quality can be approached through the mediation of visitor’s satisfaction. The general assumption is that the higher level of tourists’ satisfaction leads to higher tourists’ loyalty to those destinations. This fact is also confirmed by the conceptual model of tourist satisfaction at the destination level. Besides quality, loyalty, and tourist satisfaction, the model incorporates perceived value, cost and risk, image, and complaint behavior (Dmitrović et al. 2009).

The methods that enable us to evaluate quality in tourism from the customers’ perspective by means of analyzing customer perception are, for instance, Importance-Performance Analysis (IPA), SERVQUAL, or Priorities for Improvement (PFI) (Rašovská, Kubickova, and Ryglová 2020; Ryglová, Vajčnerová, and Šácha 2016).

Factors Affecting Visitors’ Satisfaction

From the destination visitors’ point of view, the satisfaction during a stay in a destination does not depend only on the experience with tourism services, but is also influenced by many endogenous factors such as security, hospitality, the friendliness of local inhabitants, the cleanliness of the destination, transportation infrastructure, and the level of visitor management (Ashworth and Page 2011). Visitors’ perception of the destination and their level of satisfaction with it have an impact on their willingness to repeat stays and to recommend the particular destination to other potential visitors (Oppermann 2000). Consequently, the performance of the destination leading to visitor satisfaction, and ideally to visitor loyalty, is a function of several mutually dependent factors or components, according to Xie (2011), Yoon and Uysal (2005), Žabkar, Makovec Brenčič, and Dmitrović (2010), and Kresic (2008). Among these factors, it is also necessary to include exogenous factors such as “weather” that are beyond the control of destination managers.

According to Alegre and Garau (2010), it is necessary to distinguish between factors leading to overall satisfaction and factors leading to overall dissatisfaction. The same weather might be a factor either increasing overall satisfaction with the destination or, conversely, increasing overall dissatisfaction. The authors demonstrated a low agreement between the subjective evaluation of the same destination factor when it is associated as an attribute leading to satisfaction or, conversely, dissatisfaction (e.g., cleanliness and hygiene vs. dirtiness) (Alegre and Garau 2010). According to the above-mentioned conceptual model of tourist satisfaction at the destination level, the cost and risk construct (causing disutility), which can include weather condition as well, is adversely affecting customer satisfaction (Dmitrović et al. 2009).

Weather’s impact on satisfaction with a destination has also been demonstrated in a number of studies (Jeuring 2017; Jarvis, Stoeckl, and Liu 2016; Giddy, Fitchett, and Hoogendoorn 2017; Steiger, Abegg, and Jänicke 2016; Hewer, Scott, and Gough 2015). Although it is covered in literature, this topic still deserves more research as there are some studies where significant weather impact on visitors’ satisfaction has not been proven (Bentz et al. 2016; Wu, Pearce, and Li 2017; Gössling, Abegg, and Steiger 2016). Bentz et al. (2016) in case of the Azores did not confirm the relevance of good weather conditions on tourist satisfaction although weather conditions were pointed out as a significant factor for visitors. The results could have been affected by using IPA analysis and a performance-only approach where high expectations of good weather were almost fulfilled. Wu, Pearce, and Li (2017) also indicated that the weather plays an essential role for Chinese tourists. Not only does good weather improve the positive feelings from traveling and the car drive, it also improves the quality of photos taken in the destination, which is an important activity for Chinese tourists that may affect satisfaction recall and recommendations to others. However, the results of the regression analysis did not confirm the substantial influence of weather conditions on overall tourist satisfaction. The divergent results of studies concerning connections between weather and visitors’ satisfaction imply the need for further research.

Kim et al. (2017) explored the impacts of the weather on tourist satisfaction and intention to revisit sites. It was determined that the impact of weather perception on tourist satisfaction and revisit intention is higher in rainy weather conditions. The results of the study show that to sustain tourist satisfaction and maintain revisit intentions, efforts to moderate the negative impacts of uncomfortable weather conditions are required, especially in rainy weather. Bujisic et al. (2019) revealed the relationship between perceived weather, consumers’ moods and affective experience, and word of mouth. The authors examined these connections using secondary data from restaurants in Florida, USA.

Hewer and Gough (2016), who assessed the relationship between weather and tourism turnout, concluded that turnout reacts to weather differently in each month, whether it be high and low season or in between these peaks and trough. According to these authors, the most influential factors were temperatures, rainfall, and wind. However, this study has limitations as it only deals with the turnout of a particular attraction—the Toronto Zoo, Canada. Baylis et al. (2018), who tested the weather impacts on sentiment of human expression on social media, explored that cold temperatures, hot temperatures, precipitation, narrower daily temperature ranges, humidity, and cloud cover are all associated with worsened expressions of sentiment.

Weather and climate as factors of a tourist’s decision making and their overall satisfaction were also researched by Steiger, Abegg, and Jänicke (2016), focusing on the Alps, Germany. Rain was revealed as the most important factor, and differences in perception of weather by different types of tourists were statistically supported. Older tourists are more sensitive to warmth, whereas tourists that are interested in sports activities are more concerned about cold weather. First-time visitors show higher sensitivity to rain and families with children prefer higher temperatures. Visitors’ prior expectations about weather forecasts and the climate during specific months will be important factors as well (Giddy, Fitchett, and Hoogendoorn 2017).

Satisfaction with weather is not a controllable factor from the viewpoint of destination managers and may be highly volatile. In practice, among other things, this may influence the interpretation and objectivity of results when establishing benchmarks to evaluate and assess the competitiveness of destinations. This concerns especially those cases when benchmarking of the destination performance is conducted by means of visitor satisfaction or visitor turnout (Foster and Jackson 1979; Fuchs and Weiermair 2004; Johann and Padma 2016; Kozak 2002; Štumpf, Vojtko, and Valtrová 2018).

To decrease the random impact of weather on the measurement and evaluation of overall visitor satisfaction, attempts to compensate for such inconveniences may improve the resulting satisfaction. Bambauer-Sachse and Rabeson (2015) propose this be done through price or other means. As Kozak (2002) states, if we want to benchmark tourism destinations, it is necessary to measure, analyze, and evaluate more factors and variables.

There is also a relationship between satisfaction and expectations, and this means that tourist satisfaction is positively affected by tourist expectations (Rodríguez del Bosque, San Martín, and Collado 2006). Weather expectations and previous experiences can also affect the destination choice as well as the satisfaction with holidays (Jeuring 2017). Differences between expectations and performance may affect customer satisfaction in either a positive or negative direction depending on surprises in weather outcomes. Weather expectations and previous experiences can affect not only the destination choice but also the satisfaction with holidays (Jeuring 2017), revisit intention (Hewer and Gough 2016), or word of mouth (Bujisic et al. 2019).

The weather effect on satisfaction (Steiger, Abegg, and Jänicke 2016; Kim et al. 2017) is also closely connected with the expectation of the weather, weather forecasts, and unexpected weather deviations (Jeuring 2017).

Research Framework and Hypotheses

As was already mentioned, weather and climate are mostly considered as the factors influencing the overall satisfaction. The question is which data are appropriate to enter the research measurements—subjective visitors’ weather perception or real objective weather status. There are two different approaches used in previous research studies. The authors have worked with subjective weather feeling perceived by destination visitors in most of the studies (Bujisic et al. 2019; Kim et al. 2017; Steiger, Abegg, and Jänicke 2016). In these studies, subjective perception indicates for each visitor whether the deviation from normal weather is good or bad, which can be an argument for relevance in using this approach.

Another approach argues for higher objectivity is using real data from meteorological stations instead of using subjective visitors’ weather perception. This approach is based on the real objective weather conditions. The authors used the hydrometeorological data about temperature, humidity, precipitation, cloud cover, or wind speed (Baylis et al. 2018; Hewer and Gough 2016): Baylis et al. (2018) in the case of investigating the relationship between meteorological conditions and expressions of sentiment on social media and Hewer and Gough (2016) in the case of daily weather at a particular tourist attraction.

Some studies work with both approaches and compare the subjective evaluation of weather by tourists with meteorological data (Giddy, Fitchett, and Hoogendoorn 2017). The results show the effect of weather on overall satisfaction without respondents being aware of this fact, which supports the use of objective data.

The important question related to this topic is if we can separate the satisfaction results from these weather effects to produce comparable indices for each area/place and facilitate objective benchmarking.

Based on these arguments and other arguments mentioned in the literature review, we postulate the following hypothesis:

Hypothesis 1: Weather influences the overall reported visitors’ satisfaction in a tourism destination.

The “expectation–experience–satisfaction–loyalty” paradigm has been generally accepted in tourism literature (Michalkó, Irimiás, and Timothy 2015). Some researchers deal with (perceived) performance instead of experience (Bentz et al. 2016; Song et al. 2012) and some also include the motivation factor in the model (Romão et al. 2014). Thus, destination loyalty is usually explained by the intention to revisit and/or willingness to recommend (Loi et al. 2017; Truong, Lenglet, and Mothe 2017). Generally, tourist satisfaction can be measured using overall satisfaction, comparison with expectations, and comparison with the ideal (Chen and Li 2018; Song et al. 2012). Truong, Lenglet, and Mothe (2017) analyze not only the relationship between expectations and overall satisfaction but also with the attribute satisfaction factors.

However, there are other peculiarities arising from the expectation–satisfaction paradigm. We consider whether nearby residents who make trips (especially one-day trips) within their region will be more sensitive than visitors (nonresidents) coming from other regions or countries. Nonresidents who plan holidays farther in advance may expect uncertain weather during their holidays and take this expectation into account when planning longer stays in the region, but also have less ability on short notice to avoid poor weather. Residents’ day-trips that are affected by weather may influence visitor satisfaction response differently. Visitors’ origin is connected to the length of stay (de Menezes, Moniz, and Vieira 2008; Alegre, Mateo, and Pou 2011; Boto-García, Baños-Pino, and Álvarez 2019). Our assumption can be also supported by Wang et al. (2018), who indicated the significant correlation between the length of stay and visitors’ satisfaction although it must be mentioned that they researched specific destination type and they are aware of the importance of destination status. Therefore, we can formulate the following hypothesis:

Hypothesis 2: Visitors’ satisfaction varies with the visitors’ origin.

The expectation–satisfaction model is also essential for hypothesis 3, that visitors’ satisfaction in the high season differs from the satisfaction in the shoulder season. Visitors expect good weather conditions (higher temperatures, sunny) in the high summer season (July–August), when the main summer vacation in the Czech Republic occurs. This assumption is supported by Hewer and Gough (2016), who reveal different reaction of visitors on the weather conditions in each month (in the high and the low season and also in between). Falk (2014) confirmed that the weather during the peak season altered domestic overnight stays in Austria, a neighboring country to the Czech Republic. The results show that sunshine and temperature had a positive correlation, and precipitation brought a negative effect.

Based on these arguments, we postulate the following hypothesis:

Hypothesis 3: Visitors’ satisfaction in the high season differs from the satisfaction in shoulder season.

Materials and Methods

The main goal of this research study is to identify weaknesses in the process of comparing tourism destinations from a visitor satisfaction point of view. Our aim is to reveal the influence of factors, such as weather conditions, which may bias the comparison of tourism destinations from the viewpoint of visitors’ satisfaction. The source of the potential bias is tied to the conducting of visitor satisfaction data collection in various destinations within a larger tourism destination. The data for larger destinations, such as regions or countries, are often collected on different days during the season, where the actual weather can have a significant influence on the survey results.

The above-mentioned hypotheses, drawn from a general expectation–satisfaction model and the theories supported by the existing literature, were tested in this article.

Data Collection

To analyze the influence of weather on the visitors’ satisfaction, we first combined data sets from several tourist satisfaction surveys in two Czech towns located in the South Bohemia region—České Budějovice and Písek. Both towns are mainly historical cultural tourism destinations with some similarities. These satisfaction surveys were collected using telephone-assisted personal interview/computer-assisted personal interview (TAPI/CAPI) using quota sampling during summer seasons 2016 and 2017. Altogether, the sample contains 2,278 valid responses (1,245 for České Budějovice, 1,033 for Písek).

These surveys contained the same questions over a period of several years. All analyzed variables were date of interview, place of interview, origin of respondent (from South Bohemia, from the rest of the Czech Republic, foreign), length of stay (1 day, 2–3 days, 4–7 days, more than 7 days), satisfaction (1–5 scale), and respondent demographics (gender, age, education level, and social status).

České Budějovice is predominantly a cultural destination, typical activities are visiting the main square, the city center, and the Budweiser Budvar brewery. The main activities, therefore, take place outdoors and we can expect a higher weather influence on visitors’ satisfaction.

Písek is also a cultural destination, typical activities are visiting the old stone bridge, the city center, and several indoor attractions (historical water power plant, museum). Outdoor and indoor activities are more balanced in Písek, and consequently, we can expect the weather to have comparatively less influence.

To obtain data about the weather, we used data sets from the “Сurrent Weather and Forecast” (OpenWeatherMap 2012–2019) for both towns and combined them with our survey data. By doing this, we obtained temperature and weather category (clear, clouds, rain) for each response. In České Budějovice, the final data set covered 651 responses in clear sky days, 339 in cloudy days, and 255 in rainy days. In Písek, there were 511 responses in clear sky days, 451 in cloudy days, and 71 in rainy days. Both visitors for one day and several days were included. The match between weather and satisfaction was done on the basis of survey date, which adds some ambiguity as we do not know respondents’ actual dates of stay for visits longer than a single day, and we are thus unable to include the effects of weather on prior days of their stay. Trip length was included in earlier analyses and showed no statistically significant effect on results—this variable was later removed from the model during the backwards process of variable reduction.

Because our approach is grounded in the expectation–satisfaction model, the weather has to be compared to normal weather in a particular time frame. For this, the temperature variable was recoded in quartiles, with quartiles 2 and 3 combined to get low (less than 19.9 °C), normal (between 19.9 °C and 27.2 °C), and high temperature (more than 27.2 °C).

The satisfaction scale used in the surveys was 1 (best) to 5 (worst). Because there were only few responses for high dissatisfaction (4, 5), we recategorized data to 1, 2, and 3+ values for further analysis.

Data Analysis

To analyze the data, several statistical methods were used. We started with simple descriptive statistics (frequencies, contingency tables) with chi-square tests to explore differences between data sets for both towns. This revealed statistically significant differences in many analyzed variables such as satisfaction scores, weather, origin of tourists, length of stay, and demographics.

To be able to analyze all these variables together including their interactions, we further used generalized linear model (GLM) with binomial logit link function. Subsequently, the backwards process of variable reduction based on decreasing of Akaike information criterion (AIC) and variance inflation factors (VIFs) was applied to find the final model. During the process, we also aggregated values in variables where there were no significant differences.

All the calculations were made in R Statistical Package 3.4.1 (R: The R Project for Statistical Computing, n.d.).

Results

We analyzed the influence of weather on the visitors’ satisfaction in two city tourism destinations—the town of Písek and the town of České Budějovice, located in one of the most popular tourism regions in the Czech Republic—the Region of South Bohemia.

To recall, we used GLM with binomial logit link functions. The explanatory variables of the model are shown in Table 1.

Table 1.

Explanatory Variables of the GLM.

Explanatory Variable	GLM Code	Values
Overall visitors’ satisfaction	fSatisfaction2	1; 2; 3+
Place	Town	Písek; České Budějovice
Weather status	Weather	Clouds; Rain; Sun
Origin of visitors	Origin2	InSB; OutSB
Season	fMonth2	JulAug; non JulAug
Temperature	fTemperature	Low; Typical; High

Note: 3+ = combined responses 3, 4, 5; InSB = Residents of South Bohemia; OutSB = Nonresidents of South Bohemia; JulAug = July/August; non JulAug = May/June/September.

When we made a test using GLM, all the variance inflation factors were below 1.048, which showed no serious collinearity problem, as shown in Table 2. This suggests that we are able to consider all the variables in the original model.

Table 2.

Variance Inflation Factors.

Variable	GVIF	Df	GVIF^(1/(2*Df))
Town	1.07193	1	1.03534
Weather	1.204138	2	1.047536
Origin2	1.021324	1	1.010606
fMonth2	1.05458	1	1.026927
fTemperature	1.138437	2	1.032945

Note: GVIF = generalized variance inflation factor.

We explain the overall visitors’ satisfaction (on the scale 1 = very satisfied, 2 = satisfied, 3+ = combined responses from 3 /average/, 4 /dissatisfied/, 5 /very dissatisfied/, because there were not enough responses for these categories and their aggregation improved the final model) helping with the number of explanatory variables. These variables are place (the town of Písek vs. the town of České Budějovice), weather status (cloudy, sunny, rainy), origin of the visitors (residents of South Bohemia vs. nonresidents of South Bohemia), season (July/August vs. May/June/September), and temperature (low vs. typical vs. high).

We identified statistically significant influence of all the variables included in the model, on the visitors’ satisfaction, as shown in Table 3. Recall that with a scale assigning lower values to higher levels of satisfaction, negative estimates are correlated with higher level of visitor satisfaction.

Table 3.

Statistical Significance of Explanatory Variables.

Variable	Estimate	Standard Error	z Value	Pr(>\|z\|)
(Intercept)	−0.61752	0.17259	−3.578	0.000346***
Town [T.Pisek]	−0.63171	0.09921	−6.367	1.92e–10***
Weather [T.Clouds]	0.38526	0.10906	3.532	0.000412***
Weather [T.Rain]	0.35977	0.14123	2.547	0.010856*
Origin2 [T.OutSB]	−0.56762	0.11815	−4.804	1.55e–06***
fMonth2 [T.NonJulAug]	−0.42232	0.10436	−4.047	5.19e–05***
fTemperature [T.Typical temperature]	0.59419	0.12506	4.751	2.02e–06***
fTemperature [T.High temperature]	0.65498	0.14466	4.528	5.96e–06***

Note: Significant at ***p < 0.001; *p < 0.05.

In Figure 1, we see statistically significant differences due to location, with higher visitor satisfaction reported in the town of Písek than in the town of České Budějovice. Visitors from outside South Bohemia report higher satisfaction, other things constant, than those from within South Bohemia. In addition, shoulder season visitors (May/June/September) report higher satisfaction than high season visitors (July/August). The reasons can be closely connected with the weather effect, where the high temperatures in July and August can be uncomfortable for visitors. This is especially concerning in city destinations, where the real feel of the temperature can be higher than in nature-based destinations. If urban areas are not prepared to offer services and facilities for this situation, uncomfortable temperatures can have a large impact on visitor satisfaction. There are other reasons that may also influence the overall satisfaction in the high season negatively, such as overcrowding or seasonal services that are provided with lower quality.

Figure 1.

The effect of several factors on the visitors’ satisfaction.

The GLM logit function also shows the differences in visitor satisfaction responses based on weather and temperature. Visitors report significantly higher satisfaction with clear skies than with cloudy or rainy weather, but there is no statistically significant difference between the effects of cloudy or rainy weather on satisfaction. With respect to temperature, visitors report higher satisfaction when temperatures are substantially lower than typical. There is no significant difference in satisfaction between typical temperatures and higher than typical temperatures (the lower value on y-axes represents higher satisfaction).

As the last step, we calculated the exponentiated coefficients (odds ratios) for statistically significant factors to show, how the satisfaction will be changed when the explanatory variable changes by one unit, as shown in Table 4.

Table 4.

Exponentiated Coefficients (Odds Ratios).

Variable	Exponentiated Coefficients (Odds Ratios)
(Intercept)	0.5392806
Town [T.Pisek]	0.5316823
Weather [T.Clouds]	1.4699925
Weather [T.Rain]	1.4329931
Origin2 [T.OutSB]	0.5668754
fMonth2 [T.NonJulAug]	0.6555246
fTemperature [T.Typical temperature]	1.8115646
fTemperature [T.High temperature]	1.9251031

Specifically, when the weather is cloudy, the satisfaction reported by visitors is 1.47 times as likely to be worse by one unit. According to the reporting scale, one unit is the difference between an average response and satisfied, or between satisfied and very satisfied. For rainy weather, the odds ratio is 1.43, suggesting a similar increase in the likelihood of a less satisfied response when it is rainy compared to when it is cloudy. These results are not significantly different from one another.

Furthermore, our results suggest that visitor satisfaction decreases when temperatures are typical or higher than typical when compared to low temperatures. With odds ratios of 1.81 and 1.93, respectively, respondents are nearly twice as likely to lower their satisfaction response by one unit when temperatures are higher. Again, the results for the two temperature categories are not significantly different from one another, although both are significantly different from the baseline of low temperatures.

This analysis shows that weather conditions and temperature play an important role in reporting of visitors’ satisfaction, especially in city tourism destinations. Thus, it is necessary to take these elements into consideration when measuring the visitors’ satisfaction at different times, in different places, and under different weather conditions. Differences in temperature can have an especially large influence on overall satisfaction of visitors in tourism destinations.

Discussion

This research study analyzes the relationship between overall visitors’ satisfaction and the effect of weather and other important factors for making adjustments to satisfaction comparisons among various tourism destinations. The question goes beyond if and how the weather affect the visitors’ satisfaction as shown in previous studies (Giddy, Fitchett, and Hoogendoorn 2017; Hewer and Gough 2016; Hewer, Scott, and Gough 2015; Jarvis, Stoeckl, and Liu 2016; Jeuring 2017; Steiger, Abegg, and Jänicke 2016). These authors describe the relationships among satisfaction, weather effect, and revisit intentions for particular sites or attractions (Hewer and Gough 2016; Kim et al. 2017). They also analyze how weather and weather forecasts can influence the decisions to visit a particular destination and how that decision varies by market segments (Steiger, Abegg, and Jänicke 2016). It seems reasonable to conclude that the weather effect on satisfaction is also closely connected with the expectation of the weather, weather forecasts, and unexpected weather deviations. Based on the statistical tests and results presented above, we can confirm the hypothesis that weather influences the overall reported visitors’ satisfaction in a tourism destination (hypothesis 1).

We have established the effect of weather on the overall visitors’ satisfaction in two city tourism destinations using real data from meteorological stations. Previous studies were aimed only at specific tourism attractions (Hewer and Gough 2016) or services (Baylis et al. 2018) using objective weather condition data. Moreover, the effect of objective weather conditions on visitors’ satisfaction had not previously been established in city destinations and landlocked countries. Different subjective perceptions of satisfaction determinants presented by Alegre and Garau (2010) led us to choose objective data to adjust the effect of weather on the overall satisfaction of destination visitors. Using objective meteorological data in this study is also necessary because the one-dimensional concept of satisfaction (evaluation based on subjective perception only) may be insufficient (Alegre and Garau 2010; Kozak 2002).

None of the previous studies explains how to exclude the weather effect from destination performance, when comparing different destinations from the visitors’ satisfaction viewpoint—a process that is necessary in regional policy analysis. If we consider the satisfaction of visitors as one of the main goals of tourism destinations (Mazanec, Wober, and Zins 2007; Štumpf, Vojtko, and Valtrová 2018) and a crucial factor of destination sustainability and competitiveness (Iniesta-Bonillo, Sánchez-Fernández, and Jiménez-Castillo 2016; Jarvis, Stoeckl, and Liu 2016; Sukiman et al. 2013), there is a need to benchmark the destinations from the viewpoint of visitors’ satisfaction while controlling for factors beyond the control of the destination manager. Generally, if we want to benchmark tourism destinations, it is necessary to measure, analyze, and evaluate more factors and variables (Kozak 2002), what we can consider as a limitation of this study. However, satisfaction can be considered as one of the most important parts of destination performance and thus also as one of the greatest signals of competitive advantage (Štumpf, Vojtko, and Valtrová 2018). To recall, there are several approaches that can be used for the tourism destinations benchmarking such as benchmarking against the best, or benchmarking against average values (Kozak 2004a, 2004b, 2002; Kozak and Rimmington 1999). All of them require consistent, comparable, and undistorted data to benchmark tourism destination performance correctly and efficiently.

We have proven the influence of weather conditions to visitors’ satisfaction in tourism destinations in the analysis above, where we chose only two specific city destinations. Nevertheless, the approach can be implemented internationally in other tourism destinations. This approach to benchmarking of tourism destination satisfaction is then possible to generalize in the case of the common methodology of data collection.

We can confirm the findings of Hewer and Gough (2016) that the effect of weather differs in each month in accordance with seasonality. Our GLM shows that the visitors’ satisfaction in the high season differs from the satisfaction in the shoulder season (hypothesis 3). We confirm hypothesis hypothesis 3.

Based on our empirical testing, we can state that visitors’ satisfaction in the high season differs from satisfaction in the shoulder season, and that seasonality plays an important role in visitor satisfaction. The results of our research show that shoulder season visitors (May/June/September) report higher satisfaction than high season visitors (July/August).

There are also statistically significant differences of the overall satisfaction between residents and nonresidents, and between the two analyzed destinations. We can, therefore, confirm hypothesis 2: visitors’ satisfaction varies with visitors’ origin. Different types of visitors and market segments, and variations in destination’s offerings can report different satisfaction, as was statistically proven also by Rašovská, Kubickova, and Ryglová (2020); Ryglová, Vajčnerová, and Šácha (2016); Steiger, Abegg, and Jänicke (2016).

Therefore, destination benchmarking can be distorted by more than weather when measuring visitors’ satisfaction. Collecting data in high season and shoulder season, differences in the origin of respondents, and other above-mentioned factors play important roles as well.

Based on the results, we conclude that weather affects overall visitor satisfaction, but there are other effects that might influence the differences between high and shoulder seasons, or between resident and nonresident responses, such as differences in expectations, or differences in visitor needs based on demographics, for example, gender or age (Ryglová, Rašovská, and Šácha 2017).

Thus, there is a need to remove the weather effect (and other exogenous effects) from the overall satisfaction of visitors in order to effectively benchmark destination satisfaction performance based on endogenous factors (Ashworth and Page 2011) that can be managed effectively by destination management organizations (DMOs). This focus on endogenous destination performance, where visitor satisfaction would be one of the indicators of destination success, helps to establish a consistent benchmark. Furthermore, when this indicator (among the others) determines the redistribution of financial support among benchmarked smaller destinations within the bigger one (region, country), external effects, such as weather, must be removed. Were we to benchmark tourism destinations (places, areas within the region) from the visitors’ satisfaction point of view and compare them among each other (geographically, in the period of time or with respect to the different market segments), at first we would eliminate the weather effects, as they can have a significant influence on the results. Otherwise, we would be allowing the distortions associated with weather to affect policy decisions, for which the analysis was performed. This would be especially so, if we were to consider the differences in weather perception from a time viewpoint as Hewer and Gough (2016) confirm.

Most importantly, we highlight one specific finding of this research. It was shown that visitors to this region are more satisfied when the temperature is below normal, and that reported satisfaction decreased with rising temperatures. Customer satisfaction may become more sensitive to temperature volatility in destinations that are more affected by climate change, and there may be a trend due to climate change that may have long-term consequences for some destinations. The opposite effects were identified for families with children by Steiger, Abegg, and Jänicke (2016), who find that those families are more likely to prefer higher temperatures. We can explain this with different types of tourism destination. Hot weather in city tourism destination in the high summer season is not suitable for tourist activities in towns and cities, but it is, of course, a requirement for sun and sea destinations. These results, together, suggest that climate change may alter the demographic mix of tourists for highly affected destinations.

Conclusions

The objective of this study was to identify weaknesses in current practice when comparing tourism destinations from visitors’ satisfaction point of view. The satisfaction of visitors is one of the main goals of tourism destinations and a crucial factor of destination sustainability and competitiveness. Therefore, we have to benchmark the tourism destination’s performance and to place pressure on destinations to improve their performance and competitiveness.

Summary of Findings

Visitor satisfaction data are gathered at different times and under different weather conditions for different locations. Therefore, unadjusted visitors’ satisfaction responses are not comparable and give policy makers a distorted view of relative destination performance.

We have established the effect of weather on the overall visitors’ satisfaction in two city tourism destinations using real data from meteorological stations. Visitors report significantly higher satisfaction with clear skies than with cloudy or rainy weather, and when temperatures are substantially lower than typical for these destinations. In other words, visitors are more satisfied when the temperature is below normal, and that reported satisfaction decreased with rising temperatures.

Based on our empirical testing, we can also state that the visitors’ satisfaction in the high season differs significantly from the satisfaction in the shoulder season. The results of our research show that shoulder season visitors (May/June/September) report higher satisfaction than high season visitors (July/August). Furthermore, we can confirm that different types of visitors and market segments, and variations in destinations’ offerings can report different satisfaction. We revealed statistically significant differences of the overall satisfaction between residents and nonresidents, and between the two analyzed destinations.

Theoretical Contribution

The main methodological contribution of this work is to improve the accuracy of destination benchmarking by means of visitors’ satisfaction. Our research indicated key issues about how to collect primary data about visitors’ satisfaction in a larger destination (region, country), to compare smaller units (places, areas) within the larger one, and to create a comparative benchmarking index or analyze satisfaction in time series. We highly recommend observing and controlling for the exogenous effects (such as weather) that reflect the difference between random events and the expectations of visitors, thereby influencing overall satisfaction. Furthermore, it is crucial to clean up the results from these effects to gain comparable data sets for each area/place within the larger region. The previous research studies have not brought any satisfactory explanation of how to deal with the weather effect, when comparing different destinations from the visitors’ satisfaction point of view. The effect of objective weather conditions on visitors’ satisfaction using real data from meteorological stations had not been established in city destinations and landlocked destinations in previous research studies. Thus, our findings have a role in filling this gap, thereby contributing to the current theory.

Managerial and Ethical Implications

A new system of support, certification, and financing of tourism regions and areas according to common performance indicators is being set up for implementation in the Czech Republic. If visitor satisfaction results are to be used as an indicator for evaluating destination performance and redistributing financial support, external effects, such as weather, must be removed to establish a consistent benchmark. Therefore, the findings of our study may be practically used to improve redistribution of financial support into particular tourism regions and areas, without subjecting the redistribution to the randomness of measurement errors due to weather, or other exogenous factors that affect the satisfaction with the experience rather than satisfaction with destination performance.

The results may be also used as support for decision-making policies, especially for local planning, investment, and destination marketing for city tourism destinations. The practical implications and recommendations are beneficial not only for the local tourism policy but also for the regional or national purposes. The results can be practically used by DMOs and Tourism Authorities by a tourism strategy formulation and redistribution of financial support, which must be based on relevant data and proper analysis. This approach helps to gain a clear view of the destination performance.

As we have become more conscious of climate change over the past few decades, with extremely hot weather and very high temperatures in different parts of the world, the impact of changing environmental conditions becomes an important long run consideration in tourism. Relaxation and shadow zones and green zones will be increasingly important for visitor satisfaction to city destinations, and associated investments will then be necessary. In reaction to climate change, it seems to be relevant to consider high temperatures as a potential factor that will alter the mix of preferred activities. City destinations should be especially concerned with the questions of how to cool down the city areas in sustainable manner; how to carefully deal with the existing and expanding urbanization aspects; how to build up natural green areas without water waste; or which additional alternatives they can offer and communicate properly to their visitors. Thus, the result should be to sustain or to improve visitors’ satisfaction with the stay in a tourism destination, in order to fulfill their needs and to meet their expectations, when the weather becomes uncomfortable or unbearable.

Future Research

There is a need to remove the weather effect (and other exogenous effects) from the overall satisfaction of visitors in order to properly benchmark destination satisfaction performance based on endogenous factors. In our case, the proposed GLM model could help in removing these factors, but it has been, thus far, tested and calibrated only for the two cities in this study. As a limitation, we consider the fact that we were not able to determine the actual dates of respondents’ stay when they had been spending more than one day in the destination. Therefore, we have had to link the reported satisfaction only to the meteorological data from the date of interview. It would be more accurate to include in the model the weather data from previous days of the respondent’s stay. Consequently, when surveying visitors’ satisfaction, we recommend including in the questionnaire a question of how many days the respondent has already spent in the destination, because the weather from previous days of the stay could affect the overall satisfaction as well. Thus, the main task for future research is to establish a more general methodology to remove from the results these side effects and to set clear conditions for fair comparative evaluations of tourism destinations.

Several statistical methods for removing the weather effect from the overall satisfaction can be used, when comparing several destinations. Testing of the methods on the data set of the whole region, analyzing their efficiency, and developing a more correct methodology are the main aims of the future research.

We can also recommend including a question about the weather perception in questionnaire when surveying visitors’ satisfaction. The subjective perception of weather determines for each visitor whether the deviation from normal weather is good or bad. The comparison of the real weather effect and the perceived weather effect on visitors’ satisfaction is a challenge for the future. As already mentioned in the literature review, there is a lack of studies using a combination of these two approaches. Therefore, it seems to be a gap in current knowledge that would merit further research and deeper analysis.

The primary data were gathered in 2016 and 2017 before the COVID-19 pandemic; however, the theoretical contribution as well as the managerial and ethical implications are not heavily influenced by the pandemic. The impact of the weather, as an exogenous factor, on visitor satisfaction has been proven in our research and stands independently of the COVID-19 pandemic effects on tourism. The conclusions and recommendations will be applicable in the post-COVID-19 travel and tourism world.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Supported by the Ministry of Education, Youth, and Sport of the Czech Republic—university specific research.

ORCID iDs

Viktor Vojtko

Petr Štumpf

Author Biographies

Viktor Vojtko is Assistant professor at the Department of Geography, Faculty of Education, University of South Bohemia in Ceske Budejovice, Czech Republic. His topics of research are related to destination management and marketing, consumer behaviour, and use of simulations for research and education. During his career, he was working as vice-dean for international relationships at the Faculty of Economics, University of South Bohemia (2011–2015), head of Department of Trade and Tourism at the same faculty (2011–2018). Since 2018, he serves as a deputy mayor, responsible for tourism development in Ceske Budejovice.

Petr Štumpf works as Assistant professor at the Department of Management, Faculty of Business and Economics, Mendel University in Brno, Czech Republic, where he is aimed at destination management and marketing, visitor satisfaction, and system dynamics in tourism & hospitality in his teaching and research. He worked as Project Manager at the Tourist Board of South Bohemia (2003 – 2009) and as Assistant Professor at the University of South Bohemia (2010 – 2019). He is a member of SKÅL Club, Linz, Austria. He has rich international experience with teaching in Australia, Slovenia, Austria, Slovakia, or Portugal.

Ida Rašovská is an Associate Professor at Mendel University in Brno, Czech Republic. She is the head of the Department of Management, Faculty of Business and Economics. She leads courses in Destination Management, Quality and Inovation Management, and Project Management. She is a lecturer of professional seminars and author of several scientific studies dedicated to the quality of tourism services, evaluation of the quality and attractiveness of the destination, and destination management. She is a member of the Society of Scientific Experts in Tourism, Czech Republic (SVECR).

Richard McGrath holds the rank of Professor of Economics at Georgia Southern University, after serving for twenty years at Armstrong State University. He is a fellow and past-president of the Academy of Economics and Finance, and was selected as a Fulbright-Hays program participant to South Africa in 2004. His publications and research agenda include secondary market pricing of theme park tickets, bias in customer satisfaction response, social determinants of smoking behavior, and location decisions of immigrants arriving in the United States. He teaches microeconomics and policy-oriented courses, has directed a dozen study abroad programs to six countries, and has lectured in Czechia, China, and Finland.

Kateřina Ryglová works as an Associate Professor at the Department of Marketing and Trade at the Faculty of Business and Economics of Mendel University in Brno, Czech Republic. She guarantees and ensures the lessons in study programmes that are related to tourism and marketing issues. Since 2003 she has also been a lecturer in the module “Tourism Management” at Brno International Business School (B.I.B.S) in the programmes validated by Nottingham Trent University. She focuses her research activities on the issues of tourism marketing with the aspect in the area of destination quality, visitor satisfaction, and destination loyalty. She is a member of the Society of Scientific Experts in Tourism, Czech Republic (SVECR).

References

Alegre

Garau

2010. “Tourist Satisfaction and Dissatisfaction.” Annals of Tourism Research 37 (1): 52–73.

Alegre

Mateo

Pou

2011. “A Latent Class Approach to Tourists’ Length of Stay.” Tourism Management 32 (3): 555–63.

Ashworth

Page

S. J.

2011. “Urban Tourism Research: Recent Progress and Current Paradoxes.” Tourism Management 32 (1): 1–15.

Baker

D. A.

Crompton

J. L.

2000. “Quality, Satisfaction and Behavioral Intentions.” Annals of Tourism Research 27 (3): 785–804.

Bambauer-Sachse

Rabeson

2015. “Determining Adequate Tangible Compensation in Service Recovery Processes for Developed and Developing Countries: The Role of Severity and Responsibility.” Journal of Retailing and Consumer Services 22 (January): 117–27.

Baylis

Obradovich

Kryvasheyeu

Chen

Coviello

Moro

Cebrian

Fowler

J. H.

2018. “Weather Impacts Expressed Sentiment.” PLoS One 13 (4): e0195750.

Bentz

Lopes

Calado

Dearden

2016. “Enhancing Satisfaction and Sustainable Management: Whale Watching in the Azores.” Tourism Management 54 (June): 465–76.

Blancas

F. J.

Oyola

M. L.

González

Caballero

2017. “A Dynamic Sustainable Tourism Evaluation Using Multiple Benchmarks.” Journal of Cleaner Production 174:1190–203.

Boo

Busser

Baloglu

2009. “A Model of Customer-Based Brand Equity and Its Application to Multiple Destinations.” Tourism Management 30 (2): 219–31.

10.

Boto-García

Baños-Pino

J. F.

Álvarez

2019. “Determinants of Tourists’ Length of Stay: A Hurdle Count Data Approach.” Journal of Travel Research 58 (6): 977–94.

11.

Bujisic

Bogicevic

Parsa

H. G.

Jovanovic

Sukhu

2019. “It’s Raining Complaints! How Weather Factors Drive Consumer Comments and Word-of-Mouth.” Journal of Hospitality & Tourism Research 43 (5): 656–81.

12.

Campo-Martínez

Garau-Vadell

J. B.

2010. “The Generation of Tourism Destination Satisfaction.” Tourism Economics 16 (3): 461–75.

13.

Cernat

Gourdon

2012. “Paths to Success: Benchmarking Cross-Country Sustainable Tourism.” Tourism Management 33 (5): 1044–56.

14.

Chekalina

Fuchs

Lexhagen

2018. “Customer-Based Destination Brand Equity Modeling: The Role of Destination Resources, Value for Money, and Value in Use.” Journal of Travel Research 57 (1): 31–51.

15.

Chen

X. R.

2018. “Does a Happy Destination Bring You Happiness? Evidence from Swiss Inbound Tourism.” Tourism Management 65 (April): 256–66.

16.

Chi

C. G.-Q.

2008. “Examining the Structural Relationships of Destination Image, Tourist Satisfaction and Destination Loyalty: An Integrated Approach.” Tourism Management 29 (4): 624–36.

17.

Crompton

J. L.

Love

L. L.

1995. “The Predictive Validity of Alternative Approaches to Evaluating Quality of a Festival.” Journal of Travel Research 34 (1): 11–24.

18.

de Menezes

A. G.

Moniz

Vieira

J. C.

2008. “The Determinants of Length of Stay of Tourists in the Azores.” Tourism Economics 14 (1): 205–22.

19.

de Rojas

Camarero

2008. “Visitors’ Experience, Mood and Satisfaction in a Heritage Context: Evidence from an Interpretation Center.” Tourism Management 29 (3): 525–37.

20.

Dmitrović

Knežević Cvelbar

Kolar

Makovec Brenčič

Ograjenšek

Žabkar

2009. “Conceptualizing Tourist Satisfaction at the Destination Level.” International Journal of Culture, Tourism and Hospitality Research 3 (2): 116–26.

21.

Falk

2014. “Impact of Weather Conditions on Tourism Demand in the Peak Summer Season over the Last 50 Years.” Tourism Management Perspectives 9 (January): 24–35.

22.

Fornell

Johnson

M. D.

Anderson

E. W.

Cha

Everitt Bryant

1996. “The American Customer Satisfaction Index: Nature, Purpose, and Findings.” Journal of Marketing 60 (4): 7.

23.

Foster

R. J.

Jackson

E. L.

1979. “Factors Associated with Camping Satisfaction in Alberta Provincial Park Campgrounds.” Journal of Leisure Research 11 (4): 292–306.

24.

Fuchs

2004. “Strategy Development in Tourism Destinations : A DEA Approach.” Poznań University of Economics Review 4 (1): 52–73.

25.

Fuchs

Weiermair

2004. “Destination Benchmarking: An Indicator-System’s Potential for Exploring Guest Satisfaction.” Journal of Travel Research 42 (3): 212–25.

26.

George Assaf

. 2012. “Benchmarking the Asia Pacific Tourism Industry: A Bayesian Combination of DEA and Stochastic Frontier.” Tourism Management 33 (5): 1122–27.

27.

Giddy

J. K.

Fitchett

J. M.

Hoogendoorn

2017. “Insight into American Tourists’ Experiences with Weather in South Africa.” Bulletin of Geography. Socio-Economic Series 38 (38): 57–72.

28.

Gössling

Abegg

Steiger

2016. “‘It Was Raining All the Time!’: Ex Post Tourist Weather Perceptions.” Atmosphere 7 (1): 10.

29.

Hewer

M. J.

Gough

W. A.

2016. “Weather Sensitivity for Zoo Visitation in Toronto, Canada: A Quantitative Analysis of Historical Data.” International Journal of Biometeorology 60 (11): 1645–60.

30.

Hewer

M. J.

Scott

Gough

W. A.

2015. “Tourism Climatology for Camping: A Case Study of Two Ontario Parks (Canada).” Theoretical and Applied Climatology 121 (3/4): 401–11.

31.

Hudson

2006. “Promoting Destinations via Film Tourism: An Empirical Identification of Supporting Marketing Initiatives.” Journal of Travel Research 44 (4): 387–96.

32.

Iniesta-Bonillo

M. A.

Sánchez-Fernández

Jiménez-Castillo

2016. “Sustainability, Value, and Satisfaction: Model Testing and Cross-Validation in Tourist Destinations.” Journal of Business Research 69 (11): 5002–7.

33.

Jarvis

Stoeckl

Liu

H.-B.

2016. “The Impact of Economic, Social and Environmental Factors on Trip Satisfaction and the Likelihood of Visitors Returning.” Tourism Management 52 (February): 1–18.

34.

Jeuring

J. H. G.

2017. “Weather Perceptions, Holiday Satisfaction and Perceived Attractiveness of Domestic Vacationing in the Netherlands.” Tourism Management 61 (August): 70–81.

35.

Johann

Padma

2016. “Benchmarking Holiday Experience: The Case of Senior Tourists.” Benchmarking: An International Journal 23 (7): 1860–75.

36.

Khazai

Mahdavian

Platt

2017. “Tourism Recovery Scorecard (TOURS)—Benchmarking and Monitoring Progress on Disaster Recovery in Tourism Destinations.” International Journal of Disaster Risk Reduction 27:75–84.

37.

Kim

Park

J. H.

Lee

D. K.

Son

Y.-H.

Yoon

Kim

Yun

H. J.

2017. “The Impacts of Weather on Tourist Satisfaction and Revisit Intention: A Study of South Korean Domestic Tourism.” Asia Pacific Journal of Tourism Research 22 (9): 895–908.

38.

Kozak

2002. “Destination Benchmarking.” Annals of Tourism Research 29 (2): 497–519.

39.

Kozak

2004a. Destination Benchmarking: Concepts, Practices, and Operations. Cambridge, MA: CABI.

40.

Kozak

2004b. “Introducing Destination Benchmarking: A Conceptual Approach.” Journal of Hospitality & Tourism Research 28 (3): 281–97.

41.

Kozak

Rimmington

1999. “Measuring Tourist Destination Competitiveness: Conceptual Considerations and Empirical Findings.” International Journal of Hospitality Management 18 (3): 273–83.

42.

Kresic

2008. “Index of Destination Attractiveness (IDA): A Tool for Measuring Attractiveness of Tourism Destination.” In International Conference Proceedings, 1812–26. Zagreb: University of Zagreb, Faculty of Economics and Business.

43.

Loi

L. T. I.

A. S. I.

I. S.

Fong

L. H. N.

2017. “Does the Quality of Tourist Shuttles Influence Revisit Intention through Destination Image and Satisfaction? The Case of Macao.” Journal of Hospitality and Tourism Management 32 (September): 115–23.

44.

Mazanec

J. A.

Wober

Zins

A. H.

2007. “Tourism Destination Competitiveness: From Definition to Explanation?” Journal of Travel Research 46 (1): 86–95.

45.

Michalkó

Irimiás

Timothy

D. J.

2015. “Disappointment in Tourism: Perspectives on Tourism Destination Management.” Tourism Management Perspectives 16 (October): 85–91.

46.

Moital

Dias

N. R.

Machado

D. F. C.

2013. “A Cross National Study of Golf Tourists’ Satisfaction.” Journal of Destination Marketing & Management 2 (1): 39–45.

47.

OpenWeatherMap. 2012–2019. “Сurrent Weather and Forecast.” https://openweathermap.org/ (accessed March 30, 2019).

48.

Oppermann

2000. “Tourism Destination Loyalty.” Journal of Travel Research 39 (1): 78–84.

49.

Otto

J. E.

Brent Ritchie

J. R.

1996. “The Service Experience in Tourism.” Tourism Management 17 (3): 165–74.

50.

R: The R Project for Statistical Computing. n.d. https://www.r-project.org/ (accessed March 31, 2019).

51.

Rašovská

Kubickova

Ryglová

2020. “Importance–Performance Analysis Approach to Destination Management.” Tourism Economics. Published online February 13. doi:10.1177/1354816620903913.

52.

Rodríguez del Bosque

I. A.

San Martín

Collado

2006. “The Role of Expectations in the Consumer Satisfaction Formation Process: Empirical Evidence in the Travel Agency Sector.” Tourism Management 27 (3): 410–19.

53.

Romão

Neuts

Nijkamp

Shikida

2014. “Determinants of Trip Choice, Satisfaction and Loyalty in an Eco-Tourism Destination: A Modelling Study on the Shiretoko Peninsula, Japan.” Ecological Economics 107 (November): 195–205.

54.

Ryglová

Rašovská

Šácha

2017. “Rural Tourism—Evaluating the Quality of Destination.” European Countryside 9 (4): 769–88.

55.

Ryglová

Rašovská

Šácha

Maráková

2018. “Building Customer Loyalty in Rural Destinations as a Pre-condition of Sustainable Competitiveness.” Sustainability 10 (4): 957.

56.

Ryglova

Vajcnerova

2014. “Possible Complex Approaches towards Evaluating the Quality of a Destination in the Context of Tourism Management.” Agricultural Economics 60 (5): 199–207.

57.

Ryglová

Vajčnerová

Šácha

2016. “Destination Quality Perception in the Context of Different Behavioural Characteristics of Visitors.” Czech Journal of Tourism 5 (1): 5–20.

58.

Song

van der Veen

Chen

J. L.

2012. “The Hong Kong Tourist Satisfaction Index.” Annals of Tourism Research 39 (1): 459–79.

59.

Spreng

R. A.

MacKenzie

S. B.

Olshavsky

R. W.

1996. “A Reexamination of the Determinants of Consumer Satisfaction.” Journal of Marketing 60 (3): 15.

60.

Steiger

Abegg

Jänicke

2016. “Rain, Rain, Go Away, Come Again Another Day. Weather Preferences of Summer Tourists in Mountain Environments.” Atmosphere 7 (5): 63.

61.

Štumpf

Vojtko

Valtrová

2018. “Satisfaction of European Tourists—Benchmarking of EU Countries.” Global Business & Finance Review 23 (1): 1–14.

62.

Sukiman

M. F.

Omar

S. I.

Muhibudin

Yussof

Mohamed

2013. “Tourist Satisfaction as the Key to Destination Survival in Pahang.” Procedia - Social and Behavioral Sciences 91 (October): 78–87.

63.

Truong

T. L. H.

Lenglet

Mothe

2017. “Destination Distinctiveness: Concept, Measurement, and Impact on Tourist Satisfaction.” Journal of Destination Marketing & Management 8:214–31.

64.

Wang

Fong

D. K. C.

Law

Fang

2018. “Length of Stay: Its Determinants and Outcomes.” Journal of Travel Research 57 (4): 472–82.

65.

Wilkins

de Urioste-Stone

Weiskittel

Gabe

2017. “Effects of Weather Conditions on Tourism Spending: Implications for Future Trends under Climate Change.” Journal of Travel Research 57 (8): 1042–53.

66.

Woods

Deegan

2003. “A Warm Welcome for Destination Quality Brands: The Example of the Pays Cathare Region.” International Journal of Tourism Research 5 (4): 269–82.

67.

M.-Y.

Pearce

P. L.

2017. “Chinese behind the Wheel: Factors Affecting Their Satisfaction with International Self-Drive Holidays.” Journal of Destination Marketing & Management 9:12–19.

68.

Xie

2011. “Service Quality Measurement from Customer Perception Based on Services Science, Management and Engineering.” Systems Engineering Procedia 1:337–43.

69.

Yoon

Uysal

2005. “An Examination of the Effects of Motivation and Satisfaction on Destination Loyalty: A Structural Model.” Tourism Management 26 (1): 45–56.

70.

Žabkar

Makovec Brenčič

Dmitrović

2010. “Modelling Perceived Quality, Visitor Satisfaction and Behavioural Intentions at the Destination Level.” Tourism Management 31 (4): 537–46.

71.

Zeithaml

V. A.

Bitner

M. J.

Gremler

D. D.

2006. Services Marketing: Integrating Customer Focus across the Firm, 4th ed. Boston, MA: McGraw-Hill/Irwin.