Exploring alternative methodolgies to understand the role of crowding in tourism destination choice

UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA MSc in Tourism Transport and Environmental Economics EXPLORING ALTERNATIVE METHODOLOGIES TO UNDERSTAND THE ROLE OF CROWDING IN TOURISM DESTINATION CHOICE Ilhombek Kurbanov AA 3246131 (Student's signature) Supervised by: Juan Luis Eugenio-Martin Las Palmas de Gran Canaria 12.06.2015 Acknowledgements I would like to thank to my tutor, Prof. Juan Luis Eugenio-Martin for his help and advices. While working on my thesis, he motivated me and supported with his guidance and ideas. In addition, I am very grateful of all other teachers and professors in the TIDES, University of Las Palmas, Gran Canaria because I learned a lot of knowledge about tourism, environment and transportation from them on my study. Also, I should emphasize the support my coordinator of CANEM project, Sofia Elena Siemens Barreto. She helped me to settle any problem, which I met during my stay in Las Palmas. On studying at the master course, I learned incredible good experience and gained important knowledge for my future study in PhD. Moreover, thanks to my all group mates for their kind friendship. They made a good impression on me at the course of the Master Programme. Finally, I owe my deep grateful thanks to my family, my parents and my brothers. They always motivate and give hope on me in my life. I appreciate for their unconditional love and encouraging me to study. INDEX 1. INTRODUCTION................................................................................................ 1 2. MACRO FRAMEWORK ................................................................................... 2 2.1 Competitiveness and quality ........................................................................ 2 2.2 A link between destination competitiveness and quality ............................ 8 2.3 GDP and employment ................................................................................. 9 2.4 The role of Tourism in job creation ........................................................... 12 3. MICRO FRAMEWORK................................................................................... 16 3.1 Tourism experience, satisfaction and dissatisfaction from crowding ......... 16 3.2 Demand and crowding ................................................................................ 19 4. METHODOLOGY ............................................................................................ 25 4.1 Travel cost method ..................................................................................... 26 4.2 Contingent valuation method ...................................................................... 28 4.3 Choice modelling ........................................................................................ 29 5. CONCLUSION .................................................................................................. 32 REFERENCES List of Figures Figure 1. Framework on measuring crowding effect ............................................................... 2 Figure 2. The Ritchie and Crouch model of competitiveness in Tourism ............................... 5 Figure 3: Ritchie and Crouch model for tourism destination competitiveness ........................ 6 Figure 4. Global direct GDP by industry in 2011 .................................................................. 10 Figure 5. World Direct GDP for all sector in 2011................................................................ 11 Figure 6. Global Direct Employment by industry in 2011 .................................................... 13 Figure 7. Cost benefit analysis for reducing crowding effect ................................................ 23 Figure 8. Crowding effect links between ravel cost and quality ............................................ 28 Figure 9. Demand curve. Cost of travel and number of visits ............................................... 28 List of Tables Table 1. Overview of previous destination comparison/competitiveness research ................. 3 Table 2. Tourism contribution in Gross Value Added and Employment in 2007 ................. 12 Table 3. What do tourists dislike the most on a beach? ......................................................... 20 Table 4: Attitudes to the beach and foreshore areas visited most. ......................................... 21 Table 5: Reasons for change in beach visitation .................................................................... 22 Table 6. Choice modelling method’s steps ............................................................................ 30 1. INTRODUCTION While tourism has been increasing since 1960’s year, oppositely destinations had to strengthen their competitiveness. One of the most important keys to raise the competitiveness in the tourism destination lead to the quality of the visitor experience provided to the customer. Quality in the destination consist of many factors namely poor service, bad accommodation condition, unclean site and crowding effect. Crowding is basically characterized consumer on-site behaviour in the destination as well as environmental impact. Stokols (1972) claimed that crowding is a stressed condition in the destination and it lead to less satisfaction in different activities. In some researches, it was described and related to tourist experience (Choi et al, 1976). The thesis emphasizes a crowding with related to tourists satisfaction. Besides, there is a significantly link between quality of a destination and country’s GDP, which crowding effect can define how well this relationship connected. Next, we describe these links separately into macro and micro framework. The importance of the topic is that crowding at destinations make an impression on the visitor’s experience. As a consequence of crowding effect, most of tourists may feel themselves in an uncomfortable circumstance. It encourages visitors not to come back or recommend their friends to travel to the destination. Tourism crowding has been a very important factor for the tourism area and manager at the destinations. Crowding has direct economic and social influences for environment of a destination. If natural and cultural heritage destinations need to be sustainable, decision makers must undertake or minimise its effect. For these reasons, correct estimates of crowding effect are of great importance to destination managers. The goal of the master thesis is to explore the links of crowding on the economy of a country among competitiveness and quality of the destination, consumers satisfaction, Gross Domestic Products (value added) and employment. Besides that, in this research alternative methodologies (Travel cost method, contingent valuation, choice modelling) will be discussed to understand and measure the relevance of crowding in destination. The main tasks of the thesis are (i) to monitor the role of crowding among competitiveness and quality of the destination, GDP and employment, (ii) to reveal the crowding effect and consumer’s satisfaction on saturated tourist experience, (iii) to indicate the advantages and limitations of alternative methodologies for realizing the relevance of crowding by estimating visitor’s satisfaction and WTP. Structure of the paper. The thesis consist of introduction, framework, methodology and conclusion-discussion. The part of framework is divided into macro and micro. Macro framework shows the connection between crowding and GDP as well as employment. Besides, in this section there is a discussion about competitiveness and quality of a destination regarding to crowding. Crowding effect may affect consumer’s satisfaction with heterogeneity. In the methodology section, three methods are explored to estimate the crowding effect. The advantages and limitations are analysed for all these methods. Conclusion and discussion part summaries and wraps up this work. 2. MACRO FRAMEWORK 2.1 Competitiveness and quality Crowding is a key term, whereas it has a major influence on several economic factors, for instance there is a direct affect to quality and after to competitiveness of a destination with congestion of people. Crowding level determines the quality of a site. The more level of congestion, on the contrary, the less quality image of a site. As it is so, crowding can be considered one of the main part of destination quality. Crowding, consequently, makes destination competitiveness improve, because it affects consumer’s satisfaction in a poor attitude, by this way the number of visitors coming to a site fall dawn. Improving in quality and competitiveness of a destination cause to be high satisfaction of visitors in the destination as well as make a repeat visit again (Figure 1). On the other hand after all crowding lead to increase GDP and employment, macro framework of a destination, which highlighted in the next sector. Figure 1. Framework on measuring crowding effect Tourism can be considered as an essential business field in almost all countries. It provides income and jobs to small business owners as well as large companies. It is also important in showing country’s cultural, economic and political aspects to other countries and affects internal policies. As it covers wide sphere and affects in different levels it is hard to analyse competitiveness in this field. Having reliable data in competitiveness to create policies to improve tourism is key factors, while providing reliable data itself can sometimes be difficult. Different methods and indexes exist today giving the same information in diverse ways, however this is no universal structure used to assess competitiveness. As Ireland and Hitt (1999) predicted tourism industry became more competitive in the 21st century, yet we don’t have enough research dealing with competiveness for example comparing regional with national, or national with international (Briguglio and Vella 1995; Edwards 1993). Competitiveness of touristic sites is not widely discusses topic in the business literatures (Pearce 1997). As Table 1 indicates primary data collection as well as secondary method is used in the research. Secondary mainly addressed figures and their study, likewise, Primary paid close attention to visitors’ attitude of touristic site. Table 1. Overview of previous destination competitiveness research Writers Method Criteria Webster and Ivanov (2014) Secondary data Growth decomposition methodology; Tourism and economic growth; Travel and Tourism Competitiveness Index; World Economic Forum; competitiveness; economic growth; global economy; tourism economics; tourist destination Pulido-Fernandez et at (2014) Primary data Destination competitiveness; Mediterranean countries; Tourism competitiveness Schalber and Peters (2012) Primary data Competitiveness of destinations; Health tourism; Medical wellness; Alpine destination Currie et al (2012) Generating marine-based tourism; destination competitiveness; economic impact of tourism Wang et al (2012) Secondary data China; service quality; Tourism destination competitiveness; tourism destination management Krešić and Prebežac (2011) Secondary data Destination attractiveness; Destination competitiveness; Dubrovnik-Neretva County; Index of destination attractiveness; Tourism destination Mazanec and Ring (2011) secondary Destination competitiveness; competitiveness; least squares method; modeling; tourism economics; tourism management; tourist destination Omerzel (2011) Primary data Competitiveness; Slovenia; Tourism destination; Tourism destination models; Tourism stakeholders Croes and Rivera (2010) Secondary data cointegration analysis; competitiveness; empirical analysis; error correction; Granger causality test; numerical model; tourism; tourism economics Dong et al (2012) Primary data Cooperation mode, profit allocation, tourism development, tourism supply chain. Kozak et al (2010) Primary data Competitiveness; tourism demand, multiple segments of the market Dwyer et al (2004) Secondary data Destination competitiveness, factor analysis, tourism industry. Gomezelja and Mihalic (2008) Secondary data Competitiveness indicators, competitiveness model, tourism destination, tourism destination competitiveness, tourism value added. Muller and Berger (2012) Primary data Benchmarking, destination management organizations, European foundation for quality management model, public finance, qualitative research, validity. Ritchie and Crouch (2003) Primary data A framework for understanding the complex and multifaceted nature of the factors that affect destination competitiveness, the importance of sustainability for long-term success. Zainuddin et al (2014) Secondary data Behavioural intention, competitive advantage, competitiveness, critical issues, integrated approach, perceived destination, tourist. Details proving that individuals answering visited the particular touristic sites are absent in the primary method, and up to now the research is not adequate to draw conclusions about competitiveness (Driscoll et al, 1994; Javalgi et al, 1992). We have to assume that the individuals have visited the sites and their information is correct. As today’s world is more global than it used to be, cities are competing harder and cities became focus spot for touristic site research related to competition. (Dwyer and Kim, 2003; Faulkner et al, 1999; Crouch and Ritchie, 1995, 1999, 2006; Dwyer et al, 2000; Pearce, 1997; Hu and Ritchie, 1993; Ritchie and Crouch, 1993, 2000b, 2003). Lately, we have seen rapid increase in the level competition. Therefore, touristic site need find out about their strong and weak points in order to create their path for the following years and decades. Competitive strategy can be explained as struggle to gain dominant position in the industry and having qualities to sustain that position. This includes how industry itself is coping and what position the object holds in the industry and what equivalent actions can be conducted. "Competitive strategy aims to establish a profitable and sustainable position against the forces that determine industry competition" (Porter, 1985). Bordas (1994) created a method of assessing competitiveness of a touristic site by basing the structure on demand and supply; also a number of factors outside the particular site. Ritchie and Crouch (1993) model, shown in Figure 2, is one of the well-designed models in determining the competitiveness. Figure 2. The Ritchie and Crouch model of competitiveness in Tourism Source: Ritchie and Crouch (1993) Ritchie and Crouch (1993) illustrated 5 constructions, which in turn comprises several site related indicators. It can be observed in Figure 3. Ritchie and Crouch tried to distinguish factors into the ones that bring more tourists while others push them away. If we state a few of plus factors: Natural– geography and climate, Social – culture, people characters and attitude towards tourists, Infrastructure – transport, touristic facilities (hotels, services), Economic – prices, economic stability. On the other hand the negative factors: Political and legal instability, Health problems – diseases, hospitals, low sanitation. They may serve as obstacle for tourists. Figure 3: Ritchie and Crouch model for tourism destination competitiveness Source: Adapted from Ritchie and Crouch (1993) However, competition model can be seen as separate 4 areas: main resources (core resources and attractors), secondary factors (supporting factors and resources), management (destination management) and driving qualities (qualifying determinants) (Crouch and Ritchie, 1999). Main resources would be history and related cultural elements, events that take place, relations with international market. If we state secondary elements, they are infrastructure, namely transports, public services (the elements, which almost any city would need even without tourists) Management analyses and uses main resources and secondary elements to create attractive touristic site. Driving qualities can be characterized as qualities either bad or good, which distinguishes the site from other sites. The model states that development of tourism in the site requires bettering of all spheres. If we study more deeply in the idea, we can say that competitiveness comes from applying several factors in the right order in the right time, meaning better management. Model shows that there are many forces affecting tourism, which come from inside and outside. However, the contribution of the model to the research in general is the thoroughness and broadness of all the factors and forces considered. The models point out that if all the factors work as a whole it will bring results and the fact that they should work interconnected to create attractive touristic site. Tourists may not pay attention to some drawbacks if they are filled with other factors, for instance if there are more people that wanted people can endure it as long as they hold low price services. Overall, we say that competitiveness comes from the capability to improve each factor in itself and their combination. If we speak in broad terms, Ritchie and Crouch (1993, 2000a, 2000b, 2003) tried to analyse studies in other fields in order to apply those models and frameworks in tourism sites, they tried competitiveness of companies, products, national systems and other service industries. Ritchie and Crouch (2003) stated that, “What makes a tourism destination truly competitive is its capacity to enlarge tourism expenditure, to increasingly attract visitors at the same time as providing them with satisfying unforgettable experiences”. They mention that the highest level Figure 6. Global Direct Employment by industry in 2011 Source: WTTC 2012. 8.7 percent of employed people around the world are in Tourism sector and it is definitely one of the top job providers. In 2011, 255 million people we employed in the sector, leaving behind auto industry, chemicals, mining and almost coming equal with education. If we consider the sectors that benefit from tourism in indirect channels, the share goes up to 9.09 percent. With this share, no matter which region we consider tourism will be more than auto and chemical sectors taken together. Moreover, the future perspective of tourism sector is promising having 1.9 percent annual growth in job creation within coming 10 years, while the whole economy in general have only 1.2 % growth. In addition to being essential for job providing, tourism play important role in various other ways helping economy. Tourism has more diverse connections among countries, as it is does not have to be concentrated in a single area. There is observable and quantifiable relation between tourism and other sectors. If we closely look at the process, output of one industry usually will be used as input in different sector. Increased demand in tourism will help sectors as transportation, food production, communications this is referred as supply chain effects of tourism. In almost any sector, high-qualified personnel remain one of the most important investments that any organisation should perform (Ireland and Hitt, 1999). As we can see from previously mentioned stats, tourism will require more labour resources in the near future, it demand more in person communication with clients than most of the sectors. Therefore, how local people talk with the tourists, how staff of the hotels and restaurants treat the clients, and also how the organisation work out new training systems for staff and local people will dictate the competitiveness of a touristic site. In tourism workers however, may be more temporary than other sectors, as it always requires human resources, young people, immigrants, students find job here easily but all three of them tend to quit job in a short period of time, creating new vacancies. And this circle will keep going on and on. 8 percent of all the people in world are employed in tourism, which is 230 million jobs, from 60 to 70 % is acquired by women. Also, 50 % of all the workers are youth (25 years old or less) (ILO2008). If developing countries invest into tourism more, they can lay a foundation for job creation for poor population. Environmentally friendly approach in tourism may also create more jobs as it will focus on improvement in water cleaning, sanitation, getting rid of wastes after services. It do not require special expertise so it may be hired from local people. Environmentally friendly destinations may also be basis for cultural and environmental tourism development. (Cooper et al. 2008, Mitchell et al. 2009). People get employed in tourism directly and indirectly. Based on ILO2008 a single job created in tourism leads to the creation of 1.5 new jobs in the sectors that are related to tourism. Some jobs are related to tourism for example transport systems (drivers of taxis, shuttles, buses, and airport workers), suppliers (food, drinks, souvenirs), services (hotels, daily other services.). This dependence may create different types jobs, which have temporary or permanent character, sometimes they can even be divided as official and non-official. Workplace efficiency is an aspect that significant importance for improving touristic site’s competitiveness. GDP per capita is one of the important indicators in the global economy. High level of productivity can ensure organisations’ gaining of competitive position. Efficiency in tourism are to be contrasted with other sector in the economy and the whole national economy in general to see if tourism coming higher or lower. In order that the indicator will work it is required to have a universal definition and interpretation of several term such as employment in tourism (who can be considered employed in tourism and who is not), taxation system (which currently varies in almost all countries), and PPP index. Although currently nations have definition in general some terms as part-time employment and local/regional tourism may vary. We would need data covering all of the taken time. Missing of data, time differences and influence of tourism on related industries (retail/transport) may cause difficulties for people in charge of touristic investment and strategies. Unlike normal understanding of workplace efficiency, which is output: input ratio, in tourism quality of provided service should also be taken into consideration. Consequently, this creates trouble in determining efficiency level, for example in determining quality, consumer’s utility, workers’ input. On the other hand, efficiency (in other word productivity) for the past some period of time has been calculated by a few organisations making in possible to follow the changes. As calculating productivity is internationally agreed ratio, we can easily compare the indicator throughout countries. It explains the efficiency of a worker in the sector. The level of efficiency influences level of wellbeing, competiveness of touristic site which has high value adding activities, experience and skills of labour, style of management and course of action taken by government and its legislative decisions. If the level is high, the above-mentioned aspects will also have high standards, while if they are low they will bring poor wellbeing, consumer’s low buying capability, downsides in labour development, and diminishes education, all in all low productivity. Based on this we can state that special features of tourism should be considered. In order to have the theory work we have to suppose that key individuals in the sector can understand tourism and sub-sectors in a proposed manner so that it will ensure touristic strategic development and controlling it through time. The share of tourism is the employment rate determines the significance of tourism for a particular economy, there is a data on international and national levels of a country, data on regional level is not always available. Seaside gains a lot from tourism, and it most cases it is the biggest job provider in coastal areas. The region around Mediterranean Sea is widely dependent on tourism; the level of employment in these area is as following: Iles Baleares (Spain) – 20.2 percent, Ionia Nisia (Greece) – 18.8 percent, Notio Agaio (Greece) – 18.6 percent. This high level of tourism employment is amazing compared to certain area of Italy where employment in touristic sector even in southern areas is lower than 4 percent. According to the data by CSIL, Centre of Industrial Studies in Partnership with Touring, Servizi of 2008, Anadlucia had the highest level of rise in jobs in touristic sites from 2000 till 2004 with 28.5 percent growth. Unlike Mediterranean Sea area, Baltic sea area had low level of employment in tourism. In the whole area the average is 3.3 percent. If we go into details: Stockholm – 3.7 percent, Aland – 3.6 percent, Zachodniopomorskie – 3.4 percent. The same low level remains in the North Sea area, with average rate being 4.6 percent. In particular: Zeeland – 4.7 percent, Prov. West Vlaarderen – 4.6 percent, the UK – 4.4 percent. Algarve (Portugal) has similarly high level in Atlantic coast with 18 percent of job coming from tourism. The average in the whole area is low being 4.5 percent. Black sea area has low level of employment in tourism with 1.7 percent, but the Outermost regions has however has clear dependence on tourism with 8.2 percent of employment. In particular, there is another good method to produce of Tourism importance in GDP. Namely, Granger Causality Analysis (GCA) is a method analysing economic links of Tourism and GDP. There exists a great deal of study conducted regarding tourism and economic development. For instance, Eugenio-Martin, Morales and Scarpa (2004) studies tourism and is linkage with economic growth in 21 countries located in Latin America. This study included the years of 1985 till 1998. It their work it is illustrated the significance of tourism in the economic development. They point out that in order to increase tourism in the area, the governments should rise the standards in transportation/communication/basic service, education and human safety. They also state that tourism is not a single factor, economic growth may be various depending on countries’ trade policies, FDI, and income (Chang, Khamkaev and McAleer, 2010). However, Çağlayan (2012) point out that low-income countries can not be a ideal example of Granger Causality, as in these countries social factor is more important – safety of tourists may change the number of tourists. 3. MICRO FRAMEWORK 3.1 Tourism experience, satisfaction and dissatisfaction from crowding “Happiness” of a tourist is and should be the main objective of the touristic site and institutes in it, as this the most important factor if the tourist will decide to come back or give negative review. Besides that, one issue that is currently affecting satisfaction of a tourist is crowding, crowding unlike other problems may be persistent: in airport, hotels, taxis, sightseeing. Based on several methods of analysing tourist satisfaction, a new method will be developed which will include crowding and tourist satisfaction. Famous Oxford dictionary define the word – satisfaction: “fulfilment of one’s wishes, expectations, or needs, or the pleasure derived from this”. Parasuraman et al (1994) studied satisfaction of a tourist and deducted that tourists’ happiness depends quality of service as well as unique characteristics of the service and price. Experiences during the visit is important to determine the tourist’s happiness. However, another important issue is what tourist was expecting before coming to the touristic site. These expectations will be the basis of comparison. (Oliver, 1980). Based on what tourists they were expecting their visit would be remembered as good or bad. If what they see, hear, eat will be of lower quality/price, it will create a negative attitude towards the touristic site, even if the standard is better than average. On the other hand if the visit is better than expected this will form a positive attitude, even if the service lower than standard (Oliver, 1980). So, depending on expectations of an individual customer, the visit will characterized as good or bad. Nonetheless, crowding is usually considered a problem regarding tourist’s happiness. Kalisch and KLaphake (2007) observed that crowding might have a little influence on tourist happiness. Three factors expectations, the actual experience during the trip and crowding will result in loyalty or disloyalty. Negative result will bring disloyalty, in other words tourist will not be willing to come back to the touristic site and will not promote their trip to friends and family. Therefore, we can state that loyalty is one of the vital aspects of satisfaction, because it has effect on where they want to go and if they want to come back (Ellis and Marino, 1992 Yoon and Uysal, 2005), trust (Selnes,1998), and building a reputation (Ryan et al, 1999). Crowing and all the problems associated with it may result in diminishing of reputation. In 2007, Kalisch and Klaphake investigated into ranging of age groups and sizes of the group in analysing crowding. They also state that tourists in Haliig Hooge Island would consider crowing for certain extent acceptable. In addition, Lim (1998) claims that the island had already picked in its social carrying capacity. This can be concluded from the survey taken from tourists from which 64.47 percent claimed that marine park is crowded. It is stated (Graefe et al, 1984) that crowding is not always means lowering the satisfaction of tourists, but from the survey can say that it is, as 73.97 percent of people, they would prefer fewer people in the marine park. Many researchers gave a definition about carrying capacity and learned it in various situations. The carrying capacity of a tourist destination, according to The World Tourism Organization (WTO) characterization: ‘‘the maximum number of people that may visit a tourism destination at the same time, without causing destruction of the physical, economic and socio-cultural environment and an unacceptable decrease in the quality of visitors’ satisfaction’’. Carrying capacity shows and relates evidently to sustainability. It might demonstrate the level of unsustainability, which affect negative to a destination after carrying capacity. Crowding effect is considered such a specific issue that deteriorate pollution in the social-cultural carrying capacity. Sociocultural Carrying Capacity (SCC) depends on decisions made by the managing individual/organisation based on what he/she wants tourists to experience, on what level of quality and in which price (Watson, 1988). Kalisch in 2012 claimed that SCC would have two components: descriptive and evaluation. The former pays attention to objects of the site while the latter pay attention to the degree up to which the influence is acceptable. Based on these contradicting claims, we can see there exists necessity to form new studies in SCC and crowding in relation to satisfaction. This method is supposed to analyse “happiness” in various point of view and find/list factors of crowding affecting tourists “happiness”. Expectations can be defined as what consumers think will happen; what it will be like to experience a product/place/service in a touristic site. (Ngobo, 1997; Susarla et al, 2003). A lot of research has been done having expectations in the basis of so-called equation of satisfaction. Based on all the research that has been done we can assume that expectation is important feature in choosing a touristic site, but how we can related directly expectation and satisfaction. Some attempts have been done regarding to explain antecedents of the decisions and consequences and how these to end of the process affected by expectation. (Oliver, 1980). Oliver’s model later was used by other scholars to study causes and effects of the process. (Fornell et al, 1996). These models dictates that tourist’s satisfaction can be deducted from the tourist’s expectation. We can say that tourists expect something to happen or somewhere to be like through what learn about the place and their personal knowledge and skills. After they visited the site, they will try to match what they have expected and what they actually experienced. If the match happens, it is a positive confirmation, it does not match it is known as disconfirmation. (Churchill and Suprenant, 1982; Spreng, 2003; Oliver, 1993; Kopalle and Lehmann, 2001). This theory states the better the performance of the site, the higher the positivity and consequently the higher the satisfaction. (Yi, 1990). However, in this theory there is human factor – personality of a person. If a person adapts to new environment (tend to have less culture shock and infrastructure difficulty) he/she will have higher satisfaction level than people with the same expectations but with inability to adapt (Oliver, 1980). Helson (1948) proposed following factors that may influence adapting process: 1. The product itself and person previous using 2. Brand commotion/others feature associated with the brand. 3. Communication/advertising 4. Personality – accepting different products and event differently. Recently, these factors were gathered into two broad groups: acceptance of quality and acceptance of value. (Fornell et al, 1996). A lot of research has been done into the topic (Oliver, 1977; Swan, 1977; Linda et al, 1979). However, we should mention that these studies focus on consumer attitude before he/she experienced the site. More recent research also proves that expectation plays positive role in tourist well-being in the site. (Bosque et. al, 2006). Various levels can be observed when discussing satisfaction. However, the highest point would be loyalty. Loyalty is among key requirements when predicting where a tourist will go and if they will trust and return (Yoon and Uysal, 2005; Ryan et al, 1999; Selnes, 1998). If tourist becomes satisfied with what they have expected, they will be willing to come back and also suggest their friends and relatives to come to the particular site. The level of being satisfied will determine the level of loyalty. (Yoon et al, 2010). Also, there are several factors influence tourist decision to come back or to recommended. Yoon et al (2005) state that overall site attractiveness is the predominant factor influencing tourist to return. They state that some feature may be more dominant than others may but each feature individually may not be the reason for returns and effective promotion. However, we could observe that satisfaction and recommending to other via word of mouth have linked. But, satisfaction is not always guaranty of tourists return, some of them just tend to recommend others. (Kozak and Rimmington, 2000). As recommending still is held in loyalty, satisfaction and loyalty are correlated elements. (Yoon et al, 2005). Both the image of the touristic site and satisfying feature are requirements that should influence tourist. Also, Image of the site is not 100 percent objective, having tourists’ subjective views, their behaving and choosing of the site as influential factors (Castro et al, 2007; Echtner and Ritchie, 1991). Therefore, we can assume that the image is important element affecting visitor’s wishes and deed; and their assessment of the travel. (Chi and Qu, 2008) How people accept crowds of people varies from tourist to tourist. If a tourist is part of a larger touristic group, they will be used to people and willing to deal with the crowding while tourists having their trip alone is more likely not prefer crowding (Kalisch and Klaphake, 2007). Kalisch and Klaphake (2007) also claimed that tourists’ variation in their ages and number of people they are travelling with influence their perception of crowding. The varsity of a visitor point of view can be observed in a densely visited site, although the site was crowded at certain periods, visitor did not consider it worth mentioning in their answers to the survey. (Kalisch, 2012) Satisfying the tourist expectations is very significant in the promotion of the site, as this process affects what site the visitor chooses, what and in what quantity they will buy, and whether they will come back or not. (Kozak and Rimmington, 2000). Scholars researched about tourist’s satisfaction and proposed their models and frameworks. (Oliver, 1980; Bowen, 2001; Rojas and Camarero, 2008; Chi and Qu, 2008; Xia et al, 2009). Each of these models have their focus: - Oliver (1980) – Tourists expectations and their realization - Oliver and Swan (1989) – Value of equity - Martilla and James (1977) – Performance of important segments - Tse and Wilton (1988) – Performance of overall site - Parasuraman et al (1985) – The difference between what they expected and what happened. - Sirgy (1984) – Congruity model - Pizam et al (1978) – Performance only model They have been used to analyse visitor’s satisfaction with the particular site focus. Few scholars above mentioned paid great attention to the crowding problem, but explained that crowding influences general experience of the tourist. In predicting potential tourist actions and sites’ success or failure regarding satisfaction, we must take under consideration the fact that people indeed differ. They differ from our point of view (numbers, action that can be noticed.) and their point of view (feelings, wishes). A professor from Sloan School of Management J. R. Hauser define the customer heterogeneity as “… is a very intuitive concept that refers to how consumers differ from one another in their demographics, attitudes, behaviours, and, of course, preferences for products. Each of us typically thinks of a given product as a bundle of different features and services that collectively meet our needs in various ways, but we also consider different aspects of the product as more or less critical to our purchase decision” It has been put forward that crowding is not entirely the reason for satisfaction or dissatisfaction. Also, the actual number of people in the site and how many times they meet is always the same. (Shelby, 1981). The tourist’s decision of the site choice and returning may include, as Jakus and Shaw (1997) puts it, actual, expected, anticipated, or perceived crowding. They explain that actual crowding is the one identified by someone neutral, who has 100 percent objective perspective. Perceived crowding is the tourists own opinion when he/she is in the site. Expected crowding defined by them as “the mean of a distribution”, while anticipated crowding is tourist’s own opinion of the crowing before visiting the site. They are grouped by Jakus and Shaw (1997) as ex post, and the last two as ex ante. Crowding’s external feature is a somehow problem in society as well, which brings out the problem to reconsider. In some touristic sites for example Venice, Bruges, Amsterdam highly attractive sightseeing is tend to gather crowds. (Riganti and Nijkamp, 2008). Crowding in the touristic sites especially the one having natural or cultural heritage may influence the sites in (Graham, 2005): - Less joy for tourists; - Physical damages to historical artefacts and natural objects; - Negative influence on special projects; - Financial problems (less money flow); - Causing stress for local people; - Rising in the amount of waste; - Creating permanent rush hour for local public services; - Less productivity in the services. Crowding may or may not cause damage immediately, however with time passing it sure will have effect. Natural damaging will rise, having been affected by a large number of people, and physical contact with the site. In nature, some things can grow, some animal repopulate, but only if the damage crowding is causing is not enormous. Historical heritage cannot be easily restored without human interfering and large capital, but even in this case the historical treasure will lose some of its originality. If we define crowding in simple terms, it is a coming of a large number of people to the site in the specific period of time. We can divide into: crowding among tourists and crowding of tourists in interaction with the locals. The first time has not got much attention among scholars, while the second is the cause of several models: to the development of phase models (Butler, 1980), attitudinal models (Page, 1995) and behavioural (Ap and Crompton, 1993; Carmichael, 2000). Based on the current available research on urban crowding which mainly deals with parks (Arnberger and Haider, 2007; Hammitt, 2002) and some measures (Lee and Graefe, 2003), we can say tourist crowding in the city area needs more elaborate research. Mitchell (1971) in correlational human crowding studies and Proshansky et al (1970) in their experimental human crowding studies showed that influences of crowding is mainly provoked by cultural bases and people actions. Stokols (1972) states that with time the large number of people in the site prevents participants from carrying out desired actions, from which a stress will start to arise. Stokols finalizes that lack of space is only a supporting element that comes before before stress, it is not the circumstance. 3.2 Demand and Crowding Demand is based on consumer’s want but if it can be realised in market. Consumer behaviour describes demand in a period of exact time that includes choosing goods and services among alternatives. Consumers choose the goods and services which gives the highest satisfaction on them. Formerly, consumer can to estimate benefit or comparative satisfaction of goods and services by measuring their utility for each alternative. In general, if not considering cost and quality of goods and services, crowding is a main factor, that there is a negative affect in demand of consumer wants. Crowding in a site has an impact on demand theory basically because, if where many tourists, there many suppliers are appear to satisfy consumers’ wants. The Preferences of consumer also influence to demand, so knowing and learning their behaviour we can predict the demand and use it supplying goods and services. As an example, in summer almost all people want to go to beaches but in winter not. Taking into account the marginal utility, it is impossible to find out exactly bundles of goods and services which consumers satisfied their wants. But, according to demand theory, the utility function and margins can indicate the aggregate level. So, Each destination decision maker tries to maximize consumer satisfaction Accordingly, Consumer wants caused to create demand. As an example, if we observe a beach destination in crowding, should find out what consumers want, it will be easy to cover consumers’ satisfaction. In order to know visitors’ satisfaction coming to a site, we should clarify what is the purpose of visitors from their visit to a site, what things they most dislike there. Table 3 summarizes the results of the public's perception of beach aesthetics and presents issues that tourists dislike the most on a beach (Williams et al. 2003). Survey determined the most three unsatisfied factors are firstly litter and man-made debris, secondly poor water quality, the last of third is crowding on a beach. Table 3. What do tourists dislike the most on a beach? ISSUE % Litter and man-made debris 30 Poor water quality 13.75 Crowded beach 11.25 Poor facilities 7.5 Dog waste/excrement 7.5 Noise from industry and vehicles 5 Difficult access 5 Seawalls 5 Flies and other insects 5 Washed-up seaweed 3.75 Beach erosion 2.5 Bed smells from industry 2.5 Groins 1.25 Lack of sand/shingle beach 0 Total 100% Source: Filip, 2004 As we see, one of the most affected factor is crowding. This circumstance appear some cases with seasonality whenever if there is high demand for beach but in limited resource, visitors feel miss satisfaction. Most of the tourists get unsatisfied when beach crowded. It is one of the main issue (11.25%) in the inconvenient factor The important factors are monitored in Table 4, which the visitors pay attention in choosing the beaches corresponding their behaviours. They described their behaviour and opinions about important attributes while visiting to the beaches. There is also crowding has very high mean, it shows crowding at the destination is more sensible on satisfaction. Consequently, more satisfaction gives more willingness to pay for reducing crowding. According to table 4, any kind of congestion (on beach, on foreshore, in water) at the destination exhibits negative aspect on satisfaction. The congestions decrease the marginal utility from resting or holiday on the beach destination. Table 4: Attitudes to the beach and foreshore areas visited most. Rank Attributes Mean 1. Cleanliness of beach sand 5.30 1. Cleanliness of park adjacent to beach 5.26 2. Cleanliness of ocean 5.19 3. Concerns about vandalism and theft 5.03 4. Safety and lifeguarding services 4.93 5. Parking is available 4.91 6. Showers and toilets 4.70 7. Close to where you live 4.51 8. Easy access via paths or steps 4.41 9. Less crowded on the beach 4.30 10. Less crowded on the foreshore 4.17 11. Conditions on the day 4.15 12. Less crowded in the water 4.03 13. Viewing areas 3.74 14. Jogging or cycling paths 3.74 15. BBQ facilities in park 3.72 16. Play equipment in park 3.32 17. Shops nearby 3.00 18. More privacy 2.98 19. Dogs allowed 2.72 20. Fitness classes at the beach/ park 2.30 21. Romantic location 2.28 22. Close to public transport 2.18 Source: Raybould et al. 2009. * Responses were made on a seven point scale; 0 = completely unimportant to 6 = very important On the Table 5, you can see the reasons for change in beach visitation between people who visit more and less. People who visits more to a beach, who know about crowding, they do not care for it, crowding at the destination does not affect much their satisfaction. Nevertheless, who visits less and not aware of crowding somehow, they can change the beach if they face to congestion. Table 5: Reasons for change in beach visitation Source: Raybould et al. 2009. On having a look inside demand analysis, there is a very important quality factor, Tourist satisfaction. In addition, two main factors are considered while derive the tourist demand for a destination. They are both tourist experience by travelling when coming home and advice to their friends. Tourist, before intend to travel for any kind of destination, is interested in others opinion and their feedback about that destination. They use many media facilities such as internet, TV, comments and blogs to obtain this information. Many researches emphasize that tourist’s opinion about a visited site is very important in specifying competitiveness of a destination attract and tourist satisfaction. Obtaining these evidences and study them, one may consider as quality factor while making a decision about congestion and image of the destination. There are two approach stated and revealed preference. The first approach is very good and most used in producing hypothesis economic benefit. Cost-benefit analysis (CBA) is a good tool all over the world to determine and help to find out whether a decision is acceptable or not. CBA estimates and compares the costs and benefits of new goods or services by money units. In recent times, many researchers accept this method to evaluate environmental affects and issue. It also help to develop of decisions in quality criteria. Though this cost benefit analysis is easy, there are some disadvantages in use of the method for environment. Because the difficulty when applying this technique is in evaluating coasts and benefits with monetary unit. Following we separate costs and benefits for reducing crowding effect, but as it was told before the evaluating them in monetary unit is a specific issue so we describe crowding in attributes. The Figure 7 shows us what kind of benefits and costs generally appear as well as advantages and disadvantages if crowding level decreased at the destination. People who visit more Reason People who visit less Reason 1 Family commitments changed 1 Work commitments changed 2 Work commitments changed 2 Family commitments changed 3 Relocated / moved house 3 Traffic and parking problems 4 Health / ageing issues (positive) 4 Too many people / crowding 5 Health / ageing issues (negative) 6 Relocated / moved house 7 Physical character of beach changed 8 Cultural / social character of beach changed with travel cost method Contingent valuation does not show actual behaviour of visitor because it is related to stated preference. Crowding is not be estimated by market prices because reducing crowding effect is a nonmarket attribute. So, to estimate crowding costs we need Contingent valuation method. Many researchers used this method to estimate crowding cost (Boxallet al, 2003; Cicchetti & Simth, 1973) If decreasing crowding effect at the destination, it improve recreational facilities and provide high consumer’s surplus (Cesario, 1980). Having applied Contingent valuation method, we get individuals maximum willingness to pay and minimum willingness to accept so as not to come across congestion at a site Though it is easy to construct contingent valuation method, nevertheless, the issue should be understandable and briefly need to explain to representatives. Disadvantage about the CVM  Representatives do not think very seriously for answering the questionnaire and so the results by using the method might not be correct to real as well.  Even if representatives replied seriously for questions, they may lie giving wrong answer.  The method cannot provide probability of selecting other substitute destination if there is high crowding at the destination.  Some individuals might answer high positively even their WTP a little, when they are asked about their WTP indicating amount in referendum, and others answer negative although their WTP is higher than indicated amount. 4.3 Choice modelling Choice modelling tries to create a model how a tourist decide one thing or another in a specific circumstances. Choice modelling goes outside of a normal market-related aspects and more likely to deal with beneficial sides and costs regarding environmental perspective. This model is considered as a precisely working method to determine tourist’s positive or negative tendency to pay for increase in the level of quality in many aspects. (Centre for International Economics - Review of willingness-to-pay methodologies, 2001). This model can be considered as easier and more precise as participants will choose from the list of feature and product/service alternatives, making compromises in some aspects. The model analyses the prices of feature welfare influences from implicit perspective in many situations. The model is applied in studies tourist preference for substitute services in nonmonetary perspective and possibly diminishes the stimulus of the responding people to act strategically. A usual Choice model has several important stages. (Hanley and Murato, 2001). These stages can be observed in Table 6. Participants’ wishes might be analysed in CM surveys. They will be asked to sort the option from top to bottom, giving them scores or they will be just asked to state their highest preference. These various methods matches the diverse alternative structures of CM technique. Table 6. Choice modelling method’s steps Source: Hanley and Mourato, 2001 CM works with the assumption that people choose something with clear, well-organized manner and this process of choosing has a functional form. Varying in circumstances of behaving, particular functional form can be considered as a participant to analyse. MNL (which stands for multinomial logit) is a model is applied a lot, because it has a lot of similarities with utility maximisation used in economics. In order to measure the fitting of the model some others as binary logit, probit, EBA can be used, in combination with, accompanied by proper statistics. In other words, people always try to make their utility maximal. In multinomial logit, Ojo con esto total utility has mathematical explanation in the form of linear equation, it’s an addition or subtraction of the component utilities. In MNL, after having our function of the deciding, we can find measurement of the model by conducting regression from present data. MNL model applications can be found in a number fields including tourism, transport systems and environmental sciences. (Eugenio-martin and Campos-Soria, 2011; LaMondia and Bhat, 2009; Hearne and Tuscherer. 2008; Albaladejo-Pina and Díaz-Delfa, 2009; Oppewal et al, 2015; Rodger et al, 2015). MNL model is normally measured by the highest probability methods presuming congestion effect (Carballo et al, 2014, Avila-Foucat et al, 2013; Vaske, 2008; Tseng 2009; Manning, 2010). - CE can be chosen over CV in determining the gap in the value of rises & falls in different features of environmental programmes. This can be a better option when analysing it from administration/authorities point of view than paying attention to gain/loss of the product/service, or on a distinct variations in the features. - CE can provide more information than distinct option CV because participants have several opportunities to show their wishes for a valuable product/service rather than a number of payments. - CM does not approve direct detection of participant tendency to pay. It tries to focus on respondent’s rankings/scores and best choices to determine their willingness. (The information may be inferred). Disadvantages of CM. - The major downsides of CM would be understanding of many complicated options that respondents are given, or trying to rank when dealing with a group of feature or stages. Current researchers have claimed that participant can understand, analyse and make decision with certain amount of data, if it exceeds that point, efficiency will decrease. - When determining the value of environmental project or benefit from a CE, as discrete from a variation in a feature, we have to take the value of each part combined as a equal to the value of the whole. - It can be more problematic for CE and CM to obtain values for a chain of items brought by government strategy and programme, when it was contrasted with conditional changes. Therefore, consecutive providing of products/services in multi-feature projects is likely to performed better by CV. (EFTEC, 2001) - Based on the SP method, well-being measurements taken with the help of CE are tend to study design. For instance, choosing features, stages, the way (style) the options are presented to the participants (It could be photo, text; or the format may vary scoring/ranking) may influence participants responds and cause changes in marginal utilities etc. The model which are created based on the answers of participants may be largely affected by amount of choosing task they do (Hanley, Wright, and Koop, 2000). 5. CONCLUSION Congestion of people at the destination has high psychological negative concept regarding to encroach of human norms in quality. As many cases, crowding at the destinations caused to be nervous and unsatisfied of a tourist, there is still may unknown attributes in interpret consumer behaviour on crowding situations. However, on having reviewed about the topic before, majority scientists researched crowding as a negative affect, and in reflex for crowding situations tourists behaviour do not vary much from each kind of destination except consumer behaviour. In this thesis, crowding is studied under macro and micro level of a destination on economy. Crowding in the destination might be an important implement to enhance the consumers satisfaction and quality of a destination by the influence of competitiveness. In addition, the thesis shows the relationship between crowding and GDP (gross domestic products) or employment. Moreover, this thesis learnt different alternative methods like Travel cost method, Contingent valuation, choice modelling and their advantages and limitations may be met when measuring crowding effect of the destination. Uzbekistan is currently going through a transition phase. Tourism industry is also going through this process. The touristic sites in Uzbekistan haven’t reached its full potential yet. Crowding is a topic of great significance in Uzbekistan. As, the measurement I presented in this thesis could be applied to the certain touristic destinations. I tried to present that crowding is not a necessarily negative feature it shows that there is a demand for the site and certain actions have to be taken to reach the expected well-being of the site. This thesis analyses those actions. During the touristic season (in the spring and in the summer), major tourist centres of Uzbekistan, namely, Tashkent, Khiva, Samarqand and Bukhara is becomes crowded. Because of the crowding some problems like quality of the service arises. In order to solve these problems government is paying attention to new policies and innovative approaches. This thesis will be useful tool and can be present to special departments of Uzbekistan for consideration. Since Uzbekistan is considered as dinamically developing contry with historic heritage, natural resources and outstanding touristic potential needs concrete strategy on expanding it’s touritic capacity and increasing touristic flow with less crowding damage. As mentioned before tourism sector is main job employer and every 12th person is employed in tourism for a developing country as Uzbekistan it could be essential to create new job offers to local people by widening touristic destinations and recruitment for tourists. As every strategy has pros and cons the crowding effect can bring positive or negative influence on the sphere. The cons of crowding is mainly the nature damage, massive waste, historical heritage demage but with help of massive tourism same time the level of economyand GDP rises which creates a need of extra policies to make less the general negative impact and to bring the priority of positive influence. Moreover, UNWTO famous slogan “Billion tourists, billion chances” will play a key motivation in implementation of crowding tourism strategy in Uzbekistan. The key factor of this mission is creating billion opportunities in social level as solving problems with drinking water at far destinations, preserving the culture and traditions by using them as touristic product, rising number of vacancies, developing infostructure of touristic destinations being capable to recive large number of visitors, improving quality of service in service industry. Taking steps to the crowding strategy the country also will face the need of human resources in tourism industry, more specialists needed to be educated. By increasing the touristic capacity and creating new touristic routes the country will reach touristic loyalty and positive country image helps to promote itself as world wide touristic destination. Loyalty of tourist will impact to the crowding effect and help to establish certain development strategies on positive country image. 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