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Quantitative Methods in Tourism Economics

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Nguyễn Gia Hào

Academic year: 2023

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It is worth noting that one of the leading geographers of the last century is Walter Christaller - the world. From a systemic point of view, it makes sense to distinguish three fields of forces, namely: behavioral patterns on the demand side; organizational and institutional structures on the supply side;

Introduction

Operationalizing the construct of loyalty in the tourism industry can prove to be a complex task. The interest of this current study, on a general level, lies in the need to give an answer to relationship marketing and customer loyalty in the tourist industry as questions of current importance (Bigne Oh et al. 2004; Bigne´ et al. 2008).

Customer Loyalty in the Tourism Sector .1 The Impact of Loyalty as a Strategic Objective

However, he later expanded his focus to the rest of the stakeholders involved in the interaction between customers and companies (Martı´n2005a). This depends on prior attitudes and values, the repeat behavior and the specific characteristics of the loyalty object.

Methodology

These groups show significant differences depending on tourists' intention to return or recommend the destination. 2001) notes that the literature on loyalty demonstrates a problem in its conceptualization that needs to be addressed by empirical means or operational definitions, depending on the purpose of the study. From a classical point of view, loyalty is an abstraction that is difficult to define because of the different roles it can play.

The Operationalization of the Loyalty Construct: Results

Yen et al. 2009) and on the other hand cognitive, affective and conative loyalty (Lee et al.2007; Li and Petrick2008b; Yu¨ksel et al.2010) use indicators that do not coincide with constructs of the same category. As happened before, when authors chose to use more than two items, both the intention to return and the intention to recommend appear accompanied by other indicators (Tian-Cole et al. 2002; Baloglu et al. 2003; .

Fig. 2.1 Explicit loyalty and implicit in behavioral intentions
Fig. 2.1 Explicit loyalty and implicit in behavioral intentions

Conclusions and Final Reflections

Kandampully J, Suhartanto D (2000) Customer loyalty in the hotel industry: the role of customer satisfaction and image. Tian-Cole ST, Crompton JL, Willson VL (2002) An empirical investigation of the relationship between service quality, satisfaction and behavioral intentions among visitors to a game reserve.

Introduction

The advantage of this model relative to standard logit models is that this model makes it possible to identify homogeneous and heterogeneous variables in the sample. The implications of this study are crucial in terms of defining market strategies best suited to retain these tourists whose role plays a strong role in the general development of the Algarve as a tourism destination.

Literature Review

Other studies also show that tourists of different nationalities are assumed to have different behavior patterns (see, for example, Kozak Nicolau and Ma's 2005; Richardson and Crompton 1988). Other studies claim that tourists of different nationalities are indeed assumed to have different behavioral patterns (see, for example, Kozak2002,2003; Nicolau and Ma´s2005; Richardson and Crompton1988; Pizam and Sussmann1995).

Conceptual Model and Hypotheses

In fact, several authors show that tourists of different nationalities have different behavioral patterns (Crotts and Reisinger 2010; In fact, this is the main hypothesis of the research that aims to examine the influence of nationality on the intention to return.

Fig. 3.1 Conceptual model
Fig. 3.1 Conceptual model

Methodology

Ris is the number of draws in the simulation, and SP(j) is the simulated probability to which individuals will return toj. The vi is measured by the probability that the tourist indicates an intention to return to the visited destination (Yes¼1, No¼0), labeled as no return and measured Xi as perceived characteristics.

Data and Results

The extensive examination of the validity, reliability and generalizability of the study leads to the conclusion that nothing in the evaluation indicates that it is invalid or unreliable. First, it was assumed to depend on all explanatory variables.

Table 3.2 Characterisation of the variables
Table 3.2 Characterisation of the variables

Discussion

Based on the global model, using the transformation 100(eb1), it can be said that each additional visit of the individual British tourist produces a decrease of approximately 44.95 % in the probability of returning to the Algarve, while for A German tourist is about 17.87. These findings are consistent with previous studies that argue that the determinants of tourism choice are more specific to the contextual life settings of the individual than to age cohorts or education level (March and Woodside 2005).

Conclusion

It is also necessary to consider motivations and learning processes within the set of determinants of tourists' decisions. Paper presented at the Fifth Congress of the World Leisure and Recreation Association, Sa˜o Paulo.

Introduction

Concept of Tourism

In its complexity and multiplicity, according to Ramos, it affects the people, the location and the culture of a country or region (. .) is a phenomenon with multiple characteristics that has increasingly assumed considerable importance at different levels, in relations between regions , countries and continents". It is considered as an activity that enables people to interact by exchanging different cultural, social and economic experiences (Mazon2001).

Tourism Activities

Therefore, tourism is a factor of social change that affects all factors related to the culture of societies: norms, values, ideologies and beliefs.

Youth Tourism

Despite the fact that tourist backpackers have been variously defined by academics based on their main characteristics, it is worth pointing out that a more precise definition of the term backpacker should have a less practical content. With the InterRail Global Pass you can travel for free between the 30 countries of the InterRail area: Germany, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Denmark, Slovakia, Slovenia, Spain, Finland, France, Great Britain, Greece , Hungary, Netherlands , Italy, Luxembourg, Macedonia, Montenegro, Norway, Poland, Portugal, Czech Republic, Ireland, Romania, Serbia, Sweden, Switzerland and Turkey (Trains from Portugal2009c).

Tourist Satisfaction

The methodologies used vary depending on the size of the job and the type of desired results. Qualitative research: the quality of the research requires subjective understanding of the customer's experience when buying or using a product or service.

Service Quality

To complete the research, statistical analysis of the data was made, to determine the degree of satisfaction of the customers and the assessment made by this the quality of the service. This analysis and follow-up will also give personal details about the effectiveness of its performance, by setting benchmarks that will make it easier to accurately measure the performance of future services (Gerson 1998; Mowforth and Munt 1998).

Methodology

Results

This means that there are significant differences in the averages for InterRailers' satisfaction with the variable transport between the three destinations. This means that there are significant differences in resources for InterRailer satisfaction and variable transport between the three destinations.

Table 4.2 Mauchly’s test of sphericity (transportation)
Table 4.2 Mauchly’s test of sphericity (transportation)

Conclusions

In turn, indicators showed less satisfaction from InterRailers regarding waiting times, hygiene and cleanliness and quality of equipment. The indicators used to assess satisfaction with the quality of InterRailers of Greek tourist entertainment, it is possible to conclude that the indicators that showed greater satisfaction of InterRailers regarding historical and cultural heritage and nightlife.

Introduction

Although there are a number of studies on forecasting tourist arrivals in other countries, little research is available on forecasting tourism demand in South Africa. Three tourism demand studies have been completed that identified the determinants of tourism demand for Africa and South Africa (see Saayman and Saayman 2008; Naude´ and Saayman 2005 and Seetanah et al. 2010).

Determinants of Tourism Demand

Method

Africa is not included in the empirical analysis due to lower average expenditure patterns by African visitors (SA Tourism2009:32), data limitations and the fact that some African countries use the South African rand exchange rate (Saayman and Saayman 2008:85). Hotel rooms and crude oil are also not included in the UK model due to high correlations with other independent variables.

Table 5.1 Variable description
Table 5.1 Variable description

Results

In the short term, tourist arrivals have historically had a significant impact on arrivals from Europe, North America and the United Kingdom. The increase in the number of tourist arrivals in the past is not significant in the Asian, Australian and South American model.

Table 5.7 Forecasting accuracy results
Table 5.7 Forecasting accuracy results

Conclusion

Goh C, Law R (2002) Modeling and forecasting tourism demand for arrivals with stochastic non-stationary seasonal variation and intervention. Salleh NHM, Orthman R, Ramachandran S (2007) Malaysia's tourism demands from selected countries: the ARDL approach to cointegration.

Introduction

In this respect, dynamic panel data models are suitable to model economic relationships, such as for example habit retention and education, that exist within the tourism activity and are not adequately represented by other models (Verbeek2004). Panel data models take into account variables observed over time and across units, and can identify and measure effects that are simply not detected by the purely sectional or temporal analysis of data.

Fig. 6.1 Trends in overall online travel market size in Europe (Source: Marcussen 2009)
Fig. 6.1 Trends in overall online travel market size in Europe (Source: Marcussen 2009)

Tourism Distribution and ICT

According to the World Tourism Organization (WTO 2001) the partnership between the Internet and tourism is ideal because it enables immediate and intuitive communication between agents involved in the distribution of tourism. For tourists, it provides access to relevant information about destinations and allows a quick and easy booking process.

Tourism Demand Analysis and Modelling

Based on the discussion of the previous section, the analysis of tourism demand without considering the technological environment seems reductive. It is therefore relevant to identify variables that can be used to better understand the demand for tourism in a predominantly technological environment.

Dynamic Panel Data Models

Estimating the future expected tourism demand is crucial for the planning of related activities, such as decisions on investment in destination infrastructure (airports, highways, railways, accommodation, health centers and other support services, etc.), which require planning and long-term investment. Panel data models are an ideal tool for analyzing tourism demand in this context, as these models allow us to simultaneously take into account the temporal and cross-sectional characteristics of the data and control the individual heterogeneity of each section, present more information, more variability, allow the study of dynamic adaptation that occurs unexpected, and to identify and measure effects that are simply not detected in data that is only temporal or partial.

Dynamic Modelling of Tourism Demand with Macro Panel Data

Aiis the total advertising spend per country; IIIis the number of internet users in the country. Taking into account the results of the tests presented in Table 6.2, it was concluded that all series, with the exception of the number of Internet users (I), are non-stationary.

Table 6.1 Characterization of the explanatory variables in tourism demand
Table 6.1 Characterization of the explanatory variables in tourism demand

Conclusion

Levin A, Lin CF, Chu C-SJ (2002) Root of unity testing in panel data: asymptotic and finite sample properties. Maddala GS, Wu S (1999) A comparative study of unit root tests with panel data and a new simple test.

Introduction

One of the methodologies used in empirical analysis of tourism demand is based on the estimation of a gravity model. The gravity model used controls for standard determinants of trade that could be confounded with migration, such as the size of the economy or the distance to New Zealand.

Literature Review

The theoretical underpinnings of the gravity model are thus more than justified in the current state of the literature. Consequently, the gravity model remains the most popular model to analyze the determinants of international trade flows.

Methodology

Expressed so that an increase in this variable is associated with an appreciation of the New Zealand dollar. 5Note that "random effects" and "fixed effects" apply to the distribution of the distribution parameter, not to the xbterm in the model.

Table 7.1 Explanatory variables used in the model Variable
Table 7.1 Explanatory variables used in the model Variable

Results

This is consistent with the finding in Law et al. 2009) using the same data, demonstrating a good degree of robustness of the result as the two studies use very different estimation methods for essentially the same model. This estimate is not as high as the elasticity of immigrants in Seetaram and Dwyer (2009) (which was 3.2) for Australian data, but is closer to estimates in Gheasi et al.

Summary and Conclusions

The international tourism sector in New Zealand is dependent on long-haul inbound markets, with the exception of Australia. Eryig˘it M, Kotil E, Eryig˘it R (2010) Factors influencing international tourism flows to Turkey: a gravity model approach.

Introduction

In tourism economics, there is a broad discussion on the analysis of the overall impact of the tourism sector on the development of cities and regions (see e.g. Finally, there is also a lack of research on the conditions for creating completely new tourist destinations in regions without a tradition of tourism.

Definition of the Tourism Industry and the Relevant Location Factors for the Local Tourism Industry

But in the context of this paper, such public activities are considered as a category of locational factors to explain the rise of the tourism industry. Figure 8.1 summarizes this distinction between different categories of locational factors in tourism.

Fig. 8.1 Local public infrastructure as a location factor in the area of tourism (Source: Designed by the authors)
Fig. 8.1 Local public infrastructure as a location factor in the area of tourism (Source: Designed by the authors)

The Present State of the Tourism Industry in Saxony

The average hotel size is the ratio between the number of beds offered and the number of hotels and guesthouses. A comparison of the two periods shows an increase in average overnight stays per beds offered; this ratio can be interpreted as the average utilization of bed capacities.

Table 8.1 Data included in the cluster analysis of tourism in Saxon regions (unweighted means for the counties and independent cities)
Table 8.1 Data included in the cluster analysis of tourism in Saxon regions (unweighted means for the counties and independent cities)

Empirical Findings on the Impact of Local Public

The average disposable income of a region tests the demand for accommodation and food services of the population in the same region. For example, in the city of Go¨rlitz, employment in the accommodation and catering sector has increased significantly.

Table 8.2 Counties and independent cities in Saxony and their propensity for recreational tourism, 1998–2000 and 2005–2007
Table 8.2 Counties and independent cities in Saxony and their propensity for recreational tourism, 1998–2000 and 2005–2007

Conclusions

Especially with regard to "megaprojects" in some municipalities, more light could be shed on their effect by looking at a longer period. This will only be possible in the future or by looking at "mega projects" in other regions and countries where such projects were completed several years ago.

Introduction

The second part of the chapter, on the empirical research undertaken to evaluate the economic impact of the mentioned Health Tourism Program for Seniors, begins with a characterization of that Program. Finally, conclusions from the empirical research and implications for the development of social tourism policies and strategies are provided.

Literature Review

Spain is an example of good practices in terms of social tourism programs for the elderly market. Despite the importance of the studies mentioned above, there is a significant gap in quantifying the total economic impact of social tourism worldwide, especially in relation to secondary impacts.

The Economic Impacts of a Portuguese Health Tourism Programme for the Senior Market

In 2007, within the Health Tourism Program for the elderly market (INATEL2007a), 127 trips were registered, which represented an increase of about 150%, compared to those registered in 1997 (INATEL1997). The final demand of the Health Tourism Program for the elderly was estimated in two groups: (1) the expenses incurred for the implementation of the program and (2) the expenses incurred by the elderly outside the package.

Fig. 9.1 Model used to quantify the total economic impacts of the Health Tourism Programme for the Senior Market
Fig. 9.1 Model used to quantify the total economic impacts of the Health Tourism Programme for the Senior Market

Conclusion and Contributions

This reflects the significant incentive effects of program implementation on the additional demand generated by participants. Porter PK, Fletcher D (2008) The economic impact of the Olympics: predictions in advance and reality after.

Introduction

As Gursoy et al. 2004) emphasize, understanding the perceptions of local key interest groups about the impact of the event on local communities is crucial to the success of any festival. The chapter first provides an overview of the background literature on cultural festivals and their role as regional development stimulants.

Cultural Festivals and Local Development

In recent years, festivals and special events have become one of the fastest growing types of tourist attractions. Most of the research examining the contribution of festivals and special events to local development focused on economic impact analysis (Dwyer et al.2005; Crompton et al.2001; Kim et al. 1998; Thrane2002).

Methodology

This is expected to add value to the database, enabling the analysis of the scope of the festivals to be interrogated at different geographical scales. Fixed intensity values ​​were thus assigned to each point of the Likert-type scale.

Results

Today, Patras International Festival of Film and Culture is an important cultural event of the city. The contribution of the film festival to the economic development of the host city is generally considered quite important (72 %) (Figure 10.4).

Table 10.1 Greek regional film festivals Festival profile Host city
Table 10.1 Greek regional film festivals Festival profile Host city

Discussion and Concluding Remarks

Alves HB, Campon Cerro AC, Martins AF (2010) Impacts of small tourism events in rural places. Tindall P (2005) Economic impacts of a small-scale youth sporting event: a study of the Gromfest Surf Carnival.

Introduction

A hedonic pricing model was applied to determine characteristics that influence the rental price of the accommodation units on the island, which include structural, environmental and spatial variables. The application of the methodology can serve for destination managers in the design of their promotion policies and future investment.

Methodology

The higher the value of c; the higher the penalty for introducing more regressors into the model. Although the specific shape of the kernel does not affect much, the choice of bandwidth can cause significant changes in the estimated parameters.

Study Case

When the sample is uniformly distributed over the study area, a fixed-bandwidth kernel is recommended. In the specific case of tourism research, it was used to analyze spatial variation in a study of the role of tourism and recreation in changing rural poverty rates in the US (Deller 2010).

Gambar

Table 2.3 lists the studies that take loyalty to be an indirect result of the models studied
Fig. 2.3 Number of constructs used for implicit loyalty study
Fig. 3.1 Conceptual model
Table 4.5 Descriptive statistics (accommodation)
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Referensi

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