INTERNATIONAL JOURNAL OF TOURISM RESEARCH Int J Tourism Res 12, 307–320 (2010) Published online 30 July 2009 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/jtr.749 A Study of the Non-economic Determinants in Tourism Demand Vincent Cho* Department of Management and Marketing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong ABSTRACT Traditionally, many studies have attempted to use economic demand models This paper stresses on the influence of non-economic factors on tourism demand Some researchers have suggested that tourists from different origins have various cultural and nationalistic backgrounds, and they may interpret visual imagery and experiences differently Aligning with this suggestion, we have investigated different underlying factors of tourism demand from four continents (Asia, the Americas, Europe and Oceania) Statistical data are collected from international organisations and 135 countries were covered Our results showed that there are differences and similarities among the factors in determining the tourism demand Copyright © 2009 John Wiley & Sons, Ltd Received 18 November 2008; Revised 25 June 2009; Accepted 30 June 2009 Keywords: tourism demand; non-economic determinants; holistic approach INTRODUCTION I nternational tourism today has social, cultural and political significance, as well as substantial economic benefits In the last 50 years, tourism has emerged as one of the largest and fastest growing industries in the world (Eadington and Redman, 1991; WTO, 1992) According to the World Tourism Organization *Correspondence to: V Cho, Department of Management and Marketing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong E-mail: msvcho@polyu.edu.hk (WTO), the number of international tourists worldwide increased from 25 million in 1950 to 160 million in 1970, 429 million in 1990, 689 million in 2001, 846 million in 2006 and 1.6 billion by 2020 International tourism has experienced an overwhelming boom over the last two decades and has now been called the largest industry in the world As a result of the rise in the number of tourists, and the importance of the tourism sector for many countries which have begun to channel their resources into its development (Balaguer and CantavellaJorda, 2002), tourism demand analysis has become increasingly important In general, the international tourism demand model, which is based on classical economic theory, is typically estimated as a function of tourists’ income, tourism prices in a destination relative to those in the origin country, tourism prices in the competing destinations (i.e substitute prices), exchange rates, transportation cost between destination and origin, as well as dummy variables on various special events and deterministic trends (e.g Barry and O’Hagan, 1972; Loeb, 1982; Stronge and Redman, 1982; Uysal and Crompton, 1984; Smeral, 1988; Di Matteo and Di Matteo, 1993; Crouch, 1994; Lim, 1999; Croes, 2000; Vanegas and Croes, 2000; Song et al., 2003; Chu, 2004; Li et al., 2005; Song and Witt, 2006; Wong et al., 2007; Chu, 2008; Song and Li, 2008) It postulates that factors of income and price are likely to play a central role in determining the demand for international tourism As international tourism is generally regarded to be a luxury commodity or service, it is not surprising that the study of such variables has dominated past research There are three reasons why the discussed economic framework needed to be extended First, from the consumers’ perspective, travelling overseas is one of the many options for Copyright © 2009 John Wiley & Sons, Ltd 308 them Once a decision to travel has been made, a consumer (tourist), faced with different alternatives, chooses a destination to maximise utility The tourist derives utility from spending time in a particular destination The utility stems from destinational attributes such as an agreeable climate, beautiful scenery and/or socio-cultural features These attributes are consumed along with other goods and services available at the destination The tourist’s utility function represents the preferences for travelling abroad along with other goods and services This suggests that the choice of destinations is a typical consumer choice problem (Rugg, 1973; Divisekera, 1995) In this vein, Naude and Saayman (2005) have devised a utility function based on hotel capacity, air distance, political stability, urbanisation rate, etc to estimate the tourist arrivals to Africa It was done using the regression analysis on a cross-sectional data of five-year averages from 1996 to 2002 Second, based on the theories of the behaviour-intention model, including the theory of planned behaviour and the theory of reasoned actions, it states that the perceived value and consequence of an action will affect the behaviour of a person (Ajzen and Fishbein, 1980; Ajzen, 1991) Thus, the perceived image of a destination will have an influence on the intention and actions of a person (tourist) to visit a destination Empirically, Var et al (1985) showed that destination image of a convention venue is directly proportional to the number of delegates going to the convention Third, according to Sauran (1978), the main difference between the economic and non-economic types of factors is that economic variables generally account for the total demand of an origin country and that the role of non-economic variables has more to with the types of tourism For instance, tourists in Thailand may probably go for shopping and relaxation, tourists in Europe may look for the historical heritages In this paper, we suggest to broaden the investigation on the non-economic factors based on the antecedent studies on destination image to study tourism demand This study addresses the following research problems: (1) to identify the potential factors influencing the tourism demand; Copyright © 2009 John Wiley & Sons, Ltd V Cho (2) to find out the significant underlying factors of tourism demand; and (3) to understand the tourism demand from four continents (the Americas, Europe, Asia and Oceania) The organisation of this paper is as follows First, we review on the literature relating to the potential antecedents of destination image and formulate the framework for this study Second, we describe our data collection procedures and related analysis Cross-sectional data relating to tourism of 135 destination countries are collected in this study By applying regression and neural network analyses, significant factors are sorted out These factors help to identify the most important factors behind tourism demand Interesting findings and discussions are presented, and finally there is a conclusion section LITERATURE REVIEW According to Gearing et al (1974), Ritchie and Zins (1978) and Schmidt (1979), destination image refers to an aggregated perception of attributes which make the specific location appealing as a potential destination to travellers Leading image attributes identified are nice climate, inexpensive goods and services, safety, similar lifestyles, etc To further understand the nature of destination image, we have reviewed the literature as follows Gearing et al (1974) have established an overall measure of destination image for a given region These researchers proposed eight factors including (i) accessibility of a region, (ii) attitudes towards tourists, (iii) infrastructure of a region, (iv) price levels, (v) shopping and commercial facilities, (vi) sport, recreation and education facilities, (vii) natural beauty and climate, and (viii) cultural and social characteristics By combining the score relating to the importance and actual perception of these factors by tourists, an overall value of destination image can be derived Ritchie and Zins (1978) have conducted a study on the importance of cultural and social impact on destination image using a survey on 135 respondents They identify four dimensions of cultural image of a tourism region: Int J Tourism Res 12, 307–320 (2010) DOI: 10.1002/jtr Non-economic Determinants in Tourism Demand elements of daily life, remnants of the past, good life and work habit Var et al (1985) have studied the destination image on convention tourism and found two important factors that determine the number of delegates The first one is accessibility on how close a convention is to the hometown of a delegate, and the second one is the attractiveness of the convention location Lew (1987) attempted to group the factors behind a destination image into three different perspectives: (i) ideographic nature of a location focusing on its concrete description; (ii) organisational nature stressing on the spatial, capacity and temporal characteristics of a location; and (iii) cognitive nature describing the perceptions and experience of tourists Getz (1993) applied the framework of destination image on business tourism He compared the tourism business districts in Niagara Falls (Ontario and New York) using underlying factors such as location, accessibility, design, attractions and services He concluded that in order to be a good district for business tourism, it should have three essential elements: (i) core attractions; (ii) central business district functions; and (iii) supporting services Utilising multidimensional scaling, Kim (1998) determines the relative positions of five well-known Korean national parks in terms of selection criteria and the tourists’ psychological reception to the areas He derived six features namely seasonal and cultural attractiveness, clean and peaceful environment, quality of accommodations and relaxing facilities, family-oriented amenities and safety, accessibility and reputation, and entertainment and recreational opportunities as the most important factors influencing the destination image Chen and Hsu (2000) measured the perceived image of South Korean tourists and found that travel cost, destination lifestyle, quality restaurants, freedom from language barriers and availability of interesting places to visit affects the destination choice behaviour of a Korean tourist Recently, Russo and Borg (2002) used a case study to analyse the destination image for cultural tourism in four European cities (Lyon, Lisbon, Rotterdam and Turin) They found Copyright © 2009 John Wiley & Sons, Ltd 309 that these four cities, besides their own features to attract culture tourists, should pay attention to those intangible elements, such as transportation facilities, information centre and quality of human capital in order to enhance location attractiveness Getz and Brown (2006) explored the underlying factors for a region on wine tourism Using an extensive survey on the perception on the importance of different features such as ‘the wine region is close to home’, and ‘the region is popular with wine tourists like me’, they found out there are five emerging factors: (i) core wine product; (ii) core destination appeal; (iii) core cultural product; (iv) variety; and (v) tourist oriented, and that these factors would define the image of a wine region Relating to the economic environment, Han et al (2006) found that price competitiveness is an important factor influencing Americans travelling to France, Italy and Spain, but not the UK However, as US expenditure rises, the market shares of Spain and the UK decline, while France and Italy benefit Last, but not least, Gallarza et al (2002) presented an extensive review on destination image and proposed a more comprehensive framework of destination image which contains cognitive elements, time elements and distance elements RESEARCH FRAMEWORK In our review on the literature, we classify these attributes into five categories: (i) attitude towards tourism; (ii) richness of tourism products/services; (iii) tourism support; (iv) environmental factors; and (v) economic factors The attitude of people in the destination towards tourists and their social index are under the first factor — attitude towards tourism Richness of tourism products/services includes the natural and cultural heritage of a destination, and the entertainment and recreational facilities in the destination Tourism support relates to adequacy of accommodation facilities, accessibility, road network infrastructure and safety of a destination The fourth category concerns the environmental factors such as seasonality of a destination The economic factors consist of price levels of a destination as well as the gross domestic product (GDP) of the source countries Table shows the summaries of related Int J Tourism Res 12, 307–320 (2010) DOI: 10.1002/jtr 310 V Cho Table Underlying antecedents of destination image Attitude towards tourism Tourism products/ services Tourism support Environmental factors Economic factors Attitude towards tourists (Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979; Gallarza et al., 2002; Getz and Brown, 2006) Social factors (Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979) Natural heritage (Lew, 1987; Getz and Brown, 2006; Getz, 1993; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979) Cultural heritage (Getz and Brown, 2006; Kim, 1996; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979) Entertainment and recreational facilities Shopping and relaxing facilities (Getz, 1993; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979) Sport and recreational facilities (Kim, 1998; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979) Accommodation (Kim, 1996) Accessibility of a region (Russo and Borg, 2002; Var et al., 1985; Kim, 1998; Lew, 1987; Getz, 1993; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979; Gallarza et al., 2002) Road network infra-structure of a region (Getz, 1993; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979) Clean, peaceful and safe environment (Kim, 1998) Tourism openness (Russo and Borg, 2002) Seasonality and Climate (Lew, 1987; Kim, 1998; Gallarza et al., 2002) Price levels (Han et al., 2006) studies investigating the underlying factors affecting destination image We postulate that these factors are also influential to tourism demand As this study concerns tourism demand from four different regions (Americas, Europe, Asia and Oceania) instead of from individual countries, it is hard to estimate the GDP or the exchange rates in those regions as a whole Thus, the GDP and the exchange rates are not considered in this study Moreover, it is hard to compare the consumer price index (CPI) of all 135 countries because different places have their own preferences of goods and services as well as on the weightings of those goods and services Thus, we also exclude the CPI in this study Nevertheless, without the economic factors, our findings would focus on the non-economic aspects and would have limited implications DATA COLLECTION In this study, we sourced reliable secondary data from different international organisations such as the WTO and World Development Indicators (WDI) from the World Bank Copyright © 2009 John Wiley & Sons, Ltd Initially, statistical data in 2005 from 214 countries and territories were collected These achieved data were reported in the yearbooks from different international organisations in 2007 However, there are 29 countries or territories that did not have tourist arrival data from four continents, they are Afghanistan, Canary Islands, Ceuta, Channel Islands, Cote d’Ivoire, Democratic Peoples Republic of Korea, Democratic Republic of Timor-Leste, Djibouti, Equatorial Guinea, Falkland Islands, French Guiana, Gibraltar, Greenland, Guernsey, Isle of Man, Jersey, Liberia, Madeira Island, Mauritania, Mayotte, Melilla, Netherlands Antilles, Saint-Pierre and Miquelon, San Marina, Solomon Islands, Somali Democratic Republic, Turkmenistan and Western Sahara Also, there are 25 countries or territories that did not have tourist arrival data from at least one continent (mainly from Oceania), they are Anguilla, Antigua and Barbuda, Argentina, British Virgin Islands, Cape Verde, Republic of the Congo, Curacao, Gambia, Guadeloupe, Guyana, Haiti, Luxembourg, Martinique, Mexico, Montserrat, Namibia, Norway, Qatar, Reunion, Saba, Saint Helena, Saint Maarten, Int J Tourism Res 12, 307–320 (2010) DOI: 10.1002/jtr Non-economic Determinants in Tourism Demand Saint Vincent and the Grenadines, and Sao Tome and Principe Owing to government policy, 25 countries have not recorded some variable data such as social index, tourism openness index, etc They are American Samoa, Andorra, Aruba, Bermuda, Bonaire, Cayman Islands, Cook Islands, Dominica, Eritrea, French Polynesia, Grenada, Guam, Micronesia, Marshall Islands, New Caledonia, Niue, North Mariana Island, Palau, Palestine, Puerto Rico, Saint Kitts and Nevis, Saint Lucia, Turks and Caicos Islands, Tuvalu, and United States Virgin Islands Those 79 countries or territories were neglected from the data set, which means 135 countries in total remained (as indicated in appendix A) The collected data of the 135 countries were analysed in order to sort out the determinants of tourism demand Tourism demand statistics Most destinations have used the same destination images or enticements to attract tourists regardless of their country of origin (Bonn et al., 2005) Previous research has suggested that nationals of various geographic regions interpret visual imagery and experiences differently dependent on their country of origin (Berlyne, 1977; Britton, 1979; Thurot and Thurot, 1983) In order to investigate any differences among tourists from different origins, we collect the data on tourism demand from four continents: the Americas, Asia, Europe and Oceania The tourist arrival statistics of 135 countries in 2005 were reported in the yearbook of tourism statistics published in 2007 by the WTO The WTO is a leading international organisation in the field of tourism and serves as a global forum for tourism policy issues and a practical source of tourism know-how Due to the functional form of the demand model usually in terms of powers on those underlying factors, we transformed the data using the natural logarithm, which is a common practice on most tourism demand studies In the last three decades, many studies have assumed a multiplicative form of model made linear by a logarithmic transformation of the variables (Loeb, 1982; Stronge and Redman, 1982; Summary, 1983; Arbel and Ravid, 1985; Witt and Martin, 1987; Poole, 1988; Croes, 2000; Copyright © 2009 John Wiley & Sons, Ltd 311 Vanegas and Croes, 2000; Song et al., 2003; Song and Witt, 2006; Wong et al., 2007; Song and Li, 2008) For the analysis of the underlying noneconomic factors, we have captured the data on social factors, natural and cultural heritage, accessibility, road network infrastructure, climate and distance from the origin continent of those 135 countries in 2005 On the other hand, data on safety and crime rate are only available in a few countries and factors such as lifestyle, government policy and intervention cannot be easily measured in a numerical sense Hence, these factors were neglected in this study The details of the independent variables are elaborated as follows Accessibility Accessibility is a significant attribute to destination image (Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979; Var et al., 1985; Lew, 1987; Getz, 1993; Kim, 1998; Gallarza et al., 2002; Russo and Borg, 2002) In this paper, accessibility by air is proxy by the takeoffs abroad of air carriers registered in the country The unit of registered carrier departures worldwide in this study is the number of carriers The data were collected from the WDI which is the premier data source on the global economy from the World Bank It contains statistical data for over 550 development indicators and time series data from 1960 onwards for over 220 countries and country groups with populations of more than million, as well as for China and Taiwan Natural logarithm was applied before fitting into the demand model Accessibility by road is proxy by the total road network which includes motorways, highways, and main or national roads, secondary or regional roads, and all other roads in a country The unit of total road network in this study is kilometre (km) The data were collected from the WDI Natural logarithm was used before fitting into the demand model Environmental condition Kim (1998) has indicated that environmental condition is a significant attribute to destination image In this vein, we have included the variable Carbon dioxide (CO2) emissions which Int J Tourism Res 12, 307–320 (2010) DOI: 10.1002/jtr 312 V Cho are those stemming from the burning of fossil fuels and the manufacture of cement They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring The unit of Carbon dioxide (CO2) emissions in this study is kilotons (kt) The data were collected from the WDI Natural logarithm was taken before fitting into the demand model Travelling cost In this study, we include the distance between the origin region and the destination country, which is a proxy for transport cost and effort (Tremblay, 1989) Laber (1969) found that distance between ‘origin’ and ‘destination’ plays a significant role as a determinant of tourism demand As our collected statistics only report the tourism arrivals from a region — Asia, Europe, the Americas or Oceania, thus we need to manipulate the average distance of a country from a region First, we assume that the distance between two countries is the distance between two countries’ capitals From the haversine formula as shown in Equation (1) (Sinnott, 1984), let φs, λs; φf, λf be the geographical latitude and longitude of two points respectively, and Δλ be the longitude difference Hence, Δθ is the (spherical) angular difference/distance as follows, Δq = arcsin ⎛ ⎛ φf − φs ⎞ ⎜⎝ sin ⎜⎝ ⎟⎠ ⎛ Δl ⎞ ⎞ + cos φs cos φf sin ⎜ ⎝ ⎟⎠ ⎟⎠ (1) The shape of the Earth more closely resembles a flattened spheroid with extreme values for the radius of arc of 6335.437 km at the equator (vertically) and 6399.592 km at the poles, and having an average great-circle radius of 6372.795 km (3438.461 nautical miles) Using a sphere with a radius, r, of 6372.795 km, thus results in an error of up to about 0.5% and the distance between two points of the Earth is equal to r Δ θ A matrix with 135 rows and 135 columns is formed containing the distances among the 135 countries Then we group the countries according to their continent and take the mean distance of the countries on the same continent Copyright © 2009 John Wiley & Sons, Ltd For instance, the distance from England to Asia is calculated by averaging the distance between London (England’s capital city) and all the 38 countries such as China, Hong Kong and Japan in Asia using the locations of their capital cities Appendix A shows our list of countries (38 countries in Asia, 23 countries such as USA, Canada and Mexico in the Americas, countries such as Australia, New Zealand and Fiji in Oceania, and 33 countries such as Hungary, Latvia, Norway and the UK in Europe) in different origins for the manipulation of average distance Cultural and natural heritages Cultural and natural heritages are found to be important attributes on destination images (Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979; Lew, 1987; Kim, 1996; Getz and Brown, 2006) The numbers of cultural and natural world heritage were collected from the United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage Committee, which consists of representatives from 21 of the States Party to the Convention elected by their General Assembly for terms up to six years It determines whether a property is inscribed on the World Heritage List which includes 644 cultural, 162 natural and 24 mixed properties with outstanding universal value Similar to the effect of taking natural logarithm, Table 2, which is referenced from Wikipedia, was used to transform the heritage counting Seasonality and climate Seasonality is a well-documented issue in the literature, particularly in relation to cold-water regions of Europe and North America (Aguiló and Sastre, 1984; Snepenger et al., 1990; Donatos and Zairis, 1991; Jeffrey, 1999; Kennedy, 1999; Baum and Lundtorp, 2001) Although the reasons for a significant variation in demand are also well documented (the climate, institutional patterns like school or calendar holidays, lifestyles, special events, etc.), there are a few studies on tourism seasonality (Butler, 2001) Belen-Gomez-Martin (2005) suggested that weather and climate are significant in explaining tourism demand Int J Tourism Res 12, 307–320 (2010) DOI: 10.1002/jtr Non-economic Determinants in Tourism Demand 313 Table Transformation on the number of cultural and natural heritages Cultural heritage Number Level 1–4 5–19 20–29 30+ Natural heritage Number Level 1 2–3 4–9 10+ Tyndall Centre, which brings together scientists, economists, engineers and social scientists, who together are working to develop sustainable responses to climate change, stored the daily mean temperature data by country In advance, they calculated the monthly mean temperature by taking the average value of daily mean temperature in every month We can browse the monthly mean temperature data from their website by country The unit of monthly mean temperature in this study is in Celsius The data were collected from the Tyndall Centre for Climate Change Research Standard deviation of monthly mean temperature was calculated by taking the standard deviation of monthly mean temperature in 12 months If the standard deviation is large, this would imply the destination country has a wide spread of temperature within a year or a clear seasonality Social Index As identified by Gearing et al (1974), Ritchie and Zins (1978) and Schmidt (1979), social factor is an important attribute on destination image Social Index, which is an aggregate social index, combining the Human Development Index, Newspaper Index, Personal Computer Index, and Television Index, was collected from the World Travel and Tourism Council Population Population counts all residents regardless of legal status or citizenship — except for refugees not permanently settled in the country of asylum as they are generally considered part of the population of their country of origin With higher population, which would mean a larger coverage in area and more tourists, hence we attempt to control this variable so as to reveal the significance of the other influenCopyright © 2009 John Wiley & Sons, Ltd tial factors The data were collected from the WDI Natural logarithm was used before fitting it into the demand model In sum, Table highlights the above factors which would be grouped into the relevant category as indicated in Table ANALYSIS AND FINDINGS Recently, Lim (1997), Morley (1996, 1998, 2000), Turner et al (1998) and Turner and Witt (2001) surveyed more than 100 international tourism demand studies that have attempted to model the demand for tourism Most studies are time series econometric models such as the almost ideal demand system and autoregressive distributed lag model estimated using multiple least-squares regression, which is appropriate for stationary time series data (Kulendran and Witt, 2001) As this study tries to investigate the tourism demand from another perspective using the cross-sectional data from 135 countries, we applied regression and neural network for the analysis to determine the most influential factors on tourism demand As the data are cross-sectional, common time series models are not applicable Regression is a simple yet robust linear analysis method that is capable of identifying important factors, which are assumed to be linearly related to the dependent variable However, it is not appropriate if the underlying relationship is non-linear Because of this limitation, neural network analysis, a well-developed technique which would handle both linear and non-linear data in artificial intelligence studies, is also applied to check the reliability of the results from linear regression The results of regression and neural network are shown in Tables to Those significant factors in the regression are sorted according to the weighting from the result of the neural network analysis and those insignificant factors are shown Int J Tourism Res 12, 307–320 (2010) DOI: 10.1002/jtr 314 V Cho Table Potential factors behind tourism demand Influential factors Independent variables Demographic of the destination country Accessibility by air (Russo and Borg, 2002; Var et al., 1985; Kim, 1998; Lew, 1987; Getz, 1993; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979; Gallarza et al., 2002) Cultural heritage (Getz and Brown, 2006; Kim, 1996; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979) Natural heritage (Lew, 1987; Getz and Brown, 2006; Getz, 1993; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979) Environmental condition (Kim, 1998) Infrastructure on road network (Getz, 1993; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979) Social factor (Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979) Seasonality and Climate (Lew, 1987; Kim, 1998; Gallarza et al., 2002) Population Registered aircraft departures Cultural world heritage Natural world heritage Carbon dioxide emissions, distance Total road network Social index Average of monthly mean temperature, Standard deviation of monthly mean temperature Table Regression and neural network analyses on tourist arrival from Americas Regression (R2 = 0.727) Tourist arrival from the Americas Registered Aircraft Departures Population Social Index Average distance from the Americas Standard Deviation (Temp) Roads, total network CO2 Emissions Natural world heritage Cultural world heritage Mean (Temp) Standard coefficient Significance ANN (Accuracy = 90.1) Relative importance 0.374 0.395 0.342 −0.334 −0.252 0.186 0.142 0.088 −0.051 −0.050 0.000 0.000 0.000 0.000 0.046 0.086 0.316 0.148 0.488 0.581 0.298 0.251 0.235 0.223 0.173 0.080 0.116 0.122 0.069 0.007 at the end of the list From Tables to 7, the significant factors from the regression are rather consistent with those of high relative importance in the neural network analysis All the insignificant factors have relative importance of less than 0.2 in the neural network analysis (except the CO2 emission on the tourist arrival from Americas) Hence, our results are deemed to be reliable and trustworthy DISCUSSION From the regression and data mining analyses as shown in Tables to 7, there are two common Copyright © 2009 John Wiley & Sons, Ltd factors among the tourists from the four continents — distance from the origin (all betas are negative and significant) and aircraft departure (all betas are positive and significant) That is, most tourists prefer to visit proximal countries with good accessibility In this regard, we suspect that travelling to proximal countries, which are usually associated with less time and financial effort, would be a dominant factor in selecting a destination to visit It is nice to have a mix of short and long haul trips during a year Usually, the number of short hauls would be greater than the number of long hauls for a normal traveller, unless Int J Tourism Res 12, 307–320 (2010) DOI: 10.1002/jtr Non-economic Determinants in Tourism Demand 315 Table Regression and neural network analyses on tourist arrival from Asia Regression (R2 = 0.765) Tourist arrival from Asia Average distance from Asia Population Registered aircraft departures CO2 emissions Roads, total network Social index Natural world heritage Cultural world heritage Standard deviation (Temp) Mean (Temp) Standard coefficient Significance ANN (Accuracy = 89.2) Relative importance −0.385 0.385 0.350 0.319 0.337 0.245 0.148 0.153 −0.049 0.050 0.000 0.000 0.000 0.018 0.002 0.005 0.010 0.025 0.541 0.553 0.316 0.302 0.288 0.225 0.210 0.145 0.099 0.081 0.102 0.036 Table Regression and neural network analyses on tourist arrival from Europe Regression (R2 = 0.801) Tourist arrival from Europe CO2 emissions Average distance from Europe Mean (Temp) Cultural world heritage Standard deviation (Temp) Registered aircraft departures Social index Population Natural world heritage Roads, total network Standard coefficient Significance ANN (Accuracy = 90.6) Relative importance 0.436 −0.374 −0.188 0.172 −0.158 0.188 0.108 0.004 0.047 0.147 0.000 0.000 0.016 0.008 0.027 0.015 0.178 0.968 0.373 0.128 0.423 0.293 0.242 0.130 0.113 0.108 0.108 0.101 0.043 0.021 Table Regression and neural network analyses on tourist arrival from Oceania Regression (R2 = 0.614) Tourist arrival from Oceania Registered aircraft departures Average distance from Oceania Social index Population CO2 emissions Cultural world heritage Mean (Temp) Standard deviation (Temp) Natural world heritage Roads, total network Copyright © 2009 John Wiley & Sons, Ltd Standard coefficient Significance ANN (Accuracy = 88.3) Relative importance 0.571 −0.264 0.231 0.287 −0.283 0.145 −0.086 −0.042 0.010 0.087 0.000 0.000 0.036 0.027 0.093 0.104 0.538 0.664 0.891 0.616 0.325 0.223 0.203 0.093 0.113 0.107 0.062 0.052 0.052 0.036 Int J Tourism Res 12, 307–320 (2010) DOI: 10.1002/jtr Cruise Visitors’ Experience The third section asked respondents to indicate which activities they had undertaken while in Heraklion In addition, the third section asked respondents whether they would like to stay longer and offered them the opportunity to indicate additional activities they wished to undertake but they did not have enough time The fourth section asked respondents to indicate their future intentions about their likelihood to make subsequent visits to Heraklion in the future and to recommend Heraklion to relatives and friends The final section contained questions about respondents’ profile, utilising socio-demographic variables (age, gender, marital status, education, income, employment status and geographic origin), travelling party and expenditure variables Following a review of official statistics on the nationalities of cruise passengers’ arrival in the port of Heraklion, the questionnaire was translated into five languages: English, German, French, Spanish and Italian Sampling The population of this study consisted of passengers who disembarked from arriving cruise ships for visits to the city of Heraklion, between August and November 2005 At first, four shipping agents based in the city of Heraklion were approached in June 2005 and were asked to distribute questionnaires on-board After their informed consent, they were given eight folders for distribution to each of the eight cruise ships they represented Each folder contained 200 questionnaires and a cover letter (in the English and Greek languages) The cover letter provided information about the general purpose of the study, detailed instructions for administering the questionnaires, the data collection procedure and a request to return the completed questionnaires to the shipping agent at the first return of the cruise ship The proportion of the language of the questionnaires in each folder was arranged in consultation with shipping agents on the expected nationalities of passengers on each cruise ship Following this process, only seven questionnaires were collected from one shipping agent Because of the low response rate, these questionnaires were used as a pilot study, which helped to find out whether results Copyright © 2010 John Wiley & Sons, Ltd 393 can be obtained that would justify further research on a larger scale, and were excluded from further analysis The next step was to distribute the questionnaires through three travel agents organising cruise line-sponsored bus tours In this call, only one travel agent, who represented two cruise ships, responded Two folders, similar to those of ship agents, were given to this travel agent, who was asked to administer them as the passengers re-boarded at the last stop, en route to the cruise ship This call resulted in 83 completed questionnaires, a response rate of 23.7% However, the collection of questionnaires from cruise line-sponsored bus tour passengers faces two main limitations First, such passengers not represent a probability sample but are selected simply because they were riding the buses of the travel agent who agreed to participate in the study, and second, not all cruise passengers undertake bus tours In order to increase the representativeness of the sample, a decision was taken to distribute 200 questionnaires at the Heraklion port terminal Potential respondents were approached during October and November 2005 as they were returning to the cruise ship They were told of the nature of the survey and were asked to complete the questionnaire In total, 81 respondents, or 41%, agreed to complete the questionnaires, making the overall response rate 30% In addition to the 164 completed questionnaires, participant observation provided complementary forms of material to those collected from questionnaires In more detail, one of the authors undertook a four-day excursion on a cruise ship, having Heraklion as a port of call The reason for this was to observe activities and behaviours of passengers on-board and to enable the researcher to experience directly the ways in which passengers were experiencing the cruise Finally, data were collected from shipping agents about the cruise ships’ itineraries, frequency of return and schedule of sponsored tours Data analysis A number of statistical procedures were carried out for this paper using the Statistical Package for the Social Sciences (SPSS Inc., Int J Tourism Res 12, 390–404 (2010) DOI: 10.1002/jtr 394 Chicago, IL, USA) version 15.0 First, univariate statistics (frequency distributions, percentages, standard deviations and means) were calculated where appropriate Second, to find the underlying constructs associated with cruise passengers, both motivation and satisfaction scales were grouped in one model each, using principal component analysis with a varimax rotation To determine the number of factors in each model, the criterion of eigenvalues greater than was used In both factor models, loadings of an absolute value of 0.45 or more were considered in order to load highly enough Before undertaking the factor analyses, the validity of the data in each model was tested by using the Kaiser– Meyer–Olkin test of sampling adequacy The results of the tests for both models were marvellous, according to Hair et al (1987) (a value of 0.767 for the motivation model and 0.834 for the satisfaction), indicating that both the number of variables and the sample size were appropriate for factor analyses To test the reliability of factors for both scales, Cronbach’s alphas were calculated The values of Cronbach’s alphas varied from a high of 0.859 (first factor) to a low of 0.606 (fifth factor) for the motivation scale and from 0.939 (first factor) to 0.790 (fifth factor) for the satisfaction one, thereby indicating satisfactory internal consistency reliability for both scales FINDINGS Profile of respondents The profile of cruise passengers having volunteered to participate in the study is presented in Table The sample was slightly dominated by female respondents (55.6%), indicating a greater interest on cruises by women Married couples comprised the largest segment of the sample (65.8%) Seniors and retired persons comprised a significant proportion of the passengers (34.8% and 32.1% respectively), although middle-aged adults and the employed dominated the profile (37.4% and 55.1% respectively), confirming the findings of the study of Marti (1991), who empirically identified a false impression that cruise passengers consist mainly of older retired persons Among the respondents, approximately 72% had earned Copyright © 2010 John Wiley & Sons, Ltd K Andriotis and G Agiomirgianakis Table Profile of cruise ship passengers n % 72 90 44.4 55.6 106 30 25 65.8 18.6 15.5 Age