An Analysis of Saudi Arabian Outbound Tourism

A dissertation submitted to the

Graduate School

of the University of Cincinnati

in partial fulfillment of the

requirements for the degree of

Doctor of Philosophy

in the Department of Geography

of the College of Art and Sciences

Basheer O. Alshammari

M.A. Imam University

B.A. Imam University

August 2018

Committee Chair: Robert B. South, Ph.D.

Abstract

Saudi Arabia is one of the world’s most important tourist-generating markets and the largest in the Middle East. Approximately 75 percent of Saudi citizens annually engage in an international vacation. This study is an analysis of Saudi Arabian international-outbound tourism, 2002-2015. The research focuses on two aspects of Saudi tourism - growth of outbound tourism and destination trends. Collective understanding of the characteristics of these outbound tourists is typically performed by connecting destination choice with socioeconomic and demographic variables, when those data are known. Owing largely to higher disposable incomes and liberal vacation time, Saudi Arabia has vibrant outbound tourism activity. Data collected by the Department of Tourism and Information Research

(MAS) within the Saudi Commission of Tourism and National Heritage (SCTH) on socioeconomic factors of the country’s outbound tourism population for the year 2015 provides a unique means to assess, via multiple regression, discriminant and factor analysis, if certain characteristics are common to destination choice.

The study finds that the growth of Saudi international tourism is largely a result of both push factors within the nation and Saudi wealth. Saudi outbound choice is largely influenced by those places that are Arabic speaking/Muslim nations, as well as economic/politically stable destinations. In general, most Saudis seek a cosmopolitan vacation venue where indulgence in an upscale experience in an Arabic, more liberal Muslim nation, is the

ii destination of choice. Trending data indicate emerging new western destinations for Saudi international tourists. But capturing the US 20 billion annual expenditure on outbound tourism may be elusive given Saudi preferences to vacation in culturally familiar places. The results offer some insight into a demographic understanding of Saudi outbound tourists and their destination choices, and provide an opportunity to identify motivations of travel that exist beyond the data. The results also challenge that the reliance on country groupings by other organizations and the arbitrariness of demographic data categories that could potentially skew or compromise the actual motivations and understandings of outbound travelers.

Key words: Saudi Arabia, outbound tourism, tourist growth, destination trends, familiar culture

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Acknowledgments

I would first like to thank the Department of Geography at the University of Cincinnati for accepting me into the doctoral program and providing me the platform for conducting this dissertation research. I would like to thank Dr. Robert South, who served as my Advisor and

Ph.D. Committee Chair, shepherded me through the program, offering guidance, advice, support, and criticism when necessary.

Thank you also to all my Ph.D. committee members: Dr. Kevin Raleigh, Dr. Nick

Dunning, Dr. Rainer Vom Hofe, and Dr. C.J. Kim. Each of the members of my Dissertation

Committee has provided me extensive personal and professional guidance and taught me a great deal about both scientific research and life in general.

I would especially like to thank Dr. Kevin Raleigh, who has taught me more than I could ever give him credit for here. He has shown me, by his example, what a good scientist (and person) should be.

Also thank you to my friend, Humud Alanazi, who has provided me great assistance to get the data I used in this dissertation.

Nobody has been more important to me in the pursuit of this project than the members of my family. I would like to thank my parents; whose love and guidance are with me in whatever I pursue. They are the ultimate role models. Most importantly, I wish to thank my brother, Shafi, loving and supportive wife, Hissah, and my four wonderful children, Shouq,

Raneem, Yazeed, and Basmah, who provide unending inspiration.

Table of Contents

1. Introduction...... 1 1.1 Statement of Problem ...... 1 1.2 Organization of the Dissertation...... 2 2. Literature Review ...... 4 2.1 Saudi Arabia Outbound Tourism: An Analysis of Trends and Destinations 2.1.1 Outbound tourism ……………………………………………………….4 2.1.2 Patterns and trends of outbound tourism…………………………...……4 2.1.3 Saudi Arabia outbound tourism……………………………………….…6 2.2 Saudi Arabia's Outbound Tourists: 2.2.1 Socioeconomic and Demographic Commonalities Present in International Destination Selection…………………………………………………..…7 2.3 A Social Economic Analysis of Tourists’ Destination Choice 2.3.1 Saudi Arabia…………………………………………………………….12 2.3.2 Gulf Cooperation Council (GCC) countries……………….……………13 2.3.3 The Middle East…………………………………………………………15 3. Research Article 1 ...... 17 3.1 Introduction……………………………………………………….…….…19 3.2 Methodology……………………………………………………….……...36 3.3 Results and Conclusion …………………………………………….…..…45 4. Research Article 2 ...... 52 4.1 Introduction…………………………………………………….……….…54 4.2 Methodology……………………………………………..………………..59 4.3 Results and Conclusion ………………………………..……………….…68 5. Research Article 3 ...... 91 5.1 Introduction……………………………………………………………..…92 5.2 Methodology………………………………………………………………94 5.3 Results and Conclusion ………………………………………………….100 6. Summary and Conclusions ...... 107 7. Bibliography ...... 110

List of Figures

Research Article 1

Figure 1: The Middle East………………………………………………………..….…20

Figure 2: Saudi outbound tourist travel, by month, 2015…………………….…...……26

Figure 3: Average monthly temperature and rainfall, Saudi Arabia 1990-2012………..26

Figure 4: Saudi Arabia GNI per capita 2002-2015…………………..……………....…28

Figure 5: Crude oil prices per barrel, 2000-2015 (US Dollars) …………………..……29

Figure 6: Outbound destinations by Saudi tourists, 2002……………………….……...33

Figure 7: Outbound destinations by Saudi tourists, 2012……………………....………34

Research Article 2

Figure 1: UNWTO Regional Commissions…………………………………………….60

Figure 2: UNWTO Regional Sub-groupings of the Countries of the World…………...60

Figure 3: Excerpt of Data Table on Year 2015 Saudi Outbound Tourist Trips…………61

Figure 4: Year 2015 Saudi Outbound Tourist Trips for 42 Countries…………………..62

Figure 5: Categories of Demographic Information ……………………………………..63

Figure 6: Result of Box’s M Test for the Equality of Covariances……………………..73

Figure 7: Eigenvalues and Variances of the Linear Discriminant Functions ………..…74

Figure 8: Wilks’ Lambda Eigenvalues and Variances………………………..……...…76

Figure 9: Structure Matrix Result of Discriminant Analysis …………………...….…..77

Figure 10: Classification Results of Demographics………………………………….....80

Research Article 3

Figure 1: UNWTO Regional Commissions……………………………………………..97

Figure 2: UNWTO Regional Sub-groupings of the Countries…………………………..98

Figure 3: Countries in the Analysis………………………………………...... 99

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List of Tables

Research Article 1

Table 1: International tourism-generating countries per capita (2015)……………………21

Table 2: Saudi Arabia Outbound Tourism and Expenditures, 2002-2015...... 24

Table 3: Major destinations by Saudi outbound tourists, 2002-2012……………………..31

Table 4: Catalyzing events rendering destination countries………………………………37

Table 5: Data Explanation and Variable Name Employed in Modeling………...………..39

Table 6: Pearson correlation coefficients, dependent and independent variables...... 40

Table 7: Skewness for non-categorical independent variables………………..…………..41

Table 8: Coefficients of determination (r 2 ) for regression models………………….…...43

Research Article 2

Table 1: Saudi Arabia Outbound Tourists, Overnights, and Tourism Expenditure……..56

Table 2: Per capita Purchasing Power Parity (PPP) 2015……………………………….57

Table 3: Grouping Variable for Saudi Outbound Tourism, 2015………………………..69

Table 4: Discriminant analysis results ……………………………….………………….71

Table 5: Tests of Equality of Group Means…………………….……………………….72

Research Article 3

Table 1: Table Numbers, Data, and Categorizations…………………………………….96

Table 2: Factor Analysis / Total Variance Explained………………….………………..101

Table 3: Rotated Component Matrix……………………………………...…………....102

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1. Introduction

1.1 Statement of problem

International tourism is defined as "the movement of masses across international borders for pleasure/leisure purposes, possibly for a short duration" (Yavas, 1987, p.57). The concept of international tourism can help in comprehending the concept of outbound tourism which involves people moving out of their country of origin to a particular country. (Alghamdi.

2007). Tourism has been one of the most important and consistent growth industries worldwide and is currently held to be one of the major service industries (Bansal and Eiselt,

2004; Zang et al., 2004). In 2016, one out of seven of the world’s population engaged in international tourism; “tourism ranks as [the] world’s third largest export category,” accounting for seven percent of the world’s exports with the total value exceeding US $1.4 trillion and (UNWTO, 2017a: 6).

Saudi Arabia is a leading country in the outbound tourism market. The Saudi Arabian outbound tourism market is recognized as one of the largest in the world in terms of the amount of Saudi expenditures in outbound tourism. (Alghamdi, 2007). In 2015, an estimated

15.9 million Saudi’s engaged in international travel spending an estimated $20 billion principally on shopping and leisure activities (UNWTO, 2015: 3). Geographically located in the heart of the Middle East and with relatively easy access to European and Asian destinations the nation is the most important tourist generating country in the Middle East

(UNWTO, 2003: 6).

The objective of this study is to analyze and understand the trends and patterns of Saudi

Arabia outbound tourism. The research focuses on an analysis of sociodemographic variables to provide an understanding of Saudi Arabian outbound destination choice. In addition, the study incorporates different statistical techniques in order to identify the most appropriate

1 statistical model for studies of outbound tourism. There has been a dearth of research on tourism focusing on the Middle East in general and in particular Saudi tourism. The results of the study contribute to an understanding of segmented tourist markets generated from the

Middle East and provides insight into outbound tourism in a culture that has received little attention in the literature. Finally, the findings fill a void in what is known about Saudi outbound tourism. The research provides policy-makers with information that can be useful in formulating and planning an effective strategy to domestically retain some of the Saudi export tourist dollars and provide other nations insight on how to attract a share of the $20 billion that Saudi’s currently spend on international tourism.

1.2 Organization of the dissertation

The dissertation begins with a review of relevant literature on international tourist research. A review of the international tourism literature focusing on studies of outbound choice influence international vacation selection. Further, examining current and past research on international tourism studies, specifically outbound tourism, substantiates the types of variables and methods commonly employed in such studies, and demonstrates significant progress in understanding tourism behavior. The following chapters are composed of three journal articles investigate the Saudi Arabia outbound tourism during the period of 2002-2015.

The first article is an analysis of Saudi Arabian outbound international tourism, 2002-

2015. The study focuses on two aspects of Saudi tourism. First, examines the growth of

Saudi international tourism. The study provides insight for the growth and high percentage of

Saudi engagement in international tourism. Second, the research analyzes Saudi international destination trends and outbound tourist choice. Consequently, this study undertakes a

2 statistical analysis on Saudi outbound tourism that provides analytical underpinning to the findings and a predictive basis for future tourist spatial patterns that can be replicated for other studies in other settings.

The second article attempts to understand the role of sociodemographic variables on Saudi

Arabian outbound destination selection, 2015. The analyzed variables include age, income, education level, and employment status. The results of the study contribute to an understanding of segmented tourist markets and provides insight into the relationship between tourists’ socio-demographic characteristics and destination selection. The study employs a discriminant analysis to test the relationship between Saudi tourists’ sociodemographic characteristics and destination selection.

The third article investigates the effect of several variables such as length of stay, mode of transportation, type of accommodation, and purpose of visit on Saudi outbound tourism.

Factor analysis and cluster analysis are employed to discern these relationships. Data used in this paper are derived from the Saudi national survey, 2015, which is conducted by the supreme commission of tourism and national heritage in Saudi Arabia. The finding of this paper will contribute in understand the needs of Saudi outbound tourists and assists the government in promoting the national and inbound tourism.

The dissertation concludes with a discussion of results and conclusion. The research adds to the literature on international tourism in general and specifically the Saudi Arabian tourist industry. Studying outbound tourism trends and patterns for a specific period provides an understanding of tourism to date and is a point of departure for future studies of international travel.

2. Literature Review

2.1 Saudi Arabia Outbound Tourism: An Analysis of Trends and Destinations

2.1.1 Outbound tourism

Some of the most widely quoted studies on outbound tourism focused on the development of tourism of specific countries. Elias (1993) reviewed historical development and global characteristics of the French vacation market-holiday destinations. He also evaluated

European tourism policy and provided general forecasts for the European tourism market.

Minca (1993) examined the evolution of outbound tourism, focusing on the development of

Italian international tourism from 1970-1990. This chronological analysis of outgoing traffic revealed how the Italian market had been affected by both European and world economic trends and by domestic factors. Similarly, Ohashi (1993) reviewed Japanese overseas travel trends. His study identified factors contributing to overseas travel, namely rapid appreciation of the yen in 1985 and the 1987 launching of Japan’s ministry transport plan. His study also found that the most popular destinations for Japanese travelers were South Korea, the USA, and Hong Kong.

2.1.2 Patterns and trends of outbound tourism

Analyzing patterns and trends of a tourist-generating source is a common theme among tourism studies. Williams and Zelinsky (1970) were among the first to investigate spatial patterns of international tourism by selecting a group of countries that dominated the international tourist market. Their study used secondary data from the International Union of

Official Tourist Organizations and centered on European and North American countries. By doing so, these authors established a precedent for later tourist flow studies. Likewise,

Oppermann and Chon (1995) analyzed trends in German outbound tourism from 1970 to

1992, finding that Germans were becoming more familiar with different destinations not only in Europe but also in many other countries. Their research suggested that the greater destination familiarity by Germans could increase the potential of effective word-of-mouth communication channels. Zhang and Qu (1996) studied trends of Chinese outbound travel to

Hong Kong. Their study found that the main cause of a substantial increase in the number of travelers was China’s economic boom coupled with political climate changes that eased the ability of Chinese citizens to travel to Hong Kong and neighboring countries. Huang (1996) unearthed similar results in a study of Taiwanese outbound tourism after Taiwan's government relaxed its ban on overseas travel, thus permitting residents to travel to mainland

China to visit relatives. Li et al. (2008) explored changes on the nature of travel trends as measured by tourist arrivals among Pacific Asian Travel Association (PATA) member countries for 1995 and 2004. The study identified significant growth in the Asian-Pacific region’s tourism industry for both inbound and outbound travel. Zirulia (2013) analyzed

Dutch outbound international tourist trends with the Netherlands as a place of origin. This enquiry found that the total number of international vacation trips had grown considerably within in the last three decades, denoting a significant increase in the propensity of Dutch residents to engage in international tourism. Zirulia’s study provides additional confirmation of an upward trend in European international tourism. Given the positive trend, year-by-year variations are positively associated with the Dutch Gross Domestic Product (GDP). Vijver et al. (2016) traced the evolution and shifting geographical patterns of Asia-Pacific tourism to

Australia between 1990 and 2010. The results revealed that income (GDP per capita) was the most important factor explaining tourism demand. The authors used distance as a proxy for travel costs, and they demonstrated that tourist air travel has a negative elasticity that has slightly increased over time. This study also found that the Australian vacation market has

5 been becoming increasingly mature, which corresponds to the later stages of Butler’s (1980) tourist life cycle model, perhaps a harbinger of a declining Australian tourist market.

2.1.3 Saudi Arabia outbound tourism

Few studies have addressed Saudi Arabia tourism from the perspective of either domestic tourism or outbound international travel. Bogari’s (2002) research focused on domestic tourism motivation, examining both push and pull factors of tourist behavior in an Islamic and Arabic culture. The major findings of the study were that the push factors - namely climate and “lifestyle-daily routine,” were associated with pull factors. Similarly, Albughuli

(2011) found that the primary motivations for Saudis traveling domestically were relaxation, spirituality, and family. Her study showed that relaxation (push factor) and religion (pull factor) largely influence Saudi domestic tourist activities. Alghamdi (2007) investigated motivations for Saudi outbound tourism and the influence of Saudi culture on destination selection. The study revealed that cultural factors mostly associated with family relationships are very influential on destination selection. Mumuni and Mansour (2014) empirically developed market segments for the outbound leisure travel market of Saudi Arabia based on survey data recording respondents’ stated preferences. Their study identified three main travel segments associated with outbound choice: conservatives, fun seekers, and variety seekers. Alrishaidan (2016) examined Saudi residents’ travel behavior, finding that the most important vacation activities were relaxing, shopping, and sightseeing. The results showed that demographic profiles – including gender and age – also impacted on Saudi tourist’ motivation and vacation activity.

Related literature review reveals that research on international tourism has been mostly conducted on European nations, North America, and, to a lesser extent, Asian countries.

However, minimal work has focused on the Middle East in general and particularly Saudi

Arabia. Thus, the study adds to research on international tourism in general and specifically fills a void on what is known about outbound tourism focused on a culture that has received little attention in academic tourism pursuits.

2.2 Saudi Arabia's Outbound Tourists: Socioeconomic and Demographic

Commonalities Present in International Destination Selection

This study aims to analyze the causal relationship of socio-economic and demographic factors of age, income, education level, and employment status on Saudi Arabian outbound destination selection, thereby fulfilling a greater purpose of adding to the literature on international tourism in general, and specifically with Saudi Arabia as a case study. A review of the international tourism literature focusing on studies of outbound choice confirms that demographic and socioeconomic characteristics vary by world region, and that these factors influence international vacation selection. Further, examining current and past research on international tourism studies substantiates the types of variables and methods commonly employed in such studies, and demonstrates significant progress in understanding tourism behavior, while also identifying crevices that this research aims to explore more fully.

The research of Sheldon and Mak (1987) uncovered that purchasers of package tours are likely to be elderly who prefer to travel in small groups, and are intent on visiting several destinations for short durations. In addition, this study was one of the earliest to use logistic regression, thus paving the way for (then) future usages of that and other multivariate analyses of demographic characteristics on tourist destination selection. In a similar vein,

Richardson and Crompton (1988) analyzed vacation patterns between English and French tourists in Canada by using linear modeling and analysis of covariance; they found that age,

7 education, and income were more important in determining destination selection than cultural differences between the English and French speaking populations of Canada. Oum and

Lemire (1991) found that marital status and gender could affect Japanese tourists’ destination choices by employing a multinomial logit model; interestingly, their results also determined that other variables of gender, age, and education were not critical determinants in choosing a travel destination. These and other studies through the end of the 20th century analyzed tourism choices of different international citizens while establishing effective and proven methods for modeling these behaviors.

Studies in the first decade of the 21st century have continued exploring tourism destination choice around the world via these and other methods. Particularly impactful are a number of studies on tourism behavior in the Eastern Hemisphere. In a 2001 study, Heung et al. (2001) set out to identify the underlying dimensions of vacation motives of Japanese citizens pursuing leisure travel to Hong Kong. They found that significant relationships existed among derived vacation factors (“benefits sought”, “attractions and climate”,

“cosmopolitan city”, “exploration”, and “dream fulfillment”) (267), and sociodemographic / travelling characteristics (gender, age, occupation, annual income, times to Hong Kong, length of stay, and source of travel information (266). Jang et al. (2003) analyzed a sample of

206 surveys from the 1997 In-Flight Survey of International Air Travelers, and found that

Chinese travelers to the United States were predominantly middle-aged males. Via regression modeling, Mohsin and Ryan (2004) examined the statistical significance of sociodemographic among a sample of 675 West Malaysians centering on their perception of

Australia as a tourist destination. Their resulting model suggested that income is a principal determinant of destination selection. By way of a logistic regression model, Williams et al

(2007) found that age, gender and income are the most important factors that determined if

8 tourists chose a cruise-based or land-based vacation. Using binary logistic regression,

Kattiyapornpong and Miller (2008) investigated the impact of two-way interactions between age, income, and life stage on dependent variables comprising intentions held by Australian residents to travel intrastate, interstate, or overseas for a vacation. This study found that the interactions between the constraint variables of age, income and life stage are important in explaining travel preferences. Lyons et al. (2009) used conditional logistic regression to measure the importance of destination, household, and seasonal characteristics to the tourism destination choices of Irish households, finding that household-specific characteristics such as the number of children and people over 60 in a household are important.

More recent 21st century studies continued focusing on the places in the Eastern

Hemisphere, while also exploring other methods for assessing international tourism choice.

The multiple regression results of Guillet et al. (2011) discovered that monthly household income is significantly influential on the destination choices of Hong Kong residents; unique to their study is the interesting result that other socioeconomic and demographic factors are not influential, yet trip characteristics are (e.g., purpose of trip, length of stay). Agyeiwaah et al. (2013) examined the influence of socio-demographics on tourists’ motivations for choosing homestay in the Kumasi Metropolis of Ghana. Results of t-tests and one-way analysis of variance (ANOVA) suggest that socio-demographics are influential factors on international tourists’ motivations for choosing to vacation within Ghana, rather than internationally. Perhaps working backwards, Xiang (2013) concluded that the majority of

Chinese outbound tourists were middle class and enjoyed sightseeing, and he arrived at this conclusion through qualitative methods of interviewing and content analysis. Slak Valek et al. (2014) used a linear regression to test the socio-demographic characteristics effects on tourist choice. They found that gender, age, education, and income were determining factors

9 for tourist’s choice for sports-related travel. In general, wealthier, more educated males would undertake long trips to attend international sports events. Kalabikhina and

Shishalov (2016) found that destination selection of Russians was influenced by income, family status, employment and education. In addition, they found that wealthier Russians tend to seek vacations in other more distant regions of Russia. By using factor analysis,

Seyidov and Adomaitiene (2016) determined that age, monthly income, and marital status of local Azerbaijani travelers profoundly affect their travel behavior, particularly regarding trip duration. By employing binary logistic regression, Wei et al. (2017) examined factors influencing Chinese outbound destination selection to Europe and the United States. The study found that age, education, and constraints (vacation time and income) significantly influence Chinese citizens’ outbound vacation choices. Compared with other outbound destinations, both Europe and the USA are more attractive to young, better-educated people with higher incomes and more leisure (vacation) time. Huang and Wei (2018) examined the relationship between outbound travel intention of Japanese citizens and some demographic and economic variables related to making these decisions. In their use of logistic regression, they found that education, income, and paid vacation days significantly predicted outbound travel intention. To examine how age, income, gender, education level, and occupation affect international tourists’ reasons to visit Bangkok, Suttikun et al. (2018) employed a chi-square analysis and found that varying income levels, occupations, education levels, and regions of origin all prompted travel to Bangkok, while age and gender appeared to have no impact on such decisions. All these studies have demonstrated that demographic, economic, and cultural variables have or have not impacted international tourism destination choices of many people across the world. As the focus of our research examines Saudi Arabia, it is especially noteworthy to identify the few tourism-related research pursuits unique to the

Middle East.

Al-Thagafy (1992) investigated Saudi Arabia tourism in the 1980s and 1990s, and has thus far been the earliest discoverable one investigating Saudi Arabia. While his study focused primarily on domestic tourism by the Saudis to one province, he also briefly explored outbound travel by using a chi-square test to determine if demographic characteristics influence destination choice. The study found that wealthy Saudis with more education typically indulged in longer vacations and traveled to more distant resorts in European settings. By using discriminant analysis, Metwally (2004) found that marital status and education levels significantly influenced vacation selection of Saudis to Middle Eastern Gulf

Cooperation Council countries (GCC). The findings show that young married couples preferred to spend their vacations in neighboring GCC resorts, while Saudi families preferred vacationing to non-proximate Arab resorts. Conversely, highly educated and single persons were found to prefer spending their vacations outside the Arab region. Alghamdi’s (2007) relatively recent PhD thesis pursued primary data collection with interviews and surveying of

486 Saudis who traveled to Bahrain, Egypt, and France. By employing factor analysis, he uncovered both explicit and implicit push and pull factors that explained the sample’s travel motivations.

In summary, many previous works employ regression analysis to test whether there is an effect of socio-economic and demographic factors on outbound tourism, particularly destinations selection. The literature review also reveals that research on outbound tourism has been largely conducted on East Asian nations, Oceania, North America, and Europe, with minimal work concentrating on the Middle East in general and particularly on Saudi Arabia.

Thus, this research intends to add to studies of international tourism in general while specifically filling a void on what is known about outbound tourism focused on a culture that has received little attention in academic tourism pursuits.

2.3 A Social Economic Analysis of Tourists’ Destination Choice: The Case of Saudi

Arabia

This study aims to understand the impact of Saudi tourists’ profile on outbound tourism destination choice. In the following section, we review the existing body of literature of outbound/international tourism with an emphasis on the Middle East in general and focusing on Saudi Arabia in particular. The literature review is presented in three subsections namely

Saudi Arabia, GCC countries, and the Middle East region.

2.3.1 Saudi Arabia

Very little research has focused on Saudi outbound tourism. Among the few studies are

Al-Thagafy (1992); Bogari’s (2002); Alghamdi (2007); Albughuli (2011); Mumuni and

Mansour (2014); and recently Alrishaidan (2016). The first study was conducted by Al-

Thagafy (1992) who investigated Saudi Arabia tourism in the 1980s and 1990s. While his study focused primarily on Saudi domestic tourism to one province, he also briefly explored outbound travel by using a chi-square test to determine if demographic characteristics influence destination choice. The study found that wealthy Saudis with more education typically indulged in longer vacations and traveled to more distant resorts in European settings. Alghamdi’s (2007) relatively recent PhD thesis pursued primary data collection with interviews and surveying 486 Saudis who traveled to Bahrain, Egypt, and France. By employing factor analysis, he uncovered both explicit and implicit push and pull factors that explained the sample’s travel motivations. The study revealed that cultural factors mostly associated with family relationships are very influential on destination selection. Bogari’s

(2002) research focused on domestic tourism motivation, examining both push and pull factors of tourist behavior in an Islamic and Arabic culture. The major findings of the study

12 were that the push factors - namely climate and “lifestyle-daily routine” – were positively and strongly related to pull factors. Similarly, Albughuli (2011) found that the primary motivations for Saudis traveling domestically were relaxation, spirituality, and family. Her study showed that relaxation (push factor) and religion (pull factor) predominate in influencing Saudi domestic tourist activities. Alrishaidan (2016) examined Saudi residents’ travel behavior, finding that the most important vacation activities were relaxing, shopping, and sightseeing. The results showed that demographic profiles – including gender and age – also impacted on Saudi tourist’ motivation and vacation activity. By using principal components, cluster, discriminant, and analysis of variance, Mumuni and Mansour (2014) conducted a structured survey which revealed three main travel segments regarding Saudi tourism—conservatives, fun seekers, and variety seekers. Conservatives (older, married, and male respondents) have a profound dislike for entertainment-oriented activities; fun seekers

(young, single, and female) prefer shopping and leisure activities, and variety seekers (middle aged, single, and female) like all vacation activities. In addition, Mumuni and Mansour found that “relaxation” is the vacation activity that most respondents considered most important. This is followed by visiting beaches, visiting amusement parks, recreation centers, and shopping.

2.3.2 Gulf Cooperation Council GCC countries

Previous studies have utilized different methodological approaches to study outbound tourism. For example, Balli et al. (2018) examine the impact of word-of-mouth by immigrants/ expatriates in Gulf Cooperation Council GCC countries to stimulate outbound tourism demand from the Gulf region to the countries of their origin. The study found that

GCC tourists mostly travel to places (a) where expatriates/immigrants working in the GCC

13 come from; (b) have cold to moderate temperatures; and (c) retain higher civil liberty rights.

Metwally (2004) used discriminant analysis in an analysis of Saudi vacation selection to

Middle Eastern Gulf Cooperation Council countries (GCC) and found that marital status and education levels significantly influenced destination choice. Also, Metwally findings show that young married couples preferred to spend their vacations in neighboring GCC resorts, while Saudi families preferred vacationing to non-proximate Arab resorts. Conversely, highly educated and single persons were found to prefer spending their vacations outside the Arab region. Prayag and Hosany (2014) employed cluster analysis to investigate UAE travel motivations to Paris. The study uncovered three distinct clusters; enthusiasts, unconvinced, and convivial. Additionally, the authors used multiple discriminant analysis to confirm the validity of the three-cluster solution. Michael et al. (2017 used a series of focus groups and in-depth interviews to understand travel motivations that encourage Emiratis to vacation in

Australia. They found that Emiratis are motivated to travel to Australia by three factors: physical, interpersonal and fun. The internal motivations that encourage Emiratis to engage in international travel are inseparable from Australia’s external attributes that attract the

Emiratis to the country. Michael et al. (2011) investigate the perceptions of residents of

Dubai, United Arab Emirates (UAE), both nationals and expatriates, toward the vacation in

Victoria, Australia. The study found that there are different perceptions and attitudes toward the vacation in Australia among the two groups. Emiratis mostly like shopping and lifestyle activities, while expatriates interested in outdoor activities and music. Nassar et al. (2015) analyzed the influence of travel motivation, Muslim-friendly amenities and lifestyle on

Kuwaiti travelers to visit Islamic tourism destinations. A hierarchical regression analysis tested whether and how much the influencing factors predicted a significant amount of the variance in travel. The analysis was controlled for the effects of demographic variables. They found that travel cognitive and affective images had the largest significant effects on the

Kuwaiti travelers’ intention to visit Islamic destinations. By utilizing several analysis techniques including T-tests, ANOVA, factor analysis, cluster analysis, regression, and

MANOVA, Alsawafi (2013) studied Omani outbound pleasure travelers by identifying their travel motivations and constraints and how they overcome these constraints. The study found that there is an influence of socio-demographic variables (gender, marital status, age, educational level, income, and occupation) on Omani tourists' perceptions and behaviors.

Moreover, the study finds that the Islamic teachings have an indirect influence on the choice of a pleasure travel destination and a direct influence on participation in leisure activities. In relation to market segmentation, the study finds that it is possible to segment Omani outbound tourists based on their travel motivations.

2.3.3 Middle East

Salman and Hasim (2012) investigated factors that influenced Malaysia as a vacation destination for Middle Eastern tourists and found that “safety” and “security” were the most important factors. Allan (2014) assessed 'push' and 'pull' factors that influenced Jordanian tourists to travel abroad. A sample of 500 outbound Jordanian tourists in 2013 was used. He found that the main push motivations for the respondents were ‘enjoyment’, ‘novelty’,

‘relaxation’, and ‘escape’. Whereas, the main factors underpinning pull motivations for the respondents were ‘nature and nice weather’, ‘historical and heritage attractions’, followed by

‘the interesting activities. In the same context, Dudokh (2009) investigated factors affecting destination choice for Jordanian tourists to eight countries (Oman, Saudi Arabia, Syria,

Tunisia, Yemen, Egypt, Lebanon and Bahrain) using panel data analysis. The study found that tourists from Jordan have weak demand for outbound tourism. Also, the study found that

Jordanian decisions for travelling abroad is determined by the cost of travel to different destinations and typically they choose the cheapest alternative. A study conducted by the

European Travel Commission (2012) revealed that over three-quarters of outbound tourist arrivals from the Middle East region are to destinations within the Middle East region. The share of intra-regional destinations has been steadily rising, growing from 66% in 1995 to

75% in 2010. Demographically, the study revealed that international leisure travelers from the Middle East to Europe are more male than female, with half in the 18-34 years age group, half are married with children in the household, educated and belong to the upper socioeconomic strata of society. The study also revealed that the most important travel source markets are the six GCC nations which constitute about 60% of all outbound travel and about

75% of total international tourism expenditure from the Middle East. Surprisingly, a high proportion of expatriate residents in the GCC countries, also undertake international holidays.

Finally, the study concludes with that the main barriers to holiday in Europe are high cost of travel, problems getting a need visa, and perception of lack of availability of Halal food.

The literature search for studies on Middle Eastern international tourism shows that none of the research has dealt with the outbound trip’s characteristics such as length of stay, purpose of visit, etc., and none of the research has analyzed these characteristics focusing on

Saudi Arabia. Therefore, this study contributes to the body of literature on outbound tourism in general, and on Saudi Arabia in particular.

3. Research article 1

Saudi Arabia Outbound Tourism:

An Analysis of Trends and Destinations

Basheer Alshammari, Robert South, and Kevin Raleigh

Department of Geography and GIS, University of Cincinnati, USA

Submitted to International Journal of Tourism Research (August 8th, 2018)

Abstract

Saudi Arabia is one of the world’s most important tourist-generating markets and the largest in the Middle East. Approximately 75 percent of Saudi citizens annually engage in an international vacation. This study is an analysis of Saudi Arabian international-outbound tourism, 2002-2015. The research focuses on two aspects of Saudi tourism - growth of outbound tourism and destination trends. The study finds that the growth of Saudi international tourism in largely a result of both push factors within the nation and Saudi wealth. Saudi outbound choice is largely influenced by those places that are Arabic speaking/Muslim nations, as well as economic/politically stable destinations. In general, most

Saudis seek a cosmopolitan vacation venue where indulgence in an upscale experience in an

Arabic, more liberal Muslim nation, is the destination of choice. Trending data indicate emerging new western destinations for Saudi international tourists. But capturing the $20 billion annual expenditure on outbound tourism may be elusive given Saudi preferences to vacation in culturally familiar places.

Key words: Saudi Arabia, outbound tourist growth, destination trends, familiar culture

3.1 Introduction

“International tourist flows have become one of the most critical components within the total assemblage of social and economic transactions among countries of the contemporary world.” (Williams and Zelinsky, 1970, p.549). These authors’ observations have proven to be increasingly prophetic during the nearly half century following publication of their seminal study. In 2015, there were nearly 1.2 billion international tourists - or approximately one out of seven of the world’s population - and by 2030, the United Nations World Tourism

Organization (UNWTO) estimates the industry will increase to more than 1.8 billion tourists

(UNWTO, 2016). The UNWTO reports that the total value of tourism exports generated

US$1.5 trillion in 2015 accounting for seven percent of the world’s exports ranking third in importance in terms of worldwide exports. And tourism is the world’s largest service sector industry accounting for one out of eleven jobs worldwide. Tourism is a driver of economic growth and development through the creation of jobs and enterprises, export revenues, and infrastructure development (UNWTO, 2016: 1). With the rapid growth and development of the tourist industry, especially international tourism, questions regarding spatial travel flows, principal destinations, changing destination patterns, and travel intensity are increasingly relevant and important (Li, Meng, & Uysal, 2008)

Saudi Arabia is a leading country in the outbound tourism market. In 2015, 15.9 million

Saudis engaged in international travel spending an estimated $20 billion principally on shopping and leisure activities (UNWTO, 2015, p. 3). Geographically located in the heart of the Middle East (Figure 1) and with relatively easy access to European and Asian destinations, the nation is the most important tourist generating country in the Middle East

19 accounting for approximately 40% of all outbound tourists from the region (UNWTO, 2003, p. 6).1

Figure 1. The Middle East

The Middle East is generally considered as the area from the eastern Mediterranean to Iran, including Bahrain, United Arab Emirates, Kuwait, Qatar, Oman, Syria, Jordan, Lebanon,

Iran, Iraq, Egypt, Yemen, and Saudi Arabia.

Saudi Arabia ranks seventeenth worldwide (Table 1) as measured by the ratio of outbound tourists to population or more than 76 percent of the Saudi population of 20.7 million were international tourists in 2015. Such frequency of outbound travel has become so important that the country’s government created a separate ministry of tourism in 2002 in order to

1 The Middle East is generally considered as the area from the eastern Mediterranean to Iran, including Bahrain, United Arab Emirates, Kuwait, Qatar, Oman, Syria, Jordan, Lebanon, Iran, Iraq, Egypt, Yemen, and Saudi Arabia. 20 provide greater understanding of the upward trending $20 billion annual Saudi expenditure on international tourism

Table 1. International tourism-generating countries per capita (2015)2

Rank Country Outbound Tourists Population Percent

1 Hong Kong 24,069,500 7,346,248 327.6

2 Norway 15,388,000 5,142,842 299.2

3 Hungary 17,276,000 9,911,336 174.3

4 Switzerland 13,601,000 8,238,610 165.1

5 Finland 8,904,000 5,460,592 162.8

6 Singapore 9,125,000 5,618,866 162.3

7 Sweden 15,596,000 9,693,883 160.8

8 Denmark 8,991,000 5,661,723 158.8

9 Albania 4,504,000 3,196,981 140.9

10 Austria 10,628,000 8,557,761 124.2

11 Poland 44,300,000 38,221,584 115.9

12 Netherland 18,070,000 16,844,195 107.2

13 United Kingdom 65,720,000 63,843,856 102.9

14 Germany 83,737,000 82,562,004 101.4

15 Belgium 10,835,000 11,183,411 96.8

16 Canada 32,267,000 35,871,283 89.9

17 Saudi Arabia 15,900,000 20,774,4063 76.5

2 Source: www.portal.euromonitor.com/portal/statistics 3 Most sources cite Saudi Arabian population at 31.6 million (Population Reference Bureau, 2015). However, the Kingdom of Saudi Arabia cites an official population count at 20.7 million (Saudi Arabia General Authority for Statistics GAS, 2015). Saudi Arabia does not count guest workers as part of the Saudi population, thus the discrepancy. Guest workers are also not counted as international tourists by the Kingdom of Saudi Arabia and are not included in any of the statistics in this study. 21

This study is an analysis of Saudi Arabian outbound international tourism, 2002-2015.

The research focuses on two aspects of Saudi tourism. First, the paper examines the growth of Saudi international tourism: during the time frame of this analysis, Saudi outbound tourism increased by 174 percent. The study provides insight for the growth and high percentage of

Saudi engagement in international tourism. Second, the research analyzes Saudi international destination trends and outbound tourist choice. A cursory examination of Saudi international tourism shows changing tourist trends; outbound choices in 2002 are not replicated a decade later. Some of the factors related to these changing travel patterns are immediately transparent (e.g., political events in certain countries), but other explanatory factors are less obvious. Based upon published analyses on international tourism, it is apparent that predicting future tourist spatial behavior on the basis of past patterns is weak and limited

(Mansfield, 1990). Consequently, this study undertakes a statistical analysis on Saudi outbound tourism that provides analytical underpinning to the findings and offers a predictive basis for future tourist spatial patterns that can be replicated for other studies in other settings.

The paper proceeds with a review of literature on relevant international tourist research.

The study then discusses data acquisition followed by an analysis of the growth of Saudi international tourism. Next, the research examines Saudi international destination trends followed by a statistical analysis that provides analytical robustness for Saudi outbound destination choice. The paper concludes with a discussion of results and conclusion. The research adds to the literature on international tourism in general and specifically the Saudi

Arabian tourist industry. Studying outbound tourism trends and patterns for a specific period provides an understanding of tourism to date and is a point of departure for future studies of international travel.

Data

Data on Saudi Arabian tourism were obtained from several sources. The principal data source for this study was secondary data collected by the Tourism Information and Research

Center (TIRC) of the Saudi Commission for Tourism and National Heritage (SCTH) on surveys administered annually since 2002, except for the year 2003. Additional and updated data on Saudi outbound tourism have been obtained from several tourism authorities and organizations, both locally and internationally, via direct contact and online access. These include the United Nations World Tourism Organization (UNWTO), Euromonitor

International, Eurostat Statistics Explained, The World Bank, General Authority for Statistics

(GAS), and the Saudi General Authority of Civil Aviation (GACA). Data pertaining to growth of Saudi Arabian tourism was available for the years 2002-2015. However, data on

Saudi international destinations was only available for the years 2002-2012. Consequently, the discussion on growth of Saudi international tourism spans the time period 2002-2015 while the analysis of outbound destination choice is limited to the decade 2002-2012.

Growth of Saudi international tourism

International tourism can be capsulized by two principal components: push factors provide the rationale for a populace to engage in international travel, while pull factors influence tourist destination choice (Allan, 2014). In Saudi Arabia, the number of Saudi tourists increased approximately 174% from 2002 to 2015 (5.8 million to 15.9 million), with tourist expenditures in 2015 tripling that of 2002. Note, however, that a substantial increase in outbound tourism is not reliant solely on Saudi population growth, which increased by about 53 percent during the same time period (Table 2).

Table 2. Saudi Arabia Outbound Tourism and Expenditures, 2002-20154

Year Tourists (millions) Population (millions)5 Expenditures (billions of dollars) 2002 5.8 13.5 7.3 2004 3.5 13.8 4.4 2005 3.7 14.7 9 2006 1,8 15.5 12.9 2007 3.9 15.9 21 2008 3.8 16.3 16 2009 6.1 16.8 21.3 2010 6.2 17.3 22 2011 10.4 17.9 18.2 2012 13.2 18.6 17.9 2013 9.6 19.2 18.6 2014 14.8 19.8 19.5 2015 15.9 20.7 20.2

However, tourism has a high-income elasticity of demand: as incomes increase, demand increases. This is a relationship mirrored by Saudi international tourism with expenditures, increasing from $7.3 billion in 2002 to $20.2 billion in 2012, a 177 percent increase within a decade. Though this is impressive, it is largely a result of push factors, related to the dearth of cultural and physical features within the nation that may otherwise act to attract and retain intra-national tourism. In a similar vein, Saudi Arabia is not noted internationally as a leisure tourist destination. Nonetheless, the religious tourism is a principal international tourist draw as the city of Mecca is the birthplace of the Prophet Muhammad – the founder of Islam – and is considered Islam’s holiest city. Annually, millions of Muslims

4 Source: Saudi Tourism Information and Research Center (SSTIRC), 2015 5 Saudi citizens 24 participate in religious pilgrimages to Mecca and to other places throughout the nation. Saudi citizens certainly comprise a part of this religious pilgrimage, and indeed visitations to historic mosques and religious sites explains at least a plurality of intra-national tourism.

Apart from religious tourism, the country has limited cultural and physical attractions within it that could otherwise act as a motivation for international travel. For example, Saudi

Arabia cannot boast of world class museums (e.g., Madrid’s National Prado or Paris’

Louvre); there are a limited amusement parks, no public movie theaters, and only three golf courses with grass. Similarly, the country is not noted for a variety of different entertainment options (as exemplified by Las Vegas), or for world-class shopping (similar to the Avenue de s Champs-Élysées in Paris or Rodeo Drive in Los Angeles).

In addition to an absence of these kinds of cultural attractions, physical geography of a desert country discourages international tourism. Saudi Arabia borders the Red Sea and part of the Persian Gulf, and both coasts are locations of several beach resorts (most notably

Durrah, Dana, and Movenpick); however, these resorts do not have the appeal of European

Mediterranean resorts (e.g., Côte d'Azur) or those in the Western Hemisphere (e.g., Florida and California in the United States, Mexico, or Uruguay, among others). Saudi Arabia has no ski resorts, mountainous scenic vistas, or any world-renowned physical landscapes that can compare with U.S. or Canadian National Parks. Thus, this paucity of national cultural and physical attributes is a predominant push factor that helps to explain the very high Saudi engagement in international tourism.

An additional motivation for Saudi international travel is climate. There is a distinct seasonality to Saudi outbound tourism: Over 80% of Saudi international tourism occurs during the summer months (Figure 2), a period coinciding with the highest annual temperatures and the lowest precipitation rates in the Arabian Peninsula (Figure 3).

Figure 2. Saudi outbound tourist travel, by month, 20156

1000000 900000 800000 700000 600000 500000 400000 300000 200000 100000 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Total outbound tourists

Figure 3. Average monthly temperature and rainfall, Saudi Arabia 1990-20127

14 ° 40

12 35

Rainfall (mm) 30 10 Temperature ( 25 8 20 6 15 4 10

2 5

0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rainfall Temperature

6 Source: Saudi Tourism Information and Research Center (STIRC), 2015 7 Source: Worldbank.org/climateportal 26

High summer temperatures provide rationale for the seasonality of Saudi outbound tourism, and there is considerable anecdotal evidence that supports this claim. That said, an equally important explanation is a government-recognized holiday: Saudi citizens enjoy four weeks of paid vacation during the summer months. A liberal policy of paid vacation, coinciding with months of oppressive temperatures, translates into an exodus of Saudis seeking relief in other places, both near (e.g., Lebanon or Jordan and far (e.g., North

America, Asia, Europe).

In addition to the liberal vacation policy, Saudis can also afford to engage in international tourism; the country’s per capita income is among the highest in the world. As measured by

Purchasing Power Parity, the country ranks eleventh worldwide at $53,539 (International dollars, 2015), and Saudi per capita Gross National Income (GNI) has exhibited an upward trend throughout the period of this analysis (Figure 4).

Figure 4. Saudi Arabia GNI per capita 2002-20158

53,000 51,000 50,000 47,000 44,000 44,000 43,000 40,000 39,000 38,000 37,000 35,000 35,000 34,000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Saudi wealth is largely derived from petroleum extraction dating from the 1930s, and it is currently the world’s second-largest producer and contains the world’s second largest petroleum reserves. Oil prices have a volatile history with extreme highs and lows; although there have been significant dips, oil prices have had a general upward trend starting in 2002 and ending in 2014(Figure 5).

8 Source: http://www.theglobaleconomy.com/Saudi-Arabia/GDP_per_capita, 2015 28

Figure 5. Crude oil prices per barrel, 2000-2015 (US Dollars)9

120

100

0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Oil prices

A rise in oil prices equates to a wealthier Saudi populace who spend more disposable income on amusement, entertainment, vistas, shopping and relief from desert heat in other international venues. A Spearman’s rank correlation coefficient (r) between Saudi gross per capita income and outbound tourism for the time frame of this analysis is .886 (significance

0.01) statistically confirming a relationship. The relationship between oil prices and Saudi engagement in outbound tourism with a one-year lag effect was also statistically tested by

Spearman’s rank order coefficient with a resulting r value of .846 (significance 0.01).

9 Source: http://blogs.lse.ac.uk/europpblog/files/2015/01/oilpricechar 29

Saudi international tourist destinations

The Saudi Tourism Information and Research Center is exceptional in that it is a nationally-commissioned research center purposely formed for reporting on tourism trends of its citizens. The secondary data provided by this research center is unique for tourism studies in that its breadth and scope include geographic and demographic data uncommonly available in studies of tourism flows. The secondary data collected and verified by the Saudi Tourism

Information and Research Center (TIRC) forms the basis for this analysis of Saudi international destination trends and outbound tourist choice, with a predominant focus centering on the 32 countries that had Saudi outbound tourists greater than 10,000 in 2012.

The major world destinations (by country), numbers of Saudi tourists in both 2002 and 2012, and percentage change between those years are presented in Table 3.

Table 3. Major destinations by Saudi outbound tourists, 2002-201210

2002 2012 Destination11 Rank Tourists n Rank Tourists n12 Percent Change, 2002-2012 1 Egypt 1 1,454,000 5 1,637,466 12.61 2 Syria 2 1,208,000 28 15,137 -96.26 3 United Arab Emirates 3 652,000 2 2,373,819 264.08 4 Bahrain 4 577,000 1 4,574,941 692.88 5 Jordan 5 506,000 4 1,911,481 277.76 6 Lebanon 6 235,000 8 234,432 -0.24 7 Kuwait 7 188,000 3 2,006,658 967.37 8 Malaysia 8 145,000 14 88,965 -38.64 9 Indonesia 9 143,000 11 105,796 -26.02 10 Turkey 10 78,000 7 271,969 248.68 11 United States 11 59,000 16 81,991 38.97 12 Morocco 12 57,000 17 75,441 32.35 13 France 13 56,000 18 58,216 3.96 14 United Kingdom 14 55,000 12 102,777 86.87 15 Yemen 15 45,000 9 210,000 366.66 16 Qatar 16 43,000 6 790,859 1739.21 17 Iran 17 40,000 13 92,909 132.27 18 Germany 18 35,000 15 83,584 138.81 19 Tunisia 19 22,000 21 21,646 -1.61 20 Singapore 20 18,000 19 37,686 109.37 21 Italy 21 18,000 27 15,543 -13.65 22 Thailand 22 17,000 22 20,660 21.17 23 Oman 23 16,000 10 107,399 571.24 24 China 24 14,000 20 24,667 76.19 25 Canada 25 12,000 24 17000 41.66 26 Austria 26 10,000 29 14,731 47.31 27 Netherland 27 10,000 25 15,760 57.60 28 South Korea 28 8,000 23 17,970 124.62 29 Switzerland 29 5,000 30 14,134 182.68 30 Libya 30 3,500 32 12,500 257.14 31 Hong Kong 31 2,500 26 15,678 527.12 32 Argentina 32 2,000 31 12,600 530.00 Totals 5,734,000 13,152,934

10 Source: Saudi Tourism Information and Research Center (STIRC), 2012 11 Destinations listed by ranking of the highest number of tourists in 2002 12 Destinations included for comparison are based on countries in 2012 that were visited by more than 10,000 Saudi citizens 31

Several outbound trends can be noted from these data and rankings, but the destination data also demonstrates considerable stability in outbound tourist choice. With a couple of exceptions, several of the most frequent destinations in 2002 are also major destinations a decade later (albeit with a slight shift in rankings). The largest percentage changes noting increases in Saudi outbound tourism were in Qatar (1739.21%), Kuwait (967.37%), and

Bahrain (692.88%), with Bahrain also experiencing the largest absolute gain of Saudi tourists of any country, with a net increase of nearly four million. Egypt, the #1 tourist destination in

2002, continued to be a favorite place of Saudi tourists, but its rank fell to fifth in 2012. The most noteworthy exception is Syria, which ranked second in 2002, but which also saw the largest decline in percentage change, -96.26%, between 2002 and 2012. Other noteworthy outbound tourist declines were Malaysia, Indonesia, and Lebanon.

There are several obvious explanations for the changing trends in Saudi tourist destinations. First, countries that experienced natural disasters or military / civil unrest during the first decade of the 21st century have been negatively impacted. The questionably-named

“Arab Spring” in Syria and Lebanon are solid examples, with both countries witnessing austere declines in Saudi tourism, and, speculatively, from other countries as well. Horrific events that culminated in and then followed after the Syrian Civil War of 2011, the 2006

Lebanon War, and the 2011 Egyptian Conflict further exacerbated their decline in tourism.

As a result, Saudis pursued tourism opportunities to other countries deemed to be safer, most notably Qatar, Kuwait, and Bahrain. Iraq, a country whose international reputation may appear synonymous with civil conflict, is not among the ranked Saudi tourist destinations for any year. Indonesia’s decline in tourist popularity could arguably be a result of the devastating tsunami that struck the Indian Ocean in 2004.

Second, geographic proximity is a plausible explanation for Saudi outbound tourist choice. In 2002, six of the highest seven destinations were neighboring countries, accounting for approximately 60 percent of total Saudi international tourism (Figure 6).

Figure 6. Outbound destinations by Saudi tourists, 200213

By 2012, eight of the highest ten were neighboring countries, accounting for more than

76 percent of the total number of Saudi outbound tourists (Figure 7).

13 Source: Saudi Tourism Information and Research Center (SSTIRC), 2015 33

Figure 7. Outbound destinations by Saudi tourists, 201214

The relationship between geographic proximity and international tourism is expected for people of almost any country in the world and is consistent with Tobler’s First Law of

Geography, that “everything is related to everything else, but near things are more related than distant things” (Tobler, 1970: 236). By way of example, the most frequently-visited international tourist destinations for citizens of the United States are the neighboring countries of Mexico and Canada (U.S. Department of Commerce, 2015).

Iraq and Iran are two noteworthy exceptions to Tobler’s First Law as it relates to the proximity of countries visited by Saudi tourists. Continuing civil unrest in Iraq continues to impact international tourists visiting that country. In addition, the Shia denomination of Islam

14 Source: Saudi Tourism Information and Research Center (SSTIRC), 2015 34 is prevalent in Iraq and has a strong majority in Iran, and discord between Shia Islam and the larger Sunni denomination (concentrated on the Arabian Peninsula, North Africa, Indonesia, and most areas of the world where Muslims have settled) has pervaded since the death of

Mohammed. Also noteworthy is that while Arabic is widely spoken in Iraq, it is not in Iran, where Farsi (Persian) predominates. Both religion and language differences suggest a third factor in explaining Saudi tourism cultural affinity, shorthand that we employ for purposes of this research.

Language can be viewed as a barrier to ease of travel, and overwhelmingly Saudis prefer to vacation in Arabic speaking countries. Approximately 87 percent of Saudis traveling to international destinations in 2002 went to countries where a significant majority of the population spoke Arabic; by 2012, this percentage increased to 91 percent. Religion, the second aspect of cultural affinity, is also a factor influencing Saudi tourist choice. Muslim nations, defined here as countries with a majority Muslim population, were the destinations of choice for 93 percent of Saudi tourists in 2002, increasing to 95 percent by 2012. During the decade of this analysis, Arabic-speaking Muslim nations held sway in terms of Saudi outbound tourism, and the data suggest that this in fact seems to be strengthening. It is interesting to note that Saudi vacation choice appears to be influenced by Muslim nations with what might be considered a more liberal view of Islam. Qatar provides an example:

Doha, its capital city, has become the “freewheeling hub of the Middle East”, known for world class shopping, intrigue and opulence (Walsh, 2017). While it could be argued that

Qatar may be an exception rather than the norm in the region, it may be the most plausible explanation as to why Saudi tourism to Qatar increased by 1739 percent between 2002 and

2012.

Arabic-speaking Muslim countries may be contrasted with Western, historically Christian- founded countries that include The United States, Canada, and some countries in Europe,

35 which collectively account for a relatively small percent of Saudi tourism. In 2002, five percent of Saudi tourists visited western nations, and this figure declined to approximately three percent in 2012. The United States ranked 11th in 2002, but declined to 15th by 2012, with only a modest increase in Saudi tourists. Other significant western tourist destinations for Saudi tourists in 2012 were the United Kingdom and Germany, both of which experienced gains in Saudi vacationers (47,777 and 48,584, respectively). Saudi outbound tourist choice to other western nations – including Italy, Spain, and Greece – declined.

Finally, a notable aspect of Saudi outbound tourism is the paucity of visitations to Latin

America and Africa. With the exception of north African / Mediterranean countries, no other place in Africa was an international tourist choice. Countries in Latin American fared equally poorly, with Argentina as the sole continental destination for Saudi tourists during the analysis decade.

In summary, the data identify that Saudi tourists do not travel” “off the grid”; rather, international vacations are generally limited to well-known safe havens for foreign tourists.

Additionally, the vast majority of Saudi outbound tourist choices are to areas of significant cultural affinity (Arabic-speaking, Muslim countries). We now turn to statistical analysis that provides analytical confirmation to observed trends and patterns for Saudi outbound tourism.

3.2 Quantitative analysis

The identifying qualitative factors associated with Saudi outbound tourism are better understood within a quantitative framework, so as to permit modeling for further understanding and future calibration. Saudi tourist choice is a dependent variable, as is percentage change of tourist choice of two time points. These choices are influenced by

Saudis’ preference to travel to predominately geographically-proximate countries, to places that are culturally familiar, and to politically and economically stable areas with amenable

36 climates. Thus, in order to model such tourist behavior, associated quantitative data were obtained that most ably depict those qualitative factors, described as follows.

Proximity was recorded as a Euclidean distance (in kilometers) between centroids of countries. Cultural affinity, recognized here as religion and language, were represented by data calculated from variables provided for each country from the CIA World Factbook: actual numbers of Arabic language speakers and of Muslim religious adherence were converted to percentages for each of the 32 countries examined.

A common trait of outbound tourism rests with a destination’s political and economic stability. Political stability was captured by employing a “dummy” binary variable where a

“0” was assigned if the destination was politically stable, and a “1” if it was politically unstable. Instability was assessed by the enumeration of significantly dangerous actions or political turmoil occurring in those countries within the years 2010 or 2011 (Podova, 2011).

Five of the 32 countries – Egypt, Lebanon, Libya, Syria, and Tunisia – were deemed politically unstable, precipitated by a singular catastrophic event or a succession of such events, as depicted in Table 4.

Table 4. Catalyzing events rendering destination countries as politically unstable

Country Date Event

Egypt 2011 Egyptian Revolution; Muslim Brotherhood (Kull, 2011)

Lebanon 2010 Hezbollah; Israel-Lebanon boundary dispute (Koutsoukis, 2010)

Libya 2010 First Libyan Civil War; Gaddafi execution (Smith, 2011)

Syria 2011 Assad regime thwarting citizen revolts (Malla, 2011)

Tunisia 2010 Mohammed Bouzazi set himself on fire (Watson & Karadsheh, 2011)

Though political and economic stability are often complementary, a separate economic indicator of GNI PPP per capita (2011) was used, with the data obtained from the World

Bank. GNI PPP per capita is preferred as a measurement of economic stability as it serves as a direct control for the varying magnitudes of market size (Busse and Hefeker, 2007).

Finally,” amenable climate” is postulated as a factor driving Saudi outbound tourism. Our postulation is that Saudi outbound tourism, most prevalent in summertime, is driven by desire of citizens seeking relief from desert head, prompting travel to areas with cooler temperatures. Monthly temperature data in degrees were obtained for predominant cities of travel in each of the 32 countries under analysis.15 To numerically represent “amenable climate”, we employed an arithmetic mean of the maximum temperatures (in Celsius) in

Saudi summertime – June, July, and August.

A summary of these factors, the data that we used to capture them, and the names we assigned to represent them as six independent variables in modeling Saudi tourist behavior are listed in Table 5.

15 Climate data source: ClimaTemps, www.climatemps.com . 38

Table 5. Data Explanation and Variable Name Employed in Modeling

Qualitative Factor Data Variable name

Proximity Centroid distance (in kilometers) from Saudi DISTANCE_KM

Arabia to tourist destination countries

Cultural Affinity Percentage of residents in tourist destination ARABIC_PCT

(Language) countries who speak Arabic

Cultural Affinity Percentage of residents in tourist destination MUSLIM_PCT

(Religion) countries identifying adherence to Islam

Political Stability Captured as a one-category dichotomous CIVIL_CONFLICT

dummy variable based upon recent activity

negatively impacting tourism (Table 4)

Economic Stability Gross National Income (GNI) converted to GNI_PER CAPITA_PPP_2011

international dollars by way of purchasing

power parity; reported in US dollars

Amenable Climate Average of the maximum temperatures of the SUMMER_MAX

three summer months in 2012, in Celsius, for

each of the 32 destination countries

Data analysis

Modeling behaviors of Saudi outbound tourists began with determining a Pearson correlation coefficient between the dependent variable – percentage change of outbound tourists between 2002 and 2012 – and each of the ratio-type independent variables The

Pearson correlation is a measurement of the strength of the linear relationship between two variables, with the resulting correlations and their significance noted in Table 6.

Table 6. Pearson correlation coefficients (and significance), dependent and independent variables, 2002-2012

Independent variables Pearson correlation Significance

coefficient r (two-tailed)

Proximity (DISTANCE_KN) -0.314 0.080

Cultural Affinity (ARABIC_PCT) 0.233 0.200

Cultural Affinity (MUSLIM_PCT) 0.177 0.332

Economic Stability 0.670 0.000

(GNI_PER_CAPITA_PPP_2011 in US$)

Amenable Climate (SUMMER_MAX) 0.465 0.007

As the focus of this research is to promote and investigate how to model and explain Saudi outbound tourists, a linear regression method was chosen, the general model of which is shown in Equation 1.

Eq. 1. y = a + b1x1 + b2 x2 + b3 x3 + b4 x4 + b5 x5 + b6 x6 + e

Linear regression models were obtained by using SPSS v. 23, employing a backward selection method, while also checking for common regression assumptions of variables possessing a linear relationship, multivariate normality, homoscedasticity, and an absence of both autocorrelation and collinearity. Assumptions were verified for accuracy, and while variation in proximity to normality did occur, we did find that the Central Limit Theorem referencing an approximating normal distribution for a large n (n > 30) can be invoked. “The normal approximation works well when the parameter values are such that the skewness of the distribution is small” (DasGupta, 2011: 213), and the overall skewness for each of our variables is small, as reported in Table 7.

Table 7. Skewness for non-categorical independent variables

Independent Variables Skewness

DISTANCE_KM 1.0372

ARABIC_PCT 1.1739

MUSLIM_PCT 0.6055

GNI_PER_CAPITA_PPP_2011 1.7249

SUMMER_MAX -0.0021

In verifying regression assumptions of autocorrelation and collinearity, we used common means of calculating the Durbin-Watson D statistic, the range of which is 0 < D < 4 , and where a value of D = 2 indicates no autocorrelation (Durbin and Watson, 1971). Our modeling produced a value of D = 1.954 , indicating negligible positive autocorrelation. For collinearity, the Variance Inflation Factor (VIF) score was assessed; “a common rule of thumb is that if it [VIF] is greater than 5, this indicates potential multicollinearity problems”

(Rogerson, 2015: 266). All resultant models from our backward regression analysis produced no VIFs exceeding 5, with the largest at VIF = 3.402 for MUSLIM_PCT.

Backward regression modeling is useful in that it begins with all dependent variables, and then produces subsequent models by removing one variable at a time; the variable removed is

“the one that contributes least to the r 2 value…if the reduction in r 2 is not significant”

(Rogerson, 2015: 272). The variable removal process continues so long as r 2 is not greatly compromised. In our analysis, the backward method produced five regression models, and the independent variables and r 2 values are presented in Table 8.

Table 8. Coefficients of determination (r 2 ) for regression models

Model Variables Variable Excluded r 2 1 Six independent variables: None 0.639 DISTANCE_KM ARABIC_PCT MUSLIM_PCT GNI_PPP_PER_CAPITA SUMMER_MAX CIVIL_CONFLICT 2 Five independent variables: DISTANCE_KM 0.639 ARABIC_PCT MUSLIM_PCT GNI_PPP_PER_CAPITA SUMMER_MAX CIVIL_CONFLICT 3 Four independent variables: SUMMER_MAX 0.635 ARABIC_PCT MUSLIM_PCT GNI_PPP_PER_CAPITA CIVIL_CONFLICT 4 Three independent variables: MUSLIM_PCT 0.608 ARABIC_PCT GNI_PPP_PER_CAPITA CIVIL_CONFLICT 5 Two independent variables: CIVIL_CONFLICT 0.572 ARABIC_PCT GNI_PPP_PER_CAPITA

These regression results confirm that a significant coefficient of determination is achieved without the DISTANCE_KM variable, thus disproving our supposition that Saudi tourists are more inclined to travel to places nearer to them – or, at the very least, that the influence of distance in tourist travel is perhaps not as crucial an element in modeling such behavior. This is especially surprising and inconsistent with Tobler’s First Law of Geography. Perhaps the impact of distance was merely a lurking variable for another in our model, and we postulate that its importance was diminished by our two cultural variables (ARABIC_PCT and

MUSLIM_PCT), despite any lack of collinearity among them. Likewise, the elimination of

SUMMER_MAX after the second iteration is counterintuitive to our supposition of Saudi tourists seeking tourist destinations with cooler temperatures. This suggests that the importance of amenable climate in tourist travel decision-making may be less of a factor that hypothesized; rather, it may be as simply explained that summer months coincide with Saudi paid vacations. The most common threads across the regression results confirm that two variables – ARABIC_PCT and GNI_PER_CAPITA_PPP – are present in each resultant model. Thus, cultural affinity and economic stability are prevalent factors influencing the outbound destination decisions for Saudi tourists. Based on these results, we postulate that modeling four independent variables – Arabic-speaking, Muslim adherence, economic stability, and political stability – best explain Saudi tourist behavior, as this model explains

63.5% of the variation in outbound choice for Saudi tourists. Subsequent modeling should test our results using other data for other years in order to provide the Saudi tourist board with considerations that could be useful in international tourism marketing decisions.

Perhaps more importantly the information could be useful for other countries in their attempts to capture a greater and increasing share of the $20 billion Saudis spend annually on international tourism.

3.3 Conclusion

As measured on a per capita basis, Saudi Arabia is one of the leading tourist-generating countries in the world. More than three-quarters of the nation’s population were international tourists in 2015, with predictions of increasing numbers of outbound tourists. This paper has examined growth of the Saudi outbound tourist industry, 2002-2015, and destination trends

2002-2012. The study finds that increasing numbers of Saudi international tourists is partly a reflection of the paucity of cultural and physical attributes within the nation. More importantly, the growth of Saudi international tourism is a result of Saudi wealth reflected by a liberal policy of government paid summer vacation resulting in a distinctive seasonality of outbound tourists. Saudi wealth is related to oil prices and Saudis spend an increasing amount of disposable income on international tourism. The study postulates that as long as oil prices continue an upward trend, then this will be reflected in a continued upward trend in Saudi outbound tourism.

Based upon a qualitative analysis of Saudi international tourism, the study hypothesized that destinations of choice are largely influenced by geographic proximity, cultural familiarity, political and economic stability, and climate. Linear regression modeling both confirmed and rejected these postulated causative variables. The findings confirm that culturally familiar destinations – Arabic speaking, Muslim nations, and stable economies with no civil unrest – readily explains over 63 percent of Saudi outbound tourist destinations.

The analysis shows that Saudis are not” off-the-grid” tourists; rather, they seek culturally- familiar wealthy nations that are safe vacation havens where they can indulge in a more cosmopolitan experience, and in places that practice a more liberal, tolerant interpretation of the Koran.

Finally, the trending data indicate that new Saudi tourist destinations are emerging. Both the United Kingdom and Germany show increased interest by Saudi tourists. Saudi expenditures on international tourist travel exceeded $20 billion in 2012. This is not an insignificant sum, and such a dollar figure should be of interest to western nations seeking to attract international tourism. The findings show, however, that given Saudi preference to travel to culturally familiar places, coupled with the current wave of nationalism in several western nations, that recent tourist trends may be short-lived. Other Arabic speaking, Muslim nations have a much higher probability of tapping Saudi oil that has been increasingly transposed into Saudi international tourism.

There is an important addendum to these conclusions. Saudi Arabia has recently announced initiatives that may impact Saudi outbound tourist choice. Saudi Vision 2030

(http://vision2030.gov.sa/en) aims to reduce the country’s dependence on oil and to diversify its economy (Rashad, 2016). Among the initiatives is a proposal to build an entertainment/tourist complex in Qiddiya, located 40 kilometers southwest of the Saudi capital, Riyadh (BBC World News, 2017). The proposed tourist complex will be more than twice the size of Disney World, or 100 times the size of New York’s Central Park. The entertainment project will contain recreational sports facilities that will include amusement parks, cultural attractions, arenas capable of hosting world-class sport competitions, race tracks, water parks, outdoor adventure activities, and sports educational facilities (Vivarelli,

2018). The project will also include shopping centers, restaurants, cafes, hotels, and real estate projects to house tourists and a new resident population. Attendance is projected at 17 million visitors by 2030, with an additional 12 million “day visitors” coming for shopping trips. In addition to generating intra-tourism, the project is expected to provide 57,000 new jobs for Saudis. By 2030, it is projected that Qiddiya will be one of the world’s largest entertainment complexes.

Qiddiya aims to retain not only some of the annual $20 billion currently spent by Saudis on international tourism, but also to capture a share of the exponentially growing international tourist market, particularly the Middle Eastern market. Considering the dearth of tourist venues within Saudi Arabia, this could only be addressed by a bold tourist venture on a “Saudi” (Arabian Peninsula) scale. A finding of this study is that Saudi outbound tourist choice is largely dictated by Arabic speaking Muslim nations. Qiddiya represents not only an intervening tourist opportunity for Saudis but is also arguably a tourist destination for a very large Middle Eastern population who will most likely seek a culturally familiar destination. If the proposed events come to fruition, then the map for Saudi tourism both nationally and internationally will be radically altered.

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4. Research Article 2

Saudi Arabia's Outbound Tourists: Socioeconomic and Demographic

Commonalities Present in International Destination Selection

Basheer Alshammari, Kevin Raleigh, and Robert South

Department of Geography and GIS, University of Cincinnati, OH

Submitted to Tourism and Hospitality Research Journal (August 27th, 2018)

Abstract

Tourism has emerged in recent decades as one of the most impactful and consistent growth industries for nearly all parts of the globe, ranking as the world’s third-largest export category. However, global-scale tourism trends and statistics are summations of regional- and country-specific instances people traveling outbound, across an international border from their country of residence or citizenship, for tourism purposes. Collective understanding of the characteristics of these outbound tourists is typically performed by connecting destination choice with socioeconomic and demographic variables, when those data are known. Owing largely to higher disposable incomes and liberal vacation time, Saudi Arabia has vibrant outbound tourism activity. Data collected by the Department of Tourism and Information

Research (MAS) within the Saudi Commission of Tourism and National Heritage (SCTH) on ages, incomes, education levels, and employment statuses of the country’s outbound tourism population for the year 2015 provides a unique means to assess, via discriminant analysis, if certain characteristics are common to destination choice. A review of discriminant analysis and its assumptions is followed by a performance and explanation of the method. The results offer some insight into a demographic understanding of Saudi outbound tourists and their destination choices, and provide an opportunity to identify motivations of travel that exist beyond the data. The results also challenge that the reliance on country groupings by other organizations and the arbitrariness of demographic data categories that could potentially skew or compromise the actual motivations and understandings of outbound travelers.

4.1 Introduction

In recent years, tourism has been one of the most important and consistent growth industries worldwide and is currently held to be one of the major service industries (Bansal and Eiselt,

2004; Zang et al., 2004). “Tourism has boasted virtually uninterrupted growth over time, despite occasional shocks, demonstrating the sector’s strength and resilience. International tourist arrivals have increased from 25 million globally in 1950 to 278 million in 1980, 674 million in 2000, and 1,235 million in 2016” (UNWTO, 2017a: 2). By 2030 the United

Nations World Tourism Organization (UNWTO) estimates the tourism industry will increase to more than 1.8 billion international tourists, following a steady and yearly growth rate of

3.3% for every year between 2010 and 2030 (UNWTO, 2017a: 4). In 2016, one out of seven of the world’s population engaged in international tourism; “tourism ranks as [the] world’s third largest export category,” with the total value of tourism exports exceeding US $1.4 trillion and accounting for seven percent of the world’s exports, ranking third in importance in terms of worldwide exports (UNWTO, 2017a: 6). UNWTO categorizes the countries of the world into five groups (Europe, Asia and the Pacific, The Americas, Africa, and The

Middle East) and then into 20 subgroups among those. Growth in 2016 in international arrivals occurred in five of these six regions (Europe, South Asia, East Asia and The Pacific,

The Americas, and Africa) (UNWTO, 2017a), with only one – the Middle East – experiencing a slight decline:

International arrivals in the Middle East are estimated to have decreased by 4% in 2016, with mixed results across the region where solid growth in some destinations was not sufficient to offset decreases in others. The region welcomed 54 million international tourists in 2016, or

4% of the world total, and earned US$ 58 billion in tourism receipts (5% share), while the

Middle East experienced a small decline of 4%... Growth [of international tourist arrivals] was flat in the sub-region’s top destination [of] Saudi Arabia. (UNWTO, 2017a: 12).

While inbound tourism to Saudi Arabia has recently remained stagnant, the country is nonetheless has a vibrant outbound tourism market. Geographically located in the center of the Middle East and accessible to European and Asian destinations, Saudi Arabia is perhaps the most important outbound tourist-generating country among Middle East countries, accounting for millions of tourists, millions of overnights, and billions of tourism expenditures, reported in Saudi Riyals and converted to USD using historical tables. Evident is the significant increase in Saudi outbound tourists by 412% between 2007 and 2016.

(Table 1).

Table 1. Saudi Arabia Outbound Tourists, Overnights, and Tourism Expenditure

Year Tourists Total Tourism Demand Expenses Outbound Tourism (Million) overnight (Outbound Tourism Expenditure) Expenditure (US Dollar trip (Million) (Saudi Riyal billion [SR]) billion [USD]) 2007 4.1 45.3 78.7 21.1

2008 4.2 42.1 60.6 16.2

2009 6.1 54.9 79.8 21.3

2010 7.2 89.0 55.5 14.8

2011 15.3 200.1 60.6 16.2

2012 18.7 121.3 62.9 16.8

2013 19.2 Not available 74.2 19.8

2014 19.8 147.3 78.0 20.8

2015 20.8 275.2 84.1 22.4

2016 21.0 340.4 97.3 25.9

Sources: (1) Tourists (million) and Tourists overnight trip (million): Alreshaidan (2016);

MAS Center (2016). (2) Tourism Demand Expenses (Saudi Riyals): MAS Center (2016). (3)

Tourism Demand Expenses (USD): XE (2018) Converted from Saudi Riyal figures via

https://www.xe.com/currencytables/, using December 31 of each year as the historical date.

This 412 percent increase in Saudi outbound tourism within a decade mirrors worldwide

trends that suggest international tourism for many countries has risen steadily (UNWTO,

2017a). Such increase unique to Saudi Arabia is readily accountable by several factors, one

of which is that Saudi Arabia is among the wealthiest countries of the world. Saudi wealth

and economic prosperity are related to the price of oil (Elyassi, 2018), and although oil prices

have had a volatile history the trend has been generally upward. In 2004, for example, the

56 average annual OPEC price for crude oil was $36.05 per barrel (U.S. dollars), increased to

$109.45 in 2012, and was $40.68 per barrel in 2016 (Statista, 2018). Saudi citizens also enjoy high per capita wealth, as measured by gross domestic product (at purchasing power parity) per capita, ranking 12th among sovereign countries of the world. The GDP at PPP per capita of the twelve highest countries is displayed in Table 2.

Table 2. Per capita Purchasing Power Parity (PPP) 2015

Rank Country GDP at PPP per capita

(Geary-Khamis dollars)

1 Qatar 132,870

2 Luxembourg 99,506

3 Singapore 85,382

4 Brunei 79,508

5 Kuwait 70,542

6 Norway 68,591

7 United Arab Emirates 67,217

8 Ireland 65,806

9 San Marino 62,938

10 Switzerland 58,647

11 United States 56,084

12 Saudi Arabia 53,802

Source: International Monetary Fund (IMF)

A second factor accounting for the dramatic increase in Saudi outbound tourism is time.

Saudi citizens have time available to engage in international tourism: all Saudi employees

57 have thirty days paid holiday annually, and students (primary grades to university) enjoy a summertime three-month holiday. An initial study on Saudi Tourism shortly after the establishment of the Supreme Commission for Tourism in 2000 stated that Saudis, on average, spent 24 days on international holidays (Seddon and Khoja, 2003: 958). Time and money are universal determinants for international tourism, and Saudi citizens have both

(Bogari, 2002); “a large percentage of (GCC) citizens enjoy a high standard of living that enables them to spend their short holidays and long vacations as tourists in various parts of the world” (Metwally, 2004: 131).

One of the key components of studying international tourism patterns and behaviors focuses on characteristics of the populations engaged in outbound tourism. To that end, traditional approaches have focused on socio-economic and demographic variables as exemplified by gender, age, income, education, household size, and occupation (Nyaupane et al., 2003).

Understanding the factors that affect Saudi Arabia travelers’ destination choice is of significant interest to the countries and regions that strive to increase their market share

(Seddon and Khoja, 2003; Alreshaidan, 2016).

This study attempts to understand the role of sociodemographic variables on Saudi

Arabian outbound destination selection, 2015. The analyzed variables include age, income, education level, and employment status. Saudi outbound tourist choice has important economic implications for favored destinations and for nations who compete for the billions of dollars that Saudi citizens spend on international tourism. The results of the study contribute to an understanding of segmented tourist markets and provides insight into the relationship between tourists’ socio-demographic characteristics and destination selection.

The paper proceeds with a review of relevant literature on factors related to international tourist destinations of choice. The study then discusses data acquisition followed by a

58 statistical assessment (via discriminant analysis) of the relationship between Saudi tourists’ socio-demographic characteristics and destination selection. The paper concludes with a discussion of results and trends toward future research.

4.2 Methodology

Data Description

Data employed in this research were obtained upon request to SCTH and contain both geographic and attribute information, with the focus here to determine if certain attributes can be found to occur more commonly with certain geographies.

The geographic information was any country or territory of the world identified by the SCTH as potential Saudi outbound tourism destinations that carried a recognized place name (194 total). The SCTH then organized those 194 countries geographically in the same manner and with the same names as The United Nations World Tourism Organization (UNWTO). The

UNWTO’s first-level division of the world’s sovereign states and non-sovereign territories is into one of six regional commissions (Figure 1), each presided over by a country chairman and one or more vice-chairmen. Each of those six regional commissions contains one or more second-level divisions, or subgroupings, for which there are 20 total (Figure 2), mutually exclusive across those six commissions (UNWTO, 2017b).

Figure 1. UNWTO Regional Commissions

Sources: UNWTO (2017b); MAS Center (2016)

Figure 2. UNWTO Regional Sub-groupings of the Countries of the World

Source: UNWTO (2017b); MAS Center (2016)

Aggregated and summarized geographic data on Saudi tourism has been disseminated through the SCTH-published 111-page report Tourism Statistics 2015, based on data collected by SCTH’s Tourism and Information Research department, commonly known through its translated Arabic acronym of MAS, and/or as the MAS Center (MAS Center,

2016). Presently the 2015 Tourism Statistics report is available only for purchase through the

MAS website (http://www.mas.gov.sa/publications/938), at an approximate cost of $1300

USD. An excerpt identifying the count of quarterly outbound tourist trips to North Africa from that document’s Table 3-3 confirms the geographic organization of Saudi outbound tourism data (Figure 3).

Figure 3. Excerpt of Data Table on Year 2015 Saudi Outbound Tourist Trips

Source: MAS Center (2016)

The entirety of Table 3-3 (not shown) identified outbound tourist trips per quarter, with each quarter summed to derive a total for the year, for all six regional commissions, for all 20 sub- regions, and for 42 countries. A dot map, best to visualize absolute data (Hey and Bill, 2014:

2418), depicts these phenomena (Figure 4).

Figure 4. Year 2015 Saudi Outbound Tourist Trips for 42 Countries

Source: MAS Center (2016)

The geographic information employed in this research is complemented by its associated attribute information, comprised of four demographic / socioeconomic variables of age, education level, employment status, and monthly income (reported in Saudi riyals). These data were provided as count totals by country within 23 mutually exclusive preset categories: six within age, three within education level, five within employment status, and ten within monthly income (Figure 5).

Figure 5. Categories of Demographic Information

Source: MAS Center (2015, 2016)

One aspect of SCTH, achieved through its MAS Center, centers on data collection and analysis, and dissemination of those data in the form of reports and summaries, unless otherwise proscribed. To that end – and specifically applicable to outbound tourists – are three continual surveying methods of outbound travelers: (1) Entry Card / Departure Card

(airports and ships); (2) Border / Boundary Survey (land vehicles); (3) Household Surveys, the prevailing instrument of which is the annual Domestic & Outbound Tourism Survey

(DOTS), which may necessitate an in-person follow-up visit to non-respondents to obtain data (MAS Center, 2015: 2), The Domestic & Outbound Tourism Survey (DOTS), begun in

2003, is an annual report sent to Saudis inquiring about their own demographics and their outbound travel and tourism endeavors.

The amalgamation of these primary data from cards and surveys is also a responsibility of the

MAS Center. However, unlike the geographic data identifying a specific number of trips to many countries that is widely available, the demographic data used in our analysis has not been made public. Obtaining these data are all owed to the perseverance of the lead author, who endured numerous refusals from the MAS Center both via e-mail and in person over several months, only eventually to be told that the data would be available for an exorbitant cost. Finally, this research team was able to obtain the count data on age, employment status, education level, and monthly income demographics so as to ascertain commonalities that may or may not be associated with certain outbound tourism destinations.

Method Justification

A common premise in tourism-related research is that demographic and socioeconomic factors influence destination selection of international tourists. Framed within a statistical discussion, this is often interpreted that destination selection is dependent upon one or more independent variables, and quantitative analysis is then typically pursued with multiple linear regression (cf. Dancey and Reidy, 2002; Slevents, 2002). While linear and logistic regression have been employed frequently in tourism research, there are considerations regarding their usage. Such modeling necessitates a quantitative dependent variable of interval/ratio type that is multivariate normal (approximating a normal distribution); additionally, independent variables in a linear regression analysis are assumed to distribute normally, to lack autocorrelation and collinearity, and to exhibit statistically significant equal variances

(homoscedasticity). In attempting to understand Saudi outbound tourism behavior, we find that pursuing multiple linear regression or logistic regression must be rejected for several reasons.

First, tourism destinations as presented in the data are categorical, not quantitative, and there is no notion of dependence; the data provided only indicate country of travel, with no dependent variable available to predict. Second, ascribing numerical values to a country / destination is subjective; research abounds identifying the “best” countries in the world, whether such identifications are based in economics (cf. Demetriades and Hussein 1996;

Gholizadeh et al. 2014; Becker 2016); quality of life or other social indicators (cf. Rahman et al. 2005; Giambona and Vassallo 2014); a “best-worst” scenario (cf. Auger et al., 2007;

Louviere et al., 2015; Abadi et al., 2018); or a result of tactical marketing (cf. Kotler and

Gertner, 2002; Echtner and Prasad, 2003; Sinclair-Maragh and Gursoy, 2015; Aharoni and

Grinstein, 2017).

Third, such subjectivity can be eliminated by using a data measurement based on verifiable evidence. One such measurement is distance: for example, we could convert each country of the world into a Voronoi or regular polygon, and then calculate centroid distances to each from Saudi Arabia. However, the relationship between geographic proximity and international tourism is expected for people of almost any country in the world; this is consistent with Tobler’s First Law of Geography, that “everything is related to everything else, but near things are more related than distant things” (Tobler, 1970: 236). Based only on the counts of outbound trips to countries, we determined that the First Law is largely applicable to outbound Saudi tourists (depicted on Figure 4), with a clear majority of them

(over 77%) traveling to nearby countries (Bahrain, Jordan, Egypt, Yemen, Kuwait, and the

United Arab Emirates), and then next-largest sector to of 11.4% to South Asia (MAS Center,

2016: 62). By way of a second example, the most frequently-visited international tourist destinations for citizens of the United States are the neighboring countries of Mexico and

Canada, accounting for 56% of all international travel embarked upon by U.S. citizens

(NTTO, 2018). A more complex measurement of distance would likely equally confirm

Tobler’s First Law for Saudi Arabia, and provide no insight into the demographics of age, education level, employment stats, and monthly income that may be characteristic of the people regularly traveling to certain destinations. Nyaupane et al. (2003) found that people with greater disposable incomes have greater propensity to travel farther distances as tourists, but their study was unable to discern if additional socioeconomic or demographic variables were equally compelling in explaining distance as a factor in destination choice.

Fourth, while the demographic variables appear to be of ordinal type, in actuality they were collected as count data within a nominal framework. Respondents fell into one categorical choice within each demographic indicator (see Figure 5); though independent, the categories

66 within each demographic are not necessarily ranked, and no one category is considered more desirable than another. Employing multiple logistic regression would therefore require a recoding all demographic data into dummy variables, increasing potential collinearity and in general overestimating any prediction that such a model would produce. Thus, analyzing these data must be accomplished whereby one categorical grouped variable – country of destination – is influenced by at two or more continuous, interval, or count variables that are independent. Discriminant Analysis fits these specifications.

The seminal work of discriminant analysis was published by statistician Sir Ronald Fisher in

1936, though the ideas and theories of predictive discriminant analysis appear to have emerged in the 1920s, and those of descriptive discriminant analysis in the 1960s

(Lachenbruch and Goldstein, 1979; Huberty and Olejnik, 2006). The classification / predictive aspect of discriminant analysis “is the process by which a decision is made that a specific case ‘belongs to’ or ‘most closely resembles’ one particular group,” while the interpretation / descriptive aspect focuses on interpreting the meaning explaining group differences (Klecka, 1980: 42), Regarding data types, “Discriminant analysis as a whole is concerned with the relationship between a categorical variable and a set of interrelated variables” (McLachlan, 2004: 1). As a statistical technique, however, discriminant analysis is not without its assumptions. As it is related to one-way and multivariate analysis of variance (ANOVA, MANOVA), it shares assumptions common to those techniques, as follows:

Data must follow a normal distribution. This particular assumption is common to most statistical techniques, where data are assumed to be a sample from an underlying greater (and unknown) population. However, discriminant analysis is primarily concerned with

67 classification, and such emphasis provides some “wiggle room” with respect to the hardline assumption of normality.

Homoscedasticity. Variances among variables and across groups should be equal (statistically speaking). Randomness among the relationship between the independent variables and the dependent variable is constant. However, as with normality, discriminant analysis is relatively robust to slight violations of this assumption.

No Collinearity. Discriminant analysis assumes that there are small, insignificant, or non- existent correlations among the independent variables. However, in the running of a discriminant analysis, a tolerance test is performed on the data, and variables with low tolerance (a low unique proportion of variance) are excluded.

4.3 Results

We performed a “kitchen sink” discriminant analysis using SPSS v. 23 on the numbers of trips to 125 countries that were visited by Saudis in 2015. In its own data collection and analyses, the MAS Center of SCTH followed the UNWTO grouping scheme of the world’s countries – first into one of twenty country sub-groupings employed by the UNWTO (Figure

2), and then into one of the six UNWTO regional commissions (Figure 1) – and so that scheme was adopted in our analysis. The six regional commissions were employed as our categorical / grouping variable, with the distribution of the 125 countries visited by Saudis in

2015 presented in Table 3.

Table 3. Grouping Variable for Saudi Outbound Tourism, 2015

UNWTO Group Names of countries in this group visited by Saudis in 2015 Number of Regional Number countries in Commission this group Middle East 1 Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, 13 Oman, Palestine, Qatar, Syria, UAE, Yemen South Asia 2 Afghanistan, Bangladesh, Bhutan, India, Iran, Maldives, 9 Nepal, Pakistan, Sri Lanka Europe 3 Albania, Andorra, Armenia, Austria, Azerbaijan, Belgium, 40 Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Kazakhstan, Kyrgyzstan, Malta, Moldova, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Russia, San Marino, Spain, Sweden, Switzerland, Tajikistan, Turkey, Turkmenistan, United Kingdom, Ukraine, Uzbekistan Africa 4 Algeria, Benin, Botswana, Burkina Faso, Burundi, 36 Cameron, Central African Republic, Chad, Comoros, Congo, Cote d`Ivoire, Djibouti, Eritrea, Ethiopia, Gabon, Ghana, Guinea, Kenya, Madagascar, Mali, Mauritania, Mauritius, Morocco, Niger, Nigeria, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zimbabwe East Asia and 5 Brunei, Cambodia, China, Hong Kong, Indonesia, Japan, 15 The Pacific Macau, Malaysia, Mongolia, Myanmar, Philippines, Singapore, South Korea, Thailand, Vietnam The Americas 6 Argentina, Barbados, Brazil, Canada, Colombia, 12 Dominican Republic, Ecuador, Martinique, Panama, Trinidad and Tobago, United States, Uruguay Sources: UNWTO (2017b); MAS Center (2016)

The categorical / grouping variable was then coupled with the set of interrelated variables provided to the lead author exclusively for this research, which were all count data within each category of the demographic and socioeconomic variables on age, employment status, education level, and monthly income (Figure 5) for the 125 (of 194) countries to which

Saudis traveled in 2015. The 69 countries / territories to which there were zero Saudi outbound tourists in 2015 were excluded from the analysis, and included Antarctica, many islands in Oceania and The Caribbean, and a handful of countries in Central and East Africa.

The purpose of this analysis was to discern if distinctions within those demographic variables would emerge into certain country subgroupings, and if a determination could be made that identifies certain demographic characteristics as common to certain regional commissions.

This discriminant analysis employs six categorical / grouping variables, and 23 options among four demographic / socioeconomic variables of age, employment status, education level, and monthly income, treated as predictors of outbound tourist destinations. An immediate inspection of our discriminant analysis results confirms that a few of those predictors have zero presence in certain regional classifications (Table 4).

Table 4. Grouping Variable for Saudi Outbound Tourism, 2015

UNWTO Regional Group Number of predictors Names of predictors not present

Commission Number not present in this group in this group

Middle East 1 0 Not applicable

South Asia 2 3 Age 18-21, Unemployed,

Retired

Europe 3 5 Unemployed, Student, Retired,

SR 1000 or less, SR 1000-2999

Africa 4 3 Unemployed, Retired, SR 1000-

2999

East Asia and The 5 2 Unemployed, SR 1000-2999

Pacific

The Americas 6 5 Age 18-21, Unemployed,

Retired, Self-Employed

It is perhaps unsurprising to learn that unemployed Saudis traveled nowhere beyond the

Middle East, and that retired Saudis traveled only to the Middle East and East Asia and the

Pacific. There also appears to be a tendency of students and low-income Saudis to select

tourism destinations primarily within The Middle East. Inspection has also confirmed that

zero of the 125 cases were included in the analysis (none were excluded). Additionally, the

tests of equality of group means generated by SPSS (Table 5) confirmed the independence of

each of the 23 predictor variables, and that they are able to discriminate among the six

regional commissions (as each of them exhibited a significant univariate F-statistic). The

values for Wilks’ Lambda presented here describe the degree to which a particular variable

may (or may not) be able to discriminate among groups; when equal to 0, the particular level

71 of an independent variable perfectly discriminates among groups; when equal to 1, it would carry no discriminating power.

Table 5. Tests of Equality of Group Means

A common assumption of discriminant analysis is that predictor variables follow a multivariate normal distribution. In SPSS, this is tested with Box’s M statistic, which officially tests a null hypothesis of equality among covariance matrices. A resulting p-value greater than 0.05 would cause one to accept this null hypothesis, suggesting that predictor variables are similar to a normal distribution. In this analysis, the significance of Box’s M is

0.000, suggesting that the predictor variables do differ significantly from a normal distribution (Figure 6); however, discriminant analysis is considered robust, and is able to withstand minor violations of this assumption (Lachenbruch and Goldstein, 1979; Klecka,

1980; McLachlan, 2004; Huberty and Olejnik, 2006). A possible result of major violations of this assumption may lead to incorrect classification of group membership, thus creating an increase in the number of misclassified cases (Klecka, 1980).

Figure 6. Result of Box’s M Test for the Equality of Covariances

Even with variations of normality challenged, discriminant analysis is revealing. With six groups and 23 predictor variables, discriminant analysis produces five possible canonical linear discriminant functions. The results of our analysis provided standardized and

73 unstandardized coefficients for up to 23 variables for each of the five functions, though we have not included them here due to awkwardness of their inclusion. Figure 7 provides eigenvalues, percentages of variance captured by each function, and the canonical correlation of each function.

Figure 7. Eigenvalues and Variances of the Linear Discriminant Functions

The first function captures the greatest amount of variance (57.4%), as is almost always the case, with the second function accounting for an additional 36% of the variability (for a cumulative captured variance of 93.4). Stated differently, the proportion of total variability that is explained by differences between groups is 0.574 for the first function and 0.36 for the second one. It must be noted that typically it is larger-number eigenvalues that would suggest a strong linear discriminant function, and the values here (all less than 1) may not imply function strength. The canonical correlations reported are those between discriminant scores of the 23 predictor variables and each the grouping variables (regional commissions). A high correlation indicates a discriminant function that predicts well, and in this case functions 1 and 2 may meet those criteria (values of 0.703 and 0.617, respectively).

In addition to eigenvalues, the other well-cited item summarizing characteristic of a discriminant analysis function is its corresponding value of Wilks’ Lambda, which, for each function, is equal to 1 minus the square of the canonical correlation. When discussing linear discriminant functions, the value of Wilks’ Lambda can assess how well a particular function can discriminate among groups, based on the independent variables. Put another way, it is sometimes thought in a similar manner as linear regression’s coefficient of determination r2.

Within a discussion of one-way and multivariate analysis of variance (ANOVA and

MANOVA), Wilks’ lambda is the ratio of within-groups sums of squares to total sums of squares. As shown on Figure 8, the Wilks’ Lambda associated to all linear discriminant functions combined is 0.281, while its value for four functions minus the first one is 0.556.

Both functions contribute well to group discrimination, as the significance results of 0.000 and 0.004, respectively, are the p-values resulting from the rejection of a null hypothesis testing a value of a function’s canonical correlation equaling zero, or (put another way) that functions have zero ability to discriminate among groups.

Figure 8. Eigenvalues and Variances of the Linear Discriminant Functions

Discussion

Discriminant analysis is an outstanding technique to ascertain certain or recurring characteristics of independent variables are common or present within mutually exclusive groupings of a categorical dependent variable. In addition, this particular method can reveal underlying unobservable dimensions influencing categorical assignment of data points. Those dimensions rely on creative interpretation and qualitative innovation in order to be identified.

A discriminant analysis technique can provide several venues that point to those dimensions, the most interpretable of which may be a structure matrix, which evaluates how certain levels of the independent variables contribute in different ways to each of the five discriminant functions in predicting dependent variable classification (Figure 9).

Figure 9. Structure Matrix Result of Discriminant Analysis

The structure matrix is also known as canonical loadings or discriminant loadings, but by any name it depicts correlations between each of the independent variables and the discriminant function dimensions that are created as a result of this procedure. It is important to note that as correlations, these loadings correlate to an unobservable phenomenon that underlies and runs through each linear discriminant function. In the above chart, the independent variables grayed out are those omitted due to low tolerance (low proportion of variance explained by 77 the individual variable, and a possible signifier of collinearity). Variables are ordered by absolute value within each function, thus permitting some ease in identifying or naming each particular function in helping to identify variables most common to predicting Saudi outbound tourism.

As the matrix may suggest, of the four demographic variables, age (and its levels) appear to be consistently influencing each discriminant function, while monthly income overall does not (only either low monthly incomes or refusals to divulge). Overall, this may suggest that

Saudi outbound tourism to different regional commissions of the world may be largely influential by age of traveler. The negative loadings of higher-tolerance variables in the first function indicate an inverse relationship with group predictability, suggesting that other influences besides age and education are significant to predicting Saudi outbound tourism.

Thus, if outbound tourism country predictability is negatively influenced age and education, there must be some corresponding positive influence, aspects beyond the variables analyzed in this study. Possible examples could be (1) reason for travel (business, pleasure, visiting family and friends); (2) specific attractions at the destination country; (3) physical activities

(seeking adventure, exploring, or athletic-based trips); or other factors. Conversely, in the second function, where these loadings are positive, these variables may collectively influence tourism destinations. Perhaps outbound destinations with appeal to “all ages” and to differing education levels are best categorized and explained in this second function.

After the second function, values of loadings are quite small. However, we note the singularly large correlation value between the Age 40-44 variable and functions 3, 4, and 5

(0.208, 0.442, and -0.411), suggesting the positive and negative influence those in this particular age group might exert on tourism choices. For each of these functions, this variable

78 has the highest correlation to the unobserved discriminant scores, thus influencing those functions almost solely. One explanation running through these three factors could center on this age group having the desire and means to embark upon frequent outbound tourism trips.

Another could be that it is that specific age that determines the destinations of the world to which people may travel; this age group could also be the demographic that travels the most for business and for pleasure, or who makes outbound tourism decisions for their families. A greater willingness to travel throughout adulthood may often include “first-time” travel to places that may align with greater distances, higher wealth, and desires for stable destinations. Though not explicit to the results of this discriminant analysis, these or other considerations may very well be among the unobservable characteristics represented by the discriminant scores within those functions.

The simultaneous benefits and challenges of discriminant analysis are that one obtains a relatively straightforward understanding of data and grouping patterns, but often relies on the innovation or creativity of the researcher in uncovering the unobserved dimensions inherent in the discriminant functions.

In this research, we are interested to know if certain levels of independent demographic / socioeconomic variables of age, education level, employment status, and monthly income can enlighten our pursuit of understanding Saudi outbound tourism behavior. The classification aspect of our data as a result of discriminant analysis is presented in Figure 10.

Figure 10. Classification Results of Demographics on Outbound Tourism Choices

These classification results confirm that our data have a 29.6% “hit” ratio, or that 29.6% of counts within independent demographic variables of age, education level, employment status, and monthly income are classified properly into the correct regional commission. With six different categories, and an unequal number within those categories, a “hit” ratio due to chance can be calculated as follows:

+ + + + + = 0.22496.

Thus, we have at least witnessed slight improvement of group prediction over chance with our discriminant analysis. Yet other considerations could be involved that prohibit greater predictability power of outbound destination choice based on the demographic variables used.

One such consideration concerns geography. The SCTH has collected outbound tourism data by destination country, but then the aggregation of those data upward merely relied on the categorical grouping used by the UNWTO, first into one of 20 sub-groupings, and then into one of six larger regional commissions. Such categorizations of the countries of the world are arbitrary; presumably the UNWTO has employed this classification as that is the best means for purposes known to them. However, we question if such a grouping truly conducive to understanding Saudi outbound tourism behavior. If the attempt to understand such behavior is to use a means whereby countries of the world are simulated as an isotropic plane, then perhaps the UNWTO classification is just as effective as any other. However, there are infinite ways to divide 194 entities into sub-groupings and then into larger categorizations; within geography, this phenomenon is known as the modifiable areal unit problem, or MAUP. First described by Openshaw (1983), MAUP has emerged as a critical consideration when conducting most types of spatial analysis, where both a zoning effect

(described above) and a scale effect have been identified. Changing the zoning (categorical assignments of countries) would most assuredly alter statistical results, but would also likely create model over- or underestimation, skew data to a point of possible spuriousness, or produce erroneous conclusions. This discriminant analysis engendered a smaller “hit” ratio

(29.6%) by using a preset categorization of countries, but such a ratio could so easily increase or decrease by even the smallest zoning change. Geographic research often encourages collecting data at the smallest possible unit; in the case of Saudi outbound tourism, that unit could very well be individual country. If that is too unwieldy or appears to provide no provocative insight into outbound tourism, then a re-imagining of countries is needed that would best inform such pursuit. Alternately, a movement away from defined administrative regions (identified by countries and boundaries) toward a focus on other types of formal regions (culturally-based, business-based) may be more insightful as well.

Another consideration concerns data availability and data quality. Due to the perseverance of the lead author, this research team was able to receive count data of nominal variables about Saudi tourists that are have not been made public, even though they contain no personally identifiable information. Some difficulties in obtaining data are insurmountable, and using data not publicly available in order to advance knowledge may be questioned.

Additionally, quality of secondary data is always a consideration, particularly when the source of the data is not provided. Arbitrariness also pervades data categorization. In our data, the levels ascribed within each demographic variable were provided “as is”; no clear reason was ever offered or uncovered so as to explain the mutually exclusive categories, or to offer a reason for them. Without such, quality of data may be compromised, and motivations for such categorization that may otherwise aid a research pursuit may never be known.

Conclusion

Tourism behavior of individuals, both domestic and outbound / international, is an especially insightful research pursuit that will only increase over time. As the world’s population grows, so too does its tourism sector, long resilient and ever-increasing, and its impact into local, regional, and world economics. In this research, we have attempted to provide insight into Saudi outbound tourists from the year 2015. Owing largely to a thorough data collection process by the Saudi Commission on Tourism and National Heritage (SCTH), we were able to obtain count data on the numbers of trips / visitors that Saudis took to countries of the world in 2015. We were also grateful to obtained non-public demographic data of age, education level, employment status, and monthly income connected to those numbers of trips, with an end goal to assess if these demographics – or unobservable factors

82 related to or influenced by them – were drivers of Saudi outbound tourism destination selection. Such assessment was conducted by the use of discriminant analysis, a statistical technique with classification and prediction capabilities. Though discriminant analysis has major assumptions of normality, homoscedasticity, and no collinearity, it is quite robust to minor violations of these assumptions. This classification technique is used when one has at least three continuous, interval, or count data serving as independent variables, and one categorical dependent variable.

In our assessment of Saudi outbound tourism for the year 2015, the 125 countries of the world outside of Saudi Arabia to which Saudis traveled were the dependent variable, grouped by the regional commission scheme employed by the United Nations World Tourism

Organization (UNWTO). The independent variables were the demographic data identifying counts of outbound travelers’ ages, levels of education, employment statuses, and monthly incomes. The analysis determined that 29.6% of counts within independent demographic variables of age, education level, employment status, and monthly income were found to have been properly classified into any of the six regional commissions. The assumptions associated with discriminant analysis – namely, normality, homoscedasticity, and no collinearity – were all slightly violated, as confirmed by Box’s M test, pooled within-group matrices (not shown), and tolerance values. Collinearity detected by low tolerances effectively eliminated most levels of income and employment status as factors influencing outbound tourism, while age and education level appeared to collectively combine with unobservable factors in explaining approximately 36% of the variation within the data set, as depicted by the second linear discriminant function.

The bulk of variation in the data set was explained by the first linear function, accounting for 57% explanatory power, though with most non-collinear variables exhibiting high negative correlations with the discriminant scores, or the unobservable factors. Thus, in line with the interpretation aspect of discriminant analysis, we surmised that certain motivations for travel not easily quantifiable or explained by age and education demographics must be influencing tourism destination decisions, and may include specific reasons for travel, specific attractions at places, or unique physical activities. Future research will continue to assess motivations behind Saudi outbound tourism choices, and it is our hope that this research provides understanding and a framework for those studying tourism pursuits elsewhere.

Regardless of where those tourism studies originate, we caution researchers to be mindful of the modifiable areal unit problem (MAUP), where its two effects – zoning and scale – can drastically alter an analysis and lead to spurious or erroneous conclusions. The zoning effect describes how an area subdivided into x number of smaller areas has no one singular way in which that is accomplished. The scale effect is perhaps best understood as problems of data aggregation, resulting from arbitrary classification or categorization of individual geographical units. We initiated a discussion of MAUP as we question the SCTH’s use of

UNWTO-defined world regional commissions, and instead advocated for the SCTH to employ a more thoughtful and provocative means by which to categorize destinations, including an encouragement to think of places of the world as organized not necessarily by country, but by some other phenomenon common throughout the places visited. As the literature has confirmed, people embarking upon outbound tourism have many choices or where to go, and the ultimate decision on a particular destination is often dependent on many factors, both qualitative and quantitative. Many people around the world travel outside of

84 their countries of origin for a variety of reasons and motivations. Certainly these motivations may have commonalities among them – based on geography, demographics, or other factors

– but ultimately they are all individually-driven, and that facet should remain at the forefront of tourism data collection, analysis, and synthesis. It is our hope that this analysis of Saudi outbound tourism using discriminant analysis can help to inspire the importance of geography in tourism studies and propel creativity and innovation in understanding this fascinating and ever-growing phenomenon.

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5. Research Article 3

A Social Economic Analysis of Tourists’ Destination Choice:

The Case of Saudi Arabia

Basheer Alshammari, Kevin Raleigh, and Robert South

Department of Geography and GIS, University of Cincinnati, OH 45221

5.1 Introduction

International tourism has become one of the largest and most vital economic sectors of the global economy driving huge flows of people, commodities, and capital. (Salman and Hasim,

2012, P.48). Tourism, also, has been one of the most important and consistent growth industries worldwide and is currently one of the major service industries (Bansal and Eiselt,

2004; Zang et al., 2004).

2007). In 2016, one out of seven of the world’s population engaged in international tourism;

“tourism ranks as [the] world’s third largest export category,” with the total value of tourism exports exceeding US $1.4 trillion and accounting for seven percent of the world’s exports, ranking third in importance in terms of worldwide exports (UNWTO, 2017a: 6).

The Middle East region is one of the fastest growing tourists generating regions in the world. Outbound travel from the Middle East has more than quadrupled from 8.2 million in

1990 to 55.2 million in 2016. The average annual growth rate of 9.9% during the period

2000-2010 is the highest in the world. (European Travel Commission and World Tourism

Organization, 2012). According to Ayeh (2017), International travelers from the Middle East tend to travel in large groups, as opposed to single travelers or couples, often stay longer at the destination than other travelers, and thus spend a great deal when they travel abroad.

Despite the negative impact of the socio-political unrest in the Middle East since the beginning of 2011 on tourism flows (inbound and outbound), the forecast for the tourism

92 sector remain positive. (European Travel Commission, 2012). The Middle East’s consumption economy has been driven by substantial economic gains in the region in recent decades. Spending on international tourism has been enhanced by rising disposable incomes as a result of economic gains from oil revenues and loosening restrictions on foreign travel.

(Ayeh, 2017).

Saudi Arabia is the largest tourist generating market in the Middle East accounting for

40% of all outbound travel from the region, totaling some 21.2 million trips in 2016.

(mas.gov.sa). Saudi Arabia was the region’s top spender in international tourism in 2016. The country’s international tourism expenditure was $25.9 billion in 2016. UNWTO forecasts that the country will continue to be the Middle East’s largest tourist generating country, with over

29 million arrivals worldwide in 2020. About two-thirds of Saudi outbound travel is intra- regional. Almost 70% of holiday travel takes place in the summer months – June, July,

August– when Saudi families take advantage of the school break for a long holiday.

Shopping is a major activity during the vacation, in addition to sightseeing / visiting tourist attractions, dining out, enjoying the nightlife. Destination selection is largely influenced by recommendations from friends and relatives, followed by information gathered from travel agents. (European Travel Commission, 2002).

This paper aims to assess factors that influence Saudi outbound tourism as exemplified by length of stay, mode of transportation, type of accommodation, and purpose of visit. By utilizing updated data derived from the Saudi national annual survey, the study examines the importance of destinations and geographic location by region. The results of the study contribute to an understanding of segmented tourist markets and provides insight into the

Saudi outbound tourism.

The paper is organized as follows. Previously, Section 2 reviewed relevant literature concerning analyses of outbound tourism focusing on the Middle East, Gulf Cooperation

Council (GCC) countries, and Saudi Arabia. In this research, the following section discusses the methodology, describes the data acquisition and data analysis. Next, the results and implications of the analysis are discussed. Concluding remarks are presented in the final section.

5.2 Methodology

Data Description

Saudi Arabia has far-reaching vibrant outbound tourism activity, largely due to Saudi citizens having higher disposable incomes and having more vacation times available to them. In addition, tourism is far-reaching around the globe, and typically occurs in longer blocks of time than might be otherwise common. As such, it is unsurprising that the Kingdom has recently expressed interest in assessing tourism behaviors of its citizens through the creation of the Saudi Commission of Tourism and National Heritage (SCTH) in 2013. In 2015, SCTH published a 111-page report Tourism Statistics 2015, the result of data collection and aggregation by SCTH’s Tourism and Information Research department, commonly known through its translated Arabic acronym of MAS, and/or as the MAS Center (MAS Center,

2016).

One aspect of SCTH, achieved through its MAS Center, centers on data collection, aggregation, analysis, and dissemination of those data in the form of reports and summaries, unless otherwise prohibited by law. MAS employs three continual surveying methods of

Saudi outbound travelers: (1) Entry Card / Departure Card (airports and ships); (2) Border /

Boundary Survey (land vehicles); (3) Household Surveys, the prevailing instrument of which is the annual Domestic & Outbound Tourism Survey (DOTS), which may necessitate an in- person follow-up visit to non-respondents to obtain data (MAS Center, 2015: 2), The

Domestic & Outbound Tourism Survey (DOTS), begun in 2003, is an annual report sent to

Saudis inquiring about their own demographics and their outbound travel and tourism endeavors. MAS staff then collect these departure cards and surveys, and amalgamate the data contained upon those, from which reports are published. Particular to this research are four data tables published in the most recent edition of Tourism Statistics 2015; these four tables provide an insight into Saudi outbound tourism behavior with respect to purposes of travel, modes of transport, lengths of stay, and accommodation types at the destination, with the mutually-exclusive categorizations of each of those (20 variables total) (Table 1).

Table 1. Table Numbers, Data, and Categorizations

Table number from Data Captured in this Table Mutually-Exclusive Categories of These Tourism Statistics Data 2015 3-7 (pages 67-68) Outbound Tourist Trips by • Business and Professional Destination and Purpose of • Education and Training Visit • Health and Medical Care • Leisure • Visiting Friends / Relatives (VFR) • Shopping • Sports • Others 3-9 (pages 70-71) Outbound Tourism Trips by • Air Main Destination and • Land Modes of Transport • Sea 3-11 (pages 73-74) Outbound Tourism Trips by • 1-3 Nights Destination and Length of • 4-7 Nights Stay • 8-14 Nights • 15-28 Nights • 29-91 Nights 3-17 (pages 79-80) Outbound Tourism Trips by • Hotels / Resorts Main Destinations and Type • Apartments (FUAs) of Accommodation • Private • Others

The additional dimension of these data is a reporting by country of destination, with these tales containing the specific outbound tourism data for 42 countries organized geographically in the same manner and with the same names as The United Nations World Tourism

Organization (UNWTO). The UNWTO’s first-level division of the world’s sovereign states and non-sovereign territories is into one of six regional commissions (Figure 1), with each of those six regional commissions further subdivided into one or more second-level divisions, or subgroupings, for which there are 20 total (Figure 2), mutually exclusive across those six commissions (UNWTO, 2017b).

Figure 1. UNWTO Regional Commissions

Sources: UNWTO (2017b); MAS Center (2016)

Figure 2. UNWTO Regional Sub-groupings of the Countries of the World

Source: UNWTO (2017b); MAS Center (2016)

The 20 variables contained within four data captures serve as the attribute data in this analysis, with the observations n equal to 42, identifying the number of countries for which there are data across these variables (Figure 3).

Figure 3. Countries in the Analysis

Method Justification

The data used in this analysis consists of 42 observations, geographies identified by specific country of the world, with four data categories divided into 20 variables. With 840 data points, obtaining a trend or attempting to make sense of these data rely on a data reduction technique, and related research has employed factor analysis for doing so (cf. Seyidov and

Adomaitiene (2016); Alghamdi (2007); Nassar et al. (2015); and Alsawafi (2013).

Factor Analysis emerged in the late 19th and early 20th centuries largely through Charles

Spearman and Raymond Cattell, who first applied this and its related techniques to psychology studies of intelligence. Over time, and with the development of advanced mathematics, factor analysis has come to be a useful statistical process by which data can be

99 reduced and by which latent variables or factors underlying large data sets can perhaps be extracted. While factor analysis is in no way a means by which causality can be determined, it does provide for the reduction of data into manageable factors that underlie the data trends, attempting to explain the greatest percentage of variance. As an interpretive technique, factor analysis relies on careful analysis of results in an attempt to identify, isolate, and label the specific factors that are at play in explaining variance.

Other types of methods explaining variance have certain assumptions aligning with classical inferential statistics; factor analysis, by contrast, has none, with the principal assumption being that the data are metric, of interval / ratio type, and should represent a homogeneous sample. Multivariate normality is not a requirement to perform exploratory factor analysis.

5.3 Results and Discussion

We performed an exploratory factor analysis using SPSS v. 23 on the 480 data point resulting from 20 variables across 42 observations, centering on outbound tourism behavior of Saudi

Arabian citizens in 2015. Parameters employed in this factor analysis included a extraction method of principal components with a Varimax orthogonal rotation, best when the end result of exploratory data reduction into distinctive separation into factors clearly influenced by groups of variables is desired. In obtaining the results of the factor analysis, this research team opted for factor loadings of 0.5 or greater for clarity in variance explanation, while convention often suggests that 0.3 is sufficient.

100

Assessing the contributing factors was achieved through the Kaiser stopping criterion, whereby only those factors with eigenvalues greater than 1 are to be considered. In this analysis, four factors met that criterion, explaining 89.124% of the variance within the data set (Table 2).

Table 2. Factor Analysis / Total Variance Explained

The derivation of the latent factors explaining the variance in the data set is achieved through an interpretation of the rotated component matrix, displayed in Table 3, whereby only those loadings with absolute values greater than 0.5 are displayed.

101

Table 3. Rotated Component Matrix

By performing this exploratory factor analysis, trends otherwise unobservable come to light.

Factor 4 explains 5.544% of the overall variance in the data set, and shows a high correlation

/ loading to three variables – health and medical care, sea, and others (accommodation). This suggests that likelihood of Saudi citizens embarking upon outbound travel for purposes of health and medical care may be more likely to travel by sea to do so, and will stay in accommodations not neatly categorized as a hotel, furnished apartment, or private. If health and medical care is driving such accommodation, one might postulate that “other” in this case

102 could refer to hospitals, sanitoriums, or another long-term facility. This result may suggest that approximately 5% of Saudi outbound tourism is centered on reasons connected to health and medical care.

Factor 3 explains 7.015% of the variance in the data set, and has two correlates to sports and

1-3 nights. This offers a pairing within the data that might have otherwise gone unnoticed.

Saudi citizens whose predominant purpose of travel is for sports activities appear to stay only for short durations; while this may be expected, it is nonetheless expressed clearly with an exploratory factor analysis.

Factor 2 contains a number of correlates, including Visiting Friends / Relatives (VFR), Air,

29-91 nights, Private, and Others (accommodations). In total, Factor 2 explains 16.634% of the variance in the data set. We surmise from this factor that longer periods of staying abroad

– categorized in the data set curiously as 29-91 nights – occurs in tandem with purpose of travel to visit friends and relatives, to travel by air, and to stay in either private or miscellaneous accommodations. It might be further argued that Saudis traveling outside of the country to visit friends and relatives do so with the intention of remaining abroad for a longer-than-expected vacation time.

Factor 1 in any factor analysis will have the longest eigenvalue (11.986) and will explain the greatest amount of variance in the data set (here 59.931%). Because the loadings within this factor are so high, and can easily be further subdivided into groups in 0.1 increments, it may require some additional thought to truly describe the latency capturing this amount of variance. Business travel, leisure travel, and travel for shopping have near-perfect positive correlation to this factor (loading > 0.90), while lengths of stay of 4-7 nights, 8-14 nights, and

103

15-28 nights clearly align to it as well. Type of accommodation is only correlated to hotels / resorts and furnished apartments. In some regard, this factor may confirm that business and leisure travel predominate among reasons for outbound Saudi tourism, as the factor also highly correlates to mode of transport by land.

Through factor analysis – and particularly in assessing mode of transport as it relates to these factors – we can gain a new understanding of the geographic dimensions of Saudi outbound tourism. Among the 42 countries for which data are collected, there are destinations that clearly can be reached only by air (USA, Canada, European destinations, Asian destinations), some more commonly by sea (Eritrea, Sudan), and some more commonly by land (Bahrain,

UAE, Yemen, Jordan, Egypt). Thus, we can tie those countries to the specific factors listed above that result in the highest loadings for those factors (land for Factor 1, air for factor 2, and sea for factor 4).

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6. Summary and Conclusions

The objective of this dissertation was to analyze and understand the trends and patterns of

Saudi Arabia outbound tourism. The research focused on an analysis of destination choice of

Saudi outbound tourists. In addition, the research investigated the sociodemographic variables to provide an understanding of Saudi Arabian outbound destination choice. The study incorporated different statistical techniques as exemplified by multiple linear regression, correlation, discriminant analysis, and factor analysis in order to identify the most appropriate statistical model for studies of outbound tourism. Data on Saudi Arabian tourism were obtained from several sources. The principal data source for this study was secondary data collected by the MAS center; Tourism Information and Research Center (TIRC) of the

Saudi Commission for Tourism and National Heritage (SCTH) on surveys administered annually since 2002, except for the year 2003. Additional data on Saudi outbound tourism have been obtained from several tourism authorities and organizations. These include the

United Nations World Tourism Organization (UNWTO), Euromonitor International, Eurostat

Statistics Explained, The World Bank.

107

The literature review begins with a review of the relevant international tourism literature focusing on studies on trends and patterns of outbound tourism and destination choice. The review focused on demographic and socioeconomic characteristics, factors that influence international vacation selection. The literature examined the current and past research on international tourism studies, specifically outbound tourism, substantiates the types of variables and methods commonly employed in such studies. Furthermore, previous published research demonstrates significant progress in understanding tourism behavior, while also identifying gaps that this research aims to explore more fully.

The first article is an analysis of Saudi Arabian outbound international tourism, 2002-

2015. The study focuses on two aspects of Saudi tourism. First, the research examines the growth of Saudi international tourism. The study provides insight for the growth and high percentage of Saudi engagement in international tourism. Second, the research analyzes

Saudi international destination trends and outbound tourist choice. Consequently, this study undertakes a regression analysis on Saudi outbound tourism that provides analytical underpinning to the findings and a predictive basis for future tourist spatial patterns that can be replicated for other studies in other settings.

The second article attempts to understand the role of sociodemographic variables on

Saudi Arabian outbound destination selection, 2015. The analyzed variables include age, income, education level, and employment status. The results of the study contribute to an understanding of segmented tourist markets and provides insight into the relationship between tourists’ socio-demographic characteristics and destination selection. The study employs a discriminant analysis to test the relationship between Saudi tourists’ sociodemographic characteristics and destination selection.

108

The third article investigates the effect of several variables as exemplified by length of stay, mode of transportation, type of accommodation, and purpose of visit on Saudi outbound tourism. Factor analysis is employed to identify these relationships. Data used in this paper are derived from the Saudi national survey, 2015, which is conducted by the supreme commission of tourism and national heritage in Saudi Arabia. Findings of this paper will contribute to an understanding of the needs of Saudi outbound tourists and helps the government in promoting the national and inbound tourism.

The study finds that increasing numbers of Saudi international tourists is partly a reflection of the paucity of cultural and physical attributes within the nation. More importantly, the growth of Saudi international tourism is a result of Saudi wealth reflected by a liberal policy of government paid summer vacation resulting in a distinctive seasonality of outbound tourists. The study finds that Saudi outbound tourist choice is largely dictated by

Arabic speaking Muslim nations, geographic proximity, cultural familiarity, political and economic stability, and climate, and these factors explain over 63 percent of Saudi outbound tourist destinations. The analysis shows that Saudis are not” off-the-grid” tourists; rather, they seek culturally-familiar wealthy nations that are safe vacation havens where they can indulge in a more cosmopolitan experience, and in places that practice a more liberal, tolerant interpretation of the Koran. The trending data indicate that new Saudi tourist destinations are emerging. Both the United Kingdom and Germany show increased interest by Saudi tourists.

Saudi expenditures on international tourist travel exceeded $20 billion in 2012. This is not an insignificant sum, and such a dollar figure should be of interest to western nations seeking to attract international tourism.

The research adds to the literature on international tourism in general and specifically the

Saudi Arabian tourist industry. Studying outbound tourism trends and patterns for a specific

109 period provides an understanding of tourism to date and is a point of departure for future studies of international travel. The results of the study contribute to an understanding of segmented tourist markets generated from the Middle East and provides insight into outbound tourism in a culture that has received little attention in the literature. The findings fill a void in what is known about Saudi outbound tourism. The research provides policy- makers with information that can be useful in formulating and planning an effective strategy to domestically retain some of the Saudi export tourist dollars and provide other nations insight on how to attract a share of the $20 billion that Saudi’s currently spend on international tourism. Future research will continue to assess motivations behind Saudi outbound tourism choices, and it is a hope that this research provides understanding and a framework for those studying tourism pursuits elsewhere.

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