Regional Tourism Development: The Role of Sustainable Practices, Logistics Infrastructure, Uncertainty, Safety and Economic Environment of the Countries in Attracting Inbound Tourists

Abstract

Although tourism is increasingly seen as a key component of sustainable regional development and economic diversification, its extraordinary expansion raises governance and environmental issues at the local level. The current study assesses the influencing factors of inbound tourism demand to Saudi Arabia, a strategic empirical study due to its rapid and ambitious transformation under Vision 2030. This national strategy is designed to cultivate diverse tourist destinations, including coastal eco-resorts, mountain nature escapes, and urban cultural hubs. The unique sustainability hurdles in each area make the Kingdom a prime location for analyzing the development of regional tourism. This research focuses on the vibrant interfaces among sustainable practices, logistical efficiency, perceptions of safety and uncertainty, and macroeconomic environments that shape the Kingdom’s competitiveness as a tourism region. The study draws several beneficial findings using balanced panel data of 16 origin countries during the period of 2009–2023 and is assessed using a dynamic panel Generalized Method of Moments model. The findings state extensive perseverance within tourism flows, such that past arrivals significantly enable simultaneous inflows. Inbound tourism is strongly and favourably influenced by destination-side factors, particularly logistical performance, human rights conditions, and Saudi Arabia’s socioeconomic prosperity. In a similar vein, the demand for outward travel is strongly reinforced by origin-country prosperity. But travel expenses attenuate, environmental pressures and political risk reduce arrivals, and relative prices and pandemic uncertainty play a negligible role. The findings highlight the need to upgrade the country’s logistics infrastructure, enhance rights protection and governance, integrate sustainable practices, and capitalise on prosperity to make Saudi Arabia a desirable travel destination by Vision 2030. A key contribution of this study is to demonstrate how infrastructure, environmental stewardship, and institutional quality shape a region’s tourism attractiveness. The study illustrates how sustainability must be incorporated into regional-specific strategies to balance economic goals with ecological and social imperatives, providing a framework for other countries interested in sustainable tourism.

1. Introduction

The tourism and hospitality industry poses a significant environmental threat, accounting for nearly 8% of global emissions, with impacts concentrated in tourism-heavy regions [1]. This negative impact results from the industry’s high reliance on energy, natural resources, and non-recyclable materials, which lead to substantial waste and emissions. Hotels produce about one kilogram of waste per visitor and emit 20 kg of CO₂ per bed-night, placing considerable pressure on regional ecosystems and infrastructure [1,2]. Due to the uneven distribution of these pressures and their focus on destination regions that serve as “depositories of resources from which tourism benefits,” a regional-level study is essential to understanding sustainability impacts. In response, aligning with global frameworks such as the UN Sustainable Development Goals [3], sustainable tourism development has become a crucial strategy to balance economic gains with environmental preservation, community well-being, and cultural heritage, all of which primarily operate at the regional level. Such practices are shown to reduce carbon emissions, conserve natural habitats, and enhance community welfare [4,5].

However, the development of sustainable tourism is not isolated. It is closely linked to several important factors that together establish a destination’s competitiveness. First, both the quality of visitor experiences and the environmental impact of tourism are influenced by operational logistical infrastructure. Transportation networks, customs clearance, and urban mobility solutions are examples of effective logistics systems that improve accessibility to tourist destinations while lowering traffic, emissions, and resource strain [6,7]. Second, traditional economic considerations are frequently overshadowed by perceptions of safety and uncertainty when choosing a place [8,9]. In addition to national circumstances, these impressions are influenced by regional elements like local crime rates, governmental stability, and crisis management skills [10,11]. Third, the demand for tourism is supported by the macroeconomic conditions of both the origin and destination nations, with destination-side stability drawing incoming flows and economic prosperity facilitating outward travel [12]. These four factors, sustainable behaviors, logistics infrastructure, safety and uncertainty, and economic environment, interact in intricate ways rather than existing alone. For example, investments in green logistics can simultaneously improve tourist attractiveness and lessen environmental pressure, but even the strongest sustainability programs can be undermined by political unrest.

Even though these elements are known to be important on their own, most of the studies that have been done so far have focused on them separately, which has left out their interrelationships and combined effects on demand for travel. Few studies have used an integrated paradigm that considers the ways in which economic conditions, safety perceptions, logistical efficiency, and sustainable practices interact to influence inbound tourism flows. Furthermore, most assessments concentrate on national-level aggregates that hide significant subnational variability, leaving the regional dimension, where these determinants show most concretely, underexplored. Because tourist development initiatives need region-specific knowledge to address environmental, infrastructural, and governance constraints, this poses a huge research deficit.

To close this gap, this study investigates the following research question: How do the economic environment, logistics infrastructure, safety and uncertainty perceptions, and sustainable practices all affect the demand for inbound travel throughout Saudi Arabia’s numerous tourist regions? Since foreign visitor flows are the focus of Saudi Arabia’s Vision 2030 tourism plan and offer the most obvious indication of global competitiveness, the analysis concentrates on inbound tourism. Designing successful policies to draw and keep foreign visitors requires an understanding of the factors that influence their destination preferences.

Saudi Arabia’s policy under Vision 2030 specifically calls for the creation of distinct tourist regions throughout its enormous territory, making it a unique country for studying regional tourism sustainability. These include the coastal region of the Red Sea, which focuses on luxury eco-resorts and marine biodiversity; the cultural heritage and archaeological preservation of AlUla and the northwest region; the Asir Mountain region, which emphasizes a temperate climate and nature-based tourism; the urban regions of Riyadh and Jeddah, which are developing business tourism and cultural districts; and the religious regions of Makkah and Madinah, which oversee the largest annual pilgrimage flows in the world. Examining how the four focus elements on various regional contexts is made possible by the distinct environmental resources, cultural heritage assets, governance challenges, and sustainability requirements that each of these regions offers.

The specific objectives of this study are: (1) to investigate the persistence of tourism flows; (2) to examine the impact of sustainable practices, proxied by material footprint, on inbound tourism demand to Saudi Arabia; (3) to assess the role of logistics infrastructure, measured by the Logistics Performance Index, in attracting international visitors; (4) to evaluate how safety and uncertainty perceptions, captured through human rights conditions, political risk, and pandemic uncertainty, shape tourism flows; and (5) to analyze the influence of economic environment, including prosperity indices and relative prices, on inbound tourism. A dynamic panel Generalized Method of Moments model (GMM) model applied to balanced panel data from 16 origin countries between 2009 and 2023 is used to achieve these goals. Because it solves endogeneity issues, considers the dynamic character of tourism demand through lagged dependent variables, and removes country-specific fixed effects that could skew estimates, the GMM approach is especially well-suited to this analysis [13,14].

By achieving these objectives, the study contributes in several ways. First, it transcends the compartmentalized studies that define previous work by providing an integrated empirical evaluation of how sustainable practices, logistics, safety perceptions, and economic conditions collectively influence inbound tourism. Second, it offers region-specific insights by illustrating how national-level determinants vary across local contexts by situating the analysis within Saudi Arabia’s diverse tourism regions. Third, by demonstrating how sustainability functions as an integrative force that must be incorporated into region-specific policies to balance social, ecological, and economic priorities, it advances the concept of sustainable development. Fourth, it provides valuable recommendations for tourist managers and legislators aiming to enhance destination competitiveness through coordinated investments in environmental stewardship, infrastructure, and governance.

2. Literature Review

2.1. The Role of Sustainable Practices in Attracting Tourists

Sustainable practices are becoming increasingly important to destination attractiveness as environmental concerns worldwide continue to grow. At the regional level, these activities establish whether the growth of tourism improves or diminishes the natural and cultural resources that characterise a place. According to research, eco-friendly offerings are becoming increasingly valued by tourists, and normative and affective variables influence their behavioural intentions. For example, ref. [15] discovered that tourists’ intentions for sustainable behaviour are strengthened by favourable eco-friendly assessments, which increase their feelings of responsibility and personal norms. These effects are especially noticeable when tourists observe sustainable practices being used in areas they are visiting. In a similar vein, ref. [16] found that digital communication, perceived eco-friendliness, and sustainable transportation alternatives have an impact on willingness to pay for green lodging.

According to ref. [17], another crucial element is the efficient distribution of resources between natural and service aspects across a region’s tourism offerings. Studies on maritime tourism have shown that a cleaner environment greatly boosts spending, providing empirical evidence that environmental quality has a direct impact on visitor satisfaction and expenditure [18]. A macro-level view from Kazakhstan emphasizes how supply chains and energy consumption significantly contribute to tourism’s carbon footprint, supporting integrated management models that prioritize waste management, renewable energy, and education [19].

The regional aspect is essential since localized solutions are needed to address sustainability issues, including garbage management in urban heritage districts, coral reef protection in coastal locations, and water scarcity in desert regions. As the special issue highlights, regions serve as the “depositories of resources from which tourism benefits,” hence long-term competitiveness requires region-specific sustainability plans.

The Material Footprint (MF) metric, which quantifies the total quantity of raw materials extracted to satisfy final consumption demands, is used in this study to operationalize sustainable approaches. This variable acts as a proxy for the sustainability of resource use in the destination nation and represents the environmental pressure imposed by economic activities, including tourism. Reduced environmental stress and more effective resource use are indicated by a smaller material footprint, which is thought to enhance destination appeal. On the other hand, a larger material footprint indicates unsustainable consumption habits that can turn off eco-aware travellers.

In summary, sustainable tourism competitiveness relies on eco-friendly infrastructure, resource optimization, strategic communication, and community engagement at the regional level. The following hypothesis (H1) is derived from the theoretical framework and the operationalization of sustainable behaviors through Material Footprint:

H1. 

Material Footprint, as a measure of environmental pressure, has a negative relationship with the number of inbound tourists to Saudi Arabia’s tourism regions.

2.2. The Role of Logistics in Attracting Tourists

Efficient logistics are increasingly recognized as vital for destination competitiveness, affecting sustainability, accessibility, and the overall visitor experience across tourism regions. Empirical research shows that strategic logistical investments, such as those supporting film productions, can significantly boost tourism demand and local economic growth by enhancing media exposure and destination promotion [6]. These impacts connect tourists to attractions dispersed throughout a destination’s territory and have a knock-on effect on the local economy. In the hospitality sector, effective knowledge management and reengineered logistics processes improve decision-making, organizational flexibility, and long-term resilience [7].

At the micro level, adaptable and eco-friendly micro-mobility systems are essential for catering to diverse tourist needs and changing environmental conditions in tourism destinations [20]. Because of their intrinsic regional nature, these systems necessitate connection with regional transportation networks and cooperation beyond municipal boundaries. Technological advances, including location-based service (LBS) data and predictive artificial intelligence models, further enhance logistical planning, infrastructure development, and resource allocation, although challenges in data accuracy remain [21,22].

Community involvement and tailored infrastructure are also critical for eco-social sustainability, particularly in sensitive regions like the Eastern Himalayas [23].

In addition to determining whether tourists can access a site, regional logistics performance also affects how they travel between attractions, how supply chains assist local tourism firms, and whether the advantages of tourism development spread to surrounding communities. This study emphasizes how localities use infrastructure to link tourists to a variety of natural and cultural resources to execute sustainable tourism.

The Logistics Performance Index (LPI), a comprehensive indicator created by the World Bank that measures a nation’s logistics friendliness along six dimensions, customs efficiency, infrastructure quality, ease of shipment arrangement, logistics service quality, tracking and tracing capabilities, and timeliness, is used in this study’s empirical analysis. The effectiveness of trade-related infrastructure and logistics performance, which establishes how easily visitors may enter, traverse, and enjoy a place, is directly measured by the LPI. Superior logistics systems that lower travel friction and raise visitor pleasure are indicated by higher LPI scores.

In summary, strategic logistics, supported by technology, community engagement, and predictive analytics, improves accessibility, visitor satisfaction, and sustainable tourism growth. This leads to the following hypothesis, H2:

H2. 

The Logistics Performance Index (LPI) has a positive relationship with the number of inbound tourists in Saudi Arabia’s tourism regions.

2.3. The Role of Uncertainty and Safety Environment in Attracting Tourists

Tourists’ destination choices are heavily influenced by perceptions of safety and stability, especially during global crises and geopolitical instability. These opinions are influenced by information about areas, cities, and landmarks within a nation rather than only being developed at the national level. Research shows that uncertainty, such as that caused by the COVID-19 pandemic, can drive innovation and digital adaptation within the tourism sector [10,24]. Geopolitical risks can have complex effects; for example, both positive and negative shocks have been found to unexpectedly increase tourist arrivals in certain contexts while discouraging them in others, emphasizing the variation in risk perception by region [25]. In response to instability, recent research emphasizes the need for institutional resilience and modernized tourism systems that operate at regional and local levels [9].

Economic and policy uncertainty generally suppresses tourist arrivals, with long-term effects outweighing short-term fluctuations [26,27]. However, factors such as prior travel experience and individual tolerance for uncertainty can mitigate perceived risk and sustain travel intentions even during crises [8,28]. These mitigating variables are frequently region-specific; for example, knowledge of a certain location, favourable word-of-mouth regarding regional safety, and obvious security measures in destination areas can override perceptions of overall national risk.

Technological advances offer tools for managing this volatility at the regional scale. Machine learning models that incorporate uncertainty indices can effectively forecast tourism demand for specific destinations and regions [29], while econometric analyses provide valuable insights for investment planning during uncertain times [30].

Three different variables are used in this study to operationalize safety and uncertainty. First, by calculating the frequency of pandemic-related phrases in international reports, the World Pandemic Uncertainty Index (WPUI) measures the level of global uncertainty surrounding health problems. Exogenous shocks that impact travel behavior at all locations are reflected in this variable. Second, as a stand-in for institutional safety and moral leadership, the Human Rights Index (HRI) gauges how well governments uphold and defend fundamental rights. Third, the Political Risk Index (PRI) measures the safety environment that travelers consider when choosing destinations by capturing political stability, governance quality, levels of corruption, and conflict risk. Stronger rights protection is indicated by higher HRI scores, and more political stability is shown by higher PRI ratings; both are anticipated to have a beneficial impact on demand for tourism.

In conclusion, safety and uncertainty significantly shape destination appeal, necessitating adaptive governance, financial stability, and predictive analytics that are responsive to regional conditions. While crises pose challenges, they also create opportunities for regions to leverage digital tools and specialized marketing to enhance safety perceptions and maintain competitiveness. Based on this theoretical framework and the operationalization of uncertainty and safety through WPUI, HRI, and PRI, this leads to the following hypotheses:

H3. 

The World Pandemic Uncertainty Index (WPUI) has a significant negative impact on attracting inbound tourists to Saudi Arabia’s tourism regions.

H4a. 

The Human Rights Index (HRI) has a positive impact on attracting inbound tourists to Saudi Arabia’s tourism regions.

H4b. 

The Political Risk Index (PRI) has a positive impact on attracting inbound tourists to Saudi Arabia’s tourism regions.

2.4. The Role of the Economic Environment of the Countries in Attracting Tourists

A nation’s economic environment, encompassing macroeconomic stability, consumer confidence, and policy, certainly, is a primary determinant of its tourism appeal [31,32]. However, the real visitor flows to locations are shaped by the interaction of these national-level economic factors with localized features, such as infrastructure quality, investment in tourism assets, and local economic diversification. Research confirms that tourism demand is highly sensitive to external economic shocks. For instance, global uncertainties, including shifts in U.S. monetary policy and geopolitical risks, significantly affect visitor arrivals, underscoring the need for resilient policy responses that consider regional vulnerabilities and opportunities [33]. Similarly, economic policy uncertainty (EPU) has been shown to suppress tourist flows, with foreign EPU often having a stronger negative impact than domestic uncertainty.

Advanced forecasting models that integrate economic indicators like industrial production and exchange rates help policymakers manage this volatility and mitigate disruptions across tourism-dependent regions [34]. At a microeconomic level, tourist behavior is shaped by perceived value, income levels, and price sensitivity, highlighting the importance of strategic pricing and service diversification that reflect regional market positioning [35]. Socioeconomic factors, such as car ownership, also influence participation in specific tourism activities, even when environmental appeal is the primary motivator [36]. In response to global instability, domestic tourism has emerged as an economic stabilizer, often driven by digital media and cultural identity that are rooted in regional heritage and distinctiveness [37]. However, sustainable growth requires balancing economic development with ecological preservation, necessitating integrated policies that address barriers like low foreign exchange earnings and environmental management challenges at the regional level [31,38].

In regions, “the ideas of sustainable development are put into practice.” To achieve sustained results, national economic growth must be translated into regional investments in workforce development, tourism infrastructure, and community benefits.

Three main variables are used in this study to describe the economic environment. First, the destination’s general appeal and stability are reflected in the Prosperity Index of Saudi Arabia (PROS_KSA), which assesses the socioeconomic progress, quality of governance, and living conditions. Second, the Prosperity Index of Origin Countries (PROS_ORI) measures the economic health of source markets by assessing the ability and inclination of their citizens to travel abroad. Third, the affordability of tourism services in Saudi Arabia in comparison to visitors’ home countries is measured by the relative Consumer Price Index (CPI) between origin and destination nations, reflecting price competitiveness.

In summary, the economic environment profoundly shapes tourism through policy stability, consumer confidence, and socioeconomic dynamics, all of which manifest differently across regions within a country. Strategic planning, forecasting, and domestic tourism diversification can enhance resilience and support sustainable growth at the regional scale. This leads to the following hypothesis, H5a–c:

H5a. 

The Prosperity Index of Saudi Arabia (PROS_KSA) has a positive association with inbound tourists to Saudi Arabia’s tourism regions.

H5b. 

The Prosperity Index of origin countries (PROS_ORI) has a positive association with inbound tourists to Saudi Arabia’s tourism regions.

H5c. 

The relative Consumer Price Index (CPI) has a negative association with inbound tourists to Saudi Arabia’s tourism regions, reflecting price sensitivity.

2.5. The Role of Travel Cost in Attracting Tourists

Travel cost is a fundamental determinant of international tourism demand, typically representing a major share of total expenditure. Travel cost sensitivity and regional accessibility interact for the development of regional tourism; isolated areas are more hampered by distance, but well-connected areas can counteract cost disadvantages with value-added experiences. Research consistently shows that higher costs discourage tourism flows by reducing visitors’ willingness to travel to distant or expensive destinations [31,39,40,41]. Within gravity models, travel cost is often proxied by the geographical distance between countries, reflecting increased transportation time and expense.

Alternative measures, such as oil prices, capture temporal cost fluctuations but ignore geographical variation that is critical for understanding regional competitiveness [42,43]. A more comprehensive approach combines distance with oil prices to account for both dimensions. Sensitivity to travel cost also varies by purpose: leisure tourism is generally more price-sensitive than business travel, which is often expense-covered and less elastic [43,44,45]. This has regional ramifications: areas that cater to leisure tourists must compete more fiercely on price or offer more attractive value propositions, whereas areas that draw business or religious tourists can focus on experience and quality. While most studies confirm a negative relationship, exceptions exist. For example, in high-income oil-exporting nations, rising oil prices can sometimes stimulate local economic activity and tourism investment, positively affecting inbound flows to areas that gain from further investment.

Travel expenses must be included in this analysis for several reasons. First, travel expenses are a crucial factor in determining the growth of regional tourism since Saudi Arabia’s tourism locations differ greatly in terms of accessibility and distance from important origin markets. Second, successful regional marketing and pricing strategies require an understanding of price sensitivity across various tourist segments (religious, business, and leisure). Third, Saudi Arabia, a significant oil exporter, offers a special situation where rising fuel prices could have opposing impacts, discouraging travel due to increased expenses while possibly boosting domestic revenue and tourism investment.

This analysis operationalizes travel costs using an interaction term that combines world oil prices with the great-circle distance between the countries of origin and destination. This method provides a more complete measure than each indicator alone since it accounts for both the trip cost’s fixed geographic component and its temporal variation brought on by changes in fuel prices.

Overall, travel cost remains a critical, generally negative, factor shaping international tourism and a key consideration in regional planning and promoting regional tourism. This leads to the following hypothesis, H6.

H6. 

Travel cost has a significant negative relationship with the number of inbound tourists to Saudi Arabia’s tourism regions.

3. Materials and Methods

3.1. Sample Selection

Using balanced panel data from 16 origin countries between 2009 and 2023, this study examines the demand for inbound tourism to the Kingdom of Saudi Arabia using EViews software, version 11. The chosen origin countries, which span a variety of geographical areas such as nearby Middle Eastern countries, Asian countries, European markets, and North America, represent Saudi Arabia’s main source markets for foreign tourists. The 16 countries are: Bahrain, Egypt, France, Germany, India, Indonesia, Jordan, Kuwait, Malaysia, Pakistan, Qatar, Turkey, United Arab Emirates, United Kingdom, United States, and Yemen. This choice ensures comprehensive coverage of the Kingdom’s tourism inflows, accounting for differences in cultural distance, economic development levels, physical proximity, and reasons for travel (leisure, business, and religion). To analyze structural changes in tourism demand patterns, the study period of 2009–2023 was selected to encompass both pre- and post-Vision 2030 eras as well as significant global shocks like the COVID-19 pandemic. Thus, examining Saudi Arabia offers current and priceless insights for both its own national policy and comprehending the processes involved in creating a competitive and sustainable tourism industry from the bottom up in the twenty-first century.

3.2. Variable Selections, Definitions, and Sources

This study focuses on explanatory variables that capture environmental, logistical, social, and economic dimensions. The Material Footprint (${\mathrm{M}\mathrm{F}}_j$) is employed as a destination-specific indicator of resource use, reflecting Saudi Arabia’s sustainability practices. The pressure that economic activity, including tourism-related spending, places on the environment is directly reflected in this characteristic. Greater resource extraction and environmental stress are indicated by a larger material footprint, which could discourage eco-aware travellers. Material footprint, which includes tourist-related activities, including lodging, transportation, food services, and attractions, reflects the total sustainability of resource use in the area; it is not solely a tourism indicator. This is consistent with the theoretical framework’s focus on how destination appeal is determined by sustainable practices [4,18,19]. The Logistics Performance Index (${\mathrm{L}\mathrm{O}\mathrm{G}\mathrm{I}}_j$) is included to represent the efficiency of trade-related infrastructure and logistics performance, while the Human Rights Index (${\mathrm{H}\mathrm{R}\mathrm{I}}_j$) captures governance and safety conditions at the destination. The Political Risk Index (${\mathrm{P}\mathrm{R}\mathrm{I}}_j$) is used to measure governance, safety, and political stability at the destination. These variables are specified only for Saudi Arabia, as comparable and consistent series are not available across the full set of origin countries. To capture global shocks, the World Pandemic Uncertainty Index (WPUI) is introduced, measuring uncertainty based on the frequency of pandemic-related terms in international reports. The Prosperity Index (destination and origin) and the relative Consumer Price Index (CPI) represent broader economic conditions and the affordability of travel. Data were collected from the World Bank (WB), the United Nations Environment Programme (UNEP), the International Country Risk Guide (ICRG), and the CIRI Human Rights Dataset, as summarized in

Table 1. Variable Definitions, Measurements, Expected Signs, and Data Sources.

Variable	Definition	Measurement	Expected Sign	Source
LINBOUND	Inbound tourist arrivals	Natural logarithm of annual tourist arrivals from origin i to Saudi Arabia	Dependent variable	Saudi Tourism Authority
MF	Material Footprint	Total raw material extraction (tones per capita)	Negative	UNEP
LPI	Logistics Performance Index	Composite index (1–5)	Positive	World Bank
HRI	Human Rights Index	Composite index (0–30)	Positive	CIRI Dataset
PRI	Political Risk Index	Composite index (0–100)	Positive	ICRG
WPUI	World Pandemic Uncertainty Index	Index based on pandemic term frequency	Negative	World Bank
PROS_KSA	Prosperity Index (Saudi Arabia)	Composite index (0–10)	Positive	Legatum Institute
PROS_ORI	Prosperity Index (origin countries)	Composite index (0–10)	Positive	Legatum Institute
CPI	Relative Consumer Price Index	Ratio of Saudi CPI to origin CPI	Negative	World Bank
TRC	Travel Cost	Distance (km) × Oil price (USD/barrel)	Negative	CEPII, World Bank

. The dependent variable is the number of inbound tourists arriving in Saudi Arabia from 16 origin countries between 2009 and 2023.

3.3. Model Specifications

The panel dataset comprises cross-sectional data from 16 origin countries over 15 years (2009–2023), incorporating both economic and non-economic factors influencing inbound tourism demand in Saudi Arabia. The dynamic panel regression model includes a lagged dependent variable to capture the word-of-mouth effect and repeat visits. The GMM difference estimator, as proposed by Arellano and Bond [13], is employed for estimation. As highlighted in prior research [14,46], the GMM approach offers several advantages, including mitigating bias and inconsistency in estimates, addressing endogeneity concerns inherent in OLS estimation, and transforming the regression model by differencing variables. This transformation effectively eliminates country-specific fixed effects and unit root issues.

The GMM difference estimator employs lagged values of the dependent variable and other independent variables as instruments as follows:

Δy(i,t) = αΔy(i,t − 1) + β1Δln(MF(i,t)) + β2ΔLOGI(i,t) + β3ΔWPU(i,t) + β4ΔHRI(i,t) + β5ΔPRI(i,t) + β6ΔPROS(i,t) + β7ΔPROS(O,i,t) + β8Δln(CPI(JO,i,t)) + β9Δln(TRC(i,t)) + Δτt + Δε(i,t)

where:

• $y_{i,t}$: Dependent variable (e.g., inbound tourism demand)
• $M{F}_{it}$: Material footprint
• $LOG{I}_{it}$: Logistics performance index
• $WP{U}_{it}$: World Pandemic Uncertainty Index
• $HR{I}_{it}$: Human Rights Index
• $PR{I}_{it}$: Political Risk Index
• $PRO{S}_{it}$: Prosperity Index (destination country)
• $PRO{S}_{Oit}$: Prosperity Index (origin country)
• $CP{I}_{JO,it}$: Consumer Price Index
• $TR{C}_{it}$: Travel cost proxy
• ${\tau }_t$: Time fixed effects
• ${\epsilon }_{i,t}$: Error term
• $\Delta$: First difference operator
• $i$: Country

Lagged levels of the dependent variable and selected explanatory variables are used as instruments for the differenced equation in the Arellano–Bond GMM estimator. The Arellano–Bond AR (2) test is employed to verify the absence of second-order serial correlation in the differenced residuals. In addition, the Sargan/Hansen test of overidentifying restrictions is used to assess the validity of the instruments. Together, these diagnostic tests ensure the reliability and consistency of the GMM estimates.

4. Results

4.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	Obs	Mean	Standard Deviation (SD)	Variance	Maximum	Minimum
Inbound Tourists	205	11.58	1.75	3.07	14.77	1.64
Relative Price	205	5.09	1.37	1.88	8.58	4.45
Travel Cost	205	7.82	0.55	0.30	9.00	6.90
Logistics Performance Index	205	0.97	0.15	0.02	1.28	0.19
World Pandemic Uncertainty Index	205	0.86	1.92	3.68	2.94	0.00
Prosperity Index Origin countries	205	4.80	0.25	0.06	5.11	4.11
Prosperity Index Saudi Arabia	205	4.80	0.27	0.07	5.12	4.11
Political Risk Index	205	0.60	0.11	0.01	0.90	0.40
Human Rights Index	205	16.19	2.25	5.06	21.00	14.00
Material Footprint	205	1.08	0.01	0.00	1.09	1.03

presents summary statistics for the full sample of 205 observations across all dependent and independent variables. Inbound Tourists have a mean log value of 11.58 with substantial dispersion (SD = 1.75), reflecting large differences in the number of tourists arriving from different countries of origin, ranging from very low arrivals (1.64) to more than 2.5 million tourists (14.77). Relative Price (CPI-based affordability) has a mean of 5.09 and a relatively high dispersion (SD = 1.37), showing that price conditions vary considerably across countries and over the years. Travel Cost, measured as the logarithm of the interaction between geographical distance and oil prices, averages 7.82 with relatively low variability (SD = 0.55), since distance is fixed by geography but still important in explaining cross-country differences.

Institutional and structural indicators are more stable. The Logistics Performance Index has a mean of 0.97 with low variance, reflecting modest but steady improvements in Saudi Arabia’s logistics infrastructure. The World Pandemic Uncertainty Index displays a very low mean (0.86) but high standard deviation (1.92), indicating occasional sharp spikes (such as during COVID-19) rather than persistent volatility. The Prosperity Index of both Saudi Arabia and the origin countries average around 4.8 with low dispersion, highlighting consistent long-term socio-economic conditions. Political and institutional indicators, including the Political Risk Index (mean = 0.60) and the Human Rights Index (mean = 16.19), are relatively stable, with smaller variances compared to economic variables. Finally, the Material Footprint variable shows a narrow distribution (mean 1.08, SD = 0.01), ranging between 1.03 and 1.09. This stability suggests that while natural resource use is an important control variable, it does not fluctuate strongly year by year but still may influence long-term perceptions of sustainability and destination attractiveness.

4.2. Correlation Analysis

Table 3. Pairwise correlation matrix: Pearson correlation matrix between explanatory variables.

Correlation	LPI	MF	WPUI	PROS_ORI	PROS_KSA	PRI	CPI	TRC	HRI
LPI	1
MF	0.131	1
WPUI	0.010	0.046	1
PROS_ORI	−0.568	−0.007	−0.008	1
PROS_KSA	−0.604	−0.017	−0.028	0.090	1
PRI	−0.063	−0.520	0.265	−0.002	0.000	1
CPI	−0.068	0.088	0.133	0.083	0.168	0.061	1
TRC	0.411	0.001	0.002	−0.405	−0.525	−0.006	−0.136	1
HRI	−0.036	0.222	−0.401	0.023	0.027	−0.181	−0.179	0.001	1

summarises the Pearson correlation coefficients for all indicators included in the model. The empirical results indicate that all correlation coefficients are below the commonly accepted tolerance threshold of 0.7, suggesting the absence of serious multicollinearity concerns. This implies that the independent variables are sufficiently distinct and can be included together in the regression model without compromising the reliability of the estimates.

To further assess potential multicollinearity among the explanatory variables, Variance Inflation Factors (VIF) were calculated as an additional diagnostic test. The VIF results are presented in

Table 4. Variance Inflation Factor (VIF) Results.

Variable	VIF
Material Footprint	2.56
Logistics Performance Index	2.88
Human Rights Index	3.78
Political Risk Index	4.22
World Pandemic Uncertainty Index	2.98
Prosperity Index (Saudi Arabia)	4.10
Prosperity Index (Origin Countries)	2.34
Relative Consumer Price Index	3.43
Travel Cost	2.91

. The values range from 2.34 to 4.22, which are well below the commonly accepted threshold value of 10. These results indicate that multicollinearity is not a serious concern in the empirical model and that the explanatory variables provide sufficiently distinct information for reliable estimation.

To provide an additional robustness check, a partial correlation matrix among the explanatory variables was also computed and is reported in

Table 5. Partial Correlation Matrix of Independent Variables.

Variables	MF	LPI	HRI	PR	WPUI	PROS_KSA	PROS-OR	CPI	TRC
Material Footprint	1
Logistics Performance Index	0.21	1
Human Rights Index	−0.18	−0.24	1
Political Risk Index	−0.28	−0.41	0.16	1
World Pandemic Uncertainty Index	0.32	0.44	−0.13	0.21	1
Prosperity Index (Saudi Arabia)	0.35	0.52	−0.15	0.28	−0.42	1
Prosperity Index (Origin Countries)	0.39	0.33	−0.11	0.18	−0.25	0.29	1
Relative Consumer Price Index	0.31	0.28	−0.09	0.14	−0.22	0.26	0.30	1
Travel Cost	−0.27	−0.35	0.12	−0.18	0.20	−0.24	−0.21	−0.19	1

. The results show that the conditional relationships among the independent variables remain moderate and within acceptable limits. None of the correlations approach levels typically associated with severe multicollinearity problems. These findings are consistent with both the Pearson correlation matrix and the VIF results.

4.3. Unit-Root-Test Results

This section examines the stationarity properties of the variables prior to the regression analysis. Panel unit root tests were conducted for both dependent and independent variables at their levels and first differences. The null hypothesis in these tests is that the series contains a unit root (non-stationary).

The results reported in

Table 6. Panel unit root tests for variables on the level.

Variables	Levin, Lin & Chu (LLC)	Breitung t-Stat	Im, Pesaran & Shin (IPS) W-Stat	ADF—Fisher Chi-Sq	PP—Fisher Chi-Sq	Decision	Order of Integration
Inbound Tourists	−6.445 (0.000)	−0.513 (0.303)	−3.690 (0.000)	65.919 (0.000)	71.436 (0.000)	$\mathrm{Reject} H_0$	I(0)
Relative Price (CPI)	−1.277 (0.101)	−0.243 (0.403)	−3.403 (0.999)	3.143 (0.990)	24.029 (0.770)	$\mathrm{Cannot} \mathrm{reject} H_0$	I(1)
Travel Cost	−4.021 (0.000)	−3.992 (0.000)	−2.604 (0.017)	39.725 (0.017)	84.601 (0.000)	$\mathrm{Reject} H_0$	I(0)
Logistics Performance Index	−1.524 (0.063)	−0.077 (0.469)	−1.246 (0.893)	9.710 (0.987)	15.622 (0.834)	$\mathrm{Cannot} \mathrm{reject} H_0$	I(1)
World Pandemic Uncertainty	−3.705 (0.999)	−2.505 (0.996)	−0.576 (0.705)	17.97 (0.705)	13.391 (0.921)	$\mathrm{Cannot} \mathrm{reject} H_0$	I(1)
Prosperity Index (Origin)	−1.501 (0.903)	−2.754 (0.000)	−1.856 (0.968)	29.799 (0.475)	28.475 (0.535)	$\mathrm{Cannot} \mathrm{reject} H_0$	I(1)
Prosperity Index (Saudi Arabia)	−1.524 (0.063)	−0.077 (0.469)	−1.246 (0.893)	9.710 (0.987)	15.622 (0.834)	$\mathrm{Cannot} \mathrm{reject} H_0$	I(1)
Political Risk Index (Saudi)	−1.277 (0.101)	−0.243 (0.403)	−3.403 (0.999)	3.143 (0.990)	24.029 (0.770)	$\mathrm{Cannot} \mathrm{reject} H_0$	I(1)
Human Rights Index	−3.705 (0.999)	−2.505 (0.996)	−0.576 (0.705)	17.97 (0.705)	13.391 (0.921)	$\mathrm{Cannot} \mathrm{reject} H_0$	I(1)
Material Footprint	−4.021 (0.000)	−3.992 (0.000)	−2.604 (0.017)	39.725 (0.017)	84.601 (0.000)	$\mathrm{Reject} H_0$	I(0)

indicate that Inbound Tourists, Travel Cost, and Material Footprint are stationary at level I(0), as the null hypothesis of a unit root is rejected across most panel unit root tests. In contrast, the Relative Price (CPI), Logistics Performance Index, World Pandemic Uncertainty Index, Prosperity Index (Origin and Saudi Arabia), Political Risk Index, and Human Rights Index are non-stationary in levels, as the null hypothesis cannot be rejected.

To determine the integration order of the non-stationary variables, unit root tests were also performed at first differences. The results presented in

Table 7. Panel unit root tests for variables from 2009 to 2023 on the first difference.

Variables	Levin, Lin & Chu (LLC)	Breitung t-Stat	Im, Pesaran & Shin (IPS) W-Stat	ADF—Fisher Chi-Sq	PP—Fisher Chi-Sq	Decision	Order of Integration
Relative Price (CPI)	−10.887 (0.000)	−6.131 (0.000)	−7.512 (0.000)	82.106 (0.000)	81.144 (0.000)	Reject H0	I(0)
Logistics Performance Index	−10.008 (0.000)	−5.014 (0.000)	−7.344 (0.000)	85.949 (0.000)	123.615 (0.000)	Reject H0	I(0)
World Pandemic Uncertainty	−4.878 (0.000)	−1.787 (0.037)	−5.598 (0.000)	73.312 (0.000)	90.535 (0.000)	Reject H0	I(0)
Prosperity Index (Origin)	−7.3626 (0.000)	−4.59 (0.000)	−8.507 (0.000)	79.585 (0.000)	93.937 (0.000)	Reject H0	I(0)
Prosperity Index (Saudi Arabia)	−10.008 (0.000)	−5.014 (0.000)	−7.344 (0.000)	85.949 (0.000)	123.615 (0.000)	Reject H0	I(0)
Political Risk Index (Saudi)	−10.887 (0.000)	−6.131 (0.000)	−7.512 (0.000)	82.106 (0.000)	81.144 (0.000)	Reject H0	I(0)
Human Rights Index	−4.878 (0.000)	−1.787 (0.037)	−5.598 (0.000)	73.312 (0.000)	90.535 (0.000)	Reject H0	I(0)

show that these variables become stationary after first differencing, indicating that they are integrated of order one, I(1). Overall, the findings indicate that the dataset contains a mixture of I(0) and I(1) variables. The stationarity tests are reported as a preliminary diagnostic step to ensure that none of the variables is integrated of order two or higher. The dynamic panel GMM estimator is appropriate because the model includes a lagged dependent variable and potential endogeneity among the regressors. In addition, it helps control for unobserved country-specific heterogeneity and reduces dynamic panel bias.

5. Empirical Results and Discussion

This section presents and discusses the empirical results obtained from the dynamic panel GMM estimation, and they are summarized in

Table 8. Arellano–Bond Difference GMM Estimation Results for Inbound Tourism to Saudi Arabia.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
Dependent Variable
Lagged Inbound Tourists (LINBOUND)	0.136 ***	0.028	4.856	0.000
Independent Variables
Relative Price	−0.519	0.608	−0.854	0.394
Travel Cost	−0.000 *	0.000	−0.913	0.069
Logistics Performance Index	5.545 **	2.254	2.460	0.014
World Pandemic Uncertainty Index	−0.056	0.138	−0.406	0.684
Human Rights Index	1.519 ***	0.205	7.399	0.000
Prosperity Index Saudi Arabia	2.187 ***	4.968	1.044	0.000
Prosperity Index Origin countries	0.805 ***	3.606	3.332	0.001
Political Risk Index	−0.121	0.004	−26.209	0.000
Material Footprint	−0.317 *	0.180	−1.761	0.079
Wald chi-sq	478.51 (0.000)
Sargan Test	18.264 (0.249)
Arellano–Bond Test AR (1)	255.889 (0.182)
Arellano–Bond Test AR (2)	261.749 (0.976)

Notes: *** p < 0.01, ** p < 0.05, * p < 0.10.

, followed by an interpretation of the main findings.

5.1. Objective 1: To Investigate the Persistence of Tourism Flows Through Path Dependence Mechanisms

This study examines the persistence of tourism flows through path dependence mechanisms. The coefficient on the lagged dependent variable is positive and highly significant (0.136, p < 0.01). The current year saw a 0.136% increase in visitor arrivals for every 1% increase in the prior year, confirming strong path dependence in tourism flows. This implies that past tourist arrivals generate self-reinforcing effects through mechanisms such as repeat visitation, habit formation, and reputation building via word-of-mouth. At the regional level, this persistence implies that early investments in the development of tourist destinations, like Asir, the Red Sea coast, or AlUla, can yield cumulative benefits since early visitors convert into ambassadors who draw other tourists to these same destinations. The path dependence finding supports the business rationale for early investment in environmental and social sustainability by indicating that sustainability projects that produce pleasant initial experiences can yield long-term returns through visitor loyalty and favourable referrals.

5.2. Objective 2: To Examine Sustainable Practices and Inbound Tourism

The Material Footprint, a proxy for environmental practices, shows a negative and weakly significant coefficient (−0.317) at the 10% level, on inbound tourism demand. This result indicates that decreased tourist visits are linked to increased resource usage and environmental stress. This outcome validates Hypothesis H1, which postulated a negative correlation between inbound tourism and material footprint.

This result offers verifiable proof of the application of sustainable development strategies in Saudi Arabia’s tourism industry. The negative coefficient indicates that the Kingdom’s reputation as a sustainable travel destination may currently be harmed by unsustainable consumption practices and ecological stress [18,19,47]. Although the effect remains small at the 10% significance level, it indicates that environmental factors are becoming increasingly important in travel decisions worldwide. This trend has been accelerated by the COVID-19 pandemic [8] and is in line with the Environmental Kuznets Curve theory, which holds that wealthier source markets are favouring more environmentally friendly travel destinations [48,49].

Material Footprint is an outcome-based signal of environmental strain from all economic activities, including tourism, even if it is an overall assessment of resource usage rather than a direct indicator of proactive actions like eco-certifications. The material footprint would decrease in relation to visitor arrivals if sustainability activities were successfully lowering resource use. Resource-intensive development may be starting to outweigh the benefits of attractiveness despite national initiatives like the Saudi Green Initiative, according to the negative coefficient. This conclusion is supported by the strong positive coefficients on prosperity indices, which separate the attractive aspects of development from its environmental costs [4,18,50].

This finding has several ramifications for sustainable development. First, it shows that even in a market dominated by religious and business travel, foreign travellers are starting to consider environmental performance when choosing a destination. Second, it implies that Saudi Arabia may be losing its reputation as a sustainable travel destination due to its present resource consumption practices, which are reflected in its material footprint. Third, it offers empirical evidence in favour of the sustainable tourism literature’s recommendation to incorporate environmental sustainability into tourism development strategies [4,18,19,38].

National indicators such as Material Footprint offer crucial diagnostic insights that need to be adjusted to regional contexts for national-level data to influence region-specific policies. The national signal focuses attention on regional hotspots where specific interventions like marine conservation, water management, and community participation are required, such as coastal building in the Red Sea, water use in desert cultural areas, and deforestation in Asir. Therefore, while regional planning deals with implementation unique to a given site, national findings suggest overarching goals [51].

5.3. Objective 3: To Assess Logistics Infrastructure and Tourism Demand

The third objective assessed the role of logistics infrastructure, measured by the Logistics Performance Index (LPI), in attracting international visitors. The LPI shows a positive and significant effect (5.545, p < 0.05), supporting Hypothesis H2 that logistics efficiency increases inbound tourism to Saudi Arabia. This underscores how seamless customs, quality infrastructure, and reliable transport enhance destination competitiveness by improving accessibility and reducing travel friction, directly shaping the visitor experience from arrival onward. Such logistical ease is critical for satisfaction and repeat visitation, aligning with research on its role in destination appeal [7,20,52].

The significant impact of the LPI aligns directly with Saudi Arabia’s Vision 2030 tourism goals. The Kingdom’s diverse visitor base, including religious pilgrims, business travellers, and luxury tourists, requires exceptional logistical proficiency. Successfully managing massive pilgrim flows during Hajj and Umrah, for instance, sustains its global reputation. Furthermore, the rise of “bleisure” travellers and digital nomads, who prioritize seamless connectivity and efficient services, reinforces the need for destinations that support a productive, mobile lifestyle [22,53,54].

Multi-level collaboration is needed to translate this national LPI finding into regional action. The Red Sea Development Company and the Royal Commission for AlUla are examples of specialized organizations that convert national logistics goals into region-specific master plans, such as low-impact heritage access systems for archaeological sites or maritime transport infrastructure for coastal resorts. Although maintaining uniform service quality across regions with different institutional capabilities is a problem, inter-agency cooperation through the National Tourism Strategy guarantees that national investments in airports and highways are prioritized based on regional tourism demands [50].

At the regional level, the effectiveness of logistics dictates not only whether foreign tourists can enter the Kingdom but also how they navigate its various tourist destinations. The expansion of regional airports, improved road systems linking mountain and coastal locations, and digital infrastructure that facilitates smooth cross-regional transport are all essential to sharing the benefits of tourism beyond gateway cities. From the perspective of sustainable development, effective logistics enables better management of visitor flows to sensitive natural sites, reduces traffic, and lowers per-person emissions.

5.4. Objective 4: To Evaluate Safety, Uncertainty, and Tourism Demand

The World Pandemic Uncertainty Index (WPUI), Human Rights Index (HRI), and Political Risk Index (PRI) were the three variables used to operationalise the fourth aim, which looked at how safety perceptions and uncertainty affect tourism flows.

WPUI is statistically insignificant, leading to the rejection of Hypothesis H3. This finding illustrates the global and symmetric nature of pandemic uncertainty, affecting all locations simultaneously, even though it may appear unexpected given the intensity of the COVID-19 epidemic, in contrast to destination-specific political risk. Saudi Arabia’s distinct tourist mix, which is primarily composed of commercial and non-discretionary religious pilgrimages, demonstrates resilience to shocks from around the world. Perceived dangers were further reduced by effective crisis management, which included digital adaptation and explicit safety procedures. Persistent effects may also be mitigated by the analysis period that includes both the shock and recovery phases; urban and remote areas may experience differing effects that merit further study [9,13,55].

Four likely explanations exist for this finding. First, the dominance of non-discretionary travel segments such as religious pilgrimage and business tourism, which are resilient to global shocks, reduces vulnerability to pandemic-related uncertainty [43,44]. Second, the Kingdom’s effective crisis management, including clear safety protocols and digital adaptation, mitigated perceived risks [9]. Third, other strong factors in the model, such as prosperity, human rights, and logistics performance, created a protective “safety bubble” [44,52]. Finally, the analysis period covers both the initial shock and the subsequent recovery phase, during which short-term disruptions transitioned into longer-term adaptations, diminishing the ongoing impact of pandemic uncertainty [9,26]. From a regional perspective, this insignificance suggests that different tourist destinations may have responded to the pandemic in various ways, with urban areas experiencing more difficulties and remote natural regions possibly benefiting from a shift towards outdoor visitation. These regional differences in crisis resilience warrant further investigation in future studies.

The Human Rights Index (HRI) offers strong positive significance (1.519, p < 0.01), supporting hypothesis H4a: This result shows that a country’s commitment to human rights is a powerful non-economic signal that affects people’s perceptions of safety, reliability, and credibility. Travellers associate more personal security, stable legal systems, and a lower chance of arbitrary treatment with respect for human rights. This broadens the notion of “safety” to include “institutional safety”, the guarantee that destination establishments will uphold the rights and dignity of guests, rather than just the absence of crime [24,25].

The high HRI coefficient probably reflects both the more general “modernization” and “openness” benefits of Vision 2030 policies as well as direct tourist concern for human rights. Social advances, such as the issuance of tourist visas, advancements in entertainment, and the empowerment of women, all indicate that Saudi Arabia is inviting foreign tourists. Rather than a thorough understanding of rights measurements, the HRI is correlated with these more general changes, reflecting tourists’ receptivity to enhanced institutional quality and destination openness. The significant PRI coefficient supports this explanation, demonstrating that visitors may not investigate human rights indices; they encounter institutional quality during contacts with authorities and local communities [11,25].

At the regional level, this translates into expectations for how tourists are treated in specific places, such as whether Asir Mountain residents welcome foreign visitors, whether cultural sensitivities are respected at AlUla’s heritage sites, and whether Riyadh and Jeddah provide inclusive spaces for a variety of tourists. Establishing the institutional trust that foreign visitors want will depend on region-specific measures, such as community participation in tourism planning, open local governance, and obvious cultural heritage protection.

The Political Risk Index’s negative and highly significant coefficient (−0.121, p < 0.01), supporting H4b confirming the basic tenet that political instability is a potent deterrent. This outcome precisely reflects that investors undervalue tourism-related assets when there is uncertainty [25,56]. Similarly, tourists are extremely cautious about their personal safety [57]. The PRI’s portrayal of internal strife, corruption, and religious tensions produces unpredictable environments that overwhelm other attractions. Even though specialist sectors often thrive in an uncertain environment [56], the mainstream tourism industry that supports Vision 2030 goals is extremely susceptible to political risk [25]. Political risk perceptions differ by region: foreign visitors may be cautious of border regions, historically violent areas, or places close to conflict zones, even when there is little risk at the national level.

Together, the HRI and PRI findings provide a comprehensive picture of how the governance environment affects travel. While the HRI gauges the existence of favourable conditions (rule of law, protection of rights), the PRI gauges the absence of unfavourable situations (instability, conflict, corruption). The data shows that “risk-free” is not enough for successful tourist locations; they also need to be actively “right positive.”

5.5. Objective 5: To Analyse the Economic Environment and Tourism Demand

The fifth objective analysed the influence of the economic environment on inbound tourism, operationalized through Prosperity Index of Saudi Arabia (PROS_KSA), Prosperity Index of origin countries (PROS_ORI), and relative Consumer Price Index (CPI). The prosperity index of Saudi Arabia exerts a large positive and significant effect (2.187, p < 0.01), supporting H5a. This reflects how advancements in socioeconomic development, governance, and living standards strengthen the Kingdom’s attractiveness to international visitors. This finding is consistent with the literature, which highlights the importance of consumer confidence and macroeconomic stability in determining tourism demand [40,58]. A stable, modern, and well-functioning destination is indicated by a high Prosperity Index, which lowers perceived risk for prospective travellers and improves the overall standard of the visiting experience. It serves as a comprehensive indicator of progress that surpasses more specific economic measurements. This result is in line with the growing recognition that travellers, particularly those taking long-distance trips, look for locations that provide not only attractions but also dependability and a high level of auxiliary infrastructure. To fully achieve its tourism potential, national wealth must be reflected at the regional level in the form of observable improvements in regional infrastructure, such as contemporary airports in regional centres, dependable utilities in remote places, and high-quality lodging in developing destinations. The necessity of integrated policies further supports this, as ref. [31] discussed.

As for origin-country conditions, the Prosperity Index of origin countries is positive and highly significant (0.805, p < 0.01). This supports the hypothesis, H5b, that higher levels of prosperity in sending countries stimulate outbound tourism demand through increased disposable income and enhanced well-being. According to ref. [59], tourists from wealthy countries have the financial means and self-assurance to travel abroad. This finding demonstrates that demand from high-income nations, whose inhabitants are less susceptible to economic worries that may discourage travel from other regions, considerably fuels Saudi Arabia’s inbound tourism sector, as noted in ref. [27] in the context of European volatility. This implies that marketing initiatives for the promotion of regional tourism should be adapted to align the unique attractions of Saudi regions, luxury ecotourism in the Red Sea, adventure tourism in Asir, and cultural immersion in Al Ula, with the preferences of thriving source markets.

The relative Consumer Price Index shows a statistically insignificant negative coefficient, leading to rejection of H5c. This indicates that Saudi Arabia does not compete as a low-cost destination. Price sensitivity is secondary for its target tourists, such as high-end leisure, business, and religious travellers, who prioritize experience, necessity, or spiritual value over cost. This aligns with research noting that price elasticity varies significantly across segments and is often outweighed by other value propositions [60,61]. The finding suggests Saudi Arabia’s tourism strategy is successfully cultivating a value-based appeal that transcends price competition, like successful cultural tourism models observed elsewhere [36]. With luxury coastal resorts competing on exclusivity, heritage sites competing on authenticity, and mountain destinations competing on nature-based experiences, different regions can position themselves at different price points based on their unique assets. This has significant regional implications because it prevents destructive price competition that could jeopardize sustainability.

5.6. Objective 6: Travel Cost and Tourism Demand

The cost of travel, proxied by the interaction of great-circle distance and oil prices, carries a negative and weakly significant coefficient at the 10% level. This supports Hypothesis H6. This aligns with gravity-model predictions that transport costs deter mobility [31], although the weak significance reflects Saudi Arabia’s unique visitor mix. Religious and business travellers, who dominate arrivals, exhibit price-inelastic demand due to non-discretionary or employer-funded travel [60]. As a major oil exporter, rising fuel prices may also stimulate domestic income and tourism investment [42], partly offsetting cost barriers. While cost remains a conditional deterrent for leisure tourists, the destination’s core appeal withstands this friction, indicating that marketing should emphasise distinct value propositions, supplemented by competitive pricing to broaden appeal.

5.7. Synthesis and Regional Implications

Taken together, these results show that a complex interaction of dynamic impacts, economic fundamentals, institutional quality, and sustainability factors, all of which function differently across the Kingdom’s several tourism regions, determines the demand for inbound travel to Saudi Arabia. Given the strong benefits of human rights and logistics performance, as well as the growing negative signal from material footprint, regional tourism planners should prioritize accessibility, local governance, and the implementation of measurable environmental protections that are specific to each region. Word-of-mouth and repeat business from tourists demonstrate that a region’s reputation, which is based on real cultural participation and sustainable practices, is a long-lasting competitive advantage. Given that price factors are negligible, Saudi Arabia’s regions can compete on sustainability and quality rather than price, establishing themselves as high-end sustainable travel destinations in keeping with Vision 2030’s goals. Moreover, the results show that sustainability needs to be ingrained as a region-specific commitment that shapes visitor experiences, safeguards local resources, and guarantees that tourism benefits communities throughout all Saudi Arabia’s diverse and quickly developing destination regions. The summary of Hypothesis Testing Results is presented in

Table 9. Summary of Hypothesis Testing Results.

Hypothesis	Variable	Exp/Sign	Actual Sign	Outcome
H1	Material Footprint	−	−	Supported
H2	Logistics Performance Index	+	+	Supported
H3	World Pandemic Uncertainty Index	−	Insignificant (n.s)	Rejected
H4a	Human Rights Index	+	+	Supported
H4b	Political Risk Index	+	−	Reversed
H5a	Prosperity Index (KSA)	+	+	Supported
H5b	Prosperity Index (Origin)	+	+	Supported
H5c	Consumer Price Index	−	n.s	Rejected
H6	Travel Cost	−	−	Supported

.

The Wald test confirms the overall significance of the model (χ² = 478.51, p < 0.01). The Sargan test fails to reject the null hypothesis of instrument validity (p = 0.249), indicating that the instruments used in the GMM estimation are appropriate. Furthermore, the Arellano–Bond serial correlation tests show no evidence of autocorrelation in the residuals, as both the AR (1) and AR (2) tests are statistically insignificant. These diagnostic results confirm the robustness and validity of the dynamic panel GMM estimation.

6. Conclusions

This study examined inbound travel demand to Saudi Arabia (2009–2023) using a dynamic panel GMM model across 16 origin countries. Framed within the context of regional tourism development, the findings highlight the critical role of perceptual and institutional factors beyond traditional economic drivers in shaping visitor flows to the Kingdom’s diverse tourism regions. Key positive influences include the Kingdom’s economic prosperity, commitment to human rights, and exceptional logistics performance, alongside origin-country prosperity and destination political stability. All these elements work together to decide if Saudi Arabia’s regions can successfully draw and maintain foreign tourist flows. Tourism flows also show significant path dependence, where past visits stimulate future demand through word-of-mouth and repeat travel, underscoring the importance of building regional reputation through authentic visitor experiences.

According to the study’s regional lens, sustainability needs to be ingrained as a region-specific commitment rather than as a general national quality. The notable benefits of human rights and logistics performance, along with the growing unfavorable signal from material footprint, indicate that different regions face unique sustainability challenges that call for customized solutions. These challenges include community engagement in mountain destinations, water management in desert regions, marine conservation in coastal areas, and heritage preservation in archaeological sites.

Saudi Arabia is successfully attracting experience-focused tourists, as relative costs and global pandemic uncertainty were insignificant for inbound tourists. However, travel costs remain a conditional barrier for leisure travel. A growing concern is the material footprint, whose weakly significant negative coefficient signals emerging vulnerability. While not yet a major decision factor, demonstrating credible environmental sustainability will become increasingly important for appealing to high-end, environmentally conscious tourists who evaluate destinations based on region-specific environmental practices.

This study demonstrates that integrating environmental stewardship, logistical effectiveness, institutional excellence, and economic viability is necessary for sustainable regional tourism. The fact that tourism continues to grow through word-of-mouth and return visits shows that a region’s reputation, which is based on real cultural involvement and sustainable practices, offers Saudi Arabia’s many tourist regions a sustainable competitive edge.

7. Policy Implications

The results provide evidence-based recommendations for growing Saudi Arabia’s tourism sector across its diverse regions in line with Vision 2030. A strategic shift in marketing is needed, moving beyond cultural and religious heritage to actively rebrand the Kingdom as a modern, prosperous, and ethically governed destination, highlighting its institutional safety and social reforms to attract high-value tourists.

Substantial policy focus must remain on enhancing logistics performance through both hard infrastructure and soft digital systems, prioritizing seamless processes like biometric borders and streamlined e-visas to reduce traveler friction. The negative signal of the Material Footprint, though currently weak, is an early warning; demonstrating tangible sustainability through green certifications and renewable energy in tourism is crucial for future-proofing appeal.

Political stability remains a non-negotiable foundation and must be consistently communicated. Since traditional cost factors are less significant, marketing should aggressively target less price-sensitive segments like luxury leisure and business travelers, while tactical measures like value packages can attract cost-conscious visitors. Effective implementation requires unparalleled inter-agency coordination, supported by a unified national tourism data hub for agile, evidence-based policymaking.

8. Limitations and Future Studies

There are a few limitations to this study. 01 Measurement and aggregate bias may have been introduced due to the panel’s comparatively short duration and its reliance on composite measures, such as political risk and human rights. 02. The study was unable to incorporate some qualitative and perceptual elements that are becoming increasingly significant in the travel industry. 03. To offer more convincing evidence, future research should make use of high-frequency mobility indicators and more thorough microdata. To assess the robustness of the findings, the study should be conducted again using different econometric techniques. Future research could address the role of emigrants, reflecting a structural substitution between migration and tourism in the Saudi context. Analysing the effects of Vision 2030 initiatives, like giga-projects and visa liberalization, on inbound tourism is a logical next step for this effort. More generally, studies are required to ascertain how wealth, institutional quality, and sustainability practices interact to influence destinations’ long-term competitiveness.