Development of E-Tourism to Achieve Excellence and Sustainable Development in Tourism: Ha’il Region Case Study

Abstract

E-tourism is one of the most important levers for social and economic development. The World Tourism Organization (UNWTO) has declared 2017 the year of sustainable tourism for development. When relying on sophisticated communication technologies. Certainly, these innovations in the field of this analysis aim to determine the main factors of e-tourism achieving excellence in tourism in the Ha’il region. Tourism has disrupted this sector with new and different dimensions of tourism development. This has led various regions and countries to accelerate the search for the tools necessary to achieve excellence and sustainable development in tourism. This research aims to study the development factors of e-tourism to achieve excellence and sustainable development in the tourism of the Ha’il region. Therefore, the gap in this research is represented by the lack of a comprehensive analysis of the interaction between e-tourism development factors and factors that promote tourism excellence in the Ha’il region to achieve sustainable development. For this reason, we applied two analyses: in the first one, we used a statistical analysis based on the survey sent to 500 employees from tourism companies. This analysis aimed to determine the main factors that influence e-tourism to achieve excellence in tourism in the Ha’il region. The results of the statistical analysis gave us the following elements: There are only four main factors that allow authorities to achieve excellence in tourism in the Ha’il region, namely Supporting Technology (ST), Competition Pressure (CP), Human Resource Skills (HRS), and Socio-Cultural (SC) factors. Then, we proceed with a second analysis to determine the main factors of excellence in tourism that drive the achievement of sustainable development in tourism in the Ha’il region. For this reason, we applied an artificial intelligence method based on the unsupervised machine learning technique. The results obtained from the machine learning technique show that five factors determine the excellence in tourism and reveal the sustainable development of Ha’il’s tourism. These factors are ST, CP, Financial Performance (FP), HRS, and Government Commitment (GC). Consequently, we consider that all the factors found in both analyses are important to realise excellence in tourism and sustainable development in tourism. However, to achieve sustainable development in tourism in Ha’il, Saudi authorities must develop the three common factors found in the two analyses, namely ST, CP, and HRS. Therefore, this study is expected to help introduce ways of excellence in e-tourism by marketing it using artificial intelligence, enabling the development of the tourism sector in an enormous area. It will also contribute significantly to the formulation of plans, policies, and strategies that will largely ensure the development of Ha’il’s tourism sector.

1. Introduction

In recent years, the tourism sector has faced significant impacts with the outbreak of the COVID-19 pandemic. This has made new digital technologies and innovations an important role in the significant shift towards the development of the “e-tourism” system. This phenomenon is defined as the use of digital technologies in the tourism industry to access data on events, news, and reservations related to tourism and to guide travellers during their trips. Some findings define e-tourism as a process of digitising all functions, services, and stages of management and business in the value chain of the tourism system to increase the efficiency of interaction between businesses, consumers, and the public to achieve competitive sustainability [1]. Thus, e-tourism takes into account all the services needed for online travellers. This makes travelling easier for tourists and encourages them to travel to different countries. This has created a new tourist environment that encourages competition between countries to attract more tourists. Therefore, e-tourism has become very important due to the role it plays in stimulating the tourism movement in the country, which adopts it in a systematic way that benefits the development of society and the economy.

Therefore, it has become necessary for different countries to develop and complete e-tourism to achieve excellence and sustainable tourism. In this context, Saudi Arabia is seeking to develop its tourism sector through its development strategy and Vision 2030 to change the Saudi tourism sector since the launch of the National Tourism Strategy in 2019. However, Saudi Arabia topped the United Nations Tourism Ranking for the growth of international tourist arrivals in 2023 compared with 2019, achieving an increase of 56%. This illustrates the actual context for developing the Saudi tourism approach. Consequently, it is important to analyse the following problem: how can e-tourism be developed to achieve excellence and sustainable development in tourism in Ha’il?

This study aimed to identify the most important development factors for e-tourism in the Ha’il region to achieve excellence in tourism and sustainable tourism. Therefore, this study is important to know the role of artificial intelligence in the development of electronic tourism in the Ha’il region of Saudi Arabia. Therefore, it will be a new addition to the Library of Studies and Literature on this subject. This study also supported Ha’il’s tourism sector as an effective economic sector that contributes significantly to sustainable development, addresses many social problems, and contributes significantly to the advancement of development projects. On the other hand, this study helped introduce ways of excellence in e-tourism by marketing it using artificial intelligence, allowing the development of the tourism sector in the Ha’il region. It contributes significantly to the formulation of plans, policies, and strategies that will significantly ensure the tourism sector’s development, as this study seeks to provide information.

The remainder of this paper is organised as follows. Section 2 presents a review of the literature on e-tourism, excellence in tourism, sustainable tourism, and an overview of tourism in the Ha’il region. Section 3 presents a conceptual model and hypothesis. Section 4, the methodology of this study. Section 5 provides conclusions and recommendations.

2. Review of the Literature

2.1. Theoretical Background of E-Tourism

The United Nations Conference on Trade and Development first used the term “e-tourism” in a concrete way in 2000 and was intimately related to digital trade [2]. E-tourism, often known as digital tourism, encompasses all activities related to electronic tourism acts. It refers to tourism transactions in which parties communicate online rather than directly through physical exchanges or connections. According to the United Nations World Tourism Organisation (UNWTO), “e-tourism is defined as the virtual way of making tourists travel and giving an overview of a possible trip to Internet users to turn them into tourists, by offering them electronic brochures using efficient navigation through the diversity of tourist offers” ([3] page 36), Condratov [4] defines e-tourism as a means to establish commercial relations mainly sales while using the Internet. This involves offering tourist-related products, such as flights, hotel reservations, and car rentals. Fong et al. [5] consider that the notion of digital tourism does not exist in the literature, but we speak of “digital culture” related to tourism innovation. Tourism refers to the time when traditional travel agents, tour operators, national tourist offices, airlines, car rental companies, hotels, and other accommodation providers offer Internet services that allow travellers to organise their trips online. Kazandzhieva and Santana [1] believe that e-tourism is an objective reality; its rapid dynamics and development lead to significant changes in the traditional model of the classical tourist system. Thus, the creation of an e-tourism system is a logical continuation of the digitisation of all the processes of the value chain of the travel and tourism industry. This is how the development factors and the background of tourism support the economy [6]. However, IT offers many positive perspectives and possibilities for development, particularly e-tourism, but consequently creates serious problems in terms of society, economy, politics, and individuals. Li et al. [7] described the key forms and factors to strengthen tourism through the implementation of economic, social, and environmental goals for the development of tourist destinations. His study focused on travel agencies that have integrated new technologies into their tourism services. As a result, he discovered that it had a positive impact on the environment and local communities. As a result, consumer motivations for sustainable e-tourism are increasing.

Alternatively, tourism has an important economic, environmental, and social impact in the modern world. This is how the development of this industry depends on three elements to be sustainable. The environment, skills and relationships, and market power are the three determining components of tourism development [8]. The equal and required development of the latter promotes the development of e-tourism and, as a result, sustainable development in tourism.

2.2. Sustainable Development in Tourism

The concept of sustainability was introduced into tourism based on the notion of sustainable development after the publication of the report of the World Commission on Environment and Development (WCED). After a few years, researchers defined the notion of Sustainable Tourism Development (STD) and linked it with Sustainable Development Goals (SDGs) [9]. Furthermore, the United Nations considers that the concept of sustainable development includes understanding the effects of development through the use of nontraditional characteristics to achieve development in different sectors, particularly tourism. The dynamics of tourism’s development are based on attention to privacy and quality, which requires the development of new forms of tourism. New forms include e-tourism, which at the same time improves the travel experience and are important factors in ensuring sustainable development [10]. In addition, the development of e-tourism helps to encourage responsible travel practices such as eco-friendly ones. This enhances the value of adapting to essentially innovative trends from modernity and sustainability in the ever-changing tourism industry. Fauzel and Ragoobur [11] argued that the development of tourism is essential to realise sustainable tourism because it is specific and allows us to achieve excellence. Similarly, Fennel [12] has shown that sustainable tourism development is a dynamic process that constantly faces new challenges as applied technologies and aspects of tourism consumption evolve. Therefore, sustainable tourism depends on the development of e-tourism to be more competitive and to realise the excellence of one or more regions. Aghili et al. [13] showed the importance of industrial tourism, particularly e-tourism, in the achievement of sustainable development. In addition, two approaches are found in the tourism literature to measure the excellence of destination tourism. The first finding related to competitiveness indicators is explained by survey data [14,15]. The second finding uses quantitative data measuring dimensions of destination competitiveness [16,17]. The United Nations World Tourism Organisation (UNWTO) announced in 2017 that it was important to “make tourism a catalyst for positive change”. Therefore, tourism is an essential tool to promote the SDGs in Agenda 2030, which includes 17 targets. All objectives help to achieve the development of the tourism sector directly or indirectly, but what is most important is what the United Nations has set out in the Sustainable Development Agenda (2015–2030). The United Nations has focused on sustainable development factors for tourism to achieve sustainable development through Sustainable Development Goals 8, 12, and 14. We found that item 8.9 of SDG8 is dedicated to developing and implementing policies to promote sustainable tourism that creates jobs and promotes local culture and products. We also find element 12.b of SDG12, which aims to develop and implement tools to monitor the impact of sustainable development on sustainable tourism that creates employment and promotes local culture and products. Similarly, item 14.7 of SDG14 has been identified as one of the tools for increasing the economic benefits of the least developed countries [18].

2.3. Tourism in the Ha’il Region

Ha’il is located in the northwest part of Saudi Arabia. It is bordered to the north by the northern border areas of Al-Jawf, to the west by Tabuk, and to the southwest by Medina. Ha’il represents 6% of Saudi Arabia’s territory and has a rich historical heritage. In addition, several factors make it a tourist destination, including the diversity of terrain, such as mountains, valleys, and plains. Its climate is diverse, ranging from relatively cold to hot and dry, with the presence of wild gardens. However, it has not received the necessary attention to become an international tourist destination. Therefore, it is necessary to establish a tourism development strategy in the Ha’il region to encourage internal competition between regions towards excellence in tourism. Therefore, the tourist attractions of any region are an integrated compound of human and natural characteristics and tourist establishments to form a solid base to create opportunities for the development of the tourist movement. These factors directly or indirectly affect tourism activity as attractive factors. The following table shows how much tourism in Ha’il has evolved in the last five years.

According to

Table 1. Evolution of tourism in the Ha’il region.

	2019	2020	2021	2022	2023
Number of domestic tourism trips	973	1160	1860	2174	2582
Number of tourists coming to Ha’il	15	10	90	105	124
Number of trips for business purposes	51	34	65	75	88
Number of tourists visiting the family	716	869	1254	1580	1900

Ha’il Tourism Development Association “https://dev.hail.is.sa/ (accessed on 27 June 2024)”.

, between 2019 and 2023, domestic tourism trips increased by 62.31%, inbound tourism to Ha’il increased by 87.90%, tourism trips to work increased by 42.04%, and visiting friends and relatives increased by 62.31% (all the percentages were calculated by the average annual growth rate). The improvement in these rates in the Ha’il region is due to several reasons, such as the increase in investment in the region by opening several resorts, cafes, and entertainment venues. The number of events, such as the “Ha’il International Rally”, to which the outer courtyards of the Mejlis Park have been allocated, has also intensified. The activities range from handicrafts to folk cuisine, challenge and adventure events, the effectiveness of Saudi falcons shows, the events of the Social Development Centre, the events of the Order of Knowledge and Disbelief Authority, Islamic affairs events, events of Ha’il University, health affairs events, passport events, events of the Department of Education, events of the Municipal Council, events of the General Directorate of Drug Control, and events of the Women’s Marketing Festival. All of these events coincide with vacation days in Saudi Arabia. This facilitates the mobility of the region.

Figure 1 shows us the increase in domestic tourism, especially in the Mecca region, due to its importance in religious tourism for the performance of Hajj and Umrah since Riyadh, as the capital has a strong turnout, and the eastern region due to its importance in maritime tourism, especially as a border area with Bahrain “https://stats.gov.sa/ (accessed on 2 June 2024)”. For other regions, domestic tourism is moderate to weak. Regarding the Ha’il region, we note that domestic tourism is relatively high compared with other interior talks because the region has undergone significant development in the tourism sector and provides high-quality tourist facilities, such as hotels, restaurants, and leisure centres. These facilities provide convenience and facilities to visitors and enhance the attractiveness of the area as a tourist destination.

3. Conceptual Model and Hypothesis

3.1. Conceptual

The model in this study was based on some factors conceptualised and measured in different studies in the literature [19,20]. All factors were constructed from a questionnaire survey, which was divided by theme.

Table 2. E-tourism development factors.

Dimensions	Development Factors of E-Tourism	Code	Description	References
Environment Factors	Supporting Technology	ST	Capability to implement technology to develop e-tourism	[21,22,23]
	Financial Performance	FP	Financial performance of the institution to develop e-tourism	[19]
	Government Commitment	GC	Local motivation provided by the government for e-tourism	[20]
Skills and Relationship Factors	Human Resource Skills	HRS	Human resource skills and competencies required to develope-tourism	[24]
	Awareness	AW	Degree of awareness, ease, and confidence in the adoption of e-tourism	[25,26]
	Owners’ Knowledge Management	OKM	Owners’ knowledge and information about e-tourism and its benefits and uses	[20]
	Socio-Cultural	SC	Degree of perception of culture, language, perceived benefits, thinking on e-tourism	[27]
Market Forces Factors	Competition Pressure	CP	Pressure to develop the adoption of e-tourism because of competition	[20,28]
	Market Readiness and Size	MRS	Degree of market readiness to develop e-tourism	[20]

presents different proposed factors that affect e-tourism.

3.1.1. Supporting Technology

Due to the challenges associated with e-tourism, many travel agencies are trying to use modern technology to improve their services and become more competitive in the market. Therefore, according to Subramanian and Masron [23] and Shrestha and Jeong [22], ST is one of the fundamental factors promoting the development of e-tourism. In fact, it allows for the increase in efficiency and productivity of tourism businesses in a region. Likewise, ICT integration facilitates information exchange, reduces costs, and provides a unique competitive advantage to tourism businesses.

3.1.2. Financial Performance

Some researchers, like Ji and Yin [19], suggested the importance of tourism businesses’ attachment to social responsibility. In fact, the FP of a tourism company allows it to be more efficient, since it is able to diversify its investments in the tourism sector at a high level.

3.1.3. Government Commitment

Contextual factors specific to the development of e-tourism in a region include GC. This factor helps to encourage establishments to present new initiatives [20].

3.1.4. Human Resource Skills

Many researchers believe that staff skills are manifested by their abilities to manage e-tourism services. Werthner [24] believes that the efficiency of the e-tourism services offered by tourism companies requires a relevant and precise speed of execution for the service to be efficient. This reflects real HRS.

3.1.5. Awareness

The participants believe that the adoption of e-tourism is greatly influenced by AW, convenience, clarity, and comfort. It also agrees with earlier research by [25,26], and others that highlighted simplicity of use and clarity as two common characteristics that affect e-tourism.

3.1.6. Owners’ Knowledge Management

According to Lama et al. [20], management support is also an important factor that influences the decision on e-tourism. A good relationship between OKM and employees positively influences performance.

3.1.7. Socio-Cultural

Concerning the SC aspect, it is noted as an important factor in several studies in the literature. Eyisi et al. [27] proved that e-tourism is strongly influenced by various cultural norms and values found in various countries and regions, such as language, morals, and customs.

3.1.8. Competition Pressure

Competition Pressure represents an important factor for the development of e-tourism [28]. In fact, CP motivates tourism companies to improve their services to guarantee their presence and conquer market share.

3.1.9. Market Readiness and Size

Lama et al. [20] demonstrated the importance of MRS and its diversity in the development of e-tourism. An extended and diversified market allows businesses to benefit from retail strategy. The expansion of the market and its diversification allow for the development of competitive advantage [20].

3.2. Hypotheses

The hypotheses were formulated to validate the adoption of the factor in e-tourism. The suggested topics were divided into three types of factors. Based on these themes, eight hypotheses were developed to test the impact on the acceptance of e-tourism. The hypotheses were divided according to the dimensions of e-tourism to test their effect on excellence in tourism: (i) the environment factors were tested by $({\mathrm{H}}_1$, ${\mathrm{H}}_2$, ${\mathrm{H}}_3)$; (ii) skills and relationship factors were tested by $({\mathrm{H}}_4$, ${\mathrm{H}}_5$, ${\mathrm{H}}_6, {\mathrm{H}}_7)$; and (iii) market forces factors were tested by $({\mathrm{H}}_8$, ${\mathrm{H}}_9)$.

H₀: 

There is no influence for the development of e-tourism factors on Excellence in Tourism (ET).

H₁: 

The development of ST positively influences excellence in tourism.

H₂: 

The development of FP positively influences excellence in tourism.

H₃: 

The development of GC positively influences excellence in tourism.

H₄: 

The development of HRS positively influences excellence in tourism.

H₅: 

The development of AW positively influences excellence in tourism.

H₆: 

The development of OKM positively influences excellence in tourism.

H₇: 

The development of SC positively influences excellence in tourism.

H₈: 

CP positively influences the excellence in tourism.

H₉: 

Promoting MRS positively influences excellence in tourism.

The proposed research model is shown in Figure 2.

4. Methodology

This research was divided into two sections. The first was about statistical analysis. The purpose of this analysis was to determine the main development factors of e-tourism to achieve excellence in tourism in the Ha’il region. Subsequently, the second section determined the development factors of e-tourism to achieve sustainable tourism development in the Ha’il region (see Section 4.1 and Section 4.2).

4.1. Measurement and Results of Statistical Analysis

In this phase of the statistical analysis study, we used the survey questionnaire observations method for closed data. We started by preparing the questionnaire. In fact, the survey was divided into three axes related to the implementation of e-tourism in the Ha’il region. The surveys included 25 paragraphs divided into eight components distributed into three types for the environmental factors of the development of e-tourism. Furthermore, we supposed four development factors for e-tourism, called relations factors, and two factors on the market dynamics in the Ha’il region. All of these factors were considered variables relevant to our research hypothesis. Therefore, the questionnaire was sent to the employees of tourism companies in the Ha’il region, as well as to carriers of customers travelling by road and air. After the compilation of the responses, various options were explored using the SPSS version 27.

However, after distributing the questionnaire, we received two types of responses that depended on the sets of questions for the demographic and study summary (see Table 2). This was to facilitate the understanding of the questions addressed to the Ha’il region in Saudi Arabia (the questionnaire was translated into Arabic). We also used a five-point Likert scale ranging from 1—strongly disagree to 5—strongly agree. The relative weight was also calculated to determine the appropriate range of weighted mean values for each score. So, the responses to the questionnaire distributed to 500 employees of tourist companies in the Ha’il region in September 2024, as well as customer carriers travelling by land and air, were compiled using Google Forms in the electronic design of the questions and then distributed through WhatsApp to obtain as many responses as possible. As a consequence, 489 responses were received, of which only 426 were usable and, therefore, submitted for additional data analysis.

4.1.1. Demographic Profile of the Respondents and Descriptive Statistics

In this section, we note in

Table 3. Sample demographic profile.

#	Demographic Factors	Category	Frequency	Percent
1	Gender	Male	317	74.4
		Female	109	25.6
3	Age	18 to 30 years	173	40.6
		30 to 40 years	138	32.4
		40 to 50 years	70	16.4
		Above 50 years	45	10.6
4	Experience in tourism (Work Experience)	Less than 1 year	44	10.3
		1 to 5 years	198	46.5
		5 to 10 years	95	22.3
		Above 10 years	89	20.9
5	Nationality	Saudi	127	29.8
		Non-Saudi	299	70.2

that the members of the majority of the Working Group in the tourism sector are men (74.4%) of the youth group (18–30 years) at a rate (40.6%). This shows that work in this sector has been newly developed in the Ha’il area and appeals to the young group. The share of workers’ experience in this sector is less than 5 years (46.5%). This is due to the promotion of tourism projects in recent years according to the objectives of the internal development of Saudi Arabia. We also show that most of the tourist workers in the Ha’il region are non-Saudi (70.2%). This is because many managers and entrepreneurs are Saudi, while the majority of employees are from other nationalities.

4.1.2. Discussions of the Results of the First Analysis

Statistical analysis began with the normality test, which confirmed that the data were usable with a limit of −3 to 3. The asymmetry and kurtosis of each questionnaire were below the limit. For each factor in the model, descriptive statistics were calculated. The averages ranged from mean 2.784 (OKM) to 4.078 (HRS) and were statistically significant. These results indicate that the factors explain the proposed model. That is, all components represent the average perception of the approved variable (e-tourism).

Therefore, it is important to verify the reliability and validity of the model estimation. For this, we used the alpha Crombach test to determine the internal consistency of the model factors. Furthermore, the alpha Cronbach values represented (>0.6). For this reason, it was acceptable and reliable for exploratory research. We can also consider that the values above 0.7 are recommended for the confirmation study. Furthermore,

Table 4. Correlation matrix and descriptive statistics.

Factors	Mean	St Deviation	α Crombach	AVE	ET	ST	FR	GC	HRS	AW	OKM	SC	CP	MRS
ET	3.996	0.660	0.903	0.862	1
ST	4.018	0.674	0.900	0.898	0.878 0.000	1
FP	3.051	0.833	0.873	0.865	0.295 0.000	0.278 0.000	1
GC	3.593	0.768	0.868	0.864	0.329 0.000	0.275 0.000	0.402 0.000	1
HRS	4.078	0.624	0.867	0.862	0.835 0.000	0.891 0.000	0.285 0.000	0.334 0.000	1
AW	3.641	0.745	0.835	0.861	0.286 0.000	0.333 0.000	0.393 0.000	0.304 0.000	0.387 0.001	1
OKM	2.785	0.757	0.831	0.861	0.489 0.000	0.259 0.000	0.762 0.000	0.351 0.000	0.242 0.000	0.344 0.000	1
SC	3.827	0.679	0.872	0.861	0.489 0.000	0.453 0.000	0.243 0.000	0.248 0.000	0.429 0.000	0.283 0.000	0.235 0.000	1
CP	3.500	0.817	0.873	0.886	0.403 0.000	0.334 0.000	0.395 0.000	0.826 0.000	0.374 0.000	0.283 0.000	0.346 0.000	0.259 0.000	1
MRS	2.809	0.802	0.775	0.824	0.243 0.000	0.268 0.000	0.291 0.000	0.262 0.000	0.248 0.000	0.283 0.000	0.346 0.000	0.305 0.000	0.124 0.000	1

confirms the reliability of the survey data. Regarding the validity of the content, Table 4 shows that the results of the validity and content test were verified. The validity of the content was tested by a group of three experts to verify the questionnaire questions from our study. The result showed the validity of the convergence by calculating the extracted average difference (>0.5) and the estimated load value of the normal factor load (>0.7). The validation of the distinction was also obtained by comparing the quadratic root values of the average variability extracted with the correlation values of the variables individually. The Average Variance Extracted (AVE) for each construction was also calculated (AVE > 0.5), which means that all variables explain the dependent variable. Consequently, the construct values are reliable and can be used as justification. We also verified the structural model according to the relationships formulated between the constructions. The structural model was estimated using several measurements, including Goodness of Fit (GoF): χ²/df < 5, Mean Square Error (RMSEA < 0.08). Also, the standard root residue (SRMR < 0.1) was calculated as the comparative indicator.

In addition, Table 4 shows that all correlation factors between independent variables (ST, FP, GC, HRS, AW, OKM, SC, CP, MRS) and dependent variables (ET) had a statistical function of 0.01 and 0.05. The value ranged from 0.243 (ET to MRS) to 0.489 (ET to OMK as well as SC). This indicates that the repellent relationship between all variables is relatively average but very strong in some variables, such as ST (0.878) and HRS (0.835). This demonstrated that the massive use of information technology by tourism organisations increases profits. However, the organisation needs superior human resources in the computer field. Therefore, as our study on technology and HRS shows, these are complementary and relevant factors, as shown by the ST variant, and HRS (0.891) are important for tourism organisations to achieve their ideal vision and mission.

Before discussing the analysis of multiple regression results, let us start with a summary of the different dimensions of the construction studied (environmental factors, skills and relationship factors, and market forces factors) shown in Figure 2. First, the overall averages are high (more than 4 on a five-point Likert scale) for environmental factors, particularly for ST and HRS, and for skills and relationship factors constructions that are related factors. This means that, in general, respondents consider technology to be very important in bringing customers closer to tourism firms, and the same applies to employees who must master new technologies to offer a good performance. Similarly, the respondents agree with the statements relating to the dimensions of the relationship, such as the dimension of market forces factors. This is generally small and has the lowest average (2.809). Therefore,

Table 5. Results of the measurement model.

#	B	St-Error	Standardized Coef. Beta	T	Sig
ST	0.606	0.480	0.618	12.686	0.000
CP	0.706	0.190	0.940	3.961	0.000
HRS	0.221	0.520	0.208	4.267	0.000
SC	0.930	0.240	0.096	3.882	0.000
ANOVA
Sum Square	148.7222
Mean Square	37.181
F	423.767
Sign	0.000
Model Regression
R	0.895
R-Square	0.801
Adjusted R Square	0.799
R Square Change	0.007
F Change	15.068

represents the multiple regression to measure the relationship between the dependent variable (ET) and the independent factors. The estimates’ results showed that the R square is 0. This means that about 80.10% of the variation in the number of Ha’il’s tourism excellence factors was taken into account by the quality variables of e-tourism. Furthermore, 19.9% were influenced by other external factors. This research also showed that, when examined with a 95% confidence level, the four factors of different dimensions were found to be significant (0.000). The model was found to be important. According to Santos et al. [29], the performance of e-tourism is directly and positively related to Supporting Technology and is considered an important element. Furthermore, the results of the regression measurement show that the model is important. However, according to Santos et al. [29], the performance of e-tourism is directly and positively related to technology support and is an important element as users consider their past experiences with a company relevant to their management decision. These results are consistent with previous studies, which concluded that the higher level of HRS is related to previous experiences with a company where technological support promotes skills and relationship factors. Tourists will be felt both in the company and at the selected destination. After applying several experimental models, we measured the impact relation between ET and the different variables presented in our hypothesis. We retained the best ANOVA analysis model that reflected the most variables validated in regression. Also, ST, CP, HRS, and SC identified factors as significant influencers of e-tourism, with a p-value of 0.000, indicating strong statistical significance. This suggests that these factors meaningfully contribute to enhancing the e-tourism experience. Their positive influence implies that strategies focusing on these elements could improve user engagement and satisfaction. Overall, the analysis underscores the critical role of these variables in shaping effective e-tourism initiatives. Indeed, the probability associated with Fisher’s F is 0.000. However, the variables have a good explanatory power of the model (80.1%) of the total variance with a significance threshold of p = 0.000. The Beta standard is between 0.096 and 0.618 with a statistical significance ˂ 5% (p = 0.000) that shows the positive effect of the variable on e-tourism.

Similarly, the results of the model regression analysis confirmed the findings. Skills and relationship dimensions explain 80.1% of the total variance with a significance threshold (0.007). Therefore, the hypotheses (${\mathrm{H}}_1$, ${\mathrm{H}}_4$, ${\mathrm{H}}_7$, ${\mathrm{H}}_8$) were accepted. This indicates the importance of these four factors in determining the development of e-tourism and achieving excellence in tourism in the Ha’il region. So, the hypothesis (${\mathrm{H}}_2$, ${\mathrm{H}}_3$, ${\mathrm{H}}_5$, ${\mathrm{H}}_{6,}{\mathrm{H}}_9$) were rejected.

In fact, in the first model, we only presented a single ST factor, which was excellence in tourism. The second model gave two determining factors of excellence (ST and CP). Subsequently, in a third model, the results provided ST, CP, and SC. Finally, the fourth model provides us with ST, CP, SC, and HRS. The model regression presented that the R-square was equal to 80.1%. Therefore, it provides perfect results overall.

4.2. Measurement and Analysis Using Artificial Intelligence Method

In this phase, we studied the main important development factors of e-tourism to achieve excellence in tourism in the Ha’il region. Therefore, we refer to the United Nations and the World Tourism Organisation in Agenda 21 to dispatch the factors allowed to the SDGs presented in

Table 6. Classification of e-tourism factors according to SDGs.

Development Factors of E-Tourism	Sustainable Development Goals (SDGs)	References
Supporting Technology (ST)	SDG12	[30]
Financial Performance (CP)	SDG8	[31]
Government Commitment (GC)	SDG12	[32]
Human Resource Skills (HRS)	SDG8	[33]
Awareness (AW)	SDG8	[34]
Owners’ Knowledge Management (OKM)	SDG8	[35]
Socio-Cultural (SC)	SDG12	[36]
Competition Pressure (CP)	SDG8	[37]
Market Readiness and Size (MRS)	SDG8	[38]

. In addition, we also argued that each determined link with the SDGs to which sustainable tourism refers.

Similarly, the dependent variable of excellence in tourism is related to 17 SDGs, but since we determine sustainable tourism, Table 6 focuses in particular on SDG8 and SDG12. We eliminated SDG14 since the Ha’il region does not benefit from aquatic resources.

4.2.1. Proposed Method

In this section, we proposed the second analysis based on the unsupervised machine learning technique. Recall that the objective of this analysis was to reveal the main development factors of e-tourism to achieve sustainable development in the tourism of the Ha’il region. The proposed method is based on an unsupervised machine learning technique. This unsupervised learning technique enables a system to be trained from a set of unlabelled observations and data [39].

The method we proposed is based on two pillars. The first pillar aims to partition factors into subsets (i.e., a group of similar points) using a clustering technique. By successive iterations, this clustering technique attempts to determine centroids (one per cluster) around which the data can be grouped. The second pillar consists of automatically classifying observation factors (i.e., a group of points) by calculating the using a weighted Euclidean distance [40] of each observation (i.e., each point) from a central clustering point called a centroid. Therefore, based on the weighted Euclidean distance calculation, the best data points that had a positive influence and helped ensure tourism excellence were ranked.

We programmed the proposed method using the Java programming language. The results obtained define the best support invoices for tourism excellence, and the system’s architecture is shown in Figure 3. The proposed unsupervised learning system was based on four stages. In the first step, we used a corpus of observations collected from studies carried out by the General Authority for Statistics of the Kingdom of Saudi Arabia “https://www.stats.gov.sa/ (accessed on 15 June 2024)” and the Ha’il Chamber of Commerce “https://hc.org.sa/ (accessed on 18 March 2024)”.

Data collection underwent a preprocessing and standardisation phase. This data normalisation phase aimed to put all numerical variables on the same scale to prevent certain variables from dominating the analysis because their scale is larger. This corpus was built on a set of 1464 monthly observation data during the years 2000 and 2022 in the Ha’il region. These observations were structured around statistical calculations performed on indicators and the determination of development factors of sustainable tourism, which will serve to determine sustainable development (see Table 6) to prevent certain variables from dominating the analysis due to their larger scale. Thus, this phase relies on techniques for standardising and resizing numerical variables (e.g., min-max scaling) to ensure that they are comparable on a common scale.

At the end of the preprocessing and normalisation phase, the data were consistent and comparable, and each indicator entity (i.e., each attribute) was related to homogenised and normalised data, making subsequent analysis by the training algorithm much easier.

The second step led to the construction of the “.ARFF” file (Attribute-Relation File Format). This file contains a finite set of “V” score vectors corresponding to attribute values (i.e., preprocessed and normalised factors calculated and observed from survey data).

Recall that the “.ARFF” training file contains a first header section made up of a list of attributes and their type characteristics (i.e., numeric, nominal string, date, and nominal) and a second data section with the values. Each row in the second section of the data values constructs a score vector, which has the following structure $\mathrm{V}=\{{\mathrm{v}}_1,{\mathrm{v}}_2,\dots ,{\mathrm{v}}_{\mathrm{p}}\}$, with $v_j\left(x_i\right)$ representing the preprocessed and normalised value of the calculated and observed factors.

In the third step, a clustering algorithm was used for unsupervised machine learning, namely the K-means algorithm [40]. This algorithm tries, by successive iterations, to partition a data set into K distinct clusters. Each cluster had a central grouping point called the centroid (i.e., the cluster’s centre of gravity) around which the closest data (i.e., points in the score vectors) could be grouped by calculating the Euclidean distance. This Euclidean distance was calculated between each data point and the cluster centroids. Figure 4 shows an illustration of the groupings of V-score vectors generated by the K-means algorithm with k = 2.

Following the indication of the K-means algorithm to classify the observations into two clusters (K-means with K = 2), we found two centroids to divide the observations into two distinct groups. As illustrated in Figure 4, we note that the 2-means clustering algorithm had defined two clusters of data points marked by the colours red and blue. These groupings were performed by calculating the distance of each observation (i.e., a V-score vector point) from the cluster centroid, thus automatically classifying the V-score vectors into two groups: {Tourism_Factor} and {Not_Tourism_Factor}.

In the fourth step, our system classified and evaluated the importance of a score vector point by calculating a weighted Euclidean distance to the cluster centroid. The closer the point in the score vector is to the centroid of the Tourism_Factor cluster (by calculating the weighted Euclidean distance), the more important it is, and the more it is an excellent factor for the realisation and development of e-tourism.

Recall that the weighted Euclidean distance is a measure of the geometric distance between two points in a multidimensional space. Therefore, using a 2D space, the calculation of the weighted Euclidean distance is given by the following formula: $d\left(x,y\right)=\sqrt{\sum _{i=1}^n{\left(x_i-y_i\right)}^2}$ with “x” representing the {Tourism_Factor} cluster centroid and “y” score vector V belonging to the same cluster as centroid “x”.

However, the weighted Euclidean distances determine which factors (i.e., which are presented by the points of the V-score vectors in 2D space) have more influence and importance for the development of e-tourism. The list of these factors, presented in a 2D space and sorted in descending order of importance (the factors are sorted using weighted Euclidean distances), is shown in

Table 7. Classification of e-tourism in descending order of importance using weighted Euclidean distances.

Development Factors of E-Tourism	Sustainable Development Goals (SDGs)
Supporting Technology (ST)	SDG12
Competition Pressure (CP)	SDG8
Financial Performance (FP)	SDG8
Human Resource Skills (HRS)	SDG8
Government Commitment (GC)	SDG12

.

4.2.2. Discussion of the Result of the Second Analysis

The results of this phase of the analysis indicate the main factors of the development of e-tourism that make it possible to achieve sustainable development in the tourism of the Ha’il region. To achieve this goal, we applied an unsupervised machine learning technique to a monthly tourism database in Saudi Arabia from 2000 to 2022 (about 1464 observations). In fact, the basis is global for Saudi Arabia because the achievement of sustainable tourism development is a primary goal for all regions of Saudi Arabia cited in its Vision Agenda 2030. After the achievement of standardisation, we found only five important factors of excellence that promote the achievement of tourism development in the Ha’il region. These factors are ST, CP, FP, HRS, and GC. However, we also conclude that artificial intelligence analysis has the ability to determine the factors of the most important dimension (environmental factors). E-tourism relies heavily on the social and cultural clouds of the environmental dimension. This justifies that environmental factors are a good predictor and stimulator of other factors of other dimensions (skills and relationship and market forces).

Similarly, the results of this study encourage the Ha’il authorities to turn toward the development and sustainable integration of the tourism industry. This requires a further development of the environmental dimension. In general, the results indicate a change in the link between tourism excellence and sustainable development. This explains the importance of the combination of statistical analysis and machine learning analysis that we applied in our study. It is true that the results found are specific to the Ha’il region, but they are also linked with all regions of Saudi Arabia. In fact, the concept of sustainable tourism and excellence in tourism must be reconsidered, and it is important to propose reforms in this sector for the Ha’il region and all other regions of Saudi Arabia.

5. Conclusions and Recommendations

The main objective of this paper was to study the development factors of e-tourism to achieve excellence and sustainable development in the tourism of the Ha’il region. So, we obtained two types of contributions: theoretical contributions and managerial implications.

Theoretical contributions: In this context, we applied two complementary analyses. The first was based on statistical analysis. The second was based on the artificial intelligence method, which is an unsupervised machine learning technique. In fact, in the statistical analysis, we have 426 responses from employees of tourism businesses based on the survey. The objective of this analysis was to determine the main development factors of e-tourism to achieve excellence in Ha’il. The results of this first step show that there are only four main factors that allow authorities to achieve excellence: ST, CP, HRS, and SC. Then, we applied the second analysis to determine, using the machine learning technique, the factors of excellence in tourism that allow the sustainable development of tourism in the Ha’il region. For this reason, we maintained a monthly database of tourism in Saudi Arabia from 2000 to 2022 (about 1464 observations). Therefore, we found that five development factors and the excellence of e-tourism promote the achievement of sustainable development in tourism. These factors are ST, CP, FP, HRS, and GC. Consequently, we conclude that the environment has a positive impact on the development of e-tourism in the Ha’il region.

Furthermore, the results of this study allow us to conclude that the realisation of tourism development remains an objective of the regional authorities of KSA. Therefore, the Ha’il region has specific characteristics that promote the achievement of excellence in tourism. For this reason, the development of e-tourism in the Ha’il region requires the use of a management strategy to develop tourism resources in the Ha’il region.

Managerial implication: Development factors that influence e-tourism have significant managerial implications for promoting excellence and sustainability in tourism. By integrating technology, engaging stakeholders, preserving culture, promoting environmental stewardship, diversifying economies, developing supportive policies, monitoring impacts, and raising awareness, managers can effectively contribute to a sustainable future for the tourism industry. Managers should use technology to improve the tourism experience while promoting sustainable practices. This includes using e-tourism platforms to manage resources efficiently, reduce waste, and optimise transport options to reduce carbon impacts. In addition, it is necessary to improve human resource skills to deliver high-quality services and promote innovation. This enables local tourism companies to further promote their services by enhancing their competitive value. It, thus, promotes sustainable tourism practices. It promotes responsible travel behaviours and helps increase visitor participation. Furthermore, integrating technology into tourism while integrating it with skill development for hosts and the competition challenge enables improved decision-making and strategic planning. Ultimately, excellence in service delivery contributes to e-tourism while ensuring the achievement of the region’s Sustainable Development Goals. Together, these factors (ST, CP, and HR) help the development of e-tourism in the Ha’il region, ensure excellence, and support sustainable development. Ultimately, a collaborative approach is vital to long-term success in Ha’il’s e-tourism landscape. Effective governance requires cooperation between various stakeholders, including communities, government bodies, and private institutions. The involvement of these groups ensures the alignment of tourism development with local needs and the preservation of cultural heritage while promoting economic growth. As for the preservation of culture, tourism managers must implement strategies that promote the preservation of culture. By encouraging tourists to participate in local traditions and practices, cultural appreciation is promoted, and cultural mitigation is prevented. Prioritising environmental sustainability by promoting environmentally friendly tourism practices is important. This includes supporting conservation efforts, reducing resource consumption, and encouraging travellers to adopt environmentally responsible behaviours during their visit to Ha’il. It is also important to diversify the region’s economy, as sustainable tourism can be supported by diversifying the tourism services provided. Managers must, therefore, develop community tourism initiatives that empower local people and enhance their resilience to economic shocks. Managers should also advocate for regulations that promote ecotourism, protect biodiversity, and ensure the equitable distribution of Ha’il’s economic benefits. It is also important that tourism executives in the Ha’il region develop measures to assess the social, economic, and environmental outcomes of tourism activities.

Furthermore, based on the results of these analyses, we recommend that the authorities of the Ha’il region can apply some different strategies to achieve excellence and develop tourism in the region. They are as follows: (i) maximising tourism awareness campaigns in society; (ii) encouraging the adoption of digital technology in the Ha’il region; (iii) promoting investments in the e-tourism protection programme for businesses and supporting tourism growth in the Ha’il region through increased funding; (iv) developing services for seasonal tourism programmes in the Ha’il region; (v) creating vocational training programs, especially with digital instruments; (vi) encouraging private initiatives to raise tourism standards in Ha’il; (vii) adopting flexible assessment methods for different groups of society to ensure that the tourist service is correctly assessed; (viii) encouraging investment in global community events and trying to polarise the Ha’il region. For example, the overall marketing of the effectiveness of the rally should be reviewed so that it is a victory for tourism and the excellence of the region; (ix) businesses and governments can effectively implement the results of technology, Competitive Pressure, Financial Performance, Human Resource Skills, and Government Commitment using a skills-based approach to talent management. This includes identifying the critical skills required for success and matching training programs to bridge current gaps. Collaboration between educational institutions and companies is critical to ensure that curricula meet market demands and develop a workforce with applicable competencies. Furthermore, taking advantage of government backing for innovation and offering incentives for continuous learning can help organisations adapt. Finally, companies that foster a culture of lifelong learning will be able to respond proactively to changing market circumstances.

Additionally, these recommendations require the regional development of the technological infrastructure, such as the development of security equipment and services, information and artificial intelligence. This would create skills and competencies in this area.

The limitations of this study are that it relied on a questionnaire due to a lack of quantitative statistical data. It would be more appropriate to apply a statistical model to give more efficient results. For future studies, we can analyse the comparison of e-tourism between Saudi Arabia and other countries from the Middle East or European countries to capture best practices.