Social Media Impact on Tourism Destination Decision: Evidence from International Students in China

Technological advancement has led to the adoption and usage of social media among businesses including tourism sector to disseminat e information and communicate with customers more effectively. Due to the limitless capabilities that social media provides, it is gradually transforming diverse business ecosystems. This study seeks to assess at the impact of social media on tourism destination decision-making from foreign students in China context. From the background of Uses and Gratification theory, a quantitative research approach was used to achieve the sole of the present study. This study used an online questionnaire tool to obtain response, validated using a Partial Least Square and Structural Equation Modeling and SPSS statistical software to analyze 271 samples of foreign students in China. The findings of the study show that behavioral intentions ha ve a positive and significant effect on tourism destination decision-making, and social media significant direct influence on tourism destination decision making. Furthermore, tourists’ satisfaction can enhance tourism destination decision s when mediated by social media usage. The research findings would help tourism service providers to identify and select social media platforms as a marketing strategy for tourism competitiveness.

• Social Media Usage
• Tourism Destination
• Foreign Students
• Tourism Satisfaction
• China

1. Introduction

Today, the increasing adoption and acceptance of social media has transformed business operations including the activities of the tourism sector. According to [1], social media is a “group of applications which operates on the internet Web 2.0 and allows for the exchange of information among the users”. Social media is regarded as an effective platform for the tourism industry, mainly to convey tourism information to both tourists and providers [2] [3]. Social media applications affect consumers’ decision-making and helped them to easily connect with tourism service provider’s products and services [4]. Extensive information available to tourists is crucial for making destination decisions and purchasing travel-related products. A tourist destination is defined as a location or a place with distinctive environments and tourism conditions and has values that are peculiar to other destinations [5]. [6] also described it as “a particular geographic region within which the visitor enjoys various types of travel experiences”. [7] pointed out travelers form a particular image regarding a tourism destination based on information gathered and previous experiences. The information provided aids travelers to choose a possible destination for vacation and business trips, to lower the risk and uncertainties [8]. According to [9], tourists gather information to compare prices, accommodations, transport, and services, particularly through posts on social media platforms. In this context, tourism service providers can share more visual content (photos and videos) on destinations through social media platforms such as Facebook, Youtube, Instagram, and TripAdvisor [9] [10]. [11] indicated that content generated on social media platforms by service providers can influence final tourism destination decisions. Based on this, [12] stated that social media as an effective communication channel shapes tourist needs, information search and comparing products and services, and choosing destinations. Consequently, social media affects choosing tourism destinations and helps in the growth of the tourism sector [13]. Interestingly, [5] specified that inspiration from movies and videos about travelers has a strong effect on tourism destination choice.

China as the third largest in the world country situated in eastern Asia has been one of the leading countries in Asia which continue to gain attention in terms of tourist destinations [14]. According to [15], foreign students in China form a significant segment of tourism, promising huge potential for development. China’s tourist sector has seen growth in the past few decades due to its glorious ancient architecture, splendid landscapes, and the hospitality of Chinese people [16]. A report by [17] revealed that attracting foreign students create additional value for the host countries; encouraging students to familiar with a particular country’s culture, and academics and exposing them to tourists’ destinations. The tourism sector is benefiting from foreign students by creating opportunities called “tourist spaces” through student exchange programs [18]. The development of social media applications has helped students in both academics and searching for tourist destinations [19]. Students share visited destinations and experiences on social media platforms with friends [20]. It was also observed that destination information posted online by friends and relatives influences other tourism destination decision-making [21]. As a result, students can make tourism destination decisions based on shared information or recommendations from friends. This implies that technology can play a central role in helping tourists make informed choices on the destinations they desire to visit. [22] suggested that tourism service providers can focus on the social context mobile marketing approach (SoCoMo) to attract foreign students (tourists) since it has a significant influence on tourism destination choice.

Despite the increasing role of social media in promoting tourism activities in different destinations across the globe, there is little research that has been conducted with regards to social media usage and tourism destination-decision. In addition to that, the results from existing research in the in the context of foreign students have not been consistent. A study conducted by [23] witnessed that cultural background and social media usage affects international students’ decision on destinations. Besides, [24] findings indicated that that behavioral beliefs, social media usage, and political stability have significant influence on tourism destination decision-making. Based on this limited empirical evidence, this study aims to assess the impact of social media on tourism destination decision-making among foreign students. Specifically, the study explores the roles that social media play in tourists’ choices regarding a broad range of travel components during the travel decision-making process among foreign students in China. This study would contribute to the existing knowledge on social media and tourism development. The paper is organized as follows: literature reviews, whiles methodology, and the results and discussions are detailed in content in other sections. The theoretical and practical implications of the findings are discussed in the last section including the study’s limitations and directions for future research.

2. Theoretical Background

Uses and Gratification Theory (U & G)

The foundation of the U & G theory is the belief that individuals actively use media for their own purposes and that their high interaction with the communication mediums, which they find satisfying [25]. Despite the importance of social media, theoretical knowledge regarding how and why tourists use social media is still lacking. There appears to be published material on U & G theory, especially in studies on the choices made by consumers (foreign students). The theory mainly focuses on the motivations behind people’s media use, the purposes for which they use it, and the pleasures they derive from it. Individuals, according to the theory of usage and gratification (U & G), seek out specific media to meet their needs. Previous studies have applied Uses and gratification theory in the context of tourism destination choice. [26] adopted U & G theory in their study on business model adoption based on tourism innovation. Additionally, [27] investigated why Egyptian consumers actively engage in Facebook brand pages and discovered that among the gratifications were quick to access to information and the capacity to file complaints. Similar to this, [28] used the theory to look into how customer gratification on social media affects their commitment to and trust in a brand. [29] investigation focused on how staff members of businesses interact with customers on social media. In this study, we examine how social media usage encourages the sustainability of SME firms using the U & G theory. Although the U & G theory has a great deal to do with social media, it has received little attention in research on marketing and social media. Because the author is interested in assessing the impact of social media on tourism destination decision making in China, the Uses and Gratifications theory is appropriate for this study. Further to this, travel vlogs on consumer behavior applied uses and gratification model and concluded that travel vlogs on YouTube positively influences travel intention behavior and travel decisions, from the perspective of tourism satisfaction.

3. Literature Review

3.1. Social Media

According to [30], tourism firms have recently adopted social media strategies to improve business performance, leading to a new era in the tourism industry. [31] described social media as “a group of Internet based applications that build on the ideological and technological foundations of Web 2.0, and allow the creation and exchange of user-generated content”. Social media plat-forms have been considered an effective communication tool to engage larger communities [32]. Social media’s distinctive features of interactivity and innovativeness have motivated tourism service providers to use social media platforms to engage and communicate with customers. In this context, [33] proposed that the social media tourism sector with a higher level of social media adoption and usage has better performance. Several studies have established the relevant effect of social media on tourism destination decision-making. For instance, [34] found that social media has enabled tourism service providers to share in-formation, create value, and communicate with tourism stakeholders. The study further indicated that the tourism sector used social media platforms in promoting tour-ism destinations. A study conducted by [35] revealed that tourists used social media platforms to obtain adequate trip information, destination selection, and trip planning. Additionally, [36] examined the role of social media in travelers’ decision-making and concluded that social media usage has a positive influence on travel information search and tourist destinations. Furthermore, [37] investigated online information accessed by tourists on tourists’ destinations. The study identified the significant impact of social media on the different age group of international tourists and concluded that social media positively influence foreign tourists in accessing tourist destination information. Other previous studies have established the relationship between social media and tourism sustainable performance [38] [39]. As indicated above, a study by [40] also found a positive correlation between social media us-age and tourism competitive sustainability. Finally, recent evidence by [41] found a positive association between social media platforms and tourist destination decision-making in the form of tourists’ information search, reaching target tourists, quality customer service, promoting tourist’s destination, and building customer loyalty.

3.2. Behavioral Intentions

Several studies have examined the behavioral intentions associated with tourism destination decision-making [42] [43]. Behavioral intention (BI) refers to the “stated likelihood to engage in a behavior” [44], “a person’s subjective probability that he will perform some behavior”. The study added indicated individual’s intention positively influences his/her execution of behavior. In this context, [45] observed that customers’ intention to re-experience the same tourist destination or product is mostly affected by the behavioral intention factor. A study by [46] identified behavioral intentions (i.e. local culture, meaningfulness, hedonism, refreshment, and involvement) as factors that affect tourist destination decision making. In effect, these factors influence tourist intentions to revisit the same destination.

A study conducted by [47] revealed that behavioral intentions have a significant effect on tourist destination decisions. Additionally, [48] study on behavioral intention on tourist experiences shows a positive relationship between behavioral intentions and tourist site selection. Similarly, [49] examined behavioral intentions on tourism experiences in a heritage tourism context. The study collected data from domestic cultural tourists in Kashan, Iran, and concluded that domestic culture and tourists’ site knowledge positively influences tourists’ behavioral intention toward a tourist destination. However, [50] revealed an insignificant relationship between behavioral intention and tourists’ destinations. Furthermore, [42] stated that tour leader attachment has a both direct and indirect relationship with behavioral intention. [51] findings revealed that the availability of in-trip information searches provided by the tourist service providers has a significant influence on the behavioral intentions of tourists, hence affecting their destination decision-making. Moreover, the findings of [52] confirmed that quality, perceived value, and tourists’ satisfaction have a positive influence on tourists’ behavioral intentions. Considering the above, we proposed that:

H1: Behavioral intentions will positively affect tourism destination decision making.

H2: Social media will positively mediate the relationship between behavioral intentions and tourism destination decision making.

3.3. Tourist Satisfaction

According to [53], tourist satisfaction is one of the criteria factors for tourists’ destination selection. Tourist satisfaction influences tourists’ intention to visit the same site or recommend it to friends and families. [54] advocated that quality service provision, destination image, and perceives value also influence tourists’ destination decision making. A recent study was conducted by [55] on tourists’ destination choice and satisfaction through emotional involvement; the study collected data from 382 domestic tourists and concluded that destination attributes such as site attraction, accommodation, and transportation have a significant influence on tourist satisfaction. The studies by [56] argued that tourist satisfaction and tourist delight lead to sustainable performance. The study added that food and beverages have a positive effect on tourist satisfaction. [57] argued that tourist satisfaction and tourist delight lead to sustainable performance. Similarly, [58] added that tourist satisfaction is crucial in achieving long-term performance. The study further revealed that tourist satisfaction revisits intention and income generation for the tourist service provider.

Furthermore, [59] suggested that tourist service providers should emphasize tourist satisfaction to attract tourists’ hence influencing their destination choice. A study by [60] on tourist satisfaction and quality among hotel choice decision-making revealed that tourist satisfaction has a significant influence on tourist behavioral intention toward a tourism destination. In a related study by [61], it was found that service tangible, reliability, assurance, and responsiveness positively influence tourist satisfaction and tourist destination decision-making. In achieving tourism competitiveness, [62] suggest that tourist service providers should strategically position and promote the cities, and local communities to vitalize tourists to revisit a particular site which has a long-term effect on tourist destination decision-making. Previous studies by [63] also evidenced the positive correlation between tourist satisfaction and tourism destination decision-making. However, in the context of social media and tourist destination choices in developing countries, scanty studies exist empirically. Therefore, we hypothesize the following:

H3: Tourist satisfaction will positively affect tourism destination decision-making.

H4: Social media will positively mediate the relationship between tourist satisfaction and Tourism destination decision making.

3.4. Destination Image

Destination image is pivotal in tourism destination decision making [64]. Destination image stimulates and affects travel decision-making, and influences satisfaction and loyalty. [65] described destination image as “an interactive system of thoughts, ideas, feelings, imagery, and goals toward a destination”. [66] admonished that tourist service providers’ marketing and managerial strategies enhance destination image that influences tourist destination choice in the context of Chinese outbound tourists. The authors found destination image as a significant determining factor for tourist destination decision making. Extant studies have established destination image association with tourism destination decision-making [67] [68]. [69], for instance, studied influential factors of the destination image. The study sampled tourists from Taiwan and revealed that the climate, political stability, safety, relaxation, and scenery influenced destination image, positively affecting tourist destination decision-making. Additionally, travel safety and security have a significant influence on tour-ism destination image [70]. Furthermore, [71] evidenced that destination image positively affects tourist satisfaction, hence leading to multiple return visits.

[72] investigated the transformation of tourist destinations in the context of china, it was found that website design, application of new technology, and social media applications have positively influenced tourist destination image. A study conducted by [73] investigated the influence of destination image and destination in tourists’ decision-making and established that cultural manifestation and natural attractions can positively contribute to tourist satisfaction and hence affect destination image. Preliminary work by [74] explored a sample of 385 Sarab Meymeh tourists in Dehloran City and found a positive correlation between cultural events and mental conflicts and destination image. Finally, a study by [75] found a significant link between individuals’ knowledge, feelings, and general perception of a particular destination and tourist destination choice, thus a positive influence on destination image. Therefore, based on the previous discussion, we hypothesize the following:

H5: Destination image will positively affect Tourism destination decisionmaking.

H6: Social media will positively mediate the relationship between destination image and Tourism destination decision making.

3.5. Information Provided by Other Travelers

Trust is built based on a successful relationship between consumers and service providers [76]. In the context of tourism, [77] observed that building trust between travelers and destinations is crucial in providing quality service to tourists. According to [78], the information provided by other travelers influences new travelers’ destination decision-making. Tourists’ are more abound and interested in shared experience information online by other travelers. Study of [79] on traveler decision making determinants revealed that information shared by other travelers positively influences tourism destination decision making. A study by [80] findings revealed that online information source significantly influences foreign students’ trip planning in the context of China.

Similar works by [81] [82] confirmed that the internet (social media) is most used by travelers to obtain adequate information about a tourist destination. Additionally, [83] admonished that a memorable tour-ism experience is likely to be shared by the tourists and has a significant effect on behavioral intentions. [84] conducted a study on the role of travel information search in tourist destinations. The study data from 625 tourists in Tanzania concluded that information search behaviors have a positive association with destination decision making. The study further advocated that travelers’ information shared can be used by tourism service providers to measure the satisfaction level, which in turn affects other travelers (tourists’) destination decision-making. Consequently, in line with several previous studies [85] [86] [87] [88], we proposed that information provided by other travelers (past travel experience) can positively contribute to tourism destination decision making among foreign students in china. Based on the above literature and arguments, we hypothesize the following:

H7: Information provided by other travelers will positively affect Tourism destination decision making.

H8: Social media will positively mediate the relationship between information provided by other travelers and Tourism destination decision making.

3.6. Tourism Destination Decision Making

According to [89], tourism destination decision-making is influenced by the quality services provided by tourism service suppliers. The promotion of tourism destinations and consumers’ knowledge of tourist destinations affect tourism destination decision-making [90]. Literature has demonstrated the determining factors of tourism destination decision-making [66] [91]. For instance, [92] studied key factors influencing international tourists’ decision-making and concluded that tourism infrastructure, environmental safety, price, and human resources positively influences tourism destination decision making. [93] argued that tourist risk perception significantly influences tourism decision-making. Hesitation but tourist knowledge can moderate this relationship. Similarly, [94] examined tourist perception towards tourist decision-making and found that the perception of safety and security positively influences tourist decision making. Furthermore, [95] also revealed that destination attributes have a positive relationship with tourism destination decision-making. Recently, innovation and internet applications, particularly social media have had a significant influence on tourism destination choices. In this context, [96] explored the role of social media in tourism destination decision making and found that information provided by tourism service providers online has helped foreigners to get valuable information needed for trip planning and decision on tourism destinations. Thus, tourists who always seek online information via social media platforms will be more interested and opt for the destination that provides valuable information and photos online (Țuclea, et al., 2020); [97] [98] also maintained that social media have a significant link with the travel decision-making process and destination choice. In addition to the above, [99] recently observed that active engagement on social media platforms with tourists positively affects tourism destination decision-making.

4. Methodology

4.1. Sample, Data Collection, and Analytic Techniques

The researcher was able to achieve the stated purpose of the study by employing a quantitative technique [100]. The advantage of a quantitative approach plan over others is that it can yield reliable and validated findings [101]. The study focused on foreign university students in the Province in the Southern part of China who are using social media for tourist purposes which guided the study in the quest to select the appropriate respondents. To fully achieve the purpose of the study, a structured questionnaire was developed in English in two sections: section A contains the demographics of the respondents and section B contains the questionnaires relating to the constructs of the entire study. To select participants/respondents for the research questionnaire, a type of non-probability sampling technique, specifically convenience sampling, was used. The researcher used convenience sampling because it was required to assist with data collection and to save time [102]. Once more, the sampling strategy was chosen based on the respondents’ availability, involvement, and desire to provide the data necessary for the data analysis and processing [103] [104] [105]. Formal permission was obtained from the foreign university students who were part of the selected respondents before beginning the data collection processes through their emails, text messages, WeChat, etc. The structured questionnaire which was framed in English was then administered online (Google forms) and offline to the respondents in the Province Universities. The essence of using foreign university students was a result of regular usage of social media and again, the high level of their regular travels to various tourist destinations. A total of 312 questionnaires were administered to the selected respondents in the various universities of the Province. Out of the 312 questionnaires administered, 302 were returned of which 272 representing 87.18 percent were very good for data processing and analysis after eliminating 30 of the questionnaires which contain anomalies making them unfit for the intended purpose. The data was collected between June-September, 2022. Each respondent took an average of five minutes to complete the structured questionnaire. To ensure the study’s ethical standard, the respondents’ names were not included in the questionnaire due to confidentiality. In summary, for processing and analyzing the data, the researcher was able to employ 272 (87.18%) responses that were fully genuine. The data was evaluated through SPSS and PLS-SEM, (ADANCO 2.1) software versions. The profile summary for respondents is shown in Table 1 below.

4.2. Data Analysis Technique

To test the research model, the study used partial least square and structural modeling (PLS-SEM), especially ADANCO 2.1. Instead of using Co-Variance-Based Structural Equation Modeling, (CB-SEM), PLS-SEM was used. While PLS-SEM makes no assumptions regarding data distributions, CB-SEM demands that the data be normally distributed. As a result, the use of PLS-SEM is justified because non-normal data do not contradict a statistical test’s overall findings [106]. The study adopted the Partial Least Square (PLS) approach for the data analysis since it clarified the variance of the specific variables [107] [108]. It is permissible for the study to employ PLS-SEM because it is an exploratory study [109]. The research hypotheses were put to the test using Smart PLS 3.2.9.

Table 1

Background information of respondents

Details	Groups	Frequency (N = 272)	Percentage (%)
Age	18 - 27	118	43.3
	28 - 37	142	52.2
	38 - 47 above	12	4.5
Gender	Male	190	69.9
	Female	82	30.1
Nationality	Ghana	72	26.48
	Nigeria	80	29.41
	Pakistan	75	27.57
	Others	45	16.54
Selected Provinces	Hunan	45	16.53
	Hebei	68	25.01
	Liaoning	59	21.69
	Sichuan	48	17.65
	Others	52	19.12
Level of Study	First Year	62	22.79
	Second Year	54	19.85
	Third Year	56	20.59
	Fourth Year	30	11.03
	Fifth Year	40	14.71
	Sixth Year	30	11.03
Academic Qualifications	Undergraduates	182	66.69
	Masters	68	25
	PhD	13	4.8
	Others	9	3.4
Social Media Platforms	Facebook	71	26.10
	WeChat	65	23.90
	YouTube	54	19.85
	Twitter	42	15.44
	Others	40	14.71
Do you feel motivated by social media usage	Yes	225	82.72
	No	47	17.28

Source: Field data (June 2022-September 2022), retrieved from Google form.

4.3. Measurement of the Constructs

The research constructs were measured using existing literature. The structures’ scale and measurements also underwent a minor adjustment. The current theme was assessed using new scales on a five-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree). This was used to ascertain the respondent’s level of agreement or disagreement with the constructs’ measurement. A five-point Likert scale was used since it is easier for respondents to complete and takes less time than open-ended questions [110]. By translating qualitative characteristics into numerical measurements, a Likert scale is a psychometric tool used to measure personality, character, and attitude qualities. To match the requirements of the current research, every item was altered.

4.4. Common Method Bias

First, the study evaluated whether or not CMB (common method bias) was present during the analysis. On the front page of the questionnaire, the study cited [110], in which the construct items were carefully crafted and respondents were guaranteed strict confidentiality. Simply said, the survey was designed to ensure that participants maintained their anonymity and had the freedom to discontinue participation at any time. Again, to substantiate our claim, the researcher conducted a detailed multicollinearity test, specifically on the variance inflation factor (VIF), to assess the strength of the Common Method’s evidence for variance (CMV). Because the computed VIFs are less than the cut-off of ten (10) in these calculations, CMV is not a problem [111] (see Table 3).

5. Empirical Findings and Results

5.1. Assessment of Model Appropriateness

The evaluation was based on scholarly works that used PLS-SEM in the pioneering literature (see [107] [108], such as the use of Dijkstra-rho Henseler’s with Cronbach alpha coefficients to evaluate the constructs’ reliability and validity [112]. The constructs with the highest coefficients are displayed in Table 2 below since all of the coefficient values are greater than 0.5 [108] [110]. The underlying elements of the research constructs’ psychometric properties were assessed. The test met the minimum levels for Jöreskog’s rho (pc) and Dijkstra-rho Henseler’s (pA) 0.7, which satisfied the conditions for the composite reliability of constructs as stated in (Table 2). The average variance extracted (AVE), also known as convergent validity, was finally recorded with a minimum threshold of 0.5, as demonstrated below.

Items with a factor loading of 0.50 or higher can measure the linked construct, demonstrating convergent and discriminant validity [101]. As a result, as can be seen in the table below (Table 3), all of the loadings for the different constructs were higher than the 0.5 cutoff value. To take into consideration the possibility of collinearity (variations) among the items used to measure the concept, the

Table 2

Test of validity and reliability of research construct

Constructs	Dijkstra-Henseler’s r (ρA)	Jöreskog’s rho (ρc)	Cronbach’s alpha (α)	The Average Variance Extracted (AVE)
Information Provided by other Travelers	0.877	0.940	0.826	0.592
Destination Image	0.874	0.906	0.785	0.582
Behavioral Intentions	0.902	0.934	0.784	0.570
Social Media	0897	0.936	0.874	0.636
Tourism Satisfaction	0.821	0.937	0.845	0.619
Tourism Destination Decision Making	0.792	0.984	0.830	0.725

Source: Field data from China (June-September 2022), retrieved from Google form and offline.

Table 3

Construct items, loading, and variance inflation factor (VIF)

Construct	Indicator	Loading	VIF
Information Provided by other Travelers	IP1: Information provided by other travelers drives me to visit new places.	0.702	2.300
	IP2: Information provided by other travelers seems more credible as compared to the information shared by traveling companies on their websites.	0.783	3.459
	IP3: I always ask to experience individuals before selecting a destination for tourism.	0.860	4.539
	IP4: I believed it is important to ask others before selecting a destination for tourism.	0.849	3.596
	IP5: I highly recommend getting information from different sources before making any decision regarding the destination.	0.627	3.600
Destination Image	DI1: Destination image is having a strong impact on one’s decision making.	0.773	2.437
	DI2: Destination with a good reputation is visited extensively.	0.787	3.014
	DI3: Images of tourism spots shared on social media attract me to visit that place.	0.747	2.889
	DI4: Videos shared on social media influence me to select any destination.	0.775	3.160
	DI5: Social media provides information about new tourism destinations.	0.731	3.253

Behavioral Intentions	BI1: Information shared on social media is having a strong impact on my intentions to make a decision.	0.816	3.353
	BI2: I do trust brands advertised by social media influencers I follow.	0.793	3.131
	BI3: I have favorable opinions about the tourism industry advertised by social media influencers.	0.699	2.276
	BI4: My personal experience is having a strong impact on deciding destinations for tourism.	0.716	3.236
	BI5: The way tourism companies treat complaints impacts my decision making.	0.743	4.127
	BI6: Attitude of tourism companies toward providing services impact the decision making.	0.731	4.211
	BI7: Positive behavior towards a brand impacts the decision making.	0.779	2.779
Social Media	SM1: Social media have led to a major change in consumer patterns, distribution mechanisms, and the creative processes of travel information.	0.560	2.864
	SM2: Social media have changed the way tourism material is generated, not just by destinations but also by tourists.	0.745	3.089
	SM3: Social media has created a platform to create a perspective of tourist destinations from the consumers’ perspective.	0.792	3.960
	SM4: The usage of various kinds of media to communicate travel experiences in diverse destinations enhances the target audience’s trip experience.	0.860	3.665
	SM5: Social media provides enhanced customer service.	0.852	2.873
	SM6: Social media provides information about new tourism destinations.	0.729	4.211
	SM7: Social media provides transportation information.	0.548	2.779
Tourism Satisfaction	I am satisfied with the services provided by the tourism industry.	0.785	2.645
	My satisfaction is the priority to reuse services from a tourist company.	0.850	3.415
	Tourism satisfaction is critical for me to take purchasing decisions.	0.797	4.487
	Credible information shared by tourism company increase my satisfaction level.	0.654	3.259
	The service quality provided by the company increases satisfaction.	0.627	3.195

Tourism Destination	If I were going to select a tourism company, I would consider buying this company recommended by social media celebrities.	0.792	4.269
	If I were going to visit a place, the likelihood I would likely check its visuals first.	0.821	4.311
	My willingness to visit a place depends on the reputation of that place.	0.828	4.314
	The probability I would consider visiting a place depends on the visuals provided on social media or any other platforms.	0.822	3.909

Source: Author’s processing from ADANCO 2.2.1 software.

variance inflation factor (VIF) was calculated. Therefore, a VIF lower than ten is a better value.

Furthermore, the researchers chose Fornell-Larcker’s assess the discriminant validity of the latent variables as a result of inspiration [108]. All the values in the diagonal form (bold) meet the minimal threshold of greater than 0.5, which reflects the average variance extracted (AVE) of the studied constructs, according to experts [107] [108] (see Table 3). The essential and strict presumptions of the study constructs were established after each AVE was required to have higher coefficients (both column and row position) than the other constructs, by Fornell-Larcker’s discriminant validity [109] (see Table 4 below).

5.2. Hypothesis Testing-PLS-SEM

After analyzing the model to look at potential connections between constructs, the researchers then moved on to structural modeling at this point in the project [107]. The research constructs’ significant values, t-values > 1.96 (or p-values 0.05), and regression coefficients (β) were used to compute the statistical estimates. According to Table 5 below, only three of the offered hypotheses (direct hypotheses) exhibited a significant correlation with the outcome variable (tourism destination decision making) whiles the remaining hypotheses were significant with the dependent variable. The coefficient calculation of R² of the research model is also established to provide further clarification on (Table 5) below. The coefficients show how much of the variation in the dependent variable can be explained by the independent variable. Figure 1 below shows that 18% of the outcome variable (tourism destination decision making) may be attributed to the predictive variables (See Table 5 and Figure 1 respectively).

6. Discussion, Conclusion and Implications

Social media as a technology in the 21^st century has shaped organizations and companies in diverse ways. Since the advent of social media, academics and researchers have placed a high value on the technology’s contributions [101]. This is explained by the fact that consumers are increasingly evaluating businesses’ goods and services using social media platforms or applications. It is evident from the above hypothesis that social media and behavioral intention have a positive relationship with a p-value of (0.003) indicating the findings supported the proposed hypothesis formulated against the literature reviewed. Again, the results showed that social media positively mediate the relationship between behavioral intention and tourism destination decision-making by foreign students. The results collaborated with that of [42] [43]. Behavioral intentions have a significant effect on tourist destination decisions through social media applications [47] and again signify that the availability of social media helps in-trip information searches provided by the tourist service providers has a significant influence on the behavioral intentions of tourists, hence affecting their destination decision-making [42] [49].

Table 4

Discriminant validity

Constructs	Behavioral Intentions	Destination Image	Information provided	Social Media	Tourism Destination Decision Making	Tourism Satisfaction
Behavioral Intentions	0.827
Destination Image	0.880	0.852
Information provided	0.844	0.903	0.887
Social Media	0.804	0.822	0.824	0.890
Tourism Destination Decision Making	0.039	0.059	0.098	0.043	0.793
Tourism Satisfaction	0.873	0.840	0.844	0.784	0.061	0.893

Source: processing from ADANCO 2.2.1 software.

Table 5

Hypothetical path coefficient—PLS-SEM

Constructs	Original Sample	Sample Mean	Standard Deviation	T Statistics	P Values	Decision
Behavioral Intentions → Social Media	0.212	0.214	0.072	2.937	0.003	Supported
Behavioral Intentions → Tourism Destination Decision Making	−0.095	−0.005	0.155	0.613	0.040	Supported
Destination Image → Social Media	0.232	0.228	0.089	2.604	0.000	Supported
Destination Image → Tourism Destination Decision Making	−0.073	0.056	0.197	0.371	0.021	Supported
Information Provided → Social Media	0.327	0.326	0.091	3.589	0.000	Supported
Information Provided → Tourism Destination Decision Making	0.298	0.054	0.260	1.146	0.052	Supported
Social Media → Tourism Destination Decision Making	−0.066	−0.083	0.129	0.512	0.009	Supported
Tourism Satisfaction → Social Media	0.128	0.134	0.068	1.875	0.001	Supported

Similarly, hypothesis two which states that H3: Tourist satisfaction will positively affect tourism destination decision-making and H4: Social media will positively mediate the relationship between tourist satisfaction and Tourism destination decision making are supported by the study’s findings with p-values of (0.009 and 0.001) respectively. Studies by [53] established that tourist satisfaction forms a greater decision in embarking on tourism. The results of the current tourist’s satisfaction and tourism destination decision-making are also affirmed by [46] [55] [56]. This means that there is a significant relationship between social media, tourist satisfaction, and tourism destination decision-making. To add more, the satisfaction of tourists informs their decisions for revisits which give firms and companies in the tourism businesses to increase their income generation. Again, tourist service providers should emphasize tourist satisfaction to attract tourists’ hence influencing their destination choice [59]. Tourist service providers should strategically position and promote the cities, and local communities to vitalize tourists to revisit a particular site which has a long-term effect on tourist destination decision-making [62].

Moreover, the findings also confirmed the proposed hypotheses: H5: Destination image will positively affect Tourism destination decision-making and H6: Social media will positively mediate the relationship between destination image and Tourism destination decision-making. These emphasize that a positive correlation exists between the formulated hypotheses. In deciding where to go on vacation, the destination’s image is crucial. The perception of a destination influences satisfaction and loyalty as well as stimulating and influencing travel decisions [64] [65]. In the context of Chinese outbound tourists, [66] cautioned that tourist service providers’ marketing and managerial strategies enhance destination image that influences tourist destination choice. Destination image positively affects tourist satisfaction, hence leading to multiple return visits [70]. [72] looked into how tourist destinations were changing in the context of China and discovered that social media applications, website design, and the use of new technology had a positive impact on the perception of travel destinations.

Lastly, hypotheses: H7: Information provided by other travelers will positively affect Tourism destination decision making and H8: Social media will positively mediate the relationship between information provided by other travelers and Tourism destination decision making were also strongly supported as per the results obtained. The findings of the current study confirm similar studies by [80] [82]. The advice given by experienced travelers affects where new travelers choose to go. Travelers primarily use the internet (social media) to find out enough information about a tourist destination as revealed by [82]. According to research by [80], online information sources have a big impact on how foreign students plan their trips to China. Information from other travelers (past travel experience) helps foreign students in China make wise decisions about where to visit [85] [86]. To reveal more, experiences by past travelers inform the tourist destination decision-making of current tourists in embarking on trips either within the country or outside a particular country.

The current study centered on assessing the impact of social media on tourism destination decision-making among foreign students in China. Hence, the study explores the roles that social media play in tourists’ choices regarding a broad range of travel components during the travel decision-making process among foreign students in China. By creating a structured questionnaire, the study used a quantitative research methodology to collect data from university international students in some provinces of the People’s Republic of China. Convenience sampling, a non-probability sampling approach, was utilized to choose the respondents or participants for the data gathering procedure. Furthermore, utilizing SPSS and Partial Least Square and Structural Equation Modeling (PLS-SEM) and statistical software called ADANCO 2.1, 272 valid responses from university students in China were thoroughly evaluated.

6.1. Implications

Despite the study’s constrained scope, its conclusions are quite helpful. Due to the relevance of the information or data collected in the study area, where less research or publications have been made about the use of social media for assessing the impact of social media on the decision-making process related to travel destinations among international students in China, this addition fills the gap in the literature and helps close the long-standing missing gap. According to the conclusions, the current study would also provide fresh perspectives and significant contributions to social media marketing or operations. Therefore, additional study is encouraged to reevaluate the validity and reliability of the research constructs and research model in other developed nation contexts. Again, the empirical results showed that social media is used in travel decisions.

6.2. Practical Relevance

The results of this study’s assessment of the influence of social media on foreign students in China’s choice of travel destinations will serve as inspiration for managers and owners of tourism businesses. The study also offers a clear road map for international students, tourism managers/owners, and government institutions to create a framework that incorporates the research constructs to achieve the main goal of demonstrating how they can effectively and profitably use social media for customer attractions as well as for various purposes. Above all, practitioners will also enjoy the usefulness of the research constructs (social media, tourists’ satisfaction, information provided by other travelers, destination image, behavioral intentions, and tourism destination decision-making among foreign students) in China in their marketing, information sharing, and communication activities. The findings will help practitioners and industry participants make strategic decisions about how to apply the findings to accomplish desired goals and value the existence of social media.

6.3. Limitations

The conclusions of the current study, however, cannot be extrapolated to other nearby nations because it is restricted to international university students in China. Adding interviews through qualitative research could improve the findings since the current study was only able to use quantitative research methods. In addition, the study was limited to students in China, future studies could investigate other international students in developed countries.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

References

1. Colomo-Palacios, R., Soto-Acosta, P., Ramayah, T. and Russ, M. (2013) Electronic Markets and the Future Internet: From Clouds to Semantics. Electronic Markets, 3, 89-91. https://doi.org/10.1007/s12525-013-0134-6

2. Rathonyi G. ,et al. (2013)Influence of Social Media on Tourism-Especially among Students of the University of Debrecen Applied Studies in Agribusiness and Commerce 7, 105-112.

3. Xiang, Z., Magnini, V.P. and Fesenmaier, D.R. (2015) Information Technology and Consumer Behavior in Travel and Tourism: Insights from Travel Planning Using the Internet. Journal of Retailing and Consumer Service, 22, 244-249.https://doi.org/10.1016/j.jretconser.2014.08.005

4. Buhalis, D. and Law, R. (2008) Progress in Information Technology and Tourism Management: 20 Years on and 10 Years after the Internet—The State of eTourism Research. Tourism Management, 29, 609-623.https://doi.org/10.1016/j.tourman.2008.01.005

5. Werenowska, A. and Rzepka, M. (2020) The Role of Social Media in Generation Y Travel Decision-Making Process (Case Study in Poland). Information, 11, Article 396. https://doi.org/10.3390/info11080396

6. Goeldner, C.R. and Ritchie, J.R.B. (2003) Tourism: Principles, Practices, Philosophies. 12th Edition, Willey, Hoboken.

7. Baloglu, S. and Brinberg, D. (1997) Affective Images of Tourism Destinations. Journal of Travel Research, 35, 11-15. https://doi.org/10.1177/004728759703500402

8. Roque, V. and Raposo, R. (2016) Social Media as a Communication and Marketing Tool in Tourism: An Analysis of Online Activities from International Key Player DMO. Anatolia, 27, 58-70. https://doi.org/10.1080/13032917.2015.1083209

9. Matikiti-Manvevery, R. and Kruger, M. (2019) The Role of Social Media Sites in Trip Planning and Destination Decision-Making Processes. African Journal of Hospitality, Tourism and Leisure, 8, 1-10. https://www.ajhtl.com/uploads/7/1/6/3/7163688/article_3_vol_8_5__2019_cut.pdf

10. Katsikari, C., Hatzithomas, L., Fotiadis, T. and Folinas, D. (2020) Push and Pull Travel Motivation: Segmentation of the Greek Market for Social Media Marketing in Tourism. Sustainability, 12, Article 4770. https://doi.org/10.3390/su12114770

11. Lalicic, L., Huertas, A., Moreno, A. and Jabreel, M. (2020) Emotional Brand Communication on Facebook and Twitter: Are DMOs Successful? Journal of Destination Marketing & Management, 16, Article ID: 100350. https://doi.org/10.1016/j.jdmm.2019.03.004

12. Ţuclea, C.E., Vranceanu, D.M. and Nastase, C. (2020) The Role of Social Media in Health Safety Evaluation of a Tourism Destination throughout the Travel Planning Process. Sustainability, 12, Article 6661. https://doi.org/10.3390/su12166661

13. Paul, H., Roy, D. and Mia, R. (2019) Influence of Social Media on Tourists’ Destination Selection Decision. Scholars Bulletin, 5, 658-664.https://doi.org/10.36348/sb.2019.v05i11.009

14. Balinska, A. (2015) Agritourism as a Form of Recreation for Students. In: Katsoni, V., Ed., Cultural Tourism in a Digital Era. Springer Proceedings in Business and Economics, Springer, Cham, 313-323.https://doi.org/10.1007/978-3-319-15859-4_26

15. Yang X.-Y. ,et al. (2010)A Survey on Tourist Motives and Tourism Behaviors of Foreign Students in China Journal of Sanming University 27, 478-483.

16. Zeng, B. and Gerritsen, R. (2014) What Do We Know about Social Media in Tourism? A Review. Tourism Management Perspectives, 10, 27-36.https://doi.org/10.1016/j.tmp.2014.01.001

17. World Travel and Tourism Council (WTTC) (2010) Progress and Priorities 2009-10. http://www.wttc.org/bin/pgf/original

18. Menin A. ,et al. (2017)The Successful Relationship between Tourism and Academic Mobility (A Model for Increasing the Demand of Foreign Students) European Journal of Economic and Financial Research 2, 113-119.

19. Karmila, S., Aritonang, E.Y. and Sudaryati, E. (2020) The Relationship of the Duration of Social Media Instagram Usage and Student’s Eating Behavior in University of Sumatera Utara, 2019. Britain International of Humanties and Social Sciences (BIoHS) Journal, 2, 289-295.

20. Goenawan, G. (2015) Co-Creation Communication Pengguna Instagram Dalam Foodstagram di Surabaya. Jurnal E-Komunikasi, 3, 3.

21. Fotis J., Buhalis, D. and Rossides, N. (2012) Social Media Use and Impact during the Holiday Travel Planning Process. In: Fuchs, M., Ricci, F. and Cantoni, L., Eds., Information and Communication Technologies in Tourism, Springer, Vienna, 13-24. https://doi.org/10.1007/978-3-7091-1142-0_2

22. Sicilia, M., Palazón, M. and López, M. (2020) Intentional vs. Unintentional Influences of Social Media Friends. Electronic Commerce Research and Applications, 42, Article ID: 100979. https://doi.org/10.1016/j.elerap.2020.100979

23. Lee, C. and King, B. (2016) International Students in Asia: Travel Behaviors and Destination Perceptions. Asia Pacific Journal of Tourism Research, 21, 457-476.https://doi.org/10.1080/10941665.2015.1062786

24. Baldwin, A.J. (2011) The Experience of Studying Abroad and Creation of a “How to Study Abroad Guide”. The College at Brockport, New York. https://www.semanticscholar.org/paper/The-Experience-of-Studying-Abroad-and-Creation-of-a-Baldwin/7d1be3a3c1d484c446cd42846d1567a6e7bfcf6e

25. Luo, X.M. (2002) Uses and Gratifications Theory and E-Consumer Behaviors. Journal of Interactive Advertising, 2, 34-41. https://doi.org/10.1080/15252019.2002.10722060

26. Bruce, D., Connelly, G. and Ellison, S. (2022) Different Fertility Approaches in Organic Hemp (Cannabis sativa L.) Production Alter Floral Biomass Yield but Not CBD: THC Ratio. Sustainability, 14, Article 6222. https://doi.org/10.3390/su14106222

27. Gaber, H.R., Wright, L.T. and Kooli, K. (2019) Consumer Attitudes towards Instagram Advertisements in Egypt: The Role of the Perceived Advertising Value and Personalization. Cogent Business & Management, 6, Article: 1618431.https://doi.org/10.1080/23311975.2019.1618431

28. Kamboj, S. (2019) Applying Uses and Gratifications Theory to Understand Customer Participation in Social Media Brand Communities. Asia Pacific Journal of Marketing and Logistics, 32, 205-231. https://doi.org/10.1108/APJML-11-2017-0289

29. Lian, S. and Yoong, L.C. (2018) Customer Engagement in Social Media and Tourism Brand Performance Implications. Turkish Online Journal of Design Art and Communication, 1186-1194. https://doi.org/10.7456/1080SSE/160

30. Lian, S. and Yoong, L.C. (2018) Customer Engagement in Social Media and Tourism Brand Performances Implications. Turkish Online Journal of Design Art and Communication, 2018, 1186-1194 https://doi.org/10.7456/1080SSE/160

31. Kaplan, A.M. and Haenlein, M. (2010) Users of the World, Unite! The Challenges and Opportunities of Social Media. Business Horizons, 53, 59-68.https://doi.org/10.1016/j.bushor.2009.09.003

32. Ayeh, J.K., Leung, D., Norman, A. and Law, R. (2012) Perceptions and Strategies of Hospitality and Tourism Practitioners on Social Media: An Exploratory Study. In: Fuchs, M., Ricci, F. and Cantoni, L., Eds., Information and Communication Technologies in Tourism, Springer, Vienna, 1-12. https://doi.org/10.1007/978-3-7091-1142-0_1

33. Azhar, F.N. and Fauzan, N. (2020) The Role of Twitter as a Social Media Platform of Central Java Government for Sustainable Tourism Development. International Conference on Public Organization (ICONPO) 2019, 28-30 August 2019, 1-14. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3525912

34. Yamagishi, K.D., Ocampo, L.A., Abellana, D.P., Tanaid, R.A., Tiu, A.M., Medalla, M.E., Selerio, E.F., Go, C., Olorvida, R.C., Maupo, A., Maskariño, D. and Tantoo, E. (2021) The Impact of Social Media Marketing Strategies on Promoting Sustainability of Tourism with Fuzzy Cognitive Mapping: A Case of Kalanggaman Island (Philippines). Environment, Development and Sustainability, 23, 14998-15030.https://doi.org/10.1007/s10668-021-01283-6

35. Sarkar, S.K. and George, B. (2018) Social Media Technologies in the Tourism Industry: An Analysis with Special Reference to Their Role in Sustainable Tourism Development. International Journal of Tourism Sciences, 18, 269-278.https://doi.org/10.1080/15980634.2018.1551312

36. Xiang, Z. and Gretzel, U. (2010) Role of Social Media in Online Travel Information Search. Tourism Management, 31, 179-188. https://doi.org/10.1016/j.tourman.2009.02.016

37. Chiang, L., Manthiou, A., Tang, L.R., Shin, J.Y. and Morrison, A.M. (2012) An Investigation of the Information Sources Used by International Tourists of Different Age Groups in Fiji. International Journal of Tourism Sciences, 12, 20-46.https://doi.org/10.1080/15980634.2012.11434662

38. Hysa, B., Zdonek, I. and Karasek, A. (2022) Social Media in Sustainable Tourism Recovery. Sustainability, 14, Article 760. https://doi.org/10.3390/su14020760

39. Sun, Y., Liu, Y. and Zhang, J. (2020) Excessive Enterprise Social Media Use Behavior at Work: Role of Communication Visibility and Perspective of Uses and Gratifications Theory. IEEE Access, 8, 190989-191004. https://doi.org/10.1109/ACCESS.2020.3032035

40. An, S., Kim, W., Lee, B. and Suh, J.A. (2022) Study on the Tourism-Related Information Consumption Process of Tourists on Social Networking Sites. Sustainability, 14, Article 3980. https://doi.org/10.3390/su14073980

41. Yen, C., Chen, C., Cheng, J. and Teng, H. (2018) Brand Attachment, Tour Leader Attachment, and Behavioral Intentions of Tourists. Journal of Hospitality and Tourism Research, 42, 365-391. https://doi.org/10.1177/1096348015584438

42. Coudounaris, D.N. and Sthapit, E. (2017) Antecedents of Memorable Tourism Experiences Related to Behavioral Intentions. Psychology and Marketing, 34, 1084-1093.

43. Miao Y. ,et al. (2015)The Influence of Electronic-WOM on Tourists’ Behavioral Intention to Choose a Destination: A Case of Chinese Tourists Visiting Thailand The AU-GSB e-Journal 8, 13-31.

44. Oliver, R.L. (1980) Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17, 460-469.https://doi.org/10.1177/002224378001700405

45. Gohary, A., Pourazizi, L., Madani, F. and Chan, E.Y. (2020) Examining Iranian Tourists’ Memorable Experiences on Destination Satisfaction and Behavioral Intentions. Current Issues in Tourism, 23, 131-136.https://doi.org/10.1080/13683500.2018.1560397

46. Kim, J.H., Ritchie, J.B. and McCormick, B. (2012) Development of a Scale to Measure Memorable Tourism Experiences. Journal of Travel Research, 51, 12-25.https://doi.org/10.1177/0047287510385467

47. Pohsien, C., TsaiFa, Y. and KuoKung, C. (2015) Tourists Behavioral Intentions in a Rural Area: An Integrated Aspect of SCT and TPB. Journal of Food Agriculture and Environment, 13, 168-174.

48. Tsai, C.T. (2016) Memorable Tourist Experiences and Place Attachment When Consuming Local Food. International Journal of Tourism Research, 18, 536-548.https://doi.org/10.1002/jtr.2070

49. Rasoolimanesh, S.M., Seyfi, S., Rather, R.A. and Hall, C.M. (2021) Investigating the Mediating Role of Visitor Satisfaction in the Relationship between Memorable Tourism Experiences and Behavioral Intentions in a Heritage Tourism Context. Tourism Review, 77, 687-709. https://doi.org/10.1108/TR-02-2021-0086

50. Ali, F., Ryu, K. and Hussain, K. (2016) Influence of Experiences on Memories, Satisfaction and Behavioral Intentions: A Study of Creative Tourism. Journal of Travel and Tourism Marketing, 33, 85-100. https://doi.org/10.1080/10548408.2015.1038418

51. Schmallegger, D. and Carson, D.B. (2008) Information Search and Trip Planning Behaviour of International and Domestic Four Wheel Drive Travellers in Central Australia. Proceedings of the 18th Annual CAUTHE Conference, Gold Coast, January 2008, 7.

52. Chen, C.F. and Chen, F.S. (2010) Experience Quality, Perceived Value, Satisfaction and Behavioral Intentions for Heritage Tourists. Tourism Management, 31, 29-35.https://doi.org/10.1016/j.tourman.2009.02.008

53. Cunha, C., Kastenholz, E. and Carneiro, M.J. (2020) Entrepreneurs in Rural Tourism: Do Lifestyle Motivations Contribute to Management Practices that Enhance Sustainable Entrepreneurial Ecosystems? Journal of Hospitality and Tourism Management, 44, 215-226. https://doi.org/10.1016/j.jhtm.2020.06.007

54. Jeong, Y. and Kim, S.A. (2019) A Study of Event Quality, Destination Image, Perceived Value, Tourist Satisfaction, and Destination Loyalty among Sports Tourists. Asia Pacific Journal of Marketing and Logistics, 32, 940-960.https://doi.org/10.1108/APJML-02-2019-0101

55. Biswas, C., Deb, S., Hasan, A.A. and Khandakar, M.S. (2020) Mediating Effect of Tourists’ Emotional Involvement on the Relationship between Destination Attributes and Tourist Satisfaction. Journal of Hospitality and Tourism Insights, 4, 490-510. https://doi.org/10.1108/JHTI-05-2020-0075

56. Mikulić, J., Kresic, D. and Šerić, M. (2021) The Factor Structure of Medical Tourist Satisfaction: Exploring Key Drivers of Choice, Delight, and Frustration. Journal of Hospitality & Tourism Research, 45, 1489-1512.https://doi.org/10.1177/1096348020987273

57. Anabila, P., Ameyibor, L.E.K., Allan, M.M. and Alomenu, C. (2022) Service Quality and Customer Loyalty in Ghana’s Hotel Industry: The Mediation Effects of Satisfaction and Delight. Journal of Quality Assurance in Hospitality and Tourism, 23, 748-770. https://doi.org/10.1080/1528008X.2021.1913691

58. Elgarhy, S. (2022) Effects of Service Quality, Loyalty Programs, Pricing Strategies, and Customer Engagement on Firms’ Performance in Egyptian Travel Agencies: Mediating Effects of Customer Retention. Journal of Quality Assurance in Hospitality and Tourism, 1-29. https://doi.org/10.1080/1528008X.2022.2077889

59. Osakwe, C.N. (2016) Crafting an Effective Brand-Oriented Strategic Framework for Growth-Aspiring Small Businesses: A Conceptual Study. Qualitative Report, 21, 163-177. https://doi.org/10.46743/2160-3715/2016.2484

60. Amissah, E.F. and Amenumey, K.E. (2015) Dimensions of Service Quality in Hotels in Accra, Ghana. Journal of Arts and Social Sciences, 3, 156-170.

61. Munkaila, A.D., Zakaria, M. and Clifford, B. (2018) Service Quality in Northern Region Tourism Industry: A Case Study of Hotels in Tamale Metropolis in the Northern Region of Ghana. International Journal of Scientific and Research Publications (IJSRP), 8, 766-778. https://doi.org/10.29322/IJSRP.8.10.2018.p8290

62. Owusu-Frimpong, N., Nwankwo, S., Blankson, C. and Tarnanidis, T. (2013) The Effect of Service Quality and Satisfaction on Destination Attractiveness of Sub-Saharan African Countries: The Case of Ghana. Current Issues in Tourism, 16, 627-646. https://doi.org/10.1080/13683500.2013.785479

63. Chen Q.-L. ,et al. (2009)Study on the Tourist Satisfaction Degree of Tourism Destination—A Case Study of Tongdao County in Huaihua City in Hunan Province Yunnan Geographic Environment Research 21, 97-101.

64. Ghazarian, P.G. (2016) Country Image and the Study Abroad Destination Choice of Students from Mainland China. Journal of International Students, 6, 700-711.https://doi.org/10.32674/jis.v6i3.350

65. Költringer, C. and Dickinger, A. (2015) Analyzing Destination Branding and Image from Online Sources: A Web Content Mining Approach. Journal of Business Research, 68, 1836-1843. https://doi.org/10.1016/j.jbusres.2015.01.011

66. Hsu, S., Lin, C. and Lee, C. (2017) Measuring the Effect of Outbound Chinese Tourists’ Travel Decision-Making through Tourism Destination Image and Travel Safety and Security. Journal of Information and Optimization Sciences, 38, 559-584.https://doi.org/10.1080/02522667.2016.1259854

67. Su, L., Hsu, M.K. and Swanson, S. (2017) The Effect of Traveler Relationship Perception on Destination Loyalty at a World Heritage Site in China: The Mediating Role of Overall Destination Satisfaction and Trust. Journal of Hospitality and Tourism Research, 41, 180-210. https://doi.org/10.1177/1096348014525630

68. Tang, L. and Jang, S.S. (2008) Tourism Information Trust as a Bridge between information Value and Satisfaction: An Exploratory Study. Tourism Analysis, 13, 565-578. https://doi.org/10.3727/108354208788160441

69. Jeng, C.-R., Snyder, A.T. and Chen, C.-F. (2019)Importance-Performance Analysis as a Strategic Tool for Tourism Marketers: The Case of Taiwan’s Destination Image. Tourism and Hospitality Research, 19, 112-125. https://doi.org/10.1177/1467358417704884

70. Jebbouri, A., Zhang, H., Wang, L. and Bouchiba, N. (2021) Exploring the Relationship of Image Formation on Tourist Satisfaction and Loyalty: Evidence from China. Frontiers in Psychology, 12, Article ID: 748534. https://doi.org/10.3389/fpsyg.2021.748534

71. Suid, I., Mohd Nor, N. and Omar, H. (2018) A Review on Islamic Tourism and the Practical of Islamic Attributes of Destination in Tourism Business. International Journal of Academic Research in Business and Social Sciences, 7, 255-269. https://doi.org/10.6007/IJARBSS/v7-i12/3609

72. Xiao, H.P., Gu, R.X., Chen, M. and Du, T. (2011) The Transformation and Upgrading of Tourist Destination Based on the Tourist Satisfaction: A Case Study of Chenzhou City. World Regional Studies, 20, 135-144.

73. Otway, K.M., Chitan, P.K. and Cornwall, W.S. (2011) Influence of Destination Image and Destination Brand in Tourists’ Decision-Making: A Case of Grenada. International Journal of Leisure and Tourism Marketing, 2, 209-231.https://doi.org/10.1504/IJLTM.2011.042287

74. Artigas, E.M., Chasco, C. and Pozo, V.V. (2015) Benefit Perceived by Tourists. Role of the Hospitality Offered by the Tourist Destination. International Journal of Business and Social Science, 6, 53-64.

75. Lu, C.-S., Weng, H.-K., et al. (2020) How Port Aesthetics Affect Destination Image, Tourist Satisfaction and Tourist Loyalty? Maritime Business Review, 5, 211-228. https://doi.org/10.1108/MABR-12-2019-0056

76. Cropanzano, R. and Mitchell, M.S. (2005) Social Exchange Theory: An Interdisciplinary Review. Journal of Management, 31, 874-900. https://doi.org/10.1177/0149206305279602

77. Crotts, J.C., Coppage, C.M.A. and Andibo, A. (2001) Trust-Commitment Model of Buyer-Supplier Relationships. Journal of Hospitality and Tourism Research, 25, 195-208. https://doi.org/10.1177/109634800102500206

78. García, J., Juaneda, C., Raya, J.M. and Sastre, F.A. (2015) Study of Traveller Decision-Making Determinants: Prioritizing Destination or Travel Mode? Tourism Economics, 21, 1149-1167. https://doi.org/10.5367/te.2015.0517

79. Zhu, M., Weiller, B., Young, M. and Lee, Y.L. (2015) Information Sources for Travel Decisions: Use of Internet vs Other Information by the Chinese Student Market. CAUTHE: Rising Tides and Sea Changes: Adaptation and Innovation in Tourism and Hospitality, 2-5 February 2015, Gold Coast, 789-792.

80. Choi, M., Law, R. and Heo, C.Y. (2016) Shopping Destinations and Trust-Traveler Attitudes: Scale Development and Validation. Tourism Management, 54, 490-501. https://doi.org/10.1016/j.tourman.2016.01.005

81. Bieger, T. and Laesser, C. (2004) Information Sources for Travel Decisions: Toward a Source Process Model. Journal of Travel Research, 42, 357-371. https://doi.org/10.1177/0047287504263030

82. Tran, V., Do, H.H., Phan, N.V., Nguyen, T.N. and Trang, N.V. (2016) An Impact of Social Media and Online Travel Information Search in Vietnam. Journal of Tourism Research and Hospitality, 5, 1-11. https://doi.org/10.4172/2324-8807.1000153

83. Sthapit, E. (2018) Antecedents of a Memorable Hotel Experience: Finnish Hotels Perspective. Current Issues in Tourism, 22, 2458-2461.https://doi.org/10.1080/13683500.2018.1518413

84. Victor, W.G. (2014) Impact of Travel Information Search Behavior on the Image of Tanzania as a Tourist Destination. Athens Journal of Tourism, 1, 135-146. https://doi.org/10.30958/ajt.1-2-3

85. Daniels, M.J., Rodgers, E.B.D. and Wiggins, B.P. (2005) “Travel Tales”: An Interpretive Analysis of Constraints and Negotiations to Pleasure Travel as Experienced by Persons with Physical Disabilities. Tourism Management, 26, 919-930. https://doi.org/10.1016/j.tourman.2004.06.010

86. De Vos, J. (2020) The Effect of COVID-19 and Subsequent Social Distancing on Travel Behavior. Transportation Research Interdisciplinary Perspectives, 5, Article ID: 100121. https://doi.org/10.1016/j.trip.2020.100121

87. Eitzinger, C. and Wiedemann, P.M. (2008) Trust in the Safety of Tourist Destinations: Hard to Gain, Easy to Lose? New Insights on the Asymmetry Principle. Risk Analysis: International Journal, 28, 843-853.https://doi.org/10.1111/j.1539-6924.2008.01077.x

88. Schroeder, A. and Pennington-Gray, L. (2015) The Role of Social Media in International Tourist’s Decision Making. Journal of Travel Research, 54, 584-595. https://doi.org/10.1177/0047287514528284

89. Mandasari, V. (2021) Tourists’ Decision Making in Choosing Destination Place. Journal of Economics, Finance and Management Studies, 4, 1974-1980. https://doi.org/10.47191/jefms/v4-i10-20

90. Do, T.H.N. and Shih, W. (2016) Destination Decision-Making Process Based on a Hybrid MCDM Model Combining DEMATEL and ANP: The Case of Vietnam as a Destination. Modern Economy, 7, 966-983. https://doi.org/10.4236/me.2016.79099

91. Kyriakaki, A., Stavrinoudis, T. And Daskalopoulou, G. (2020) Investigating the Key Factors Influencing the International Tourists’ Decision-Making on Choosing a Destination. In: Katsoni, V., Spyriadis, T., Eds., Cultural and Tourism Innovation in the Digital Era. Springer Proceedings in Business and Economics. Springer, Cham, 335–352. https://doi.org/10.1007/978-3-030-36342-0_27

92. Seyidov, J. and Adomaitienė, R. (2017) Factors Influencing Local Tourists’ Decision-Making on Choosing a Destination: A Case of Azerbaijan. Ekonomika, 95, 112-127. https://doi.org/10.15388/Ekon.2016.3.10332

93. Wong, J.Y. and Yeh, C. (2009) Tourist Hesitation in Destination Decision Making. Annals of Tourism Research, 36, 6-23. https://doi.org/10.1016/j.annals.2008.09.005

94. Garg A. ,et al. (2013)A Study of Tourist Perception towards Travel Risk Factors in Tourist Decision Making Asian Journal of Tourism and Hospitality Research 7, 47-57.

95. Nuraeni, S., Arru, A.P. and Novani, S. (2015) Understanding Consumer Decision-Making in Tourism Sector: Conjoint Analysis. Procedia-Social and Behavioral Sciences, 169, 312-317. https://doi.org/10.1016/j.sbspro.2015.01.315

96. Qu, Z. and Chang, S.H. (2016) The Relationships between Destination Images Formed through Visual Media Travel Motivation and Travel Decision Making: Chinese Tourists to Korea. Korea Journal of Tourism Research, 31, 41-60. https://doi.org/10.21719/KJTR.31.4.3

97. Al-Sheebani, G. and Abdallah, G. (2021) Impact of Travel Destination on Tourist Behavior. International Journal of Research Publications, 87, 6-22. https://doi.org/10.47119/IJRP1008711020212365

98. Sealy, W. and Wickens, E. (2008) The Potential Impact of Mega Sports Media on the Travel Decision-Making Process and Destination Choice—The Case of Portugal and Euro 2004. Journal of Travel and Tourism Marketing, 24, 127-137. https://doi.org/10.1080/10548400802092601

99. Syamsu, M.N., Sasongko, G., Adari, R.K. and Supramono, S. (2022) The Relationship of Experience, Satisfaction, and Trust of Y Generation Tourist Instagram Social Media Users to Tourism Destination Loyalty in Yogyakarta. Technium Social Sciences Journal, 33, 516-527. https://doi.org/10.47577/tssj.v33i1.6775

100. Amoah, J. and Jibril, A.B. (2021) Social Media as a Promotional Tool towards SME’s Development: Evidence from the Financial Industry in a Developing Economy. Cogent Business and Management, 8, Article 1923357.https://doi.org/10.1080/23311975.2021.1923357

101. Heckathorn, D.D. (2011) Comment: “Snowball versus Respondent-Driven Sampling”. Sociological Methodology, 41, 355-366. https://doi.org/10.1111/j.1467-9531.2011.01244.x

102. Amoah, J. and Jibril, A.B. (2020) Inhibitors of Social Media as an Innovative Tool for Advertising and Marketing Communication: Evidence from SMES in a Developing Country. Innovative Marketing, 16, 164-179. https://doi.org/10.21511/im.16(4).2020.15

103. Etikan, I., Musa, S.A. and Alkassim, R.S. (2016) Comparison of Convenience Sampling and Purposive Sampling. American Journal of Theoretical and Applied Statistics, 5, 1-4. https://doi.org/10.11648/j.ajtas.20160501.11

104. Haseeb, K., Abbas, N., Saleem, M.Q., Sheta, O.E., Awan, K., Islam, N., et al. (2019) Correction: RCER: Reliable Cluster-Based Energy-Aware Routing Protocol for Heterogeneous Wireless Sensor Networks. PLOS ONE, 14, e0224319.https://doi.org/10.1371/journal.pone.0224319

105. Goodhue, D.L., Lewis, W. and Thompson, R. (2012) Does PLS Have Advantages for Small Sample Sizes or Non-Moral Data? MIS Quarterly, 36, 981-1001. https://doi.org/10.2307/41703490

106. Hair, J., Hollingsworth, C.L., Randolph, A.B. and Chong, A.Y.L. (2017) An Updated and Expanded Assessment of PLS-SEM in Information Systems Research. Industrial Management and Data Systems, 117, 442-458. https://doi.org/10.1108/IMDS-04-2016-0130

107. Hair, J.F., Risher, J.J., Sarstedt, M. and Ringle, C.M. (2019) When to Use and How to Report the Results of PLS-SEM. European Business Review, 31, 2-24. https://doi.org/10.1108/EBR-11-2018-0203

108. Jibril, A.B., Kwarteng, M.A., Chovancova, M. and Pilik, M. (2019) The Impact of Social Media on Consumer Brand Loyalty: A Mediating Role of Online Based-Brand Community. Cogent Business and Management, 6, Article ID: 1673640. https://doi.org/10.1080/23311975.2019.1673640

109. Issa, B., Leow, R. and Morgan-Short, K. (2014) Leung and Williams (2011) Revisited: Addressing Issues of Internal Validity. https://www.researchgate.net/publication/270570687_Leung_and_Williams_2011_Revisited_Addressing_issues_of_internal_validity

110. Bagozzi, R.P. and Yi, Y. (1988) On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16, 74-94.https://doi.org/10.1007/BF02723327

111. Liu, Y.D., Zhu, B.F., Fan, C.L., Qin, Y.Y. and Chen, H.Y. (2022) The Performance Changes and Migration Behavior of PLA/Nano-Silver Composite Film by High-Pressure Treatment in Food Simulation Solution. Journal of Food Safety, 42, e12974. https://doi.org/10.1111/jfs.12974

112. Hubona, G.S., Schuberth, F. and Henseler, J. (2021) Clarification of Confirmatory Composite Analysis (CCA). International Journal of Information Management, 61, Article ID: 102399. https://doi.org/10.1016/j.ijinfomgt.2021.102399