Keywords

Social Media Interaction, Perceived Destination Image, Travel Intentions, Saudi Arabia.

Introduction

Tourism can have a significant impact on a country’s economy; as Chu (2018) has noted, it can positively affect a nation’s employment figures, export markets, and the overall GDP. Rahayu (2018) and Rahman (2014) have emphasized the benefit of increases in leisure travel, noting the significant rise in business in areas such as public transport, shopping, resorts and the entertainment industry, as a consequence of high demand.

In an effort to reduce its reliance on oil revenues, which account for 52.6% of Saudi Arabia’s GDP, the Saudi government has been placing increased emphasis on the country’s tourism potential over the past twenty years, as Khizindar (2012) has noted. To this end, a new destination, Neom City, has been under development (Middle East Institution, 2019), with a substantial amount of attention being paid to its potential for tourism.

Encouraging visitors from overseas is a key concern of many governments and, as Mosbah & Saleh (2014) have pointed out, targeting specific destinations for incoming travelers has been prioritized. According to Chu (2018), with such emphasis placed on the tourism industry by many countries around the world, strategists are taking steps to gain an understanding of the decision-making process through which travelers choose their destinations in the hope that such an understanding might be helpful in successfully influencing those choices.

This focus on tourism as an industry is not new, however; there have been a number of studies in recent decades that consider the impact of travelers’ decision-making processes (Lam & Hsu, 2006).

For Echtner & Ritchie (1991), this leads to a situation in which national tourist agencies need to be competitive in an increasingly over-subscribed market, which has been further complicated in the wake of the Global Financial Crisis. Beerli & Martin (2004) have asserted the importance of a destination’s image in the decision-making process carried out by potential tourists, while Sheikh (2019) has demonstrated the extent to which a potential traveler’s perception of a destination can influence the competitiveness of that destination in the marketplace. In order to understand the ways in which a positive image is generated, it is necessary to examine the ways in which potential visitors’ perception of a destination is engineered.

Blazquez-Resino et al. (2016) have highlighted the extent to which studies have engaged with questions relating to the production and consumption of information, the variables that are most influential in the decision-making process, and the relative persuasiveness of the various types of argument or format used. There is an argument to suggest that it is by visiting a place that the most comprehensive understanding of it is gained (Girard & Gartner, 1993); without having been there, potential travelers lack the experience and knowledge necessary for informed decision making, and are left in an inevitable state of uncertainty. In such circumstances, travelers rely on internet resources and other sources of information to aid the process.

Internet and Social media have become an important channel of such information in recent years, in that it encourages peers to communicate their experiences in an open and trustworthy way, through feedback, descriptive posts, collaborative discussions and polls (Rizky et al., 2017; Al-Rawashdeh et al., 2019). According to Evans (2010), social media can be an immersive marketing experience for users, naturalizing the communication process; customer discussion and feedback promote interaction on a communal level as well as allowing for communication with the company itself. With this in mind, this study considers the extent to which social media serves in a moderating capacity in the dissemination of information that is influential to the image and appeal of a destination to potential tourists, with a specific focus on Neom City.

Literature Review

Travel Intentions

Choice and decision-making processes are at the root of behavioral intention; as McCormick (2016) has suggested, travelers base their decisions on social influence (the extent to which their peers approve of an individual making a particular choice) and their perception of the benefits that making a particular decision (to visit a destination or purchase a product, for example) might provide. Consideration of these factors, along with an assessment of an alternative course of action, is integral to the decision-making process. Intentions are subject to change, however; between the choice and decision stages, unanticipated factors can influence intentions, and the likelihood of this increases the longer the process takes.

According to Chu (2018) and Rizky et al. (2017), two key factors in the formation of intentions are the traveler’s confidence in a destination and their inhibitions toward it, which can influence their behavior in ways that contradict their basic attitudes. These intentions, which are defined by the subjective likelihood of a traveler committing to specific actions in relation to a service or product, constitute, as Kim & Jun (2016) have pointed out, a particular type of belief which the traveler holds regarding whether or not they will travel to a particular destination within a given timeframe.

The analysis which follows adheres to the model proposed by Chu (2018) in assessing the likelihood of travelers visiting particular destinations, the potential for their plans to change, and the process through which the customer’s intention is formed.

Perceived Destination Image

In 1979, Crompton proposed that there are two elements to the formation of a destination’s image, these being affective and cognitive components; since then, the impact of attitudes on the creation of a destination’s image have been extensively discussed in numerous studies. Several researchers have employed different terminology in the definition of destination image; according to Gedikoglu et al. (2020), some are subjective (“perception”, “mental representation” and “impression”, for example), owing to the discrepancies between the reality and the image that is perceived. This is supported by Hunt (1975), who acknowledges the mental image relied upon by tourists in their decision-making process may differ significantly from the actual reality. This subjective understanding of the real is further confirmed by Bigne et al. (2001). In line with this, Canally (2010) suggests that the perceived image of a destination is an amalgam of objective knowledge, emotions, imaginings, prejudices and general impressions through which an object or place is judged by an individual.

There has been a substantial amount of research published that has emphasized the significance of such images in the formation of perceptions among potential travelers, which impacts directly upon the decision-making process. A tourist destination’s real potential is dependent on the image it generates (Rodríguez del Bosque et al., 2009), which highlights the importance of the dissemination of information in the management of customer uncertainty. This in turn is influential in the formation of expectations relating to future visits to the region. Mun et al.’s (2018) arguments relating to customer perception and destination image are drawn upon in the discussion of the ways in which marketing strategy takes into account customer perception and expectation in the management of tourism.

Social Media Interaction

In recent years, social media has become a key networking and marketing platform for a growing number of e-commerce ventures such as Instagram and Facebook, in part because it responds to a basic human need: social interaction. It provides a space for people of a similar nature to communicate, share ideas and establish common values.

Social interaction plays a crucial role in behavioral motivation. In social interaction theory, as Stephen et al. (2010) have explained, it is access to a large number of a social commerce network’s users, rather than their centrality within that network, that benefits the seller to the largest degree. The phenomenon of clustering, whereby a community’s participants are influenced by the proximity of their friends and the behavior and character of those friends (Zhang & Benyoucef, 2016), leads to changes in their own consumer behavior; as Goldfarb et al. (2015) have pointed out, though, these changes can also be influenced by the nature of the retail platform itself.

Studies of the psychological factors and mechanisms that primarily influence customer purchasing intentions in e-commerce contexts have continued to prioritize perceptions of risk and trust; this has been less well documented in the area of social interaction, with customer perceptions and the ways in which they influence buying intentions remaining under-researched. Word-of-mouth has been shown to be a highly effective marketing mechanism in social commerce environments, where much of the content is user generated. In these contexts, it is through an affinity between users that interactive information sharing, including user feedback and recommendations, that users come to accept the content that is generated. As Liang et al. (2011) have pointed out, when this affinity is at its strongest (in close relationships with friends, for example), interaction is likely to be more frequent.

Intimacy

Intimacy is a measure of how close a relationship is, taking into account emotional bonds and feelings of spiritual connection and approval (Yin et al., 2019; Lee et al., 2011). For the purposes of this study, intimacy will refer to the emotional link between the user and the commercial promoter in a social media context and will take into account levels of interactivity and the nature of the psychological connection between these.

Perceived Risk

Perceived risk is a measure of the degree to which uncertainty impacts upon the decision-making process. As Yin et al. (2019) observe, when shopping online for products or services, the outcome of the transaction cannot always be accurately predicted, meaning that there is likelihood that the customer will be required to take a risk.

Trust

Yin et al. (2019) goes on to describe trust as a cooperative relationship, whereby a party has an adequate level of belief that the other party is both honest and reliable. With this in mind, this study will interpret trust as being the degree of confidence that exists among customers with regard to other users and the online shopping platform itself.

The analysis takes into account three elements proposed by Yin et al. (2019), these being: a) the level of emotional intimacy between the consumer and the promoter that is generated through social media channels; b) the anticipated risks associated with purchasing online; and c) the level of trust that is established between the user and product marketer.

Relationship Between (Perceived Destination Image; Travel Intentions; Social Media Interaction)

Studies have reached different conclusions regarding the relationship between travelers’ intentions and their perceived image of a destination. Some (including Chew & Jahari (2014); Hallmann et al. (2015); Endah et al. (2017) and Kanwel et al. (2019)) identified a positive correlation between perceived image and the intention to travel or visit. Conversely, Chen et al. (2013) noted a negative correlation between perceived images of a destination and travel intentions. McLellan & Foushee (1983) and Qi (2005) concluded that positive perceptions can be mixed with negative perceptions in forming an overall image. Woodside & Lysonski (1989) have asserted that travel decision-making favors destinations with a positive image; similarly, McLellan & Foushee (1983) suggest that it is necessary in positive decision making for the favorable image of a destination to outweigh the unfavorable. This study proposes the following hypothesis, on the assumption that travelers make destination choices based on their perception and image of that destination:

H₁ The impact of perceived destination image on travel intention is positive.

Social media has been shown to impact in a positive way on travelers’ intentions and plays a role in influencing the perceived image of a destination (Kim et al., 2017; Alnsour et al., 2018; Bernkopf & Nixon, 2019; Sultan et al., 2019). While social media has a demonstrable effect on perception and intention with regard to a destination, in relative terms the connection between content and emotional engagement in social media contexts scored moderately, according to Voorveld et al. (2018).

It can be concluded, then, that as the correlation between intention and a destination’s image is not always positive (as shown in the studies cited above), the relationship can be said to be inconsistent, with Voorveld et al. (2018) observing the moderating effect of social media on the relationship between content and emotional connection. On this basis, the following hypothesis is proposed:

H₂ The relationship between perceived destination image and intention to travel is moderated by social media interaction.

Methodology

This research can be defined as descriptive, meaning that it aims to provide a description of relevant conditions and phenomena. Such research offers a description of the existing situation, followed by a decision-making process. The current situation forms the basis of hypotheses that are formulated and then tested. For the purposes of this research, questionnaires were handed out to Malaysian travelers at Kuala Lumpur Airport (KUL) who regularly make use of social media and who have some knowledge of Neom City and Saudi Arabia, with the intention of gathering information on the factors than influenced their intention to travel. A convenience sampling technique was employed, owing to their ability to make decisions on the choices available. The questionnaire was divided into four sections. The first requested personal and demographic information on topics such as age, gender, status and education. The second used the models proposed by Mun et al. (2018) to gather, through eight items, the perceived destination images held by the travelers. The third section, which drew upon Yin et al. (2019) was concerned with social media use, focusing on three areas: trust (five items); perceived risk (four items); and intimacy (three items). The final section, which drew from Chu (2018) included three items focused on the intention to travel.

For the items in Section 2, five Likert scales were used. Each item was adjusted for the specific purposes of the study. The questionnaire was assessed by external academics and determined to be valid, before having its internal consistency evaluated by applying the Cronbach alpha to the variables. The results of the latter were: perceived destination image = 0.82; perceived risk = 0.73; intimacy = 0.89; trust = 76; intention to travel = 89, which confirmed the questionnaire’s reliability and validity. It was essential that the sample size conformed to the power of analysis, through which a minimum sample size for the study is encompassed based on the model’s complexity. A minimum sample size of 68 was determined for the study, in line with Green’s (1991) table; two predictors from the research framework were included, with medium effect (Gefen et al., 2011). 250 questionnaires were distributed, in accordance with Hair et al.’s (2010) recommendation that the sample size should exceed 100 in order for the result to be accurate. For the analysis of the model, the partial least squares method was applied using the Smart PLS 3.0 software.

Result

The total number of questionnaires returned was 180; 30 were incomplete, while the remaining 150 were used in the study.

Moderating Analysis Approach

The analysis was carried out using the partial least squares method, meaning that a number of approaches to analysis of the interaction term (moderator) are possible. These include: a) the product indicator approach with reflective indicators (which suffers from weak statistical potency); and b) a two-stage approach with formative indicators, but not this limitation not mandatory (Fassott et al., 2016). When the aim of the study is to determine whether the moderation effect is significant or not, the two-stage method is recommended, according to Chin (2010) and Hair et al. (2017). With this in mind, a two-stage method was adopted for the analysis of the moderator through existing reflective constructs and indicators, eliminating the problems associated with the product indicator approach’s weak statistical power. In the first stage, the convergent validity and discriminant validity (excluding the interaction term) were extracted in the second stage. The structural model requirements were extracted, in accordance with Hair et al. (2017) in calculating the product indicator relating to the second-stage analysis, which introduced the interaction term alongside the predictor and moderator variables.

Measurement Model

Three primary variables are accounted for in the study: perceived destination image as a first-order contract; intention to travel as a first-order contract; and social media interaction as a second-order contract. Three factors were considered when assessing social media interaction; for the purposes of this project, social media interaction was treated as a second-order reflective construct. First-order constructs, including trust, intimacy and perceived risk provide reflective measurements that make reference to the reflective-reflexive type. Social media interaction was considered to be suitable for treatment as a second-order element, with a view to gaining a wider understanding of its conceptual and consensus aspects. Following Hair et al. (2016), in order for the PLS path model to be economical and easily interpreted, the second order was used to reduce the quantity of relationships, and also the quantity of hypotheses to be considered, in the structural model.

The two-stage approach was applied in line with Becker et al. (2012). Thus, in the first stage, the repeated indicator approach is applied, through which first-order scores were gathered for first-order constructs; in the second stage, the weighting of the first-order variables was used to calculate the second-order contract’s CR.AVE.

The measurement model was assessed using the convergent validity and discriminant validity. The evaluation of convergent validity involves analyzing composite reliability, average variance extract (AVE) and factor loading. As shown in Figure 1 and Table 1, the results demonstrated that, aside from P-IM 1 and P-IM 8 (which were deleted were less than 0.6), each item’s loading was above 0.6; AVE figures were above 0.5 and CR figures exceeded 0.7.

Table 1 Measurement Model
First Order Construct	Items	Factor loading	CR	AVE
P-IM	P-IM 1	0.597	0.939	0.720
	P-IM 2	0.867
	P-IM 3	0.843
	P-IM 4	0.842
	P-IM 5	0.850
	P-IM 6	0.879
	P-IM 7	0.808
	P-IM 8	0.564
TI	TI 1	0.883	0.884	0.718
	TI 2	0.854
	TI 3	0.802
PR	PR 1	0.834	0.918	0.737
	PR 2	0.883
	PR 3	0.867
	PR 4	0.849
IN	IN 1	0. 856	0.894	0.738
	IN 2	0.879
	IN 3	0.842
T	T 1	0.891	0.923	0.707
	T 2	0.817
	T 3	0.805
	T 4	0.795
	T 5	0.891
Second Order Contract
SOI	PR	0.721	0.906	0.766
	IN	0.934
	T	0.951

HTMT is used to measure the discriminant validity of the model returning HTMT construct values below 0.90 (ranging from 0.309 to 0.838) (see Table 2). This confirms that, in line with Henseler et al. (2015), each latent construct measurement was wholly discriminant against each of the others.

Table 2 Discriminant Validity (HTMT)
	IN	P-IM	PR	SOI	T	TI
IN
P-IM	0.351
PR	0.559	0.438
SOI	0.642	0.405	0.838
T	0.713	0.309	0.554	0.779
TI	0.780	0.475	0.744	0.853	0.773

Following an analysis of the convergent validity (Table 1) and discriminant validity (Table 2) of the measurement model, it can be concluded that the scale of measurement used in evaluating the constructs and their relative items in the CFA model was both valid and reliable.

Structural Model

Hair et al. (2016) has proposed that, in the assessment of the structural model, R², beta, t-values via bootstrapping with a 1,000 resample, the predictive relevance (Q²), (VIF) was carried out. Social media interaction was assessed on the second-order construct (see Figure 2).

Table 3 shows an R² value of 0.567 for travel intention (TI), indicating a 56.7% degree of variation in TI, which satisfies Chin’s (1998) required cut-off figure of 0.19, and is therefore clarified by its predictors. The Q² figure relating to TI was 0.399, which confirms the relevance of the predictive model outlined by Chin (2010) in that it is significantly above zero. On this basis, it can be concluded that the model is acceptable, with a high level of predictive relevance. Further, in accordance with Hair et al. (2016), the VIF values are lower than 5, being recorded at 1.193 and 1.038.

Table 3 also shows that a positive influence on TI was demonstrated by the Perceived Destination Image (P-IM) predictor, which returned values of β = 0.197 and p = 0.000 < 0.05. In relation to H1, therefore, P-IM on TI is supported. Regarding H2, which is concerned with the moderating influence of social media interaction on the relationship between P-IM and TI, the figures returned were β = 0.108 and p = 0.003 < 0.05. As Figure 3 shows, the lines are not parallel. There is a suggestion that social media interaction reinforces the positive relationship between P-IM and TI, along with the change that occurred in R2 (before entering the interaction effect of 0.559 – see Figure 1) and increased upon entering an interaction figure of 0.567 (Figure 2). The relationship between P-IM and TI is shown to be moderated, and therefore there is support for H2.

Table 3 Structural Model
	S. B	S. D	R2	Q2	VIF	F2	T-value	P-values
P-IM > TI	0.197	0.037	0.567	0.399	1.193	0.075	5.264	0.000
P-IM*SOI > TI	0.108	0.040			1.038	0.020	2.733	0.003

Conclusion and Future Work

The conclusion to be drawn from this research, which corresponds with those of Endah et al. (2017) and Kanwel et al. (2019), is that travelers’ intentions are positively influenced by the traveler’s perception of a destination, which supports H1. Image is, therefore, an important factor in influencing travelers’ perception of a destination and affects the outcome of their decision-making process regarding visiting, while also impacting upon the potential of a destination to attract tourists. The dissemination of information can therefore serve to reduce traveler’s levels of uncertainty regarding a destination. Further, social media has been shown to be influential in reinforcing the connection between travelers’ perception of a destination and their intention to travel there, thereby supporting H2. Social media has the potential to lower perceptions of risk while simultaneously increasing levels of intimacy, which helps to establish trust; by enhancing the visibility of a destination’s real character, the intention of travelers to visit that destination rises.

To summarize, according to the literature review, the role played by the selected research variables was to assess the moderating influence of interaction via social media channels on the perceptions of a destination’s image and travelers’ intentions to visit. The research involved the use of Protection Motivation Theory (PMT) (Rippetoe & Rogers, 1987), the Technology Acceptance Model (TAM) (Davis, 1989), and the Theory of Planned Behaviour (TPB) (Ajzen, 1991), in relation to the rise in intention to travel to Neom City, which is facilitated by the enhancement of the region’s perceived image through social media interaction. The results were analyzed and interpreted using PLS-SEM methods via using two stage approach to analyze the reflective constructs and indicators.

The recommendations that can be made in light of this research are that: a) because the study only involved participants living in Malaysia, further research should be carried out in other countries; b) because the study analysed the moderating influence of social media, in the decision-making process of travelers, the use of other variables, such as the impact of information intensity on the moderation process, would be beneficial in further studies; and c) because of the moderating effect in this study achieved by suing the two-stage approach to analyze the reflective constructs and indicators, further studies can use this approach with the same cases.

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