Abstract
In light of the implementation of the strategies of Vision 2030 in the Kingdom of Saudi Arabia, and the remarkable development in the tourism sector in particular, this paper examined the influence of using social media to access travel-related information on the intention to visit from a domestic tourism perspective in Saudi Arabia. The paper used a correlation research design and quantitative and deductive research methods to examine the proposed hypotheses of this study. A structured questionnaire was used to gather the respondent’s views on tourist visit intention. A sample of 394 local tourist respondents was selected to administer the survey questionnaire. The paper used the convenience sampling method to collect the responses from the respondents. The empirical findings of data showed a significant direct relationship of social influence, price value, and habit with e-word-of-mouth, as well as confirm the significant direct relationship of e-WOM with intention to visit. The findings confirmed the mediating role of e-WOM in influencing the relationship of social influence and habit with tourist visit intention, while it was found that there was no mediating effect in the relationship of price value with tourist visit intention. This paper provides several practical contributions to practitioners in electronic tourism field.
Keywords: Domestic tourism, e-WOM, Habit, Intention to visit, Price value, Social influence, Social media, UTAUT2.
Received: 10 March 2022 / Revised: 25 April 2022 / Accepted: 12 May 2022/ Published: 30 May 2022
Contribution/ Originality
The paper offers a novel contribution on how e-word-of-mouth is used as a mediator toward behavioural intention in a non-organizational setting such as domestic tourism. This paper provides several practical contributions to practitioners in the electronic tourism field.
1. INTRODUCTION
The advent of the internet has afforded consumers today unrestricted access to information than ever before, making it tough to persuade them as even advertising is not seen as truthful. Thus, customers seek out a trustworthy source that they can rely on, and they look to other consumers as a credible source of trust. Thus, as technology advances, e-word-of-mouth (e-WOM)is becoming increasingly prevalent, particularly on social media platforms (Dinh & Doan, 2020). In other words, the internet has changed the way consumers evaluate the value of products and services in general. In recent times, consumers use a wide range of internet-based platforms to review varieties of products and services before making a final decision. There exists a plethora of influencing content shared via social media, online review sites, blogs, and discussion forums, to spread the electronic word of mouth (e-WOM) (Mukhopadhyay, Pandey, & Rishi, 2022).
According to Jamu and Sari (2022), e-WOM as a social activity has grown alongside internet penetration as the intangible character of tourism. It is difficult to evaluate the extent of this penetration prior to consumption since tourism enthusiasts have only social influence. The e-WOM is their only primary source of information as tourists can more easily share their vacation thoughts and stories thanks to the rapid growth of social media. Because of this, e-tourism is fast becoming the status quo as internet users in Saudi Arabia, which stands at 33.58million is projected to grow to 36.2million by 2025, thereby further deepening the dependency of tourists on social influence and e-WOM as World Tourism Organization (WTO) declared that 95 percent of online users turn to the internet for travel information, and 93 percent consult tourism websites when planning their vacations, according to Huertas and Marine-Roig (2018).
Tourism, otherwise regarded as tourist visit intentions, may be defined as the subjective likelihood that a traveler or visitor is willing or not willing to make based on certain available factors and conditions, according to Gretzel, Koo, Sigala, and Xiang (2015). A prospective individual intent to travel to a destination choice is based on their subjective probability of visiting the location within a certain period (Loi, So, Lo, & Fong, 2017). Lyu (2016) stated that travel visit intentions are studied based on individual readiness to make travel-related purchase decisions which are often conditioned as dynamic, complicated decision-making and behavioral processes that are influenced by numerous determinants and interconnected components. In terms of tourism business, according to Meng and Choi (2019), the majority of tourist marketing efforts are created to increase the number of visitors to a tourist destination. Such marketing strategies aim to influence travelers’ behavioral intentions and enhance the likelihood of a visit to a destination (Merli, Preziosi, Acampora, & Ali, 2019).
Thus, anticipating traveler’s future behaviors is important for destination marketers’ for planning the services as anticipated by visitors and also forecasting the number of tourist visits in the future (Hall & Prayag, 2021). To create successful marketing strategies aimed at increasing visitors to a particular location, tourism marketers must understand when and how the target audience develops behavioral intentions and the factors that influence those intentions (Merli et al., 2019). According to Gretzel, Sigala, Xiang, and Koo (2015), behavioral intentions are formed through a process of available choices and decision-making to perform or not to perform a certain action. Consumer behaviors are motivated by individual views about the advantages of a specific activity (such as purchasing a product or traveling to a location) and their subjective judgment of whether others want the consumer to participate in that behavior (Hall & Prayag, 2021). Individual intentions may vary overtime throughout the choices available and the state of the decision-making process (Wu & Wu, 2018). The proposed study, therefore, has made an effort in order to investigate the travel visit intention of individuals based on using social media to access travel-related information by specific constructs from Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is one of the most recent theories to analyse customer behaviour.
The UTAUT2 was formulated with the objective of providing a rigorous framework for explaining technology usage and adoption. As compared to prior technology acceptance models, the UTAUT2 has relatively shown a higher explanation of variance in endogenous constructs. Venkatesh, Thong, and Xu (2012) reported an increase in the explanatory power of UTAUT2 from 56% to 74% in terms of behavioral intention and an increase from 40% to 52% in terms of actual usage (Verkijika, 2018). The UTAUT2 theory has been extensively used and validated in a number of consumer decision-making studies, thereby establishing its predictive ability and validity (Arenas Gaitán, Peral Peral, & Ramón Jerónimo, 2015; Baptista & Oliveira, 2015), including the tourism industry (Morosan & DeFranco, 2016). However, from past literature, it is indicative that the findings from these researches on predictive ability and interrelationship between the constructs have been very diverse, which assert that there is still a need for the systematic study to refine, modify, validate or extend UTAUT2 formulation.
While countries often prioritize international tourism in order to maximize income from exports, domestic tourism continues to be the most popular type of tourism, serving as a critical instrument for regional economic growth and development. This proposed study has been undertaken from the context of domestic tourism. Domestic tourism accounted for 73% of worldwide tourist spending (US$3,971 billion) in 2017. Indeed, domestic tourism accounted for at least 50% of overall Travel & Tourism expenditure in 22 of the 31 nations studied by (Turner, 2018). Domestic tourism is strong in the majority of these countries as a result of a growing number of the middle class, rapid increase in purchasing power of countries, government policies to promote new tourist destinations, initiatives to improve transportation infrastructure as well as establish economic linkage between internal regions and the vastness in size of those nations (Turner, 2018). Saudi Arabia has made significant efforts in recent years to diversify its economy away from oil. Previously, Saudi Arabia was exclusively accessible to individuals with business permits, religious pilgrims, and expatriate employees (Arab News, 2021). The kingdom’s decision to release tourist visas in 2019 is certain to have a long-term beneficial effect on the ’kingdom’s tourism industry. Additionally, Saudi has a long-term strategy centered on tourist growth and has made significant expenditures toward this goal (Poncet, 2020).
The annual spending of Saudis on tourism is in the billions of dollars (Arab News, 2016). However, only a small amount of percentage is being spent on domestic tours. Since international tourism is a considerable burden on the current account, the Saudi government has been trying to convince its people to spend holidays at home, where the Kingdom’s tourism industry is speeding its growth in the future via the announcement of various projects and initiatives (Arab News, 2021). Saudi tourism is diverse geographically and historically, emphasizing natural resources, archaeological riches, and historical sites that fulfill visitor expectations. It established performance metrics to track progress toward the Kingdom’s Vision 2030 objective of increasing annual visits to 100 million tourists by 2030, 55 million of whom are domestic and 45 million foreigners, creating 1 million jobs and increasing the country’s GDP by 10% (Poncet, 2020). Hence, the present study aims to understand the creation of local tourist visits intention in Saudi Arabia.
In the tourism industry, Electronic-Word-of-Mouth (e-WOM) is regarded as the most influential and trustworthy source of information, having a significant impact on tourist destination selection. According to Chen, Shang, and Li (2014) electronic word of mouth is relevant with regard to tourists’ intention toward visiting a particular destination. They also study explored the underlying e-Word of mouth relationships between a traveler’s perception regarding a particular tourist destination and the actual intention to visit. Their study’s goal was to highlight the positive relationship between e-word of mouth and successful marketing, which has a significant impact on the tourism industry. Numerous scholars have discovered in the tourism literature that WOM and e-WOM are the two most important factors that influence visitors’ travel intentions (Abubakar & Ilkan, 2016). In comparison to word of mouth, studies on e-WOM from the perspective of tourism, specifically domestic tourism, are scarce in the literature. Accordingly, the study’s purpose is to understand the influence of the mediating role of e-WOM on local tourists’ visit intention in Saudi Arabia.
2. THEORETICAL BACKGROUND
In UTAUT2 theory, Venkatesh et al. (2012) proposed that individual behavioral intention to use new information systems or technological applications like social media can be predicted by seven explanatory variables, namely: performance expectancy (PE), habit (H), social influence (SI), price value (PV), effort expectancy (EE), facilitating conditions (FC), habit and hedonic motivation (HV). The present study is modified and narrowed-down version of UTUAT2 theory. In this study, three constructs of UTUAT2 theory have been considered to study local traveler visit intention in terms of using information and communication technology like social media for the purpose of travel. The proposed study examined the role of e-WOM as a moderator between the relationship of social influence and visit intention, price value and visit intention, and habit and visit intention. Figure 1 shows the current research model.
Figure 1. Research model.
2.1. Social Influence
“Social influence has been defined as the extent to which an individual perceives that other people within his or her network such as friends, relatives, family, peers, and acquaintances anticipate him/her to utilize the new information system or technology made available (Alvarez, Campo, & Fuchs, 2020)”. Venkatesh et al. (2012) examine the impact of social influence on behavioral intention in both involuntary and mandatory settings and found the impact to be statistically significant. However, Venkatesh et al. (2012) found the relationship to be more robust only during the initial phase of adoption in a mandatory setting and was found to diminish over time. Therefore, the role of social influence on individual behavioral intention was described as complicated (Venkatesh et al., 2012) and is much debated, although there is empirical evidence that it has a significant impact on the latter. Similarly, in terms of travel-related e-WOM from powerful individuals, influential groups, or the sheer volume of people who use the online travel agency may exert a stronger influence. With the increasing number of users of social media, e-WOM would also increase, which would result in more user-generated content regarding travel services and products as well as their reviews and recommendations. The relationship between social influence and electronic word of mouth is still debatable. Therefore, the study developed the following hypothesis:
Hypothesis1: Social influence has a direct and positive influence on e-WOM.
2.2. Price Value
The price value was derived from the perceived value, which is frequently seen as a key variable in determining the buying behavior, which may affect the competitive advantage of a business (Alalwan, Rana, Dwivedi, & Algharabat, 2017). The concept of price value is traditionally a trade-off of advantages and sacrifices one has to undergo while using or accepting a new system (Venkatesh et al., 2012). The price value was recently highlighted by several researchers and marketers of consumer electronic gadgets in the areas of information technology (Escobar-Rodriguez & Carvajal-Trujillo, 2014).
According to Gupta and Dogra (2017) the reason why price value had no effect on behavioral intention was that the cost of technology apps, such as mapping apps for mobile devices was low and easily available. In fact, the majority of the applications discovered are available for free, making the cost insignificant. The only cost incurred or spent for using such applications is the cost of mobile internet. The price value of travel-related products and services via e-WOM by using social media applications must be acceptable and provide value to the tourist so that the tourist does not hesitate to give up his intention to visit. As a result, the study hypothesized the following hypothesis:
Hypothesis2: Price Value has a direct and positive influence on e-WOM.
2.3. Habit
According to Venkatesh et al. (2012), a habit is defined as an unintentional behavior that occurs automatically rather than deliberately. Chen and Chao (2011) stated that a person might develop a habit when he or she frequently utilizes a new system tool or information technology with curiosity, reinforcement, repetitions, conceptual clarity, and the capacity to learn. According to Chávez Herting, Cladellas Pros, and Castelló Tarrida (2020), habitual behaviors may be triggered by external and internal moods that are partly linked to prevailing circumstances. Habits often forecast future behavior, and individuals are more likely to have a positive attitude when they do actions they have done often enough in the past (Huang & Kao, 2015). Once a habit is prevalent, individuals tend to place a greater emphasis on habit than on other external sources or decision methods (Chen & Chao, 2011). Additionally, Venkatesh et al. (2012) discovered that behavioral intention to utilize technology is affected by habit. Individual users who have a habit of using social media are more likely to seek travel-related e-WOM information and make their destination choices. Individuals use social media without any second thought, and few are addicted to it, which reciprocates them from finding e-WOM related to travel purposes. When behavior is repeated many times, it becomes habitual thereafter (Ye, Li, Wang, & Law, 2014). Thus, once users begin to use the app, the activity develops into practice and habit, which encourages people to utilize the app. Therefore, the study developed the following hypothesis:
Hypothesis3: Habit has a direct and positive influence on e-WOM.
2.4. Mediator- Electronic Word of Mouth
Electronic Word of Mouth refers to any online, interpersonal and non-commercial information communication of a service or a product (Serra & Salvi, 2014). Bronner and de Hoog (2011) posit that tourists have become more reliant on electronic platforms for obtaining information related to travel planning. From the hospitality industry context, it was revealed that online platforms such as e-WOM or other forms of electronic mediums exert a positive impact on the decision-making process of tourists and making their destination choice (Ladhari & Michaud, 2015). According to Chong and Ngai (2013), with technological advancement in recent years, various social network sites have come up that have relatively high visitor traffic. These network sites become means of e-Word of mouth as they can reach out to a large number of individuals all at once and include reviews and comments based on their personal experiences of the contributors. The study by Chong and Ngai (2013) found that e-Word of mouth has a significant positive relationship with traveler’s intention to use location-based social media apps.
The result of the study by Herrero and Martín (2017) through the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) revealed that there are mainly three drivers of generating user intentions on social network sites or e-word of mouth, namely performance expectancy, hedonic motivation, and habit. Herrero and Martín (2017) found that other factors inclusive of societal influence, privacy concerns, do not have any influence on the travel intentions of individuals for the use of e-WOM. Additionally, the results concluded that e-word of mouth or user-generated content on social network sites had a relationship between UTAUT2 constructs and behavioral intentions like intention to visit a tourist destination. Although the existing literature posits the positive influence of e-WOM on the travel-related decision-making process, there is also a lack of empirical research to validate the impact of electronic WOM specifically on local tourists’ behavior intention on particular.
To study the role of e-WOM as a mediator between independent variables (social influence, price value, and habit) and the intention of visiting local tourists, the study hypothesized that:
Hypothesis4: Electronic word of mouth has a direct and positive influence on intention to visit.
Hypothesis5: Electronic word of mouth mediates the relationship between social influence and intention to visit.
Hypothesis6: Electronic word of mouth mediates the relationship between price value and intention to visit.
Hypothesis7: Electronic word of mouth mediates the relationship between habit and intention to visit.
3. METHODOLOGY
The study used a primary data collection procedure in order to collect respondents’ data. A structured questionnaire was used to gather the respondent’s views on tourist visit intention. The population considered for the study included Saudi' tourists who use social media. A total of 420 respondent’s questionnaires were administered from 600 surveys have been distributed. Out of which 394 responses were found to be valid and complete. Thus the response rate was 70%. The study used the convenience sampling method in order to collect the responses from the respondent sample.
3.1. Data Analysis
Data was analyzed using SmartPLS 3. A Partial Least Square (PLS) based structured equation modeling method was used in order to test the proposed research model. PLS-SEM was undertaken as it was found suitable for the proposed study to know the interrelationship between the constructs as well as test the mediation effect of the proposed research model. According to Dijkstra and Henseler (2015), PLS-SEM is preferred in terms of confirmatory analysis of the proposed research model, for which has been found suitable for use in this study. A 7-point Likert scale was used in order to collect the perspectives of the constructs like tourist visit intention, social influence, habit, price value, and electronic word of mouth. The indicator items considered to measure a construct were taken from prior literature and are as shown in Table 1.
4. FINDINGS
4.1. Assessment of Measurement Model (Outer Model)
4.1.1. Convergent Validity
Goodhue, Lewis, and Thompson (2012) suggest examining average variance extracted, composite reliability and item loading in order to evaluate the convergent validity of the outer model in PLS-SEM. As specified by Hair, Hult, M., Ringle, and Sarstedt (2014), the average variance extracted value has to be more than 0.5 or higher to ascertain convergent validity, have composite reliability, and item loading score of 0.7 or higher, respectively. The value of average variance extracted, composite reliability, and item loadings of each construct for this study were found to be within the specified ranges and as per specification, thereby establishing the convergent validity of the outer model. For instance, the composite reliability score obtained was of value range (0.80 to 0.88), which is greater than the specified value of 0.7. Item loading of several construct values ranged from 0.70 to 0.95, which are again greater than the acceptable threshold value of 0.7. For the average variance extracted, the value of constructs ranged from (0.66 to 0.76) which are again greater than acceptable values of 0.50 or higher. Therefore, convergent validity could be established for the research model measurement.
Table 1. Construct measurement items.
| Construct | Code | Items | Sources |
| Social Influence (SI) | SI1 | People who influence my behavior think that I should use social media on my tourist destination. | Venkatesh et al. (2012) |
| SI2 | People who are important to me feel that I should use social media on my tourist destination. | ||
| SI3 | I make a reservation through social media because many people are doing so. | ||
| Price Value (PV) | PV1 | Products prices on social media platforms are acceptable prices. | Venkatesh et al. (2012) |
| PV2 | At the current price, social media platforms provide a good value. | ||
| PV3 | For tourism, I have never given up purchasing on social media platforms. | ||
| Habit (H) | H1 | The use of social media has become a habit for me. | Venkatesh et al. (2012) |
| H2 | Using social media is something I do without thinking. | ||
| H3 | I am addicted to using social media. | ||
| Intention to Visit (ITV) | ITV1 | I desire to visit tourist places in Saudi Arabia in the near future. | Han, Kim, and Hyun (2011) |
| ITV2 | If I plan a trip, I will visit tourist places in my country. | ||
| ITV3 | In short, I think Saudi Arabia is a good place worth visiting. | ||
| Electronic Word of Mouth (e-WOM) | e-WOM 1 | I feel more comfortable travelling when I have other people’s online travel opinions. | San Martin, Collado, and Rodriguez del Bosque (2013) |
| e-WOM 2 | When I consider travelling, I seek opinions and advice online from commercial as well as independent sources. | ||
| e-WOM 3 | Online opinions influence my choice of travel. |
4.1.2. Discriminant Validity
In order to ensure discriminant validity of the outer model, variable correlation, or Fornell Larker Criterion (Fornell & Lacker, 1981), cross-loadings of factors and HTMT were measured. As per the rule of thumb, the square root of the average variance extracted of each construct has to be higher as compared to corresponding values with other constructs to maintain discriminant validity (Goodhue et al., 2012). The square root of each construct was found to be higher as compared to other corresponding values. Another important criterion to check for discriminant validity is to examine the cross-loadings of items of each construct. Again, the cross-loading of each item indicators have to load the highest value against the construct that it measures as compared to other constructs (Dijkstra & Henseler, 2015). The analysis showed each item indicator value to have the highest loading against the corresponding construct that it measured. A more advanced and sophisticated method to determine the discriminant validity is to examine the HTMT. The HTMT values must not exceed the maximum value of 0.8 in order to ensure discriminant validity (Hair et al., 2014). The HTMT values were found to be within the range of (0.026 to 0.591) for all the latent constructs, which are within the specified range as stated by Cohen (1988). Thus discriminant validity could be established for the research model measurement constructs.
4.2. Assessment of Structured Model (Inner Model)
4.2.1. Path Coefficient-Hypothesis Testing
The bootstrap procedure shows the significance values of each independent construct in explaining and predicting the dependent construct (Dijkstra & Henseler, 2015). The p-values of all the three exogenous constructs were found to be less than 0.05, supporting the three direct hypotheses. In addition, the p-values of e-WOM were found to be less than 0.05, supporting the fourth direct hypothesis. Thus, it was revealed from the analysis that social influence, price value, and habit have a direct and positive influence on e-WOM, as well that e-WOM has a direct and positive influence on tourist visit intention, as shown in Table 2.
The hypotheses support indicated that social influence, price value, and habit significantly influence e-WOM where friends, family, and other known peers and relatives encourage using social media to obtain travel-related information from social media. Likewise, individuals also feel prices of services and tourist-related product costs are reasonable and appropriate across social media. Similarly, the habit of using social media or its addiction enhances exposed the user many of e-WOM related to travel and tourism. In addition, the fourth direct hypothesis indicates that e-WOM has a direct and positive influence on intention to visit. Thus, it can be concluded that travelers feel more comfortable traveling when they have other people’s online travel opinions and experiences shared through social media.
Table 2. Path coefficient of direct hypotheses.
| Path Coefficient of the Research Hypotheses | ||||||
| Hypo | Direct Relationship | Std. Beta |
Std. Error |
T- Value |
P- Value |
Decision |
| H1 | Social Influence → e-WOM | 0.264 |
0.106 |
2.482 |
0.013 |
Supported |
| H2 | Price Value → e-WOM | 0.258 |
0.111 |
2.329 |
0.020 |
Supported |
| H3 | Habit → e-WOM | 0.273 |
0.100 |
2.726 |
0.007 |
Supported |
| H4 | e-WOM → Intention to visit | 0.657 |
0.047 |
14.025 |
0.00 |
Supported |
Figure 2. Research model path coefficient.
4.2.2. Coefficient of Determination (R-Square)
R-Square measures the predictive ability of the structured model in explaining the endogenous construct variance (Yin, Wang, & Yang, 2014). According to Hair, Celsi, Ortinau, and Bush (2010), R2 determines the model variables’ quality. It refers to the percentage of variance explained to the dependent variables by independent variables. According to Chin (1998), R2 value of 0.67 or higher specifies high variance or explanatory power, R2 values between the ranges (0.33 to 0.67) as moderate explanatory power, a value between the range of (0.19 to 0.33) as weak explanatory power, and R2 value below 0.19 as unacceptable. Figure 2 illustrates the predictive power for intention to visit is 0.431, which indicates moderate explanatory power, 43.1% of the variance has been explained by e-WOM by intention to visit. Additionally, the predictive power of social influence, price value, and habit on e-WOM was found to be moderate at 0.454.
4.2.3. Effect Size (F-Square)
The effect size explains the strength of the relationship between the two variables (Bagozzi & Yi, 1994). As stated by Cohen (1988), the effect size is important, as it indicates the variability in the extent of variance explained by exogenous variables to the endogenous variables. As per specified values of Cohen (1988) f2 > 0.35 is considered as large, 0.15 > f2 < 0.35 is considered as Medium, 0.02 > f2 < 0.15 is treated as small and f2 < 0.02 is regarded as having no effect size. The bootstrapping procedure showed that the effect size of social influence on e-WOM is small at 0.064, price value effect size on e-WOM is small at 0.072, and the impact of habit on e-WOM is small at 0.080, while the impact of e-WOM on intention to visit is large at 0.758.
4.2.4. Predictive Relevance (Q-Square)
Hair, Hult, Ringle, and Sarstedt (2017) state that in order to utilize SmartPLS for prediction, a measure to evaluate the predictive relevance and significance is needed. SmartPLS uses a blindfolding procedure to evaluate both predictive relevance and the significance of the model fit (Bagozzi & Yi, 1994). Q2 analyses, not just the model’s built-around values but also the model’s parameter estimations (Hair, Sarstedt, & Mena, 2012). According to Hair et al. (2017), an endogenous variable with a Q2 value greater than zero indicates that the path model has a predictive relevance. The structured model assessment results indicate that the Q2 value for the endogenous latent variables is more than 0.00, which were found at 0.195 for intention to visit and 0.254 for e-WOM, suggesting that the model has a high degree of predictive relevance.
4.2.5. Goodness of Fit Index (GoF)
As stated by Hsu, Chen, and Hsieh (2006), there are several indications of a path model to evaluate the goodness-of-fit, and the majority of SEM researchers suggest assessing models using several indicators. Hair et al. (2017) suggest that optimal fit indices should meet the following criteria: simplicity in interpretation, independence of sample size, and consistency and accuracy in model comparison. Goodness of fit was evaluated using SRMR values. As per the rule of thumb, SRMR value less than or equal to 0.08 indicates a good fit of the model (Hair... et al., 2017). The goodness of fit index of the structured model was established through SRMR as the computed value was found to be 0.07, which is within the specified and acceptable range.
4.2.6. Mediation Analysis
With respect to Mediation analysis, e-WOM was found to mediate the relationship between social influence and intention to visit and between the relationship habit and intention to visit, while e-WOM was found to not mediate the relationship between price value and intention to visit, as shown in Table 3.
Table 3. Mediating effect.
Mediating Effect |
Bootstrapped Confidence Interval |
|||||||
Path |
Path a |
Path b |
Indirect effect |
Std. Error |
T- Value |
95% LL |
95% UL |
Decision |
SI → e-WOM → ITV |
0.264 |
0.657 |
0.173 |
0.072 |
2.409 |
0.032 |
0.315 |
Mediation |
PV → e-WOM → ITV |
0.258 |
0.657 |
0.170 |
0.770 |
0.220 |
-1.340 |
1.679 |
Not Mediation |
H → e-WOM → ITV |
0.273 |
0.657 |
0.179 |
0.069 |
2.599 |
0.044 |
0.315 |
Mediation |
5. DISCUSSION
The objective of the study was to examine the relationship of three UTUAT2 constructs, namely social influence, habit, and price value, with electronic word of mouth. The study results confirmed that there is a positive and significant direct relationship between social influence and e-WOM, price value, and e-WOM and habit and e-WOM. The hypotheses set in the study were empirically supported and confirmed statistically. Social influence constructs were found exerting a positive impact on e-WOM. It was also observed that as the price value of travel, related products, services were found to be acceptable and reasonable, social media users were more likely to be impacted.
As individuals form the habit of using social media, they are likely to be influenced by travel-based information, opinions, product, and services. Similarly, e-WOM made a direct and positive influence on intention to visit. In addition, the study had aimed to examine the direct relationship of e-WOM and intention to visit and to examine the mediating role of electronic word of mouth between the relationships of social influence, habit, price value with the intention to visit. The study results confirmed that there is a positive and significant direct relationship between e-WOM and intention to visit, as was also confirmed the role of electronic word of mouth as a mediator to influence the relationship of social influence and habit with the intention to visit, while it was found that e-WOM experienced no mediator influence in the relationship of price value with the intention to visit. The hypotheses were empirically supported and confirmed statistically; Table 4 shows the summary of the results of the hypotheses.
The findings that social influence exerts direct and positive influence towards e-WOM have also been supported by prior studies in tourism literature like Sridhar and Srinivasan (2012). In terms of social influence, it is indicative that when an individual contemplates adopting new technology, they are often influenced by other people’s views and opinions, like family, peers, and close friends. Previous research like Huang and Kao (2015); Verkijika (2018) have revealed social influence as a crucial factor predicting using new technology like mobile applications, use of e-learning, etc. The study findings of Sridhar and Srinivasan (2012) confirmed the power of social influence effects via social media which may be a double-edged sword on intention.
Table 4. Summary of results of hypothesis.
| Hypo | Statement | Results |
| H1 | Social influence has a direct and positive influence Electronic word of mouth | Supported |
| H2 | Price Value has a direct and positive influence on Electronic word of mouth | Supported |
| H3 | Habit has a direct and positive influence on Electronic word of mouth | Supported |
| H4 | Electronic word of mouth has a direct and positive influence on intention to visit. | Supported |
| H5 | Electronic word of mouth mediates the relationship between social influence and intention to visit | Supported |
| H6 | Electronic word of mouth mediates the relationship between price value and intention to visit | Not Supported |
| H7 | Electronic word of mouth mediates the relationship between habit and intention to visit | Supported |
Price value has been revealed as a significant predictor by numerous other studies in the past like Lee et al. (2013); Kim (2014); Yi, Mérel, Lee, Farzin, and Six (2014), who studied prices considering from different perspectives and dimensions such as social media. If price values are found to be acceptable and reasonable with the value that the product or service offer, people are more likely to buy such product or services and leave positive comments.
Wang, Li, and Li (2013) revealed a positive and significant relationship between habit and behavior using the UTAUT2 model. Chong and Ngai (2013) examined travelers’ usage of social media throughout their travels and found that passengers’ habit significantly influenced their behavioral intention and usage behavior using the UTUAT2 model. Thus the findings of this study that social influence, price value, and habit have influenced e-WOM have been supported by several other past studies like Lyu (2016); Gretzel. et al. (2015); Loi et al. (2017); Meng and Choi (2019); Merli et al. (2019).
The Mediation analyses using SEM-PLS indicate that the e-WOM mediates and strengthens the relationships between social influence, price value and habit, and traveler visit intention; to the best of the researcher’s knowledge, this finding is novel in the e-WOM literature and may be confirmed in the future by other researchers. The mediation analysis of this study considering e-WOM as a mediator assert that consumers who have an Influential social relationship and habit of using social media for travel purpose are more likely to increase their visit intention. On the another hand, the study’s findings assert that e-WOM do not experience any mediator effect on Price value, which means the consumers who have an interest in the price value of any tourism good or service via social media are less likely to increase their visit intention. There is a lack of studies pertaining to examining e-WOM as a mediator between UTUAT2 constructs and travel visit intention. Hence, there is a need to validate these findings in further studies in the future.
5.1. Practical Implication
The findings of this study provide several practical contributions to marketers’ travel and tourism firms and the overall tourism industry. Firstly, the study findings have extensively highlighted the importance of social influence on social media. Therefore, travel firms, marketers, and travel business can re-orient their social media marketing efforts to streamline the overall travel process and thereby help the traveler to enhance their overall experience. Information asymmetry can be ensured so that the travel planning process becomes systematic and travelers can have access to social media information related to travel in a very organized manner. Secondly, the study findings also revealed that travelers are very sensitive to price value discovered through social media platforms. Therefore, travel firms must ensure that prices that have been made available through social media platforms are exact and accurate. Price value posted by experienced travelers via social media platforms must be ensured that they are up to date and reflect the overall expense of traveling.
Thirdly, the study findings also revealed the importance and influence of tourists’ habit of using social media platforms. In this regard, the creation of more user-friendly social networking sites, the publishing process being made simpler, more fun, and exciting, may be caused by a link between the site and the user, and with continuous use, the habit is formed, and a greater amount of e-WOM generates user material. Lastly, the study findings have highlighted the role of e-WOM as a mediator; since e-WOM may originate from a source that is appealing to prospective businesses, this user-generated material also enhances the marketing potential of the social networking site in question. This indicates that there is a strong potential in this area for suitable content creation across social media since consumers seem to be generally receptive to information sharing (e.g., publishing content about experiences).
From the viewpoint of social media businesses, the findings may be used to create strategies for increasing the usage of their platforms to post material regarding personal experiences, which can then be implemented. On one hand, the publishing of more experiences-related material will enrich the social networking site since they are assets that can be consumed by other users, thus enhancing the value of e-WOM on the platform.
5.2. Limitation and Future Scope of Research
The study included only three constructs from UTAUT2 to study individual traveler’s visit intention to Saudi Arabia. Therefore, in future, more constructs can be incorporated, and other destination choices of travel other than Saudi Arabia can be considered for the study. In addition, the empirical research to validate the impact of e-WOM as a mediator needs further verification with other constructs in future studies. The study has used UTUAT2 theory as the basic framework underpinning this study. Therefore, other theories or combinations of different theories of user acceptance and usage of information and communication systems like social media; e-learning can be evaluated in the future. The present study has used convenience sampling, a technique for surveying primary data for which the findings and results of the research may not be generalized to a greater audience. In further research, a different methodical approach using the probability-sampling technique can be used. The study also used a survey questionnaire in order to collect the respondent data. A 7-point Likert scale item was used to obtain the responses of constructs. Therefore, limitations associated with such survey instruments, like limiting the response to certain predefined options, could affect the study results and findings.
6. CONCLUSION
According to Buhalis and Law (2008), the development of social media has had a synergistic effect on the global growth of tourism. Social networking has fundamentally altered how visitors look for, plan, and experience their vacations (Kavoura & Stavrianeas, 2015). From the customer’s perspective, it enables them to search for, plan, and personalize their vacation packages in ways that were previously impossible with conventional information techniques (Fotis, Buhalis, & Rossides, 2011). The research concludes that UTAUT2 is a powerful theory in providing a robust explanation of tourist behavioral intention in terms of using social media applications for travel planning or making travel-related purchase decisions. This was confirmed from the findings, as it was empirically established that UTAUT2 constructs, social influence, price value, and habit have a significant and direct relation with e-WOM. In addition, e-WOM was found to mediate the relationship of social influence and habit with the intention to visit, while it was found that there is no mediating effect in the relationship of price value with tourist visit intention. It is evident from the study that this high level of contact, also known as customer-to-customer participation or e-WOM, is rapidly influencing the tourist sector (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004).
This e-WOM is seen as critical in the development of the destination’s image, as well as being of enormous value to travel brokers and tourist business organizations. There is a wealth of study on the functional, psychological, and social antecedents of tourist decision-making through social media (Parra-López, Bulchand-Gidumal, Gutiérrez-Taño, & Díaz-Armas, 2011), but less on e-WOM and its mediating role in visit intention. This study provides theoretical and practical contributions and recommendations for future research specifically related to the social media dimensions. Lastly, this study also helps understand the importance of social media for the development of domestic tourism, which generally leads to the activation and improvement of tourism in the kingdom of Saudi Arabia.
Funding: This study received no specific financial support. |
Competing Interests: The authors declare that they have no competing interests. |
Authors’ Contributions: All authors contributed equally to the conception and design of the study. |
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