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Article

The Effects of Social Networking Services on Tourists’ Intention to Visit Mega-Events during the Riyadh Season: A Theory of Planned Behavior Model

by
Dayal Ali W. Al-Khaldy
1,
Thowayeb H. Hassan
1,2,*,
Ahmed Hassan Abdou
1,3,
Mostafa A. Abdelmoaty
4,* and
Amany E. Salem
1,2
1
Social Studies Department, College of Arts, King Faisal University, Al Ahsa 400, Saudi Arabia
2
Tourism Studies Department, Faculty of Tourism and Hotel Management, Helwan University, Cairo 12612, Egypt
3
Hotel Studies Department, Faculty of Tourism and Hotels, Mansoura University, Mansoura 35516, Egypt
4
Independent Researcher, Giza 12573, Egypt
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(21), 14481; https://doi.org/10.3390/su142114481
Submission received: 20 October 2022 / Revised: 1 November 2022 / Accepted: 2 November 2022 / Published: 4 November 2022
(This article belongs to the Special Issue Sustainability of Festivals and Events)
Browse Figure
Review Reports Versions Notes

Abstract

:
The economic, cultural and environmental benefits of mega-events justify conducting tourism research articles based on tourists’ intention to visit the event destination. These decisions are likely influenced by behavioral attributes and social interaction. In the current study, we assessed potential predictors of tourists’ intention to visit the Riyadh Season using a theory of planned behavior (TPB) model and three variables of social networking service (SNS) variables, including SNS use, SNS content and trip experience sharing (TES). Results showed that the perceived behavioral control and subjective norms were antecedent TPB predictors of participants’ intentions to visit the destination. Additionally, the benefits of SNS content in organizing/planning events were independently associated with behavioral motivations. SNS attributes were not significant moderators of the relationship between subjective norms and tourists’ intentions. National authorities are required to focus on improving the content of SNS while planning for future mega-events to enrich tourists’ experience and support their motivation and decision.

1. Introduction

Mega-events have been well established as a significant motivator of tourism and an important driver of improving the development and marketing plans of different destinations [1]. Within the tourism sector, the effects and roles of planned events have been prominent such that they gained importance for destination competitiveness over the past two decades [2]. This way, events represent a key element in both the origin and destination areas. Interestingly, the benefits of events are particularly apparent with the successful hosting of mega-events, which is reflected in the economic aspects of the hosting region/city, increased city visibility, increased pride of the hosting city by local and regional communities, and increased tourism revenues [3]. Mega-events are known for their heavy tourist attractions (that exceed one million visitors) and wide mediated reach [4]. Furthermore, large events typically cost millions, or even billions, of US dollars that go primarily into infrastructure and event organization. In the literature, mega-events include sports events (Football World Cup, Summer and Winter Olympics, etc.) [5,6], as well as Expos, political summits and large festivals [7,8].
In Saudi Arabia, the Riyadh Season is a megafestival that is held by the local General Entertainment Authority (GEA), and it features many entertainment events and festivals over five months. Since its beginning, the number of visitors to the Riyadh Season has increased from 10 million in 2019 to 11 million in 2022. About 10% of the visitors were international tourists, and they belonged to 125 countries [9]. The event program includes a number of sport, cultural and business events, which consider the Kingdom’s cultural and historic features. Additionally, the organizing bodies ensure the highest levels of service quality, such as in accommodation and transportation [10]. As with other mega-events, the Riyadh Season might imply several benefits in multiple fields, such as economic benefits (i.e., employment, income and tax revenues), social benefits (support cultural exchange and increase awareness about the destination) and environmental benefits (conservation of natural resources and increased environmental awareness) [2,11,12]. Accordingly, the Riyadh Season provides an excellent opportunity for destination branding, business leverage and enhanced tourism activities. Therefore, it is necessary to focus research on the drivers of tourists’ intention to visit the mega-event.
Interestingly, information sharing and social interaction among visitors could play an important role in tourists’ decisions and their intentions to visit a destination [13]. With the continuous rise of social networking service (SNS) use, the amount of information that an individual could attain has extensively increased. Social media has been integrated into multiple marketing strategies because it satisfies social interaction as one of the basic human needs. Expectedly, SNS use can influence behavioral motivation via access to a large number of social network users. Eventually, participants within a given network would be influenced by the proximity of their socially interactive peers and the characters and behaviors of those friends [14].
However, individuals’ decisions might also be affected by other behavioral constructs that can be exerted in a very complicated process. To better understand human behavior, the theory of planned behavior (TPB) was developed by Ajzen [15], and it consisted of three major domains, including personal attitudes towards behavioral engagement (attitude), the level of personal control over engaging in a behavior (perceived behavior control) and the engagement in a particular behavior based on the perceived social pressure from others (subjective norm). The TPB has been frequently cited as a significant predictor of intentions with an enhanced predictive ability in tourism research [16,17,18].
Based on these observations, the present study was conducted to assess the role of SNS use on the intention to visit the event (the Riyadh Season) using the TPB model via three main pathways. First, we analyzed the impact of the TPB model on participants’ intentions. Second, we assessed the impact of SNS use on tourists’ decisions. Third, we explored the potential moderating role of SNS use on the relationship between the TPB model and participants’ behavioral intentions.

2. Literature Review

2.1. Mega-Events

Festivals and events have characterized most cultures throughout history; however, mega-events have only been increasingly recognized in the last 30 years as agents of destination marketing and branding [12]. There are a number of reasons governments and societies attempt to attract and develop mega-events. However, general economic considerations represent an essential hindering factor. These considerations include generating foreign exchange earnings, boosting GDP and employment, as well as creating new economic opportunities [12]. Additionally, events may play a role as an animator of the local attractiveness, and they are more likely to help promote the destinations within the context of the increasing competitiveness to attain visitor spending. These events would support the destination system, where attractions, accommodation, transport systems and ancillary services are utilized or specifically enhanced to support the infrastructure, particularly for mega-events. This would eventually expand the tourism potential and increase the destination capacity beyond the original limits that had focused on leisure-based tourism. It has also been demonstrated that while events can improve visitor attractions, they would also compensate for the seasonal drop in tourism demand during off-peak seasons via residents and domestic visitors [19]. Mega-events are often justified on the assumption that increased tourism will improve local economies and provide other benefits to the host country; however, these benefits can be overstated as multiple factors are at play [20]. Organizers should take note that mega-events can attract large crowds, thus putting the local environment and infrastructure under severe stress and disturbing the daily lives of residents [21]. According to Jago et al. [12], failure to display mega-events as part of a long-term strategy by event organizers and managers contributes to mega-events that do not generate the expected benefits for the host site. It is therefore essential for the hosting country and organizers to address the issues that act as a barrier that prevents mega-events from delivering the expected benefits and optimizing their potential.

2.2. The Theory of Planned Behavior

A person’s intentions are a significant motivational factor that plays a key role in determining whether they will engage in a particular behavior. When it comes to the attitude and perceived behavior of tourists to visit mega-events, the use of TPB is considered one of the best conceptual frameworks that can explain this intention and of tourists to visit the event [22]. This theory explores the effects of three factors on intention to make a decision, including perceived behavior control, attitude and subjective norms, on the intention to make a decision, such as visiting a mega-event [23]. One of the key advantages of adopting the TPB conceptual framework is its adaptability, which means that other constructs may be added to the model to increase its predictive usefulness in a specific behavioral setting [24].
To conceptualize and predict a wide range of behaviors, TPB has been extensively utilized in numerous research papers. In entertainment and sports research, Potwarka [24] proposed and used an extended TPB model to explore the intention of individuals to become more active as a response to the Vancouver Olympics. The study results showed that attitude toward the behavior, past behavior and descriptive norms were significant predictors for individual intentions, and explained 50.7% of the variance in respondents’ intention scores. Furthermore, Jeong, Kim and Yu [25] employed the TPB model to explain sports fans’ decisions to attend stadium sports matches during the COVID-19 pandemic. Sports fans’ intentions to attend sporting events were positively influenced by attitude, subjective norms and perceived behavioral control, according to Jeong, Kim and Yu [25]. A contribution of three TPB components, namely attitude, subjective norms and perception of behavioral control, was found to be positively correlated with adolescent participation intention in new sports, which further results in actual participation [26]. In another study by Cheunkamon, Jomnonkwao and Ratanavaraha [22], subjective norms and perceived behavioral control were found to have a positive indirect effect on intentions to use social media. As for attitude, Cunningham and Kwon [27] sought to understand why fans attend sporting events, arguing that fan attitudes toward their favorite teams play a significant role in influencing the intention to attend sporting events.

2.3. The Effect of Social Network Services on Individual Intentions and Potential Moderators

The spread of globalization has increased the dependence of organizations and individuals on SNS, which has affected the behavior, decisions and intentions of individuals, especially during major events [28]. The internet has fundamentally shaped the electronic means of data sharing in multiple societal activities. It not only acts as an essential source of information for many trending worldwide subjects, but also offers an exceptional means of promoting megatrends, which would eventually affect public opinions and preferences. In the internet era, early studies showed that electronic word-of-mouth on social media plays a significant role in the tourism marketing of businesses and destinations, providing an important channel for the communication and marketing of tourism services [29,30]. Accordingly, data exchange can significantly moderate tourists’ responses to a given destination; thus, improving marketing strategies can potentially influence tourists’ decision making [30]. A number of studies have investigated the relationship between SNS use and changing behavior and intentions. For instance, Kim, Choi and Koo [31] incorporated information and communication technologies such as SNS into mega-events in order to understand their influence on national image formation and future visitor behavior. The study found that these technologies have positively affected the overall satisfaction of visitors, and shaped a positive perception of the host country, thereby influencing future individual behavior [31]. Another paper by Jin and Cheng [28] looked at the role and relations of the key participants and the formation of networks arising from the Commonwealth Games’ communication on Twitter. The study found that event marketing and event-based destination marketing had a great influence on individual perceptions and argued host cities and event organizations to involve key SNS influencers at the different stages of event planning and execution [28].
When it comes to the application of the theory of planned behavior to understand SNS intentions, McLaughlin and Stephens [32] emphasized that the application of conceptual framework models such as TPB and its three constructs is critical, as it greatly contributes to the understanding of individuals’ social media intentions, as well as the potential moderators to these intentions. Shang et al. [33] found that perceived behavior control, attitude and subjective norms significantly influence tourists’ visit intention. Moreover, Shang et al. [33] reported that both subjective norms and intention to visit are positively moderated by social media use. Another study by Joo, Seok and Nam [34] also utilized TPB to understand the moderating effects of social media use on sustainable rural tourism. The results showed tourist behavior was controlled by subjective norms as well as perceived behavior control, with subjective norms having the greatest effect. Moreover, SNS usage by consumers contributed significantly to the robust relationship between intention to visit and subjective norms [34]. As Kasim et al. [35] recommend, developing marketing and communication strategies for tourism using online contexts, such as social media and other web-based technologies, has a greater potential impact on tourists’ behavior.
Based on the above-mentioned observations, we postulated the following hypotheses:
Hypothesis 1 (H1).
Attitude toward mega-events significantly influences tourists’ intention to visit mega-events in the Riyadh Season.
Hypothesis 2 (H2).
Perceived behavior control significantly influences tourists’ intention to visit mega-events in the Riyadh Season.
Hypothesis 3 (H3).
Subjective norms significantly influence tourists’ intention to visit mega-events in the Riyadh Season.
Hypothesis 4 (H4).
Intensity of social network servicing use significantly influences tourists’ intention to visit mega-events in the Riyadh Season.
Hypothesis 5 (H5).
The content of social network servicing significantly influences tourists’ intention to visit mega-events in the Riyadh Season.
Hypothesis 6 (H6).
Intensity of trip-sharing experience significantly influences tourists’ intention to visit mega-events in the Riyadh Season.
Hypothesis 7 (H7).
Intensity of social network servicing use moderates the effect of subjective norms on tourists’ intention to visit mega-events in the Riyadh Season.
Hypothesis 8 (H8).
The content of social network servicing moderates the effect of subjective norms on tourists’ intention to visit mega-events in the Riyadh Season.
Hypothesis 9 (H9).
Intensity of trip-sharing experience moderates the effect of subjective norms on tourists’ intention to visit mega-events in the Riyadh Season.
The above-mentioned theoretical framework is depicted in Figure 1.

3. Materials and Methods

3.1. Study Procedures and the Used Instrument

A survey-based study was carried out among potential visitors of the Riyadh Season during the period between 1 November 2021 and 31 January 2022. The questionnaire used was developed based on a literature review of previous studies [15,34,36,37]. The survey was created on an online platform (Google Forms), and the survey link was sent to participants via travel agents dedicated to organizing trips to the event location. The questionnaire consisted of a total of 23 items and 6 main domains. The first domain was related to the demographic characteristics (six items), including participants’ sex, age, marital status, number of children, educational level and monthly income. The theory of planned behavior construct was the second domain, and it consisted of three subdomains, including attitude (four items), perceived behavior control (three items) and subjective norms (three items). The responses to these items were collected on a five-point Likert scale (strongly disagree = 1 to strongly agree = 5). The third domain investigated the intentions to visit aspects (four items), and the responses were graded on a five-point scale (strongly disagree = 1 to strongly agree = 5). The remaining domains of SNS consisted of three domains (one item in each) to assess the content of SNS, intensity of SNS use and trip-sharing experience. More details about different domains and items are provided in the Supplementary Materials (see Supplementary Materials Table S1).

3.2. Statistical Analysis

In the current study, we employed a partial least-squares structural equation modeling (PLS-SEM) technique. Rather than the common factor-based SEM (CB-SEM), which considers the domains as common factors that account for the covariation between its distinct indicators [38], the PLS-SEM model deals with the domains of interest as composites [39]. In other words, the indicators of the measurement model are linearly combined to establish composite variables. Most importantly, the PLS-SEM is best suited for the assessment of moderation analysis using the product indicator method [40,41]; therefore, the use of PLS-SEM is appropriate to conduct SEM in addition to the moderation analysis. The consistency and accuracy of the study instrument were investigated by reliability analyses of the measurement model. This included the analysis of the convergent validity, including factor loadings, composite reliability (CR), the exact (or consistent) reliability coefficient (RhoA), Cronbach’s alpha (α) and average variance extracted (AVE) [42,43]. The discriminant validity was also evaluated by comparing the AVE square roots to the correlation coefficients of constructs and assessing the model heterotrait–monotrait ratio (HTMT). Indicator collinearity (the existence of highly correlated indicators in the model) was assessed using the variance inflation factor (VIF) values.

4. Results

4.1. Demographic Characteristics

A total of 319 participants responded to the online form. More than half of them were females (60.8%), had a university degree or higher (62.4%) and had no children (51.4%). The majority of participants were aged less than 40 years (70.8%). Less than half of the respondents were single (48.9%) and had a monthly income of SAR < 8000 (47.3%, Table 1).

4.2. Convergent Validity and Internal Consistency Reliability

First, we ran an exploratory factor analysis (EFA) to assess the degree to which the indicators were significantly loaded to distinct constructs. Based on the EFA, we excluded one item from the attitude domain and one item from the intention to visit the domain. The included items in the final model are shown in Table 2. Subsequently, a confirmatory factor analysis was carried out using the PLS-SEM approach. As demonstrated in Table 2, the statistics of the convergent validity showed that factor loadings of individual indicators were generally greater than 0.7. Furthermore, both CR values and RhoA values were above 0.7. Cronbach’s alpha coefficients ranged between 0.735 and 0.872, indicating a good internal consistency. Finally, AVE values for each domain exceeded the 0.5 threshold [44].

4.3. Discriminant Validity

To investigate the discriminant validity of the applied model, we constructed the square roots of AVE values for each factor and compared these values to the correlation between different factors. As shown in Table 3, the square roots of AVE were greater than the correlation coefficients for all the constructs [40,44], which corroborates the validity of the model. Additionally, based on the outcomes of the model HTMT (see Supplementary Materials Table S2), the validity of the model was apparent since the HTMT values did not exceed 0.85 [45]. HTMT values were not significantly different from 1 because the bootstrap confidence intervals for the HTMT values did not include 1 (see Supplementary Materials Table S3).

4.4. Structural Model

Prior to conducting the structural model analysis, we should have investigated multicollinearity between the endogenous variables by assessing the VIF values. As indicated in Table 2, the interaction term of SNS content × SNM_01 had the highest VIF values (2.753). Therefore, all VIF values were generally below 3 (the conservative threshold) [46]. Moreover, VID antecedents of each construct were also below 3. These included VIF values of participants’ attitudes (1.489), perceived behavior control (1.929), subjective norms (2.118), social media use (1.029), content of SNS (1.263) and trip experience sharing (1.033), as well as the interaction terms between subjective norms and social media use (1.089), social media content (1.310) and trip experience sharing (1.064). As a consequence, we conclude that the items incorporated in the model did not reach the critical level of collinearity.
Considering the outcomes of the structural model, as indicated in Table 4, we found that H2, H3 and H5 were supported; that is, the intention to visit mega-events in the Riyadh Season was independently associated with two subdomains of the TPB model, including the perceived behavior control (β = 0.19, 95%CI, 0.06 to 0.32, p = 0.001) and subjective norms (β = 0.38, 95%CI, 0.25 to 0.51, p < 0.0001). Furthermore, the content of social media was an antecedent predictor of participants’ intention to visit the mega-events (β = 0.18, 95%CI, 0.07 to 0.28, p = 0.001). The moderating effects of social media use, social media content and trip-sharing experience were non-significant for subjective norms on participants’ intention to visit mega-events (Table 4).

5. Discussion

The intention to visit mega-events can be altered and explained by multiple factors and using different conceptual framework models. In the current study, a total of nine hypotheses were generated in order to understand the effect of social media and behavioral constructs on the intention to visit mega-events. We utilized the effects of the three domains of TPB, as well as the effect of SNS, use on the intention to visit the Riyadh Season (Table 4). We found that subjective norms were significantly associated with the intention to visit mega-events in the Riyadh Season (p = 0.001), which indicates that the popularity of mega-events increases when a good public image from the individual family and friends is created. This finding was consistent with the findings from multiple prior studies that reported an association between subjective norms and intention to visit mega-events [22,24,25,26]. Sorrentino et al. [47] further emphasized that satisfaction is a key attribute in intention motivation, since the participants in their study were inclined to recommend the host destination and return if they were satisfied with the event. We also revealed a significant association between the perceived behavior control (p < 0.0001) and the intention to visit Mega-events in the Riyadh Season. This finding was consistent with the results from earlier studies, which reported an association between perceived behavior control and intention to visit mega-events [22,24,25,26]. It is important to mention also that the impact of the subjective norms on the intention to visit mega-events was twice the impact of the perceived behavior control (Table 4).
Our multiple regression model showed that every one-unit increase in subjective norms was associated with a 0.376 unit increase in the intention to visit the mega-event. In contrast, a one-unit increase in perceived behavior control was associated with a 0.193-unit increase in the intention to visit the mega-event. This finding was consistent with the findings by Joo, Seok and Nam [34], who reported greater effects of subjective norms on the intention to visit mega-events compared with perceived behavior control. On the other hand, Mejías et al. [48] utilized the TPB in their study and predicted tourist operators’ pro-environmental behavior intent more accurately from the perceived behavioral control, followed by subjective norms. Participants’ attitudes, the third domain of the TPB, were not significantly associated with the intention to visit Riyadh’s mega-event (p = 0.066). This finding contradicted the findings from other studies in which there was a significant association between attitudes and the intention to visit mega-events [25,26,27]. Conversely, the results of the current study were similar to the finding by Mejías et al. [48], who did not find a significant association between attitude and behavioral intent. Seemingly, the mega-event’s image has no or little impact on participants’ intentions to visit during the season. A possible explanation for such a finding is the fact that mega-events may be very important to some tourists, while others do not consider such events as a priority; thus, there was a significant variation in attitudes and a non-significant effect on motivation [28]. Importantly, the type of sponsorship, whether local or international, may exert a significant impact on tourists’ attitudes [49], and this should be an area of future research in the context of the Riyadh Season.
Concerning the effect of SNS use on the intention to visit mega-events, both the intensity of SNS use and the intensity of trip-sharing experience were not significantly associated with the behavioral intentions. Moreover, the moderating effects of social media use, social media content and trip-sharing experience were non-significant for subjective norms on participants’ intention to visit mega-events. In contrast, the content of SNSs was an antecedent predictor of participants’ intention to visit the mega-events (p = 0.001). This underlines the significance of SNS content in providing effective guidance for the planning and organization of attending different activities at the event. Accordingly, Information provided on social media platforms can be effective in marketing strategies to support participants’ intentions. The organizing bodies of the Riyadh Season are required to support their social media accounts with structured information and guidance in future events.
According to Hede [50], effective marketing strategies with the right content are crucial to maximizing the benefits and minimizing the negative effects of SNS. Hede [50] further explained that the effective broadcasting of the 2004 Summer Olympic games provided potential tourists with a virtual experience of the destination, which assisted them in developing a good impression of Greece as a tourism destination. Similarly, Kosmaczewska and Czarnecki [51] found that visitors’ intention to visit EURO 2012TM was significantly influenced by the host country’s image, and the authors argued for countries to advocate its image and reputation using methods such as multimedia promotional campaigns. Another study by Knott et al. [52] noted that the destination brand can be significantly affected if social media was not properly utilized during events, and they stressed the importance of using SNS wisely with providing proper content across different platforms. The significance of SNS content is also evident in the example of the 2010 FIFA World Cup, where media coverage was capitalized as a part of the mega-event planning. With the use of positive media content, event organizers were able to reposit the distorted country image, which is dominated by inflation, crime and xenophobic rioting [3]. Interestingly, the content of SNS may be linked to participants’ attitudes. In a study by Lu [53], participants were exposed to different types of media content, showing a significant variation in attitude between the different content types. This argues the need to further explore the type of SNS content to understand precisely its contribution to attitude change.
The current study has some limitations. Though the research instrument used showed reliability and good internal consistency, the use of a self-reporting method increases the chances of response bias or social desirability bias, which affect the validity. Sampling bias may be also apparent in our analysis. In other words, the sample under study may not fully represent all visitors because we employed a convenience sampling technique; hence, future studies may consider using a random sampling method. From another perspective, we did not include some important factors that may influence visitors’ attributes, particularly in the context of the COVID-19 outbreak. For example, we did not assess the psychological attributes of international visitors regarding the perceived risk of getting a COVID-19 infection and the intention to vaccinate for traveling to the destination [54]. Furthermore, the items used in the current study to measure the moderating effects of social media are not sufficient to give a complete picture of the moderating effects of social media on subjective norms. Therefore, future studies should implement more potential moderating items to assess this relationship. Future research should also focus on travelers’ perception of mega-events, as it is considered one of the potential predictors that determine their attitudes, and thus their intent to attend the activities. Although most of the current research focuses on resident perceptions of mega-event hosting countries, we recommend further studies that focus on local residents’ perceptions to gain the residents’ confidence, especially considering the massive development of Saudi Arabia in recent years. Moreover, we recommend conducting studies that focus on the type of sponsorship provided, as it was shown to contribute to the attitudes and intentions of individuals.

6. Conclusions

To conclude, tourism intentions and the role of social media in influencing these intentions were examined in this study. We used a PLS-SEM approach to construct a reliable and validated model using the TPB and SNS items. We showed that the intentions to attend the activities of the Riyadh Season were significantly predicted by two domains of the TPB model, namely, subjective norms and perceived behavior control, whereas the attitudes had no impact on tourists’ intentions. The content of SNS also had a significant impact on participants’ intentions via facilitating the organization and planning of event activities. It is evident that planning mega-events can be a challenging process and that it requires careful consideration of numerous factors to attain the maximum benefits.
Conducting research to understand tourist behaviors and intentions is considered a critical step in the planning process in any country which is planning a mega-event. Host countries and governments are advised to utilize SNS in order to support the planning and organization of activities and to gain confidence and a positive image among both local visitors and international tourists. More studies are required to assess the moderating effects of social media use, social media content and trip-sharing experience on subjective norms and intentions to visit mega-events. Future investigations might also assess the role of different types of sponsorship on participants’ attitudes, and the potential effects of travel perceptions on tourists’ attitudes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su142114481/s1, Table S1: Items of the questionnaire that were submitted to the participants. Table S2: Outcomes of the model heterotrait–monotrait ratio (HTMT). Table S3: Results of the mean and confidence intervals for the bootstrap heterotrait–monotrait ratio (HTMT).

Author Contributions

Conceptualization, T.H.H. and A.E.S.; methodology, T.H.H. and M.A.A.; software, A.E.S. and D.A.W.A.-K.; validation, D.A.W.A.-K. and M.A.A.; formal analysis, M.A.A. and D.A.W.A.-K.; investigation, A.E.S. and D.A.W.A.-K.; resources, T.H.H. and A.H.A.; data curation, M.A.A. and A.H.A.; writing—original draft preparation, A.H.A. and D.A.W.A.-K.; writing—review and editing, A.E.S. and A.H.A.; visualization, M.A.A. and D.A.W.A.-K.; supervision, T.H.H. and A.E.S.; project administration, T.H.H. and A.E.S.; funding acquisition, T.H.H. and A.H.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University (KFU), Saudi Arabia (Grant No.: 1820), through its KFU Research Summer initiative.

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Deanship of Scientific Research Ethical Committee, King Faisal University (Grant No.: 1820).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available on request due to privacy/ethical restrictions.

Acknowledgments

This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University (KFU), Saudi Arabia (Grant No.: 1820), through its KFU Research Summer initiative.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Getz, D.; Page, S.J. Progress and prospects for event tourism research. Tour. Manag. 2016, 52, 593–631. [Google Scholar] [CrossRef] [Green Version]
  2. Getz, D. Event tourism: Definition, evolution, and research. Tour. Manag. 2008, 29, 403–428. [Google Scholar] [CrossRef]
  3. Knott, B.; Fyall, A.; Jones, I. The nation branding opportunities provided by a sport mega-event: South Africa and the 2010 FIFA World Cup. J. Destin. Mark. Manag. 2015, 4, 46–56. [Google Scholar] [CrossRef]
  4. Müller, M. What makes an event a mega-event? Definitions and sizes. Leis. Stud. 2015, 34, 627–642. [Google Scholar] [CrossRef] [Green Version]
  5. Nyikana, S.; Tichaawa, T.M.; Swart, K. Sport, tourism and mega-event impacts on host cities: A case study of the 2010 FIFA World Cup in Port Elizabeth: Tourism. Afr. J. Phys. Health Educ. Recreat. Danc. 2014, 20, 548–556. [Google Scholar]
  6. Gursoy, D.; Milito, M.C.; Nunkoo, R. Residents’ support for a mega-event: The case of the 2014 FIFA World Cup, Natal, Brazil. J. Destin. Mark. Manag. 2017, 6, 344–352. [Google Scholar] [CrossRef]
  7. Hiller, H.H. Conventions as mega-events: A new model for convention-host city relationships. Tour. Manag. 1995, 16, 375–379. [Google Scholar] [CrossRef]
  8. Rose, A.K.; Spiegel, M.M. The Olympic Effect. Econ. J. 2011, 121, 652–677. [Google Scholar] [CrossRef]
  9. Saudi Press Agency Riyadh Season Visitors Exceed 11 Million. Available online: https://www.spa.gov.sa/viewfullstory.php?lang=en&newsid=2329348 (accessed on 4 October 2022).
  10. Aldakhil, F.M. Tourist Responses to Tourism Experiences in Saudi Arabia. School of Hospitality and Tourism Management. Master’s Thesis, Florida International University, Miami, FL, USA, 2020. [Google Scholar]
  11. Kim, H.J.; Gursoy, D.; Lee, S.-B. The impact of the 2002 World Cup on South Korea: Comparisons of pre- and post-games. Tour. Manag. 2006, 27, 86–96. [Google Scholar] [CrossRef]
  12. Jago, L.; Dwyer, L.; Lipman, G.; van Lill, D.; Vorster, S. Optimising the potential of mega-events: An overview. Int. J. Event Festiv. Manag. 2010, 1, 220–237. [Google Scholar] [CrossRef]
  13. Munar, A.M.; Jacobsen, J.K.S. Motivations for sharing tourism experiences through social media. Tour. Manag. 2014, 43, 46–54. [Google Scholar] [CrossRef]
  14. Zhang, K.Z.K.; Benyoucef, M. Consumer behavior in social commerce: A literature review. Decis. Support Syst. 2016, 86, 95–108. [Google Scholar] [CrossRef]
  15. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Processes 1991, 50, 179–211. [Google Scholar] [CrossRef]
  16. Norman, P.; Clark, T.; Walker, G. The theory of planned behavior, descriptive norms, and the moderating role of group identification. J. Appl. Soc. Psychol. 2005, 35, 1008–1029. [Google Scholar] [CrossRef]
  17. Chatzisarantis, N.L.D.; Hagger, M.S. Mindfulness and the Intention-Behavior Relationship Within the Theory of Planned Behavior. Personal. Soc. Psychol. Bull. 2007, 33, 663–676. [Google Scholar] [CrossRef]
  18. Verma, V.K.; Chandra, B. An application of theory of planned behavior to predict young Indian consumers’ green hotel visit intention. J. Clean. Prod. 2018, 172, 1152–1162. [Google Scholar] [CrossRef]
  19. Connell, J.; Page, S.J.; Meyer, D. Visitor attractions and events: Responding to seasonality. Tour. Manag. 2015, 46, 283–298. [Google Scholar] [CrossRef] [Green Version]
  20. Mills, B.M.; Rosentraub, M.S. Hosting mega-events: A guide to the evaluation of development effects in integrated metropolitan regions. Tour. Manag. 2013, 34, 238–246. [Google Scholar] [CrossRef]
  21. Müller, M. The mega-event syndrome: Why so much goes wrong in mega-event planning and what to do about it. J. Am. Plan. Assoc. 2015, 81, 6–17. [Google Scholar] [CrossRef] [Green Version]
  22. Cheunkamon, E.; Jomnonkwao, S.; Ratanavaraha, V. Determinant Factors Influencing Thai Tourists’ Intentions to Use Social Media for Travel Planning. Sustainability 2020, 12, 7252. [Google Scholar] [CrossRef]
  23. Wang, J. Perceptions of individual behavior in green event—From the theory of planned behavior perspective. Am. J. Ind. Bus. Manag. 2017, 7, 973–988. [Google Scholar] [CrossRef]
  24. Potwarka, L.R. Exploring Physical Activity Intention as a Response to the Vancouver Olympics: An Application and Extension of the Theory of Planned Behavior. Event Manag. 2015, 19, 73–92. [Google Scholar] [CrossRef]
  25. Jeong, Y.; Kim, S.-K.; Yu, J.-G. Examining the Process Behind the Decision of Sports Fans to Attend Sports Matches at Stadiums Amid the SARS-CoV-2 Pandemic: The Case of South Korea. Sustainability 2021, 13, 3403. [Google Scholar] [CrossRef]
  26. Bae, J.; Won, D.; Lee, C.; Pack, S.M. Adolescent participation in new sports: Extended theory of planned behavior. J. Phys. Educ. Sport 2020, 20, 2246–2252. [Google Scholar]
  27. Cunningham, G.B.; Kwon, H. The Theory of Planned Behaviour and Intentions to Attend a Sport Event. Sport Manag. Rev. 2003, 6, 127–145. [Google Scholar] [CrossRef]
  28. Jin, X.; Cheng, M. Communicating mega events on Twitter: Implications for destination marketing. J. Travel Tour. Mark. 2020, 37, 739–755. [Google Scholar] [CrossRef]
  29. Sotiriadis, M.D.; van Zyl, C. Electronic word-of-mouth and online reviews in tourism services: The use of twitter by tourists. Electron. Commer. Res. 2013, 13, 103–124. [Google Scholar] [CrossRef]
  30. Pantano, E.; Priporas, C.-V.; Stylos, N. ‘You will like it!’ using open data to predict tourists’ response to a tourist attraction. Tour. Manag. 2017, 60, 430–438. [Google Scholar] [CrossRef]
  31. Kim, S.S.; Choi, J.Y.; Koo, C. Effects of ICTs in mega events on national image formation: The case of PyeongChang Winter Olympic Games in South Korea. J. Hosp. Tour. Technol. 2022, 13, 217–239. [Google Scholar] [CrossRef]
  32. McLaughlin, C.; Stephens, S. The Theory of Planned Behavior: The Social Media Intentions of SMEs. 2019, pp. 1–30. Available online: https://www.researchgate.net/profile/Simon-Stephens-2/publication/330412288_The_theory_of_planned_behavior_the_social_media_intentions_of_SMEs/links/5c3f056e299bf12be3cb7ab8/The-theory-of-planned-behavior-the-social-media-intentions-of-SMEs.pdf (accessed on 19 October 2022).
  33. Shang, Y.; Mehmood, K.; Iftikhar, Y.; Aziz, A.; Tao, X.; Shi, L. Energizing Intention to Visit Rural Destinations: How Social Media Disposition and Social Media Use Boost Tourism Through Information Publicity. Front. Psychol. 2021, 12, 782461. [Google Scholar] [CrossRef] [PubMed]
  34. Joo, Y.; Seok, H.; Nam, Y. The Moderating Effect of Social Media Use on Sustainable Rural Tourism: A Theory of Planned Behavior Model. Sustainability 2020, 12, 4095. [Google Scholar] [CrossRef]
  35. Kasim, H.; Abdurachman, E.; Furinto, A.; Kosasih, W. Social network for the choice of tourist destination: Attitude and behavioral intention. Manag. Sci. Lett. 2019, 9, 2415–2420. [Google Scholar] [CrossRef]
  36. Bynum Boley, B.; Magnini, V.P.; Tuten, T.L. Social media picture posting and souvenir purchasing behavior: Some initial findings. Tour. Manag. 2013, 37, 27–30. [Google Scholar] [CrossRef]
  37. Narangajavana, Y.; Callarisa Fiol, L.J.; Moliner Tena, M.Á.; Rodríguez Artola, R.M.; Sánchez García, J. The influence of social media in creating expectations. An empirical study for a tourist destination. Ann. Tour. Res. 2017, 65, 60–70. [Google Scholar] [CrossRef]
  38. Bollen, K.A.; Davis, W.R. Causal indicator models: Identification, estimation, and testing. Struct. Equ. Modeling A Multidiscip. J. 2009, 16, 498–522. [Google Scholar] [CrossRef]
  39. Hwang, H.; Sarstedt, M.; Cheah, J.H.; Ringle, C.M. A concept analysis of methodological research on composite-based structural equation modeling: Bridging PLSPM and GSCA. Behaviormetrika 2020, 47, 219–241. [Google Scholar] [CrossRef]
  40. Chin, W.W. The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 1998, 295, 295–336. [Google Scholar]
  41. Kenny, D.A.; Judd, C.M. Estimating the nonlinear and interactive effects of latent variables. Psychol. Bull. 1984, 96, 201. [Google Scholar] [CrossRef]
  42. Trizano-Hermosilla, I.; Alvarado, J.M. Best alternatives to Cronbach’s alpha reliability in realistic conditions: Congeneric and asymmetrical measurements. Front. Psychol. 2016, 7, 769. [Google Scholar] [CrossRef] [Green Version]
  43. Dijkstra, T.K. Latent variables and indices: Herman Wold’s basic design and partial least squares. In Handbook of Partial Least Squares: Concepts, Methods and Applications; Vinzi, V., Chin, W., Henseler, J., Wang, H., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; Volume II, pp. 23–46. [Google Scholar]
  44. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  45. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
  46. Becker, J.-M.; Ringle, C.M.; Sarstedt, M.; Völckner, F. How collinearity affects mixture regression results. Mark. Lett. 2015, 26, 643–659. [Google Scholar] [CrossRef]
  47. Sorrentino, A.; Fu, X.; Romano, R.; Quintano, M.; Risitano, M. Measuring event experience and its behavioral consequences in the context of a sports mega-event. J. Hosp. Tour. Insights 2020, 3, 589–605. [Google Scholar] [CrossRef]
  48. García Mejías, M.V.; Arroyo-Arcos, L.; Segrado-Pavón, R.G.; De Marez, L.; Ponnet, K. Factors influencing the intention to adopt a pro-environmental behavior by tourist operators of a Mexican national marine park. Tur. Y Soc. 2021, 29, 221–238. [Google Scholar] [CrossRef]
  49. Chang, C.-H.; King, B.E.M.; Shu, S.-T. Tourist attitudes to mega-event sponsors: Where does patriotism fit? J. Vacat. Mark. 2020, 26, 320–333. [Google Scholar] [CrossRef]
  50. Hede, A.-M. Sports-events, tourism and destination marketing strategies: An Australian case study of Athens 2004 and its media telecast. J. Sport Tour. 2005, 10, 187–200. [Google Scholar] [CrossRef]
  51. Kosmaczewska, J.; Czarnecki, R. The Role of a Host Country Image and Mega-Event’s Experience for Revisit Intention: The Case of Poland. Available at SSRN 2586145 2013. [Google Scholar] [CrossRef]
  52. Knott, B.; Fyall, A.; Jones, I. Leveraging nation branding opportunities through sport mega-events. Int. J. Cult. Tour. Hosp. Res. 2016, 10, 105–118. [Google Scholar] [CrossRef]
  53. Lu, Q. Media Effect on Resident Attitude Toward Hosting the Olympic Games: A Cross-National Study Between China and the USA. Ph.D. Thesis, University of South Carolina, Columbia, SC, USA, 2015. [Google Scholar]
  54. Morar, C.; Tiba, A.; Jovanovic, T.; Valjarević, A.; Ripp, M.; Vujičić, M.D.; Stankov, U.; Basarin, B.; Ratković, R.; Popović, M.; et al. Supporting Tourism by Assessing the Predictors of COVID-19 Vaccination for Travel Reasons. Int. J. Environ. Res. Public Health 2022, 19, 918. [Google Scholar] [CrossRef]
Sustainability 14 14481 g001 550
Figure 1. Theoretical framework of the current study. Solid lines indicate direct relationships, whereas dashed lines indicate moderating relationships. SNS: social networking service.
Figure 1. Theoretical framework of the current study. Solid lines indicate direct relationships, whereas dashed lines indicate moderating relationships. SNS: social networking service.
Sustainability 14 14481 g001
Table
Table 1. Demographic characteristics of the participants.
Table 1. Demographic characteristics of the participants.
ParameterCategoryN (%)
SexFemale194 (60.8%)
Male125 (39.2%)
Age (years)20 to <30126 (39.5%)
30 to <40100 (31.3%)
40 to <5064 (20.1%)
50 and above29 (9.1%)
Marital statusSingle156 (48.9%)
Married140 (43.9%)
Other23 (7.2%)
Number of childrenNone164 (51.4%)
134 (10.7%)
240 (12.5%)
345 (14.1%)
431 (9.7%)
5 above5 (1.6%)
Educational levelHigh School48 (15.0%)
College72 (22.6%)
University106 (33.2%)
Graduate school and above93 (29.2%)
Monthly income (SAR)1000 to <400077 (24.1%)
4000 to <800074 (23.2%)
8000 to <12,00080 (25.1%)
≥12,00088 (27.6%)
Table
Table 2. Results of the convergent validity of the measurement model.
Table 2. Results of the convergent validity of the measurement model.
DomainItemFLVIFαCRAVERhoA
ATT 0.8120.8860.7230.841
ATT_010.8011.927
ATT_020.8962.297
ATT_030.8501.581
PBC 0.8370.9020.7540.837
PBC_010.8802.128
PBC_020.8651.917
PBC_030.8591.874
SNM 0.7860.8750.7010.787
SNM_010.8561.801
SNM_020.8481.732
SNM_030.8061.497
INT 0.7670.8650.6820.767
INT_010.8291.602
INT_020.8271.556
INT_030.8221.529
SNS content × SNM 1 0.8721.0361.1151.000
SNS content × SNM_011.0772.753
SNS content × SNM_021.0762.601
SNS content × SNM_031.0131.973
SNS use × SNM 1 0.7350.8400.6371.000
SNS use × SNM_010.7671.533
SNS use × SNM_020.8451.757
SNS use × SNM_030.7801.337
TES × SNM 1 0.7560.8740.6981.000
TES × SNM_010.8501.780
TES × SNM_020.8581.619
TES × SNM_030.7971.377
α: Cronbach’s alpha; CR: composite reliability; FL: factor loading; AVE: average variance extracted; ATT: attitude; PBC: perceived behavior control; SNM: subjective norms; SNS: social networking service; INT: intention to visit; TES: trip experience sharing; VIF: variance inflation factor.
Table
Table 3. Results of the discriminant validity of the applied model.
Table 3. Results of the discriminant validity of the applied model.
Parameter12345678910
1. ATT0.850
2. PBC0.5020.868
3. SNM0.5270.6450.837
4. SNS use0.0990.0960.1271.000
5. SNS content0.2890.3840.412−0.0151.000
6. TES0.0430.1360.0940.0550.0961.000
7. SNS use × SNM−0.090−0.157−0.159−0.035−0.0900.0260.798
8. SNS content × SNM−0.287−0.397−0.446−0.066−0.214−0.0940.1921.056
9. TES × SNM−0.080−0.092−0.1060.027−0.123−0.0560.2030.1150.835
10. INT0.4460.5770.6480.1080.4480.173−0.134−0.361−0.0890.826
The square roots of AVE are demonstrated on the diagonal (bold) and the correlations between constructs are shown on the lower triangle. ATT: attitude; AVE: average variance extracted; Cα: Cronbach’s alpha; CR: composite reliability; FL: factor loading; PBC: perceived behavior control; SNM: subjective norms; SNS: social networking service; INT: intention to visit; TES: trip experience sharing.
Table
Table 4. Results of the structural model.
Table 4. Results of the structural model.
Parameterβ95%CIT Statisticp-Value
ATT -> INT (H1)0.079−0.018 to 0.1881.5090.066
PBC -> INT (H2)0.1930.064 to 0.3212.9990.001
SNM -> INT (H3)0.3760.248 to 0.5095.795<0.0001
SNS use -> INT (H4)0.028−0.054 to 0.1090.6880.246
SNS content -> INT (H5)0.1790.068 to 0.2753.2910.001
TES -> INT (H6)0.086−0.015 to 0.1721.8030.360
SNM × SNS use -> INT (H7)−0.015−0.116 to 0.078−0.3190.625
SNM × SNS content -> INT (H8)−0.037−0.112 to 0.046−0.9230.822
SNM × TES -> INT (H9)0.009−0.104 to 0.1110.1580.437
ATT: attitude; PBC: perceived behavior control; SNM: subjective norms; SNS: social network servicing; INT: intention to visit; TES: trip experience sharing.
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Al-Khaldy, D.A.W.; Hassan, T.H.; Abdou, A.H.; Abdelmoaty, M.A.; Salem, A.E. The Effects of Social Networking Services on Tourists’ Intention to Visit Mega-Events during the Riyadh Season: A Theory of Planned Behavior Model. Sustainability 2022, 14, 14481. https://doi.org/10.3390/su142114481

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Al-Khaldy DAW, Hassan TH, Abdou AH, Abdelmoaty MA, Salem AE. The Effects of Social Networking Services on Tourists’ Intention to Visit Mega-Events during the Riyadh Season: A Theory of Planned Behavior Model. Sustainability. 2022; 14(21):14481. https://doi.org/10.3390/su142114481

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Al-Khaldy, Dayal Ali W., Thowayeb H. Hassan, Ahmed Hassan Abdou, Mostafa A. Abdelmoaty, and Amany E. Salem. 2022. "The Effects of Social Networking Services on Tourists’ Intention to Visit Mega-Events during the Riyadh Season: A Theory of Planned Behavior Model" Sustainability 14, no. 21: 14481. https://doi.org/10.3390/su142114481

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