Examining the Impact of the Fear of Missing Out on Museum Visit Intentions

Fear of Missing Out

Based on SDT (Ryan & Deci, 2000), Przybylski and colleagues (2013) undertook the initial empirical research on the fear of missing out (FOMO), which is defined as a desire to remain continuously connected with what other people are doing (Przybylski et al., 2013). According to SDT, an individual’s psychological health and self-regulation are based on the satisfaction of three basic needs: autonomy, competence (the capability to act in life successfully), and relatedness (the need to connect with others; Ryan & Deci, 2000). FOMO is a self-regulatory state that arises from poorly satisfied psychological needs (Przybylski et al., 2013). FOMO can also be defined as an individual’s pervasive understanding that others enjoy better experiences than their own, and the fear of skipping enjoyable experiences that they wants to have (Zhang et al., 2020). Based on previous studies, this research briefly defines FOMO as an uneasy feeling, anxiety, and fear that emerges from knowing the existence of attractive experiences and activities one cannot attend.

FOMO is mainly studied in social media (Przybylski et al., 2013). It is mentioned as a specific type of addiction, which can be an outcome of internet usage (Lee et al., 2020). However, it can also be associated with offline events, such as missing out on a party (Milyavskaya et al., 2018) or a concert (Zhang et al., 2020). For example, missing an academic conference in another city that could have created a chance for increased professional collaboration can cause FOMO (Yakar & Kwee, 2020). Similarly, people can experience FOMO because of the difficulty of catching up with rich digital content, such as movies or TV series, on streaming platforms (Hayran & Anik, 2021). Briefly, an individual may feel FOMO when missing out on an experience, which results in a threat perception regarding the private self (the self-evaluation of oneself) and public self (the self-evaluation of how other people see oneself; Zhang et al., 2020).

The antecedents of FOMO are various. FOMO, as a negative feeling, is associated with knowing about or seeing attractive activities that one cannot attend (Bui et al., 2022; Hayran & Anik, 2021). Unsatisfied personal needs can lead to FOMO, resulting in increased social media usage (Przybylski et al., 2013). Social media intensifies FOMO by showing the numerous opportunities and experiences that can be enjoyed (Zhang et al., 2020). Friends’ social media posts about traveling or attending a joyful event that a person cannot participate in can trigger FOMO (Hayran & Anik, 2021). The abundance of choices for experiences can lead to FOMO, even if a person thinks he made the best selection available (Milyavskaya et al., 2018). An interdependent self-construal, defining the self as a part of social relationships rather than conceptualizing it as independent from others, is also linked to FOMO (Dogan, 2019). FOMO is generally linked with unfavorable outcomes such as substance abuse (Riordan et al., 2015), stress, decreased sleep, fatigue (Milyavskaya et al., 2018), information overload, negative mood (Bui et al., 2022), social media addiction, and negative affect (Tandon et al., 2021). FOMO can refer to missing experiences regarding the public or private self, for example, the fear of missing public events related to personal ideals (Zhang et al., 2020) or continuous learning goals.

The Desire for Continuous Learning and Its Relationship With FOMO

Continuous learning is a person’s deliberate actions to acquire new knowledge or skills (Maurer & Weiss, 2010; Watanabe et al., 2010). Since the Internet opens new opportunities for continuous learning without time and place restrictions, the rich digital content and virtual activities available to be explored may lead to FOMO (Hayran & Anik, 2021), particularly for people who value continuous learning. According to SDT, feeling competent and engaging in self-developing activities such as continuous learning is crucial for facilitating a person’s social development, behavioral self-regulation, and well-being. SDT states that individuals have a natural and intrinsic inclination toward mastery and exploration that will function as a main source of cognitive and social development and life enjoyment (Lemay et al., 2019). From a personal need perspective, an unsatisfied personal need is associated with a greater degree of FOMO (Przybylski et al., 2013; Xie et al., 2018).

Continuous learning relates to a person’s need for competence and behavioral regulations and can be examined from the perspective of SDT because personal growth relates positively to need satisfaction (Anderson et al., 2020; Ryan & Deci, 2000; Weigold et al., 2021). The desire for continuous learning is associated with personal growth, an intrinsic life aspiration in the terminology of SDT, and is directly linked to an individual’s basic psychological need for competence (Janke & Dickhäuser, 2019). FOMO can act as a self-regulatory feeling that arises from social comparison (Dinh & Lee, 2022), inner motivations for competence and new knowledge acquisition, or the desire for continuous learning (Przybylski et al., 2013). The desire for continuous learning is expected to influence FOMO since FOMO is linked to unsatisfied personal needs (Xie et al., 2018), self-regulation, productivity, and academic performance (Milyavskaya et al., 2018; Riordan et al., 2015; Tandon et al., 2021). In parallel, continuous learning is related to intrinsic motivation, which is required to reach goals (Watanabe et al., 2010). A person with a strong desire for continuous learning will be more likely to wonder about the missing possibilities regarding self-development and develop FOMO concerning self-concept maintenance (Zhang et al., 2020). People with a strong desire for continuous learning are more likely to feel sad or anxious because of missing self-improving activities that can help them achieve competence. Thus,

FOMO and Its Relationship With Museum Visit Intentions

FOMO is associated with increased social media usage and the rising trend of experiential consumption, as many people feel anxiety because of missing enjoyable activities (Zhang et al., 2020). According to SDT, behavior can depend on psychological needs to reduce a negative emotional state, such as FOMO (Ryan & Deci, 2000; Teng & Tsai, 2020). Need satisfaction is related to behavioral regulation; for example, if their basic needs of competence and relatedness are poorly satisfied, consumers can be more likely to visit museums because museum visits can be utilized as a resource to develop competence and connect with others (Przybylski et al., 2013). Previous studies also showed that FOMO can influence behavioral intentions: it affects buying intention towards products endorsed on social media (Dinh & Lee, 2022); motivates people to adopt new technologies (Gartner et al., 2022); and leads individuals to keep up and connect with others (Lee et al., 2020). FOMO is linked to higher academic performance due to the student’s need for social acceptance (Lemay et al., 2019). People can feel FOMO when they miss out on attending a desired event, such as a party, concert, or professional seminar (Milyavskaya et al., 2018; Yakar & Kwee, 2020; Zhang et al., 2020). Following this perspective, FOMO can positively influence museum visit intentions, as an individual may dread missing traveling experiences (Zaman et al., 2022). Therefore,

Involvement With Museums and Visitor Attitudes Toward Museums

Involvement can be explained as the personal relevance of an object based on the consumer’s interests, needs, and values (Zaichkowsky, 1985). Involvement can also be defined as the importance, personal relevance, and cognitive value of a marketing offer and a primary psychological structure and motivating factor that shapes consumer attitudes (Arghashi & Yuksel, 2023; Foxall & Bahte, 1993; Josiam et al., 1999). It is the perceived importance of an object or experience and a consumer’s perception that it meets essential values (Flynn et al., 2000; Mataracı & Kurtuluş, 2020). In the research aim of this study, involvement, or involvement with museums, is a personal motivator associated with museums’ perceived importance and value.

Valuing an activity personally, or the existence of external influences, can motivate people for a behavior (Ryan & Deci, 2000), so potential tourists who are highly involved with an activity or destination would be a good target market for the destination (Josiam et al., 1999). Involvement level positively affects attitudes and purchase intentions (Mataracı & Kurtuluş, 2020). In advertising, a higher ad relevance leads to higher involvement and a more favorable attitude (Huang & Yoon, 2022). Involvement level influences attitude structures: a higher involvement with positive online consumer reviews, that is, finding the vacation experience exciting, appealing, or interesting, leads to a positive change in attitudes toward it (Park et al., 2019). Involvement positively influences brand attitudes (Cruz et al., 2017). Tourist involvement is related to traveling motivation (Josiam et al., 1999) and the positive images of virtual tourism destinations (Tsai, 2022). Visitors who are highly involved with museums are expected to have a positive attitude toward visiting museums. Therefore,

Theory of Reasoned Action (TRA) and Museum Visit Intentions

According to TRA, behavioral intentions are shaped by attitudes, beliefs about the behavior and its expected outcomes, and subjective norms—the social influence on one’s behavior (Ajzen & Fishbein, 1980). Ajzen (1991) has expanded TRA into the theory of planned behavior (TPB) by including perceived behavioral control as a predictor of behavioral intentions. TRA is still a valid theoretical framework researchers use (Chung et al., 2018; Jang & Cho, 2022; Song et al., 2022; Youn et al., 2021). TRA and TPB state that consumers with more favorable attitudes toward an object are likelier to perform favorable behaviors (Arghashi & Yuksel, 2023). Besides attitudes and subjective norms, TPB examines the influence of perceived behavioral control on behavioral intentions Ajzen (1991). If behavior is beyond an individual’s control due to lacking necessary resources or dependence on others, perceived behavioral control covers the uncertainty factor (Oh & Yoon, 2014). However, since visiting a museum is under the control of an individual, the current study used TRA as its theoretical framework and focused on the influence of attitudes and subjective norms on museum visit intentions. According to TRA and TPB, good attitudes toward a destination website positively influence visit intentions (Loureiro, 2015). Similarly, Tussyadiah and colleagues (2018) and Alyahya and McLean (2022) have shown the impact of tourists’ attitudes toward a destination on visit intentions. Besides attitudes, subjective norms, which are the opinions of essential others, are significant factors that shape behavioral intentions (Jang & Cho, 2022; Mataracı & Kurtuluş, 2020). In agreement, Lu and colleagues (2022) have shown the positive impact of attitudes and subjective norms on virtual tourism. Therefore,

Enjoyment From the Virtual Travel Experience

Virtual tourism can be experienced in various forms, such as live streaming supplemented with narration and sound, a video on a connected computer, and 360-degree virtual tours that may be combined with more advanced devices such as VR glasses (Lu et al., 2022; Tsai, 2022). The simplest form of virtual tourism is a video where visitors can see the relevant content via a device such as a computer or a smartphone (Verma et al., 2022). VTE is the total of the tourists’ cognitive and emotional responses regarding their interaction with the virtual environment (Godovykh et al., 2022). Previous research has shown that enjoyable VTEs positively affect the visit intentions of tourists (Alyahya & McLean, 2022; Atzeni et al., 2022; Choi et al., 2018; Godovykh et al., 2022; Kim et al., 2020; Lee & Kim, 2021; Lu et al., 2022; McLean & Barhorst, 2022; Tussyadiah et al., 2018; Ying et al., 2022; Yuce et al., 2020) and can promote destinations (Walters et al., 2022; Zirbes, 2021). For example, in Lu and colleagues’ (2022) study, participants who had seen a museum for the first time online stated that they would like to visit the museum in the future. Similarly, Barreto and colleagues (2019) stated that experiencing a destination online before seeing it positively influences visit intentions. Visitors’ satisfaction and positive feelings about a destination website positively impact visit intentions (Koo et al., 2016; Loureiro, 2015).

The current study focuses on the moderating role of the enjoyment from the VTE on the attitude-visit intentions and subjective norms-visit intentions relationships. Enjoyment from the VTE can be expected to increase the influence of attitudes and subjective norms on visit intentions. Entertaining and informative online environments can make a museum more exciting and familiar to prospective tourists (Choi et al., 2016), which can lead to an interaction effect on attitudes and subjective norms. Since VTEs require significant mental effort and intense cognitive processing (Walters et al., 2022), an increase in the level of enjoyment from the VTE can lead to more vivid and pleasant memories and increase the influence of attitudes and subjective norms on visit intentions. Furthermore, VTEs provide hedonic value and influence tourists’ liking and interest in the actual environment (Tussyadiah et al., 2018; Yuce et al., 2020), so the enjoyment from a VTE can strengthen the influence of attitudes and subjective norms on visit intentions. Thus,

Research Setting

Following Atzeni and colleagues (2022), Choi and colleagues (2018), McLean & Barhorst (2022), and Tsai (2022), this study utilized a virtual environment that could be navigated by using a computer. Participants were requested to visit the national museum website and interactively navigate in a museum they chose. The virtual museum page (www.sanalmuze.gov.tr) includes 3D models of museums and historical ruins that can be visited interactively over the Internet. The Ministry of Culture and Tourism of the Republic of Turkey established the webpage for free public use on March 25, 2020. On this virtual museum page, 32 museums, historical ruins, and a temporary exhibition can be visited online. During the period of March 2020 to April 2021, the virtual museums were seen 12,529,246 times. Göbeklitepe is the most visited virtual museums, with 3,383,985 visits. The War of Independence Museum, with 1,869,319 visits, is in second place, and the Ephesus Ruins, with 1,350,742, is the third most visited virtual museum. The Troy Museum follows the Ephesus Ruins with 1,100,147 visits and the Museum of Anatolian Civilizations with 1,031,447 visits (www.sanalmuze.gov.tr). In total, virtual museums had 25 million visitors in 2021 (Republic of Turkey Ministry of Culture and Tourism, 2022).

Measurement Tools

The desire for continuous learning was measured using 11 items adapted from Watanabe and colleagues (2011) and Maurer and Weiss (2010). For the involvement variable, three items were adapted from Flynn and colleagues (2000). FOMO was measured by the two-dimensional scale of Zhang and colleagues (2020, which consisted of personal FOMO (five items) and social FOMO (four items). Attitude and subjective norm items were adapted from the study of Vallerand and colleagues (1992). Enjoyment from the VTE was measured by four questions adapted from Wu and Lai (2022). Finally, visit intention was measured by a two-dimensional scale that consisted of the intention to visit an on-site museum (three items) and the intention to see a virtual museum (three items), which were adapted from the studies of Vallerand and colleagues (1992) and Wu and Lai (2022). All items are listed in Table 2. Attitudes were measured using a 5-point semantic differential scale, while all other constructs were measured using a 5-point (1 = strongly disagree, 5 = strongly agree) Likert scale.

Data Collection

The questionnaire was designed as an online survey on Google forms (https://docs.google.com/forms/) since online surveys are a commonly used tool due to the fast response rate (Wright, 2005). Because the current study investigates the virtual experiences of consumers, an online survey can be considered a particularly appropriate data collection method. The population for this study was comprised of consumers from Turkey over 18 years of age. A convenience sampling method was used for time and cost efficiencies. Participation in the research was voluntary, and participants were free to leave the survey whenever they wanted. After the virtual museum experience on www.sanalmuze.gov.tr, the participants were asked to complete the online questionnaire. They answered 39 questions about the research variables and six questions regarding their demographic characteristics. The data collection lasted 1 week, from May 30 to June 7, 2022. In all, 416 questionnaires were collected. Data screening was applied to check the dataset, and it was found that 10 questionnaires were incomplete or invalid. After removing incomplete or invalid questionnaires, the data cleaning process was continued with an examination of outliers by examining a combination of Mahalanobis distance, Cook’s distance, and residual analyses to identify and eliminate potential outliers (Hair et al., 2021). Twenty-one outliers were found and eliminated from the data, and the remaining 385 questionnaires were analyzed. A sample size of 384 is sufficient when p = .5 and q = 0.5 are taken at a margin of error of 5% and a confidence interval (CI) of 95% (Cohen et al., 2017). A sample size of at least 300 respondents was targeted in line with the requirements of Structural Equation Modeling (SEM), which is this study’s primary data analysis technique. Thus, the number of participants was adequate to represent the population.

Data Analysis

Data were analyzed by examining descriptive statistics and frequencies on SPSS (Version 24). Visit intention (on-site and virtual visits), and FOMO (personal and social FOMO) were converted into second-order constructs. Then, the measurement model, structural equation modeling, slope test, and hypotheses were tested. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test the reliability and validity of measurement items and associated relationships among the constructs. SmartPLS 4 was used in a two-step process to assess the reliability and validity of the measurement model, followed by the strength of the structural model (Hair et al., 2021). The slope test result was examined to see the slope graph for the moderator variable. One item (social FOMO 3) indicated with an asterisk in Table 2 was deleted in PLS due to high VIF values over 5.0 that were checked for multicollinearity (Kock, 2015).

As with all self-reported data, there is potential for common method bias (CMB; Podsakoff et al., 2003). Multiple tests were carried out to evaluate this issue to examine the severity of CMB. First, CMB was examined by comparing correlations among constructs following the procedure established by Pavlou and colleagues (2007). The correlations between constructs in the data ranged from 0.11 to 0.66 (Table 3), indicating no systematic bias in the data. Second, following Podsakoff and colleagues (2003) and Liang and colleagues (2007), a common method factor was employed whose indicators included all items in PLS. The variance explained by a common method factor and the substantive factors were compared to determine whether the common method factor explained the majority of covariance. As shown in Appendix A, the results demonstrate that the average substantively explained variance of the indicators is 0.73, while the average method-based variance is 0.006. The ratio of substantive variance to method variance is about 122:1. In addition, most method factor loadings are insignificant. Based on the above multiple tests, CMB is unlikely to be a severe concern in this study.

Sample Characteristics

The mean age of the participants was χ = 34.27. Sixty-three percent of the participants were male, and 37% were female. Sixty-two percent of the participants had a bachelor’s degree. The distribution of the participants across income levels, occupations, and cities of residence is outlined in Table 1.

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Table 1.

Demographic Characteristics of Participants.

<

Percentage (%) or Mean (χ)
Variables	Visitors (n = 385)
Age (χ)	34.27
Gender (%)
Female	37.1
Male	62.9
Educational Degree (%)
Primary school	0.8
High school	13.0
University	61.8
Master’s degree/PhD	24.4
Monthly Income (%)
Minimum wage or less	28
Minimum wage–₺7000	18.7
₺7001–₺10.000	20.8
₺10.001–₺15.000	17.7
₺15001–₺20.000	10.4
₺20.001–₺25.000	2.3
₺25001 or more	1.6
No response	0.5
Occupation (%)
Student	18.4
White-collar worker	51.7
Blue-collar worker	19.5
Own business	2.3
Homemaker	2.9
Not working or retired	3.1
Other	2.1
City of Residence (%)
Diyarbakır	50.1
Adana	10.1
Antalya	4.5
Muğla	4.4
İstanbul<	3.6
Ankara	3.1
Bursa	1.8
Other*	22.4

Note. *Includes 32 Turkish cities such as Izmir, Gaziantep, Samsun, and Eskişehir. 1 US Dollar ($) = 18.75 Turkish Liras (₺), as of January 2023.

Reliability and Validity Tests

The PLS-SEM method uses a measurement model for confirmatory factor analysis (CFA) (Afthanorhan, 2013). The internal consistency reliability, convergent, and discriminant validity criteria were evaluated to test the measurement model’s validity and reliability. Cronbach’s Alpha and Composite Reliability (CR) values for internal consistency reliability were investigated. Standardized factor loadings, t values, and Average Variance Extracted (AVE) scores were examined for convergent validity. Factor loadings should be above 0.60, the CR value and Cronbach’s Alpha value should be above 0.70, and the AVE scores should be above 0.50 (Hair et al., 2014). As listed in Table 2, the standardized factor loadings were above 0.70, and the t values were above 2.56. All dimensions in the measurement model had AVE scores above 0.50, and convergent validity was achieved (Hair et al., 2021).

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Table 2.

Descriptive Statistics, Reliability, and Validity Analyses of the Measurement Model.

Constructs and Items (n = 385)
First Order	Standardized Factor Loadings	T Values	Mean	SD	VIFValues
Continuous Learning (Cronbach’s Alpha = 0.930; CR = 0.94; AVE = 0.59)			4.40
I try to acquire the knowledge and skills necessary in life.	0.704	16.347	4.48	0.573	2.072
I continuously improve my knowledge.	0.815	36.106	4.41	0.588	3.105
I try to anticipate future needs and fill in the gaps in my current knowledge and skills.	0.855	45.944	4.40	0.569	3.645
I study regularly to acquire new skills.	0.756	23.384	4.27	0.665	2.147
I am willing to spend money and invest in myself to improve my skills and knowledge.	0.707	17.158	4.32	0.689	1.801
I think about what I learned from my past experiences and apply them to my life.	0.829	41.415	4.38	0.583	2.755
I am trying to improve myself.	0.843	43.245	4.41	0.571	3.074
I want to pursue developmental activities in endeavors that are important to me.	0.760	23.945	4.50	0.550	2.343
I strive to do my best, even when I could get by with less.	0.746	25.362	4.43	0.634	2.088
I like learning new things.	0.757	26.868	4.55	0.538	2.209
I know my own strengths and weaknesses.	0.640	16.799	4.31	0.674	1.531
Involvement (Cronbach’s Alpha = 0.865; CR = 0.92; AVE = 0.79)			4.21
I have a strong interest in museums.	0.879	46.046	4.10	0.773	2.480
Museums are very important to me.	0.938	120.626	4.20	0.705	3.452
For me, museums are valuable.	0.844	34.279	4.34	0.659	2.025
Enjoyment from VTE (Cronbach’s Alpha = 0.926; CR = 0.95; AVE = 0.82)			4.26
Experiencing the virtual museum tour. . .
was enjoyable for me.	0.893	34.776	4.26	0.788	3.349
was pleasurable for me.	0.924	59.205	4.24	0.793	3.932
was fun for me.	0.911	50.011	4.27	0.731	3.387
kept me happy.	0.892	43.527	4.28	0.728	2.875
Attitude (Cronbach’s Alpha = 0.889; CR = 0.93; AVE = 0.82)			4.76
I think seeing museums that interest me is. . .
Bad/Good	0.896	45.569	4.74	0.518	2.484
Not wise at all/Very wise	0.913	60.585	4.78	0.485	2.635
Harmful/Beneficial	0.905	40.601	4.76	0.512	2.637
Subjective Norm (Cronbach’s Alpha = 0.803; CR = 0.88; AVE = 0.72)			4.18
My friends would think that it is good to go to the museum.	0.823	29.956	4.21	0.729	1.745
Most people who are important to me would find it helpful to see museums.	0.881	58.877	4.18	0.736	1.988
My family would think that it is good to visit museums.	0.836	30.813	4.15	0.750	1.610
Personal FOMO (Cronbach’s Alpha = 0.899; CR = 0.93; AVE = 0.71)			3.83
I feel anxious when I do not experience events/opportunities.	0.844	41.163	3.97	0.971	2.549
I believe I am falling behind compared with others when I miss events/opportunities.	0.848	39.752	3.69	1.126	2.452
I feel anxious because I know something important or fun must happen when I miss events or opportunities.	0.903	74.460	3.68	1.091	3.336
I feel sad if I am not capable of participating in events due to constraints of other things.	0.789	28.143	3.97	0.946	1.920
I feel regretful of missing events/opportunities.	0.836	36.018	3.83	1.030	2.201
Social FOMO (Cronbach’s Alpha = 0.925; CR = 0.95; AVE = 0.87)			2.99
I think my social groups view me as unimportant when I miss events/opportunities.	0.935	111.807	3.05	1.350	3.648
I think I do not fit in social groups when I miss events/opportunities.	0.936	96.582	3.16	1.293	3.702
I think I am excluded by my social groups when I miss events/opportunities.*	0.952	128.697	2.88	1.360	6.968
I feel ignored/forgotten by my social groups when I miss events/opportunities.	0.927	87.109	2.88	1.342	3.348
On-site Visit Intention (Cronbach’s Alpha = 0.818; CR = 0.89; AVE = 0.74)			4.31
I would like to go to one of these museums and see the real environment.	0.898	22.249	4.50	0.626	1.428
I intend to visit one of these museums in the near future.	0.937	60.196	4.15	0.826	2.702
I want to go to one of these museums soon.	0.903	59.156	4.26	0.740	2.897
Virtual Visit Intention (Cronbach’s Alpha = 0.850; CR = 0.91; AVE = 0.77)			4.13
I would like to visit these museums in virtual environment.	0.767	35.067	4.16	0.846	2.288
I intend to visit one of these museums online/virtually in the near future.	0.891	73.114	4.08	0.902	2.790
I want to go to one of these virtual museums soon.	0.909	39.178	4.15	0.871	1.795
Second Order
FOMO (Cronbach’s Alpha = 0.911; CR = 0.93; AVE = 0.62)
Personal FOMO	0.937	172.194
Social FOMO	0.835	46.406
Visit Intention (Cronbach’s Alpha = 0.854; CR = 0.89; AVE = 0.58)
On-site Visit	0.865	49.653
Virtual Visit	0.893	75.290

Note. The significance level of all factor loadings was p < .001, and bootstrapping was performed over 5000 samples. SD = standard deviation.

*

Item deleted in PLS due to high VIF value above 5.

Since the Cronbach’s Alpha values were between (α) 0.930 and 0.803; CR values were between 0.95 and 0.88, it can be stated that internal consistency reliability was achieved. As for all variables, standardized factor loadings were between 0.952 and 0.704, and AVE scores were between 0.87 and 0.58. It can be concluded that convergent validity was also achieved.

Fornell and Larcker’s criteria were used to determine discriminant validity (Fornell & Larcker, 1981; Hair et al., 2014). According to the Fornell and Larcker criteria, shown in Table 3, the square root of the AVE scores in the research should be higher than the correlations between the other structures in the study (Wong, 2013). The results of the Fornell and Larcker criterion in Table 3 indicate that discriminant validity was provided.

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Table 3.

Discriminant Validity-Fornell Larcker Criterion.

	Attitude	Continuous Learning	Enjoyment From the VTE	Involvement	VI-Virtual Visit	VI-On-Site Visit	Personal FOMO	Social FOMO	Subjective Norm
Attitude	0.904
Continuous Learning	0.356	0.767
Enjoyment From the VTE	0.303	0.485	0.905
Involvement	0.329	0.484	0.397	0.887
VI-Virtual Visit	0.297	0.512	0.661	0.444	0.877
VI-On-Site Visit	0.313	0.496	0.426	0.532	0.546	0.858
FOMO-Personal FOMO	0.225	0.402	0.285	0.399	0.310	0.329	0.845
FOMO-Social FOMO	0.132	0.118	0.158	0.283	0.239	0.111	0.589	0.933
Subjective Norm	0.292	0.506	0.495	0.511	0.541	0.575	0.324	0.220	0.847

Note. Bolded figures are the square roots of the AVE (Average Variance Extracted). Figures below the AVE line are the correlations between the factors. FOMO = fear of missing out (second-order construct); VI = visit intention (second-order construct); VTE = virtual travel experience.

The structural validity of the model is ensured by its validity and reliability. The model’s hypotheses can be tested when it is established that a model is highly compatible with all of the results gathered for the model.

PLS-SEM Analysis and Hypothesis Test Results

The structural equation model is shown in Figure 2. The structural equation model was analyzed using PLS-SEM. Five thousand sub-samples were taken from the sample, and bootstrapping analysis was operated to calculate the t values used to evaluate the significance of the PLS path coefficients.

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Figure 2.

Regression Results, R², and P Values in PLS.

Table 4 summarizes the hypotheses tests, the variables’ effect size coefficients (f2), and the VIF values. Continuous learning significantly affects FOMO (β = 0.324; t = 7.300; p < .001), and H₁ was supported. FOMO (β = 0.096; t = 2.443; p < .05), attitude (β = 0.167; t = 3.427; p < .001), and subjective norm (β = 0.376; t = 8.312; p < .001) significantly affected visit intentions. Involvement had a significant impact on attitudes (β = 0.329; t = 7.525; p < .001). Therefore, hypotheses H₂, H₃, H_4, and H₅ have been accepted. The t values were also greater than 2.56 (Hair et al., 2021).

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Table 4.

Hypothesis Tests.

	Path	Coefficient	P Value	T Value	F Square	VIF	Result
H1	Continuous Learning → FOMO	0.324***	.000	7.300	0.117	1.000	Supported
H2	FOMO → Visit Intention	0.096***	0.015	2.443	0.018	1.155	Supported
H3	Involvement → Attitude	0.329***	.000	7.525	0.121	1.000	Supported
H4	Attitude → Visit Intention	0.167**	.001	3.427	0.041	1.528	Supported
H5	Subjective Norm → Visit Intention	0.376***	.000	8.312	0.221	1.449	Supported
H6	Attitude x Enjoyment From the VTE → Visit Intention	0.118**	.002	3.051	0.027	1.407	Supported
H7	Subjective Norm x Enjoyment From the VTE → Visit Intention	-0.034ns	.371	0.895	0.003	1.141	Not supported

Note. ns = non-significant.

*

P value < .05, **p value < .01, ***p value < .001.

According to the R² values in the structural model shown in Figure 2, FOMO and attitude accounted for 11%, and visit intention for 56% of the variance. The R² values were higher than the 0.10 threshold value (Falk & Miller, 1992). These findings indicate a moderate disclosure rate for the research’s endogenous variables (Henseler et al., 2014). Examining the VIF values across the variables in Table 4 reveals that linearity is not an issue because these values (1.000–1.528) are much lower than the cutoff value of 10 (Hair et al., 2021). In addition, the effect size coefficients of the variables included in the model (f²) are evaluated as low, 0.02 as a medium, 0.15, and 0.35 as high (Cohen, 1988). Accordingly, when the effect size coefficients (f²) in Table 4 are examined, it is understood that the variables have low and medium effect sizes.

The Moderating Role of Enjoyment From the VTE

The interactions between attitude and enjoyment from the VTE positively and significantly influenced visit intention (β = 0.118; t = 3.055; p < .001). Moreover, it has been determined that there is no significant and positive moderator effect of enjoyment from the VTE variable (β = −0.035; t = 0.908; p > .05) on the impact of the subjective norm variable on visit intention.

These results indicate that enjoyment from the VTE positively moderated the effects of attitude on visit intention. Thus, H₆ was supported, but H₇ was rejected. A simple slope test was performed to understand better the moderating impact of enjoyment from the VTE (Figure 3).

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Figure 3.

Simple Slope Test.

When the simple Slope test in Figure 3 is examined, the slope graph is constructed according to the −1 and +1 standard deviation values of the mean enjoyment from the VTE variable. The slope graphs’ non-parallel lines indicate the existence of a moderator effect.

Managerial Implications

Virtual tourism can provide new opportunities for tourism and hospitality businesses. VTE can be a risk-reducing marketing tool to attract people to a specific tourism destination or facility (Zirbes, 2021). The current study showed that FOMO could be effective within a self-improvement frame in the tourism context. Individuals who value self-improvement and feel anxious when missing events are more likely to travel virtually or physically. Thus, continuous learning and FOMO can be used in tourism initiatives’ positioning and marketing communication activities. Museums can utilize continuous learning and self-development appeals in their value propositions. Rich content on a museum website can appeal to people who constantly want to improve themselves. The benefits of visiting museums in terms of personal development can be emphasized on the web banners of museum websites.

The findings also show the importance of providing an enjoyable and exciting virtual museum experience. Accordingly, creating an engaging website and application is critical. The borders between virtual and offline experiences are becoming blurred in a rapidly digitizing world. Virtual tourism provides excellent opportunities for differentiation and creating substantial value propositions. Positive emotions, such as enjoyment from a VTE, are associated with increased visit intentions. Therefore, museum managers and public administrators can consider different traveler segments to design enjoyable virtual tours. For example, a museum can be presented virtually with various content complexity and length options. Tourism managers can develop their websites considering the visitors with different involvement levels.

Social Implications

This study has shown that consumers can experience FOMO in tourism. Individuals who value continuous learning can feel anxious about missing out on an experience relevant to their interests. Their visit intentions can be triggered by the anxiety of skipping experiences related to their personal growth ideals. Besides influencing attitudes and subjective norms, FOMO shapes behavioral intentions. From a consumer well-being perspective, this finding points out the risk of experiencing FOMO because of abundant choices and numerous opportunities, even in the tourism sector, a sector that is traditionally associated with relaxation, fun, and refreshment. Since FOMO is experienced with appealing activities that are unwillingly skipped, associating missed tourism activities with FOMO is consistent with previous studies. However, the results highlight the importance of robust self-regulation, task prioritization, and personal time management in the digital age. Governments and managers of schools and lifelong learning institutions must consider those points in designing and communicating their virtual programs. Specific programs tailored to potential travelers’ needs and general availability, mindfulness training, and personal learning plans, may help keep FOMO at reasonable limits.

On the other hand, virtual tourism can be an engaging educational tool. Virtual tourism is increasingly used as a safe, interactive, and easily accessible edutainment tool at schools (Njerekai, 2020). This study showed that virtual museum visits could appeal to adults for continuous learning. VTEs can transform museum visit experiences and elevate them to a more comprehensive and engaging activity that would appeal to many potential tourists. Therefore, public administrators and managers of higher education institutions can offer enjoyable virtual tours to increase awareness about cultural heritage and enrich people’s perspectives with interesting information and appealing virtual experiences.

Study Limitations and Future Research Directions

As with any research, this study has some limitations. Although convenience sampling is appropriate considering the general target market of virtual tourism, future studies could utilize random or stratified sampling methods. The model can be examined further with different consumer samples. Second, future studies could examine FOMO and the continuous learning relationship in other contexts, such as sports or arts tourism, while controlling for the website characteristics or potential tourists’ technological capabilities. Furthermore, the continuous learning–FOMO relationship could be examined by conducting qualitative research that reveals the inner motives and psychological needs that lead to FOMO in the personal growth context. Third, the interrelationships between continuous learning, independent and inter-dependent self-construal, and virtual tourism intentions could be investigated by considering the personal and social dimensions of FOMO. Further studies could focus on specific aspects of virtual tours, such as website interactivity, narratives, and museum properties.

This study has measured participants’ general attitudes and perceptions of subjective norms regarding museum visits and did not explicitly differentiate between the attitudes and subjective norms toward virtual and on-site museum visits. Future studies could solely focus on virtual visit intentions. Furthermore, exploring visit intentions across different levels of involvement and FOMO and analyzing possible changes in the relationship strengths between the TRA constructs could be interesting. As another fruitful research direction, examining traveler responses and visit intentions before, during, or after a virtual museum visit could be interesting. Such research could also reinvestigate the influence VTEs on visit intentions while considering attitudes and subjective norms. Lastly, further analyses could be done by comparing different museums.