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Article

The Role of Social Media on Tourists’ Behavior: An Empirical Analysis of Millennials from the Czech Republic

by
Mohsin Javed
1,*,
Zuzana Tučková
1 and
Abdul Bashiru Jibril
2
1
Department of Business Administration, Faculty of Management and Economics, Tomas Bata University in Zlin, Mostni 5139, 76001 Zlin, Czech Republic
2
Department of Management and Marketing, Faculty of Management and Economics, Tomas Bata University in Zlin, Mostni 5139, 76001 Zlin, Czech Republic
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(18), 7735; https://0-doi-org.brum.beds.ac.uk/10.3390/su12187735
Submission received: 8 August 2020 / Revised: 15 September 2020 / Accepted: 16 September 2020 / Published: 18 September 2020
(This article belongs to the Special Issue Web 2.0 in Tourism and Hospitality Industries)
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Abstract

:
Due to the emergence of Web 2.0 and consequently the increased use of social media, this study aims to explore the role of social media in changing the behavior of tourists and the choice of a destination. Despite the massive popularity of social media, the studies with a comprehensive set of driving constructs along with indicators of social media regarding the changing behavior of tourists are lacking in the literature. Therefore, framed by the theory of planned behavior (TPB), the present study fulfills this research gap by developing a set of driving constructs and carrying out an empirical analysis by collecting data from millennials in the selected universities of the Czech Republic. Through the non-random sampling technique, precisely convenience sampling, 261 valid responses were received, and partial least squares-structural equation modeling (PLS-SEM) analysis was performed to achieve the objectives of this study. The results indicate that social media channels have a significant impact on behavioral intention and the actual behavior of tourists (significant direct and indirect effect). The constructs tourist information search and tourism promotion were partially supported toward predicting the behavior of tourists. Control–variables related to socio-economic characteristics such as gender and educational level also have a significant impact in determining the actual behavior of tourists. Hence, overall, the study concludes with the significant and considerable impact of social media on the behavior of tourists. The contributions of the study and future directions are discussed at the end of the paper.

1. Introduction

Social media and social networking sites have become an important tool for obtaining expeditiously detailed information about a certain destination [1]. Tourists plan their trips accordingly under the significant influence of social media [2,3,4]. During travel, the active use of social media in terms of sharing stories, pictures, and videos also influences other potential tourists for travelling toward exotic destinations. Although people still use internet browsers such as “Internet Explorer” and “Google Chrome”, etc. to search and plan for their holidays, however, the emergence of Web 2.0 and social media has made the process more interactive [5]. This digital transformation provides accessibility and allows the users to create and exchange the content (i.e., user-generated content [6,7,8]). Nonetheless, the continuous and free Internet access at most of the holiday destinations in Europe has also supported the use of social media to share stories and tourism experiences in real time [9]. Such a scenario changes the behavior of tourists [10], which is why nowadays, tourism and hospitality companies are fully integrating on social media to meet the changing needs and desires of customers [11,12,13,14].
Social media and networking sites are considered as web-based platforms, where people interact with each other, create their stories, share information, and exchange ideas in a virtually connected community [15,16,17]. According to Kaplan and Horizons [18], social media is “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content”. It is worth noting that the definition of social media evolved over time due to its increasing use and expansion. In this vein, Cohen abridged the debate about the explanation of social media by elaborating 30 diverse definitions of social media and described important characteristics of social media [19]. These characteristics include that (a) social media uses online applications, tools, and platforms, hence solely depend on information technology for technical operationalization; (b) social media refers to communication channels that enable content creation and exchange of information and collaboration among participants and the public, leading to pervasive changes; and (c) social media connects users within a virtual community, and consequently has impacts on the behavior of people in real life [20].
Recently, some researchers have analyzed the different definitions of social media [21]. Based on the integration ideology of Web 2.0, online platforms and Internet tools allow for the programmatic integration of applications such as websites, mobile applications, and online systems with Web 2.0 applications. Hence, a comprehensive definition of social media includes technological, ideological, and functional components [21]. However, it is worthwhile noting that most researchers have focused on the “social” nature and defined social media as information exchange and interpersonal communication [18,22,23,24,25].
In light of the extensive social media usage in the tourism industry, social media helps tourists to plan their travel and eventually in some way influences the behavior and decision making of tourists. To reiterate the significance of social media in the context of tourism, the technology has noted be impactful on the behavior of tourists, depending upon their purpose of travel and aim of visitation [20] such as business travelers might use social media differently than tourists traveling for leisure [26]. Some recent studies have shown an increasing importance of social media such as a study carried out in Romania that explored the role and impact of YouTube for prosumers and found that YouTube had become an important source for the development of tourism destinations [27]. Similarly, another study applied in the town of Longyearbyen explored motivational factors based on the influence of social media [28]. In this vein, the users of social media had some influence on the behavior of potential tourists. These central travelers are sometimes referred to as “travel opinion leader”; although small in proportion, they influence the behavior of tourists depending upon the shared information and destination choice [29,30].
However, despite the increasing importance of social media in the tourism industry, the channels through which the behavioral intention and actual behavior of tourists influence are overlooked in the literature. Importantly, the use of social media and mobile Internet usage is having prevalence among teens and young adults [31]. Some studies have also found that social media influencers typically impact the behavioral intentions of millennials as indicated in the case of rural tourism in Greece [32]. This is why millennials can be referred to as the Internet generation [33]. In light of this, the present study particularly focused on Millennials as respondents to carry out this research. Such a trailblazing exploration is also necessary for deep understanding due to its increasing significance in tourist decision-making and marketing strategies from the point of view of firms. Therefore, based on the theory of planned behavior, the main objective of this study was to explore the role of social media in changing the behavior of tourists based on the proposed research constructs. In addition, as a partial objective, the study aimed to test the reliability and validity of such social media-based information channels. Theoretically, this study will contribute to the literature by giving a conceptual model and exploration of how the behavioral intention and the actual behavior of tourists are influenced by social media usage in the tourism industry. Practically, this study will help business managers understand the role of social media better and formulate policies accordingly. Hence, the understanding of such social media involvement and influence is very crucial, whilst the present study will provide such clarity of affairs by offering recommendations to them to formulate profitable marketing strategies through different available social media platforms.
The rest of the paper is organized as follows. Section 2 presents the theory of planned behavior (TPB) as a theoretical background of the research. In Section 3, we define constructs, discuss the conceptual framework, and develop the propositions. Section 4 primarily explains the materials and methods by describing the sampling and procedure, construct measurement, and analysis of the measurement model, whilst Section 5 gives the data analysis and results along with the tests for reliability and validity of the study. Section 6 presents a discussion of the findings and results of the study. Finally, Section 7 summarizes the article with our conclusions and implications and also describes the limitations and offers suggestions for future research.

2. Theoretical Background: Theory of Planned Behavior

The TPB extends an earlier theory known as the theory of reasoned action (TRA) [34] to account for conditions in which individuals do not have absolute control over the situation. According to the theory (TPB), human action is guided by three kinds of considerations: (a) behavioral beliefs about the likely outcomes of the behavior and the evaluations of these outcomes; (b) normative beliefs about the normative expectations of others and the motivation to comply with these expectations; and (c) control beliefs about the resources and opportunities possessed (or not possessed) by the individual and also the anticipated obstacles or impediments toward performing the target behavior [35]. By extension, behavioral beliefs produce a favorable or unfavorable attitude toward the behavior; normative beliefs result in perceived social pressure or subjective norm; while control beliefs consequently give rise to perceived behavioral control. In this light, the current study takes inspirations from the theory of planned behavior to underpin the proposed research model (see Figure 1).

3. Conceptual Framework and Propositions Development: Definitions of Constructs

3.1. Social Media

Social media is a powerful networking tool with strong integration into the real world, ranging from social to economic life [20]. Different authors have explained the notion of social media. Boyd refers to social media as social networking sites, blogging platforms, video-sharing sites as well as other related tools allowing participants to generate and share their content [36]. Another author indicated that social media increases the capacity to share and cooperate, outside the framework of traditional institutions [37], whilst Jenkins et al. [38] argued that the novelty of social media lies in the fact of having access to broadcast media by the people who never had such opportunities to share their videos and experiences if they have a smartphone and a connection to the Internet. Meikle defined social media as a manifestation of convergence between personal communication and public media [39].
In the tourism industry, the application of digital technologies in the search of tourist destinations has received considerable attention from practitioners (tourism operators). However, the study considered a thin line between the use of the Internet and social media for travel planning and precisely considered the usage of social media and not Internet websites for general information. Therefore, the avenue of social media for such interaction, communication, co-operation, and the creation of user-generated content may influence the tourism industry and tourists, and subsequently the intentional behavior and actual behavior of tourists. Therefore, we hypothesize that,
Hypothesis 1 (H1).
The use of social media would positively and significantly predict the behavioral intention of tourists’ choice of destination.

3.2. Tourist Information Search

Tourist information search refers to the search of information from various sources before making a decision to purchase [40]. This leads to three major factors: motives, determinants, and sources. The main motive behind the tourist information search is to find good traveling opportunities at good prices [41] and also to improve the quality of the trips [42]. Hence, a tourist information search helps the tourists to choose a destination and assists in making decisions such as travel mode and location activities as well as boarding and lodging [43,44]. Again, the tourism information search reduces the uncertainty associated with traveling and increases the quality of trips [45].
The determinants of a tourist information search are based on categories such as the composition of the traveling party, past experience, friends and relatives at the destination, and the novelty of the destination [43]. Previously, information sources for a tourist information search used to be past experiences, brochures, guidebooks, travel agents, magazines and newspapers, and friends and relatives [41,46]. However, nowadays, social media as a source of tourist information search has surpassed all traditional sources [47,48]. Consequently, the attitude and behavior of tourists could possibly have changed. In light of this, it can rightly be hypothesized that:
Hypothesis 2a (H2a).
The use of social media for a tourist information search would positively and significantly predict the behavioral intention of the tourists’ choice of destination.
Hypothesis 2b (H2b).
The use of social media for a tourist information search would positively and significantly predict the actual behavior of the tourists’ choice of destination.
Hypothesis 2c (H2c).
The behavioral intention of tourists would positively and significantly mediate the relationship between a tourist information search and the actual behavior of tourists.

3.3. Tourism Promotion

Social media and Web 2.0 have changed the marketing process, promotional (advertising) activities, and also impacted throughout the tourism industry [4]. Interestingly, the emergence and widespread popularity have also shown mistrust as well as reduced the impact of traditional mainstream media [49]. Social media has become a very effective channel for tourism promotion due to its integrated communications [50]. Social media has not only provided a new way for tourism promotion, but has also made it inevitable for tourism organizations to reengineer their ways of operation and business models [20], and such recognition and reengineering have become necessary for sustaining the tourism business and reaping widespread benefits.
In the tourism industry, a study carried out by Crofton and Parker gave measurable evidence that social media has contributed significantly by increasing the number of tourists in Atlantic Canada [51]. Some consider social media as a new strategy of online marketing for tourism promotion by exploiting interactive virtual relationships with users of social media platforms [52], which also helps to build customer loyalty [53]. The dynamic change and virtual interaction have possibly impacted the attitudes and behaviors of tourists, this leading us to formulate the following hypotheses:
Hypothesis 3a (H3a).
The use of social media for tourism promotion positively and significantly predicts the behavioral intention of tourists’ choice of destinations.
Hypothesis 3b (H3b).
The use of social media for tourism promotion positively and significantly predicts the actual behavior of tourists’ choice of destination.
Hypothesis 3c (H3c).
The behavioral intention of tourists would significantly mediate the relationship between tourism promotion and the actual behavior of tourists.

3.4. Behavioral Intention and Actual Behavior of Tourists

Millennials, and for that matter, young-adults, are noted as mostly early adopters of technology/innovation [54,55]. The research construct behavioral intention describes the extent to which a person has expressed conscious efforts/plans to perform or not perform some specified future anticipated behavior [56] while the expression conceived by the person is triggered by the perceived excitement and ease accompanied by the new technology or an innovation [57]. Whereas, actual behavior indicates the resultant action that was ignited (caused) by the behavioral intention [58]. Therefore, social media usage within the context of the tourism industry has seen a facelift by tourist practitioners regarding their marketing strategies toward reaching mass visitors. Again, it is worth noting that the millennials form a significant number of visitors who equally pay attention to zoo attractions or destinations. Similarly, these young-adults (Millennials) are prone to the use of digital devices and for that matter, social media usage [59,60].
Hence, to obtain a more comprehensive understanding of the role of social media on tourist behavior, we examined the interplay of tourist inclination using social media to identify/search for a particular tourist destination. Some studies [61,62,63] consistently revealed that experienced individuals were more likely to have intentions and then use technology in their tasks. In light of this, we outline the following hypotheses:
Hypothesis 4a (H4a).
The behavioral intention of tourists would consequently and significantly affect the actual behavior of tourists regarding their choice of destination.
Hypothesis 4b (H4b).
The behavioral intention of tourists would significantly mediate the relationship between social media and the actual behavior of tourists regarding their choice of destination.
Notably, the study also incorporated some control variables related to the socio-demographic characteristics to have a look on the possible impact of gender, age, and educational level. Hence, in light of this aspect, we hypothesize a further three hypotheses:
Hypothesis 5 (H5).
Gender as a control variable significantly predicts the actual behavior of tourists.
Hypothesis 6 (H6).
Age as a control variable significantly determines the actual behavior of tourists.
Hypothesis 7 (H7).
Educational level of respondents significantly leads to determine the actual behavior of tourists.

4. Materials and Methods

4.1. Sampling and Procedure

The study employed a quantitative research approach from a deductive research paradigm to implement and achieve the research objectives, suitable to make inferences about behavior and attitude [64]. A structured questionnaire was developed and data collected through the survey method due to its appropriateness in data collection [65]. The study focused on Millennials due to their higher prevalence of social media usage [31]. Hence, to carry out the survey, students at some of the public sector universities in the Czech Republic (Tomas Bata University in Zlin, University of Ostrava, Brno University of Technology, and Masaryk University) were selected due to the higher prevalence of social media usage amongst university students. Again, the survey respondents (university students) fell into the age groups of 18–25 years and 26–33 years where the use of Internet and technology is higher, as confirmed by previous research [66]. Hence, social media usage is also higher [67]. The questionnaire, through a self-administered and online survey approach, was employed to collect data during the months of February 2020 to May 2020. The carefully designed questionnaire only took an average of seven minutes to fill in. Hence, this convenience sampling method ended up by distributing 280 questionnaires, out of which 261 were valid and completed. Table 1 below gives a quick preview of the demographic characteristics of respondents who participated in this research study. The demographic characteristics included were age, gender, educational level, and marital status, and the medium of social media engagement of the respondents.

4.2. Construct Measurement

All the items used in this study for the measurement of the research constructs were adopted from the previous literature. The responses and opinions of respondents were measured through a five-point Likert scale ranging from strongly disagree to strongly agree (i.e., Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, and Strongly Agree = 5). Importantly, all the measurement items used were positive statements [68] to ascertain meaningful analysis. Hence, Table 2 indicates the same procedure, a summary of constructs along with the operationalization and their respective literature.

4.3. Analysis of the Measurement Model

The study used partial least squares-structural equation modeling (PLS-SEM) for the statistical analysis and response evaluation based on the proposed conceptual model. PLS-SEM is considered as a popular tool due to its applicability to conduct an analysis of complex relationships with diverse types of constructs and indicators such as moderated-mediated analysis and direct or indirect relationships [79,80]. Thus, in this study, PLS-SEM has been employed to test the proposed research model and posited hypotheses, a variance-based technique of structural equation modeling [81]. PLS-SEM is considered to be a better technique and is preferable over covariance based-SEM as it handles many predictors despite their multicollinearity [80,82]. Furthermore, non-normal data, minimum sample size, and scale of measurement are also some of the reasons to consider PLS-SEM for response evaluation and statistical analysis [83], while PLS-SEM is also preferable when the exact nature of data is not known [84].
In the literature, there is no consensus with regard to sample size for structural equation modeling (SEM). Some authors are of the view that even a small sample size could be tested meaningfully [85,86,87], while some consider that the minimum sample size required for structural equation modeling is between 100 and 150 [88,89,90,91]. However, some scholars have recommended a relatively larger sample size, for example, a sample size of 200 respondents is quite appropriate for carrying out analysis through SEM [92,93,94,95,96]. Therefore, the present study considered more than 200 respondents (university students) as a sample size to fulfill this requirement.

4.4. Test of Common Method Bias

Most of the researchers linked the common method bias with the measurement method. In this regard, a study explored the issue of method bias extensively and linked it as a primary source of measurement error [97]. Previously, many authors highlighted the same issue [98,99,100] and warned that such a systematic error variance has an influence on the results and consequently leads to misleading conclusions. Hence, common method bias (CMB) is a considerable problem and researchers should put efforts to control it. The careful assessment of potential sources of bias and the implementation of control methods related to procedural and structural vain can help to tackle the issue of CMB [97].
In the present study, some qualitative measures were taken to control CMB. The authors stated in the header section of the questionnaire that no answer was right or wrong for the asked questions. Furthermore, the authors also assured the respondents of their anonymous status regarding the provided information and proclaimed that the filling of the questionnaire could be stopped at any stage due to some inconvenience. Apart from this, the suggestions given in the recent literature of PLS-SEM have been followed, especially the full collinearity approach suggested by Kock and Hadaya [101]. Along this vein, the variance inflation factor (VIF) was employed for the detection of CMB due to multicollinearity. The results in Table 4 shows that all the computed VIFs were near or less than two, considering the problematic range of VIFs between 5 to 10, as suggested by Alin [102]. Therefore, the issues related to CMB were minimal for this study.

5. Empirical Findings and Results

5.1. Model Assessment

The study used ADANCO 2.0.1 to estimate the model and obtain results. The reliability of the model can be assessed through Dijkstra–Henseler’s rho [103], composite reliability [104], and Cronbach’s alpha [105]. According to Henseler [106], the Dijkstra–Henseler’s rho is a measure of reliability with obtained construct scores through PLS path modeling, whereas, for Joreskog’s rho, the composite reliability is estimated through sum scores of the measurement model. According to the recommended threshold of 0.5 for construct reliability [107,108], Table 3 indicates that coefficients for reliability fulfill this requirement. The composite reliability measured by Joreskog’s roh, has also met the threshold of 0.7 [83] whilst the constructs also fulfilled the condition for unidimensionality and convergent validity by having a value of average variance extracted (AVE) greater than 0.5 [109].
Furthermore, with reference to the factor loadings of the related constructs, all indicators were loaded sufficiently. Basically, a factor loading is a regression slope and equivalent to the correlation between an item and its related construct. A threshold level of 0.6 or more is recommended [107] for the significant measurement of a latent construct; hence, Table 4 indicates that all factor loadings were greater than 0.6, except one item of the construct behavioral intention that is more than 0.5 and does not make a problem. Therefore, these indicators measure what actually has to be measured.
In addition, the outer model was assessed through discriminant validity. By discriminant validity, we mean that the two constructs were statistically different. The well-known criterion for the discriminant validity Fornell–Larcker criterion was used in this study [110]. The Fornell–Larcker criterion for discriminant validity postulates that a latent construct’s average variance with its indicators should be more when compared to other latent constructs in the structural equation model [110]. In other words, a model is discriminant valid if the square root of AVE for a construct is greater than the correlation coefficient with other latent constructs [106,111]. The current study fulfills this criterion of discriminant validity as Table 5 shows that all the values in the diagonal of the table (in bold) is greater than any other value in the row or column position with reference to any other construct.

5.2. Structural Equation Modeling and Hypotheses Testing

After the assessment of the reliability and validity of the measurement model, SEM was used to proceed ahead for hypothesis testing. Along this vein, SEM analogs regression analysis by using the mean approach to carry out inference analysis [112]. Therefore, the study used SEM for hypotheses testing to see the impact of certain constructs on behavioral intention and the actual behavior of tourists based on direct and indirect effects.

5.2.1. Direct Effects

Based on the proposed hypothesized conceptual model (Figure 1), path analysis of the SEM was carried out through ADANCO Version 2.0.1., which gives direct effects such as the impact of social media channels on the behavioral intention of tourists, tourism promotion, and tourist information search by taking the case of Millennials in the selected universities in the Czech Republic. The results in Table 6 show that behavioral intention, tourism promotion as well as two control variables—gender and educational level—significantly and directly influenced the actual behavior of tourists. Specifically, the regression coefficients and corresponding t-values depict that the impact of behavioral intention on actual behavior of tourists (β = 0.3180, t-value = 4.3290), the impact of tourism promotion on actual behavior of tourists (β = 0.1720, t-value = 2.6880), the impact of gender on tourist information search (β = 0.1489, t-value = 2.8257), the impact of educational level on the actual behavior of tourists (β = −0.1982, t-value = −4.4817) were significant (Table 6). Likewise, Table 6 also shows that social media channels and tourist information search also significantly and directly influence the behavioral intention of tourists (β = 0.3408, t-value = 5.0133; β = 0.2763, t-value = 4.1284) whilst tourism promotion on behavioral intention (β = 0.0849, t-value = 0.7789) and tourist information search on the actual behavior of the tourist (β = 0.1720, t-value = 1.7715) and a control variable—age—on the behavior of the tourist had insignificant relationships (β = −0.0468, t-value = −0.9187) (Table 6). In light of this, it can be said that the H1, H2a, H3b, and H4a hypotheses are supported. Then again, hypotheses H2b and H3a are not supported considering the context of direct effects in the study. All the significant relationships supporting the corresponding hypothesis are shown in ‘bold’ font and ‘supported’ empirical remarks (Table 6) (see also Appendix A). Moreover, an index of effect size Cohen’s f2 for measuring the strength of the relationship is also shown in Table 6. According to the Cohen’s recommended values (strong effect if f2 ≥ 0.35, moderate effect if 0.15 ≤ f2 < 0.35, weak effect if 0.02 ≤ f2 < 0.15), most of the significant relationships fell in the category of medium strength [113,114,115].

5.2.2. Indirect Effects

The present study also has important indirect relationships, as indicated in the proposed conceptual model. The results in Table 7 show the indirect effects related to the mediation effect of behavioral intention. Notably, Table 7 depicts that social media channels and tourist information search significantly influence the actual behavior of tourists through the intercession of behavioral intention (β = 0.1084, t-value = 2.5968; β = 0.0879, t-value = 3.0051). Hence, it can be rightly said that the H2c and H3c hypotheses were empirically supported. In contrast, the H3c hypothesis was not supported because the behavioral intention of tourists did not significantly mediate the relationship between tourism promotion and the actual behavior of tourists regarding their choice of destination (β = 0.0270, t-value = 0.7481) (see also Appendix A).

5.2.3. Coefficient of Determination (R2)

The coefficient of determination (R2) assesses the predictive power of the research constructs. To put it another way, this coefficient shows the percentage of the variation of the dependent variable explained by the independent variable [106] whereas there was a slight modification for the adjusted R2, which takes into account the sample size to compensate for the added independent variables in the model. Table 8 shows that the estimated R2 for behavioral intention explains 32% variability in the behavioral intention by the independent constructs, tourism promotion, social media channels, and tourist information search whilst the adjusted R2 showed 31.29% of explained variation. Likewise, for the actual behavior of the tourist, the value of the coefficient of determination showed that 38.58% of variability was explained by the independent constructs, tourism promotion, social media channels, tourist information search, and behavioral intention with almost the same percentage of variation (37.13%) of adjusted R2. The same can also be verified with the estimated research model in Figure A1 (see also Appendix A).

6. Discussion and Study Implications

6.1. General Discussion

The purpose of this study was to explore the role of social media in changing the behavior of tourists in the Czech Republic. In addition, the partial objective of the study was to test the reliability and validity of such social media-based information channels. To achieve the objectives of the study, the data were collected from Millennials of the selected universities in the Czech Republic. The study yielded very important findings of the role of social media and its influence on the tourists’ behavior.
First, social media channels came up as a construct to significantly influence the behavioral intention of tourists (Table 6). Similarly, Table 6 also shows that the construct behavioral intention significantly influences the actual behavior of tourists. There was also a significant indirect effect of social media channels on the actual behavior of tourists through the mediation of behavioral intention (Table 7). Hence, Hypothesis 1, Hypotheses 4a and 4b are supported in this study. These results are compatible with most of the studies carried out on social media and its influence [116,117,118].
Second, the results of the study suggest that social media as a source of tourist information search significantly predicts the behavioral intention of the tourists’ choice of destination. This indicates that Hypothesis H2a is supported. The same was found by Jacobsen and Munar [47] that in the digital age of social media, tourist information search matters, especially within online communities while the results indicate that social media as a source of tourist information search does not significantly predict the actual behavior of tourists. Hence, this led to the rejection of Hypothesis H2b for this research. Although this result is unprecedented, it confirms the points raised by Tham, Mair, and Croy [2] that the context is important [119,120] where the level of social media engagement, the novelty of destination, and complexity in planning have an important role. However, the indirect effect of a tourist information search on the actual behavior of tourists was significant through the mediation of behavioral intention that is logically based, leading to the support of Hypothesis H2c.
Third, the results indicate that social media as a source of tourism promotion does not predict the behavioral intention of tourists by not supporting Hypothesis H3a. Likewise, the indirect effect of tourism promotion on the actual behavior of tourists was also insignificant and the H3c hypothesis was not supported. This confirmed the findings of a study carried out on Chinese tourists by reporting that the behavioral intentions of tourists were only affected by internal values related to travel motivations [121]. Such findings also validate the points raised by Li and Cai [2].
Fourth, the study also considered the socio-demographic factors to analyze the impacts of variables like gender, age, and educational level. Pritchard highlighted the importance of such relationships [122]. Along this vein, the variable gender and educational level had a significant impact on the actual behavior of tourists and supported Hypothesis H5, and H7. Such a relationship, particularly in a social media setting, has not been explored before but in general, this finding is compatible with Frew and Shaw [123], where they found that gender and tourist behavior had a significant association. Likewise, educational level also had an impact on the attitude that was close to behavior [124].

6.2. Theoretical Implications

Theoretically, this study identified certain constructs related to social media and found their impact on behavioral intention and the actual behavior of tourists based on the theory of planned behavior (TPB) [35]. Such exploration adds to the growing body of knowledge related to the research on social media and its increasing use [117,118]. Due to the higher prevalence of social media usage, this study took the case of Millennial tourists from the universities of the Czech Republic and contributed to understanding the phenomenon better. The inclusion of socio-economic characteristics as controlled variables in analyzing the behavior of tourists in a social media usage context is a novel aspect of this study and theoretical contribution.

6.3. Practical Implications

Practically, this study has significant implications. The findings related to social media and its impact on behavioral intention and the actual behavior of tourists have important implications for business managers. As in the case of Millennials, the significant impact of social media channels on behavioral intention and the actual behavior of tourists reiterates such importance to business managers to seriously consider social media to sustain tourism business. Social media as a tourist information search has a significant influence on behavioral intention, which makes it imperative to be up-do-date in providing relevant information and be available on such tourist information searches. Importantly, tourism promotion has a significant impact on the actual behavior of tourists, therefore, tourism promotion on social media has become an important tool to gain the attention of tourists and tourist managers should adopt a suitable tourism promotion strategy for their businesses to entice more Millennials and obtain their business share in a competitive business environment.

7. Conclusions, Limitations, and Future Research Directions

Based on the enhanced importance and increasing use of social media, this study considered social media channels and related constructs to explore such relationships. Taking inspiration from the theory of planned behavior (TPB) [35], the impact of social media channels on behavioral intention and the actual behavior of tourists were explored. In order to achieve the objectives of this research study, a research model was proposed based on underlying constructs and their indicators from the literature. Due to the higher prevalence of social media usage among millennials, the study considered Millennials as respondents. Therefore, by taking Millennials from universities in the Czech Republic (Tomas Bata University in Zlin, University of Ostrava, Brno University of Technology, and Masaryk University), the data were collected and analyzed through PLS-SEM to achieve the objectives of this study.
In light of the findings, the study concludes that social media channels are important in the contemporary era and has a significant impact on the behavioral intention and actual behavior of tourists. This influence on the behavior of tourists leads to choosing certain destinations and making travel-related decisions. A tourist information search has a strong impact on behavioral intention but not on the actual behavior. However, tourism promotion due to social media has a significant impact on the actual behavior of tourists, highlighting that businesses cannot grow and obtain their due share without a presence and promotion on social media. Additionally, the control variables depict that age and educational level are important in influencing actual behavior; hence, gender choices and the needs of tourists with different educational levels need to be addressed and considered to keep them intact.
No study is without limitations, and this study has some limitations and requires further scientific inquiry. First, the study only included Millennials as respondents, therefore the findings cannot be generalized to all age groups and further research in this regard can help to generalize the results. Second, as this study included only four universities to collect data, these results cannot be generalized to all of the Czech Republic, however, the prevalence can be helpful to understand the behavior of Millennials. Third, the study only includes the viewpoint of Millennials regarding social media usage and its influence, it seems worthwhile if future researchers include some business managers and carry out semi-structured interviews to take their view on the increasing use of using media and changing avenues of tourist destinations and attractions due to the behavior of tourists.

Author Contributions

M.J.—conceptualization, methodology, validation, investigation, writing—original draft preparation, Z.T.—conceptualization, supervision, methodology, review, and editing, A.B.J.—conceptualization, methodology, validation, and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Internal Grant Agency of FaME, Tomas Bata University in Zlín, project No. IGA/FaME/2020/005; and project no. FSR-S/2020/FaME/001.

Acknowledgments

We would like to thank David Tuček (Dean, Faculty of Management and Economics, Tomas Bata University in Zlin, Czech Republic), and Lubor Homolka (Vice-Dean for Ph.D. Study), all at Faculty of Management and Economics-TBU in Zlin for their consistency and persistence scholarly advice and support for this new epistle.

Conflicts of Interest

The authors declare no conflict of interest of whatsoever.

Appendix A

Sustainability 12 07735 g0a1 550
Figure A1. Empirically tested research Model.
Figure A1. Empirically tested research Model.
Sustainability 12 07735 g0a1

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Sustainability 12 07735 g001 550
Figure 1. A conceptual model. Source: Authors’ own.
Figure 1. A conceptual model. Source: Authors’ own.
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Table
Table 1. Demographic characteristics.
Table 1. Demographic characteristics.
VariableDetailsFrequencyPercentage
GenderMale12046
Female14154
Age (years)18–2514154
26–3310239.1
34–40186.9
Educational LevelBachelor diploma11443.7
Master diploma7227.6
PhD3011.5
Others4517.2
Occupational StatusStudent17767.8
Private employed4216.1
Government employed218
Others218
Marital StatusSingle12046
Married5119.5
In a Relationship9034.5
Divorced00
Total (n) 261100
Source: Authors’ field survey, Feb–May, 2020 in the Czech Republic.
Table
Table 2. Operationalization of constructs.
Table 2. Operationalization of constructs.
ConstructsIndicatorsLiterature Adapted
Social Media Channels
1. 
I actively use social media channels (Facebook, Twitter, YouTube, Instagram, and Blogs, etc.) during travel and updates my social status.
2. 
I actively upload pictures and videos during travel and tourism.
3. 
I think, social media impacted me to use it more during travel to show mobility in competitive ways.
4. 
I think I travel more to show my competitive mobility and seek attention of my friends and family to enhance psychological well-being.
[69,70,71,72,73,74]
Tourism Promotion
1. 
I think, tourism promotion/advertisement via social media also entices more travelers to travel towards a destination.
2. 
I think, social media sites and Internet-based social media technologies helped to promote tourism.
3. 
I think, the emergence of Web 2.0 (creating and sharing of user-generated content) played important role in tourism marketing and promotion.
[20,75,76,77,78,79]
Tourist Information Search
1. 
I think, social media plays important role in information search and decision-making behavior of tourists.
2. 
I think, social media sites help to search credible, and reliable information for making rational decisions.
3. 
I think, external information search (such as through social media) is typically helpful for newcomers to a destination search.
[20,45,47,49]
Behavioral Intention
1. 
I think I am capable of searching for a tourist destination over internet (social media).
2. 
Searching for interesting places on the social media is something I intend to do.
3. 
I have the resource and the knowledge and the ability to look for interesting destination to visit.
[55,57,60,80]
Actual Behavior of Tourist
1. 
I have been using internet often to search for tourist destinations.
2. 
I love visiting social media to see new places of interest.
3. 
I will continue to use and recommend social media channel for friends and relatives in search for tourist destinations.
[57,60,61,62,63]
Table
Table 3. Reliability and validity of constructs.
Table 3. Reliability and validity of constructs.
ConstructsDijkstra–Henseler’s rho (ρA)Jöreskog’s rho (ρc)Average Variance Extracted (AVE)Cronbach’s Alpha (α)
Behavioral Intention0.50430.78240.50000.5010
Tourism Promotion0.88280.87050.69280.7923
Social Media Channels0.80470.86310.61290.7894
Tourist Information Search0.73850.84090.63840.7191
Actual Behavior of Tourists0.70560.82340.70310.7532
Source: Authors’ processing from ADANCO 2.0.1.
Table
Table 4. Variance Inflation Factors (VIFs) and factor loadings.
Table 4. Variance Inflation Factors (VIFs) and factor loadings.
IndicatorsVIFBehavioral IntentionTourism PromotionSocial Media ChannelsTourist Information SearchActual Behavior of Tourists
SMC11.2679 0.7104
SMC21.6674 0.7791
SMC32.0092 0.8606
SMC41.7667 0.7743
TP12.0991 0.8692
TP21.7200 0.7360
TP31.5516 0.8838
TIS11.3957 0.7696
TIS21.3906 0.7773
TIS41.4477 0.8479
BI11.24230.7794
BI21.05570.7573
BI31.19490.5088
ABT11.3529 0.8911
ABT21.3529 0.8452
Source: Authors’ processing from ADANCO 2.0.1.
Table
Table 5. Discriminant validity; Fornell–Larcker criteria.
Table 5. Discriminant validity; Fornell–Larcker criteria.
ConstructsBehavioral IntentionTourism PromotionSocial Media ChannelsTourist Information SearchActual Behavior of Tourists
Behavioral Intention0.5000
Tourism Promotion0.12130.6928
Social Media Channels0.22760.08190.6129
Tourist Information Search0.21660.36030.16420.6384
Actual Behavior of Tourists0.20900.21210.23960.19650.7031
Note: Values at the diagonal line (in bold) are the square root of AVE. Source: Authors’ processing from ADANCO 2.0.1.
Table
Table 6. Path analysis: direct effects.
Table 6. Path analysis: direct effects.
Direct EffectsOriginal Coefficient (β)Mean ValueStandard Errort-ValueCohen’s f2Remarks
Behavioral Intention -> Actual Behavior of Tourists0.31800.32060.07354.32900.2617Supported
Tourism Promotion -> Behavioral Intention0.08490.08700.10900.77890.0068Not Supported
Tourism Promotion -> Actual Behavior of Tourist0.17200.17870.06402.68800.2704Supported
Social Media Channels -> Behavioral Intention0.34080.35030.06805.01330.1925Supported
Tourist Information Search -> Behavioral Intention0.27630.27390.06694.12840.2052Supported
Tourist Information Search -> Actual Behavior of Tourist0.17200.17170.09711.77150.0264Not Supported
Gender -> Actual Behavior of Tourist0.14890.14770.05272.82570.1263Supported
Age -> Actual Behavior of Tourist−0.0468−0.04600.0510−0.91870.0027Not Supported
Educational-Level -> Actual Behavior of Tourist−0.1982−0.19280.0442−4.48170.1954Supported
Source: Authors’ Processing from ADANCO 2.0.1.
Table
Table 7. Path analysis: indirect effects.
Table 7. Path analysis: indirect effects.
Indirect EffectsOriginal Coefficient (β)Mean ValueStandard Errort-Value
Tourism Promotion -> Behavioral Intention -> Actual Behavior of Tourist0.02700.02680.03610.7481
Social Media Channels -> Behavioral Intention -> Actual Behavior of Tourist0.10840.11470.04172.5968
Tourist Information Search -> Behavioral Intention -> Actual Behavior of Tourist0.08790.08760.02923.0051
Source: Authors’ processing from ADANCO 2.0.1.
Table
Table 8. Coefficient of determination.
Table 8. Coefficient of determination.
ConstructCoefficient of Determination (R2)Adjusted R2
Behavioral Intention0.32080.3129
Actual Behavior of Tourist0.38580.3713
Source: Authors’ processing from ADANCO 2.0.1.

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MDPI and ACS Style

Javed, M.; Tučková, Z.; Jibril, A.B. The Role of Social Media on Tourists’ Behavior: An Empirical Analysis of Millennials from the Czech Republic. Sustainability 2020, 12, 7735. https://0-doi-org.brum.beds.ac.uk/10.3390/su12187735

AMA Style

Javed M, Tučková Z, Jibril AB. The Role of Social Media on Tourists’ Behavior: An Empirical Analysis of Millennials from the Czech Republic. Sustainability. 2020; 12(18):7735. https://0-doi-org.brum.beds.ac.uk/10.3390/su12187735

Chicago/Turabian Style

Javed, Mohsin, Zuzana Tučková, and Abdul Bashiru Jibril. 2020. "The Role of Social Media on Tourists’ Behavior: An Empirical Analysis of Millennials from the Czech Republic" Sustainability 12, no. 18: 7735. https://0-doi-org.brum.beds.ac.uk/10.3390/su12187735

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