Keywords

1 Introduction

Tourism, as a leading global industry, could generate significant wealth for a country and contribute to higher employment in the tourism industry (Fang et al. 2016; Duarte et al. 2018, 2020). In developed countries, a more thriving tourism industry means higher income diversity and economic improvement (Pourahmad et al. 2018; Oliveira et al. 2018). In developing countries, however, the tourism industry offers export opportunities that could be exploited more quickly than through the traditional channels (Shahhoseini et al. 2017; Ratten et al. 2018). The Iranian government tends to invest in the tourism industry according to the country’s natural attractions and cultural heritage in local areas as a way to gain competitive advantages (Torabi Farsani et al. 2012). Although the tourism industry in Iran suffers from negative perceptions in the world, social media can be used to eliminate and correct these perceptions and to encourage foreign tourists to travel to the country and visit its different sights (Mousazadeh et al. 2018). In this line, geotourism is another way to attract more tourists (Ghazi et al. 2013). Given the unstable and challenging environments in the new century, there is a pressing need for creativity and innovation through entrepreneurial efforts at the organisational levels to achieve the desired outcomes (Tajpour and Salamzadeh 2019; Ratten et al. 2019a). With the present economic conditions and modern-day life problems as factors that cause high levels of stress, geotourism could be a choice for those who seek to get away from the everyday stressful life and focus more on their entertainment and wellbeing (Yalgouz-Agaj et al. 2010; Salamzadeh and Dana 2020). Travel to visit geological heritage and touristic destinations is one of the most important goals of geotourism, and often, in addition to receiving touristic services, leisure activities are also added to the travel package (Eshraghi et al. 2012). In addition to the optimal use of domestic capital, governments could encourage growth in geotourism as a national strategy for increasing income and thus achieving national security. Thus, it is crystal clear that there is a theoretical gap that could be studied by investigating the relationship between geotourism and destination branding.

2 Statement of the Problem

Geotourism is considered as one of the most lucrative and competitive industries in the world and is one of the new areas of advanced tourism. At the macro-level, governments are interested in gaining the economic benefits of this industry (Bastaman 2018). There is also increasing competition between different countries, especially in developing Asian countries, to attract geotourists. Globalisation and trade liberalisation in the field of geotouristic services are the basis for the rapid growth of tourism in developing countries (Radovic Markovic and Salamzadeh 2012; Salamzadeh et al. 2021; Millaningtyas and Hatneny 2019). Due to the low cost and high profitability of this industry, many countries interested in tourism development focus on and develop plans for this area of the tourism industry. In its Year 2021 Strategic Plan, Iran has also been considered to become one of the main hubs of geotourism in the region (Hamidi et al. 2020). In this regard, Iran can monitor the outflow of foreign currencies and the workforce to other countries.

With the emergence of global standards and rules in different parts of the world, people began to seek higher quality touristic services at lower and more competitive prices (Estiri et al. 2018). That is why social media plays a vital role in the selection of a tourist destination (Foroudi et al. 2016). There are no accurate statistics on the number of people by country who travel to other countries for such services, and these figures are sometimes contradictory (Momeni et al. 2018). Nonetheless, the number of geotourists is increasing, and it is expected to increase sharply in the coming years. This study aims to fill the theoretical gap by providing a model for understanding the impact of social media on geotourism (Salamzadeh 2020). The model is expected to help various stakeholders such as policymakers and academics to use social media to promote geotourism and modify the negative! perceptions about Iran. To achieve this goal, a researcher-made model for foreign users on social networks has been selected as the research strategy.

3 Background

3.1 Geotourism in Iran

Iran is a large country with a variety of locations in terms of weather, culture and geological heritage (Tavallaei et al. 2012). Therefore, geotourism in the country has a long history which is full of eye-catching destinations. As geotourism deals with ‘non-living parts of the natural environment’ (Sadry 2009), which is an integral part of the country, there are several opportunities to be explored, evaluated and exploited by those who are in this industry (Moradipour et al. 2020). The geotouristic destinations in Iran are increasingly drawing the attention of visitors as there are different attractions in those destinations, including geological and geomorphological sites, as well as ancient and cultural heritages (Ranjbaran et al. 2020). Hopefully, Iranian researchers have focused on this interesting area during the last two decades. Amrikazemi conducted the first research in 2002. He has published several books on the capacities of the Iranian geoparks and geotourism and continued his research on indigenous geosites and landforms in Iran (Shahhoseini et al. 2017).

Numerous authors have followed his work, and these led to new streams of research and practice in this domain (Molchanova and Ruban 2019). Most of the research in this domain is concentrated on particularities of various geotourism destinations, including but not limited to Manesht and Ghelarang (Mokhtari et al. 2019), Qeshm Island (Shahhoseini et al. 2017; Pourahmad et al. 2018), Khorramabad (Moradipour et al. 2020), Isfahan (Shafiei et al. 2017), Bangestan (Molchanova and Ruban 2019), Takht-e Soleymān (Khoshraftar and Farsani 2019), Shiraz (Habibi et al. 2018), the Lut Desert (Maghsoudi et al. 2019), Ali-Sadr Cave (Safarabadi and Shahzeidi 2018), Gachsaran (Habibi and Ruban 2017), Dasht-e-Kavir (Bahak 2016), Lorestan (Maghsoudi and Rahmati 2018), and Neyriz (Habibi and Ruban 2018). These locations are among the most well-known geotourism destinations in Iran; however, as a rule of thumb, these are less than 1% of the whole destinations.

3.2 Geotourism and Destination [Brand] Selection

Although the destination selection is affected by several factors, such as the image, reputation (Salamzadeh et al. 2016; Bañegil-Palacios and Sánchez-Hernández 2018), and the like, yet previous research on geotourism and destination [brand] selection is very limited and rare (Estima et al. 2014). Nevertheless, a few research papers have implicitly pointed out such a relationship. For instance, Chan and Zhang (2018) concentrated on the gap between the projected image with the perceived image of the destination and its relationship with the development of geotourism. Soliman and Abou-Shouk (2017) also followed a behavioural approach toward destination selection of geotourists. They believed that predicting the behavioural intention of geotourists could affect geotourism industry of a typical country. Moreover, Boley et al. (2018) argued that the development of geotourism could affect the intention to select a destination as well as the social return. Some researchers, such as Awaritefe (2004), concentrated on the differences between prospective and actual geotourists’ approach toward destination image. They believed that the image could be affected by geotourists approach. In another seminal research, Dryglas and Lubowiecki-Vikuk (2019) investigated the image of Poland as perceived by German and British tourists. They believed that destination selection was affected by tourists’ approach toward the image of Poland as their destination.

In sum, as mentioned earlier, the relationship between geotourism and the destination selection is implicitly mentioned in the literature. Moreover, destination brand selection is also marginally studied in the extant literature of tourism. For instance, Bhattacharya and Kumar (2017a, b) scrutinised the factors affecting tourists’ destination brand selection behaviour in India. They listed some factors to create improved relationships between the preferences of prospective tourists’ and the marketing mix of the destination brands. Besides, Shafiei et al. (2017) made the connection between geotourism and destination brand selection by concentrating on geo-branding as a linking pin. Although this concept has been previously mentioned by scholars such as Brown and Campelo (2014), Freire (2005, 2006), and Ilieș and Ilieș (2015), yet its connection to geotourism was poor. Therefore, in this chapter, the authors put more emphasise on the concept of destination brand selection and its connection to geotourism.

3.3 Geotourism and Social Media

Previous scholars extensively studied the role of social media in the tourism industry (e.g. see, Munar and Jacobsen 2013; Harrigan et al. 2017). Nevertheless, findings in different contexts are contradictory to some extent (Zeng and Gerritsen 2014; Salamzadeh et al. 2017). While some studies have confirmed the positive impact of social media platforms (e.g. see, Miguéns et al. 2008; Hays et al. 2013; Munar and Jacobsen 2014), others have rejected such a significant effect (e.g. see, Wozniak et al. 2017). In the realm of geotourism, this connection is studied by a number of authors. For instance, Tormey (2019) offered the use of new approaches toward social media to improve geoheritage. Also, Green (2017) and Prendivoj (2018) considered social media posts and comments as marginal triggers to motivate potential visitors to become geotourists. Therefore, according to the points mentioned above of view, it is essential to see if geotourism and social media platforms, as they are currently operating, have any relationship, or in better words, is the relationship between geotourism and destination brand selection affected by social media platforms in the studied context?

4 Theoretical Foundations

The theoretical model of this research has been developed by the researchers based on Ólafsdóttir and Tverijonaite (2018). Geotourism is defined by four components, i.e. macro facilitators (Gil-Saura et al. 2013), information search (Lee and Chhabra 2015), demand triggers (Hassan and Einafshar 2012), and personal factors and travel experiences (Boley et al. 2011). It has been updated with recent findings on the role of social media in destination branding (Ebrahimi et al. 2020). In this theoretical model, geotourism is an independent variable that affects the choice of destinations. The destination brand is also considered as a dependent variable. The research hypotheses are derived from this model and are as follows.

Few studies have paid attention to macro-level facilitating factors of geotourism (Adem Esmail and Suleiman 2020), yet, this issue has been previously investigated in the tourism industry at a broader scope. For instance, according to Németh et al. (2017), volcanic geoheritage has been listed as some macro facilitating factors which affect tourism in areas with tremendous potential for hosting visitors. Besides, facilitating geo-knowledge management is another issue to be considered while exploiting geotourism-related opportunities (Farsani et al. 2018). Shafiei et al. (2017) also consider such factors critical for choosing a destination brand for geotourists, studying the rural geotourism destinations in Iran. In addition to such approaches, Mwesiumo and Halpern (2019) believe that facilitating factors at macro-levels could impact the internationalisation of geotouristic destinations. Scholars such as Farsani et al. (2012) argue that while managing the tourism crises in geoparks in order to develop geotourism, one should consider facilitating factors, and this could affect the selection of geotouristic destinations. Besides, Mulec and Wise (2012) investigated the strategic guidelines for the potential geotourism destinations, and they implicitly indicated that during such strategic planning, one must take macro-level factors into account. Such a consideration might lead to the selection of a destination by geotourists. Therefore, the following hypothesis is proposed.

H1

Macro facilitating factors have a significant impact on the choice of a destination brand by geotourists.

Individuals in geotourism industry frequently use social media as a tool for introducing and promoting destination brands, as well as for answering the questions of potential geotourists and interacting with them (Robertson 2015). Besides, Rozenkiewicz et al. (2020) argue that even the national tourism organisations in selected central European countries use their social media pages as well as web pages to provide more information about geotouristic destinations and promote their brands. By doing so, they could improve the chance of a geotouristic location to be selected by potential visitors. For instance, Tikoudi et al. (2016) and Hemmonsbey and Tichaawa (2018) discuss that social media platforms are used to leverage geotourism and destination branding. Some of the social media platforms, such as Instagram and Facebook, are considered as most used platforms to promote destination brands for geotourists. By highlighting the facilitating factors and macro-level advantages, geotourists might become more interested in travelling to specific geotouristic destinations (e.g. Fatanti and Suyadnya 2015). Thus, we proposed the following hypotheses and highlighted such a moderating role.

H1a

Macro facilitating factors that affect the choice of a destination brand are moderated by social media by geotourists.

In addition to macro facilitating factors, information search is considered as a critical concern in choosing destination brands by geotourists (Widawski et al. 2018a, b). Geotourists must search and gather data and information about their destinations (Nelson 2014; Ezebilo 2014). Thus, the more data would be available and searchable for them regarding their destinations, the more likely they will choose that destination (Robertson 2015; Rozenkiewicz et al. 2020). By the way, online information search could improve the visibility of a brand and therefore improve its brand awareness, especially regarding geotouristic destinations (Park and Kim 2010; Pawłowska et al. 2015). Therefore, we believe that information search could affect the choice of a destination brand by geotourists, and then we proposed the following hypotheses.

H2

Information search has a significant impact on the choice of a destination brand by geotourists.

Social media plays a significant role in tourism, and more specifically, the geotourism industry, as such platforms could facilitate information search by providing potential geotourists with more information about their destination brands (Boley et al. 2013; Widawski et al. 2018a, b). Therefore, social media platforms play a critical role in introducing, promoting and choosing destination brands (Robertson 2015; Duan et al. 2020). Some researchers like Rozenkiewicz et al. (2020) believe that social media and the Internet have facilitated communications and therefore have changed the ways players of the tourism industry used to promote their planned destinations. Nevertheless, we wonder if social media platforms could moderate the impact of information search on choosing a destination brand. Then, the following hypothesis is proposed accordingly.

H2a

Information search on choosing a destination brand by geotourists is moderated by social media.

Several demand triggers motivate potential geotourists to choose specific destination brands. For instance, Cetinski et al. (2006) propose that there are ‘elements of a destination’s tourism offering that are of the utmost importance for tourism demand markets’. They mention some of the essential elements in their study and suggest that for example natural and cultural elements could affect destination brands. Besides, Dulău et al. (2010) consider the appropriate management of tourist demand a vital issue in promoting tourism and destination branding. There are a series of studies that both implicitly and explicitly highlight the importance of demand triggers (e.g. see, Dioko et al. 2011; Dryglas and Lubowiecki-Vikuk 2019). Therefore, we proposed the following hypothesis to investigate whether such demand triggers could affect the choice of a destination brand by geotourists or not.

H3

Demand triggers have a significant effect on the choice of a destination brand by geotourists.

Besides, social media platforms could be considered as useful tools for pushing demand triggers (Chatzigeorgiou and Christou 2020). For instance, by disseminating promotional campaigns and reminding potential geotourists about specific destination brands through reinforcing customer engagement and interactions, these platforms might improve the level of geotourism in specific locations (Agapito et al. 2017). A few scholars have marginally investigated this issue (e.g. see, Berselli et al. 2019; Duan et al. 2020). Therefore, by proposing the following hypothesis, we would like to scrutinise the possible impact of social media on the relationship between demand triggers and choosing a destination brand.

H3a

Demand triggers that affect the choice of a destination brand by geotourists are moderated by social media.

Personal factors and previous travel experiences could also be a determinant for choosing a destination brand by geotourists. For instance, Jafari et al. (2017) investigated the factors affecting tourism destination brands. In their study, they mention some of the personal factors and previous travel experiences as critical elements in choosing destination brands. Several issues such as personal beliefs (Cascón-Pereira and Hernández-Lara 2014), inter-personal elements (Smith 2015), willingness (Nematolahi et al. 2017), personal issues (Božić et al. 2017), personal religious considerations (Różycki and Dryglas 2017), as well as personal experiences (Dryglas and Lubowiecki-Vikuk 2019) have been considered as critical elements for choosing destination brands. Thus, we proposed the following hypothesis accordingly.

H4

Personal factors and travel experiences have a significant impact on the choice of a destination brand by geotourists.

Finally, as social media platforms could affect personal factors and remind people of their previous travel experiences, this could affect the choice of a destination brand by geotourists (Hemmonsbey and Tichaawa 2018; Maia et al. 2018; Moghadamzadeh et al. 2020; Duan et al. 2020). This might be due to the various specifications of social media platforms, such as notifications of memories which could make people remember their previous travel experiences (Smith 2015; Agapito 2020). Therefore, we investigated the moderating effect of social media on the relationship between personal factors and travel experiences of geotourists on the choice of destination brand, using the following hypothesis.

H4a

Personal factors and travel experiences that affect the choice of a destination brand by geotourists are moderated by social media.

Figure 1 shows the conceptual framework of the research, which includes the above-mentioned hypotheses.

Fig. 1
figure 1

The conceptual model of the research

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5 Methodology

5.1 Sample

The statistical population of this study includes foreign tourists in three social media networks of Facebook, Instagram and Telegram in 2019 who had made at least one trip to selected geotourism destinations in Iran. In order to collect data, an immediately available sample of individuals who had the final say in the selection of Iran as the destination was selected as the study sample. As the number of individuals in the statistical population could not be determined, the population size was considered unlimited; then, by referring to Krejcie and Morgan’s (1970) table, a sample size of 384 individuals was determined. It is worth mentioning that communities with 100,000 people and more have been assigned a sample size of 384 individuals in this table. In the end, 327 valid questionnaires were collected from the sample. The data collection instrument is a researcher-made questionnaire whose content validity was confirmed by tourism experts, and its construct validity was confirmed through conducting a confirmatory factor analysis in the SmartPLS software. Also, the Cronbach’s alpha coefficient was used to calculate the reliability of the questionnaire. In this instrument, answers to each question received different numerical values, which were considered as having acceptable reliability when they were above 0.7. The table shows the reliability of the questionnaire. Finally, the collected data were analysed with the Smart PLS 3.0 software (Fig. 2).

Fig. 2
figure 2

T-statistics moderated by social media

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5.2 Reliability and Validity

In order to evaluate the relationships between the variables of the conceptual model, the data were collected with a questionnaire. The research questionnaire was designed based on a review of the literature and the model indices. It consists of two sections: the respondents’ demographic information and the research questions which were designed across a five-point Likert scale from 1 (very low) to 5 (very high). Five questions were asked in order to measure each variable. Finally, Smart PLS 3.0 was used to analyse the data. This method involves a statistical model for examining the relationships between latent and observed variables. In order to ensure the accuracy of the research results, the technical features of the questionnaire were evaluated in terms of validity and reliability using different criteria (Henseler et al. 2015). Construct and content validity have been used in this study to examine the validity of the questionnaire. To this end, first, the questionnaire was given to five experts and faculty members to measure the content validity of the questionnaire; then, some modifications were made in the questionnaire according to their comments. Cronbach’s alpha coefficient and combined reliability were used to evaluate the reliability of the instrument (Fornell and Larcker 1981). As can be seen in Table 1, Cronbach’s alpha coefficients for all the variables are higher than the minimum acceptable value, i.e. 0.7, so it can be said that the research instrument has good reliability.

Table 1 The relationship between the variables and the questionnaire items
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6 Findings

6.1 Descriptive Statistics

Table 2 shows the demographic information of the respondents.

Table 2 Demographic information
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6.2 Inferential Statistics

The partial least squares method was used in order to evaluate the reliability of the questionnaire. In this method, reliability is measured by two criteria: factor loadings and combined reliability. The loading factor is between 0 and 1, which indicates the power of the observed variable (question) in measuring the latent variable (main variable). The closer the number is to 1, the stronger will be the item. Also, items with loading factors greater than 0.4 are acceptable. The validity and reliability of the measurement model are reported in Table 3.

Table 3 Composite and shared reliability and convergent validity
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In this study, as shown in Table 2, all the coefficients indicate that this criterion is correct. All the factor loadings above 0.4% and at the 99% confidence level are significant, suggesting that the indicators explain the conceptual variables well. The results show that the Cronbach’s alpha coefficient and the combined reliability of all the constructs are higher than the minimum acceptable value, i.e. 0.7. Therefore, the constructs of this study have acceptable reliability. Also, the Average Extracted Variance (AVE) and the reliability measures show that all the constructs have values higher than the minimum acceptable value, i.e. 0.5. Therefore, the constructs of this study have acceptable convergent validity. According to the results (Table 3), all the indicators have AVE values higher than 0.5, which demonstrate their convergent validity.

In order to evaluate the convergent and divergent validity, the average variance extracted (AVE) and the root of AVE measures was used, respectively. As Table 4 shows, the AVE values are higher than the minimum acceptable value of 0.5. Therefore, the research variables have convergent validity. Additionally, since the AVE values are higher than the correlation of the respective variable with the other variables, divergent validity is only acceptable if the numbers on the main diagonal are higher than the numbers below it (Fornell and Larcker 1981). Therefore, we can say that the variables are valid, and their convergent validity is also confirmed.

Table 4 Divergent validity
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Based on the above and the output of the SmartPLS 3.0 software in Tables 3 and 4, the measurement model has good reliability. The model was examined at three levels of measurement, structure and its general design in order to evaluate its fit (Hair et al. 2018). Several criteria are used to evaluate the fit of a structural model by using the partial least squares regression method. The primary criterion is the significance coefficients or the t-statistics, where they must be greater than 1.96 to be confirmed at the 95% confidence level. The second criterion for assessing the fit of a structural model is the R2 coefficients which capture the endogenous latent variables of the model. R2 is a measure that shows the effect of exogenous variables on an endogenous variable, and 0.19, 0.33, and 0.67 are considered weak, moderate, and strong R2 values (Fornell and Larcker 1981). In this study, a strong R2 value was obtained (R2 = 0.807, which is higher than 0.67); therefore, the structural model has a good fit according to this criterion.

The overall fit of a model takes into account both its measurement and structural features. Therefore, the overall fit of a model can be assessed with the help of a GoF test. The GoF test returned a value of 0.962 for the research model, which indicates a very good overall fit. The GoF values range between 0 and 1 with the cut-off values of 0.1, 025, and 0.36, which have been considered as poor, acceptable and good, respectively. A GoF value of 0.962 for this criterion indicates a strong overall fit for the research model.

6.3 Hypotheses Testing

At this stage, the t-statistics have been used to investigate the proposed relationships between the variables. Four sub-hypotheses have been used to measure the main hypothesis, and according to Table 5, the t-statistics in the eight relationships have been confirmed. Therefore, the main hypothesis was confirmed. To determine the effect of predictor variables on dependent variables, the standardised coefficients of the factor loadings related to the pathways of each hypothesis were investigated. These coefficients indicate that change in dependent variables is captured up to a few per cent by independent variables.

Table 5 T-statistics and coefficients
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7 Conclusion

Social media has changed the way people relate to different aspects of their lives and how they decide to travel. People use social media to obtain information to plan their travels, and they also share their experiences on social media by, for example making comments and recommending places and activities (e.g. see, Mokarram and Sathyamoorthy 2016; Pilogallo et al. 2019). Also, opportunities exist in the environment and are waiting to be discovered; hence, those with a greater level of human capital are able to discover opportunities more consciously (e.g. see, Tavallaei et al. 2012; Chitsaz et al. 2019). This research is a model for examining the variables that play a role in choosing a destination brand. It can be said that all the hypotheses that indicate a direct impact are confirmed; however, when social media is introduced as a moderating variable, it is not significant enough to affect the results. This could mean that simply sharing photos and videos of Iran’s attractions is not enough to choose a destination in the minds of prospective tourists.

The results of this study show that the Information search component has the most impact on the choice of a destination brand. Therefore, information plays an important role in attracting geotourists. Facilities, insurance companies, travel agencies and accommodation centers play an important role in providing information. There should be a communication network between these different institutions that are involved in geotourism, and they should adopt a more integrative approach in implementing promotion and marketing activities. Also, timely promotion activities that provide tourists with information could result in the attraction of more geotourists to Iran. That is why an integrated promotion network is particularly essential (Ratten et al. 2019b).

In the same line, the officials of the Cultural Heritage and Tourism Organisation are suggested to launch promotion platforms by creating official websites and social networks on the Internet to introduce Iranian geotourism destinations better. It is also recommended that short films be made and shared on social media since they could act as local guides and provide the audience with the needed information about the destinations. Additionally, there are highly visited websites whose services could be purchased for promoting geological destinations in Iran. The Arabic and English languages can be used to promote and introduce touristic centers and the types of services that are provided. Cumbersome rules and regulations that have made room for brokers prevent entrepreneurs from operating in this field and should be therefore eliminated. Finally, it can be said that social media could be used to encourage prospective tourists to visit a particular commercial place. For this reason, and in the absence of professional management of social media by municipalities and tourism agencies, it is recommended that the cities of Iran use social media wisely to share interesting features and facts about their attractions. One of the limitations of this research was the difficulty of communicating with the individuals in the research sample who were foreign tourists.