Assessing the drivers to share the content on social media in tourism: the empirical evidence on a world heritage city

Abstract

Purpose

This study seeks to provide a greater understanding of the variables that influence travellers’ intention to participate in social media, paying special attention to (1) the direct impact of perceived usefulness (PU) of social media and (2) the moderating impact of tourists’ altruism and self-interest.

Design/methodology/approach

The proposed conceptual model was empirically tested using an online questionnaire distributed to a sample of 394 tourists visiting a World Heritage city.

Findings

The findings show that perceived social media usefulness has a significant effect on users’ intention to share experiences. Additionally, self-interest appears to moderate the relationship between perceived social media usefulness and users’ sharing intention, but the results do not support the moderating effect of altruism.

Originality/value

Despite scholars’ growing interest in social networks as sources of tourist information, little is known about the aspects that encourage users’ participation in these platforms. This paper offers key contributions to the relevant literature in this field and offers compelling recommendations for tour operators' management of social networks.

研究目的

本研究擬讓我們更清楚了解驅使旅行人士參與社交媒體上的交流活動的變數;為求達至這研究目的，研究人員特別對以下兩方面加以注意和研究：(一) 、旅行人士對社交媒體的感知效用所帶來的直接影響;(二) 、旅行人士的利他主義，以及其對個人利益的考慮所帶來的緩和影響。

研究設計/方法/理念

研究人員對其提出之概念模型進行實證測試，方法乃透過收集一個包含394名曾參觀世界遺產城市的旅行人士的樣本所回應的網上問卷數據，並進行數據分析。

研究結果

研究結果顯示，旅行人士若覺得社交媒體是有用的話，則他們會更願意在那裡分享旅行經歷;而且，他們對自己個人利益的考慮，似會緩和他們對社交媒體的感知效用與其分享經歷的願意程度之間的關係;唯研究結果沒有證實利他主義會帶來緩和的影響。

研究的原創性

雖然學者對社交網絡作為提供資訊的來源感到興趣，而且這興趣不斷增加，但我們對促進旅行人士參與社交網絡平台活動之因素的了解仍然淺薄，就此而言，本研究於有關的文獻提供了重要的貢獻;研究亦為旅遊經營者就應如何管理社交網絡提供了具說服力的建議。

Citation

García-Haro, M.Á., Ruiz-Palomino, P., Martínez-Cañas, R. and Martínez-Ruiz, M.P. (2024), "Assessing the drivers to share the content on social media in tourism: the empirical evidence on a world heritage city", European Journal of Management and Business Economics, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EJMBE-03-2023-0084

1. Introduction

Social media has gained enormous significance for tourism destinations becoming an indispensable means of both information and communication, especially after COVID-19 pandemic (Pachucki et al., 2022). Consequently, such platforms are now the main method for travellers to share their tourism experiences (Lin and Rasoolimanesh, 2022). Before taking their trip, users look for travel information and recommendations in order to plan, organise and learn about tourism products and services. Consumers thus frequently encounter a multitude of messages from various sources offering differing, and often inconsistent, opinions regarding the same product or service (Bigne et al., 2020). Importantly, after the trip, users can use social networking platforms to share their experiences and evaluations about previously experienced destinations, tourism offers and services (Oliveira et al., 2020).

Thanks to this trend, people increasingly rely on other consumers’ opinions when deciding to visit a tourist destination, which has resulted in a vast amount of user-generated content that is centrally important for travel planning (Oliveira et al., 2020) and tourism marketing (Kim et al., 2018). As a result, reviews and opinions can have a considerable impact on the demand for tourism services (Dedeoğlu et al., 2020). Furthermore, the visual content they enjoy through social networks may also impact their decisions (Bigne et al., 2020). Despite the importance that tourists attribute to information sharing on social networks, academia lacks certain knowledge in this area (Dedeoğlu et al., 2020). For instance, little attention has been paid to the factors that encourage user participation in social networks to share tourist experiences (Lin and Rasoolimanesh, 2022); knowledge in this area remains inconsistent and fragmented (Wang et al., 2022). Indeed, according to Oliveira et al. (2020), few studies have analysed the determinants that directly or indirectly affect users’ intention to participate online on social media platforms.

Moreover, there is still no conceptual model that integrates the factors influencing the intention to share tourism experiences on social networks (Lin and Rasoolimanesh, 2022). Thus, there is a need to uncover not only the determinants of users' intention to participate in social networks (Lin, 2006), but also there is the need to better identity “what” influences the acceptance of social networks as a useful tool for travel planning (Park et al., 2013). To this end, we rely on the technology acceptance model (TAM) (Davis, 1989), which the tourism sector has widely adopted to understand the importance of information and communication technologies (hereafter, ICT) in people’s decision-making process and intention to visit a destination (Kaplanidou and Vogt, 2006).

Although TAM underscores that a user’s behavioural intention to use an information system is explained through two critical elements: perceived usefulness (PU) and ease of use (EU) (Davis, 1989), PU is especially critical: people generally have to consider behaviour useful before they can develop intentions towards it (Ayeh et al., 2013). Indeed, inasmuch as travellers search for tourism information to better build their decision on where to travel (cf., Vogt and Fesenmaier, 1998), travellers appear to be more willing to share their travel experiences if they consider that the information provided by social networks has been useful in planning their trip (Casaló et al., 2010). Thus, if users find a review useful and credible, they will be more confident in making a purchase decision (Bigne et al., 2020). Moreover, if this review is endorsed by many users, it becomes even more persuasive and trustworthy for users to base their decisions on (Bigne et al., 2021). As such, it is expected that the greater the PU of social media for trip planning, the greater the willingness of users to share information on this platform.

As a support of this, PU has been previously found to have a positive impact on not only customers' purchasing decisions, but also on the generation of positive electronic word-of-mouth (hereafter, eWOM) within travel communities (Agag and El-Masry, 2016) or on the participation in an online travel community (Casaló et al., 2010) and on knowledge-sharing (Hung and Cheng, 2013). Furthermore, we know that virtual communities are used as knowledge repositories where people can absorb or share information (Hung and Cheng, 2013), so individuals are believed to be more likely to share knowledge in these online media only and only if they perceive it as valuable. The notion that social media is an instrument for facilitating knowledge-sharing has evolved gradually over time (Ahmed et al., 2019).

Previous research has analysed the relationship between different variables and sharing intention in the online context, such as source credibility (Hur et al., 2017); interaction between tourists and bloggers (Zhao et al., 2022) or even the use of eye-tracking measures (Bigne et al., 2020) and neurophysiological tools (Bigne et al., 2021). However, the models proposed to date have neglected the PU of social media as a potential antecedent of people’s online sharing intention, especially in the tourism sector. Indeed, most studies have focussed on two main aspects: (1) examining the impact of the PU of a website or app on users’ intention to search for information and/or book (tourism) products and services online (Agag and El-Masry, 2016) and (2) analysing the relationship between the PU of social media and people’s intention to visit a tourist destination (Chung et al., 2015).

Thus, as Oliveira et al. (2020) state, previous research has yet to explore several aspects of tourists’ behaviours, whilst Hur et al. (2017) note that there is a shortage of theoretical and practical studies on the factors that influence people’s choice to use social networks for tourism decisions. Similarly, Park et al. (2013) highlight the paucity of research on how knowledge is shared via online social networks. Finally, and more generally, Wang and Li (2019) underline the need for more research on the relationship between PU and consumer behaviour. As important to advance this research field, we note that, yet little attention has been paid to background of users’ intentions to participate in online communities (Tsai and Pai, 2014), which we believe can be valuable.

Investigating this aspect could help understand why PU has been observed in previous research not to affect users' intention to share videos on YouTube (Yang et al., 2016) or content in entertainment environments such as social networks (Hsu and Lin, 2008). In this regard, several authors have highlighted the need to identify possible moderators in the direct relationship between PU and consumer behaviour (Hussein et al., 2016). As Oliveira et al. (2020) asserts, there are certain factors that facilitate the practice of sharing travel experiences. Consumers do not always share opinions about products, services or travel experiences just to “inform others”. They can also be motivated, for instance, by the desire to receive social recognition or “likes” (Dedeoğlu et al., 2020). In fact, the existing literature recognises a lack of knowledge about the relationship between the sharing platform used and the potential motive(s) behind consumer-generated content (Dedeoğlu et al., 2020).

Investigations into the moderating effect of motivations on user behaviour are relatively new (Gutierrez-Garcia et al., 2019), so there is not yet a great body of work on this topic, especially in tourism (Xu and Chan, 2016). Nonetheless, motivations may have a significant moderating effect on the relationship, as they influence a tourist’s decision-making processes and the selection of a destination (Moreno-Gil et al., 2012). Moreover, motivations are known to be a critical antecedent of consumer behaviour (Mansfeld, 1992), but little is known about how these factors compel users to share their travel experiences on social networks (Oliveira et al., 2020). Amongst the different types of motivations commonly analysed, the largest debate in the literature revolves around altruism (or goodwill) versus self-interest (Heaton et al., 2019), especially in the tourism sector (Paraskevaidis and Andriotis, 2017).

However, most studies to date have analysed these two motivations only in the context of voluntary tourism (Benson and Seibert, 2009). Although studies have looked at the relationship between PU of social media and consumer behaviour, to our knowledge, none has attempted to examine the role of altruism and self-interest in this relationship. This study adopts a broader perspective. First, it proposes a conceptual model that includes one of the most influential determinants of user behaviour: PU of social media or social networks. Second, the study contributes to a better understanding of the motivational factors that influence the intention to share tourism experiences on social networks, determining the moderating effect of altruism and self-interest in the proposed relationship.

The findings of this research will provide important implications for the strategies tourism professionals should develop in their social network profiles. Thus, this research aims to answer the following questions: How does the PU of social media influence travellers’ intention to share their tourism experiences? And what is the moderating role of altruism and self-interest in this relationship? The paper is organised as follows. First, we present the theoretical framework and hypotheses. Second, we describe the research design and the method used to test the model. Finally, we discuss the study’s conclusions and limitations and illuminate how they can inform future studies.

2. Literature review

2.1 Perceived usefulness (PU) of social media and tourists’ sharing intent

Today, user-generated content is the basis of innovative knowledge-sharing networks (Öz, 2015), and its importance is such that it allows and helps tourists to directly obtain knowledge from other tourists’ experiences. As the number of social networks grows, the concept of PU becomes increasingly integral to users’ intention to share information and give pieces of advice to others (Hajli, 2014). Indeed, PU is considered a fundamental determinant of users’ acceptance or rejection of a technology (Davis, 1989).

In social networks, PU -defined as the extent to which users believe that a particular social network will help them achieve their goal(s) (Kusyanti et al., 2018)- reflects the trust that users place in the characteristics and capabilities of these platforms to capture and/or generate the information that meets their requirements and improves their decision-making (Hua et al., 2017). So, if users perceive social networks as a space or tool for acquiring travel information, they are more likely to adopt this information media or platform to plan their trip (Chung et al., 2015). In fact, websites are known to improve their ability to encourage more visits, engagement and sales (Liu and Park, 2015) when consumers perceive the website’s images, comments and reviews as useful to some extent (Kumar and Benbasat, 2006).

To better understand PU, scholars have developed different approaches to explain users’ decision to accept, reject or adapt to a technology (Izuagbe et al., 2019). However, the Technology Acceptance Model (TAM) is the most used for analysing the impact of information systems on users’ behaviour (Davis, 1989), which considers PU as a key determinant of user behaviour (Agag and El-Masry, 2016). The direct relationship between PU and behavioural intent arises from the notion that people develop their intentions towards behaviours that they consider useful (Ayeh et al., 2013). Consequently, as users’ perception of a system’s usefulness improves, so does their attitude towards sharing knowledge through said system (Hung et al., 2015).

A positive experience will stimulate their interest in participation, whilst a negative experience may generate frustration that reduces the motivation to participate again (Deci and Ryan, 2002). Therefore, users’ behaviour towards sharing information flows from their judgement about its usefulness (Yan et al., 2016). In the tourism domain, and particularly in online travel communities, one of the main challenges is simply consumer participation in social networks (Agag and El-Masry, 2016). If tourists receive useful information from other travellers, they are more likely to contribute their own knowledge in the same way (Park et al., 2013). Consequently, people are more likely to share their travel experiences on the social networks that served as a source of trip information to them (Kang and Schuett, 2013), and therefore, if they perceived such information as useful. Based on the above, we hypothesise that:

H1.

The PU of social media has a positive and significant effect on tourists’ intent of sharing their travel experiences in this media or platform.

2.2 Moderating effect of tourist’s altruism and self-interest

A growing body of literature highlights the importance of motivation in the creation of online content (Wang and Li, 2014) and the exchange of experiences through social networks (Oliveira et al., 2020). This debate about motivations has centred on two, in particular: altruism and self-interest. It is important to note, however, that altruism and self-interest do not occur in a “pure” sense; instead, the two concepts are intertwined. Human beings are complex beings who do not necessarily operate according to the two extremes (Diacon, 2014), as a person may find personal benefits in most altruistic actions (Batson, 1990). Recent research has mainly analysed altruism or self-interest (Paraskevaidis and Andriotis, 2017) with regards to the motives and behaviours of volunteer tourists and has concluded that both types of motives underlie the actions of this type of tourists (Wearing and McGehee, 2013).

In addition, there is evidence that intrinsic motivation (which includes self-interest and altruism) may serve to moderate the consumers' reactions to different situations (Chen and Chen, 2011). Thus, in a context of sharing the tourism experience on social networks, PU of social networks could influence more positively on such sharing intent in as much as these motives (i.e. altruism, self-interest) are present amongst the tourists or social media users. In other words, the relationship between social networks’ PU and the intent to share the tourism experience could be boosted based on what drives (altruism, self-interest) tourists to participate in this online media or platform.

2.2.1 Moderating effect of altruism

Altruism is about renouncing opportunistic behaviour—not for the sake of sanctions or rewards, but because it is seen as the “right” thing to do (Hardy et al., 1998), which applied to consumerism, principally means sharing positive and negative experiences in order to help other consumers make better decisions (Oliveira et al., 2020). In fact, altruism is considered one of the main motivations for sharing information, especially with regard to sharing travel experiences. Indeed, advising other users and prevent them from choosing bad products or tourist destinations (Oliveira et al., 2020) or contributing to websites that are considered useful and valuable (Munar and Jacobsen, 2014) is generally one principal motivation amongst travellers. Accordingly, we then propose:

H2.

The effect of PU of social media on tourists’ sharing intent is positively moderated by tourists’ level of altruism

2.2.2 Moderating effect of self-interest

Self-interest also drives tourists to do volunteering tourism-tourists who undertake holidays that might involve aiding or alleviating the material poverty of some groups in society- (Wearing and McGehee, 2013), so it is of no surprise that this motivating factor can play a role in explaining why tourists share content online. However, unlike altruism, self-interest drives consumers to share content online for the personal gains it entails (cf., Paraskevaidis and Andriotis, 2017). These gains may involve tangible, economic benefits, such as discounts, prizes or rewards, or they may be intangible and non-economic, such as the possibility of gaining respect, status and recognition, increasing social ties, improving one’s image, attracting attention, increasing self-esteem and deriving enjoyment and fun from the activity and/or achieving greater cooperation in return, amongst others (Lin and Rasoolimanesh, 2022; Oliveira et al., 2020; Yoo and Gretzel, 2008). Accordingly, then:

H3.

The effect of PU of social media on tourists’ sharing intent is positively moderated by tourists’ level of self-interest.

3. Methodology

3.1 Sample and procedure

To test the proposed hypotheses, we administered an online questionnaire to tourists and travellers, for at least 18 years of age, who use social networks to seek tourist information about the city of Cuenca (Spain), a small city that United Nations Educational, Scientific and Cultural Organisation (UNESCO) declared a World Heritage City in 1996. We launched this questionnaire in the following platforms: Official Cuenca City Council webpage and its main profiles on social networks, with prior contact and authorisation from the City Council Press Officer; official webpage and social networks profile of the Faculty of Social Sciences of Cuenca, with the prior authorisation from the Dean of the Faculty; social networks’ profile of CuencaOn, the main newsletter of the Cuenca University Campus; the tourism blog “Esto es Cuenca” which currently has about 10.000 monthly visits of which 80% of visitors are people from other different cities to Cuenca; personal platforms such as social networks (Facebook, Instagram, Twitter and WhatsApp) and emails to friends and acquaintances, requesting them to share it to more users.

After five weeks, we obtained 394 surveys; however, some were incomplete with missing data for some of the items used to measure our core study variables. After removing these surveys, a final sample of 247 valid questionnaires was used for our study (see Table 1). The study sample included 123 respondents (49.8% of the sample) from diverse locations of the large province of Cuenca, which covers an area of 17,141 km² and has more than 238 population centres. However, the inclusion of this sample of respondents in our study did not alter the results obtained, as they were not from the World Heritage Site of Cuenca but from different, multiple locations within the province. A t-test analysis to compare the mean values of all the variables used in this study across two independent groups (tourists from the province of Cuenca versus tourists from other locations) revealed no significant differences (for social media usefulness, F = 1.935, p-value = 0.165; for altruism, F = 0.005, p-value = 0.941; for self-interest, F = 2.468, p-value = 0.117; for sharing intent, F = 0.914, p-value = 0.340), which suggests that the inclusion of respondents from Cuenca province in our study is likely not to have biased our findings.

The study was designed in a cross-sectional manner and used self-report measures, so common method variance (CMV) and social desirability were potential concerns. To limit the incidence of such problems in our data, we designed the questionnaire in accordance with Podsakoff et al.’s (2003) recommendations. Finally, by assuming that late respondents are more similar to non-respondents than to early ones (e.g. Armstrong and Overton, 1977), we compared the first and last quartiles of received submissions by conducting independent sample t-tests for all the study variables. Because no significant differences were found, non-response bias is not a major problem in this study. Following previous recommendations about how to treat latent variables in partial least squares (PLS) (Hair et al., 2017), our study included formative and reflective first-order constructs. Table 2 shows the items for each variable that were measured on a five-point Likert format response basis.

3.1.1 Social media usefulness (reflective)

In order to measure PU of social networks in tourism, we adapted Yoo and Gretzel (2016) 12-item scale. The items captured participants’ opinions on the usefulness of social media when they look for tourism information about a destination. The items were related to aspects such as usefulness of information obtained; trustworthiness and reliability of social networks and planning of the trip (see Table 2).

3.1.2 Altruism (reflective) and self-interest (formative)

To measure altruism and self-interest, we adapted items used by Yoo and Gretzel (2011). Altruism was a two-item reflective construct that assessed whether people use social networks “to advise” and “to help” other users (see Table 2), whilst self-interest was a formative construct that assessed whether people use social networks “to store information important to me” and “to get rewards” (see Table 4). The items used to measure altruism meet all the criteria that indicate these items are reflective, as outlined by Mackenzie et al. (2005) and Martínez-Cañas et al. (2023). These criteria include that the items should covary, be manifestations of the construct, are interchangeable and have same antecedents and consequences.

Thus, the items used to measure altruism are supposed to be highly correlated, are clear and direct manifestations of altruism, are interchangeable (advising, helping) and are supposed to have the same antecedent (i.e. when an individual is an altruist, he/she is expected to help others with useful advice, Oliveira et al., 2020). For self-interest, in contrast, the two indicators (to store information important to me, to get rewards) refer to aspects that define and capture unique, distinctive concepts of the construct (e.g. the reasons why an individual is self-interested when he/she shares information through social networks can be many and varied). The indicators are not expected to be strongly correlated (people can share for the rewards they gain or because by doing so they store this information for themselves) and may have distinct antecedents or consequences (they may get rewards by sharing information on social networks or by doing other things). This suggests that these indicators are likely to be formative rather than reflective, according to MacKenzie et al.’s (2005) guidelines.

3.1.3 Sharing intent of tourism experience

To measure the willingness to share travel experiences on social networks, we only used one item adapted from Kohler et al. (2011) and used by Füller et al. (2011) (Table 1).

3.2 Data analysis

We used Smart PLS 3.2.8 (Ringle et al., 2005) to test the hypotheses. Following Hair et al. (2017), we applied bootstrapping (5,000 resamples) to generate standard errors and t-statistics for the hypothesis testing.

4. Results

4.1 Measurement model

Tables 2 and 3 collectively show the reliability and validity for all the constructs used. Specifically, Table 2 reports the reliability of individual constructs and the convergent validity for reflective constructs. Table 3 reports the results for discriminant validity and provides the correlations across all incorporated variables.

Table 2 reveals that the majority of items are reliable, with most loadings exceeding the desired threshold of 0.707 (Hair et al., 2017). Two items showed poor values of around 0.50 (SNUTI1 and SNUTI3) and were deleted. Although five items related to social networks usefulness (SNUTI2, SNUTI4, SNUTI5, SNUTI6 and SNUTI7) reflected values lower than 0.707, they were still higher than 0.60 and thus were retained (Hair et al., 2017). In terms of construct reliability, Cronbach’s alphas and Dijkstra–Henseler’s composite reliabilities (ρA) were above 0.70, as recommended (Hair et al., 2017). The criteria for convergent validity was also met, as the average variance extracted (AVE) for each variable was greater than 0.50 (Table 3), as recommended (Hair et al., 2017).

Table 3 also shows that the heterotrait-monotrait (HTMT) values are below the most restrictive threshold of 0.85 and significantly different from 1, thus confirming discriminant validity between each pair of variables of our study (Hair et al., 2017). The Fornell–Larcker criterion provided another confirmation of discriminant validity: The square roots of AVE for each variable are greater than the correlation of each variable with the others, as also required (Hair et al., 2017) (see Table 3).

Table 4 reports the co-linearity values (FIV), weights, t-student, p-values and confidence intervals for the self-interest variable, as it is the only formative construct in our study. Table 4 illustrates that all items of this construct have significant weights. In addition, all FIV values are less than 3.3, which affirm that the construct does not suffer from multicollinearity problems. Thus, we have confidence in our measurement model for the variable of self-interest.

4.2 Hypothesis testing

Figure 1 contains the findings related to our hypotheses. In support of H1, the results reveal that the PU of social media for tourism has a positive and significant effect on tourists’ sharing intent (Beta = 0.151, p < 0.01). With regards to H2, the results fail to confirm that users’ altruism strengthens this relationship (Beta = 0.021, p > 0.05); Figure 2a shows that when this interaction term is plotted, the low and high altruism regression lines are parallel, meaning that the relationship between perceived social media usefulness and users’ or tourists’ sharing intent is the same for high and low altruism conditions. Thus, H2 cannot be supported.

In contrast, the results show a positive and significant effect of self-interest on the relationship between PU of social media and users’ sharing intent (Beta = 0.106, p < 0.05), which means that the relationship between social network usefulness and tourists’ experience-sharing intention is stronger for higher levels of self-interest. When the interaction term is plotted (see Figure 2), the slope is clearly more pronounced for higher than for lower self-interest conditions (see Figure 2b). Thus, H3 is fully supported.

In terms of the model’s explanatory power, the R² and Q² (predictive relevance of the endogenous variable) yielded satisfactory values. First, the model explains the R² adjusted value of 0.224 for tourists’ sharing intent. In addition, the Stone–Geisser blindfolding sample reuse technique reveals a Q² value for our dependent variable –tourists’ sharing intent – (Q² = 0.179) larger than zero, thus indicating that our predicted model has good predictive power to explain such a variable (Hair et al., 2017).

5. Conclusions and implications

Due to the importance of social networks as a channel for travellers to share their travel experiences today, the topic of social media has attracted great interest lately. However, most studies have focussed on analysing the consequences of consumers’ participation in online communities, whether in terms of satisfaction, purchase decisions, or intention to visit a destination; few are the studies focussed on identifying the background variables that drive travellers to share tourism information on social networks. Against that background, the present paper pursued two main objectives that advance literature. Firstly, we sought to examine perceived social media usefulness and its relationship with users’ intention to share their travel experiences on these platforms. Secondly, we sought to analyse the moderating role of users’ altruism and self-interest in this relationship.

Regarding the first objective, the results confirmed the existence of a positive, significant relationship between perceived social media usefulness and the user’s intent to share his/her tourist experience. Specifically, our data reveals that if users perceive the information in social networks as useful for organising and planning their trip, their intention to share tourism experiences with other users will be greater. Concerning our second objective, the results fail to support that altruism strengthens the positive impact of perceived social media usefulness on sharing intent. This is an unexpected finding as altruism is said to be an important motive for people to share travelling experiences on social networks (Oliveira et al., 2020). Although our findings align with previous research, such as that shown in Figure 1, where altruism is a positive factor that directly influences users’ sharing intent, the study findings do not allow us to support our prediction that the PU of social media should have a stronger positive effect on the sharing intent of the most altruistic social media users.

However, our findings are consistent with a substantial body of previous research that suggests that self-interest incentives have a strong and positive influence on content sharing intention amongst users (Fu et al., 2017), as well as a significant moderating effect on customers’ intention decision formation (Gremler et al., 2001). Specifically, our results reveal that it is self-interest that reinforces the positive effect of the PU of social media on users’ sharing intention; therefore, it is users’ self-interest that has the most significant contribution in strengthening the positive effect of PU on users’ sharing intent. Indeed, the results reveal a significant effect of self-interest on user’s decision to exchange travel experiences and that if users are guided by self-interest motives, PU of social media has a stronger positive effect on their sharing intent.

Thus, our findings reveal that social network users are more motivated to share their tourism experience if they receive rewards for their contribution (gifts, discounts, offers) or if storing information about the trip is of great importance to them. These findings contrast with previous research suggesting that users who create and share online content are primarily motivated by altruistic and hedonic motives (Munar and Jacobsen, 2014; Oliveira et al., 2020). However, our results may reflect the dynamics of the tourism sector, which is characterised by the continuous enjoyment of services and experiences and in which tourism organisations are increasingly noting the benefits of social networks for enhancing their reputation and attractiveness (Pop et al., 2022). After all, tourism products and services cannot be consumed or experienced before the trip, so the opinion of other consumers is an important factor in inspiring and motivating other users to demand these products and services.

That’s why tourism companies are increasing trying to motivate consumers to share their experiences through prize draws, promotions or discounts for future travel, which is likely to have motivated that consumers become inclined to share their travelling experiences in exchange for some type of external rewards (e.g. discounts, coupons, recognition, reputation) or simply to store important information such as the destination, the accommodation where they stayed or the activities they did -all of them strongly rooted in self-interest motivations.

Thus, although altruism could be a positive influence on the intention of people to share on social networks, our findings reveal that when an individual perceives that the information shared on social networks is highly useful to achieve their own goals (e.g. acquiring good travel information to plan their trip), they may be more interested in sharing information on social networks for self-interested motives, not altruistic ones. In other words, to the extent that consumers (who are also social media user) view social networks as a space within which they and third parties can obtain useful information to achieve their goals, self-interest will likely be a more powerful motive (vs. altruism) for driving them to share content on social networks.

Despite these important contributions to the literature, our study has several limitations that could inspire future compelling lines of research. First, our work focusses on examining the usefulness perceived by the user on a specific type of social media, social networks, so generalisations of our findings to other social media platforms should not be assumed. Therefore, it would be interesting to examine the PU of other social media platforms, beyond social networks, such as tourism blogs, intermediary websites or booking portals. The results obtained in this study and the theoretical model tested could be an important starting point for future research. Second, whilst this study examines the usefulness of social networks for sharing tourist experiences, it does not take into account different strategies that companies can use on these platforms to engage users, such as those directed at increasing value co-creation.

In this sense, we encourage future researchers to investigate the potential moderating effect of value co-creation on the relationship between PU and sharing intention. We think that the strength of the relationship may vary depending on whether there is a co-creation activity. Thus, the perceived utility of the social network will be greater if the user also participates in an entertaining and fun activity with the company in question, such that they are likely to be more willing to share their tourist experience. Third, the current study considered the moderating effect of altruism and self-interest motivations, but the literature suggests that other types of motivational factors – such as entertainment or enjoyment of the activity – may affect consumer behaviour. Thus, we propose to incorporate into the theoretical model new motivations that may influence the user’s decision-making. For example, it is possible that the entertainment motive has a more positive influence than the self-interest motive on the relationship between PU and sharing intention.

Likewise, future research could consider different groups of users and other moderating effects, such as age, gender or the frequency of social network usage and then demonstrate the robustness of the model fit in different contexts. It should be noted that, contrary to expectations, altruism did not positively moderate the relationship between perceived social media usefulness and sharing intention. Thus, it would be valuable to uncover which other variables are less relevant than they appear. As a final limitation, the questionnaire was only completed by tourists from a single destination. Therefore, future works should strive to replicate the results in other cities or locations.

Figures

Figure 1

Measurement and structural model: hypothesis testing

Figure 2

Interacting effect between PU in social media and sharing intent

Table 1

Respondent profiles (n = 247)

		Frequency	Percentage (%)
Age	≤24 years old	44	17.8
	25–44 years old	154	62.3
	45–64 years old	49	19.8
Gender	Male	86	34.8
	Female	161	65.2
Level of studies	Primary studies	7	2.8
	Secondary studies	53	21.5
	University education	128	51.8
	Postgraduate (master’s degree and doctoral degree)	59	23.9
Occupation	Self-employed	38	15.4
	Employed full time	114	46.2
	Employed part time	20	8.1
	Student	40	16.2
	Student and self-employed	3	1.2
	Student and employed	15	6.1
	Housework	3	1.2
	Unemployed	12	4.9
	Retired	2	0.8
Family life cycle	Young people	63	25.5
	Young couple	62	25.1
	Couple with children	73	29.6
	Mature couple	26	10.5
	Grandparents	1	0.4
	Group of friends	21	8.5
	Other	1	0.4
Gross family income	From 0 to 15,000€	81	32.8
	From 15,001 to 25,000€	82	33.2
	From 25,001 to 40,000€	55	22.3
	From 40,001 to 60,000€	21	8.5
	More than 60,000€	8	3.2
Residence	Province of Cuenca	123	49.8
	Out of Spain	1	0.4
	Another province of Castilla-La Mancha	53	21.5
	Another province of Spain	70	28.3

Source(s): Table by authors

Table 2

Reflective constructs, their corresponding items and reliability and validity criteria

	Loadings	Construct reliability			AVE
		Cronbach’s Alpha	Dijkstra-Henseler’s ρA	Composite reability
Social Networks usefulness		0.895	0.897	0.915	0.520
SNUTI2. Recommendations shown by other users on social networks are reliable	0.622
SNUTI4. The information on tourism pages on social networks is convincing	0.673
SNUTI5. The information on tourism pages on social networks is very useful for consumers	0.621
SNUTI6. The information on tourism pages on social networks is reliable	0.669
SNUTI7. In general, I trust the comments and materials published by other travellers	0.661
SNUTI8. Through social networks, I can find more information about a destination and decide where to go during my visit	0.811
SNUTI9. Through social networks, I am able to evaluate what I can do during my visit to the destination	0.818
SNUTI10. Through social networks, I can get more involved in planning my trip	0.816
SNUTI11. Through social networks, I can imagine what the destination is like	0.772
SNUTI12. Through social networks, I can save time planning my trip	0.706
Altruism		0.795	0.810	0.907	0.514
ALTRU1. I participate in social networks … to advise other users	0.756
ALTRU2. I participate in social networks … to help other people	0.668
Sharing intent
I would be willing to share my tourism experience on social networks

Source(s): Table by authors

Table 3

Descriptive statistics, correlation matrix and square roots of the reflective constructs’ AVE

	Mean	SD	1	2	3	4
1. Social network usefulness	3.37	0.90	0.721	0.392 [0.277;0.508]	----	0.328 [0.224;0.430]
2. Altruism	3.28	1.15	0.339**	0.910	----	0.460 [0.356;0.563]
3. Self-interest	2.72	1.17	0.334**	0.351**	----	----
4. Sharing intent	3.97	0.95	0.313**	0.412**	0.308**	----

Note(s): Italic values on the diagonal are square roots of AVE (variance shared between the constructs and their measures). Off-diagonal elements below the diagonal are correlations amongst the constructs; **p < 0.01, *p < 0.05 (two-tailed test). Off-diagonal elements above the diagonal are the heterotrait-monotrait ratios of correlations (HTMT), and their corresponding 95% confidence intervals. SD = standard deviation

Source(s): Table by authors

Table 4

Formative construct and their respective items

Self-interest	VIF	Weights and significance	t-value	p-value	95% confidence interval
SELF1. I participate in social networks to … to store information important to me	1.003	0.814**	7.019	0.000	0.574–0.953
SELF2. I participate in social networks to … to get rewards	1.003	0.536**	3.407	0.000	0.263–0.780

Note(s): **p < 0.01: (t _(4,999) =  = 2.33). Thus, each one of the items that help to build up the formative construct of self-interest adds to the building up of the construct. Notes. VIF = Variance inflation factor. VIFs lower than the strictest threshold of 3.3 indicate that multicollinearity is not a problem, so each item provide information to build up the construct

Source(s): Table by authors