Information Systems Frontiers

, Volume 20, Issue 3, pp 471–483 | Cite as

Sharing of Sponsored Advertisements on Social Media: A Uses and Gratifications Perspective

  • Cherniece J. Plume
  • Emma L. SladeEmail author
Open Access


Organisations are increasingly utilising social media to advertise to, and interact with, consumers. Sponsored advertisements embedded into targeted users’ social media feeds appear less invasive than standalone advertisements but, unlike organic postings, incur financial cost. Given that friends’ posts attract most attention, this research employs Uses and Gratifications theory to determine salient motivations for users’ intentions to share sponsored advertisements, framed in the tourism context. Survey data was collected (n = 487) and analysis revealed altruism, entertainment, socialising, and information seeking to be significant positive drivers of intention to share tourism-related sponsored advertisements on Facebook. Notably, information sharing was found to have a negative effect, while self-expression had no significant effect. In addition, the motivations were not found to significantly differ between males and females. This study contributes to theoretical understanding of users’ intentions to share sponsored advertisements within the social media environment and provides practical recommendations to help tourism marketers maximise reach.


Social media Sponsored advertisements Sharing Uses and gratifications Gender differences 

1 Introduction

The sharing of, and exposure to, content has made social media a tool that marketers can leverage to build brand awareness and brand loyalty (Zhou et al. 2012). More than 4.75 million items are shared on Facebook daily and users are potentially exposed to more than 1500 pieces of individual content each time they visit the site (Fu et al. 2017). However, participation on some of the dominant social networks is beginning to decline (Gottbrecht 2017) as excessive commercialisation dilutes their appeal. This threatens the core of many platforms’ business models and heightens the challenge for marketers to produce unobtrusive content which successfully engages target audiences.

Advertising is a critical source of revenue for social media platforms - exceeding $9bn in the second quarter of 2017 for Facebook alone (Boland 2017) - hence they offer a variety of advertising formats and advert reporting tools (e.g. Facebook 2017; Pinterest 2017; Snap Inc. 2017). However, further research of the effectiveness of advertising on social media is needed (Khang et al. 2012) as the increasing clutter of advertisements on platforms has caused growing concern among consumers about credibility. ‘Sponsored content’ is one attempt to address the issue (Cunningham and Bright 2012); by embedding advertisements into the format of a social networking site where engagement is greatest (Lipsman et al. 2012), the sponsored advertisement format helps to reduce consumers’ scepticism (Tutaj and Reijmersdal 2012).

While scholars have examined online sharing of user-generated content (UGC), research regarding motivations to share sponsored advertising - or marketer-generated content (MGC) - on social media is lacking. It has been found that overall UGC exhibits a stronger impact than MGC on consumer purchase behaviour (Goh et al. 2013) and that UGC on social media can equal the effects of large advertising spends on traditional media (Lee et al. 2017). Content sharing is especially useful for service industries such as tourism as their intangible nature and lack of trialability results in consumers seeking information from a variety of different sources during the research phase and making purchase decisions heavily influenced by the information gathered (Leung et al. 2013). Trustworthiness is a key antecedent in determining consumers’ decisions to use information on social media (Leung et al. 2013) but Burgess et al. (2011) found that there are differences in the level of trust for online travel information from different sources. Therefore, understanding and then harnessing motivations affecting intentions to share tourism-related sponsored advertisements will help reach and engage a wider audience to maximise return on investment.

The structure of the paper is as follows. First the literature is discussed in relation to social media advertising and the importance of social media in the tourism industry. This is followed by an outline of the theoretical framework and hypotheses development. The paper then moves on to highlight the methods used for data collection and analysis, with further sections presenting and discussing the results with reference to theoretical and managerial implications. Finally, the paper is concluded, outlining limitations and suggestions for future research.

2 Literature Review

Numerous studies have been undertaken to explore the behavioural side of social media, including examination of factors influencing usage of the platform and/or its features (e.g. Chiu and Huang 2015; Idemudia et al. 2016; Smock et al. 2011), yet extant research regarding behaviour and sponsored advertisements on social media is limited. Cunningham and Bright (2012) explored how celebrity sponsors affect consumers’ attitudes towards advertisements on Twitter but not how attitudes are affected by brand sponsored advertisements. Murphy and Schram (2014) undertook a holistic review of what constitutes sponsored advertising and how it is being utilised on social media, but did not conduct an empirical investigation. Studies that have explored the effect of earned and owned social media on purchase intention did not decipher sub-types of ‘earned’ or ‘owned’ such as sponsored advertisements (e.g. Xie and Lee 2015). Others have started to empirically consider how consumer privacy concerns influence attitudes towards sponsored advertisements on social media and general purchase intent (e.g. Lin and Kim 2016) but findings are limited due to the lack of consideration of variances associated with different product/service categories.

Research in the area of social media and tourism stresses the impact that this new communication technology is having in the industry, particularly in the areas of promotion, management, and research (Leung et al. 2013; Xiang and Gretzel 2010). From the early 2000s research found that travellers utilise the internet to help in their decision making (Jeng and Fesenmaier 2002). While many have noted the utmost importance of social media for the engagement and retention of consumers within the tourism and hospitality industry (Cabiddu et al. 2014; Hudson et al. 2015; Munar and Jacobsen 2014; Yan et al. 2015), Harrigan et al. (2017) state that both social media and engagement within the context of tourism is under-researched. Akin to social media research more broadly, much research of social media within this industry is focused on sharing of UGC (e.g. Litvin et al. 2008; Wu and Pearce 2016; Xiang and Gretzel 2010; Yan et al. 2015) rather than sharing of MGC such as sponsored advertisements. Moreover, according to a review by Zeng and Gerritsen (2014), social media research in the context of tourism has tended to adopt a qualitative approach, limiting the generalisability of findings. Thus, a quantitative exploration of sharing of tourism-related sponsored advertisements will not only offer theoretical contributions for social media researchers but also practical recommendations for tourism organisations.

3 Theoretical Framework and Hypotheses Development

Social media researchers have utilised numerous social psychology theories and concepts such as social identity theory (e.g. Lee et al. 2011; Yang and Lai 2011), social capital theory (e.g. Choi and Scott 2013), and need to belong theory (e.g. Ma and Chan 2014; Ma and Yuen 2011) to explain a variety of sharing behaviours. On the other hand, Uses and Gratifications (U&G) theory has been the dominant theoretical approach for studying how and why individuals utilise particular media for many decades. It identifies the needs and desires that an individual has to use a particular media channel (Katz et al. 1974). These provide the basis for the motivations of an individual to communicate which thus influences the type of media that they will use and how they use and interpret the content that the media facilitates (Rubin 2009). Rather than looking at what it is that media does to people it looks at the functions that the media provides for people, considering the motivations of an audience as critical. Considered one of the most effective frameworks, scholars have used U&G theory to explore a variety of social media sharing behaviour, including sharing links (Baek et al. 2011), news (Hanson and Haridakis 2008; Lee and Ma 2012) and photos (Malik et al. 2016; Sung et al. 2016). Given that social psychology theories focus on relationship establishment and group behaviour, as well as the extensive application of U&G theory in examining the variety of motivations in social media, it was decided that U&G was an acceptable theoretical foundation for the study.

The dominant U&G factors identified by social media literature are entertainment, socialising, information seeking, and self-expression (Lee and Ma 2012; Park et al. 2009). However, in order to formulate a comprehensive model for this study, the numerous motivations considered in 49 studies of sharing in a social media or online context were analysed. In addition to this, 53 studies that utilise U&G theory in a social media context but not specifically related to sharing behaviour were also identified and motivations explored. From this review, six motivations were selected to build a conceptual model (Fig. 1) of users’ intentions to share tourism-related sponsored advertisements on social media, namely: entertainment, altruism, information sharing, information seeking, socialising, and self-expression.
Fig. 1

Conceptual Model

3.1 Entertainment

The entertainment gratification refers to the way in which social media enables individuals to pass time, escape their everyday lives, and engage in behaviours they find entertaining. Literature on sharing behaviour has identified entertainment as a strong motivation (e.g. Baek et al. 2011; Holton et al. 2014; Malik et al. 2016; Sung et al. 2016; Taylor et al. 2012), with U&G studies also finding that it is one of the most dominant gratifications (e.g. Lee and Ma 2012; Park et al. 2009). Entertainment has been associated with link sharing (Holton et al. 2014), news sharing (Hanson and Haridakis 2008), online advertisement sharing (Taylor et al. 2012), as well as photo sharing (Malik et al. 2016). The use of social networking sites by brands enables entertainment through their use of the platform and the content they post (Kim and Ko 2012). Content that facilitates entertainment for individuals on social media may provoke them to be more involved in interacting with others to discuss it (Lee and Ma 2012). Given that the nature of tourism is experiential and about facilitating the enjoyment of experience (Babin et al. 1994) it is suggested that:
  • H1: Entertainment has a positive effect on consumers’ intentions to share tourism-related sponsored advertisements on Facebook

3.2 Altruism

The motivation of altruism reflects an individual’s desire to help others (Batson 1987), differing from the motivation of concern for consumers in that those motivated by altruism share information merely because of the enjoyment of helping others. Studies concerning sharing on social media have highlighted an altruistic motivation (e.g. Ho and Dempsey 2010; Ma and Chan 2014; Munar and Jacobsen 2014). Lee and Kim (2011) found that consumers view UGC on social media as being motivated by altruism, unlike advertising generated by advertisers. Existing tourism literature also highlights an altruistic motivation for sharing within the social media environment (e.g. Wang and Fesenmaier 2003; Wu and Pearce 2016). Yoo and Gretzel (2011) found that consumer-generated travel media was motivated by altruism. Consumers high in this motivation are informative and efficient in their approach to helping other people, and are more likely to rely on factual content (Bronner and de Hoog 2011); therefore, it is suggested that consumers motivated by this are likely so share sponsored advertisements on Facebook.
  • H2: Altruism has a positive effect on consumers’ intentions to share tourism-related sponsored advertisements on Facebook

3.3 Information Sharing

Information sharing refers to the motivation to provide information for others. Studies on sharing behaviour in both an online and social media context have validated the positive effect of the information sharing motivation (e.g. Alhabash et al. 2014; Baek et al. 2011; Holton et al. 2014; Johnson and Yang 2009; Lu et al. 2010; Malik et al. 2016; Quinn 2016). However, prior studies that identified information sharing as a significant positive predictor of behaviour were focussed on non-commercial contexts, which are not directly comparable with this study. For example, Malik et al. (2016) focused on the sharing of digital photos on Facebook which, unlike commercially sponsored advertisements, are very personal in nature. There are already a variety of dedicated platforms to share tourism-related information. Therefore, because of the commercial nature of tourism-related sponsored advertisements on platforms like Facebook, consumers motivated to share content for informational purposes may not be inclined to share this type of content. Users may perceive that, due to the bombardment of marketing messages already on social media, sharing tourism-related sponsored advertisements will only serve to increase clutter rather than provide useful information, which users feel is an important characteristic of information shared on social networking sites (Ren et al. 2012). Therefore, while consumers motivated to provide information to others may be inclined to share information about first-hand tourism-related experiences they may not feel the same about sponsored advertisements on social media.
  • H3: Information sharing has a negative effect on consumers’ intentions to share tourism-related sponsored advertisements on Facebook

3.4 Information Seeking

Information seeking is one of the most commonly identified gratifications of social media (e.g. Dunne et al. 2010; Johnson and Yang 2009; Kim et al. 2011; Lee and Ma 2012; Park et al. 2009) and refers to the search for information and the act of learning through the consumption of information. Studies of sharing behaviour in both an online and social media context have validated the information seeking motivation (e.g. Kairam et al. 2012; Kim 2014; Oh and Syn 2015; Raacke and Bonds-Raacke 2008). The only study in the sharing literature that has found information seeking not to have a significant effect on behaviour was Hanson and Haridakis (2008) in the context of traditional and comedy news sharing on YouTube. Raacke and Bonds-Raacke (2008) identified ‘to learn about events’ as one of the gratifications on Myspace and Facebook, whereby individuals will actively engage in information seeking to enhance their decision making. Due to the intangible nature of tourism, consumers often use the internet to seek out information to help them make a more informed decision (Jeng and Fesenmaier 2002). The internet and subsequent rise of social media has had a colossal impact on the way that information is both sought and shared, especially in relation to the tourism and hospitality industry (Xiang and Gretzel 2010). Therefore, consumers may be inclined to share tourism-related sponsored advertisements on Facebook as a result of motivations to seek information.
  • H4: Information seeking has a positive effect on consumers’ intentions to share tourism-related sponsored advertisements on Facebook

3.5 Socialising

Socialising in the context of U&G refers to the need to build and develop relationships and connect with others. The fundamental nature of social media is participatory, through which the sharing of content is both a form of expression and means of relationship building (van House et al. 2005). The mere act of sharing content online means that relationships are created and maintained specifically with individuals that show a particular interest, opinion or problem concerning the content shared. When individuals interact with each other they achieve a sense of belonging, which is an innate human need. Social interaction has been identified as a significant factor related to social media use (e.g. Ho and Dempsey 2010; Ma and Chan 2014; Ma and Yuen 2011; Park et al. 2009). The majority of U&G research on social media has elaborated on a social gratification, through which people enjoy forming ties with others and facilitating continued interaction (e.g. Alhabash et al. 2012; Hollenbaugh and Ferris 2014; Quinn 2016; Smock et al. 2011). To meet social needs, tourism literature suggests that individuals will communicate with others and participate in the relevant environments (Yoo and Gretzel 2008). The interaction that consumers have with each other has also been found to influence attitudes towards advertising (de Gregorio and Sung 2010) and Wolny and Mueller 2013 found that those who are motivated by social interaction are more likely to engage in brand related eWOM more frequently than those who are not. Therefore, it is hypothesised that:
  • H5: Socialising has a positive effect on consumers’ intentions to share tourism-related sponsored advertisements on Facebook

3.6 Self-Expression

The self-expression gratification relates to the need for expression of one’s self and personal experiences with others. In the context of sharing, self-expression has been linked to viral marketing (Ho and Dempsey 2010), information sharing (Kairam et al. 2012), music sharing (Lee et al. 2011), sharing of online advertisements (Taylor et al. 2012), online community contribution (Wang and Fesenmaier 2003), tourism experience sharing (Wu and Pearce 2016), and photo sharing (Malik et al. 2016). Self-expression is also highlighted within the U&G literature (e.g. Alhabash et al. 2014; Balakrishnan and Shamim 2013; Johnson and Yang 2009; Kim 2014). In their study on information sharing on Google+, Kairam et al. (2012) found that desire to share about oneself or self-expression is one of the two primary reasons for sharing, with the other being the value of content shared. Within this motivation, individuals highlighted the need for sharing of personal experiences and sharing their stories with others. Self-expression is a notable motivation in services and hedonic experiences such as tourism; for example, respondents in Wu and Pearce’s (2016) study, which explored motivations behind writing an overseas travel blog, most strongly agreed with items measuring self-documentation and sharing. As the sharing of tourism-related sponsored advertisements on social media may be a way for consumers to show their personality, it is proposed that:
  • H6: Self-expression has a positive effect on consumers’ intentions to share tourism-related sponsored advertisements on Facebook

3.7 Gender

Market segmentation is an essential part of marketing strategy and gender continues to be one of the most common forms of segmentation in marketing practice. However, it appears that gender differences are becoming less clear-cut. Even in earlier research, although statistically significant, only small effect sizes were found for gender differences in computer-related attitudes and behaviour (e.g. Whitely Jr. 1997), and newer research has found that the gender gap is closing (Imhof et al. 2007; Pascual-Miguel et al. 2015; Taylor et al. 2011). As Eisend (2010) found that marketers tend to react to gender-related developments in society and use existing values in a society, confirming or refuting gender differences will help to prevent offending consumers through inappropriate sponsored advertisement strategies.

Advertisements on Facebook have been found to have a greater effect on females than males (Hargittai and Hsieh 2010). Research has found that although internet use is not affected by gender (Fallows 2005), motivations on how time is spent whilst in this online environment signifies inherent differences. For example, females are more likely to be motivated by social interaction and relationship maintenance than males who are more likely to be focused on information seeking activities (Guadagno and Cialdini 2002; Lucas and Sherry 2004; Weiser 2000). Previous research has found evidence for differences in males and females when engaging in information sharing behaviour in blogs (Lu et al. 2010), suggesting that this may also be a moderator affecting other social media sharing behaviours. Both males and females have been found to engage in altruistic behaviours (e.g. Eisenberg and Fabes 1998). However, it has been suggested that study characteristics and type of helping behaviour play an important role in determining the differences between genders with regards to altruism (Eagly and Crowley 1986). Gender has also been found to be an influential factor for self-expression on blogs (Argamon et al. 2007). Based on the existing research it is suggested that gender will affect the motivations of consumers’ intentions to share tourism-related sponsored advertisements on social media.
  • H7: Gender moderates the proposed model’s hypotheses in the UK context

4 Method

A survey method was employed given the availability of validated scales measuring the defined constructs. Following a covering letter and qualifying questions, the survey comprised of two sections. The first section requested demographic information and provided contextual details, including two tourism-related sponsored advertisement examples (Appendix 1) in order to solicit a real-time, instead of a recall, response and enhance measurement reliability. The second section of the survey contained the measurement items. Constructs were measured using multiple items from existing research (Appendix 2), selected from a review of previous studies’ scales that were consistent with the definitions of the constructs in this research. Items were measured using a seven-point Likert scale, anchored by strongly disagree and strongly agree.

The target population consisted of UK Facebook users aged 18 and over. Due to the constraints around privacy and access to the Facebook platform it was unfeasible to secure a reliable sampling frame of UK based Facebook users. Therefore, it was deemed appropriate to use non-probability techniques of convenience and snowball sampling. Both paper-print and web-based survey approaches were employed in order to maximize participation, which were identical in content and structure. The link to the online survey was distributed via social media platforms and the offline survey was distributed at an institution with more than 15,000 undergraduate/postgraduate students and 3000 staff.

Structural equation modelling (SEM) was used for quantitative data analysis as it allows simultaneous analysis of all relationships of both observed and latent variables (Tabachnick and Fidell 2013) and also accounts for measurement error (Gefen et al. 2000; Hair et al. 2006) providing overall model fit statistics. Adhering to the two-stage analytical process, confirmatory factor analysis was conducted in AMOS v.22, followed by analysis of the structural paths to examine the proposed hypotheses.

5 Results

A total of 531 responses were collected and, following screening and cleaning of the data in SPSS, 487 usable responses were retained. The sample consisted of slightly more male respondents (51.5%) compared to female (48.5%). Most respondents fell into the youngest three categories of 18–24 (30.4%), 25–34 (26.9%), and 35–44 (24.4%), with the fewest respondents in the 65+ category (3.9%). 80.5% of respondents classified themselves as “White: English/ Welsh/ Scottish/ Northern Irish/ British” (Table 1).
Table 1

Descriptive Information
































White: English/ Welsh/ Scottish/ Northern Irish/ British



White: Irish



White: Gypsy or Irish Traveller



Any other White Background



Black: African



Black: Caribbean



Any other Black background



Asian: Indian



Asian: Pakistani



Asian: Bangladeshi



Asian: Chinese



Any other Asian background



Mixed/ multiple ethnic group: White & Black Caribbean



Mixed/ multiple ethnic group: White & Black African



Mixed/ multiple ethic group: White & Asian



Any other ethnic group background



Prefer not to say



Examining the contextual items from the survey (Table 2), an overwhelming 76.4% of respondents had been Facebook users for more than four years, with only 1.2% of respondents having been Facebook users for one year or less. The majority of respondents spent between 31 and 59 min on Facebook per day (27.7%) with only 6.2% spending more than 3 h per day. The number of ‘friends’ respondents had on Facebook varied with 23.2% having up to 100 but 22.6% having 501+. 83.8% of respondents had not shared tourism-related sponsored advertisements on Facebook before.
Table 2

Contextual information





Length of time as a Facebook user

1 year or less



1–2 years



2–3 years



3–4 years



More than 4 years



Time spent on Facebook per day

Less than 10 min



11–30 min



31–59 min



1–2 h



2–3 h



more than 3 h



Number of Facebook friends



















Shared tourism-related sponsored advertisements on Facebook before







5.1 Measurement Model

Prior to CFA, the measurement model was assessed for both construct reliability and validity (Table 3). Cronbach’s alpha was used to test measurement reliability, with all values higher than the recommended 0.7 threshold (Nunnally 1994). Composite reliabilities (CR) were also higher than the recommended value of .70 (Hair et al. 2010), with CR values greater than AVE values, demonstrating internal consistency. The overall model fit was assessed by five goodness-of-fit measures: Normed Chi-square (CMIN/df), Adjusted Goodness of Fit (AGFI), Comparative Fit Index (CFI), Parsimony Normed Fit Index (PNFI), and Root Mean Square Error of Approximation (RMSEA). For the model to have sufficiently good fit these measures needed to be <3, ≥ .80, ≥ .95, > .50, ≤ .70 respectively. Through analysis of the model fit indices, standardized regression weights, covariance modification indices, and standardized residual covariance estimates, it was decided to remove INSH2, SOC2, SE3, and ALT4. This significantly improved the model fit indices to within the recommended values (CMIN/df 2.687; AGFI .876; CFI .967; PNFI .765; RMSEA .059).
Table 3

Construct reliability, composite reliability and AVE values



Standardised loadings

Construct reliability

Composite reliability


























Information sharing
























Information seeking
























Behavioural intention










5.2 Structural Model

Model fit of the structural model was good (CMIN/df 2.687; AGFI .876; CFI .967; PNFI .765; RMSEA .059). Path analysis revealed that five of the structural hypotheses were supported (Table 4). The effects of entertainment (γ = .458, p = .001), altruism (γ = .376, p = .001), information seeking (γ = .223, p = .005), and socialising (γ = .160, p = .021) on sharing intention were all significant and positive, thus H1, H2, H4, and H5 were supported. Information sharing (γ = −.429, p = .007) had a significant negative effect on intention to share tourism-related sponsored advertisements on Facebook, supporting H3. Self-expression (γ = .076, p = .172) was the only non-significant predictor, thus H6 was not supported. The model explained 53% of variance in intention to share tourism-related sponsored advertisements.
Table 4

Results of hypotheses testing


Proposed effect




p- value


Entertainment → BI






Altruism → BI






Information sharing → BI





Information seeking → BI






Socialising → BI






Self-expression → BI





In order to examine the moderation effect of gender, the data was divided into two groups: 251 males and 236 females. The chi-square difference test (Table 5) revealed measurement invariance between the two groups. The addition of constraints on structural paths did not lead to significant differences at the 95% confidence level, thus groups were not different. Therefore, it was concluded that gender did not significantly moderate the proposed hypotheses and H7 was rejected.
Table 5

Results of invariance testing







Nested model




1. Unconstrained







2. Measurement weights constrained










3. Measurement weights and structural paths constrained










6 Discussion

This study adopted U&G theory to explore motivations to share tourism-related sponsored advertisements on social media. The results of this research found that entertainment was the most significant predictor of intention to share tourism-related sponsored advertisements. The results of this study are concurrent with current literature that has found enjoyment to be a crucial factor that influences how users behave within social networks. A study by Celebi (2015) found that consumers who are highly motivated by entertainment have more favourable attitudes towards advertising on the internet, which is also echoed in a study by Zhou and Bao (2002). Celebi (2015) also found that entertainment was positively related to Facebook advertising which subsequently affected the use of Facebook features including one-to-many communication features such as the sharing function. Thus, it can be concluded that consumers are motivated to share tourism-related sponsored advertisements on Facebook based of the perceived entertainment value which makes them respond more positively, resulting in the intention to share.

Altruism has also been found to exist in most decisions to contribute on social media (Wang and Fesenmaier 2004). Results of this study revealed that altruism was the second most positive motivation behind intention to share tourism-related sponsored advertisements on Facebook. The messages conveyed by consumers on social media such as Facebook are seen to be motivated by altruism, unlike those that come directly from the advertiser (Lee and Kim 2011). Previous studies have found that consumers are also likely to share tourism-related UGC for altruistic purposes (e.g. Wu and Pearce 2016; Yoo and Gretzel 2011).

The results of this study establish that gratifications of information sharing negatively affect intention to share tourism-related sponsored advertisements on social media. This suggests that individuals who are more motivated to share content for the purposes of information sharing are less inclined to share tourism-related sponsored advertisements. While sponsored advertisements may appear more credible given that they are embedded into an individual’s newsfeed, they are still commercial in nature. The proliferation of platforms where users can share and access endless tourism-related UGC (Munar and Jacobsen 2014) perhaps offers sufficient information that is also perceived as more trustworthy (Leung et al. 2013), hence the resistance by those motivated to share content for the purposes of information sharing to share tourism-related sponsored advertisements.

It has been found that travellers are more likely to utilise social media while they are searching for information before making a purchase decision (Xiang and Gretzel 2010). The finding of this study that information seeking positively affects sharing intentions of tourism-related sponsored advertisements suggests that users want to garner their network’s opinions as a result of sharing this type of content. Raacke and Bonds-Raacke (2008) identified ‘to learn about events’ as one of the gratifications on Myspace and Facebook, whereby individuals will actively engage in information seeking to enhance their decision making, and it appears that users have similar motivations behind sharing tourism-related sponsored advertisements.

The results of this research found that socialising was also a significant predictor of intention to share tourism-related sponsored advertisements on social media; however, it had the smallest significant effect of the motivations considered. Social networking sites such as Facebook facilitate the connection and maintenance of relationships between individuals (Kane et al. 2009), enabling social ties to be strengthened. Munar and Jacobsen (2014) found that social connections were one of the most significant predictors for sharing tourism experiences through social media. Fu et al. (2017) also found that individuals were more likely to re-share content that their friends had already shared on Facebook, as this was a way of ensuring that their entire network received the same information. Tourists are utilising social networking sites for a variety of reasons including maintaining social connections, sharing tips and experiences with each other, and finding others to travel with, hence users may surmise that sharing tourism-related sponsored advertisements may provide a stimulus to satisfy social gratifications.

Contrary to H6, the results of this research found that self-expression was the only motivation that did not significantly affect intention to share tourism-related sponsored advertisements on social media. Lee et al. (2011) found that self-expression was the only significant factor to influence social identity through social presence in their study on music sharing behaviour on social networks. This suggests that individuals are concerned with egotistic motivations that are self-serving rather than motivations which are perhaps altruistic in nature. As altruism and socialising were found to be significant predictors of intention to share tourism-related sponsored advertisements, it could be posited that these two motivations conflict with self-expression in this context. Thus, individuals who are motivated to share based on an altruistic or social basis are not motivated to do so for purposes of self-expression. On the other hand, it may be that as an individual already has a variety of ways to easily express personal tourism experiences the sharing of MGC is not perceived as an appropriate method to express their original and alternative forms of self.

Gender was not found to have any significant moderating effect which could be linked to increasing gender equality in the UK, giving support for newer research which has found that the gender gap is closing (Imhof et al. 2007; Pascual-Miguel et al. 2015). Past research has shown clear differences between genders in terms of socialising and information seeking (e.g. Guadagno and Cialdini 2002; Lucas and Sherry 2004; Weiser 2000), information sharing (e.g. Lu et al. 2010), altruism (e.g. Eisenberg and Fabes 1998), and self-expression (e.g. Argamon et al. 2007). As the bombardment of content on social media is not gender specific it may be that the motivations to add to the existing clutter are the same for both males and females in the context of sharing tourism-related sponsored advertisements.

6.1 Theoretical Implications

Research considering sharing behaviours on social media has focused heavily on mechanisms such as blogging. Jansen et al. (2011) noted that sharing information within social networking sites needs more attention given that sharing on other platforms is much better understood. This study contributes to theory in providing a conceptually grounded and empirically tested model of U&G to explain individuals’ motivations to share sponsored advertisements on the social networking site Facebook. While the model has been validated in the context of tourism-related sponsored advertisements, it can be applied to explore other types of sponsored advertisement which will further contribute to understanding the mechanisms that make advertisements successful in the social media environment. Furthermore, the quantitative approach adopted offers a valuable addition to tourism research which has tended to adopt a qualitative approach, and the large sample of UK consumers contributes to U&G literature as the theory has rarely been applied in the UK context. Finally, given that gender was not found to have a moderating effect, further evidence has been provided of the closing of the gender gap in terms of digital media.

6.2 Practical Implications

This study has taken a step to answers calls to explore how technology can be utilised by an organisation for better promotion of their product or service (Kannan and Lim 2017). Understanding why consumers share specific types of content online is critical for organisations if they want to effectively use this resource. This study identified five motivations as significant factors affecting intention to share sponsored advertisements - altruism, entertainment, socialising, information sharing, and information seeking - suggesting organisations can develop more appropriate advertisements reflecting the importance of these different motivations. Given that information sharing was found to be negatively related to intention it may be necessary for organisations to approach paid social media marketing differently (Gossieaux and Moran 2010). This study revealed that entertainment was the strongest predictor of intention to share tourism-related sponsored advertisements on Facebook. Therefore, tourism organisations should focus on making their sponsored advertisements entertaining rather than trying to provide information through this medium.

Brands should only engage in utilising Facebook for advertising purposes if they are prepared to fully engage with the user and spend time building and maintaining a relationship (Brettel et al. 2015). Maurer & Weigmann (Maurer and Wiegmann 2011) suggest organisations should focus on ‘friendvertising’ and build relationships with consumers to maintain their loyalty rather than trying to utilise social media for commercial purposes. There has also been much research that shows that UGC is preferred by consumers rather than MGC. However, by creating sponsored advertisements that appeal to the motivations identified in this study, tourism organisations are likely to see their content reach a wider audience as a result of user sharing. Given that this study revealed that not many consumers have shared tourism-related sponsored advertisements on Facebook before, tourism organisations could attempt to encourage this behaviour by rewarding those who share one of their sponsored advertisements with something that satisfies the salient motivations.

7 Conclusion

Several studies have utilised U&G theory to understand why consumers use social media but there has been less research applying the theory to understand the motivations of specific behaviours on social media. Additionally, there has been no previous research using U&G theory in the context of sharing sponsored advertisements on social media, and more specifically in the tourism context. Therefore, this study contributes to a variety of literature through validation of the model in the context of sharing tourism-related sponsored advertisements via social media. This study identified altruism, entertainment, information seeking, socialising, and information sharing as significant motivations affecting behavioural intention to share sponsored advertisements. This is especially useful within the marketing context to help brands understand and develop their strategies to encourage users of social media to share these advertisements.

7.1 Limitations and Future Research

Despite its contributions this study is not without limitations and these provide fruitful avenues for further research. Firstly, this study was undertaken in the context of tourism-related sponsored advertisements and consequently findings may not be generalizable to other industries. Future research should seek to understand whether the motivations highlighted in this study are still significant in other contexts. Given that gender was not found to be a significant moderating variable, future research could explore other potential moderating variables, for example age and personal innovativeness. On the other hand, further research might adopt an experimental approach utilising gender-specific tourism-related sponsored advertisements to explore differences. Thirdly, this research utilised both convenience and snowball sampling methods which are associated with less generalisability. Therefore, future research should seek to utilise random samples to test the proposed model. Finally, this study was undertaken in the UK and therefore findings may not be generalised to other countries. Future research should utilise the validated model in other contexts to investigate the possibility of cultural influences on motivations. The reasons that people use social networking sites has been found to be related to the inherent social and individual needs of people, meaning the U&G an individual has from using a particular social media platform are not the same for everyone. Thus, individual difference indicators between consumers should be examined to understand the differing motivations between consumers.


  1. Alhabash, S., Park, H., Kononova, A., Chiang, Y.-H., & Wise, K. (2012). Exploring the motivations of Facebook use in Taiwan. Cyberpsychology, Behavior, and Social Networking, 15, 304–311. Scholar
  2. Alhabash, S., Chiang, Y.-H., & Huang, K. (2014). MAM & U&G in Taiwan: Differences in the uses and gratifications of Facebook as a function of motivational reactivity. Computers in Human Behavior, 35, 423–430. Scholar
  3. Argamon, S., Koppel, M., Pennebaker, J. W., & Schler, J. (2007). Mining the blogosphere: Age, gender and the varieties of self-expression. First Monday, 12(9).
  4. Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. The Journal of Consumer Research, 20(4), 644–656. Scholar
  5. Baek, K., Holton, A., Harp, D., & Yaschur, C. (2011). The links that bind: Uncovering novel motivations for linking on Facebook. Computers in Human Behavior, 27(6), 2243–2248. Scholar
  6. Balakrishnan, V., & Shamim, A. (2013). Malaysian Facebookers: Motives and addictive behaviours unraveled. Computers in Human Behavior, 29(4), 1342–1349. Scholar
  7. Batson, C. D. (1987). Prosocial motivation: Is it ever truly altruistic? In L. Berkowitz (Ed.), Advances in experimental social psychology (pp. 65–122). New York: Academic Press.Google Scholar
  8. Boland, H. (2017). Facebook revenue soars as mobile advertising sales boom. The Telegraph. Accessed 9 Aug 2017.
  9. Brettel, M., Reich, J.-C., Gavilanes, J. M., & Flatten, T. C. (2015). What drives advertising success on Facebook? An advertising-effectiveness model measuring the effects on sales of “likes” and other social-network stimuli. Journal of Advertising Research, 55(2), 162–175. Scholar
  10. Bronner, F., & de Hoog, R. (2011). Vacationers and eWOM: Who posts, and why, where, and what? Journal of Travel Research, 50(1), 15–26. Scholar
  11. Burgess, S., Sellitto, C., Cox, C., & Buultjens, J. (2011). Trust perceptions of online travel information by different content curators: Some social and legal implications. Information Systems Frontiers, 13(2), 221–235. Scholar
  12. Cabiddu, F., De Carlo, M., & Piccoli, G. (2014). Social media affordances: Enabling customer engagement. Annals of Tourism Research, 48, 175–192. Scholar
  13. Celebi, A. I. (2015). How do motives affect attitudes and behaviors toward internet advertising and Facebook advertising? Computers in Human Behavior, 51, 312–324. Scholar
  14. Cheung, C., Chiu, P-Y., & Lee, M. (2011). Online social networks: Why do students use Facebook. Computers in Human Behavior, 27, 1337–1343.Google Scholar
  15. Chiu, C., & Huang, H. (2015). Examining the antecedents of user gratification and its effects on individuals’ social network services usage: The moderating role of habit. European Journal of Information Systems, 24(4), 411–430. Scholar
  16. Choi, J. H., & Scott, J. E. (2013). Electronic word of mouth and knowledge sharing on social network sites: A social capital perspective. Journal of Theoretical and Applied Electronic Commerce Research, 8(1), 69–82.CrossRefGoogle Scholar
  17. Cunningham, L. F., & Bright, N. (2012). The tweet is in your court: Measuring attitude towards athlete endorsements in social media. International Journal of Integrated Marketing Communications, 4(2), 73–87.Google Scholar
  18. de Gregorio, F., & Sung, T. (2010). Understanding attitudes toward and behaviors in response to product placement. Journal of Advertising, 39(1), 83–96. Scholar
  19. Dunne, Á., Lawlor, M., & Rowley, J. (2010). Young people’s use of online social networking sites – A uses and gratifications perspective. Journal of Research in Interactive Marketing, 4(1), 46–58.Google Scholar
  20. Eagly, A. H., & Crowley, M. (1986). Gender and helping behavior. A meta-analytic review of the social psychological literature. Psychological Bulletin, 100(3), 283–308. Scholar
  21. Eisenberg, N., & Fabes, R. (1998). Prosocial development. In W. Damon (Series Ed.) and N. Eisenberg (Vol. Ed.) Handbook of Child Psychology, Vol. 3: Social, emotional, and personality development (5th Ed.). (pp. 701–778) New York: Wiley.Google Scholar
  22. Eisend, M. (2010). A meta-analysis of gender roles in advertising. Journal of the Academy of Marketing Science, 38(4), 418–440. Scholar
  23. Facebook. (2017). Marketing on Facebook. Accessed 9 Aug 2017.
  24. Fallows, D. (2005). How women and men use the internet. Pew Research Centre. Accessed 5 Jan 2017.
  25. Fu, P.-W., Wu, C.-C., & Cho, Y.-J. (2017). What makes users share content on Facebook? Compatibility among psychological incentive, social capital focus, and content type. Computers in Human Behavior, 67, 23–32. Scholar
  26. Gefen, D., Straub, D., & Boudreau, M. (2000). Structural equation modelling and regression: Guidelines for research practice. Communications of AIS, 4(7), 1–79.Google Scholar
  27. Goh, K. Y., Heng, C. S., & Lin, Z. (2013). Social media brand community and consumer behavior: Quantifying the relative impact of user- and marketer-generated content. Information Systems Research, 24(1), 88–107. Scholar
  28. Gossieaux, F., & Moran, E. (2010). The hyper-social organization: Eclipse your competition by leveraging social media. New York: McGraw-Hill.Google Scholar
  29. Gottbrecht, L. (2017). 8 User-Generated Content Trends We Learned from 25 Million Facebook Posts. Accessed 8 Aug 2017.
  30. Guadagno, R. E., & Cialdini, R. B. (2002). Online persuasion: An examination of gender differences in computer-mediated interpersonal influence. Group Dynamics: Theory Research and Practice, 6(1), 38–51. Scholar
  31. Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis (6th ed.). New Jersey: Prentice Hall.Google Scholar
  32. Hair, J., Black, W., Babin, B. & Anderson, R. (2010). Multivariate data analysis: A global perspective (7th ed.). New Jersey: Pearson Education.Google Scholar
  33. Hanson, G., & Haridakis, P. M. (2008). YouTube users watching and sharing the news: A uses and gratifications approach. Journal of Electronic Publishing, 11(3).
  34. Hargittai, E., & Hsieh, Y. L. P. (2010). Predictors and consequences of differentiated practices on social network sites. Information, Communication and Society, 13(4), 515–536. Scholar
  35. Harrigan, P., Evers, U., Miles, M., & Daly, T. (2017). Customer engagement with tourism social media brands. Tourism Management, 59, 597–609. Scholar
  36. Ho, J., & Dempsey, M. (2010). Viral marketing: Motivations to forward online content. Journal of Business Research, 63(9-10), 1000–1006. Scholar
  37. Hollenbaugh, E. E., & Ferris, A. L. (2014). Facebook self-disclosure: Examining the role of traits, social cohesions, and motives. Computers in Human Behavior, 30, 50–58. Scholar
  38. Holton, A. E., Baek, K., Coddington, M., Yaschur, C., & C. (2014). Seeking and sharing: Motivations for linking on twitter. Communication Research Reports, 31(1), 33–40. Scholar
  39. Hudson, S., Roth, M. S., Madden, T. J., & Hudson, R. (2015). The effects of social media on emotions, brand relationship quality, and word of mouth: An empirical study of music festival attendees. Tourism Management, 47, 68–76. Scholar
  40. Idemudia, C., Raisinghani, M. S., & Samuel-Ojo, O. (2016). The contributing factors of continuance usage of social media: An empirical analysis. Information Systems Frontiers.
  41. Imhof, M., Vollmeyer, R., & Beierlein, C. (2007). Computer use and the gender gap: The issue of access, use, motivation, and performance. Computers in Human Behavior, 23(6), 2823–2837. Scholar
  42. Jansen, B. J., Sobel, K., & Cook, G. (2011). Classifying ecommerce information sharing behaviour by youths on social networking sites. Journal of Information Science, 37(2), 120–136. Scholar
  43. Jeng, J., & Fesenmaier, D. R. (2002). Conceptualizing the travel decision-making hierarchy: A review of recent developments. Tourism Analysis, 7(1), 15–32. Scholar
  44. Johnson, P. R., & Yang, S.-U. (2009). Uses and gratifications of twitter: An examination of user motives and satisfaction of twitter use. Communication Technology Division of the annual convention of the Association for Education in Journalism and Mass Communication in Boston, MA.Google Scholar
  45. Kairam, S., Brzozowski, M. J., Huffaker, D., & Chi, E. H. (2012). Talking in circles: Selective sharing in Google+. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ‘12), ACM, Austin, Texas, USA (pp. 1065-1074).Google Scholar
  46. Kane, G. C., Fichman, R. G., Gallaugher, J., & Glaser, J. (2009). Community relations 2.0. Harvard Business Review, 87, 45–50.Google Scholar
  47. Kannan, P. K., & Lim, H. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22–45. Scholar
  48. Katz, E., Blumler, J. G., & Gurevitch, M. (1974). Utilization of mass communication by the individual. In J. G. Blumler & E. Katz (Eds.), The uses of mass communications: Current perspectives on gratifications research (pp. 19–32). Beverly Hills: Sage.Google Scholar
  49. Khang, H., Ki, E.-J., & Ye, L. (2012). Social media research in advertising, communication, marketing, and public relations, 1997–2010. Journalism & Mass Communication Quarterly, 89(2), 279–298. Scholar
  50. Kim, J. W. (2014). Scan and click: The uses and gratifications of social recommendation systems. Computers in Human Behavior, 33, 184–191. Scholar
  51. Kim, A. J., & Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. Journal of Business Research, 65(10), 1480–1486. Scholar
  52. Kim, Y., Sohn, D., & Choi, S. M. (2011). Cultural difference in motivations for using social network sites: A comparative study of American and Korean college students. Computers in Human Behavior, 27(1), 365–372. Scholar
  53. Lee, J., & Kim, J. K. (2011). The impact of online brand community type on consumer’s community engagement behaviors: Consumer-created vs marketer created online brand community in SNW. Cyberpsychology, Behavior and Social Networking, 14(1), 59–63. Scholar
  54. Lee, C. S., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior, 28(2), 331–339. Scholar
  55. Lee, D., Park, J. Y., Kim, J., Kim, J., & Moon, J. (2011). Understanding music sharing behaviour on social network services. Online Information Review, 35(5), 716–733. Scholar
  56. Lee, Y.-J., Keeling, K., & Urbaczewski, A. (2017). The economic value of online user reviews with ad spending on movie box-office sales. Information Systems Frontiers.
  57. Leung, D., Law, R., van Hoof, H., & Buhalis, D. (2013). Social media in tourism and hospitality: A literature review. Journal of Travel & Tourism Marketing, 30(1-2), 3–22. Scholar
  58. Lin, C., & Kim, T. (2016). Predicting user response to sponsored advertising on social media via the technology acceptance model. Computers in Human Behaviour, 64, 710–718. Scholar
  59. Lipsman, A., Mudd, G., Rich, M., & Bruich, S. (2012). The power of ‘like’: How brands reach (and influence) fans through social-media marketing. Journal of Advertising Research, 52(1), 40–52. Scholar
  60. Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458–468. Scholar
  61. Lu, H.-P., Lin, J. C.-C., Hsiao, K.-L., & Cheng, L.-T. (2010). Information sharing behaviour on blogs in Taiwan: Effects of interactivities and gender differences. Journal of Information Science, 36(3), 401–416. Scholar
  62. Lucas, K., & Sherry, J. L. (2004). Sex differences in video game play: A communication-based explanation. Communication Research, 31(5), 499–523. Scholar
  63. Ma, W. W. K., & Chan, A. (2014). Knowledge sharing and social media: Altruism, perceived online attachment motivation, and perceived online relationship commitment. Computers in Human Behavior, 39, 51–58. Scholar
  64. Ma, W. W. K., & Yuen, A. H. K. (2011). Understanding online knowledge sharing: An interpersonal relationship perspective. Computers & Education, 56(1), 210–219. Scholar
  65. Malik, A., Dhir, A., & Nieminen, M. (2016). Uses and gratifications of digital photo sharing on Facebook. Telematics & Informatics, 33(1), 129–138. Scholar
  66. Maurer, C., & Wiegmann, R. (2011). Effectiveness of advertising on social network sites: A case study on Facebook. In R. Law, M. Fuchs, & F. Ricci (Eds.), Information and communication Technologies in Tourism. Vienna: Springer. Scholar
  67. Munar, A. M., & Jacobsen, J. K. S. (2014). Motivations for sharing tourism experiences through social media. Tourism Management, 43, 46–54. Scholar
  68. Murphy, T., & Schram, R. (2014). What is it worth? The value chasm between brand and influencers. Journal of Brand Strategy, 3(1), 31–40.Google Scholar
  69. Nunnally, J. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.Google Scholar
  70. Oh, S., & Syn, S. Y. (2015). Motivations for sharing information and social support in social media: A comparative analysis of Facebook, twitter, delicious, YouTube, and Flickr. Journal of the Association for Information Science and Technology, 66(10), 2045–2060. Scholar
  71. Park, N., Kee, K. F., & Valenzuela, S. V. (2009). Being immersed in social networking environment: Facebook groups, uses and gratifications and social outcomes. Cyber Psychology & Behavior, 12(6), 729–733. Scholar
  72. Pascual-Miguel, F., Agudo-Peregrina, A., & Chaparro-Peláez, J. (2015). Influences of gender and product type on online purchasing. Journal of Business Research, 68(7), 1550–1556. Scholar
  73. Pinterest (2017). Why Pinterest ads work. Accessed 25 Sept 2017.
  74. Quinn, K. (2016). Contextual social capital: Linking the contexts of social media use to its outcomes. Information, Communication & Society, 19(5), 582–600. Scholar
  75. Raacke, J., & Bonds-Raacke, J. (2008). MySpace and Facebook: Applying the uses and gratifications theory to exploring friend-networking sites. Cyberpsychology & Behavior, 11(2), 169–174. Scholar
  76. Ren, Y., Harper, F. M., Drenner, S., Terveen, L., Kiesler, S., Riedl, J., & Kraut, R. E. (2012). Building member attachment in online communities: Applying theories of group identity and interpersonal bonds. MIS Quarterly, 36(3), 841–864.Google Scholar
  77. Rubin, A. M. (2009). The uses-and-gratifications perspective on media effects. In J. Bryant & M. B. Oliver (Eds.), Media effects: Advances in theory and research (3rd ed., pp. 165–184). New York: Routledge.Google Scholar
  78. Smock, A. D., Ellison, N. B., Lampe, C., & Wohn, D. Y. (2011). Facebook as a toolkit: A uses and gratification approach to unbundling feature use. Computers in Human Behavior, 27(6), 2322–2329. Scholar
  79. Snap Inc. (2017). Snapchat. It’s your business - on mobile. Accessed 1 Oct 2017.
  80. Sung, Y., Lee, J.-A., Kim, E., & Choi, S. M. (2016). Why we post selfies: Understanding motivations for posting pictures of oneself. Personality and Individual Differences, 97, 260–265. Scholar
  81. Tabachnick, B., & Fidell, L. (2013). Using multivariate statistics (6th ed.). Harlow: Pearson Education Limited.Google Scholar
  82. Taylor, D. G., Lewin, J., & Strutton, D. (2011). Friends, fans and followers: Do ads work on social networks? Journal of Advertising Research, 51(1), 258–275.Google Scholar
  83. Taylor, D. G., Strutton, D., & Thompson, K. (2012). Self-enhancement as a motivation for sharing online advertising. Journal of Interactive Advertising, 12(2), 13–28. Scholar
  84. Tutaj, K., & Reijmersdal, E. A. (2012). Effects of online advertising format and persuasion knowledge on audience reactions. Journal of Marketing Communications, 18(1), 5–18. Scholar
  85. van House, N., Davis, M., Ames, M., Finn, M., & Viswanathan, V. (2005). The uses of personal networked digital imaging: An empirical study of cameraphone photos and sharing. In: Conference on Human Factors In Computing 2005, Technology, Safety, and Community, April 2–7, Portland, Oregon, USA.
  86. Wang, Y., & Fesenmaier, D. R. (2003). Understanding the motivation of contribution in online communities: An empirical investigation of an online travel community. Electronic Markets, 13, 33–45.CrossRefGoogle Scholar
  87. Wang, Y., & Fesenmaier, D. R. (2004). Modeling participation in an online travel community. Journal of Travel Research, 42(3), 261–270. Scholar
  88. Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35–57. Scholar
  89. Weiser, E. (2000). Gender differences in internet use patterns and internet application preferences: A two-sample comparison. Cyberpsychology & Behavior, 4, 167–178.CrossRefGoogle Scholar
  90. Whitely Jr., B. (1997). Gender differences in computer-related attitudes and behavior: A meta-analysis. Computers in Human Behavior, 13(1), 1–22. Scholar
  91. Wolny, J., & Mueller, C. (2013). Analysis of fashion consumers’ motives to engage in electronic word-of-mouth communication through social media platforms. Journal of Marketing Management, 29(5–6), 562–583. Scholar
  92. Wu, M.-Y., & Pearce, P. L. (2016). Tourism blogging motivations: Why do Chinese tourists create little “lonely planets”? Journal of Travel Research, 55(4), 537–549.CrossRefGoogle Scholar
  93. Xiang, Z., & Gretzel, U. (2010). Role of social media in online travel information search. Tourism Management, 31(2), 179–188. Scholar
  94. Xie, K., & Lee, Y.-J. (2015). Social media and brand purchase: Quantifying the effects of exposures to earned and owned social media activities in a two-stage decision making model. Journal of Management Information Systems, 32(2), 204–238. Scholar
  95. Yan, X., Wang, J., & Chau, M. (2015). Customer revisit intention to restaurants: Evidence from online reviews. Information Systems Frontiers, 17(3), 645–657. Scholar
  96. Yang, H.-L., & Lai, C.-Y. (2011). Understanding knowledge-sharing behaviour in Wikipedia. Behaviour & Information Technology, 30(1), 31–142.CrossRefGoogle Scholar
  97. Yoo, K.-H., & Gretzel, U. (2008). What motivates consumers to write online travel reviews? Information Technology & Tourism, 10(4), 283–295. Scholar
  98. Yoo, K.-H., & Gretzel, U. (2011). Influence of personality on travel-related consumer-generated media creation. Computers in Human Behavior, 27(2), 609–621. Scholar
  99. Zeng, B., & Gerritsen, R. (2014). What do we know about social media in tourism? A review. Tourism Management Perspectives, 10, 27–36. Scholar
  100. Zhou, Z., & Bao, Y. (2002). Users' attitudes toward web advertising: Effects of internet motivation and internet ability. Advertising Consumer Research, 29, 71–78.Google Scholar
  101. Zhou, Z., Zhang, Q., Su, C., & Zhou, N. (2012). How do brand communities generate brand relationships? Intermediate mechanisms. Journal of Business Research, 65(7), 890–895. Scholar

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Authors and Affiliations

  1. 1.School of ManagementSwansea University Bay CampusSwanseaUK
  2. 2.School of Economics, Finance and ManagementUniversity of BristolBristolUK

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