The influence of social media in creating expectations. An empirical study for a tourist destination

Annals of Tourism Research 65 (2017) 60–70

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The influence of social media in creating expectations. An empirical study for a tourist destination Yeamduan Narangajavana ⇑, Luis José Callarisa Fiol, Miguel Ángel Moliner Tena, Rosa María Rodríguez Artola, Javier Sánchez García Departamento de Administración de Empresas y Marketing, Universitat Jaume I, Castellón de la Plana 12071, Spain

a r t i c l e

i n f o

Article history: Received 26 November 2015 Revised 15 January 2017 Accepted 5 May 2017

Keywords: User-generated content Trust Expectations MIMIC model SEM model Social media

a b s t r a c t Social media are transforming the tourism industry from its traditional pattern into an intense informational pattern. Our study aims to investigate the causes underlying the use of user-generated contents (UGC) to receive tourist information and its effect on tourists’ expectations. Our empirical work was analysed by means of a multiple indicators multiple causes model (MIMIC) and a structural equation model (SEM). The main finding showed that when users receive UGC related to tourist destinations, they will create expectations about the destination by placing their trust in the contents received. It is recommended that tourism organizations should maintain the quality level in order to allow more UGC, and then further trust in the contents of social media and expectations will occur. Ó 2017 Elsevier Ltd. All rights reserved.

Introduction Social media have an impact on tourism, especially in the way the way travellers access and use tourism information (Xiang, Magnini, & Fesenmaier, 2015). These media have affected the tourism environment by changing the behaviour of both tourists and business sectors (Jacobsen & Munar, 2012). While social media and the Internet was becoming popular among tourists (Xiang & Gretzel, 2010), the tourism industry turned into an information-intense industry, since the social media allow tourists to challenge and collaborate in producing, consuming and distributing travel information through the Internet (Yoo & Gretzel, 2009). In addition, Xiang et al. (2015) added that the arrival of online and cloud access through mobile devices can create new sources of information to be searched, which later tend to become progressively more prominent in guiding travel decisions. Due to the fact that the social media have various utilities, they have gained a substantial amount of popularity in travellers’ use of the Internet (Nezakati et al., 2015; Zeng & Gerritsen, 2014). The reasons for this popularity are that social media allow large numbers of people to express opinions, feeling, experiences, etc. in an innovative way (Luo & Zhong, 2015). Similarly, in our case, these people are tourists who can search for, read and receive information regarding tourist suppliers and tourist destinations through the reviews that were posted by other tourists via social media (Chung & Koo, 2015; Sigala, Christou, & Gretzel, 2012). Hence, the reviews posted on social media, which recent literature has named user-generated

⇑ Corresponding author. E-mail addresses: [email protected] (Y. Narangajavana), [email protected] (L.J. Callarisa Fiol), [email protected] (M.Ángel Moliner Tena), [email protected] (R.M. Rodríguez Artola), [email protected] (J. Sánchez García). http://dx.doi.org/10.1016/j.annals.2017.05.002 0160-7383/Ó 2017 Elsevier Ltd. All rights reserved.

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content (UGC) or consumer-generated media (CGM), are important for both generating and acquiring information related to travel. The study of UGC related to tourism on social media is still in an early stage (Zeng & Gerritsen, 2014), although we can separate it into two phases. The first includes most of the literature concentrated on general information and how it affects the tourism industry. It was found that earlier research works, such as that of Conrady (2007), in their respective studies, analysed the influence of the new technologies on tourism. Further, other researchers, such as Cox, Burgess, Sellitto, and Buultjens (2009), Gretzel and Yoo (2008) analysed the impact of the UGC on travel planning decisions, especially those concerning accommodation. The second refers to the literature that attempts to link UGC on social media to other aspects such as tourists’ behaviour (Gretzel, Lee, Tussyadiah, & Fesenmaier, 2009), intention to buy/use (Cox et al., 2009), attitudinal factors and their influence on the use and creation of UGC (Daugherty, Eastin, & Bright, 2008), trust in and the creditability of the websites on which the UGC is posted and their impact on trip planning (Yoo, Lee, & Gretzel, 2007; Yoo, Lee, Gretzel, & Fesenmaier, 2009), and loyalty and the effect of electronic work-of-mouth (eWOM) on the final destination choices (Luo & Zhong, 2015). Authors such as Lim, Chung, and Weaver (2012) or more recently Zeng and Gerritsen (2014) express their interest in discovering how UGC on social media inform the different players in the tourism industry, so as to enrich tourist experiences and to promote tourist services and destinations. Nevertheless, the research on UGC in the tourism sector still needs to expand and fill the research gap in several different areas. For example, various studies focus on the motive for creating such contents (Munar & Jacobsen, 2014; Daugherty et al., 2008) or the motivations for visiting a destination (Llodrá-Riera, Martínez-Ruiz, Jiménez-Zarco, & Izquierdo-Yusta, 2015), but none of them concentrate on the motivations for receiving the contents and how this can encourage tourists to visit the destination. Moreover, to date there are no studies dealing with the relationships between UGC in social media and tourists’ expectations. In order to further the knowledge of UGC related to the tourism industry and to close the gap, our study aims to analyse the impact of received UGC on tourists’ expectations, and, more particularly, to determine how their expectations about a tourist destination are generated via the use of social media. Specifically, it will examine how the intensity of social media usage can influence the motivations for receiving UGC and how it can persuade tourists to visit the destination, with special attention given to the effect on tourists’ expectations. Thus, we have designed a research study concentrated on a specific tourist destination: Valencia, Spain. This research has two different parts. First, we focused on tourists to see how and why the tourists use UGC. Second, from the first part, we discover how UGC influences tourists’ behaviour, particularly their expectations and trust. Finally, we discuss the contributions made by the study, recommendations, and future research for the academic field of tourism. Conceptual framework and theorical model During the late 1960s and early 1970s, the expectancy disconfirmation paradigm was applied for the evaluation of satisfaction on product performance (Oliver, 1977). Later, in the 1980s, more researchers expanded it to include knowledge about service satisfaction. The problematic issues started when Parasuraman, Zeithaml and Berry presented a SERVQUAL in 1988 to measure perception on service quality. It was criticized and became one of the most prolific debates in the 1990s. For example, Cronin and Taylor (1992) compared SERVQUAL with SERVPERF, a tool which is based only on the measurement of perception. However, the issue still needed to be studied further in order to gain an understanding and measure of customer satisfaction with expectations and service quality. The process of tourist satisfaction formation is typically explained by the expectancy disconfirmation paradigm (Oliver, 1980). The expectation-disconfirmation model says that tourists develop expectations about a product or service before purchasing it. Subsequently, they compare actual performance with those expectations. Tourists usually have initial expectations regarding the type and quality of services to be offered at a particular destination (Lorenzo, Avilés, & Centeno, 2010). The extent to which tourist expectations are met will eventually determine the level of tourist satisfaction. If the overall performance, while or after visiting a destination, exceeds or meets the initial expectations, then the tourist is considered satisfied. Otherwise, the tourist may be dissatisfied. Expectations are regarded as standards against which tourists assess a provider’s performance (Meirovich & Little, 2013). Researchers acknowledge the existence of various classes of expectations, among which growing interest is focused on two particular types: normative and predictive expectations. The notion of normative (should) expectations was developed in the service quality literature as an element of the SERVQUAL instrument (Parasuraman, Zeithaml, & Berry, 1985). These expectations constitute customers’ beliefs about what a service provider should offer and represent standards against which customers compare their perceptions of product or service quality. On the other hand, the concept of predictive (will) expectations emerged in customer satisfaction literature as a component of the expectation-disconfirmation model (Oliver, 1980). Within the framework of the expectation-disconfirmation model, predictive expectation is an experiencebased prediction or anticipation of what is likely to happen in the future. The formation of tourists’ expectations is important not only because it influences satisfaction, but also because it is a first element of the purchasing decision. The appearance of Internet, and especially the social media, in the tourism business has changed the rules. Nowadays, tourists’ expectations do not depend on only traditional word of mouth from relatives and acquaintances or on the communication that is made by travel agencies or tourist destination. Social media and UGC allow

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tourists to transmit their experiences to one another so that the contents later have an influence on the travelling expectations of potential tourists. Therefore, under the conceptual framework and based on all the foregoing explanation, the theoretical model (see Figure 1) is designed to analyse UGC and expectations in the tourism sector, and will be justified in the next section through the different hypotheses of the study. Literature review and hypotheses development An influence of the intensity of social media usage on the received UGC in tourism Social media have become a habit for daily communication (Luo & Zhong, 2015). Four out of five Internet users have a social media account, Facebook being the most frequently used in the world (GlobalWebIndex, 2014). The statistical figures act as evidence of the intense behaviour in using social media. According to Kaplan and Haenlein (2010), increasing intensity and experience in virtual social network usage demonstrates behaviour that is becoming more similar to real life. For example, people can consult and interact with others through social media (Anderson, Knight, Pookulangara, & Josiam, 2014). Additionally, the study by Ellison, Steinfield, and Lampe (2007) confirms that an intensive usage of social media, in their case Facebook, is closely related to behaviour in maintaining relationships with friends. Consequently, the intensity of social media usage can represent the extent to which this behaviour is carried out through social media usage. The origins of this behaviour are motivated by different reasons, but the two main causes that make users visit their sites frequently are: (1) the need to socialize and set up relationships, and (2) to share and search for interesting contents (Correa, Hinsley, & De Zuniga, 2010; Xiang & Gretzel, 2010). Firstly, according to Cho, Kim, Park and Lee (2014, pp. 1364), socializing was defined ‘‘as the desire to maintain and create relationships”. As social media have different features to support and develop relationships among the users (Lee & Ma, 2012), they allow users to interact with one another (Cho et al., 2014). Previous studies support this claim (Luo & Zhong, 2015; Correa et al., 2010). The second reason, to share and search for information, has a greater influence on the tourism sector. Social media, through the participation of tourists, can contribute UGC by sharing and receiving tourism information or their photographs, videos and comments about their travel experiences with others (Luo & Zhong, 2015; Xiang & Gretzel, 2010). Tourists can exchange ideas about their future trip and consult others who have experienced the same or similar journeys (Munar & Jacobsen, 2014; Zeng & Gerritsen, 2014). These benefits allow tourists to utilize social media as a search engine to plan their trip during the pre-travel period. Since UGC on social media is acknowledged as a significant information resource that may possibly support and develop their travel plan or eventually influence their travel-related decision-making, particularly as regards the tourist destination (Nezakati et al., 2015; Zeng & Gerritsen, 2014; Xiang & Gretzel, 2010), tourists can decide where to travel through the information received. However, the study by Ayeh, Au, and Law (2013) found that most Internet users do not use UGC to plan their travels, yet little is currently known about the relevant factors determining UGC usage for the specific purpose of travel planning. Previous studies explain that there is a relationship between social media usage and the UGC from social media. For example, Jacobsen and Munar (2012) mentioned that increased usage of social media produces eWOM from UGC, which is an important source of information for travel planning and travel decision-making. Ellison et al. (2007) explained how more intensive Facebook users report greater satisfaction than those who use Facebook less because they receive better

Fig. 1. Theoretical model.

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information and opportunities. Therefore, tourists who make high intensity use of social media are likely to receive better UGC. However, although the significance and the value of this relationship have been recognized, there is still a lack of empirical research about social media usage for searching for travel information (Chung & Koo, 2015). For all the reasons set out above, the first hypothesis is proposed as: H1. The intensity of social media usage will be significantly and positively associated to the user-generated content (UGC) received in the virtual search while planning the trip. The creation of tourists’ expectations in social media Since users or tourists themselves are the ones who independently generate and update all the contents on social media, and tourists usually look for reliable and credible information provided by others (Sigala et al., 2012), UGC become a valuable tourism information resource (Yoo et al., 2009). The posts, whether texts, photos or videos done by tourists about their travel experience, as well as the contents and the assessments that were written by the website owner and other users, have all improved over time and this has allowed the level of trust in social media to increase (Yoo et al., 2009). Furthermore, it is known that UGC provides more trustworthy and up-to-date information (Yoo & Gretzel, 2011) and is considered a tourism reference, although UGC are perceived as less trustworthy than traditional word-of-mouth (Yoo et al., 2009).This information is usually required by other users who are planning their holiday activities (Yoo & Gretzel, 2011) and so these potential travellers can rely on others’ experiences for their decision-making (Zeng & Gerritsen, 2014). Another point of view regards UGC as being perceived in the form of recommendations provided by friends, family members or even ‘like-minded souls’ (Zeng & Gerritsen, 2014), and thus the role of UGC providers who post their travel experiences is essential. These people usually create contents which may help other users to minimize the risks in their decisionmaking (Nezakati et al., 2015). This could decrease uncertainty and increase the usefulness of exchanges of information among users that the potential tourists could consider less biased and more trustworthy compared to information provided by tourism service providers (Zeng & Gerritsen, 2014). However, the study by Cox et al. (2009) disagrees with this. They explained that although UGC is well-accepted, it is still not considered to be as credible or reliable as existing sources of travel information provided by government-sponsored tourism websites. Therefore, our research will seek to verify this fact with the following hypotheses: H2.1. The motive for receiving user-generated content (UGC) in the social media, related to their travelling experiences, has a significant positive effect on trust in its contents H2.2. The motive for receiving user-generated content (UGC) in the social media, related to their travelling experiences, has a significant positive effect on trust in the content provider. With all the strengths of UGC in the social media, online user-generated reviews in relation to their trip to a tourist destination and tourism services become important information sources for travellers (Nezakati et al., 2015), which means that social media also become the hub of information for travellers when it comes to planning and booking their travels (Yoo & Gretzel, 2011). Zeng and Gerritsen (2014) added that information that was shared on social media is acknowledged as a significant information resource which can help tourists plan their trips or even influence their travel decision-making and the way to get there. Social media have become more a tool than just a means of planning the travel process, as can be seen from previous studies which have demonstrated that online travel reviews may affect the decision-making of travellers, including their degree of satisfaction and dissatisfaction (Gretzel & Yoo, 2008). Based on disconfirmation theory, expectations are created before purchasing products or services. The need to make the right decisions is the motivation in receiving UGC, so that the collected tourism information will later generate their expectation about their next trip. With these reasons, we formulate the following hypotheses. H3.1. The motive for receiving user-generated content (UGC) in the social media, related to their travel experiences, has a significant positive effect on the tourist’s expectations about the core resources at the destination. H3.2. The motive for receiving user-generated content (UGC) in the social media, related to their travel experiences, has a significant positive effect on the tourist’s expectations about the supporting factors at the destination. According to Yu and Zou (2015), the credibility of posts related to tourism influences consumers’ purchase intentions. Therefore, the higher the credibility of the UGC, the greater expectations of consumer purchasing there will be. From this perspective, trust in UGC and trust in other users can influence the tourist’s perspective, so that planning a journey by using social media allows tourists to make a decision with a better level of knowledge gained from the experiences of others, including friends or colleagues. Consequently, they can have a higher level of trust, since they can bring their travel expectations closer to reality (Gretzel & Yoo, 2008; Yoo et al., 2009). With these reasons in mind, we formulate the following hypotheses:

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H4.1. Trust in user-generated content (UGC) has a significant positive effect on tourist expectations about core resources at the destination. H4.2. Trust in user-generated content (UGC) has a significant positive effect on tourist expectations about supporting factors at the destination. H5.1. Trust in the user-generated content provider (UGC provider) has a significant positive effect on tourist expectations about core resources at the destination. H5.2. Trust in the user-generated content provider (UGC provider) has a significant positive effect on tourist expectations.

Research methodology Data collection and sampling design A questionnaire with a series of questions related to tourists’ use of the social media and their trip was created. We first screened the samples by selecting only tourists who had used social media to search for travel information and were travelling to the main tourist attractions in Valencia, Spain. Spain has been one of the top three countries in terms of the number of tourists in the world for over ten years and the Valencia Community has always been in the top five positions within Spain (Frontur, 2015). Moreover, the questionnaires were completed by a personal interview, since such a method based on the respondents’ real experiences allows us to acquire a more accurate and rational measurement of social media usage. In agreement with Pappas (2016), regarding the sample size for the case of an unknown population proportion, the sampling error of the study was in the range of ±5.16% and a confidence interval of 95.5%, and p = q = 0.5. Our calculations yielded a sample size of 375. Therefore, 375 completed questionnaires were collected from respondents of over 40 nationalities, which represented a response rate of 24.5% over the total number of questionnaires administered between December 2014 and March 2015. The computation method used for missing values was ML estimators based on the pairwise covariances (Satorra & Bentler, 1994). Measurement scales Following the reasons given by Kyle, Graefe, Manning, and Bacon (2003), and Gross and Brown (2008) regarding the reliability and valid number of items in each dimension and scales, a five-point Likert scale questionnaire was designed (1 = absolutely disagree, 5 = absolutely agree) to measure the variables of this study. Two items of the intensity of social media usage adapted from Ellison et al. (2007), Anderson et al. (2014) and Correa et al. (2010), four items from the motivation for social media use adapted from Jalilvand and Samiei (2012) and Ayeh et al. (2013), five items from trust in UGC adapted from Kang (2011), six items on trust in a UGC provider adapted from Chow and Chan (2008) and six items on tourist expectations adapted from Crouch (2011), and Vengesayi (2008) were analysed. Analysis methods A multiple indicators multiple causes (MIMIC) model was used to analyse the first part of the research, namely, the reasons for using UGC to receive tourist information about a specific tourist destination, such as Valencia. Then, an SEM was used to examine the relation between all the constructs in order to understand how UGC influence tourists’ behaviour, particularly their expectations and trust. MIMIC analysis. MIMIC was initially proposed by Jöreskog and Goldberger (1975) and is a special case of SEM. It is used to reach a theoretical explanation by introducing the causes of the latent variable. It allows SEM to be considered a latent variable which connects to a number of observed causal variables and another set of observable indicators. The observed variables result from the latent factors and these latent factors are caused by other exogenous variables (Krishnakumar & Nagar, 2008). To use the MIMIC model, it is necessary to confirm that there will not be any discrepancy in the measurement and that these exogenous variables or the determinant causes do not have strong correlations. Thus, according to the SEM classification, which is adapted to our MIMIC model in our case, and as explained in the literature, the intensity of social media usage (gj) is analysed as a latent variable which has the two important determinant causes that make users use social media on a frequent basis, i.e. (1) using it to socialize and create relationships (X1), and (2) updating news and searching for information (X2) (Correa et al., 2010). The equation with the relationships between the latent variable and the causes can be explained as follows:

gj ¼

q X i¼1

cij X i ¼ fj

ð1Þ

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Another relationship in our case is that between the latent variable, which is the intensity of social media usage, and the observed variables or the indicators (Yi), which are: 1) the frequency of use of social media (Y1), and (2) the number of hours spent on their social media website (Y2). The equation that connects them can be described as follows:

Y i ¼ kji gj þ ei

ð2Þ

Structural equation model (SEM). The study used SEM to investigate whether the research hypotheses are confirmed or would be rejected, and utilizes the two-step structural equations methodology (Anderson & Gerbing, 1988). First of all, the reliability of the measurement scales will be analysed, and then the study will focus on the causal model by using SEM and concentrating on the relationship between the intensity of social media usage and the motives for receiving UGC for tourism. Both the MIMIC and the SEM models were evaluated by the maximum likelihood estimate. Chi-square, df, RMSEA and others were developed by the statistical software EQS 6.2. Findings Results of the MIMIC model After examining the relationships between the causes and the latent variable, the results agree with the previous literature mentioned above in that there is a positive relationship between the reason for socializing and behaviour. The results indicated that socializing and creating relationships contribute to the intensity of social media usage (b = 0.22, t = 3.03) and, besides, updating and searching for information are also the cause underlying the level of the intensity of social media usage (b = 0.29, t = 3.66). Moreover, we found that the level of intensity of social media usage affects the frequency of social media use, such as how often users log in to their social media site (b = 0.81, t = 4.76), and the number of hours users spend on it per week (b = 0.60, t = 4.48) respectively. Chi-square is statistically significant and is recommended for evaluating an overall model if there is good fit of the scales, which need to be higher than 0.5 (Jöreskog & Sörbom, 1996); the results of v2 = 1.1592 and other results such as df = 1; p = 0.28164; SRMR: 0.041;RMSEA = 0.021; CFI: = 0.998; and NNFI = 0.989 are satisfactory. Consequently, the results agreed with the previous literature (Correa et al., 2010) in that the level of intensity of social media usage is formed by socializing and creating relationships, as well as updating news and searching for information. According to the characteristics of the formative model, MIMIC, a change in these causes will give rise to a change in the latent variables (Jarvis, MacKenzie, & Podsakoff, 2003), which explains how changes in the reasons ’socializing and creating relationships’ or ’updating and searching for information’ in turn give rise to changes in the level of intensity of social media usage. In contrast, the change in intensity does not affect any change in these causes, although it has a strong effect on the other two indicators: the frequency of use and the number of hours spent on their social media website, as mentioned by Cho et al. (2014) with regard to the positive relationship with the behaviour of users. The results showed that the effects of the latent variable on the indicators are quite strong. This explains the higher level of intensity of social media usage, the higher frequency of social media usage, and the greater number of hours spent on their social media. This MIMIC model was developed in the general model to analyse the hypotheses. The results of SEM analysis The first step of the SEM analysis started with the analysis of the measurement model (Garrigós, Palacios, & Narangajavana, 2008). Following Anderson and Gerbing’s (1988) method, SEM needs to assess the dimensionality, reliability and validity (convergent and discriminant) of the measurement model to ensure that the constructs that we are measuring are the most appropriate ones. This analysis (CFA) was performed only for the reflective observed variables and, hence, the two formative variables of the MIMIC model were not included. In any case, prior to CFA, an EFA was conducted to detect scale dimensionality and it was proved that six dimensions were obtained and that it was not necessary to delete any item of the scales used because all associated factor loadings were in all cases higher than 0.5. This indicates that EFA is suitable because the total Variances explained was 78.12%, KMO was close to unity (0.931) and the probability of Bartlett’s test of sphericity was 0.000, as suggested by Gorsuch (2003). Six dimensions were obtained with the CFA. As can be observed in Table 1, the probability associated with chi-squared reaches a value higher than 0.05 (0.08741), indicating an overall good fit of the scale (Jöreskog & Sörbom, 1996). The convergent validity is demonstrated in two ways. On the one hand, the factor loadings are above 0.6 in every item and significant because the t-values of all items are between 3 and 17, higher than 1.96 (Veasna, Wu, & Huang, 2013). On the other, the average variance extracted (AVE) for each of the factors is higher than 0.5 (Fornell & Larcker, 1981). The reliability of the scale is demonstrated because the composite reliability indices of each of the dimensions obtained are higher than 0.6 (Bagozzi & Yi, 1988). Table 2 shows the discriminant validity of all the constructs considered, which were determined by comparing the square root of the AVE with each Pearson correlation between each pair of constructs. The results show that the square root of the

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AVE estimates is higher than the corresponding inter-construct correlation estimates, which means that there is discriminant validity (Fornell & Larcker, 1981).

Table 1 Analysis of dimensionality, reliability and validity of all measurement scales (Fully standard solution).

Intensity of social media usage (AVE = 0.63; CR = 0.73; a = 0.75) How often do you connect to social media?* How many hours do you spend per week on social media?** Motives for receiving UGC (AVE = 0.56; CR = 0.79; a = 0.77) I often read other tourists’ online travel reviews to know what destinations make good impressions on others. To make sure I choose the right destination, I often read other tourists’ online travel reviews. I often consult other tourists’ online travel reviews to choose an attractive destination. When I travel to a destination, tourists’ online travel reviews make me more confident about travelling to the destination. Trust in UGC (AVE = 0.58; CR = 0.80; a = 0.83) Comments about the destination on social media are true. Photos or pictures of the destination on social media match the real situation. With social media consultation, I feel I know what to expect from the destination before I travel there. I believe that what people have posted about their holiday in VALENCIA on social media is reliable. After reading/seeing comments or pictures on social media, I believe that the destination or tourism companies will provide me with what I expected. Trust in UGC providers (AVE = 0.59; CR = 0.86; a = 0.85) They know what I like. They recommend me sites that I like. They are honest with the information they send to me. When there is something that can be of benefit to me for my stay in VALENCIA, they tell me immediately. They are aware of the things I need during my stay in VALENCIA. If I need to search for something about Valencia, they will help me to find it. Expectations (Core Resources) (AVE = 0.60; CR = 0.77; a = 0.77) Culture and History (unique, interesting culture/history; artwork, handicrafts, performances, etc.). Activities and special events (different types of tourism programmes, festivals, events such as sports competitions, exhibitions, etc.). Tourism Superstructures (well-known architecture, popular cuisine, accommodation facilities, food services, transportation facilities, etc.). Expectations (Supporting Factors) (AVE = 0.59; CR = 0.77; a = 0.76) Hospitality (friendliness of the local people, the VALENCIAN community’s attitudes towards visitors). Service Quality (reliable, responsive and highly customized service for visitors). Safety/Security (visitors feel safe and secure at all times during their stay).

Factor loading

T-value

Mean

SD

Skewness

kurtosis

0.79 0.73

3.66 3.60

5.04 2.02

1.09 0.88

-0.85 0.53

0.79 -0.29

0.74

9.73

3.50

1.16

-0.48

-0.45

0.70

10.00

3.42

1.24

-0.045

-0.72

0.67 0.66

8.67 8.72

3.31 3.35

1.19 1.05

-0.027 -0.36

-0.74 -0.22

0.72 0.68 0.73

10.70 13.09 12.29

3.49 3.81 3.47

0.85 0.93 0.90

-0.31 -0.71 -0.25

0.26 0.47 -0.09

0.73

11.35

3.45

0.92

-0.18

-0.22

0.69

9.91

3.47

0.84

-0.34

0.43

0.69 0.67 0.66 0.79

10.31 16.26 14.26 13.07

3.67 3.59 3.78 3.61

1.02 1.02 1.01 1.05

-0.66 -0.56 -0.71 -0.45

0.07 0.02 0.13 -0.21

0.74 0.70

11.35 11.53

3.28 3.55

1.11 1.05

-0.22 -0.47

-0.52 -0.20

0.71

7.57

3.55

0.93

-0.43

0.12

0.72

7.94

3.30

1.03

-0.39

-0.20

0.75

7.38

3.63

0.91

-0.33

0.08

0.73

10.73

3.62

0.96

-0.40

-0.02

0.80 0.61

10.33 7.67

3.55 3.77

0.91 0.96

-0.46 -0.55

0.41 0.04

Note: the model fits Chi-square: 242.5937; df: 214; p: 0.08741; SRMR: 0.043; RMSEA: 0.021; CFI: 0.985; NNFI: 0.982. Global a-Cronbach = 0.87. AVE is the average variance extracted, CR is the composite reliability. * ➀Rarely, ➁ Once a month, ➂ Several times a month, ➃ Several times a week, ⑤ Daily, ⑥ Several times a day. ** ➀ Less than 1 h ➁ 1–5 h ➂ 6–10 h ➃ More than 10 h.

Table 2 Discriminant validity of all the constructs considered for the model.

Intensity Motivation Trust in UGC Trust in UGC Providers Expectations (Core Resources) Expectations (Supporting Factors)

Intensity

Motivation

Trust in UGC

Trust in UGC Providers

Expectations (Core Resources)

0.79 0.29 0.31 0.08 0.11

0.75 0.55 0.44 0.39

0.76 0.55 0.44

0.77 0.33

0.77

0.10

0.30

0.44

0.36

0.67

Note: Diagonal: correlation estimated between the factors. Diagonal: square root of AVE.

Expectations (Supporting Factors)

0.77

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After examining the quality of all the constructs, the second step is to analyse the causal relationships in order to test our hypotheses (see the results in Table 3). First of all, let us start with the first relationships in the hypothesis (H1), focused on how the intensity of social media usage influences the motives for receiving UGC. The results support H1 with b = 0.45 (t = 4.92), which posited that the intensity affects the motives for receiving UGC in the virtual search while planning the trip. Another result about the relationship between the motives for receiving UGC and trust showed that the motives for receiving UGC are an antecedent of trust, regardless of whether it is trust in UGC content or trust in UGC providers. The results show a strong, direct and positive relationship between the motives for receiving UGC and the trust in UGC, in the same way as the relationship between the motives for receiving UGC and the trust in the UGC provider; and so H2.1 b = 0.63(t = 7.09) and H2.2 b = 0.51(t = 5.70) are accepted. However, the results reveal that the motives for receiving UGC do not influence tourist expectations in terms of core resources or supporting factors, and thus H3.1 b = 0.16(t = 1.20) and H3.2 b = 0.03(t = 0.21) are not accepted. In the relation between trustworthiness and tourist expectations, we found that there are significant positive relationships between trustworthiness regarding UGC and tourist expectations in both the core resources and the supporting factors aspects, so that H4.1 b = 0.30(t = 2.45) and H4.2 b = 0.37(t = 3.25) are accepted. Conversely, the results show that the relationships between trustworthiness regarding a UGC provider and tourist expectations in both the core resources and the supporting factors aspects are not significant, and so H5.1 b = 0.10(t = 1.02) and H5.2 b = 0.17(t = 1.91) are rejected. Discussion Intensity of social media usage Responding to our initial research issue, the origin of and reasons for social media usage are reviewed in this section as a part of the MIMIC analysis. As explained in the literature, one of the main reasons for using UGC is for socializing and creating relationships. Our results proved to be similar to those of the empirical study by Ellison et al. (2007) and Cho et al. (2014), and confirmed that socializing and creation of relationships have positive effects on the intensive usage of social media. Thus, we can definitely state that the socializing reason creates the behaviour of social media usage. Logically, this kind of people will have a high tendency to visit their social media sites. Another reason that was found and also matched previous literature, such as Chung and Koo (2015), Xiang et al. (2015), Ayeh et al. (2013) and Zhang, Wu, and Mattila (2014), is to search for information. This reason has a truly significant impact on every level in the tourism industry, from the destinations and tourism suppliers to the tourists. Since tourism products are an intangible and experimental product (Smith, 1994), obtaining information is especially necessary in the pre-travel period. It is needed for travel planning and to make a purchase decision, as it can reduce risk. Although the relationships between the reasons and the intensity are very similar, the ’searching for information’ reason is a little less significant than the ’socializing and creating relationships’ reason. However, it is sufficient to explain how the users or tourists use social media contents to acquire more tourist information. Consequently, the results of our study concur with previous studies (Nezakati et al., 2015; Xiang et al., 2015;) in the literature on the fact that the contents from social media are one of the most important sources for obtaining all kinds of tourist information. Moreover, we also found that the intensity of social media usage reflects how often tourists use social media and how long they use their social media for each week. This makes it clear that the intensity of social media usage is a sign of the frequency of social media usage and the number of hours spent on them. If the intensity of social media usage is higher, then the frequency and the number of hours spent on them are also higher. The relationships of all constructs in generating tourist’s expectations Our study extends the knowledge gained from the empirical study of Ellison et al. (2007) by concentrating on the usage of social media in general. Since the confirmation about the intensity of social media usage is an antecedent of the motivation

Table 3 Structural model relationships obtained.

H1 Intensity?Motives H2.1 Motives?Trust UGC H2.2 Motives?Trust UGC providers H3.1 Motives?Expectations (Core Resources) H3.2 Motives?Expectations (Supporting Factors) H4.1 Trust in UGC?Expectations (Core Resources) H4.2 Trust in UGC?Expectations (Supporting Factors) H5.1 Trust in UGC providers?Expectations (Core Resources) H5.2 Trust in UGC providers?Expectations (Supporting Factors)

Relationships

T-value

Results

0.47 0.63 0.51 0.16 0.03 0.30 0.37 0.10 0.17

4.89 6.96 5.67 1.20 (n.s) 0.21 (n.s) 2.45 3.25 1.02 (n.s) 1.91(n.s)

Supported Supported Supported Not supported Not supported Supported Supported No supported No supported

Model fit: Chi-square: 294.6959; df: 263; p: 0.08716; SRMR: 0.051; RMSEA: 0.028; CFI: 0.967; NNFI: 0.962.

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for receiving UGC, this reflects the fact that the higher the intensity is, the better the UGC received will be. Tourists or users have a greater interest in receiving information through social media. They are able to learn more about the destinations from other users’ reviews, which can make them feel more confident when it comes to choosing the right things and the right destinations for their trip. Trust is another variable which is significant in this process. Not only do we agree with previous literature about the trustworthiness of the information provided in social media on the tourism industry, but we also enrich the knowledge by intensely analysing two categories of trust: trust in UGC and trust in UGC provider. We have proved that the motives for receiving UGC affect both categories of trust. Regarding trust in UGC, the future tourists might assume that there is a high possibility of encountering the same or similar situations as those described by other tourists in the comments or images in their messages. They may also believe that the destination or tourism establishments can offer what they expected. As regards trust in the UGC providers, many of them are friends, relatives or acquaintances and so the contents seem to be more like recommendations (Zeng & Gerritsen, 2014) than general contents, as they are likely to know what the users prefer and the users can rely on this information because they trust those people. This action can reduce the risks in their travel decision-making (Nezakati et al., 2015). After all the processes have been carried out, and based on disconfirmation theory, the expectations will be created before purchasing any tourism products. Our results did not support the possibility that the motivation for receiving UGC in order to make correct decisions is likely to directly generate the expectations about the trip, as explained in the literature. We can state that trust acts as a mediator between the motives for receiving UGC and expectations about the destination. Additionally, we argue with the study of Cox et al. (2009) about the UGC provided by friends, family is less reliable than those from tourism organizations as we found that the users’ trust in UGC providers does not have any influence on their expectations about the tourism destination, regarding neither the core resources nor the supporting functions of the destination. Instead, it focuses on the contents. The users’ trust in UGC has a strong effect on the expectations about the destination in terms of both the core resources and the supporting functions. This reflects the idea that tourists’ expectations will be created when the tourists believe in or trust the UGC on social media regardless of who the person that posts the contents is. By trusting UGC, they will be able to meet their expectations. Conclusions and limitations To sum up, our study has expanded on previous studies and proposed several significant contributions. It has provided some better knowledge about the influence of UGC in the social media on generating tourists’ expectations about tourist destinations. First of all, our study provides a theoretical and innovative study that offers a global model of how a series of tourists’ expectations are set up. To date, only some partial relationships among the variables have been described in the literature. The model combines two types of analysis: MIMIC and SEM models, which represent the formative process of tourists’ expectations that contain several variables and cover areas that range from social media usage behaviour to the intensity of the usage, as well as the motives for receiving UGC, trust in UGC and the providers, and end with the tourists’ expectations. The global model allows comprehension of the reasons for and the outcome of social media usage in the tourism sector, particularly the influence of UGC on tourists’ expectation. Secondly, the study has demonstrated how, before deciding to travel, potential tourists tend to look for more tourism information by using social media, and then step up the intensity of their social media usage. From that initial moment when the first information arrived to the other moment when users can distinguish between relevant and irrelevant information, the motives for receiving UGC will be increased due to the new knowledge they have received about the tourist destination. After that, they create a motive for receiving UGC that allows them to be more confident about where they travel by receiving more contents and more information. While they are receiving tourism information, they are generating trust in those contents, and end up expecting to have experiences in that destination that are similar to those of other users if they follow those contents. Another important finding for the tourism industry is that to create expectations about a tourism destination only ‘trust in UGC’ in the social media can drive the creation of tourists’ expectations. ‘Trust in UGC providers’ has no influence on their expectations. This reflects the fact that tourists are not interested in where the information is from – it does not matter whether it comes from friends, acquaintances or family. Rather, they are more concerned with the content of the information, so that if it is positive content, it may encourage positive expectations about the destination. In contrast, negative contents may lead to negative expectations about the destination. This point is essential as it leads to satisfaction or dissatisfaction later on. Lastly, the study proved that the intensity of social media usage is a good indicator of motives for receiving UGC, which is a main antecedent of trust in UGC. Moreover, the intensity of social media is a variable which is easy to observe and makes it possible to predict the importance that tourists attribute to user-generated contents when forming their expectations. As we have seen from the study, tourists use UGC as basic tourism information to plan their trip, and so tourism establishments should take advantage of the situation to distribute related information and to create the destination image. The social media are not only useful for the dissemination of information, but can also have a influence on tourists’ expectations, as we explained above. Companies should understand the fact that tourists will create their expectations from the UGC received, although they will need to trust the contents before creating any expectations. This means the destination or

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tourism establishments should have a sustained or better level of quality and be able to offer what the tourist expects, so that tourists will finally feel satisfied with their trip to the destination, as the expectation/disconfirmation theory explains. Following this, positive contents will be maintained and tourists will be encouraged to share their good experiences with other tourists. Therefore, trust in the contents will gradually rise and, finally, expectations about destinations will be created. There are, however, some limitations to the study. Thus, we could mention the number of samples and the period of collecting them. Although the number of samples is small, they were collected by a face-to-face interview based on a questionnaire in different tourist attractions, which can have significant advantages and is more reliable than an online questionnaire method. Moreover, it provides heterogeneous data due to the fact that it represents the opinions of tourists from different countries. Another limitation is that the survey was carried out in a specific period of time, and the recommendation should be a longitudinal study that would give much better results that can be extrapolated reliably. Moreover, the findings of the study are only aimed at discovering the relations among all the constructs: the intensity of social media usage, the motives for receiving UGC, trust, and expectations before the trip takes place. Finally, for future research, it would be interesting to explore an interrelation or bilateral relation among factors in generating tourists’ expectations, especially a possibility of tourists’ expectations having an effect on trust and the received UGC. Furthermore, it would also be interesting to apply a mixed qualitative-quantitative technique like QCA (Ordanini, Parasuraman, & Rubera, 2014), which can be applied to investigate complex configurations of constructs, as is the case in this research. Moreover, we would recommend expanding on our study, which is based on the UGC received on social media, by comparing these findings with the tourist’s real perception after travelling, applying disconfirmation theory and SERVQUAL knowledge to analyse tourist satisfaction. The research could be further enriched by analysing how the result of the trip affects the creation of eWOM on social media and its influence on the UGC received by other users. By so doing, it will be possible to examine a process that goes full circle from the beginning to the end of the travel experience. Research interests of the authors 1. 2. 3. 4. 5.

Yeamduan Narangajavana, Tourism Marketing and Strategies, Customer Behaviour, and Social Media. Luis José Callarisa Fiol, Tourism Management and Marketing, Relationship Marketing, and Digital Marketing. Miguel Ángel Moliner Tena, Relationship marketing and customer loyalty. Rosa María Rodríguez Artola, International marketing and consumer behavior. Javier Sánchez García, Tourism Management and Marketing, and Service Marketing.

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