Social Network for the Choice of Tourist Destination Attitude and Behavioural Intention

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Social network for the choice of tourist destination: attitude and behavioural intention

60 Received 29 April 2011 Reviewed 8 July 2011 Revised 27 September 2011 Accepted 24 October 2011

Loredana Di Pietro and Francesca Di Virgilio Department of Management, University of Molise, Campobasso, Italy, and

Eleonora Pantano Department of Linguistics, University of Calabria, Arcavacata di Rende, Italy Abstract Purpose – The aim of this paper is to investigate how social networks can become the main tool for achieving fast and detailed information for the choice of tourism destination, in order to deeply understand the benefits of these media for promoting tourism destinations in a global perspective, reaching a wider range of potential visitors, and developing ad hoc and marketing strategies with benefits for competitive advantage on the market. Design/methodology/approach – The research focuses on an extended Technology Acceptance Model (TAM) which also integrates the constructs e-word-of-mouth communication and enjoyment. In particular, 1,397 experience users have been involved. Findings – The main findings are related to the key role of e-word of mouth communication on both the perception of usefulness and the attitude towards the use of social network as powerful tool for the choice of tourism destinations; as well as to enjoyment which underlines the role of the fun provided by the social network and represents a stronger predictor for consumer attitude and tourism behavior intention. Research limitations/implications – This research does not focus on a specific tourists’ destinations, thus the presence of different destinations may affect consumers in different ways, according to their involvement towards to a particular destination. This study contributes to deepening the scientific debate on the tourist’s destinations. Practical implications – The findings of this research support the development of tourism marketing and communication strategies focused on the online contexts as factors capable of influencing tourists’ behaviour in a more efficient way. Originality/value – This paper focuses on the web-based technologies, like social media, in order to deeply understand to what extend tourists accept the usage of these technologies for the choice of destination, by providing issues for researchers and practitioners. The present research is of a multidisciplinary value, by linking business science, psychology and social science. Keywords Tourist behaviour, E-word-of-mouth communication, Social networks, Technology Acceptance Model (TAM), Consumer behaviour Paper type Research paper

Journal of Hospitality and Tourism Technology Vol. 3 No. 1, 2012 pp. 60-76 q Emerald Group Publishing Limited 1757-9880 DOI 10.1108/17579881211206543

1. Introduction The increasing changes in tourism sector prompted by both current advances in technologies and tourist’ changeable needs forces the sector to outline a new framework based on a systemic approach, in destination governance as well as in tourism destination and marketing strategy. In fact, several factors, such as more experienced consumers, global economic restructuring, and environmental boundaries to growth

require rapid changes, especially in tourism destination for respond to market trends (Pechlaner and Tschurtschenthaler, 2003). In fact, the advances in web-based technologies, as well as the increasing interest in social networking systems prompt industry to reconsider the way for planning and consuming tourism products and services. As a consequence, there is a wide potential for marketers to use internet for tourism purposes. Internet plays a new role as intermediary, by overcoming the traditional role of tour operators and travel agencies and providing tourist the possibility to buy several tourist products and services by themselves (Buhalis and Law, 2008). For instance, half of European internet users makes decisions on their travel plans using eMarketer[1], which implies that out of every three European tourists two use the internet to upload their blogs and share reviews about their holidays with other people (Li and Bernoff, 2008). According to ISTAT (the Italian national statistics centre), the use of internet is growing more than in the past (Del Chiappa, 2011). In 2010, 52.4 per cent of Italian households accessed to internet, whereas in 2009, Italy’s share of the European travel market was about 7.7 per cent and its online travel market share was only 4.6 per cent, thus predictive studies see the Italian online travel market growing considerably over the next few years (PhoCusWright, 2010). Social media technologies provide the tools to both produce and distribute information. These technologies support collaborative writing (e.g. wikis), content sharing (e.g. text, video, and images), social networking (e.g. Facebook, Twitter), social bookmarking (e.g. ratings, tagging), and syndication (e.g. RSS feeds) (Dawson, 2007; O’Reilly, 2005). Furthermore, they increase the potentiality of the web sites, by combining interactive functions. In particular, the social network offers new powerful tools which can be exploited in tourism contexts for the promotion of local resources in a global perspective in a fast and innovative way. Social networks are becoming an efficient tool for IT-based business, by providing several services for tourism market. In fact, through the social network, the way people plan for, buy and consume tourist products and services dramatically change the role of tourism intermediaries (Buhalis and Law, 2008; Kracht and Wang, 2009). Tourists can post their thoughts and opinions about holidays and past experiences, by making them available to the global community of internet users (Dellarocas, 2003). On the other hand, internet increases the productivity and efficiency of hotels’ marketing efforts, allowing hospitality companies to reach their customers directly in order to offer them promotions and sales (Law and Lau, 2005; Pantano et al., 2011; Tse, 2003), as well as to improve the role of traditional tour operators and travel agencies (Ye et al., 2011). For instance, in February 2010, Twitter attracted an average of 21 million unique visitors over the month, and sent about 50 million tweets every day (TechCrunch.com, 2010). This information sharing process includes the increasing use of social network to link actors across market boundaries, to share common knowledge (Cheng, 2010), and to create new connections among users and between firm and clients (Boyd and Ellison, 2007). In fact, these virtual and free spaces play an important role in information diffusion among tourists capable of influencing their behavioural intentions (de Valck et al., 2009; Pantano and Servidio, 2011) as well as on the creation of a common topic-oriented knowledge. As a consequence, the communication mediated by social networks has positive or negative effects for tourist’s judgments and on their subsequent decisions (Knights and Willmott, 2007; Pantano et al., 2011).

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Hence, due to the powerful tools of social networks such as interactivity, high quality visualization, and fast messaging and searching, social network can become the main tool for the choice of tourism destination (Hogg, 2010; Kim, W. et al., 2010; Kwon and Wen, 2010; Pantano et al., 2010). The use of social network for tourism purposes is a topic relatively new (Kasavana et al., 2010). For instance, in the Italian context, to what extend Italian tourists uses social networks for the choice of tourism destinations is still under investigated. Since tourists prefer enhanced graphical web site design and ease of use tools for their choice (Pantano et al., 2011; Stringam and Gerdes, 2010), travel agencies and destination marketers are prompted to provide several high quality photos and videos of destination through social network for attracting more consumers’ interest. In this scenario, social networks increases the traditional tools offered by web-based technologies, due to the interactivity and their free and fast access from everywhere (Isacsson and Gretzel, 2011; Machlouzarides, 2010; Pantano and Servidio, 2011; Tatsiopoulos and Boutsinas, 2010). Social networks can be a technology more appealing if compared to the ones more oriented to tourism sector, capable of catching consumers’ interest and influencing their decision. Hence, travel agencies and tourism operators should take into account these tools for the development of more efficient marketing strategies. The aim of this paper is to model the use of social networks as powerful tool for the choice of tourism destinations, by extending the use of technology acceptance model (TAM). In particular, the first part of this paper analyses the most important evidences on TAM model and on the influence of perceived enjoyment and e-word of mouth (eWOM) communication on tourist decision choices, whereas the second one is devoted to a quantitative analysis which involves 1397 experience users in order to predict their intention to use social network as tool for supporting their decision choice of tourism destinations. The present research has a multidisciplinary value, by linking Business Science, Psychology and Social Science. 2. Theoretical background 2.1 Technology acceptance model TAM is one of the most used tools for predicting user’s adoption of a new technology. Currently, it is largely used for predicting users intention to accept new technologies in several sectors, such as for information technologies (Wu et al., 2011; Al-Somali et al., 2009; Kim, J.U. et al., 2010), with emphasis in participation to online communities (Chung et al., 2010), for learning (Bourgonjon et al., 2010; Saade´ and Bahli, 2005), for shopping (Baier and Stuber, 2010; Doong et al., 2011), for tourism and hospitality. In this scenario, meaningful examples are the analysis of the influence of web sites on the intention to travel and on the hotel reservations (Kaplanidou and Vogt, 2006; Morosan and Jeong, 2008), the acceptance of hotel front office system (Kim et al., 2008), consumers’ intention to use the airline business-to-consumer ecommerce web sites (Casalo` et al., 2010). TAM is based on the theory of reasoned action (Fishbein and Ajzen, 1975) with the aim to investigate computer usage behaviour, by mainly focusing on the constructs of perceived ease of use and perceived usefulness, and on the subsequent acceptance of technology in terms of attitude towards the technology, intention to use and effective use (Wu et al., 2011; Al-Somali et al., 2009). Perceived ease of use represents the degree

to which a user believes that using that technology would not require effort (Chung and Tan, 2004; Davis, 1989), whereas perceived usefulness represents the degree to which a user believes that using that technology will enhance his/her job performance (Chung and Tan, 2004; Davis, 1989). In particular, the model focuses on the effects of perceived ease of use and usefulness on the attitude in using the technology and on the subsequent users’ behaviour; where attitude consist of user assessment toward the technology and behavioural intention represents the degree to which a person is prompt to accomplish a certain behaviour (Davis, 1989). In fact, if the technology requires no effort and enhances the job performance, then users are prompt to make an extensive use of the technology in terms of more frequency and time. Since the particular characteristics of social network and the importance of social influence expressed as interactions among users, this research extends the knowledge on TAM by adding the constructs related to enjoyment and eWOM communication. Thus, we hypothesize the following relationships: H1. Perceived usefulness of this technology affects the tourist attitude towards the use of the social networks for the choice of tourism destinations. H2. Perceived ease of use has a direct influence on the tourist’s attitude towards the use of the social networks for the choice of tourism destination. H3. Tourist’s attitude towards the use of the social networks has a direct influence on his/her subsequent behavioural intention in using this technology for the choice of tourism destination. Furthermore, other constructs might be added to the previously identified, such as emotional ones. In fact, several authors suggest the introduction of perceived enjoyment (Hsu and Lin, 2008; Kwon and Wen, 2011; Al-maghrabi et al., 2011; Liao and Tsou, 2009), which is effectively linked to web use. Since the main characteristics of social networks are meeting other users and achieving/sharing information (i.e. videos, texts, audio, etc.) in an ease and entertaining way and the attention paid to the enjoyment in the case of online technologies is largely recognized (Known and Wen, 2010), we suppose to extend the traditional TAM by analyzing the role of the enjoyment and the influence emerging from the social interactions while online. In fact, the role of social digital media in the tourists choices is a interesting new area of research (Casalo` et al., 2010; Parra-Lo´pez et al., 2011; Susskind and Stefanone, 2010), thus the use these media for the choice of tourism destination is still underdeveloped, as well as the conjoint role of enjoyment and eWOM influences in this direction. 2.2 The influence of perceived enjoyment Interacting in a social network is a process through which users pursue also hedonic value, such as enjoyment, pleasure, and new experiences. Since the hedonic value of a technology influences the usage of a certain technology (Kim and Han, 2011), enjoyment emerged from the online experience has added to traditional TAM, due to its positive effect of attitude toward using the technology (Chen and Chen, 2011; Kang and Lee, 2010; Liao and Tsou, 2009). In particular, it can be defined as the degree to which perform a task is perceived as providing pleasure (Venkatesh, 2000). Indeed, numerous studies demonstrated the role of an entertaining

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context for supporting several processes (Pantano, 2010) and providing benefits in computer-mediated environments capable of influencing users behavior (Kim, J.U. et al., 2010). In fact, it is an emotional state which prompts users in persisting in such behaviour (Oh et al., 2009; Jiang, 2009), as using online technologies (Cheung et al., 2011). As a consequence, people who perceive the online technology as enjoyable are more likely to have favourable feelings towards it, as well as people who exhibit a high degree of fun while online spend longer visiting that web site and are more likely to choose it again for their needs. Furthermore, perceived enjoyment can be influenced by the quality of interaction while online, in terms of ease of use of the interactive tools, quality of navigation, etc. (Yoon and Kim, 2007). To extend which it evokes hedonic feelings might impact tourist online experience are crucial topics for the current researches. Therefore, we hypothesize: H4. Perceived enjoyment of the social networks affects the perceived usefulness of the media as supporting tool for tourism destination. H5. Perceived ease of use has a direct effect on the perceived enjoyment. H6. Enjoyment has a direct influence on the attitude toward the social networks. 2.3 The influence of eWOM communication Since the characteristics of internet in terms of ease of knowledge sharing and fast messaging, web-based technologies are giving a new meaning to WOM modalities. This represents an informal communication mode between individuals concerning the evaluation of such a product. Due to its characteristics of low cost and high reliability in information transmission, it extends the consumers’ choice for achieving information on products and services (Jeong and Jang, 2011), with several consequences in decision-making process if compared to the traditional WOM, by facilitating the search for information (Zhang et al., 2010). In particular, it includes informal communications directed at consumers through internet-based technology related to the usage or characteristics of particular goods and services or their sellers (Litvin et al., 2008). It is possible to consider eWOM as different to traditional WOM communication because it reaches audiences of unprecedented scale and allows organizations to monitor and control their operations (products, services, events). Since the intangible characteristics of many tourism products, consumers cannot be evaluated before the consumption experience, therefore the online suggestions/recommendations reduce the risk involved in the process (Jeong and Jang, 2011). In social networks people develop a sense of community and trust in the comments posted online (de Valck et al., 2009; Utz et al., 2011) as if they were interacting face-to-face (Yadav and Varadarajan, 2005). Since the information is generated through the interaction among several consumers, the process enhances the richness and reliability of information. Furthermore, other studies highlighted users’s interest in participating in the social life promoted by the social network, due to the provided fun (Hsu and Lin, 2008). In fact, positive online suggestions/comments/reviews are capable of improving the tourist’ perception of the travel product among potential users, thus online suggestions/comments provided by other users has a significant effect on the online sales of tourism products

(i.e. online rooms booking) (Ye et al., 2011; Utz et al., 2011). Previous research also showed that people usually buy convenience and standard goods online, while they deeply rely on traditional intermediaries when buying complex products (Werthner and Klein, 1999). Similarly, tourists are more willing to buy low-involvement products through the internet than high-involvement products (Chu, 2001). In this way, eWOM provides further and high customized product information, on the other one it measures the product popularity (Casalo` et al., 2010; Chan and Li, 2010; Park and Kim, 2008), through the access to social digital media, ad hoc virtual communities, blogs, etc. As a consequence, consumers are prompt to use the social media for searching information on possible tourism destinations, visualizing images, access to tourist’s previous experiences, in order to gain the sufficient elements for the best choice (Sicilia and Ruiz, 2010). Therefore, understanding how tourist is influenced by the interaction with other consumers becomes a key factor for the development of efficient tourism destination and marketing strategies (Ye et al., 2011) with consequences in the destination image (Lee et al., 2002). On this basis, we hypothesize the following relationships:

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H7. eWOM communication has a direct influence on the perceived usefulness of the social digital media for the choice of tourism destinations. H8. eWOM communication affects the perceived enjoyment. H9. eWOM communication has a direct influence on the tourist’s attitude in using the social digital media for the choice of tourism destinations. 3. Research model Based on the theoretical background, the variables of the research model are perceived usefulness, ease of use, enjoinment, eWOM communication, attitude, and behavioural intention. Figure 1 summarizes the relationships among the identified variables. E-word-of-mouth communication H7

H8

Perceived usefulness

H9 H1

Attitude

H4

H3 Behavioural intention

H2 Ease of use H5 Enjoyment

H6

Figure 1. Research framework

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3.1 Measurement scale The participants were asked to fill a questionnaire developed on 21 items, including 16 items on perceived usefulness, ease of use, enjoyment, eWOM communication, attitude and behavioural intention which were measured with five-points Likerts’ scale (1– strongly disagree; 5 – strongly agree) and six items on consumer profile including gender, age, education, as well as the most used place for connecting to virtual community, the experience on the system in terms of months from the first access, and the time spent during each visit. Table I represents the constructs adapted from the related literature. A pre-test with a convenient sample has been carried out in order to calculate the minimum time to complete the questionnaire. It was 5.2 minutes (mean ¼ 5.1, maximum ¼ 5.9), which implied that some subjects may not have taken the questionnaire seriously, and may not have read the items before answering. To avoid the increased error variance introduced by such subjects, 5.2 minutes was used as a cutoff for inclusion in the analysis. The data were edited by checking and adjusting for errors, omissions, legibility and consistency in order to ensure completeness, consistency, and readability. Since the large use of structural equation model (SEM) for evaluating relationships between variables and expressing complex variable in one analysis such as in TAM (Gefen et al., 2000), authors used LISREL software for analysing the covariance-based SEM. Each measurement item has been validated by analyzing the value of Cronbach’s a through SPSS 17.0 software (Table II). Since the value for each variable satisfies the suggested ones (Cronbach and Shavelson, 2004), the proposed research model is reliable. 3.2 Sample The sample consists of undergraduate students, employees and academics from the areas of study of Touristic Sciences and Tourism/Hospitality at Universities of Southern Italy. We assumed that their previous experience with the medium might

Table I. Constructs adapted from literature

Constructs

Source

Perceived usefulness Perceived ease of use Perceived enjoyment eWOM Attitude Behavioural intention

Wu et al. (2011) and Al-Somali et al. (2009) Wu et al. (2011) and Al-Somali et al. (2009) Kim and Han (2011) and Liao and Tsou (2009) Jeong and Jang (2011) Wu et al. (2011) and Al-Somali et al. (2009) Kim and Han (2011), Davis (1989) and Al-Somali et al. (2009)

Factors

Table II. Cronbach’s a value

Perceived usefulness (PU) Perceived ease of use (EOU) Perceived enjoyment (ENJ) E-word of mouth communication (EWOM) Attitude (A) Behavioural intention (BI)

Items

Cronbach’s a

3 3 3 2 3 2

0.803 0.865 0.792 0.779 0.816 0.882

be more influenced by familiarity with the system on the perception of the touristic destination’s presence on the social network, as suggested by Casalo` et al. (2010). Participants were asked to complete anonymous surveys within a period of three months from October 2010 to December 2010. A total of 1,509 questionnaires were returned, by generating 1,397 usable responses for the statistical analysis. Table III shows the tourists’ demographics. The male and female respondents are similar in number (51.2 per cent female; 48.7 per cent male), whereas the largest age group was composed of students under 25 years of age (59.0 per cent). In fact, just the 15.2 per cent were over 36 while 25.8 per cent were between 36 and 35 years of age. Most respondents reported a secondary school qualification (74.7 per cent) whereas 21.4 per cent had a university degree or a postgraduate degree (3.7 per cent). The other demographic information concern the level of technology being used, both in terms of ease navigation, searching, uploading and downloading of tourism material. Respondents reported a high mean for involvement in the social life in the network due to the duration of each visit (the 43.5 per cent spends 1-3 hours each time) and the long lasting presence on virtual community (the 70.2 per cent is virtual community user from more than one year).

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3.3 Findings Further fitness indexes to evaluate the reliability of the proposed model are analysed through LISREL, such as x 2 to degree of freedom, goodness-of-fit index (GFI), normed fit index (NFI), comparative fit index (CFI), root-mean-square error of approximation (RMSEA). Table IV makes a comparison between values recommended by literature (Casalo` et al., 2010; Ye et al., 2011) and the results of the model. The results confirm the good fit of the model and of the data structure. Measure

Items

%

Gender

Male Female Missing Under 25 26-35 Over 36 High school Master degree PhD/specialization Missing Home University/office Other Under three months Three to nine months More than one year Less than 1 hour 1-3 hours More than 3 hours Missing

48.7 51.2 0.1 59.0 25.8 15.2 74.7 21.4 3.7 0.2 82.5 10.6 6.9 10.4 19.4 70.2 42.8 43.5 13.6 0.1

Age Education

Place for connecting to virtual community Experience in virtual community Time in virtual community each time

Table III. Tourist’s profile

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Results support all the hypotheses. Figure 2 shows the results of the structural model analysis, by underlining the value of the explained variance (R 2) and the standardized coefficients, significant at p , 0.001. H1 is supported, thus it predicts a positive casual relationship between perceived usefulness and tourists attitude towards the use of the social network for the choice of tourism destination (b ¼ 0.33), with a value of R 2 which explains the 0.26 of variance. Consistent with H2 and H6, attitude is influenced by perceived ease of use (b ¼ 0.20) and enjoyment (b ¼ 0.32). In particular, perceived enjoyment is the variables with the strongest influence on attitude, which affects tourists’ behavioural intention towards the use of social network for the choice of tourism destination (b ¼ 0.38). H4 is also confirmed, thus perceived enjoyment is significantly related to perceived usefulness of the social network for the choice of tourism destination, with a standardized coefficient value of 0.18 and a R 2 which explains the 0.25 of variance. Moreover, findings show the positive casual relationship between perceived enjoyment and perceived ease of use (H5), with a path coefficient value of 0.l9 and R 2 ¼ 0.36. Analysis carried out the influence of eWOM communication on perceived usefulness (H7, b ¼ 0.34), perceived enjoyment (H8, b ¼ 0.11), and tourists’ attitude (H9, b ¼ 0.28), with a value of R 2 (0.25) which may exclude the presence of other variables. Fit indexes

Table IV. Model fit indexes values compared to the ones suggested by literature

Recommended value

Result

,3 . 0.8 . 0.8 . 0.9 . 0.9 , 0.08

2.98 0.80 0.81 0.91 0.90 0.016

x 2/degrees of freedom GFI AGFI NFI CFI RMSEA

E-word-of-mouth communication R2 = 0.25

0.11

0.34 0.28

Perceived usefulness 0.18

0.33

R2 = 0.25 Attitude 0.20

2=

R

Ease of use R2 = 0.19

Figure 2. Casual relationships values in the structural model

0.19

0.32

Enjoyment R2 = 0.36

*p