Drivers of social network adoption in post-travel phase: An empirical study in Thai tourists

Abstract

As tourism is a fast-growing industry, it is worth focusing on understanding travelers’ information generation behavior. The previous research also found that research in the motivation of travelers in sharing travel information through SNSs is quite limited. This research aims to study social network site usage intention in the post-travel phase by adding information features, media features, and user factors. This paper used a quantitative approach with the SEM technique. Questionnaires completed by 450 Thai travelers were analyzed by using a structural equation modeling test. The results suggested that perceived usefulness, customer involvement, and information accuracy were the significant factors that enhanced travelers’ social network site’s adoption intention after their trip. Positive relationships existed between perceived usefulness, subjective norm, and accessibility factors positively influenced perceived usefulness. This study suggests that tourism businesses should create their SNSs accounts in well-known and easy accessed social media to increase the number of reviews and feedbacks. The present paper contributes to previous research by identifying the impact of the information feature, media features, social and user factors on social network sites adoption intention, specifically in the post-travel phase.

Keywords:

• social network sites
• behavioral intention
• smart tourism technologies
• technology acceptance

PUBLIC INTEREST STATEMENT

This research highlights social network sites usage intention after tourists finish their trips. It focuses on how tourists share their travel information through social network sites. This study shows that perception of tourists that their information are useful and accurate, in cooperate with their involvement will enhance the likelihood of their intention to share travel information in social network sites after their trip. Moreover, if social network sites are easy access and their peers would love them to use the sites, they will feel that using social network sites is useful. Thus, this study recommends that tourism businesses should have their own social network site accounts because they can have relationship with travelers through reviews and feedbacks.

1. Introduction

Internet, social network sites (SNSs), and mobile devices play crucial roles in individuals’ information exchange. People use the internet to search for information easily and efficiently, while SNSs can help people connect with each other. Internet and SNSs users do not only search for information but also generate and share their information with others (Bronner & de Hoog, 2011; Sigala, 2012). SNSs users can be both information providers and receivers because information sharing is one of the characteristics of SNSs (Chung, Han, & Koo, 2015). SNSs provide users with general information as well as sources of specific information during their searches (Parra-López, Gutiérrez-Taño, Díaz-Armas, & Bulchand-Gidumal, 2012). Mobile devices can help individuals to connect with internet and SNSs when in transit. Smartphones with the Internet of Things (IoT) offer mobile location-based services (Almobaideen, Krayshan, Allan, & Saadeh, 2017). With the combinations of these information and communication technologies, individuals can exchange information easily anywhere and anytime.

Not only using these technologies for their daily lives, but individuals also use them for their travel. These technologies in the tourism industry, called smart tourism technologies, can help travelers connecting with travel information and service providers easily during all stage of their trips, from pre-trip to post-trip. Travelers use the internet for information searching and acquire important travel information (Chung & Koo, 2015). In term of mobile devices, smartphones can help travelers acquire services which making their trips become more effortless and harmless (Almobaideen et al., 2017), which later on create distinct travel experiences for travelers (Chung, Lee, Lee, & Koo, 2015). Previous scholars (Inversini & Masiero, 2014; Law, Leung, Lo, Leung, & Fong, 2015) pointed out the important roles of SNSs in the travel decision-making process because travelers use SNSs to acquire and provide travel-related information. Thus, SNSs have become powerful travel-related sources for travelers to gather information during their travel decision-making process and allow travelers to share their experiences through reviews, ratings, and comments (Bilgihan, Barreda, Okumus, & Nusair, 2016). In tourism aspects, SNSs are important for information searches, travel decision-making, the interaction between travelers and service providers (Zeng & Gerritsen, 2014). With the information sharing character, SNSs get a lot of attention (Choi, 2013; Koo, Wati, & Jung, 2011). For example, travelers use online reviews to share their tourism-related products and services with others (Harrigan, Evers, Miles, & Daly, 2017). After their trips, travelers can share travel information and experiences online and send feedback to tourism providers for future improvements. Thus, the usage of SNSs varies at each stage of the travel decision-making process.

Governmental and business organizations can utilize information from travelers gained through SNSs. The tourism industry intends to utilize ICTs to enhance management and practices in both governmental and business aspects to generate more tourist experiences and increase organizational competitiveness (Gretzel, Werthner, Koo, & Lamsfus, 2015; Hunter, Chung, Gretzel, & Koo, 2015; Koo, Gretzel, Hunter, & Chung, 2015). In this perspective, SNSs can assist tourism organizations to enhance their competitive advantage, because organizations can use the information from travelers to extract and understand customers’ behaviors and needs. They, afterward, can provide products/services which serve travelers’ needs and attract more travelers. The technologies rapidly transform travelers’ needs and expectations, business environments, and industry structure (Gretzel, Yuan, & Fesenmaier, 2000). Therefore, it is important for both academics and practitioners to maintain careful observation of SNSs because of their importance toward travelers’ travel decision making behavior.

As the tourism industry is a fast-growing industry (Almobaideen et al., 2017), it is worth focusing on understanding travelers’ information generation behavior. Regarding Bilgihan et al. (2016), research in travelers’ motivation to share travel information through SNSs is quite rare. Moreover, Del Chiappa and Baggio (2015) pointed out that there are a small number of researches that aimed to analyze the information sharing processes in smart tourism destinations. This study aims to identify the drivers of smart tourism technologies implementation of travelers after their trips. Therefore, the objectives of this study are as follow: (1) to examine factors affecting the perceived usefulness and intention to adopt SNSs after the trip, and (2) to analyze the influence of perceived usefulness on intention to adopt SNSs after the trip. Results from this study will explain travelers’ intention to use SNSs after their trips, and its’ influential elements. The current study contributes an understanding of critical motivators related to SNSs adoption intention specifically at the post-trip stage. The main contribution of this study is to provide communication factors which stimulate SNSs usage intention in the stage of post purchase behavior in consumer decision making process theory. Moreover, with the high volumes of data transactions of tourism industry, this research also contributes to the tourism industry in particular, in order to clarify how tourists intend to adopt the SNSs in post-travel stage, in which their roles are information providers instead of information receivers.

2. Theoretical background

The concept of smart tourism technologies (STTs) is associated with the concepts of smart city and smart tourism destinations because the technologies implemented in destinations can enhance both locals’ way of living and travelers’ travel experiences. From a smart tourism destination perspective, technological implications by both travelers and local stakeholders are all important. Nam and Pardo (2011) pointed out that the priorities of smart tourism destinations are to improve the travel experience of travelers and prepare platforms for local organizations’ information utilization. Therefore, the demand and supply side of information are needed to be considered equitably. Organizations adopting STTs depend on useful information and transform this into a value proposition (Gretzel, Sigala, Xiang, & Koo, 2015). Travelers play crucial roles in both data consumption and generation. Due to the advancement of ICTs, travelers can access information easily. They can also effortlessly share travel information immediately after their destination visit. With the support of SNSs, travelers can connect with information, travel organizations, other travelers, and their peers at all travel stages. For example, travelers apply social media in searching, collecting, and communicating travel information from people’s reviews (Parra-López et al., 2012). Because SNSs provide platforms for users to create, comment and share their contents (Harrigan et al., 2017), travelers can share their travel experiences through SNSs, which other people may utilize for their trips. Travelers can utilize both general and traveling social media sites. In the tourism industry, social media utilization is high (Leung, Bai, & Stahura, 2015) and is increasing in popularity and attraction (Cabiddu, De, & Piccoli, 2014; Filieri, Alguezaui, & McLeay, 2015; Munar & Jacobsen, 2014). Thus, travel information consumption and creation in SNSs are linked as a cycle.

With the platform of SNSs that allows users to share travel information freely. In the tourism industry, the crucial role of SNSs is travel information communication, which combines both information receiving and providing (Chung et al., 2015). Zhang and Watts (2003) indicated that the success of information sharing requires giving and taking together. The contents created in SNSs can be acquired by using search engines (Yoo, Goo, Huang, Nam, & Woo, 2017). This circumstance encourages travelers to share their travel information. Qu and Lee (2011) mentioned that travel information sharing is related to travel expert knowledge and experiences. According to Y. Huang, Basu, and Hsu (2010), obtaining travel information, information dissemination, and personal documentation are crucial motivators for travelers to share travel knowledge through SNSs. Regard to Kang and Schuett (2013), social factors, such as identification and internalization, positively affect travel experience sharing on SNSs. Therefore, travelers can be motivated to share travel information by the characteristic of the information and relationship with others. They may also provide feedback to the service providers in order to improve service practices. Thus, information from travelers can help both other users and local stakeholders.

The technology acceptance model (TAM) is a model aiming to understand the factors affecting technologies. The TAM was basically made for an information system context to predict acceptance and usage of IS technologies (Venkatesh, Morris, Davis, & Davis, 2003). Core constructs of the TAM cover perceived usefulness and perceived ease of use. Later, in TAM2 subjective norm was added to improve the model (Venkatesh et al., 2003). Subjective norm is also an important construct of the theory of planned behavior (TPB), which aims to connect people believe with their behavior. This theory was proposed by Ajzen (1985) from the theory of reasoned action (TRA) to enhance predictive power (Ajzen, 1991). The core independent constructs of the theory consist of attitude toward behavior, subjective norm, and perceived behavioral control. These factors influence an individual’s behavioral intention and actual behavior. Scholars in technological studies have adopted TAM with their focused topics to assess predicting the power of technological acceptance behavior, including tourism-related technologies scholars (e.g. Chen & Tsai, 2017; Hew, Leong, Tan, Lee, & Ooi, 2018; Xia, Zhang, & Zhang, 2018). TRA has also been widely adopted in technology adoption studies (e.g. Alavion, Allahyari, Al-Rimawi, & Surujlal, 2017; Dezdar, 2017; Yang, Lee, & Zo, 2017). With SNSs circumstance, this study would like to adapt and adjust the constructs from TAM and TPB to understand travelers’ intention to adopt SNSs, specifically in the post-travel stage. Taken from TAM and TPB, perceived usefulness and subjective norm were added into this study. Moreover, information accuracy, customer involvement, and accessibility are added as independent factors. Lastly, intention to adopt SNSs in the post-trip stage participates as a dependent factor.

In tourism industry related-research, there are several factors that can stimulus tourists’ intention to use information technologies, including SNSs. Buhalis and Law (2008), Law, Qi, and Buhalis (2010) and Xiang, Wang, O’Leary, and Fesenmaier (2015) agreed that information accessibility is associated with tourism website utilization. With accessibility through tourism information technologies, travelers can acquire travel information (Yoo et al., 2017). Information accuracy was also studied in the context of tourism information technologies and found to be the strongest indicator in a travel online review adoption (Filieri & McLeay, 2014). The study of Bilgihan et al. (2016) mentioned that an imperative person of travelers is a key factor to stimulate individuals not to switch their travel-related SNSs because they think travelers should utilize them. In terms of customer involvement, Harrigan et al. (2017) stated that tourism SNSs customer involvement can lead to customer engagement and increase loyalty. Lastly, perceived usefulness has been applied in tourism studies and was found to affect travel information adoption (Chung et al., 2015; Kucukusta, Law, Besbes, & Legohérel, 2015). With the mentioned above studies, it can be found that these factors have a relationship with tourism-related information technologies, including SNSs adoption. Thus, this study applies the above-mentioned factors to understand how they impact on SNSs adoption intention, specifically in post-travel phase.

3. Model variables and hypotheses

3.1. Accessibility

Accessibility is the degree to which customers effortlessly discover information from online resources (Ho & Lee, 2007). In other words, accessibility is assessed by the effort users use to access websites to get to the content (Petrie & Kheir, 2007). The fewer effort users have to use, the more accessibility they would perceive. Regarding C. D. Huang, Goo, Nam, and Yoo (2017), accessibility in the tourism industry is defined as “the degree to which travelers can easily access and use online tourism information sources”. Accessibility is one of the attributes of an online tourism information source (No & Kim, 2015; Yoo et al., 2017). It is also one of the factors determining the usability of tourism websites (Muhtaseb, Lakiotaki, & Matsatsinis, 2012). This attribute can elicit customer travel planning (Huang et al., 2017). Previous studies agree that a high level of accessibility can elicit perceived ease of use in travel websites for travel search at the pre-trip stage (Buhalis & Law, 2008; Law et al., 2010; Xiang et al., 2015). Moreover, accessibility can increase travelers’ overall travel quality (Perdue, 2002). Information accessibility is related to the usability of tourism websites (Buhalis & Law, 2008; Law et al., 2010; Xiang et al., 2015). Good accessibility can help travelers use tourism information technologies, e.g. websites, to search and navigate travel information (Yoo et al., 2017). After experiencing destinations, accessibility can also help travelers to share travel experiences easily through SNSs. With easy access to the system, travelers can sense that the technologies are useful travel decision support tools (Yoo et al., 2017) and are willing to provide useful information for other travelers and service providers. This study adopted the accessibility of SNSs after travelers had completed their travel experience. In the context of SNSs adoption after travel, this study assumed that perceived usefulness was positively influenced by accessibility. Hence, this hypothesis is proposed:

H1: Accessibility positively influences travelers’ perceived usefulness toward using SNSs.

3.2. Subjective norm

In regard to Ajzen (1991), the subjective norm is defined as “the perceived social pressure to perform or not to perform the behavior in question” (p. 188). It is an awareness of individuals of how an influential person think the individual should behave (Teo, 2009). Subjective norm attempts to force an individual to engage in a specific behavior (Fishbein & Ajzen, 1974, 1975). In TRA, subjective norm plays a crucial role in individuals’ behavioral intentions (Kucukusta et al., 2015). Intimates of individuals may share opinions or provide suggestions. Individuals may accept certain behaviors of their surrounding intimates, in which attract their behavioral intention (Pavlou & Chai, 2002). Moreover, Venkatesh and Davis (2000) propose that subjective norm has a direct effect on perceived usefulness. In this case, travelers’ intimates may influence how travelers perceive the usefulness of using SNSs. A study by Bilgihan et al. (2016) found that individuals will not switch their travel-related SNSs if their important person thinks they should use it. Studies in tourism studies have hypothesized that subjective norm is a key factor of perceived usefulness, such as airline B2C e-commerce website (Kim, Kim, & Shin, 2009), and electronic booking (Bhatiasevi & Yoopetch, 2015). Thus, travelers may be encouraged by their influential person to recognize the utility of SNSs. When travelers sense that their intimates have positive viewpoints toward SNSs in sharing travel experience and information, they will have a greater awareness of SNSs’ effectiveness. This study investigated the influence of subjective norm on perceives usefulness to use SNSs after a trip. Hence, this hypothesis is proposed:

H2: Subjective norm positively influences perceived usefulness toward using SNSs.

3.3. Information accuracy

With reference to Nelson, Todd, and Wixom (2005), information accuracy is the exactitude of the information measurement to the proper state that the information explains. Information accuracy is one of the information quality dimensions (Filieri & McLeay, 2014; Wang & Strong, 1996). Moreover, information accuracy was the strongest predictor in the information quality dimensions in an online review of travel information adoption (Filieri & McLeay, 2014). With a variety of information available on SNSs, travelers need to search and sort the information before they select accurate information for making trips. In this case, information accuracy relies on the recognition by the travelers whether the information is correct, believable, accurate, and credible (Wang & Strong, 1996). Travelers usually gather travel information through SNSs’ reviews and comments. Most of the comments are from independent travelers, which may create a perception for information seekers that the information is correct, straightforward, and free from bias (Filieri & McLeay, 2014). Compared to other information sources, information from other travelers is perceived to be more trustworthy and accurate (Bickart & Schindler, 2001; Dickinger, 2011; Senecal & Nantel, 2004; Smith, Menon, & Sivakumar, 2005). Moreover, when travelers have experienced from their trips, they may desire to comment and share accurate information based on the experience of their trips. Therefore, this article would like to explain another perspective of users, as information generators. Perception of information accuracy from travelers’ direct experience will lead to an increase in the possibility of travelers adopting SNSs in creating their review or comment. In the context of this study, when travelers realize that SNSs can provide accurate information from their travel experience, they will have a greater intention to use them. Hence, this hypothesis is proposed:

H3: Information accuracy positively influences travelers’ intention to use SNSs after their trips.

3.4. Customer involvement

With regard to Zaichkowsky (1985), involvement is defined as “a person’s perceived relevance of an object based on inherent needs, values, and interest”. Customer involvement occurs when customers sense the pertinence of an object grounded on customers’ focused perspectives. In the tourism industry, tourism organizations try to create their brand and attract customers to choose their businesses. In this case, businesses try to pursue customer engagement through customer involvement. Businesses use various SNS to stimulate involvement in different customer types such as review, rating, photo sharing, storytelling, and so on. Harrigan et al. (2017) mentioned that customer involvement in tourism SNSs leads to customer engagement, which later on, increase users’ intention of loyalty. The study mentions that if travel providers would like to have effective customer engagement, they should use SNSs to stimulate the involvement of users (Harrigan et al., 2017). Therefore, customer involvement can be a great help for tourism organizations. In the context of SNSs adoption after travel, this study assumes that travelers’ intention to use SNSs after their trips is positively influenced by customer involvement. Hence, this hypothesis is proposed:

H4: Customer involvement positively influences travelers’ intention to use SNSs after their trips.

3.5. Perceived usefulness

Davis (1989), perceived usefulness is defined as “the degree to which a person believes that the use of a particular system would enhance his or her job performance”. Perceived usefulness is a dimension in TAM. Usefulness plays a significant role in encouraging behavioral intention to use technology (Davis, Bagozzi, & Warshaw, 1989). Comparing to another TAM leading factor, perceived ease of use, previous scholars found that users perceive the importance of usefulness at a higher degree than ease of use (Davis, 1989; Lee, Kim, & Lee, 2006; Morosan, 2012; Ruiz-Mafé, Sanz-Blas, & Aldás-Manzano, 2009). Perceived usefulness can help travelers enhance their information search for tourist destinations, services, or products (Muñoz‐Leiva, Hernández‐Méndez, & Sánchez‐Fernández, 2012). Perceived usefulness has a direct relationship with behavioral intention to use some technological devices (Davis, 1989; Munir, Idrus, Kadir, & Jusni, 2013). In the tourism industry, perceived usefulness was found to influence travel information adoption (Chung et al., 2015) such as online travel booking (Kucukusta et al., 2015). Moreover, scholars found that perceived usefulness plays an important role in intention to use internet for tourist information search (Castaneda, Frias, Munoz-Leiva, & Rodriguez, 2007; Luque-Martínez, Castañeda-García, Frías-Jamilena, Muñoz-Leiva, & Rodríguez-Molina, 2007). Since SNSs are anticipated to be helpful for travelers to share their travel experiences, this study investigated the influence of perceived usefulness on the intention to use SNSs after their trip. Hence, this hypothesis is proposed:

H5: Perceived usefulness positively influences travelers’ intention to use SNSs after their trips. Figure 1. illustrates the research structure and hypotheses of the current study.

Figure 1. Illustration of the framework and hypotheses of this study.

4. Methodology

A questionnaire was outlined in the above-mentioned research framework and hypotheses. The questionnaire was divided into two parts, including the demographic background of the respondents and questions related to the constructs in this study. The questionnaires asked the questions based on the unspecified name of SNSs that they use because this study would like to focus on the characteristics of SNSs, information, and users. This study used the 7-point Likert scale to measure item scores. One represented “strongly disagree” and seven meant “strongly agree”. All construct items were formed based on previous studies. The questionnaire was produced in English based on previous studies, then translated into Thai. Later on, to certify the conciseness of the translation, the Thai language edition was evaluated by two scholars, one from management and the other from marketing.

After the translation was checked, a pilot test was conducted using 60 questionnaires with Thai travelers. After the pilot test, the questionnaire was revised and adjusted. Finally, the final edition of the questionnaire contained 18 items, in 6 constructs. The survey was carried out by using Google Form, an online survey platform. Later on, a hyperlink generated from the system was disseminated through online SNSs. This study was conducted through online data collection by using Facebook because Facebook is one of the most renowned social network in Thailand, which match well with the study. Facebook is a popular SNSs in Thailand and had users in Thailand of 20.62 million people in the year 2017 (Statista, 2018) from a 66.19 million Thai population (Trading Economics, 2018). Using online data collection has various advantages. Data can be collected in short response time, reduced cost, effortless to entry data, adjustability of format (Granello & Wheaton, 2004). This study uses Facebook to disseminate the data because users can provide information through the site and this study aimed to understand travelers’ intention to adopt SNSs in the post-travel phrases. Hence, online data collection was chosen in this study. This study took 2 months to collect data. Instead of calculating the sample size by the population, one acceptable way of sample size determination is through a calculation with the number of question items. Hair, Black, Babin, and Anderson (2009) have mentioned that the passable way of determination is a 1:10 ratio or one question for 10 samples. Thus, this research holds 18 question items and the sample can be at least 180 samples. In total 490 completed questionnaires were obtained. After deducting incomplete submitted questionnaires, a sample of 450 usable questionnaires was used in this study.

Table 1 shows the demographic information of the respondents. In terms of gender, the proportion of females was 65.6%, which was higher than male by 34.4%. In terms of age distribution, most of the respondents fell between the ages of 15 to 25 (44.9%), followed by between 26 to 35 (43.3%), between 36 to 45 (7.8%), and between 46 to 55 (3.1%). The number of travelers aged lower than 15 years old and older than 55 years was relatively few. In addition, most of the respondents, in the present study, fell under the travel frequency of 2–3 times a year with 45.3%, followed by more than 5 times a year (27.3%), 4–5 times a year (14.4%), and 0–1 time a year (12.9%).

Table 1. The demographic composition of the samples

Variable	Categories	Frequency	Percent (%)
Gender	Male	155	34.4
	Female	295	65.6
Age	Less than 15 years old	1	0.2
	between 15-25	202	44.9
	between 26-35	195	43.3
	between 36-45	35	7.8
	between 46-55	14	3.1
	More than 55	3	0.7
Travel Frequency (Per year)	1 time	58	12.9
	2-3 times	204	45.3
	4-5 times	65	14.4
	More than 5 times	123	27.3
Total		450	100.0

5. Results

Data analysis was conducted using SPSS20 and AMOS 23. The reliability and validity analysis was assessed first. Next, the overall goodness-of-fit of the model was assessed. Later on, structural equation modeling (SEM) was utilized to access the proposed model and the hypothetical relationship between the variables. Table 2 shows the result of the convergent validity and Cronbach’s alpha of the measurement model. Cronbach’s alpha was utilized to assess the internal consistency and scales reliability. The Cronbach’s alphas of measurement scale were from 0.921 to 0.973 which was above the threshold of 0.7 (Nunnally, 1978). Hence, it indicated good internal consistency and reliability. Composite reliability (CR) was performed to check the degree of internal relations among the indicators. The CR values of the latent variables were from 0.924 to 0.973 which were above the threshold of 0.7 (Fornell & Larcker, 1981). The results showed a high degree of internal consistency of the latent variables. Later, standardized factor loading and average variance extracted (AVE) were performed to assess the convergent validity of the measurement model. The results illustrated that the standardized factor loadings of the observed variables were from 0.835 to 0.973, which was above the threshold of 0.5 (Tracey, Vonderembse, & Lim, 1999). The results indicated a high explanatory power of the observed variables on their latent variables. The results of the AVE values of all the latent variables were from 0.801 to 0.922 which are above the threshold of 0.5 (Fornell & Larcker, 1981). It pointed out that the average exposition power of each item in the construct was adequate.

Table 2. The results of the measurement model analysis

Construct	Items	Loading	Cronbach’s alpha	CR	AVE
Accessibility	A1 SNSs allow travel information to be readily accessible to me	0.954
	A2 Travel information is easy to access through SNSs	0.973	0.973	0.973	0.922
	A3 Travel information is accessible to me through SNSs	0.954
Subjective norm	SN1 People who are important to me would think that I should use SNSs	0.931
	SN2 People who influence me would think I should use SNSs	0.951	0.963	0.964	0.899
	SN3 People whose opinions are valued to me would prefer that I should use SNSs	0.962
Information Accuracy	IA1 I expect travel information on SNSs to be accurate	0.944
	IA2 I expect SNSs to provide me with accurate travel information	0.847	0.921	0.924	0.801
	IA3 SNSs provide correct travel information	0.892
Customer Involvement	CI1 If I have a useful idea on how to improve SNSs, I let them know	0.835
	CI2 When I experience a problem using SNSs, I let them know about it	0.940	0.924	0.926	0.807
	CI3 I would like to be included in the development of new SNSs products and services	0.916
Perceived Usefulness	PU1 SNSs are useful on my trip	0.951
	PU2 SNSs enhance the quality of my trips	0.941	0.963	0.963	0.897
	PU3 SNSs enable me to have more convenient trips	0.950
Post-travel intention	PI1 I intend to share my travel experience in SNSs application in the future	0.906	0.948	0.949	0.861
	PI2 I plan to share travel information and photos in Tourism website, social media and mobile application after “my next trip”	0.942
	PI3 I expect to send my trip response in Tourism website, social media and mobile application	0.936

Note: AVE = Average Variance Extracted.

The square roots of the AVEs of all the latent variables and the correlation coefficients between them are shown in Table 3. To test the discriminant validity, the square roots of the AVE of the latent variables and the correlation coefficients between the latent variables were compared with each other. The test showed that the square roots of the AVEs in each latent variable were greater than the correlation coefficient between the latent variable and other latent variables. Therefore, the results illustrate clear discriminant validity (Fornell & Larcker, 1981). The above-mentioned results assure that the proposed model explained essential validity and reliability. Thus, it was available for further analysis.

Table 3. Correlation between the constructs

Factor correlation matrix with the square roots of AVE on the diagonal
Construct	A	SN	IA	CI	PU	PI
A	0.960
SN	0.725	0.948
IA	0.664	0.576	0.895
CI	0.547	0.553	0.624	0.898
PU	0.800	0.892	0.600	0.518	0.947
PI	0.642	0.702	0.595	0.556	0.669	0.928

Note: (1. The diagonal elements in boldface are the square roots of AVEs; 2. Others represent correlation with significant at the 0.01 level.

The CFA results illustrated the measurement model fit. The measurement model had a normed chi-square (x2/d.f.) of 1.616. The goodness-of-fit index (GFI) was 0.956 and the comparative fit index (CFI) value was 0.993, which showed a good model fit. The root mean square error of approximation (RMSEA) was 0.037. All the fit indicators were satisfactory, with the threshold value and adequate with the hypothesized measurement model (Bentler, 1990; Bollen, 1989; Hair et al., 2009; Hooper, Coughlan, & Mullen, 2008; MacCallum, Browne, & Sugawara, 1996; Steiger, 1990). Later on, a model fit measurement of the hypothesized structural model was estimated. The related structural model fit indices were x2/d.f. = 1.785, GFI = 0.95, AGFI = 0.931, NFI = 0.979, and RMSEA = 0.042. The results show that the structural model achieved a good fit.

Table 4 shows the results of path the coefficients and hypothesis assessment. Every path coefficient was statistically significant. Concerning the factors affecting perceived usefulness, subjective norm (β = 0.647, t = 18.557, p < 0.001) and accessibility (β = 0.290, t = 9.682, p < 0.001) were significantly related to perceived usefulness. Hence, the hypotheses H1 and H2 are supported. Concerning the factors affecting post-travel intention, information accuracy (β = 0.192, t = 3.902, p < 0.001), and customer involvement (β = 0.220, t = 3.917, p < 0.001) had significant direct effects on post-travel intention. Hence, hypotheses H3 and H4 are supported. Lastly, concerning mediating effect affecting post-travel intention, perceived usefulness (β = 0.490, t = 9.713, p < 0.001) was significantly related to post-travel intention. Hence, hypotheses H5 is supported. Figure 2 demonstrates the hypothesized structural model.

Figure 2. Structured model testing results.

Table 4. Maximum likelihood estimates for the research model

	Standardized Path Estimate	Critical Ratio (t- value)	Hypothesis Supported
H1: A→ PU	0.290***	9.682	Supported
H2: SN→PU	0.647***	18.557	Supported
H3: IA→PI	0.192***	3.902	Supported
H4: CI→PI	0.220***	3.917	Supported
H5: PU→PI	0.490***	9.713	Supported

Notes: ***p < 0.001; **p < 0.01; *p < 0.05

6. Discussion

Regarding the intense technological utilization in the tourism industry, travelers exchange their travel information and experiences in substantial amounts. This study aimed to understand the factors affecting the intention to use SNSs in the post-travel stage. The results of this study explain that travelers’ intention to use SNSs after their trips could be forecast by the five constructs presented in this study. The results indicate that the accessibility of technologies and influence from surrounding peers are the important factors to stimulate travelers’ perceived usefulness of SNSs. Moreover, perceived usefulness was found to be the most influential factor affecting travelers’ intention to use SNSs after their trip, followed by information accuracy and customer involvement. These results confirm a previous study that perceived usefulness influences on tourists’ mobile social tourism shopping intention (Hew et al., 2018). The results also confirm the previous knowledge that the use of certain technologies can lead to the intention to use or actual usage of the technologies (Abramson, Dawson, & Stevens, 2015; Bhatiasevi & Yoopetch, 2015; Kuo & Yen, 2009). In practice, individuals will perceive the usefulness of certain technologies because others think in the same way.

Regarding the factors that influence perceived usefulness, both accessibility and subjective norm are significantly related to perceived usefulness. Subjective norm was the strongest positive determinant of perceived usefulness. This confirms previous findings (Bhatiasevi & Yoopetch, 2015; Kim et al., 2009) that a positive relationship between subjective norm and perceived usefulness is more likely to be found in tourism-related technological studies. In the tourism context, travelers will realize the usefulness of SNSs because the people surrounding them think in the same way. Moreover, accessibility has a significant effect on perceived usefulness. This confirms previous a study that with accessibility enhancements, SNSs can increase the perception of travelers towards the usefulness of the technology (Tom Dieck, Jung, Kim, & Moon, 2017). With the easy access system, travelers also sense the usefulness of SNSs because they can access the system easily and very quickly. In the tourism context, travelers will share experiences by posting texts, videos, and photos. The accessibility will help travelers share experience easily. Then, they will feel the benefit of using SNSs.

Information accuracy was found to be a significant-factors affecting intention, complying with the study of Gao and Bai (2014) that if travel websites can guarantee information accuracy, consumers are more likely to participate in their websites. Thus, this study would like to add that if the travelers feel that their travel experiences can provide accurate information for other travelers, they would prefer to share their experiences through SNSs. Moreover, customer involvement was found to be a significant factor affecting intention, confirming the study of Cevdet Altunel and Erkurt (2015) which found a significant relationship between involvement and recommendation intention. Travelers with a high level of involvement will have a strong desire to share their travel experiences through SNSs. This suggests that information accuracy and customer involvement are significant in predicting intention to use SNSs in the post-trip stage.

6.1. Limitation

Although this study has provided some interesting results, there exist some limitations to the study. Firstly, the research was limited to Thai travelers only. The restricted respondents setting, with a small sample size, may not represent overall travelers’ behavior. In future studies, samples of other groups from diversified regions should be included in the survey. Secondly, this study used an online survey through social media as the collecting tool. This respondent collection method may lead to self-selection and nonresponse biases (Kraut et al., 2004). Future studies should use more offline surveys to ascertain the result of this study. Thirdly, there are various theories in technological behavior researches with distinct advantages and limitations. Future research should adopt different theories such as UTAUT to generate deep analysis in this research area. Lastly, this study focus on behavioral intention as the dependent variable. However, behavioral intention and actual behavior are different from each other (Ajzen & Fishbein, 1980). Thus, future studies may focus on actual behavior.

7. Implications and conclusion

The findings of this study hold both practical and theoretical implications. From a theoretical standpoint, the findings discovered the influential factors of travelers’ intention to use SNSs. Firstly, this study provides an enhanced theoretical understanding of predicting factors that influence the intention to use SNSs in the post-travel stage. Existing studies tend to focus on searching intention, whereas, this study focuses on sharing intention. Furthermore, this study applies the features of technology, information factor, social factor, and user factor with an extension of TAM and TPB model, which are perceived usefulness and subjective norm to create a combined model of communication elements. The integrated model offers a richer clarification of SNSs adoption intention than the original models alone. Future studies may possibly concentrate on other factors that can shape travelers’ behavioral intentions. Lastly, this study focused on SNSs as tourism technology. The model with proposed constructs can be interpreted in different industrial backgrounds to determine the various degree of influences on behavioral intention.

From a practical standpoint, the findings have several implications for the practitioners in a tourism context, in terms of the approaches that they can implement to improve users’ acceptance of SNSs. Firstly, the results highlighted that if travelers realize the benefit of SNSs, they will intend to use the technologies. Subjective norm takes an important role in stimulating the travelers’ realization. Thus, SNSs would increase their active users through the connection of people. If users realize that their peers use the technologies, ones may realize the usefulness of SNSs. In addition to the subjective norm, the technologies need to have an easy-access platform and system. Once travelers realize how easy they can connect to SNSs, they will be aware of the usefulness of the technologies. Thus, SNSs should create a user-based and easy-access system.

In conclusion, comparatively little research has examined SNSs adoption in the tourism industry, specifically in the post-travel stage. This study offers the results of explanatory factors affecting travelers’ intention to adopt SNSs after their trips. The results suggest that travelers’ perception of technology’s usefulness is an important element in their consideration of SNSs adoption. Consequently, subjective norm and accessibility are important elements enhancing perception toward usefulness. Furthermore, it is suggested that customer involvement and information accuracy are also included to enhance the intention to use SNSs in the post-travel stage. The practical implications of the findings can support social media marketing practitioners in the tourism industry who seek to apply suitable strategies for existing and upcoming travelers. Based on the limitations, this study suggests that it will be worth in replicating this research in other contexts to understand more the predicting power of the proposed model.

Correction

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Nathatenee Gebsombut

Nathatenee Gebsombut is a PhD student in the Institute of Technology Management at National Chung Hsing University and is also a lecturer in International College, Khon Kaen University. Her current research interests include customer relationship management, e-business management and technology adoption.

Chien-Ta Bruce Ho

Chien-Ta Bruce Ho is a professor in the Institute of Technology Management at National Chung Hsing University and is also a director of Electronic Commerce &Knowledge Economics Research Centre. His research interests include customer relationship management, value chain management, performance evaluation, e-business management and finance technology. He has authored and co-authored books in both Chinese and English languages. He has published over 100 papers in various journals and conference proceedings.