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Article

Examining Vietnamese Hard-Adventure Tourists’ Visit Intention Using an Extended Model of Goal-Directed Behavior

by
Ngoc Anh Bui
1 and
Kiattipoom Kiatkawsin
2,*
1
Department of Hospitality and Tourism Management, Sejong University, Seoul 05006, Korea
2
Tourism Industry Data Analytics Lab (TIDAL), Department of Hospitality and Tourism Management, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(5), 1747; https://doi.org/10.3390/su12051747
Submission received: 2 February 2020 / Revised: 24 February 2020 / Accepted: 25 February 2020 / Published: 26 February 2020
(This article belongs to the Collection Intention and Tourism/Hospitality Development)
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Versions Notes

Abstract

:
Although adventure tourism is divided into two subgroups based on the level of risk, most previous research has not made a distinction in their research. However, due to the high risk, skills, experience, and physical condition required of participants, hard adventure tourism should be studied separately from soft tourism activities. Thus, this study investigates the antecedents influencing hard adventure tourists’ visit intention by adopting the model of goal-directed behavior. Furthermore, two new variables, social media consumption and hardy tourism knowledge, were added to improve the model’s predictive ability. Using 441 Vietnamese hard adventure tourists as research samples, the results have shown the conceptual model to be robust and effective in explaining behavioral intention. Generally, the results were consistent with previous research. However, subjective norms, negative anticipated emotion, and frequency of past behavior produced unexpected results.

1. Introduction

As an industry of approximately USD 1.7 trillion in 2018, tourism is one of the world’s largest industries [1]. International tourism recorded higher growth in 2018 than the global economy. The global gross domestic product (GDP) has seen an increase of 3.6%, while the tourism receipts increased by 4.4%. Both the revenue generated and the total number of tourist arrivals have also increased by 4% and 5% respectively [1]. However, within the vast scope of the tourism industries, a wide variety of tourism products and activities exist [2]. Since the different motivations and purposes of each visit can vary, it becomes a necessity to divide the tourism industry into smaller subgroups [3,4,5]. One subgroup that has experienced strong growth over the past few years has been the adventure tourism sector. In some countries, adventure tourism accounts for the majority of their tourism product offering [6,7,8]. Adventure tourism was first seen as a niche sector and was a part of Adventure Culture Ecotourism (ACE) and Nature Eco Adventure Tourism (NEAT) sectors [9]. The Adventure Travel Trade Association (ATTA) defined adventure tourism as a trip that includes at least two out of the following three elements: natural environment, physical activities, and cultural immersion [5,10,11]. In 2013, a study found that 42% of travelers have some level of adventure activities included on their trips, making adventure tourism one of the most prominent subgroups in the tourism industry [12].
Researchers categorized tourism subgroups based on primary activities during the trip. In other words, a trip can include multiple elements such as historical site visits and adventure activities. However, if adventure activities are the primary motivation for the journey, the trip is considered an adventure tourism trip [4]. Then, based on the primary activities during an adventure trip, adventure tourism is divided into soft and hard [6,10,11]. Soft adventure tourism refers to any low-risk activities that do not require experience or specific skills to participate (e.g., camping, birdwatching, and canoeing). On the contrary, hard adventure tourism, also known as hardy tourism, includes high-risk activities that require travelers to be well-equipped with knowledge and experience of the activities, a good physical condition, skills, and specialized safety equipment [6,13]. Caving, mountain/ice/rock climbing, trekking are examples of the three most popular hardy tourism activities [14,15]. These high-risk activities in hardy tours have caused significant injuries and even the deaths of participants [16]. Despite the risks, the overall adventure tourism sector is still being seen as a highly lucrative segment [5,17,18]. Most notably, researchers suggest that hard adventure tourists tend to be the trailblazers for new adventure tourism destination development [19,20]. This is due to their tendency to prefer unexplored destinations and to seek the next destination after they gain mass touristic appeal [4,5,20]. Hence, understanding hardy tourists’ decision-making processes can enlighten businesses and governments of new adventure destinations and activities for further development.
Academically, scholars have continuously been researching about adventure tourism. Well-established research pertaining to adventure tourism includes perceived risk, fear, and injuries [16,21,22]. Another group of research centers around adventure tourists’ motivation [3,19,21,23]. These studies have applied a broad range of motivational theories such as several rush-related concepts [9,24], flow and thrill concepts [25,26], edgework [27,28], sensation seeking [29,30], and serious leisure [31]. Yet, most previous research did not distinguish the difference between soft and hard adventure tourism. Although the terms soft and hard tourism can be seen sparingly in some industry and academic sources prior, it was not until the UNWTO’s adoption of the ATTA’s classification in its 2014 global report on adventure tourism that the soft-hard was seen as the arbiter units of classification [5,12,19,20,32]. The level of risk has been cited as the main criteria to differentiate between soft and hard tourism activities. As a result, tourists’ decision-making and behaviors can be substantially different when engaging in either soft or hard activities.
The model of goal-directed behavior (MGD) emphasized that people’s intention and behavior are affected by not only attitude, subjective norms, and perceived behavior control, but also other habitual (past behavior), motivational (desire), and affective (positive and negative anticipated emotions) aspects [33,34,35,36]. With this significant development, MGD was used to explain humans’ behavioral intention in various fields such as sporting goods online purchasing [37], mobile device usage [38], software learning [34], and restaurant re-patronage [39]. In tourism, researchers have applied the MGD in different contexts of tourism to explain tourists’ behavioral intention such as festival visitors’ decision-making process [36], slow tourist’s intention [40], and the oriental medicine festival tourists’ behavioral intention [41]. With the MGD’s inclusion of factors beyond an individual’s psychological evaluation such as attitude, the MGD has been to be found useful in research contexts that required a high level of involvement such as hardy tourism. However, the MGD has yet to be applied to the context of adventure tourism.
Furthermore, social media became an integral part of many human lives [42]. Tourism is no different. Travel-related social media became a necessary platform that helps users to consume and share information [43]. Thus, the advent of social media has changed the travelers’ decision-making process and pre-trip destination impression substantially [42]. However, well-established theories in consumer behaviors such as the MGD have not yet been updated with crucial dimensions that have been embedded in most contemporary tourists’ decision-making processes. Therefore, this present study seeks to extend the MGD with social media consumption and hardy tourism knowledge. Previous research found out that the more consumers become involved with the product or services, the more knowledge they accumulate [44]. In other words, the knowledge of a particular product can be gathered by directly experiencing or searching for information about this product [45].
In sum, adventure tourism is an important segment for many countries [6,8,46]. In the last few years, Vietnam has emerged as one of the leading destinations in the region [46]. Moreover, adventure tourism has been one of the primary sectors for both domestic and international visitors for the country [47,48]. Although adventure tourism activities have been categorized into soft and hard, most previous academic research does not distinguish between the two [5]. Yet, hardy tourists are found to be pioneers when it comes to discovering new destinations and activities [20]. Thus, exploring their decision-making process could help develop marketing tools for mass market development. Reviewing previous literature reveals the MGD to be a suitable theoretical framework. The MGD proposes key antecedents predicting tourists’ visit intention [38,40,41]. Moreover, this present study attempts to update and extend the MGD by adding two new constructs. In other words, this study aims to investigate the antecedents explaining tourists’ intention to engage in hard adventure tours. In order to achieve the aim, three objectives are proposed. First, adopting the empirically validated MGD to form the basis of the conceptual model. Second, extending the MGD with two new variables, namely social media consumption and hardy tourism knowledge. Third, validating the proposed study model with Vietnamese hard adventure tourists. As a result, this present research was designed to contribute both practically and theoretically in a few ways. The literature review section introduces key concepts and variables in this study. In addition, the development of the study model and research hypotheses are discussed. Next, the methodology and results sections explain the steps taken to obtain the results. Lastly, discussions and conclusions are presented.

2. Literature Review

2.1. Adventure Tourism

Adventure tourism was described as a journey close to nature accompanied by activities [4,9]. Furthermore, the purposes of adventure tourists are understood to be seeking unfamiliar places, meeting unfamiliar people in order to explore, and study or for recreation [3,17,49]. Through time, the definition of adventure tourism has been expanded to cover a combination of travel, sport, and outdoor recreation [10]. The interpretation of how one considers an activity to be either adventure, educational, or cultural can be a marketing choice [5,19,20]. For example, a snowmobile safari through the winter wilderness of the Finnish Lapland is considered non-adventurous by the local people, as it involves no risk-taking and is highly commercialized [32]. However, for others, the same activity can be viewed as either a cultural tour or an educational one. Thus, adventure tourism is often conceptualized as a crisp classification rather than an abstract concept, wherein its usage can be highly dependent on the destination marketing and branding choice [5,19].
Although adventure tourism has been closely associated with the risk involved, not all activities during an adventure trip must be risky or involve high risk [11]. As a result, adventure tourism has been categorized into soft and hard [10,31]. Soft adventure tourism activities are usually led by experienced and skilled guides. Thus, participation requires little to no prior experience and skills [3,19,31]. Researchers found that the main motivations of soft adventure activities can include to escape daily life, experience new things, and to discover oneself [23,49]. On the contrary, hard adventure tourism, also known as hardy tourism, involves high-risk activities that usually require strenuous physical exertion from tourists [31]. Typical hardy activities are rock climbing, downhill ski racing, and scuba diving. The main difference between soft adventure tourism activities is the level of danger introduced to participate and how real the risk is [6]. Therefore, hardy tourists depend heavily on their own strengths and experience to overcome the challenges rather than just relying on the guidance and support of the guides [4].
Researchers have argued that if tourists are classified on the mass and adventurer continuum, mass tourists would typically seek activities with the lowest risk possible. On the other hand, adventurers would not only prefer some level of risky elements but would continue to seek even higher risk activities as their skills and experience increase [5,19,20]. At the same time, destinations rely on adventurers to pave the way for tourism development. However, as destinations gain more popularity among the general public, it is imperative to provide safer and less intimidating activities [50,51]. In other words, as the adventurers continue to seek newer destinations, existing destinations learn to develop activities that are more suitable for a wider audience. Therefore, focusing on the psychological construct of hardy tourists can guide marketers to choose effective tools and strategies for the development of such destinations and activities.

2.2. The Model of Goal-Directed Behavior

The MGD was proposed by Perugini and Bagozzi (2001) as an attempt to broadened and deepened the popular theory of planned behavior (TPB) [33,39]. The aim was to improve upon the TPB’s limitations by including motivational, affective processes, and past behavior elements [41]. As a result, the MGB added new variables. They are desire, anticipated emotions, and frequency of past behavior [33,34]. The addition of these three new variables was to develop a model that can effectively predict complicated, goal-orientated, or highly involved human behaviors [34,35,37]. Among these three new variables, desire is conceptualized as a mediator between attitude, anticipated emotions, subjective norms, perceived behavior control, frequency of past behavior, and behavioral intention [34]. Meanwhile, anticipated emotions represent the emotional responses in the behaviors of humans [52]. Then, past behavior is posited as a theoretical factor by symbolizing the habitual aspect in human behavior that influences human desires, intentions, and actions [34,41]. Empirical studies have supposed the MGD in terms of both validity and predictive ability across various contexts, including tourism [40,41,53].
Attitude is described as a favorable or unfavorable evaluation of individuals toward a particular behavior [54]. In the TPB, attitude toward an act is considered a significant predictor of intention [39]. However, many researchers believe that attitude only has an indirect influence on the intention with desire playing the role of a mediator [35,40]. Therefore, a positive attitude could strongly encourage an individual’s desire to engage in that behavior [41]. In other words, when an individual holds a positive attitude toward an act based on their knowledge, information, experience, and perception in daily life, the desire to engage in the act is strengthened [36,41,54].
Anticipated emotion is proposed as a determinant of intention [55]. Most individuals anticipate feelings about future behavior, which could be either negative or positive, leading to the decision-making process of pursuing goals [35]. In other words, anticipated emotions explain the motive of encouraging positive situations and avoiding negative ones. Several research studies have empirical support that emotion plays an essential role in predicting attitude and subsequently behavioral intentions [34,36,55,56,57]. For example, among international tourists in Thailand, those with positive emotions together with positive attitudes are more likely to return than those with positive attitudes alone [56]. Moreover, skydivers were found to have a higher likelihood to partake in the activity if they anticipate they could achieve excitement by doing so, but some will avoid skydiving if they anticipate negative emotion [55].
Subjective norm is understood as a perceived social pressure referring to what important salient referents think an individual should or should not do and the individual’s motivation to comply with this advice [35,54]. Furthermore, perceived behavioral control, a non-volitional dimension regarding one’s confidence or capacity to carry out certain behavior, predicts that individuals’ intentions to act will be stronger when they have the resources or opportunities required [40,54]. In other words, perceived behavioral control is a reflection of the perceived ease of difficulty in producing behavior that could facilitate or reinforce desire and behavioral intention [35]. Past studies found both subjective norm and perceived behavioral control to be significant predictors of desire and eventually behavioral intention as well [35,36,41,58,59,60].
Desire has been considered as the most influential variable in predicting intention and has a positive link to behavioral intention [35,39]. Thus, desire is defined as a motivation element that is required to explain why someone intends to do something [41,53]. In the Korean wine tour context, desire was found to strongly predict visit intention [60]. Moreover, the frequency of past behavior refers to how often the behavior was performed in the past by individuals [35,61]. Researchers believe that when a person performs a particular activity regularly and habitually in the past, their behavioral intention to do that kind of activity again will be enhanced [38,39,54]. Past research projects have produced evidence supporting the role of frequency of past behavior in predicting behavioral intentions in many tourism contexts [11,40,53]. Based on the MGD framework and empirical results, the following hypotheses were developed.
Hypothesis 1 (H1).
Attitude has a significant and positive effect on desire.
Hypothesis 2 (H2).
Positive anticipated emotion has a significant and positive effect on desire.
Hypothesis 3 (H3).
Positive anticipated emotion has a significant and positive effect on attitude.
Hypothesis 4 (H4).
Negative anticipated emotion has a significant and negative effect on desire.
Hypothesis 5 (H5).
Subjective norms has a significant and positive effect on desire.
Hypothesis 6 (H6).
Perceived behavior control has a significant and positive effect on desire.
Hypothesis 7 (H7).
Perceived behavior control has a significant and positive effect on behavioral intention.
Hypothesis 8 (H8).
Desire has a significant and positive effect on behavioral intention.
Hypothesis 9 (H9).
The frequency of past behavior has a significant and positive effect on behavioral intention.

2.3. Social Media Consumption and Hardy Tourism Knowledge

Due to the utilities of social media among tourists, it has been widely used as a tool of marketing in tourism applications [62,63]. Research has shown that the interaction and information shared among users on the social network have a positive impact on consumer’s trust and intention [64]. Using social media in the tourism decision-making process is also no longer prevalent among only young travelers, but also senior travelers [65]. Thus, individuals with a high level of social media consumption are those interested, aroused, and involved in social media [43,66]. In the tourism context, research found that the more often people read online reviews, the more likely they are to be influenced by reviews from other travelers [43,45,63,66]. As a result, those with a high level of social media consumption would likely have accumulated a pool of knowledge related to that particular topic. Hence, knowledge refers to information such as brand perceptions, product attributes, and more that are stored in memory [42,45,62]. Interns, a high level of knowledge, or in the context of this present research, hardy tourism knowledge, help with the decision-making process and eventually behavioral intention [67,68].
People who are highly interested in tourism-related social media often stay active on the Internet to share, post, and search for more information related to travel [66]. Under these online interactions among travelers, the tourism-related knowledge that was accumulated would likely be higher than those who are less involved online [66,68]. Previous research has also supported the relationship between involvement (social media consumption) and knowledge (hardy tourism knowledge). For example, luxury diners who often discuss and read information about gastronomy have reported a higher knowledge about luxury restaurants in general [69]. Furthermore, the attitude toward a behavior can be influenced by the knowledge of the consumer. Those who have a high level of knowledge of a product tend to perceive that product to be necessary and less likely to have a negative attitude toward the product or the company offering the products [67,70,71]. According to previous research, knowledge could be seen to have a positive relationship with consumers’ confidence as well as a stronger attitude toward specific products or behaviors [67,72]. Previous research has also illustrated that knowledge has influenced loyalty intention [69]. Therefore, knowledge is especially crucial in adventure tourism contexts due to the risky nature of the activities. The following hypotheses were developed to reflect the discussions above. In addition, Figure 1 illustrates the study variables and all the proposed hypotheses.
Hypothesis 10 (H10).
The frequency of past behavior has a significant and positive effect on social media consumption.
Hypothesis 11 (H11).
Social media consumption has a significant and positive effect on hardy tourism knowledge.
Hypothesis 12 (H12).
Hardy tourism knowledge has a significant and positive effect on attitude.
Hypothesis 13 (H13).
Hardy tourism knowledge has a significant and positive effect on desire.

3. Methods

3.1. Measurement Items and Survey Development

All measurement items in this present study were adopted from previous research. Specifically, the items measuring the constructs of the MGD were adopted from Perugini and Bagozzi (2001) and Lee et al. (2012) [27,44]. They are four items each for measuring attitude, positive anticipated emotion, negative anticipated emotion, and subjective norms. Perceived behavioral control was measured using three items. Then, the desire and behavioral intention constructs used four items each. Social media consumption and hardy tourism knowledge have three and four items, respectively. Items for both constructs were adopted from studies done by Amaro and Duarte (2015) and Algesheimer et al. (2005) [57,64]. Next, all items were adapted to fit the context of this present study. A five-point Likert scale ranging from (1) strongly disagree to (5) strongly agree was used for all items except the frequency of past behavior. For that, another five-point continuous scale was used ranging from (1) never to (5) many times.
At the start of the questionnaire, a brief cover letter including a description of hardy tourism is attached. In addition, the respondents’ background information including demographic and other descriptive characteristics was also included. Then, a screening question asking the respondents if they had participated in a hardy adventure tour over the past two years and if they have a plan for a hard adventure tour in the next two years. The screening questions were included to ensure the respondents are at least involved in some form of hard adventure tourism. After the original version of the questionnaire in English has been finalized, a pre-test was conducted. The pre-test includes experts in tourism, senior academics, and general tourists. Next, the survey was translated from English to Vietnamese by a bilingual speaker. Then, a second bilingual blindly translated it back into English. Both versions were compared to ensure translation accuracy. Another round of pre-test was subsequently conducted, this time among Vietnamese speakers. After collecting the results of both pre-tests, the deviation was checked or modified each time.

3.2. Sampling and Data Collection

The research setting for this present study was Vietnam. The tourism sector was considered an essential economic sector from 1976 to 1990 for Vietnam due to the benefits that tourism has brought to the country [8]. In 2018, the travel and tourism sector accounted for 8.39% of the total GDP [48]. During the same year, the country attracted over 15.5 million international arrivals, who contributed an estimated VND 637 trillion in tourism receipts [46,47]. Vietnam is best known for a variety of tourism options, and adventure is one of the leading tour themes. It can be attributed to the country’s advantages in terms of topography and natural resources [7,50]. For example, the world’s largest cave, Son Doong cave, attracted 3.3 million tourists in 2017 [73]. Moreover, other topographic characteristics of Vietnamese mountains, hills, and rivers also offer suitable conditions for adventure activities such as trekking, mountain climbing, and cave expedition [7,8,49,50]. As a result, the number of tourists visiting adventure tourism destinations throughout Vietnam has increased sharply in recent years [50]. Subsequently, Vietnam was named “Asia’s leading destination” for the first time in 2018 [46]. Nevertheless, the rise in the number of tourists has not been proportionately reflected in research attention. Hence, Vietnam provides a suitable setting to conduct research on hard adventure tourism.
The research samples include any Vietnamese tourists who either have participated in a hard adventure tour previously or interested/planning to participate in the next two years. Those who answered no to both screening questions were asked to end their participation. The survey was collected online using the Google Forms platform. Facebook groups related to a variety of hard adventure travel groups (such as trekking groups, cave exploring groups, and mountain climbing groups) were used to distribute the survey. Several offline collections in the form of self-administered questionnaires were also used. A mixture of both snowball sampling and convenience sampling methods can be best used to describe the sampling techniques. The data collection period was between June and July 2019.

3.3. Sample Profile

A total of 441 questionnaires were collected during the collection period. Incomplete questionnaires were disregarded. Subsequently, the standard deviation of each respondent was calculated to identify potential unengaged responses [74]. No outliers were identified and removed. After screening and the removal of incomplete and unengaged responses, 308 cases were retained for further analysis.
Among the 308 respondents, a total of 53.6% are male and 46.1% are female. In terms of age, the most substantial portion is between 27 and 35 years old (50.6%). Singles and married were the two largest groups with 60.1% and 33.1%, respectively. More than half of the respondents (55.8%) hold a bachelor’s degree, and nearly 21.8% hold a master’s degree. Monthly income between VND 10 million and 20 million was the largest group (39.3%). Full-time employees accounted for 54.2% of all participants. The largest portion of respondents who had participated in at least one hardy tour reported that Northern Vietnam was the last visit location (48.4%), followed by Central Vietnam (21.4%), outside of Vietnam (19.2%), and Southern Vietnam (11%). Most of the respondents traveled with their friends (63.6%). Their last activity reported was mountain climbing (39.9%), trekking (22.4%), cave exploring (20.5%), and waterfall diving (8.1%). Table 1 summarizes the demographic information.

4. Results

4.1. Confirmatory Factor Analysis

The data were analyzed using SPSS version 22 and SPSS AMOS version 20. According to Anderson and Gerbing’s (1988) recommendation [75], a two-step approach was adopted. In the first stage, Confirmatory Factor Analysis (CFA) was conducted to assess the measurement items’ validity and reliability. In the second stage, Structural Equation Modeling (SEM) was used to test the proposed hypotheses. The two-step approach has been the prominent analysis method for research projects, adopting the MGD as it first helps establish the reliability and validity between the data and the model before conducting path analysis to test the proposed hypotheses [34,38,39]. Moreover, the data were initially screened for potential violations of the basic multivariate assumptions. Skewness and kurtosis scores were all within the recommended thresholds of 2.2 and 3, respectively [76]. No other basic assumptions were violated. Then, the goodness-of-fit statistics have also produced a satisfactory fit (χ2 = 775.444, df = 425, p < 0.001, χ2/df = 1.825, RMSEA = 0.052, CFI = 0.960, IFI = 960). The composite reliability scores were calculated, and the results were between 0.774 and 0.926, which were higher than the recommended threshold of 0.7 [76]. Hence, the evidence of reliability exists. The average variance extracted (AVE) scores were also greater than the minimum requirement of 0.5 [77] ranging from 0.599 to 0.807. Lastly, the AVE for each construct was greater than the squared correlation scores between a pair of constructs, which provides the existence of the discriminant validity [78]. The summary of the CFA results can be seen in Table 2.

4.2. Structural Equation Modeling

At the structural level, the goodness-of-fit statistics have also shown satisfactory fit (χ2 = 952.495, df = 469, χ2/df = 2.031, RMSEA = 0.058, CFI = 0.945, NFI = 0.898, PGFI = 0.707). The variables have also been demonstrated as effective in explaining the dependent variable, behavioral intention, which was explained by 86.7% of the variance. Desire was also proved to be a strong mediator of the model, which was explained by 75.3% of the variance. Attitude, social media consumption, and hardy tourism knowledge were all strongly explained by their antecedents with the R-square results of 0.570, 0.551, and 0.345 respectively. The frequency of past behavior has the largest total impact on behavioral intention (0.728) followed by desire (0.489), illustrating their importance in the study model. For the full summary of the SEM results, please see Table 3.
Hypotheses testing showed that hypotheses 1 (β = 0.223, p < 0.001), 2 (β = 0.223, p < 0.05) and 3 (β = 0.223, p < 0.001) were both significant and positive. However, hypotheses 4 (positive anticipated emotion and attitude) and 5 (negative anticipated emotion and desire) did not produce any significant relationships. Perceived behavioral control produced a significant relationship to desire but behavioral intention did not. Thus, hypothesis 6 (β = 0.615, p < 0.001) was supported but not hypothesis 7. Next, both desire and frequency of past behavior significantly impacted behavioral intention. Hence, both hypothesis 8 (β = 0.487, p = 0.001) and hypothesis 9 (β = −0.261, p < 0.001) were supported by the data. Notably, hypothesis 9 produced a negative impact. The two newly added constructs and their relationship were all statistically significant. Hypothesis 10, frequency of past behavior, significantly predicted social media consumption (β = 0.742, p < 0.001). Hypothesis 11, social media consumption, also significantly impacted hardy tourism knowledge (β = 0.588, p < 0.001). Lastly, hardy tourism knowledge significantly affected both attitude and desire. Therefore, hypothesis 12 (β = 0.167, p < 0.01) and hypothesis 13 (β = −0.099, p < 0.05) were all supported. Figure 2 illustrates the study model and the SEM results.

4.3. Indirect-Impact Assessment

The indirect-impact assessment allowed further testing of the mediating effect of the model. The results show that desire plays a significant role in mediating between many variables and behavioral intention. Both attitude (β = 0.109, p < 0.001) and positive anticipated emotion (β = 0.153, p < 0.001) are partially mediated by desire. Negative anticipated emotion produced a significant indirect impact on behavioral intention (β = −0.036, p < 0.05) but it was not directly significant. Hence, desire does not play a mediating role between these two variables in this present study context. Subjective norms did not produce any significant relationship, both directly and indirectly, whereas desire played a fully mediating role between hardy tourism knowledge and intention. Attitude was found to partially mediate the relationships between positive anticipated emotion (β = 0.152, p < 0.001) and hardy tourism knowledge (β = 0.037, p < 0.01). Lastly, social media consumption was also a partial mediator between the frequency of past behavior and hardy tourism knowledge (β = 0.436, p < 0.001). Table 4 summarized the results of the indirect-impact assessment.

5. Discussion

5.1. General Discussion

The overall model fits, and other indicators have shown that the integration of social media consumption and hardy tourism knowledge have been harmonious and helped explained the visit intention among hardy tourists. This present study also validates the MGD in the adventure tourism context, which expands the usage of the model in tourism from extant successful implementations of the method in other tourism contexts, such as intention to attend an oriental medical festival and a slow tourism tour [40,41]. Specifically, the results suggest that among Vietnamese hardy tourism enthusiasts, attitude, positive anticipated emotions, and frequency of past behavior are among the most critical indicators of visit intention. Consistent with the original conceptualization of the MGD and previous studies, desire played a central role in mediating between the independent variables and the dependent variable effectively [34,38,39]. This lends credit to the concept that for individuals to engage in a hardy activity, they first need to form a level of desire for the activity. Attitude has also shown to be another critical driver of tourists’ visit intentions, as it had demonstrated in previous studies [35,40,56]. Attitude is usually formed as either favorable or unfavorable, and logic dictates that people are more likely to desire something if they have a favorable attitude toward it. Hence, the study model validates that the decision-making process appears to be a causal chain.
It also came as no surprise that both positive anticipated emotion and hardy tourism knowledge not only directly impact desire but also attitude, implying that hardy tours generated positive emotional expectations and those with prior experience and knowledge tend to form a favorable attitude toward such trips. Thus, these findings were also consistent with previous research [40,41]. The relationships between social media consumption and hardy tourism knowledge were also consistent with previous research [23,43,60,66,67,69]. In other words, individuals who spend more time reading and interacting on social media tend to have more knowledge about hardy tourism. In addition, the significant path from the frequency of past behavior to social media consumption indicated that Vietnamese tourists who have joined in hardy trip are likely to be more active on social media.
On the other hand, negative anticipated emotion, although it was not directly influencing desire, was still indirectly influencing behavioral intention. However, subjective norms were not significantly linked to desire nor behavioral intention. Needless to say, the result was not consistent with previous research and the conceptualization of the MGD. It can be interpreted that hardy tourists are fully aware of the potential danger and risks associated with the tours, and their participation in such tours may not be supported by their friends and family. Hence, the subjective norm had a non-significant role [40,41,53]. The collectivist nature of Vietnamese people and the general tendency to be risk-averse could explain this unexpected result. Thus, these cultural traits may lead to hardy tourists’ anticipation that their involvement in such activities would not be approved and supported by their families and close friends. In addition, the results indicated that hardy tourists might primarily focus on their own experience and enjoyment more than the opinions of those surrounding them. Lastly, it is essential to note that the frequency of past behavior produced a significant but negative relationship to behavioral intention. At the same time, it has a strong and positive impact on social media consumption. Thus, this finding points out how those who have previously participated may not necessarily want to visit again, but they are likely to share and read others’ experiences online, akin to sharing their achievement with others.

5.2. Theoretical Implications

Although the MGD has been previously been applied to various contexts in tourism [37,38,39,40,53], it has never been validated in the hardy tourism context until now. Thus, the leading contribution of this study was the successful validation of the MGD in a new context. Although not all hypotheses of the original MGD were supported by the data, it has shown that the model is adequate in explaining visit intention among hardy tourists. The non-supported hypotheses can also be due to the different context than that for which the MGD was originally designed. However, the extension of the MGD via the addition of social media consumption and hardy tourism knowledge was proven to be successful. It also highlighted the importance of social media and the broader role of user-generated content in the tourism field, especially in the niche segment of hard adventure tourism. In other words, it can be perceived that by adding the two new variables, this present study has updated the MGD to be more effective in the contemporary tourism application.

5.3. Practical Implications

The popularity of hard adventure tours implicated concerns not only among the tour operators but also the government and other health and safety organizations. Unlike soft adventure activities, hardy tours involve high physical risks, and any unwanted incidents may cause negative publicities to the entire market region. Thus, the results of this study can be useful to many stakeholders involved. First, as attitude and positive anticipated emotion are two important factors directly impacting hardy tourists’ visit intention, the marketing should focus on information and building that favorable attitude. The information such as the tour route, protective equipment, insurance policy, and more can help strengthen travelers’ confidence. The results of this study have shown that illustrating the potential emotional response that can be achieved by participating can help form a favorable attitude. Additionally, because of the danger and risk associated, the availability of information on various platforms such as social media can also help build that pool of knowledge that is necessary to help make an informed decision about participation in hardy tours, which is crucial. Due to the potential danger, those who are interested in hardy tours may not wish to share their interests with their close family members. Thus, marketers can work to link like-minded people together through various platforms such as blogs, forums, and specialized groups on social networks. At the same time, such a platform can provide the opportunity to share their achievements among the members. Moreover, the government can contribute by means of enhancing the response systems in case of any accidents, further eliminating any safety concerns.

5.4. Limitations and Recommendations for Future Researches

As in other research, this research project also is no exception regarding its limitations. The samples and background of this study are Vietnamese hard adventure tourists in Vietnam. As a result, the generalizability of results may be limited when applying to other countries. The types of activities and infrastructures around the tourism sites may also be different. Therefore, the results of this study should be view with caution. Additionally, the conceptual model, while statistically robust, should be validated in a wider range of contexts to confirm its validity. Nevertheless, the sound extension of the MDG should encourage researchers to deploy the model in other similar fields of research. Future research projects could also explore the role of negative anticipated emotion in terms of what might be a more effective mediator between it and visit intention. The relationships of hardy tour participants and their families also provide an intriguing avenue for future research. For example, variables such as perceived risk and anticipated pride could help to deepen the understanding of hardy tourists’ visit intention. Moreover, hardy tourists’ perception of the risk and appeal of each activity may depend heavily on territorial settings. In other words, the same activity may elicit a different level of perceived safety and security, depending on the country and destination due to externalities, such as national healthcare systems and other infrastructures. Therefore, future research is encouraged to explore tourists’ decision-making process in relation to the local territorial infrastructures.

6. Conclusions

This present study adopted a well-established framework in explaining the consumer decision-making process, the MGD. Although the MGD has been well established in a variety of consumer research, including hospitality and tourism research, the variables used in past research are outdated and do not truly reflect the contemporary lifestyles of tourists today. Thus, the ubiquitous influence of social media consumption and knowledge were added to modernize the MGD, as well as improve its predictive ability. The research sample, hardy tourists in Vietnam, showed that the proposed conceptual model has a strong predictive ability as 86.7% of the variance explained the intention to engage in hardy tourism. The study also found that many hardy tourists are aware that their close family and friends may not approve of the activities but found social media platforms a preferred community to share information and experiences. From the government perspective, news and announcements should not be confined to traditional channels but should look at social media platforms as the new mainstream outlets. Hence, safety information and regulations should be integrated into online social media networks, as many tourists now rely on such platforms for information. Likewise, marketers should look to be actively engaged in social media for product announcements and community engagement.

Author Contributions

N.A.B. designed the research model, analyzed the data, and wrote the paper. K.K. analyzed the data, edited, and supervised the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 12 01747 g001 550
Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Sustainability 12 01747 g002 550
Figure 2. Conceptual model and the SEM results. Note: * p < 0.001, ** p < 0.01, *** p < 0.05.
Figure 2. Conceptual model and the SEM results. Note: * p < 0.001, ** p < 0.01, *** p < 0.05.
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Table
Table 1. Demographic information (N = 308).
Table 1. Demographic information (N = 308).
VariablesCategoryDistributionValid Percentage
GenderMale16553.6
Female14246.1
Age (years old)19–22247.8
23–267925.6
27–3515650.6
36–453511.4
More than 45144.5
Marital statusSingle18560.1
Married10233.1
Divorced82.6
Others134.2
Educational BackgroundHigh school or below206.5
Bachelor’s degree17255.8
Master’s degree6721.8
Doctorate82.6
Others4113.3
Household monthly income (VND)Under 10,000,00010433.8
10,000,000–20,000,00012139.3
20,000,000–30,000,0004715.3
30,000,000–40,000,000154.9
Over 40,000,000216.8
OccupationStudent5216.9
Part-time92.9
Full-time16754.2
Self-employed6822.1
Unemployed61.9
Retired61.9
Location of the last tourNorth of Vietnam14948.4
Middle of Vietnam6621.4
South of Vietnam3411
Outside of Vietnam5919.2
Visit companionshipAlone3511.4
Family3812.3
Friends19663.6
Colleagues134.2
Others268.4
Activity of the last tourTrekking6922.4
Mountain climbing12339.9
Waterfall diving258.1
Cave Exploring6320.5
Others289.1
Table
Table 2. Summary of Confirmatory Factor Analysis results.
Table 2. Summary of Confirmatory Factor Analysis results.
ATPAENAESNPBCDEBISMCHTK
AT0.774a0.534 c0.0710.2110.4450.4970.5540.2970.194
PAE0.731 b0.8800.1080.0930.4170.4860.5520.2740.162
NAE−0.267−0.3290.8740.0020.1040.1380.0830.0360.025
SN0.4590.305−0.0420.7850.1040.0860.1200.1210.163
PBC0.6670.646−0.3220.3230.8330.6720.6590.4290.264
DE0.7050.697−0.3710.2940.8200.8780.7430.3790.141
BI0.7440.743−0.2880.3460.8120.8620.8820.4080.290
SMC0.5450.523−0.1910.3480.6550.6160.6390.8930.326
HTK0.4400.403−0.1590.4040.5140.3750.5390.5710.926
AVE0.5990.7750.7630.6170.6930.7720.7790.7970.807
Notes: Goodness-of-fit statistics: χ2 = 775.444, df = 425, p < 0.001, χ2/df = 1.825, RMSEA = 0.052, CFI = 0.960, IFI = 0.960. AT = Attitude, PAE = Positive anticipated emotions, NAE = Negative anticipated emotions, SN = Subjective norms, PBC = Perceived behavior control, DE = Desire, BI = Behavioral intention, SMC = Social media consumption, HTK = Hardy tourism knowledge. a = Composite reliabilities are along the diagonal in bold. b = Correlations. c = Squared correlations.
Table
Table 3. Summary of Structural Equation Modeling results.
Table 3. Summary of Structural Equation Modeling results.
Standardized Estimatet-ValueGoodness-of-Fit StatisticTotal Variance ExplainedTotal Impact of Behavior Intention
H1: Attitude →Desire0.2233.310*χ2 = 952.495,
df = 469,
χ2/df = 2.031,
RMSEA = 0.058,
CFI = 0.945,
IFI = 0.946,
TLI = 0.938,
NFI = 0.898,
PGFI = 0.707		R2 of BI = 0.867
R2 of DE = 0.753
R2 of AT = 0.570
R2 of HTK = 0.345
R2 of SMC = 0.551		DE = 0.489
AT = 0.109
HTK = −0.030
SMC = −0.018
PAE = 0.153
NAE = −0.036
SN = −0.006
PBC = 0.040
FPB = 0.728
H2: Positive anticipated emotion→Desire0.1602.275***
H3: Positive anticipated emotion→Attitude0.68310.892*
H4: Negative anticipated emotion→Desire−0.074−1.903
H5: Subjective norms→Desire−0.013−0.336
H6: Perceived behavioral control→Desire0.6158.914*
H7: Perceived behavioral control→Behavioral intention−0.261−1.389
H8: Desire→Behavioral intention0.4876.623*
H9: Frequency of past behavior→Behavioral intention−0.2613.502*
H10: Frequency of past behavior→Social media consumption0.7427.520*
H11: Social media consumption→Hardy tourism knowledge0.58810.726*
H12: Hardy tourism knowledge→Attitude0.1673.185**
H13: Hardy tourism knowledge→Desire−0.099−2.367***
Notes: AT = Attitude, PAE = Positive anticipated emotion, NAE = Negative anticipated emotion, SN = Subjective norms, PBC = Perceived behavior control, FPB = Frequency of past behavior, DE = Desire, BI = Behavioral intention, SMC = Social media consumption, HTK = Hardy tourism knowledge. * p < 0.001, ** p < 0.01, *** p < 0.05
Table
Table 4. Indirect-impact assessment results.
Table 4. Indirect-impact assessment results.
Indirect Effect ofOn
HTKATDEBI
FPB0.436*-−0.027−0.013
SMC-0.098*−0.036−0.018
PBC-0.073*-0.301*
HTK--0.037**−0.030
PAE--0.152*0.153*
AT---0.109*
NAE---−0.036***
SN---−0.006
Note: AT = Attitude, PAE = Positive anticipated emotions, NAE = Negative anticipated emotions, SN = Subjective norms, PBC = Perceived behavior control, FPB = Frequency of past behavior, DE = Desire, BI = Behavioral intention, SMC = Social media consumption, HTK = Hardy tourism knowledge. * p < 0.01, ** p < 0.05, *** p < 0.10.

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Bui, N.A.; Kiatkawsin, K. Examining Vietnamese Hard-Adventure Tourists’ Visit Intention Using an Extended Model of Goal-Directed Behavior. Sustainability 2020, 12, 1747. https://doi.org/10.3390/su12051747

AMA Style

Bui NA, Kiatkawsin K. Examining Vietnamese Hard-Adventure Tourists’ Visit Intention Using an Extended Model of Goal-Directed Behavior. Sustainability. 2020; 12(5):1747. https://doi.org/10.3390/su12051747

Chicago/Turabian Style

Bui, Ngoc Anh, and Kiattipoom Kiatkawsin. 2020. "Examining Vietnamese Hard-Adventure Tourists’ Visit Intention Using an Extended Model of Goal-Directed Behavior" Sustainability 12, no. 5: 1747. https://doi.org/10.3390/su12051747

APA Style

Bui, N. A., & Kiatkawsin, K. (2020). Examining Vietnamese Hard-Adventure Tourists’ Visit Intention Using an Extended Model of Goal-Directed Behavior. Sustainability, 12(5), 1747. https://doi.org/10.3390/su12051747

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