International Tourism Advertisements on Social Media: Impact of Argument Quality and Source

Abstract

To guarantee sustainable international tourism market growth, challenges for international tourism advertisements (ITAs) include how and by whom they are made. Different to traditional ITAs, a new type of ITA has been created by the international tourists themselves; it contains not only pictures but also their own tour stories, and it is distributed via social media (e.g., Youtube.com). However, few studies have investigated the impacts of this type of ITA. I was challenged to empirically validate the impacts on potential tourist reactions of argument quality and the peer tourist source of ITAs. I developed my research model based on Toulmin’s model of argument, institution-based trust, the information adoption model, and consumer reaction literature. I conducted the quasi-experiment using three types of ITAs that vary by argument quality and advertisement source. A total of 387 data were collected and analyzed using ANOVA and the partial least squares (PLS) analysis. The results indicate that argument quality and peer tourist source significantly increase perceived ITA quality, ITA fit-to-task and trusting belief, and decrease perceived risk. Argument quality and peer tourist source could also significantly increase tourist reactions, such as ITA adoption, planned/unplanned visit, and word-of-mouth intention. These findings could make ITAs more persuasive on social media.

1. Introduction

International tourism has become one of the largest and fastest-growing industries [1]. The number of international tourist arrivals is expected to increase by an average of 3.3% per year from 2010 to 2030. International tourist arrivals will increase by approximately 43 million per year and will reach 1.4 billion by 2020 and 1.8 billion by 2030 [1]. Along with this quantitative market growth, tourism could be a key driver of socio-economic progress through export revenue, job creation, and national infrastructure development. Tourism could additionally generate 29% of service exports, 11 jobs per tourist, 9% of GDP and 6% of worldwide exports [1]. In line with this trend, some private and public agencies produce international tourism advertisements to attract international tourists. International tourism advertisements (ITAs) are tourism-related advertisements targeted at potential tourists and non-tourists alike and are made by private and public agencies [1]. As a type of traditional ITA, destination advertisement shows merely the beautiful sights of the target nation. For example, the destination advertisement of the Malaysia Tourism Promotion Board, ‘Malaysia Truly Asia—The Essence of Asia’ shows a variety of beautiful Malaysian sights (http://www.tourism.gov.my).

Different to traditional ITAs, a new type of ITA has recently been created by tourists themselves and deployed via social media. Social media users can easily interact with a mass of friends [2,3,4]. Companies could strategically use social media to interact with potential consumers and to exercise more persuasive marketing practices [5,6]. Characteristics of a new type of ITA could be described as follows: it could be made by the international tourists themselves, it contains not only pictures but also tour stories and tourists’ experiences about a destination, and it has been published and distributed on social media platforms such as Youtube.com. As a type of user-created content, I call this new type of ITA tourist-created ITA (TCITA) in this study. TCITA differs from the traditional ITA, which presents only pictures and which is made by governmental tourism agencies; the argument quality and the source of the ITA could vary. Challenges for making the ITA are how and by whom it should be made.

Additionally, it would be beneficial to create a virtuous cycle using the user-created content (i.e., TCITA in this study) to attract international tourists and to guarantee sustainable international tourism market growth. ITAs are temporally and artificially made by the governmental tourism agency; however, international tourists voluntarily create TCITAs by posting pictures and by sharing their own tour stories with their friends on social media. A potential international tourist could easily identify him- or herself with peer international tourists and empathize with their tour stories. When potential international tourists perceive the TCITA made by peer tourists to be helpful, they will also post their own tour stories reciprocally. Therefore, we could effectively and efficiently use TCITAs as a marketing practice, and the value creation process of TCITAs could form the virtuous cycle of reproduction. So TCITAs, the subject of this study, could be essential for creating sustainable international tourism market growth.

However, few studies have been conducted regarding the impact of TCITAs on potential tourist reactions. Previous studies have focused on validating the effectiveness of ITAs. They have verified the causal model of the impact of ITAs on advertisement awareness, positive destination image, attitude and visit intention [7,8] by media channel [9] and by cultural differences [10]. Previous studies have focused on the tourist reactions of traditional ITAs, but they could not explain how ITAs should be produced and what argument structure of ITAs could be beneficial to persuade potential tourists. Some studies have investigated the effect of an ITA’s photographic image as a design element [10]. It would be beneficial to verify the effect of multimedia ITAs, which could contain a tourist’s own tour stories in social media. The sources of traditional ITAs are governmental tourism agencies, who cannot capture the effect of peer tourist sources on the performances of ITAs.

Therefore, I was challenged to empirically validate the impact of ITAs by considering the argument quality and the peer tourist source on tourist reactions, such as ITA adoption, planned/unplanned visits and word-of-mouth intention. The findings of this study could help us understand how TCITAs could be strategically used, how ITAs should be structured, and by whom ITAs could be made in order to persuade potential international tourists.

2. Theoretical Background

2.1. Toulmin’s Model of Argument

Because ITAs are the advertisement message for persuading potential tourists, ITAs could also be regarded as a type of argument that aims to reach a mutual understanding between communicators and that could be adopted by communicators [11,12]. Therefore, I adopted Toulmin’s model of argument, which explains an effective structure of argument in daily communication [13,14,15,16,17,18]. Toulmin (1958) [17] identified six inter-related elements of argument: ‘claim’, ‘data’, ‘warrant’, ‘backing’, ‘rebuttal’, and ‘qualifier’. A claim is the assertion or statement itself for which one is arguing. Data are the supportive evidence on which the claim is based. Warrant is a proposition that links the data to the claim; the warrant often remains unexpressed, although its implicit existence is generally assumed in daily communication [14]. Backing is the additional evidence which explains why the data and the warrant are acceptable. Backing could be added as the essential element, especially in a persuasive argument, because this element could help in making a more robust argument by providing a ground to connect the data and the warrant. Compared with data, which directly supports the claim, the backing could indirectly support the claim through supporting the data and warrant on which the claim was based. The last two elements, rebuttal and qualifier, may not be essential elements of argument, because these elements could be selectively used to assume an argument situation or to express the nuance and confidence level of claim. Descriptions of the six elements in Toulmin’s model of argument are shown in

Table 1. Six elements in Toulmin’s Model of Argument.

Elements	Description	Examples	Sources
Claim	The assertions or conclusions put forward for general acceptance.	If a person tries to convince a listener that he is a US citizen, the claim would be “I am a US citizen.”	Ye & Johnson (1995) [15] (p. 159)
Data	The evidence used to support a claim.	The person can support his claim with the supporting data “I was born in New York.”	VerLinden (1998) [16]
Warrant	The propositions that establish links between data and claim.	For bridging, the person must supply a warrant to bridge the gap between the above claim and data with the statement “A man born in New York will legally be a US citizen.”	VerLinden (1998) [16], Toulmin (1958) [17]
Backing	The evidence explaining why warrant and data should be acceptable.	If the listener does not deem the warrant as credible, the speaker will supply the legal provisions as backing statement to show that it is true that “A man born in New York will legally be a US citizen by US law.”	VerLinden (1998) [16], Toulmin (1958) [17]
Rebuttal	Statements recognizing the restrictions to which the claim may legitimately be applied.	“A man born in New York will legally be a US citizen, unless he has betrayed US and has become a spy of another country.”	Toulmin (1958) [17]
Qualifier	Words or phrases expressing the speaker’s degree of force or certainty concerning the claim.	Such words or phrases include “possible”, “probably”, “impossible”, “certainly”, “presumably”, “as far as the evidence goes”, or “necessarily”. The claim “I am definitely a British citizen” has a greater degree of force than the claim “I am a US citizen, presumably”.	Toulmin (1958) [17]

.

Toulmin (1958) argues that a hierarchy of argument exists that can be combined to enhance the veracity of a claim [14,17]. Argument in daily communication can be composed of these six elements, in the sequential order of claim, data, warrant, backing, rebuttal, and qualifier [16]. We can imagine an exceptional combination of data without a claim, but this form would appear infrequently in daily communication [14]. Claim, data, and backing should be considered essential to make the argument more persuasive, because they frequently consist of the body of argument in daily communication [14,16]. Someone would easily accept the claim of an argument if they could accept its data (e.g., evidence) and warrant (e.g., logic of argument) [14]. If someone could not believe in the data and warrant, the backing could provide the reason why they should be acceptable [14]. Therefore, a claim with the other composites of data, warrant and backing could be better structured to persuade others than a bare argument with a claim only [13]. Impacts of this well-structured form of argument for persuasion have been empirically validated in the context of expert system users [15] and online consumers [14]. In an advertising context, Munch et al. (1993) found that having the warrant from Toulmin’s model in adverting message increases consumer’s beliefs about products and attitudes toward the product [18]. Ye & Johnson (1995) [15] found that the recommendation message, which consists of elements of Toulmin’s model, was more persuasive when expert system users were considering its adoption. Kim & Benbasat (2006) [14] tested the claim, data and backing structure of Toulmin’s model in an online shopping context and found that this combination could enhance an online consumer’s trust more than other cases of a bare argument.

Elements in a well-structured form of argument, which has the claim with the ground, are closely related to each other with logical links and orders, and someone could not easily find the void of logic in the argument. Therefore, a well-structured form of argument could cause a message receiver to evaluate the target information as highly qualified, form a positive attitude about the message sender, and be easily persuaded by the message sender [14,15,18]. Perceived information quality, information fit-to-task, trusting belief about the message sender and perceived risk of information adoption have been regarded as the antecedents of information adoption in the persuading process [19,20,21]. Perceived information quality refers to the information user’s reaction to the characteristics of information to meet their information requirements [19] and refers to an information user’s cognitive beliefs about the favorable or unfavorable characteristics of the currency, accuracy, completeness, relevance, and reliability of the information [20,21]. Information fit-to-task refers to the extent to which information presented on a website is appropriate for performing the task at hand [22]. Perceived information quality focuses on the judgement of the target information by the various quality criteria, but information fit-to-task pertains to the practical applicability of the target information. Trusting beliefs refers to someone’s belief that the partner in a social-exchange relationship is honest (i.e., has integrity and keeps promises), benevolent (i.e., is responsive to the partner’s interests), and competent (i.e., has the ability to do what the partner needs) [23]. Perceived risk refers to someone’s belief that uncertainty exists as to whether the desirable outcomes will be obtained [21,24].

A well-structured form of argument could increase these four antecedents of information adoption and persuasion. A well-structured form of argument could be logically strong and could provide more various cues and grounds to support claims than a bare argument of claim only [25]. A well-structured form of argument could cause someone to evaluate the target information as currant, accurate, complete, relevant, and reliable, thereby increasing perceived information quality. A well-structured form of argument could also provide contextual information for the data, the warrant and the backing for the claim. Also, it could provide target information in an easily adaptable form so someone could use it to perform a task in a specific situation, thereby increasing information fit-to-task. When a reader can find a claim with more various cues and grounds, a message sender has intentionally disclosed this information to help the reader; the sender is interested in keeping the reader’s interest in matters of competence, benevolence and integrity and should be considered a trustable partner [14,26]. So trusting belief in a message sender would be increased. Compared with the case in which the reader must make a decision based on a bare argument of claim only, they could escape from unexpected loss and undesirable outcomes by making a decision based on the well-structured form of argument. So, perceived risk of information adoption could be decreased [21].

2.2. Institution Based Trust

Along with the well-structured form of argument, peer tourist source of argument could also increase the four antecedents of information adoption. Generally, in an online marketplace, online shoppers and online sellers are in a dyadic relationship [23], and online sellers could sometimes try to cheat online shoppers by distorting the deal information using concealment, equivocation, and falsification [27]. Online shoppers can trust a community of sellers and buy something in an online marketplace because institutional mechanisms play a role to protect online shoppers from this potential threat [23]. Institution-based trust is ‘the belief that needed structural conditions are present’ for guaranteeing transactional success in online environment [14,23]. An example of trust-assuring structural conditions is the intervention of an essentially trusted third party. A community of peer consumers is regarded as the essentially trusted third party, because they exist in the dyadic relationship as well [23].

Traditional ITAs are artificially made by a governmental agency, which exists in a dyadic relationship, has a narrow view about the destination, and cannot help providing a limited amount of travel information. TCITAs could provide an amount of highly-qualified travel information, because a community of peer tourists has their own tour stories that would be accurate, completed, relevant, and reliable; moreover, TCITAs would be useful because tourists could synchronously share that information on social media platforms. Therefore, peer sources of ITAs could increase the perceived content quality of ITAs. Peer tourists can provide vivid travel information about destinations based on their own experiences, that can be easily adopted by other tourists. Therefore, peer sources of ITAs could increase the information fit-to-task of ITAs. Similar to the online feedback mechanisms, peer tourists have no need to provide fraudulent travel information, and they would want to keep their own reputation in a community of tourists by providing high-quality travel information [23]. Therefore, peer sources of ITAs could increase the trusting belief in the ITA’s creator. Tourists could feel low risk by following the highly-qualified, easily adoptable tourism information from the trustable source as well in the dyadic relationship. Therefore, peer sources of ITAs could decrease the perceived risk of ITA adoption.

2.3. Information Adoption Model

The information adoption model [28], which describes a message receiver’s elaboration likelihood for information adoption and successfully captures the dual processes of a message receiver’s perception, attitude and behavioral intention formation for information adoption, could be used to illustrate the effects of four antecedents on information adoption intention in this study. The information adoption model proposes two routes of information adoption as the central and peripheral routes [29]. In the central route of information adoption, a message receiver actively concentrates on the information itself and tries to understand, evaluate and judge it. When the quality of information is high, a message receiver can judge the target information to be useful and become willing to adopt it to solve problems [28]. In the peripheral route of information adoption, a message receiver would not concentrate on the information itself but on the affective aspect of the information source. When the credibility of the information source is high, a message receiver could judge the target information to be useful and then become willing to adopt it to solve the problem [29]. When too much effort and too small of a benefit would be expected to evaluate the information, when the problem is too trivial or when too much uncertainty exists, a message receiver could easily choose the peripheral route of information adoption [28,30]. This phenomenon has been validated, especially in user reactions to the knowledge management system [28].

If message receivers perceive the information to be highly qualified and to be adoptable in a specific situation, they concentrate on the information itself and exist on the central route of information adoption. If message receivers trust the message senders and perceive a low risk to adopting the information, the receivers consider the affective aspect of the information source and that they exist in the peripheral route of information adoption. In both routes, message receivers could perceive the information to be useful and dare to adopt it to solve the problems. So, four antecedents of information adoption in this study could increase the information usefulness and information adoption intention.

2.4. Tourist Reaction

Impacts of information usage on information user’s perception, attitude and behavioral intention have already been identified [31,32]; I use them to capture tourist reactions. Perceived usefulness, perceived enjoyment, and satisfaction are frequently mentioned performances of information usage. Perceived usefulness refers to the perception about enhanced effectiveness achieved through the use of a service [33]. Perceived enjoyment refers to the extent to which an activity is perceived to be enjoyable, apart from any performances that may be anticipated (Davis et al., 1992 [34], p. 1113). Satisfaction refers to favorable feeling toward the service in question [35]. Perceived usefulness and enjoyment stand for utilitarian and hedonic, respectively, performances of information usage. Satisfaction as the attitude toward the service in question mediates between the perception and the behavioral intention of information usage [36]. When information users perceive the target information to be useful and enjoyable, they form a favorable feeling toward the information usage experience and form various behavioral intentions [36,37].

ITA adoption, planned/unplanned visits [32], and positive word-of-mouth intention about ITAs have been adopted to illustrate the ITA performances in this study. Message receivers will adopt the target information when they perceive it to be useful and are satisfied with the information usage experience. Two types of purchase intention exist: planned and unplanned [38]. Sometimes, a consumer has already determined their shopping plan to buy something before entering the store. A consumer could also buy something impulsively without any shopping plan [39]. A consumer could easily be attracted by the various in-store and out-of-store marketing practices that cause them to recognize that given their needs they could buy something impulsively [39]. Word-of-mouth intention refers to a customer’s intention to recommend the service to others [33]. When consumers are satisfied with the service, they perceive the psychological inequity, and they frequently try to reward the service provider by spreading positive word-of-mouth to other consumers [33].

3. Hypotheses Development & Research Model

TCITAs would have two salient characteristics of the well-structured form of argument and peer tourist source of argument in this study. Because ITAs are also the message for persuading potential tourists, ITAs could be a type of argument [11]. A claim of ITAs could be an assertion that the destination is attractive to visit. Data could be the beautiful pictures about the destination. Warrant could be the proposition that destination visitors would enjoy their journey, which pictures would show. Backing could be the stories, schedules, tips, knowledge, know-how, and experiences that explain how visitors enjoy their journey, such as pictures. Different from traditional ITAs, which merely show pictures, TCITAs can provide almost all the claim, data, and backing. TCITAs could be regarded as a well-structured form of argument that could be considered highly qualified information and could be easily adapted to a specific situation by providing the contextual backing information. TCITAs could also be considered as the intentional disclosure of information to help tourists and to protect tourists from unexpected loss. The argument quality (i.e., well-structured form of argument) of ITAs could increase four antecedents of information adoption. Therefore, I hypothesized the following:

Hypothesis 1 (H1).

Argument quality of ITAs could positively affect perceived ITA quality.

Hypothesis 2 (H2).

Argument quality of ITAs could positively affect ITA fit-to-task.

Hypothesis 3 (H3).

Argument quality of ITAs could positively affect trust in the ITA creator.

Hypothesis 4 (H4).

Argument quality of ITAs could negatively affect perceived risk.

Peer tourists could be considered as the essentially trusted third party [23], and they have no need to distort the travel information. They could also provide an amount of accurate, current, completed, relevant, reliable and vivid travel information by posting and sharing their own tour stories in social media. This information could be highly qualified by criteria and could be adaptably applied to a specific situation, because it depends on the real experiences of peer tourists. Peer tourist could intentionally disclose and share their tour stories to help other tourists, and some tourists could escape from unexpected loss by following the advice of peer tourists. Peer tourists as a source of ITAs could increase four antecedents of information adoption. Therefore, I hypothesized the following:

Hypothesis 5 (H5).

Peer tourist source of ITAs could positively affect perceived ITA quality.

Hypothesis 6 (H6).

Peer tourist source of ITAs could positively affect ITA fit-to-task.

Hypothesis 7 (H7).

Peer tourist source of ITAs could positively affect trust in the ITA creator.

Hypothesis 8 (H8).

Peer tourist source of ITAs could negatively affect perceived risk.

If tourists perceive the ITA to be highly qualified and adaptable to their own travel, they concentrate on the ITA itself in the central route of information adoption. If they trust the source of ITAs and dare adopt the advice in an ITA, feeling low risk, they consider the affective aspect of information source in the peripheral route of information adoption. A tourist could perceive the ITA to be useful and to be enjoyable in both utilitarian and hedonic performances of content consumption. ITAs can innately create the fantasy about the destination and should be enjoyable. TCITAs could cause tourists to acknowledge the social presence of peer tourists, and they could easily feel a sense of relatedness [40]. It could be the source of intrinsic motivation for a tourist to consume the ITA. Therefore, I hypothesized the following:

Hypothesis 9 (H9).

Perceived ITA quality could positively affect the perceived usefulness of ITAs.

Hypothesis 10 (H10).

Information fit-to-task of ITAs could positively affect the perceived usefulness of ITAs.

Hypothesis 11 (H11).

Trusting belief in ITAs could positively affect the perceived usefulness of the ITA.

Hypothesis 12 (H12).

Perceived risk could negatively affect the perceived usefulness of ITAs.

Hypothesis 13 (H13).

Perceived ITA quality could positively affect the perceived enjoyment of ITAs.

Hypothesis 14 (H14).

Information fit-to-task of ITAs could positively affect the perceived enjoyment of ITAs.

Hypothesis 15 (H15).

Trusting belief in ITAs could positively affect the perceived enjoyment of ITAs.

Hypothesis 16 (H16).

Perceived risk could negatively affect the perceived enjoyment of ITAs.

If tourists perceive the ITA to be useful and enjoyable, they could form favorable feelings toward the ITA, be satisfied with the ITA consumption experience, and then form various positive behavioral intentions [36,37]. When online shoppers are satisfied with the service, they intend to purchase this product and service [41], to follow the advices and recommendations of the service provider [28] and to spread the positive word-of-mouth about the service and the service provider [33]. Tourists who are satisfied with ITAs will visit the destination by following the ITA and spreading the positive word-of-mouth about the ITA. Therefore, I hypothesize the following:

Hypothesis 17 (H17).

Perceived usefulness could positively affect the satisfaction.

Hypothesis 18 (H18).

Perceived enjoyment could positively affect the satisfaction.

Hypothesis 19 (H19).

Satisfaction could positively affect the ITA adoption intention.

Hypothesis 20 (H20).

Satisfaction could positively affect the planned visit intention.

Hypothesis 21 (H21).

Satisfaction could positively affect the unplanned visit intention.

Hypothesis 22 (H22).

Satisfaction could positively affect the word-of-mouth intention.

I decided to control variables such as media richness [42], information overload [43], involvement and expertise [28], because ITAs could be deployed in social media and because these four variables are considered as the major exogenous variables to influence the process of multimedia contents adoption. Media richness theorists [25,42] argued that media richness and information overload, as the cue convey-ability of media and absorptive capacity of media users, could affect the information adopter’s reactions. Sussman and Siegal (2002) [28] argued that the involvement and expertise in the information issues could be regarded as the motivation and ability factors of the information adopter, and that they could also affect the information adopter’s reactions. Also, I adopted the privacy concern as the marker variables to check the common method bias. My research model is shown as Figure 1.

4. Methodology

4.1. Operationalization of Constructs

Measurement items of each construct were adapted from the previous studies to guarantee validity [44]. Because each construct has more than three measurement items, I performed the two validation processes in two stages [45]. I recruited five professionals (e.g., professors in Managment Information Systems and Marketing) and followed their advice to change words and expressions to access the face validity of measurement items. I also recruited ten graduate students to access understandability and clarity of the meanings of the material. Measurement items of each construct are shown in

Table 2. Measurement items of each construct.

Constructs	Measurement Items	Sources
Perceived information Quality (PIQ)	In my thought, the given international tourism advertisement (ITA)…	Nicolaou & McKnight (2006) [21]
	(PIQ1) is current enough to meet my needs.
	(PIQ 2) is accurate enough to meet my needs.
	(PIQ 3) is pretty much what I need.
	(PIQ 4) actually fulfill my needs.
	(PIQ 5) is an appropriate level of detail for my needs.
	(PIQ 6) can be relied upon.
	(PIQ 7) can reflect the real feature of the destination and cannot be distorted.
Information fit-to-task (IFT)	To know about the destination, this ITA…	Parboteeah et al. (2009) [32]
	(IFT1) is effective.
	(IFT2) adequately meets my information needs.
	(IFT3) is pretty much what I need to carry out my task.
Trusting belief (TB)	Compared with the other ITA creators, this ITA creator…	Pavlou & Gefen (2004) [23]
	(TB1) can be trusted at all times.
	(TB2) has high integrity.
	(TB3) is competent and knowledgeable.
Perceived risk (PR)	Believing this ITA …	Nicolaou & McKnight (2006) [21]
	(PR1) is overall risky.
	(PR2) is much more risky than my acceptable level.
	(PR3) could expose me to a significant threat.
	(PR4) could expose me to the potential for loss.
	(PR5) could expose me to a negative situation.
Perceived usefulness (PU)	This ITA is…	Sussman & Siegal (2003) [28]
	(PU1) valuable.
	(PU2) informative.
	(PU3) helpful.
Perceived enjoyment (EJ)	This ITA is…	Parboteeah et al. (2009) [32]
	(EJ1) enjoyable.
	(EJ2) exciting.
	(EJ3) pleasant.
Satisfaction (SAT)	(SAT1) I am contented to see this ITA in social media.	Kim & Son (2009) [33]
	(SAT2) I am satisfied with this ITA in social media.
	(SAT3) This ITA in social media meets what I expect for this type of service.
Information adoption intention (IAD)	After being shown this ITA…	Sussman & Siegal (2003) [28]
	(IAD1) I intend to understand the destination following this ITA without any modification.
	(IAD2) This ITA makes me highly motivated to understand the destination in the ITA.
	(IAD3) I completely agree with the description about the destination in this ITA
Planned visit intention (PVI)	When I would visit some destination…	Song & Zahedi (2005) [41]
	(PVI1) The probability of visit the destination in this ITA would be probable.
	(PVI 2) The likelihood that I would visit the destination is highly likely.
	(PVI 3) My willingness to visit the destination is highly willing.
	(PVI 4) The probability that I would consider visiting the destination is highly probable.
Unplanned visit intention (UVI)	After being shown this ITA…	Parboteeah et al. (2009) [32]
	(UVI1) I had the urge to visit the destination in the ITA other than or in addition to my specific travel goal.
	(UVI2) I had a desire to visit the destination in the ITA that did not pertain to my specific travel goal.
	(UVI3) I had the inclination to visit the destination in ITA outside my specific travel goal.
Word-of-Mouth intention (WM)	(WM1) I will say positive things about this ITA in social media to other people.	Kim & Son (2009) [33]
	(WM2) I will recommend this ITA in social media to anyone who seeks my advice.
	(WM3) I will refer my acquaintances to this ITA in social media.
Media richness (MR, control)	(MR1) This ITA provides the information about the destination which could be easily understood.	Dennis & Kinney (1998) [42], Kahai & Cooper (2003) [25]
	(MR2) This ITA helps me to understand the destination.
	(MR3) This ITA could not get in the way of understanding the destination.
	(MR4) I could easily explain the destination in this ITA.
	(MR5) This ITA helped me understand the destination quickly.
	(MR6) This ITA could provide the various cues which help me easily to understand the destination.
	(MR7) This ITA could provide the various cues which help me to better understand the destination.
	(MR8) This ITA could provide the various cues which help me to quickly understand the destination.
Information overload (IO, control)	(IO1) I need more time to understand this ITA.	Paul & Nazareth (2010) [43]
	(IO2) This ITA contains information that is too complex for me to understand.
	(IO3) This ITA contains too much information for me to understand.
Involvement (IV, control)	(IV1) I am much involved in the topic of this ITA.	Sussman & Siegal (2003) [28]
	(IV2) Much of the issue discussed in this ITA has been on my mind lately.
Expertise (EP, control)	(EP1) I was much informed on the subject matter of this issue in the ITA.	Sussman & Siegal (2003) [28]
	(EP2) I am an expert on the topic of this ITA.
Privacy concern (PC, marker variable for check common method bias)	(PC1) I am concerned that the information I submit to the Internet could be misused.	Son & Kim (2008) [46]
	(PC2) I am concerned that a person can find private information about me on the Internet.
	(PC3) I am concerned about providing personal information to the Internet, because of what others might do with it.
	(PC4) I am concerned about providing personal information to the Internet, because it could be used in a way I did not foresee.

.

4.2. Data Collection

I performed the quasi-experiment to verify my hypotheses in this study [47]. Laboratory experiment methods could have advantages in maintaining the internal validity of research by controlling exogenous variables, but they could not entirely reflect the practical applications [47]. Survey methods could not be applied in this study, because I had difficulty in recruiting the potential tourists on social media, because social media was reluctant to recruit users because of consumer protection policies [48]. For performing the quasi-experiment, I captured three types of ITAs on Youtube.com. Three types of ITAs could vary by the argument quality and the source of advertisement. The first one (group A) is a typical destination advertisement of the traditional ITA. This ITA was made by the governmental tourism agency, and it shows a series of beautiful pictures of the destination (e.g., https://www.youtube.com/watch?v=OVaEq4Sk5D8). It is an ill-structured form of argument, which consists of claim with data. The claim of this ITA could be an assertion that Korea could be an attractive destination to visit. Data could be a series of beautiful pictures in Korea. The second one (group B) was also made by the governmental tourism agency, but it shows a series of beautiful pictures and narrates the stories, tips, and travel advice about the destination (e.g., https://www.youtube.com/watch?v=ck3s0T2n6Ao). It could be regarded as a well-structured form of argument, because it consists of a claim with data, warrant, and backing. With the above claim and data, its warrant could be the proposition that visitors in Korea could enjoy their journey with the pictures shown, and its backing could be the stories, schedule, tips, knowledge, know-how, and experiences explaining how visitors to Korea could enjoy their journey with similar pictures. The third one (group C) was made by peer international tourists, but it could be regarded as the typical destination advertisement which shows a series of beautiful pictures (e.g., http://www.youtube.com/watch?v=rx2whStnQsk). The difference between the first and second one is the argument quality of the ITA, and the difference between the second and third one is the source of the ITA.

The population is the potential tourist who has not been exposed to TCITAs. I recruited volunteer student participants. A total of 400 participants were recruited, but 8 among them could not participate in the quasi-experiment for their own private reasons. Then, 392 participants were randomly assigned to each group. The participants in each group were exposed to one of three ITAs, and participants were requested to fill in the survey material. $10 per participant was paid to encourage sincere responses. Five responses were irresponsible (all responses were 1 or 7), and 387 responses were gathered for analysis. The demographic data of participants is shown in

Table 3. Demographic data.

G	Num.	%	M	Num.	%	W	Num.	%	I	Num.	%
Male	225	58.1%	Humanities	21	5.4%	0–1 year	60	15.5%	1 time	33	8.5%
Female	162	41.9%	Business	135	34.9%	1–2 years	48	12.4%	2 times	147	38.0%
			Natural Sci.	81	20.9%	2–3 years	66	17.1%	3 times	144	37.2%
			Engineering	69	17.8%	4–5 years	120	31.0%	4–5 times	45	11.6%
			Social Sci.	33	8.5%	6–7 years	45	11.6%	6–7 times	6	1.6%
			Life Sci.	9	2.3%	8–10 years	27	7.0%	8-9 times	6	1.6%
			Art	39	10.1%	10 years -	21	5.4%	10 times -	6	1.6%

Cf. G: gender; M: major; W: web experinece; I: international travel experience per a year; Num.: Frequency.

.

5. Analysis and Results

The argument quality and the source of ITAs were manipulated in this study, and I performed two stages of ANOVA and partial least-squares (PLS) analysis to verify my hypotheses. I performed ANOVA to verify the impacts of the argument quality and the source of ITAs on four antecedents of information adoption (H1~H8). ANOVA results indicate that the means between groups were significantly different from one another and that the mean of group C was significantly higher than the others in the order of group C, B, and A in three antecedents of information adoption (this order could be reversed in the perceived risk). Upper bounding and lower bounding of means were not superposed with one another. The difference between group A and B reflects the impact of the argument quality of the ITA, and the difference between group B and C reflects the impacts of peer tourist source of the ITA. Therefore, it could be argued that the argument quality and peer tourist source of the ITA can significantly increase the three antecedents of perceived ITA quality, ITA fit-to-task, trusting belief of an ITA creator, and decreased perceived risk of ITA adoption. Therefore, H1~H8 were supported. ANOVA results are shown in

Table 4. ANOVA Results.

ANOVA Results
				Value				F			Sig.
λ				0.471				12.895			0.000
Mean Difference Estimates									Turkey HSD Comparison Results
Index	III	df	MS		F	Sig	R2	Ad. R2	Group	Mean	SD	95%
												Lower	Upper
PIQ	188.940	2	94.470		109.807	0.000	0.364	0.361	A	4.023	0.082	3.863	4.184
									B	4.813	0.082	4.652	4.973
									C	5.733	0.082	5.573	5.894
ITF	231.997	2	115.999		74.047	0.000	0.278	0.275	A	3.695	0.110	3.478	3.912
									B	4.726	0.110	4.509	4.943
									C	5.589	0.110	5.372	5.806
TB	119.413	2	59.706		56.141	0.000	0.226	0.222	A	4.256	0.091	4.077	4.434
									B	5.049	0.091	4.871	5.228
									C	5.610	0.091	5.431	5.788
PR	119.772	2	59.886		36.521	0.000	0.160	0.155	A	4.011	0.113	3.789	4.233
									B	3.060	0.113	2.839	3.282
									C	2.690	0.113	2.468	2.912
PU	133.089	2	66.545		53.850	0.000	0.219	0.215	A	3.734	0.098	3.541	3.926
									B	4.522	0.098	4.330	4.714
									C	5.168	0.098	4.976	5.360
EJ	197.427	2	98.713		74.331	0.000	0.279	0.275	A	4.307	0.101	4.108	4.507
									B	5.160	0.101	4.961	5.360
									C	6.057	0.101	5.857	6.256
SAT	213.241	2	106.620		76.674	0.000	0.285	0.282	A	4.052	0.104	3.848	4.256
									B	5.028	0.104	4.824	5.233
									C	5.868	0.104	5.664	6.072

Cf. λ: Wilks’s λ, III: type III sum of square, df: degree of freedom, MS: mean squred, Sig.: significance, Ad. R²: adjusted R2, 95%: 95% significant interval.

.

The remaining hypotheses were validated by PLS analysis [49], which is simple and reliable, needs relatively small amounts of observation, and is adopted in various contexts [50]. The PLS algorithm is performed in two stages of analysis, one of which assesses the reliability and validity of the measurement model, and the other of which assesses the causality of the structural model.

5.1. Measurement Model

It is possible to examine the average variance extracted (AVE) and composite reliability to assess the convergent validity of constructs and the reliability of measurement items [51]. AVE values should exceed the 0.50 cut-off level, and they indicate that the majority of the variance could be explained by each construct. Composite reliability values should exceed 0.70 cut-off levels, and they indicate that the indicators are internally consistent [52]. AVE and composite reliability values are shown on the left side of

Table 5. Latent variable correlations.

	AVE	CR	R2	α	PIQ	IFT	TB	PR	PU	EJ	SAT	IAD	PVI	UVI	WM	MR	IO	IV	EP	PC
PIQ	0.722	0.948		0.935	0.850
IFT	0.921	0.972		0.957	0.801	0.959
TB	0.888	0.960		0.937	0.765	0.757	0.943
PR	0.841	0.964		0.953	−0.569	−0.582	−0.635	0.917
PU	0.919	0.971	0.691	0.956	0.702	0.753	0.761	−0.618	0.958
EJ	0.940	0.979	0.557	0.968	0.662	0.653	0.708	−0.521	0.691	0.970
SAT	0.918	0.971	0.677	0.955	0.709	0.731	0.761	−0.582	0.738	0.772	0.958
IAD	0.806	0.926	0.637	0.880	0.734	0.731	0.757	−0.572	0.747	0.728	0.798	0.898
PVI	0.894	0.971	0.571	0.961	0.692	0.681	0.722	−0.533	0.680	0.728	0.756	0.792	0.946
UVI	0.945	0.981	0.633	0.971	0.670	0.670	0.726	−0.532	0.672	0.724	0.796	0.761	0.868	0.972
WM	0.899	0.964	0.517	0.944	0.623	0.636	0.658	−0.472	0.591	0.657	0.719	0.735	0.765	0.815	0.948
MR	0.784	0.967		0.960	0.764	0.805	0.758	−0.571	0.758	0.647	0.756	0.744	0.672	0.676	0.636	0.885
IO	0.830	0.936		0.900	−0.238	−0.244	−0.281	0.369	−0.300	−0.302	−0.294	−0.300	−0.245	−0.245	−0.278	−0.316	0.911
IV	0.949	0.974		0.946	0.287	0.300	0.384	−0.160	0.262	0.342	0.383	0.382	0.406	0.480	0.473	0.246	0.015	0.974
EP	0.896	0.945		0.884	0.260	0.285	0.339	−0.183	0.266	0.292	0.362	0.330	0.348	0.438	0.444	0.228	0.001	0.833	0.946
PC	0.738	0.919		0.887	0.142	0.078	0.133	0.033	0.058	0.108	0.070	0.090	0.101	0.105	0.167	0.061	−0.109	0.026	0.055	0.859

* AVE; average variance extracted, CR; composite reliability, α: Cronbach’s α; ** Diagonal cells in right side are the square root of AVE of each construct. Off-diagonal cells are squared correlations.

. I could compare the square root values of the AVE of each construct with the correlations of the other constructs to access the discriminant validity, which means that a construct should be different from the other constructs [53]. All square root values of AVE (which are diagonal values in the right part of Table 5) exceed the correlations of the other latent variables (which are off-diagonal values in the right part of Table 5) [50].

We could also access the convergent and discriminant validity to compare the factor loadings of indicators of a construct with the factor loadings of the other constructs. Indicators should have higher loadings in their construct than any other constructs [50]. Factor loadings in a construct were higher than the cross-loadings of any other constructs in

Table 6. Factor loadings.

	PIQ	IFT	TB	PR	PU	EJ	SAT	IAD	PVI	UVI	WM	MR	IO	IV	EP	PC
PIQ1	0.765	0.538	0.523	−0.390	0.488	0.531	0.538	0.528	0.539	0.522	0.467	0.519	−0.136	0.168	0.131	0.095
PIQ2	0.889	0.682	0.679	−0.472	0.640	0.551	0.599	0.628	0.600	0.579	0.522	0.639	−0.219	0.253	0.227	0.128
PIQ3	0.855	0.676	0.610	−0.447	0.569	0.568	0.593	0.624	0.586	0.555	0.503	0.627	−0.171	0.233	0.199	0.089
PIQ4	0.903	0.714	0.671	−0.512	0.597	0.598	0.638	0.658	0.623	0.604	0.574	0.699	−0.213	0.236	0.212	0.177
PIQ5	0.857	0.743	0.635	−0.492	0.621	0.560	0.634	0.639	0.605	0.589	0.580	0.717	−0.208	0.265	0.255	0.088
PIQ6	0.860	0.693	0.713	−0.515	0.625	0.554	0.620	0.642	0.602	0.596	0.550	0.691	−0.212	0.332	0.312	0.125
PIQ7	0.810	0.701	0.700	−0.545	0.622	0.572	0.590	0.637	0.554	0.533	0.502	0.635	−0.249	0.207	0.201	0.135
IFT1	0.757	0.960	0.737	−0.541	0.725	0.628	0.709	0.704	0.666	0.642	0.612	0.777	−0.241	0.264	0.257	0.093
IFT2	0.768	0.971	0.722	−0.555	0.717	0.640	0.704	0.710	0.643	0.640	0.614	0.762	−0.250	0.292	0.263	0.080
IFT3	0.781	0.947	0.720	−0.579	0.726	0.610	0.691	0.688	0.651	0.645	0.605	0.780	−0.212	0.308	0.301	0.051
TB1	0.738	0.731	0.962	−0.626	0.736	0.679	0.727	0.713	0.691	0.694	0.624	0.734	−0.283	0.360	0.324	0.119
TB2	0.718	0.709	0.956	−0.626	0.729	0.675	0.717	0.722	0.682	0.676	0.612	0.715	−0.291	0.330	0.298	0.106
TB3	0.707	0.701	0.909	−0.542	0.686	0.647	0.708	0.706	0.669	0.684	0.626	0.695	−0.220	0.396	0.338	0.152
PR1	−0.503	−0.526	−0.581	0.923	−0.560	−0.451	−0.509	−0.494	−0.454	−0.450	−0.381	−0.512	0.339	−0.119	−0.131	0.021
PR2	−0.490	−0.523	−0.572	0.933	−0.576	−0.467	−0.519	−0.527	−0.464	−0.472	−0.418	−0.510	0.325	−0.139	−0.160	0.046
PR3	−0.566	−0.555	−0.625	0.928	−0.579	−0.525	−0.584	−0.568	−0.542	−0.547	−0.497	−0.553	0.346	−0.207	−0.227	0.014
PR4	−0.554	−0.573	−0.598	0.917	−0.568	−0.490	−0.550	−0.548	−0.535	−0.535	−0.480	−0.541	0.356	−0.194	−0.237	0.044
PR5	−0.493	−0.486	−0.532	0.883	−0.549	−0.452	−0.502	−0.480	−0.445	−0.428	−0.380	−0.500	0.325	−0.067	−0.076	0.027
PU1	0.662	0.715	0.728	−0.595	0.956	0.658	0.694	0.703	0.640	0.637	0.550	0.711	−0.272	0.257	0.251	0.062
PU2	0.684	0.729	0.724	−0.594	0.959	0.639	0.689	0.712	0.625	0.615	0.549	0.729	−0.285	0.243	0.250	0.037
PU3	0.673	0.721	0.736	−0.588	0.961	0.689	0.739	0.734	0.688	0.678	0.601	0.739	−0.306	0.253	0.265	0.067
EJ1	0.645	0.635	0.701	−0.495	0.663	0.975	0.745	0.716	0.704	0.703	0.636	0.634	−0.304	0.335	0.282	0.126
EJ2	0.633	0.636	0.684	−0.505	0.674	0.972	0.739	0.697	0.700	0.684	0.624	0.632	−0.302	0.318	0.264	0.105
EJ3	0.647	0.628	0.674	−0.515	0.673	0.962	0.761	0.705	0.714	0.719	0.649	0.617	−0.271	0.341	0.303	0.082
SAT1	0.697	0.679	0.724	−0.555	0.690	0.743	0.962	0.753	0.720	0.773	0.691	0.713	−0.282	0.389	0.362	0.056
SAT2	0.684	0.706	0.752	−0.563	0.727	0.764	0.971	0.783	0.746	0.777	0.699	0.729	−0.269	0.380	0.360	0.081
SAT3	0.657	0.716	0.711	−0.556	0.705	0.711	0.940	0.756	0.706	0.736	0.675	0.730	−0.296	0.329	0.316	0.064
IAD1	0.649	0.635	0.612	−0.473	0.636	0.561	0.655	0.873	0.659	0.596	0.627	0.656	−0.278	0.244	0.217	0.115
IAD2	0.626	0.654	0.686	−0.528	0.682	0.735	0.732	0.911	0.760	0.748	0.694	0.662	−0.301	0.390	0.343	0.107
IAD3	0.703	0.679	0.735	−0.537	0.693	0.658	0.757	0.910	0.712	0.699	0.659	0.687	−0.233	0.385	0.323	0.027
PVI1	0.671	0.647	0.698	−0.518	0.658	0.715	0.720	0.755	0.959	0.810	0.714	0.637	−0.263	0.336	0.293	0.111
PVI2	0.661	0.655	0.689	−0.493	0.634	0.705	0.694	0.751	0.953	0.810	0.721	0.634	−0.237	0.369	0.295	0.113
PVI3	0.650	0.634	0.693	−0.512	0.630	0.657	0.739	0.762	0.942	0.850	0.742	0.652	−0.228	0.438	0.385	0.109
PVI4	0.634	0.641	0.650	−0.493	0.649	0.679	0.704	0.728	0.929	0.812	0.714	0.617	−0.200	0.389	0.340	0.048
UVI1	0.655	0.665	0.722	−0.530	0.668	0.713	0.787	0.751	0.852	0.974	0.791	0.679	−0.243	0.460	0.419	0.104
UVI2	0.650	0.641	0.706	−0.521	0.649	0.700	0.775	0.742	0.838	0.974	0.790	0.651	−0.232	0.467	0.439	0.100
UVI3	0.648	0.647	0.689	−0.500	0.642	0.698	0.759	0.727	0.841	0.968	0.796	0.643	−0.238	0.472	0.421	0.102
WM1	0.593	0.625	0.661	−0.441	0.566	0.643	0.694	0.715	0.714	0.770	0.946	0.631	−0.289	0.453	0.415	0.161
WM2	0.621	0.598	0.608	−0.458	0.561	0.610	0.680	0.682	0.741	0.790	0.951	0.599	−0.243	0.467	0.427	0.134
WM3	0.558	0.585	0.600	−0.443	0.555	0.614	0.669	0.693	0.719	0.757	0.946	0.578	−0.256	0.426	0.421	0.181
MR1	0.672	0.683	0.645	−0.481	0.630	0.547	0.654	0.657	0.581	0.525	0.525	0.858	−0.310	0.151	0.131	0.085
MR2	0.699	0.728	0.685	−0.532	0.709	0.573	0.691	0.682	0.596	0.607	0.577	0.903	−0.314	0.229	0.220	0.065
MR3	0.664	0.725	0.648	−0.526	0.671	0.480	0.640	0.652	0.557	0.541	0.533	0.881	−0.283	0.196	0.189	−0.008
MR4	0.693	0.764	0.708	−0.546	0.697	0.607	0.653	0.685	0.622	0.612	0.563	0.871	−0.288	0.256	0.201	0.002
MR5	0.655	0.697	0.689	−0.471	0.658	0.609	0.674	0.663	0.603	0.619	0.584	0.869	−0.327	0.246	0.255	0.101
MR6	0.663	0.709	0.659	−0.467	0.675	0.587	0.662	0.644	0.587	0.593	0.554	0.908	−0.216	0.229	0.203	0.083
MR7	0.686	0.692	0.654	−0.497	0.670	0.587	0.687	0.639	0.586	0.619	0.571	0.899	−0.267	0.214	0.214	0.050
MR8	0.675	0.703	0.676	−0.524	0.657	0.584	0.689	0.647	0.620	0.664	0.591	0.891	−0.229	0.213	0.193	0.049
IO1	−0.275	−0.259	−0.285	0.360	−0.339	−0.309	−0.293	−0.287	−0.236	−0.232	−0.244	−0.312	0.896	0.016	−0.011	−0.063
IO2	−0.180	−0.197	−0.225	0.305	−0.210	−0.259	−0.260	−0.262	−0.218	−0.229	−0.262	−0.279	0.915	0.037	0.028	−0.121
IO3	−0.175	−0.199	−0.247	0.332	−0.246	−0.243	−0.241	−0.266	−0.211	−0.203	−0.255	−0.262	0.922	−0.010	−0.009	−0.127
IV1	0.278	0.294	0.385	−0.139	0.252	0.354	0.375	0.367	0.410	0.478	0.460	0.242	0.011	0.973	0.799	0.032
IV2	0.281	0.291	0.363	−0.173	0.259	0.313	0.371	0.378	0.381	0.457	0.462	0.237	0.018	0.975	0.823	0.020
EP1	0.238	0.272	0.314	−0.139	0.225	0.293	0.338	0.313	0.328	0.421	0.406	0.228	0.007	0.804	0.935	0.038
EP2	0.254	0.269	0.326	−0.202	0.275	0.263	0.346	0.313	0.330	0.411	0.433	0.206	−0.004	0.777	0.957	0.063
PC1	0.125	0.060	0.105	0.050	0.030	0.081	0.059	0.091	0.108	0.083	0.150	0.037	−0.100	−0.006	0.007	0.872
PC2	0.103	0.075	0.081	0.063	0.034	0.059	0.025	0.045	0.072	0.063	0.104	0.033	−0.073	−0.037	−0.028	0.853
PC3	0.105	0.053	0.112	0.014	0.056	0.104	0.062	0.075	0.043	0.054	0.158	0.057	−0.113	0.014	0.046	0.848
PC4	0.141	0.083	0.135	0.011	0.066	0.105	0.072	0.081	0.113	0.137	0.140	0.067	−0.080	0.074	0.108	0.865

. Factor loadings and cross-loadings are shown in Table 6. Considering all of these assessments, I could confirm the convergent and discriminant validity and reliability of my measurement model to be adaptable.

Because the data was collected using surveys, common method variance (CMV) could affect the results of this study. I used the marker-variable technique to access CMV. I added a theoretically unrelated variable (i.e., privacy concern) as the marker variable. If the co-efficient is less than 0.10, CMV is not substantial and serious. The results indicate that CMV was not substantial because the average correlation coefficient was close to 0 (r = −0.007, n.s.).

5.2. PLS Analysis Results

The hypotheses from H9~H22 were validated using 200 iterations of the bootstrapping technique in PLS algorithm [54,55]. The explanatory power of each dependent variable could be accessed by checking its R² value. I assessed the standardized path-coefficient and p-values of each path, which is the two-tail test result with a significance level of 0.05. The PLS results are shown in Figure 2. All hypotheses except three (H5: the path from perceived ITA quality to perceived usefulness, H10: the path from information fit-to-task to perceived enjoyment, and H12: the path from perceived risk to perceived enjoyment) were significantly approved. R² values of the dependent variables are calculated as perceived usefulness (0.691), perceived enjoyment (0.557), satisfaction (0.677), ITA adoption intention (0.637), planned visit intention (0.571), unplanned visit intention (0.633), and word-of-mouth intention (0.517).

6. Discussion

ANOVA results indicate that the argument quality and peer tourist source of ITAs could significantly increase the perceived ITA quality, ITA fit-to-task and trusting belief of an ITA creator, and could decrease the perceived risk of ITA adoption. This result means that TCITAs—which are the claim with data, warrant and backing—could be a highly qualified and trustable type of ITA. This result is consistent with the findings of Kim & Benbasat (2006) stating that the trust-assuring argument composed of claim, data, warrant and backing could promote trusting belief in Internet stores [14]. Along with PLS results, the argument quality and peer tourist source of ITAs could significantly increase perceived usefulness, perceived enjoyment, and satisfaction, and could increase ITA adoption, planned/unplanned visit and word-of-mouth intention of the potential tourists. These results could also be consistent with prior studies’ findings that the effectiveness of ITAs could be determined by the destination awareness [8] and destination visit [9]. The results of this study successfully capture how the argument quality and peer tourist source of ITAs could induce the potential tourists’ perceptions, attitudes and behavioral intentions toward the ITA. We can observe the logical path for how the argument quality and peer tourist source of ITAs could increase the various positive perceptions of perceived ITA quality, ITA fit-to-task, trusting belief of an ITA creator, perceived usefulness, and perceived enjoyment and could decrease the negative perception of the perceived risk of ITA adoption. Also I found the progressive impacts of these changes on satisfaction as the general attitude and four beneficial behavioral intentions. We can successfully observe the detailed causal relationships between the independent and dependent variables.

Results could also contribute to implications about how we can make more persuasive ITAs by suggesting a well-structured form of ITAs and by suggesting the strategical use of peer tourist sources of ITAs, which is a type of user-created content (UCC). Results of this study indicate that the well-structured form of ITAs should contain the claim with data and backing (i.e., advertisement copy with picture and stories). Compared with traditional ITAs that contain a claim only (i.e., advertisement copy) or a claim with data (i.e., pictures), the well-structured form of ITAs could be more persuasive to potential tourists. The results of this study also indicate that the peer tourist source of ITAs could increase various dependent variables, and it means that we could strategically use the peer tourist’s tour story UCC on social media as the ITA for attracting other international tourists.

Some of my hypotheses were rejected in this study. These are that perceived ITA quality could not significantly increase perceived usefulness, ITA fit-to-task could not significantly increase perceived enjoyment, and perceived risk could not significantly decrease perceived enjoyment. To explain why these paths could not be confirmed, I performed an additional interview with several participants of the quasi-experiment. Interviewees said that perceived ITA quality could not always guarantee the perceived usefulness of ITAs, because ITAs that were too high in quality could make a potential tourist feel as if there was too much information about the destination. Tourists always have limited time and resources to visit and enjoy the destination, but they could not easily determine their own travel plan when too many opportunities exist. They also said that ITAs that were too complete could not attract the potential tourist when tourists already know too many things about the destination. They said that ITAs that are too accurate could sometimes undermine the fantasy about the destination when ITAs show some negative aspects of the destination. Interviewees also said that ITA fit-to-task could not always guarantee the perceived enjoyment of ITAs. They said that tourists are not simple information adopters; they will sometimes make their own travel plans to enjoy the destination in their own ways. ITA fit-to-task, which means adoptability in a specific travel situation, could undermine novelty and serendipity of the destination and the autonomy of tourists and could decrease perceived enjoyment. Interviewees also said that perceived risk could not always decrease the perceived enjoyment of ITAs. They said that one travel goal is to feel thrilled by performing challenges in the destination, and some tourists could regard the endurable risk to be one source of enjoyment in the destination. Tourists react dynamically, and the abovementioned interview results could not be expected in the design stage of this study. Reflecting on these unexpected reasons, some future studies could more thoroughly investigate the impacts of ITAs on tourist reactions.

7. Conclusions

This study empirically validated the impact of argument quality and peer tourist source of ITAs on tourist reactions. ITAs are a novel research area both in information systems and tourism, and the challenges to investigate how ITAs should be structured and by whom ITAs could be made would be beneficial to create more persuasive ITAs. I found that the argument quality and the peer tourist source could be essential elements for attracting potential tourists and causing them to visit the destination. Findings could provide some academic and practical implications to design ITA. Some academic implications and contributions could be considered as follows. First, I successfully captured TCITAs, which are a type of UCC and a new phenomenon on social media with potential as a powerful marketing practice, and I tried to include this artifact in the research area. I identified two salient characteristics of ITAs—the argument quality and the peer tourist source—and successfully validated their impacts on the various tourist reactions. Second, I reintroduced Toulmin’s model of argument, which insightfully explains what structure of argument could be needed for composing more persuasive arguments. Despite the simplicity and clarity of this model, few studies have adopted it to explain the process of persuasion [14].

Some practical implications could also be considered. First, ITAs—which are a well-structured form of argument with a claim, data, warrant and backing—could provide practical implications for designing and creating ITAs in the tourism industry. The role of pictures as data has already been validated to make effective ITAs. The results of this study should emphasize the role of stories, tips and advice in ITAs as backing, which is the ground supporting data and warrant and would be essential to make logically strong ITAs and critical to make them more highly qualified and persuasive. Second, I introduce a case for how the tourism agency and company could strategically use social media platforms to enhance their business practice [56]. The user-created content (UCC) of the international tourist’s story could be used to manipulate the peer tourist sources of ITAs. Social media users could be the source of UCC and their tour-related UCC could be strategically used for attracting other international tourists. I proposed the power of a community of tourist as consumers, co-creators, and sustainable market structure developers. Lots of social media users could interact with their friends, could voluntarily create their own UCC, and could share it with themselves on social media. TCITAs could be examples of this UCC, and findings of this study could provide implications about how it could be used for enhancing business practices to attract other tourists, for encouraging peer tourists to reciprocally post their stories in social media, and for building a sustainable structure for market growth. In one example, a Korean tourism agency used a UCC contest campaign to motivate tourists to create their own ITA, which is ‘To me, Korea is…’ (e.g., http://www.youtube.com/watch?v=5o7OXgJRud0). This example could be referred to by other governmental tourism agencies.

There are many inescapable limitations in this study. First, I captured three types of ITAs from social media that vary by the argument quality and source of the ITAs. In fact, I should have used four types of ITAs (in the 2 × 2 matrix) and captured the case of a high argument quality and a peer tourist source for my research design. However, I could not find any fit samples of TCITAs on Youtube.com. Second, the quasi-experiment method was used in this study, but a variety of research methods could be adopted in future studies. Third, subjects of this study are potential tourists, but the majority of subjects were voluntary students. Recruiting real tourists in various culturally different settings [10] could be beneficial to capture events in the real world and to enhance the external validity of this study. Fourth, future studies could gather the big data of tourist’s reactions about ITAs and travel experiences. Despite these limitations, I expect that the findings of this study could contribute in designing ITAs and could inspire other researchers to make more persuasive ITAs and to create a sustainable tourism market structure.