The Influence of Authenticity of Online Reviews on Trust Formation among Travelers

Abstract

Travelers have increasingly used mega review sites as an information source during the decision-making processes. This study focuses on the significant role of the authenticity of online reviews on mega review sites in formulating travelers’ behavioral intention as well as trust toward both websites and their service (i.e., destinations). Extending trust transfer theory, this study aims to formulate a research model and investigate associations among three aspects of trust (i.e., cognitive and affective trust toward mega review sites and trust toward destinations the websites recommend) to predict travelers’ behavioral intention (e.g., purchase). The empirical results indicate the significant roles of perceived authenticity of online comments and trust in the context of online tourism. This study provides some implications for online review management among website administrators.

Keywords

authenticity online reviews mega review site trust trust transfer theory

Introduction

Over the last decade, the emergence of online content reviews, which are generated by users (i.e., user-generated content), on mega review sites (e.g., TripAdvisor) has revolutionized tourists’ perceptions, attitudes, and even behaviors with regard to destinations as well as hotels and restaurants (Boo and Busser 2018; Narangajavana Kaosiri et al. 2019). When planning a trip, travelers have increasingly visited mega review sites to search for travel-related information, including online reviews, because they believe the reviews help them to make the proper purchase decision (Cheng and Jin 2019; Narangajavana Kaosiri et al. 2019). This trend is closely related to some characteristics of tourism (e.g., intangibility and experience). More specifically, travel-related information, such as online reviews, may play a significant role in helping travelers to reduce uncertainty about tourism services (Antón, Camarero, and Garrido 2018). Thus, online review management becomes important as a tourism marketing tool to enhance and maintain high levels of reputation among travelers (Antón, Camarero, and Garrido 2018; Baka 2016).

The impact of online reviews on travelers’ decision-making processes has been studied by practitioners and scholars in hospitality and tourism, including investigating attitudes toward destination hotels and online sales of hotel rooms (Boo and Busser 2018; Cheng and Jin 2019; Filieri and McLeay 2014; Hotel News Resource 2018; Schuckert, Liu, and Law 2015; Sparks and Browning 2011; StayNTouch 2018; Trivago 2017). Also, prior research has found that negative online review management can be used for service recovery strategies for hotels (Sparks, So, and Bradley 2016). So, based on the extant literature, can we conclude that positive online reviews from mega review sites have positive influences on travelers’ decision-making processes and negative reviews have negative impacts? What if travelers believe that the online reviews are not authentic but unreliable or artificial to only promote hotels or destinations?

On the mega review sites, technology can serve as “the rarefaction process as a facilitator through customization technologies” (Koiso-Kanttila 2005, p. 65). Hence, prior research in consumer behavior has argued that consumers engage in a constant quest for “authenticity” of a website and its contents (Evrard and Krebs 2018; Koiso-Kanttila 2005). Authentic contents, which are generated by both the website and its users, mean to be original, credible, trustworthy, reliable, genuine, real, and actual as opposed to artificial, fake, secondhand, and copied (Napoli et al. 2014). The mega review sites do not provide any real human interaction between travelers (i.e., no face-to-face interaction). In addition, as mentioned above, travelers cannot experience destination services without visiting the destination in person. Therefore, within this digital context, travelers’ perceptions about the authenticity of online reviews generated by other (virtual) users can influence their perceptions, attitudes, and behavior (or purchase intention) toward all-digital tourism products on the mega review sites (Evrard and Krebs 2018; Scheinbaum 2012). For example, even if online reviews on a particular destination are positive, travelers may be more likely to become skeptical about the destination if the reviews seem to be copied and replicated for a destination-marketing purpose. In this case, the travelers’ inauthenticity perception about the reviews may formulate distrust toward the mega review sites and even generate negative attitudes toward the destination (Kwok, Xie, and Richards 2017; Napoli et al. 2014).

However, travelers are increasingly dependent on online reviews because they trust the mega review sites for travel planning (Ukpabi and Karjaluoto 2018; Sparks and Browning 2011), even though the websites do not provide the travelers with any physical evidence to support the reviews are real. Trust is referred to as the travelers’ expectation that the websites are dependable/authentic and will deliver on their promises (e.g., authentic travel-related information and contents) (Sirdeshmukh, Singh, and Sabol 2002). Thus, the present study assumes that travelers’ perceived authenticity of online reviews can increase their trust toward the mega review sites by directly and significantly influencing the bottom line of the websites (Eggers et al. 2013). More importantly, the travelers’ trust toward the websites may be transferred to their trust of destinations recommended by the websites because the travelers may perceive that the websites and destinations are associated (Lee et al. 2014). According to trust transfer theory (Strub and Priest 1976), when evaluating a particular object with little experience or knowledge (i.e., destinations in this study), individuals tend to depend on their trusted source (i.e., the mega review sites) and associate its image and/or attitude with the lesser-known object, thus creating a psychological association between the trusted source and object. Therefore, travelers who trust a mega review site because of its authentic online reviews may engage more in the trust-transferring process by relating their trust toward the websites to trust toward destinations the website recommended (Novelli, Schmitz, and Spencer 2006). If the travelers’ trust toward the mega review site and destinations is favorable, consequently, they may be more likely to visit the destination the website recommends (Novelli, Schmitz, and Spencer 2006; Ukpabi and Karjaluoto 2018).

Based on the aforementioned notion, the following research model (see Figure 1) was formulated to (1) investigate the influences of perceived authenticity of online reviews about destinations on two aspects of travelers’ trust toward mega review sites (i.e., cognitive and affective); (2) examine the impacts of the trust toward the websites on travelers’ trust and behavioral intention toward destinations the website recommended; and (3) test the influence of destination trust on behavioral intention. This study formulates avenues for baseline assessments of online travelers regarding their trust-formulation and decision-making processes.

Figure 1.

A research model.

Literature

Theoretical Background

In general, trust refers to an individual (trustor)’s willingness to take risks to another one (trustee)’s action according to the expectation this other one will take important action (Mayer, Davis, and Schoorman 1995). There are three reasons why trust is essential in the online tourism context (Belanche et al. 2014; Kim, Chung, and Lee 2011). First, the inherent characteristics of a destination’s services (e.g., intangibility, inseparability, and perishability) may reduce outcomes’ predictability and increase uncertainty. Second, the online channel may mask the identity of interacting parties (or unobservability). Third, the e-travel agents require travelers to fill out their personal information (e.g., passport number and credit card information), and the information may be recorded for future transactions. Therefore, it is important for the administrators of the e-travel agents to build trust in the relationship with travelers by overcoming vulnerability perceptions and uncertainty among the travelers (Kim et al. 2011).

According to trust transfer theory, individuals tend to accept vulnerability for distinct, particular agents and assign their trust toward the agents to different reference points, including processes, objects, and people (Belanche et al. 2014). More specifically, individuals’ trust toward an object can be transferred to other entities the individuals perceive as being related to the object (Stewart 2003). In other words, the trust transfer process is that individuals develop trust toward an object because of their trust toward a related object. This process is dependent on the individuals’ assessment of the object’s proximity, similarity, or belongingness to a trusted reference (Grayson, Johnson, and Chen 2008). Thus, the trust transfer process can occur from salespeople to their firms, from products to their firms, and even from firms to other firms that operate in the same sector (Belanche et al. 2014; Doney and Cannon 1997; Grayson, Johnson, and Chen 2008).

Trust transfer theory also suggests two types of the trust transfer process: (1) interchannel (trust transfer between different channels, such as online and offline) and (2) intrachannel (trust may affect the evaluation of a service and/or product in the same channel, such as online or offline) (Lu et al. 2011). Prior research has investigated trust transfer from offline stores to online stores because trust toward the offline stores increased consumers’ positive perceptions about the online stores (i.e., perceived extent of future use, structural assurance, flow, and perceived website satisfaction), which can formulate trust toward the online stores (Lee, Kang, and McKnight 2007). In addition, consumers’ trust toward offline stores can reduce their uncertainty of online stores, which can significantly affect their trust toward the online stores (Lu et al. 2011). Regarding the intrachannel trust transfer process, it occurs between trust toward the well-known website between trust toward an unknown website due to their perceived links among consumers (Stewart 2003). Thus, this study assumes that trust toward mega review sites can influence (or be transferred to) travelers’ favorable perception of corresponding destination services. Although the mega review sites do not provide travelers with direct interactions with destination services (i.e., they only provide a description of the destination services on behalf of destination marketing organizations), the travelers are more likely to trust the destinations because the trusted websites recommended them (i.e., perceived links). Prior research has overlooked travelers’ distinct trust formation toward mega review sites and destination services. This study builds a theoretical framework of travelers’ trust formation process and intention to visit destinations in the online tourism context.

Authenticity

Commonly, authenticity refers to originality, sincerity, genuineness, reality, or truthfulness of an object (Lu, Gursoy, and Lu 2015). In tourism, authenticity has been studied based on two aspects (Liang, Choi, and Joppe 2018; Steiner and Reisinger 2006): (1) active-related authenticity (i.e., referring to human nature) and (2) objective-related authenticity (i.e., referring to things’ realness and/or genuineness). Prior research has conceptualized the authenticity construct as a perception of authenticity based on the notion that authenticity is “a social construction that may change due to different evaluators’ perceptions and interpretations of the place, situation, person, or object” (Grayson and Martinec 2004, p. 298). The current study adopts this perspective on authenticity (i.e., perceived authenticity) because of the research context. Travelers have used mega review sites to search for tourism-related information. They are expecting online reviews to be the “real” experiences of visiting destinations and/or experiencing destination services. Hence, in this study, perceived authenticity is defined as travelers’ perceptions of online reviews about destinations on the websites generated from other users’ “real” experiences (Ramkissoon and Uysal 2011). Interestingly, the formation of perceived authenticity is different between travelers based on their own interests, experience, existence, and knowledge instead of truth or reality (Liedtka 2008). In other words, travelers may perceive authenticity based on their own interpretation of what is observed in online reviews regardless of the inherent accuracy of the reviews (Beverland 2005). This means that travelers can develop their own perception of authenticity even in the absence of objective criteria in the online reviews (Lu et al. 2015). Therefore, in this study, authenticity is defined as travelers’ subjective evaluation of the genuineness of online reviews (Napoli et al. 2014).

Trust

In this study, travelers’ trust toward mega review sites is their willingness and/or tendency to rely on the websites for tourism-related information based on their beliefs about the websites, even though the information about destinations may result in the travelers experiencing uncertainty and/or risk (Chaudhuri and Holbrook 2001). This study divides travelers’ trust into two components: affective perceptions about websites and cognitive beliefs (Delgado-Ballester, Munuera-Aleman, and Yague-Guillen 2003; Elliott and Yannopoulou 2007). Prior research has primarily investigated the cognitive aspect of trust formulated by individuals’ expectations of competence, consistency, reliability, and predictability of performances of all services/products under the same company/brand (Becerra and Badrinarayanan 2013; Delgado-Ballester, Munuera-Aleman, and Yague-Guillen 2003). Cognitive trust is formulated by the individuals’ accumulated knowledge, which helps to form anticipation regarding the company/brand without risks and uncertainty (Johnson and Grayson 2005). However, trust also embraces individuals’ affective evaluations of the products/services based on expectations about the performance and attributes of the products/services (Becerra and Korgaonkar 2011). The affective aspects of trust are formulated by confidence and feelings toward an object based on levels of concern and care demonstrated by the object (Johnson and Grayson 2005). In this case, the individuals rely on the object because of their emotions. The deeper the emotional connections among travelers, the more the travelers trust mega review sites regardless of their accumulated knowledge (for the formulation of cognitive trust).

Destination marketing’s ultimate goal is to develop and maintain a strong bond with travelers whose main component is trust (Abubakar and Ilkan 2016). Previous studies have found that travelers’ trust tends to minimize their perceptions of uncertainty and risk of destinations, and that travelers visit destinations perceived as dependable and trustworthy (Han and Hyun 2013). Hence, travelers are more likely to visit a particular destination because of destination trust (i.e., quality assurance, competence, integrity, and reliability) (Abubakar and Ilkan 2016).

Behavioral Intention

According to Ajzen (1991), “intentions are assumed to capture the motivational factors that influence a behavior” (p. 181). Hence, if an individual has a stronger intention to take an action, he or she is more likely to take the actual action (Cheon et al. 2012). This positive association between behavioral intention and actual behavior has been confirmed by Venkatesh, Morris, and Ackerman (2000) and Venkatesh and Davis (2000). Given that the research context is travelers’ information-searching stage before they select a particular destination to visit, their actual behavior may result in an incorrect inference. Therefore, this study considers behavioral intention a final outcome because it has been viewed as the most immediate driver of actual behavior (Ajzen 2002; Cheon et al. 2012).

Research Hypotheses Development

Previous research has addressed that perceived authenticity can lead consumers to exhibit favorable reactions (e.g., positive attitudes and trust) to a particular service, product, company, brand, and even activities (Alhouti, Johnson, and Holloway 2016). In the context of this study, a mega review site’s online reviews may play a role in its authentic desire to contribute to travelers’ authentic experience and correspond to the website’s trust feelings and dispositions according to attribution theory (Kelley and Michela 1980). Consequently, the travelers are more likely to trust and accept the websites and their contents (e.g., online reviews and destination services) without any questions (Filieri 2016). In addition, perceived authentic and true information (or reviews) regarding intangible or virtual products and/or services on the mega review site tend to increase users’ certainty about them (i.e., destination in this study) and are positively related to formation of trust toward the destination products/services that the mega review site offers/recommends (Busser and Shulga 2019). More specifically, according to signaling theory, consumers use their perceptions of a website’s genuine contents (i.e., authentic online reviews and information) as signals or cues to ensure that the products and/or services the website offers (i.e., destinations in this study) are trustworthy and meet with their expectations (Kim and Kim 2018). Thus, travelers’ perceived authenticity is likely to formulate higher perceptions of trust toward mega review sites and their destinations the site recommends as follows:

Hypothesis 1: Perceived authenticity is positively associated with cognitive trust.

Hypothesis 2: Perceived authenticity is positively associated with affective trust.

Hypothesis 3: Perceived authenticity is positively associated with destination trust.

Prior research has considered cognitive trust a base for the formation of emotional trust (Johnson and Grayson 2005). Zhang, Cheung, and Lee (2014) also suggested that consumers’ cognitive trust for online stores develops their affective trust. For example, when travelers visit a new or strange website (e.g., mega review sites), they may formulate low affective trust because they feel uncomfortable at first. However, the travelers are more likely to feel comfortable on the e-travel site (i.e., formulation of high affective trust) after they find evidence of a reputable certification authority and professional information on the website (i.e., formulate of high cognitive trust) (Hong and Cho 2011; Zhang, Cheung, and Lee 2014).

Purchasing a destination service through mega review sites requires travelers’ cognitive resources because of the related anxiety and ambiguity (Johnson and Grayson 2005). Therefore, when travelers are confident in the websites’ information, competency, and knowledge in providing destination services (i.e., cognitive resources), they will not hesitate to purchase the websites’ destination services. Also, having affective trust requires the travelers to feel comfortable about the websites’ destination services without anxiety and worry (Zhang, Cheung, and Lee 2014).

The trust transfer process is based on the notion that individuals’ trust for unknown objects may be derived from their trust for well-known objects that are associated with the unknown object (Wang, Shen, and Sun 2013). In this study, three actors are involved in the mechanism (Stewart 2003): (1) travelers (or trustors who make a judgment on whether to trust others); (2) destinations (or unknown/unexperienced trustees who are assessed by the trustors); and (3) mega review sites (or well-known online brokers in the relationship between trustors and unknown trustees). The logic of the mechanism is based on trust transfer theory (Wang, Shen, and Sun 2013), such that when travelers trust mega review sites and the mega review sites recommend a particular destination (formulate associations), the travelers’ trust toward the mega review sites can be transferred to the destinations. Accordingly, the mega review sites are “the source of trust transfer” and the destinations are “the target of trust transfer” (Wang, Shen, and Sun 2013, p. 1396). The logic of the mechanism can be also explained by source and target similarity (Stewart 2003). If the source and target have similarity (e.g., the intangibility of virtual destination services in this study), it makes individuals have the same perceptions about the source and target. Therefore, this study formulates the following hypotheses to explain the trust formulation process among travelers in the online tourism context:

Hypothesis 4: Cognitive trust is positively associated with affective trust.

Hypothesis 5: Cognitive trust is positively associated with destination trust.

Hypothesis 6: Cognitive trust is positively associated with behavioral intention.

Hypothesis 7: Affective trust is positively associated with destination trust.

Hypothesis 8: Affective trust is positively associated with behavioral intention.

Consumers’ trust toward a product/service/brand has positive influences on their attitudes (e.g., favorable evaluation of its value and commitment) and behaviors (e.g., purchases and referrals) (Becerra and Badrinarayanan 2013; Elliott and Yannopoulou 2007). In the tourism context, while purchasing a destination service entails travelers’ particular risks (i.e., uncertainty and experience), trust can alleviate the risks among travelers (Becerra and Badrinarayanan 2013). In the online environment, trust in a website also increases consumers’ intention to purchase its online products/services (Becerra and Korgaonkar 2011). Similarly, if consumers establish trust toward a website, they are more likely to promote the website for referrals (Reichheld 2003). Based on the aforementioned notion, this study assumes that travelers’ trust toward a destination through mega review sites makes them purchase the destination service. Thus,

Hypothesis 9: Destination trust is positively associated with behavioral intention.

Methods

Sample and Data Collection

The sample for this study was travelers in the United States who used a mega review site and read online reviews to search for tourism-related information. To conduct a web-based survey for data collection with appropriate samples, the authors of the current study hired a marketing research company that works with online travel agents. After receiving lists of customers from 15 online travel agents that agreed to participate in this study, the marketing research company was ready to distribute the questionnaires to the travel agents’ customers who had purchased a destination service through mega review sites (e.g., TripAdvisor). In order for the marketing research company to minimize potential biases, a nationwide random stratified sampling method was employed (Xie, Kerstetter, and Mattila 2012). Consequently, 1,200 participants were selected from the lists for survey distribution to achieve sufficient Statistical Power (i.e., to recruit representative samples, who reflect the mega review websites’ user population profile regarding gender [male: 45% vs. female: 55%] and age [18–34 years: 46% vs. 35–54 years: 38%] based on TripBarometer 2016 as conducted by TripAdvisor, from about 30% of the participants [n = 36,444]) and asked to complete the questionnaire during two weeks in November 2018 based on their most recent experience at a mega review site. The marketing research company provided the participants with an opportunity to win one of four $10 online gift certificates. Finally, out of 388 surveys collected, 346 were used for data analyses because 42 surveys contained missing values (i.e., a usable response rate: 28.8%).

Measures

Multiple items were adapted to measure all constructs for this study, which were already well developed and rigorously validated in prior studies. The items were modified by the authors to reflect the research context and then were checked by two professors in the tourism field. Before finalizing the questionnaire, this study performed pretesting with 35 travelers of mega review sites to test and improve its overall quality. This process led to some minor adjustments in flow, wording, and interpretation of each item. First, a brief description of a mega review website was provided on the questionnaire, and then participants were asked to select one mega review website they were frequently visiting. To arouse their cognition of the mega review website, second, they were asked to write their most recent experience at the website. They were then asked to respond to each question regarding the selected mega review website.

All items except for demographic characteristics were measured on a 7-point Likert-type scale anchored by “strongly disagree” and “strongly agree.” First, perceived authenticity was measured with 5 items that referred to the studies of Crompton and McKay (1997) and Kerstetter, Confer, and Graefe (2001) (i.e., travelers perceive that online reviews, generated by other users to provide travelers with information regarding destination experiences as well as products and/or services on mega review sites, are real and genuine). Second, two aspects of trust toward mega review sites were measured with 10 items (i.e., 5 items per component) from the study of Dabholkar, van Dolen, and Ruyter (2009) (i.e., cognitive trust: perceived reliability in mega review sites; affective trust: emotional trustworthiness in mega review sites). Third, destination trust was operationalized with five items from Delgado-Ballester’s (2004) study to measure travelers’ trust in the mega review site’s destination recommendation (i.e., tendency to trust destinations that the mega review site recommends to travelers through its algorithm). In addition to results of searches done by travelers, the mega review site can offer various tourism products/services with personalized pricing based on online activities and characteristics of the travelers, such as search and purchase histories. Last, behavioral intention was measured with five items from the study of Fishbein and Ajzen (2010) (i.e., willingness to visit the destinations the mega review sites recommended).

Results

Sample Characteristics

Table 1 demonstrates the demographic characteristics of the participants (i.e., gender, age, marital status, education, and vocation). The sample for this study was 59.8% female and 39.9% male. The most prevalent participant age group was 20 to 29 years (46.2%), followed by 30 to 39 years (39.0%) and 40 to 49 years (11.8%). In terms of the marital status of the samples, 62.4% were single and 35.5% were married. They were mostly college or university degree holders (91.6%). The most prevalent respondent vocation was student (58.9%), followed by office job (39.3%). The demographic features ensured that the participants had a similar profile regarding gender and age with the 2016 profile of mega review websites’ user populations.

Table 1.

Demographic Analysis of Respondents.

Demographic Variables	n = 346	Percent (%)
Gender
Female	207	59.8
Male	138	39.9

Age
20–29	160	46.2
30–39	135	39.0
40–49	41	11.8
50 and older	7	2.0

Marital status
Single	216	62.4
Married	123	35.5

Education
High school	7	2.0
College or university degree	317	91.6
Graduate degree	19	5.5

Vocation
Student	204	58.9
Office job	136	39.3
Owner-operator	4	1.2

Measurement Model

To test the reliability and validity of all measures employed in the questionnaire, this study followed the two-step approach of Anderson and Gerbing (1992) (i.e., from reliability and validity to structural equation modeling). First, the authors used Cronbach’s alpha coefficients with SPSS 24.0 to check the reliability of each construct (i.e., perceived authenticity = 0.929; cognitive trust = 0.872; affective trust = 0.930; destination trust = 0.930; and behavioral intention = 0.938). According to Nunnally (1978), the acceptable coefficient for reliability is more than 0.70 in the social science field. After assessing reliabilities of all constructs, the authors performed confirmatory factor analysis (CFA) with AMOS 24.0 to test their validity (Anderson and Gerbing 1992). One of the items measuring the perceived authenticity construct (e.g., “Online destination reviews on the mega review site provide me with natural information”) was dropped because it did not meet acceptable levels of convergent and discriminant validity.

The CFA results of the measurement model indicated acceptable fit indices suggested by Hair et al. (2010): χ² = 685.616, df = 242, p < .001, root mean square error of approximation (RMSEA) = 0.073, normed fit index (NFI) = 0.912, comparative fit index (CFI) = 0.941, Tucker–Lewis index (TLI) = 0.932. The standardized factor loadings of all indicators exceeded 0.7 (p < .01) after the purification procedure (i.e., removing one item with less than 0.50 of the standardized factor loading), which signified convergent validity of each construct (see Table 2). Then, to check the discriminant validity of each construct, the authors calculated the proportion of average variance extracted (AVE) in all constructs. As indicated in Table 2, all constructs satisfied discriminant validity because the AVEs of each construct exceeded 0.50, which is an acceptable magnitude (Bagozzi, Yi, and Phillips 1991; Hair et al. 2010) (see Table 2).

Table 2.

Measurement Model from Confirmatory Factor Analysis.

Constructs and Operationalization	Items	Factor Loading	t Value
Perceived authenticity (α = 0.929; AVE = 0.768; CCR = 0.930)	Online destination reviews on the mega review site provide me with authentic information.	0.841	Fixed
	Online destination reviews on the mega review site provide me with natural information.	–	–
	Online destination reviews on the mega review site provide me with exceptional information.	0.867	20.485
	Online destination reviews on the mega review site provide me with genuine information.	0.909	22.138
	Online destination reviews on the mega review site provide me with true information.	0.887	21.250
Cognitive trust (α = 0.872; AVE = 0.583; CCR = 0.874)	Even when the mega review site seems questionable, I am confident that it is telling me the truth.	0.682	Fixed
	I think the mega review site would keep the promises made to me.	0.791	12.981
	I know that the information given to me is based on mega review site’s best judgment.	0.823	13.420
	I trust the mega review site because it seems to be very dependable.	0.777	12.789
	I can count on the mega review site to be sincere.	0.737	12.221
Affective trust (α = 0.930; AVE = 0.730; CCR = 0.931)	In all circumstances, the mega review site is ready to offer me assistance and support.	0.859	Fixed
	When giving advice, the mega review site is concerned about my welfare.	0.859	21.368
	In the future, I can count on the mega review site service to consider how its decisions and actions will affect me.	0.875	22.107
	I trust the mega review site because it seems to care about their customers.	0.815	19.431
	When it comes to things that are important to me, I can depend on the support provided by the mega review site.	0.862	21.519
Destination trust (α = 0.930; AVE = 0.727; CCR = 0.930)	The destinations the mega review site recommends meet my expectation.	0.838	Fixed
	I feel confident with the destinations the mega review site recommends.	0.865	20.229
	The destinations the mega review site recommends guarantee satisfaction.	0.844	19.414
	I will not be disappointed with the destinations the mega review site recommends.	0.871	20.441
	The destination that mega review site recommends would make any effort to satisfy me.	0.846	19.507
Behavioral intention (α = 0.938; AVE = 0.755; CCR = 0.939)	If I wanted, I could easily visit the destinations the mega review site recommends from now on.	0.868	Fixed
	I am confident that I am able to visit the destinations the mega review site recommends.	0.890	23.453
	The decision to visit the destinations the mega review site recommends is up to me.	0.869	22.349
	I intend to visit the destinations the mega review site recommends in the future.	0.852	21.489
	It is likely that I will visit the destinations the mega review site recommends in the future.	0.864	22.062

Note: χ² = 685.616, df = 242, p < .001. AVE = average variance extracted; CCR = composite construct reliability. Root mean square error of approximation (RMSEA) = 0.073, normed fit index (NFI) = 0.912, comparative fit index (CFI) = 0.941, Tucker–Lewis index (TLI) = 0.932. All items were measured on a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree).

The authors then conducted another analysis to more rigorously test all measures’ discriminant validity. Following the study of Rust, Moorman, and Dickson (2002), the authors performed CFA to investigate whether the unconstrained model that consists of each pair of primary measures is significantly different from the constrained model that was set when the measures are same. The result of this analysis demonstrated evidence of discriminant validity among all constructs used in this study (see Table 3).

Table 3.

Chi-square Difference Test for Discriminant Validity of the Measures.

	Constrained		Unconstrained
	χ²	df	χ²	df	∆χ²	p ^*
Perceived authenticity vs. Cognitive trust	130.169	26	171.663	27	41.494	.00
Perceived authenticity vs. Affective trust	120.319	26	147.055	27	26.736	.00
Perceived authenticity vs. Destination trust	142.027	26	150.453	27	8.426	.00
Perceived authenticity vs. Behavioral intention	118.580	26	153.004	27	34.424	.00
Cognitive trust vs. Affective trust	166.068	34	216.129	35	50.061	.00
Cognitive trust vs. Destination trust	158.930	34	190.771	35	31.841	.00
Cognitive trust vs. Behavioral intention	148.136	34	203.269	35	55.133	.00
Affective trust vs. Destination trust	148.304	34	175.155	35	26.851	.00
Affective trust vs. Behavioral intention	185.729	34	197.320	35	11.591	.00
Destination trust vs. Behavioral intention	160.340	34	194.442	35	34.102	.00

p < .01.

The same survey respondents were used to measure the independent and dependent variables, which may lead to common method variance (CMV) because this study setting may be more likely to produce a correlation between the constructs (Bagozzi and Yi 1990; Jakobsen and Jensen 2015). As exhibited in the study of Podsakoff and Organ (1986), the authors conducted Harman’s one-factor test to statistically test CMV. This test compares chi-square and df of a single-factor model (i.e., assumes one latent factor accounts for all constructs or variables) against chi-square and df of a multidimensional model (or research model). If the multidimensional model’s chi-square and df are better than those of the single-factor model, it can be concluded that CMV may not be a serious threat (Podsakoff and Organ 1986). The result of this test indicated the following: (1) chi-square = 685.616 with df = 242 (the proposed model); and (2) chi-square = 3,143.244 with df = 252 (the single-factor model). Consequently, this study concluded that CMV might not be serious based on the test result.

Testing of the Hypothesized Structural Model

AMOS 24.0W was used to conduct structural equation modeling for assessments of each hypothesized parameter. The fit indices of the research model satisfied the acceptable levels suggested by Hair et al. (2010): χ² = 685.823, df = 243, p < .001, RMSEA = 0.073, NFI = 0.912, CFI = 0.941, TLI = 0.933. The maximum likelihood estimates (MLEs) for the model’s parameters are illustrated in Figure 2 and demonstrated in Table 4.

Figure 2.

Estimates of structural equation modeling (SEM).

Table 4.

Standardized Structural Estimates.

Path		Standardized Estimates	Standardized Error	t Value
Hypothesis 1	Perceived authenticity → Cognitive trust	0.537	0.044	8.481^**
Hypothesis 2	Perceived authenticity → Affective trust	0.274	0.056	4.576^**
Hypothesis 3	Perceived authenticity → Destination trust	0.379	0.053	6.834^**
Hypothesis 4	Cognitive trust → Affective trust	0.400	0.089	6.043^**
Hypothesis 5	Cognitive trust → Destination trust	0.372	0.086	5.959^**
Hypothesis 6	Cognitive trust → Behavioral intention	0.060	0.047	1.556
Hypothesis 7	Affective trust → Destination trust	0.138	0.055	2.578^**
Hypothesis 8	Affective trust → Behavioral intention	0.941	0.046	18.591^**
Hypothesis 9	Destination trust → Behavioral intention	−0.022	0.032	−0.597
Indirect path		Standardized estimates		p value
	Perceived authenticity → Affective trust	0.215		.002
	Perceived authenticity → Destination trust	0.267		.001
	Perceived authenticity → Behavioral intention	0.478		.002
	Cognitive trust → Destination trust	0.055		.005
	Cognitive trust → Behavioral intention	0.367		.003
Endogenous variables		SMC (R²)
	Cognitive trust	0.288 (28.8%)
	Affective trust	0.353 (35.3%)
	Destination trust	0.560 (56.0%)
	Behavioral intention	0.928 (92.8%)

Note: χ² = 685.823, df = 243, p < .001; root mean square error of approximation = 0.073, normed fit index = 0.912, comparative fit index = 0.941, Tucker–Lewis index = 0.933.

p < .01, *p < .05.

Hypotheses 1 to 3 posited that travelers’ perceived authenticity would affect trust toward mega review sites and destinations. The results showed that perceived authenticity had significant, positive influences on cognitive trust (coefficient = 0.536, t value = 8.481, p < .01), affective trust (coefficient = 0.274, t value = 4.576, p < .01), and destination trust (coefficient = 0.379, t value = 6.834, p < .01), supporting hypotheses 1, H, and 3.

Hypotheses 4 to 6 speculated that travelers’ cognitive trust toward mega review sites would influence affective trust toward the websites and destination trust. The findings demonstrated that cognitive trust had significant, positive impacts on affective trust (coefficient = 0.400, t value = 6.043, p < .01) and destination trust (coefficient = 0.372, t value = 5.949, p < .01), but it did not significantly influence behavioral intention (coefficient = 0.060, t value = 1.556, p > .05), thus supporting only hypotheses 4 and 5. Hypotheses 7 and H8 predicted that travelers’ affective trust toward mega review sites would affect destination trust and behavioral intention. The results showed that affective trust had significant, positive influences on destination trust (coefficient = 0.138, t value = 2.578, p < .01) and behavioral intention (coefficient = 0.941, t value = 18.591, p < .01), supporting hypotheses 7 and 8. Finally, travelers’ destination trust did not have a significant impact on behavioral intention (coefficient = −0.022, t value = −0.597, p > .05).

To rigorously establish the validity of the research model, this study formulated and tested an alternative model (i.e., trust → perceived authenticity → behavioral intention). The alternative model produced the following fit indices: RMSEA = 0.103, NFI = 0.853, CFI = 0.880, TLI = 0.864. In addition to fit indices, this study performed a chi-square difference test to see which model is significantly better (i.e., research model vs. alternative model) (Anderson and Gerbing 1992). The comparison of both models’ chi-square statistics indicated that the proposed model is significantly more valid than the alternative model (Δχ² = 457.282, Δdf = 1, p < .001). This finding verified the validity of the research model.

Conclusion and Implications

By expanding trust transfer theory, the empirical results of this research in the online tourism context supported the impact of perceived authenticity of online reviews on mega review sites on trust and behavior toward destinations as well as website trust among travelers. Theoretically, this study attempts to refine a trust formation model in the virtual tourism context by considering the critical role of perceived authenticity of online reviews on mega review sites. In psychology, authenticity is handled with the trait-like abilities of individuals to demonstrate self-awareness, self-regulation, and self-determination with respect to their behaviors (Kernis 2003). This approach to authenticity tends to be based on a social constructivist perspective that emphasizes meaning, existence, and experience instead of knowledge, reality, and truth (Liedtka 2008). In other words, individuals may perceive authenticity by interpreting what is observed or experienced instead of what properties are inherent in a certain object (Beverland 2005). Thus, this study focused on travelers’ perceptions of the authenticity of online reviews on mega review sites (i.e., perceived as real by the travelers).

The tourism industry provides travelers with experience services of which the travelers are unable to know their quality before purchasing them (Klein 1998). Because of the characteristics of tourism, travelers have relied on reviews and/or word of mouth on mega review sites as a reference for the experience goods (Kwok, Xie, and Richards 2017; Narangajavana Kaosiri et al. 2019). Responding to this characteristic and the current trend (i.e., adoption of Internet technology in the tourism industry), the influence of online reviews has been studied in tourism to formulate travelers’ decision-making process and predict hospitality companies’ business performance (Goldenberg, Libai, and Muller 2001; Gretzel and Yoo 2008; Jang and Moutinho 2019; Vermeulen and Seegers 2009). However, the online contents, such as reviews, tend to be easily generated, copied, and distributed anywhere by anyone and for any reason (Koiso-Kanttila 2005). Many consumers have already recognized this repetition because of current news reports and because they have already been skeptical about online reviews (Baek, Ahn, and Choi 2012; Davis, Sheriff, and Owen 2019). Despite this, there is a limited body of existing research in the online tourism context that considers the influence of perceived authenticity on travel review sites and destinations. To fill this gap, the present study was conducted to explore whether travelers’ perceptions of the authenticity of online reviews would contribute to their trust formulation process regarding the mega review site and its products (i.e., destinations). This perspective proposed that perceived authenticity of online reviews enabled travelers to trust mega review sites, and the research model provides avenues for scholars to examine the trust formation process in the virtual environment context.

Unlike prior studies (Agag and El-Masry 2017; Filieri and McLeay 2014; Filieri, Alguezaui, and McLeay 2015), this research also divided travelers’ trust toward mega review sites into cognitive and affective aspects. To date, trust scholars have distinguished between cognitive and affective aspects of trust because individuals’ trust formation process tends to be simultaneously influenced by their cognition and emotion (Lee et al. 2015). Although travelers are in the online context, the virtual contents of websites act as a cue that makes the travelers trust the websites and/or their contents (Filieri, Alguezaui, and McLeay 2015). However, the contents may also lead the travelers to feel good or bad (Rodríguez-Molina, Frías-Jamilena, and Castañeda-García 2015), which affects whether the travelers emotionally trust the websites. Interestingly, according to trust transfer theory (Strub and Priest 1976), travelers may trust mega review sites’ virtual products (e.g., destination services) if they perceive that the mega review sites meet their expectations and provide authentic online reviews. Thus, it is important for scholars to further explore the trust transfer process in the virtual tourism environment (e.g., trust toward virtual peers). This study takes the next step by empirically investigating how trust is transferred to travelers’ online decision-making process. In consumer behavior, “trust” is already considered one of the most powerful antecedents that motivate individuals to virtually purchase a product (Bilgihan and Bujisic 2015; King et al. 2016) and in tourism (Agag and El-Masry 2017; Filieri, Alguezaui, and McLeay 2015). This research updates prior online tourism studies by extending the impact of trust toward websites on travelers’ trust toward their online products and behavioral intention.

Finally, affective trust has a significantly positive influence on behavioral intention whereas the direct path from cognitive trust to behavioral intention does not. This association is consistent with the “think-feel-do hierarchy” proposed by Lavidge and Steiner (1961). The decision-making process assumes that consumers build certain beliefs in a shopping environment by cognitively acquiring and processing information (i.e., cognitive trust). Then, they produce emotional responses by evaluating the formulated beliefs (i.e., affective trust), which are in turn translated into behavior (Verhagen and Bloemers 2018). This study also supports the assumption that travelers’ cognitive trust recedes and predicts affective trust before influencing behavioral intention in the context of mega review sites. More specifically, when visiting a mega review site, travelers evaluate information using its criteria associated with reviews (e.g., how to align with their goals and plans), which leads to the formation of cognitive trust toward the site. Next, the travelers’ cognitive evaluation elicits affective trust toward the site, which matches with the evaluation’s consequences. Finally, in response to affective trust, the travelers perform favorable behavior for the site (e.g., recommendation).

From a practical perspective, this study shows that travelers need to perceive online reviews on mega review sites as authentic, exceptional, genuine, and true. This is because the travelers tend to use the reviews as a cue for assessment not of the users who posted but of the websites that host the users and provide this user-generated information. For administrators of the mega review sites, it is very important to note that a wide range of information about destination services or experiences may not lead travelers to formulate a high level of authenticity of online reviews (Filieri 2016). This means that administrators need to focus on how to make travelers perceive the online reviews as authentic through natural and genuine information when developing algorithms or software. This is because some organizations hire online users to leave promotional reviews on mega review sites (i.e., one type of artificial information) (Gartner 2012). Also, some of the reviews are generated by an organization focusing on its marketing purpose (i.e., one type of untrue information). The reviews promote a particular company or destination in a direct way. However, if the travelers recognize information in the reviews as pretended or untrue, they will not trust the website and switch to another mega review site. Therefore, this study suggests that site administrators need to develop algorithms that can track online users’ activity history so as to detect inauthentic reviews. If the algorithm finds a user who has left many more reviews than others, the administrators need to check his or her review activity with a focus on his or her purchase and travel history (i.e., check whether the reviews came from real experiences).

Second, mega review sites must develop high levels of trust among travelers because the websites provide only virtual information about destination services. Therefore, they need to ensure that all ratings and reviews are trustworthy and genuine although they are generated by their users. So, how can the administrators of mega review sites manage their users and make them leave authentic reviews? The administrators may establish the user classifications based on the quantity and quality of online reviews the users have posted and their purchase history. When reading reviews, other users are able to see the posters’ category, reputation level (e.g., experienced traveler I, II, III), and the number of postings so far. This is based on the notion that the number of reviews left by one user can also be used for a source of assessment of trustworthiness on mega review websites (Filieri 2016). Also, the administrators may hire local residents of destinations to provide more accurate information for travelers and to identify incorrect information about the destination. This is because the role of local residents on mega review sites tends to be more influential in destination, food and beverage, and accommodation recommendations (Arsal, Backman, and Baldwin 2010). Finally, the administrators should not monitor reviews only on their websites. They need to monitor other review websites, too. This effort will help the administrators to find copied and pasted reviews (i.e., inauthentic reviews). What if a traveler reads an online review on a mega review site and then reads the same review on another review site? The traveler may not trust both sites anymore and instead will visit another review site to gather tourism-related information (Filieri, Alguezaui, and McLeay 2015; Kwok, Xie, and Richards 2017). This is very important because the finding indicated that travelers tend to purchase destination services from the mega review website not because of trust toward the destination but because of trust directed toward the website. From a statistical perspective, the possible reason the direct impact of destination trust on purchase intention was not supported is the relatively stronger impact of affective trust toward the website on purchase intention (i.e., destination trust had been significantly correlated with purchase intention, but it was not in SEM). Thus, the administrators of the mega review websites may need to develop an integrated system, such as “duplicated review detector” software (e.g., iThenticate: academic similarity detection software) to increase travelers’ trust toward their websites, which directly leads them to consume the services and/or products.

Limitations and Research Agenda

In spite of the theoretical and practical implications, this study has three limitations. First, this study was conducted in the context of mega review sites. However, in addition to the websites, travelers use multiple sources to search for tourism-related information (e.g., social media, news articles, and commercials). Hence, the research model needs to be empirically tested in other online sources to explore whether the associated implications are able to be generalized further to the online tourism context. In addition to the research context, the sample composition would be another limitation of this study. To obtain representative samples from the mega review websites’ user population, this study focused more on gender and age than on education and vocation. This approach led the samples to be skewed toward students. Although an independent t test between student and nonstudent samples did not indicate statistically significant differences in all variables’ means, it might have influenced the study findings (e.g., no behavioral intention to travel). Thus, further research with more representative samples needs to be utilized to improve its external validity (i.e., generalizability). Second, although this study considered perceived authenticity a core driver of the trust formulation process, the authenticity construct may have studied multiple aspects rather than one aspect (e.g., sincerity and consistency, separately; Napoli et al. 2014). Future research needs to be conducted that embraces multiple dimensions of the authenticity construct to explore travelers’ trust formation process in a more psychologically detailed manner. Also, travelers’ perceptions of online reviews’ authenticity may have been influenced by situational (e.g., promotion pricing) and individual factors (e.g., personal value orientation). The potential factors may lead consumers to not purchase a destination even if they perceive the mega review website’s information about the destination as authentic and trustworthy. Therefore, the potential variables need to be included to demonstrate the proposed relationship of this study in a psychologically detailed manner. Finally, a longitudinal study on travelers’ actual behavior after the trust and behavioral intention formation processes may be useful in exploring the potential impact of perceived authenticity on favorable outcomes for destinations (e.g., online and offline positive word of mouth and recommendation as well as revisit). Given the importance of perceived authenticity among consumers in a virtual environment, the findings of this research provide scholars with an avenue for future research in the virtual tourism context.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Minseong Kim

Author Biographies

Minseong Kim is an Assistant Professor in the Department of Management and Marketing at Louisiana State University in Shreveport. His research focuses mainly on consumer behavior and marketing strategy. He has taught principles of management, hospitality management, research methods, and marketing strategy.

Jihye Kim is an Assistant Professor in the Department of Integrated Strategic Communication at the University of Kentucky. Her research interests include advertising and public relations strategies, digital communication, cause-related marketing, and health communication. She has taught digital strategies, research methods, international advertising, media planning, and health communication campaign.

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