Marketing China to U.S. travelers through electronic word-of-mouth and destination image: Taking Beijing as an example

Abstract

The tourism industry in China has grown significantly over the last two decades. Most of the growth, however, is fueled by domestic tourism. As one of the biggest tourism markets in the world, U.S. tourists might be reluctant to travel to China due to reasons such as unfamiliarity, cultural differences, visa requirements, and long flights. Building on the Theory of Planned Behavior (TPB) with relevant constructs, this research proposes that building a strong destination image via eWOM may influence the attitude and intention of U.S. travelers to visit Beijing. More specifically, the current research aims to examine the impact of eWOM and destination image on travel intention of tourists. This study used a quantitative research method and online data collection was conducted through Qualtrics. A total of 413 valid responses from U.S. residents were collected. The statistical software SPSS 21.0 and Mplus 7.0 were used to analyze the data. Study results show a strong relationship between eWOM utilitarian function and eWOM credibility, and eWOM credibility has a significant influence on destination image. Although there was no direct impact of destination image on tourists’ future travel intention, destination image plays a mediating role between eWOM credibility and perceived behavioral control (and tourists’ attitudes as well). Finally, perceived behavioral control and tourists’ attitudes mediate the impact of destination image on travel intention.

Keywords

Asia pacific attitudes behavioral control destination image electronic word-of-mouth eWOM credibility inbound tourism online review theory of planned behavior tourism marketing travel intention

Introduction

The Internet has provided a unique channel for users from different countries to share their ideas and opinions online. Electronic word-of-mouth (eWOM) creates an opportunity for developing countries to build up their destination images. With the growing amount of Internet users worldwide, consumers can easily write and read online reviews, which enhances the outreach and influence of eWOM (Belarmino and Koh, 2018; Huete-Alcocer, 2017; Serra-Cantallops et al., 2020). Destination information, transportation, food and beverage options, unique experiences, hotel ratings and reviews, local attractions, safety and service quality have been generated and shared by online users from different countries, and these online reviews and insights provide important information for travelers, especially for those planning their trips (Li et al., 2019; Lo and Yao, 2019; Mariani et al., 2018; Pan et al., 2007). eWOM has gotten extensively popular in tourists’ information searches and subsequent purchase decisions (Chang and Wang, 2019; Kim et al., 2018; Yan et al., 2018). Prior to planning a trip, about 60% of all travelers search for travel information online (Chang and Wang, 2019). In return, reading such reviews impacts customers’ decision-making processes (Goldenberg et al., 2001). eWOM provides a unique channel for countries and cities to market themselves and increase their global attractiveness, making eWOM an important tool of a destination marketing strategy (Jalilvand and Heidari, 2017).

Although the use of eWOM is becoming popular in tourism marketing, there is a lack of empirical evidence about interrelationships among eWOM, destination image, tourists’ attitudes, perceived behavioral control, and visit intention (Jalilvand et al., 2012). Empirical studies about eWOM on tourism marketing in the context of China’s inbound tourism industry still need more investigation. Destination image influences tourists’ purchase decisions, perceptions and behaviors. Building a strong image is vital for the competitiveness of a travel destination (Tasci and Gartner, 2007). eWOM and destination image are two important factors influencing tourists’ visiting intention. Therefore, it is necessary to design and put into practice specific strategies to improve the destination image.

China is one of the leading international destinations, and tourism is a key pillar of the Chinese economy. China’s inbound tourism market has dramatically expanded over the past two decades (from 7.428 million tourists in 1997 to 28.13 million tourists in 2016) according to the National Bureau of Statistics of China (2021). The city is interested in tourism growth and underwent a rapid economic development to improve its offerings (Singh and Zhou, 2016). However, inbound tourism from U.S. has yet to reach its potential. Although the U.S. is China’s primary North American tourism market, inbound tourists from the U.S. only rank fifth when compared to visitors from all countries. In total, U.S. inbound tourists only accounted for 8% of China’s total inbound tourists in 2016. On one hand, as a developed country, U.S. tourists should be a major target for China’s inbound tourist marketing. On the other, U.S. tourists might be reluctant to travel to China due to reasons such as unfamiliarity, visa requirements and long flights. Last but not least, air pollution and food security problems cause some foreign tourists to worry about the travel conditions and cancel their plans for China. The World Economic Forum assembles The Travel and Tourism Competitiveness Index by ranking countries that are attracting greater attention, and China usually does not rank well in such ranking systems (Zhang et al., 2018). Beijing is one of the most visited tourist destinations in China, so the development of the U.S. tourist market should be a high priority due to its potential.

As global competition has increased, tourism policymakers and stakeholders are now aware of the significant impact that positive and credible online reviews and comments can have on a city’s image. Using the Theory of Planned Behavior (TPB) (Ajzen, 1991) as its backbone, this study aims to build an overarching theory of travel intention. The broad goal of the current study is to investigate the effect of eWOM on Beijing’s destination image, and further test the impact of whether having a strong destination image affects tourists’ intention to travel to the city. Destination image is a major theme in the literature as it is crucial for tourism development in a locale (Gibson et al., 2008). In the tourism and hospitality academic fields, destination image has been an important area of inquiry for more than 50 years (Tseng et al., 2015). It is a key mediating factor in tourists’ decision-making process. An empirical study shows that the destination image can be affected by eWOM (Setiawan and Armanu, 2014). Image is one of the most important factors in tourists’ decision-making process (Fakeye and Crompton, 1991), and it also plays a key role in influencing tourists’ attitudes, decision-making and behavioral intentions from a marketing perspective (Bigne et al., 2001; Chen and Tsai, 2007; Chi and Qu, 2008). This study aims to extend the TPB model in line with the current development of the internet technology such as internet searching, online reviews, social media, by including destination image and considering the dual role of eWOM, in order to make the most use of this theory on the marketing strategy in China’s inbound tourism industry.

The foremost motives of the current research include: first, it attempts to develop a model that examines the relationships between eWOM, U.S. residents’ perceived behavioral control, tourists’ attitudes, destination image, and future travel intentions to Beijing; second, it aims to investigate the impact of eWOM on potential tourists’ travel intentions; third, it provides theoretical and practical implications to promote Beijing as an international destination among U.S. residents. Traditional marketing model investigated the relationships among tourists’ attitudes, destination image and future travel intentions (e.g., Huang and Hsu, 2009; Jordan et al., 2018; Lee, 2009). For example, Jordan et al. (2018) used the TPB to investigate U.S. residents’ intentions to travel to Cuba. Lately, the impact of eWOM on destination image and travel intention is becoming a hot topic in line with the fast development of the Internet and social media. The latest research highlights that eWOM is an important source of information for and influence upon travelers (Litvin et al., 2018). While China is a big tourist destination, we still do not know much regarding the eWOM and destination image of China. Empirical studies investigating the link between eWOM’s utilitarian function and credibility destination image, and travel intention to China need more attention.

This study links widely adopted TPB with influential eWOM to offer an understanding of attitudes, destination image, and perceived behavioral control of U.S. residents about potentially traveling to Beijing, and it is expected to provide useful suggestions for travel agencies and other stakeholders, as well as to increase government awareness of the online marketing strategies used to promote Beijing. Another goal of this study is to provide theoretical and practical implications to promote Beijing as an international tourist destination among U.S. residents who had never been to Beijing. We aim to provide practical implications by offering a systematic marketing strategy to promote tourism.

Literature review

This study was based on Theory of Planned Behavior model (see Figure 1). TPB proposes that perceived behavior control (PBC), subjective norms and attitudes determines an individual’s intention to perform a certain behavior. This model generates a theoretical framework for analyzing and predicting human behavior (Ajzen, 1991).

Figure 1.

Model of theory of planned behavior.

There are interrelationships among PBC, attitudes, behavioral intentions, and subjective norms according to the TPB (Ajzen, 1991). The TPB predicts an individual’s intention to engage in a behavior. The theory links one’s beliefs and behavior. The TPB proposes that PBC, subjective norms and attitudes determine an individual’s intention to perform a certain behavior. Attitude is the degree to which a person has a favorable or unfavorable evaluation of the behavior of interest. Subjective norms are the beliefs regarding other peoples’ approval or disapproval of the behavior of interest. The stronger the person feels the intention to perform the behavior, the more likely he/she will perform the behavior. This model generates a theoretical framework for analyzing and predicting human behavior (Ajzen, 1991). Therefore, TPB has been widely applied in social sciences studies (Chen and Tung, 2014; Han and Ryu, 2012).

However, there are some limitations of TPB in regard to investigating people’s behavioral intentions and actual behaviors. For example, it neglects additional variables such as environment related variables, motivation (Hsu and Huang, 2012), perceived uncertainty and risk (Quintal et al., 2010), and anticipated emotions and desire (Perugini and Bagozzi, 2001). TPB presumes that individuals acquire resources and opportunities to be successful in performing the desired behavior, regardless of the intention. Consequently, adding more related variables can strengthen TPB’s predicting power for tourists’ future travel intentions (Perugini and Bagozzi, 2001).

Tourism scholars have found that many other factors that influence the destination image such as the information that tourists obtain from external sources (Gibson et al., 2008). Many studies have extended the concept of TPB by adding supplementary variables (e.g. Han, 2015; Quintal et al., 2010, 2015). Throughout the decision-making process, there is a positive relationship between destination image and travel intention (Chen et al., 2013a; Gibson et al., 2008). Therefore, destination image could be an additional variable to the TPB model in order to predict tourists’ travel intention (Chen et al., 2013a, 2013b; Chew and Jahari, 2014). Additionally, it was found that TPB could also be influenced by tourists’ WOM (Cheng et al., 2006). These variables and relations among them are introduced in detail in the following sections.

eWOM

The fast diffusion of the Internet provides a new platform for people to share their ideas. The introduction of new ICTs made WOM more easily accessible and influential than ever (Chu et al., 2019). WOM plays a prominent role in influencing customer purchase intentions because some aspects of services are intangible in hospitality and tourism sector (Chen and Law, 2016). In online platforms, tourists exchange their knowledge and information about the destination and their travel experiences (Munar and Jacobsen, 2014; Wang and Li, 2019), and these online positive or negative statements about products, destinations, and institutions are defined as eWOM (Hennig-Thurau et al., 2004). eWOM provides information related to product quality, experiences and services. eWOM can be recommendations (Park et al., 2007) that are influential to the public because they serve as a written form of consumers’ experiences (Bickart and Schindler, 2001). Users consider these reviews more trustworthy than traditional marketing information (Abubakar and Ilkan, 2016; Smith et al., 2005). Compared with the traditional method, studies have shown that eWOM attracts more customers (Trusov et al., 2009). Travelers perceive eWOM to be more reliable than tour agents, around 30% of online shoppers have rated products online (Lenhart, 2006), and more than 70% of tourists visited tourism websites, forums, or virtual communities before making their final decision (Forrester, 2006). Consumers believe that online reviews are crucial or extremely important before they make a purchase (Leong et al., 2019; Lo and Yao, 2019). Empirical research reveals that tourists consult eWOM prior to making travel decisions (Bilgihan et al., 2016). Previous studies indicate that there is a positive relationship between eWOM and tourists’ intention to travel and revisit (Abubakar and Ilkan, 2016; Abubakar et al., 2017; Chen and Law, 2016; Jalilvand et al., 2013). In addition, positive online reviews received by consumers influence their likelihood to book a resort or hotel (Abubakar and Ilkan, 2016; Duverger, 2013; Goh, 2015; Ogut and Tas, 2012; Vermeulen and Seegers, 2009). In short, eWOM is important in tourism marketing, tourists’ decision-making (Abubakar and Ilkan, 2016; Abubakar et al., 2017), and shaping the perception of destination image.

With the fast diffusion of the Internet and online social networks, people can find a wide range of information offered by others to resolve problems and reduce risks associated with purchases (Dowling and Staelin, 1994). Consumers try not to buy negatively rated products (Amblee and Bui, 2008) from which arise the utilitarian function; this positive link between consumers’ attitudes and utilitarian functions was established by Hennig-Thurau and Walsh (2004 ). The utilitarian dimension is characterized by rational and economic evaluations (Ranaweera and Karjaluoto, 2017). When tourists are choosing a destination, they want to reduce the risks and uncertainty by reading online comments. The utilitarian eWOM function is expected to compel tourists to make better informed rational decisions. The complexity of tourism leads to risk and uncertainty; one way of dealing with this risk is to learn from the wisdom of others (Han et al., 2019). Utilitarian functionality of eWOM, therefore, is important for consumers as it provides useful elements such as price and variety. Studies show that the utilitarian function of eWOM emerges from various benefits to the customer as they may be better informed regarding product comparisons, product attributes, features, warranties, etc. (Reichelt et al., 2014).

In addition to the eWOM utilitarian function, credibility is another important element of eWOM. Studies show that source credibility, which includes trustworthiness and expertise, can influence consumers’ attitudes and behavioral intentions (Ayeh et al., 2013; Chen and Law, 2016). Studies also show that online credibility is different from the traditional WOM (Chen and Law, 2016). Jeacle and Carter (2011) found that government sponsored tourism websites and travel agents’ official websites perceived to be more credible (Chen and Law, 2016). Unlike traditional WOM from direct sources such as friends and family, eWOM generally originates from unknown individuals and therefore consumers have difficulty assessing the reliability of the information (Kim et al., 2018). Travel related eWOM is usually posted by anonymous users about attractions, accommodation, and restaurants available in a destination (Filieri et al., 2015). The credibility of the source is often crucial. If potential travelers find such information from reviews is credible, they will trust the message more (Filieri et al., 2015), in return, it will have a greater impact on the traveler’s behavior (Chu and Kamal, 2008; Lee and Koo, 2012). Trustworthiness and expertise are the two most important elements of credibility (Hovland et al., 1953). A person’s credibility is defined by his or her extensive related experience and knowledge (Feick and Higie, 1992). Credibility heavily depends on a person’s willingness to honestly transmit information. Online virtual community members prefer to search for reviewers who provide trustworthy and reliable suggestions, and the more consumers treat the online reviews as credible, the greater impact it will have on their behaviors (Chu and Kamal, 2008; Zhang and Watts, 2008). If tourists find that the eWOM is functional, they will be more likely to possess trusting beliefs about the destination’s credibility, integrity, and benevolence (Bilgihan et al., 2016). Therefore, the following hypothesis is proposed:

H₁: eWOM utilitarian function has a positive influence on eWOM credibility.

Perceived behavioral control, attitude, and travel intention

Perceived behavioral control (PBC) refers to perceived ease or difficulty of performing a behavior, and it reflects previous experiences and anticipated impediments and consequences (Ajzen, 1991). Controlled belief (e.g., individual’s skills and resources), which represents the sense of self-availability, is a factor of PBC; perceived facilitation is another factor of PBC, representing the evaluation process of the importance of the skills, or opportunities for preferred outcomes (Jalilvand and Samiei, 2012). To measure PBC such items may be used: ‘I have sufficient amount of time to visit Beijing’, or ‘I have sufficient amount of money to travel to Beijing’. As for the tourist destination choice, PBC is associated with the opportunities for visiting a certain destination, and it depends on the person’s self-confidence in his/her ability to go to the place (Jalilvand et al., 2012). In that regard, PBC has been introduced as a key influential factor of travel intention.

Tourists’ attitude describes the psychological intents expressed by their positive or negative evaluations when they engage in certain behaviors (Ajzen, 1991). Vincent et al. (2002) found that attitude consists of three factors: (a) the cognitive response that forms an attitude determined by evaluations, (b) the affective response that expresses the psychological preference of a tourist to a place, and (c) the behavioral component that represents an individual’s verbal indication for visiting intention. Attitude encourages people to perform or act in a certain manner (Lee, 2007; Sparks, 2007). Tourist attitude can therefore be seen as an important predictor of tourists’ decision for visiting a certain place.

Visiting intention refers to a tourist’s willingness to visit a destination (Chen et al., 2014), and the decision to visit a destination is a consequence of a comparison of the benefits and costs among different destinations. There is a positive relationship between attitude and an individual’s intention to visit the place (Ajzen, 2001), and it is important in choosing a final destination and even future travel behavior of tourists (Sirakaya and Woodside, 2005). PBC varies across situations and actions, which results in a person having varying perceptions of behavioral control depending on the situation. Therefore, perceived credibility of other peoples’ opinions may have an influence on PBC. Previous research also indicated that eWOM is important in destination marketing and for tourists’ travel intention (Jalilvand et al., 2011; Litvin et al., 2008; Vermeulen and Seegers, 2009). It was also found that eWOM has positive relationships with consumer attitudes and behavioral intentions (Abubakar et al., 2017; Goh, 2015; Jalilvand and Samiei, 2012; Sen and Lerman, 2007; Xia and Bechwati, 2008). Consequently, we proposed that:

H₂: eWOM credibility has a positive influence on tourist’s perceived behavioral control (PBC).

H₃: eWOM credibility has a positive influence on tourists’ attitudes (ATT).

H₄: Perceived behavioral control (PBC) has a positive influence on travel intention (TI).

H₅: Tourists’ attitudes (ATT) have a positive influence on travel intention (TI).

Destination image

Earlier studies on destination image primarily concentrated on perceptions of destination image in developed countries in Europe and North America, while few studies focused on destination image in Asian countries. Yet, studies concentrating on Asia and other developing countries have increased rapidly since the early to mid-2000s (Pike, 2007; Stepchenkova and Mills, 2010). Destination image is one’s overall perception of the total set of impressions of a place, and it is the mix of a destination’s natural, cultural, and social attributes, as well as tourism infrastructure (Milman and Pizam, 1995; Phelps, 1986). It is also outlined as an individual’s mental representation of knowledge and feelings of the characteristics of a destination (Chi et al., 2008; Crompton, 1979; Fakeye and Crompton, 1991), or the psychological interpretation of a tourist’s perception (Alhemoud and Armstrong, 1996). Different approaches have been used to assess destination image (Chen and Tsai, 2007; Court and Lupton, 1997; Echtner and Ritchie, 1993; Lin et al., 2003), and researchers have conceptualized the destination image (Echtner et al., 1993; Gallarza et al., 2002; Tasci et al., 2007), measurement, and the destination decision-making process (Chi et al., 2008; Tasci et al., 2007). A single-item method was used to evaluate the overall destination image (Balloglu and McCleary, 1999; Bigne et al., 2001), and a multidimensional concept including cognitive, affective and behavioral factors was suggested (Baloglu and Brinberg, 1997, Chi and Qu, 2008; Pike and Ryan, 2004; White, 2004).

Destination image can be influenced by individual characteristics that include socio-demographics, past travel experiences, and nationality (Gibson et al., 2008; Mazursky, 1989). Previous research established that eWOM communications positively influences brand image (Jalilvand and Samiei, 2012; Moro and Rita, 2018). Among professional advice, eWOM, advertisements, and books or movies, only eWOM has a positive relationship with the destination image (Baloglu and McCleary, 1999). Unsatisfied consumers tend to share their experiences with others (Morgan et al., 2003), so there can be a significant impact of eWOM on a destination’s image (Setiawan et al., 2014). However, only a few studies have focused on the effects of online communication. Jalilvand et al. (2012) for example, find that eWOM impacts the destination image, tourist attitude and travel intention. Their research further calls to action for studying the eWOM and decision-making process in a destination image context for different nations and cultures. With the growing influence of social media (Chu et al., 2020; Law et al., 2019; Moro and Rita, 2018; Nusair, 2020; Nusair et al., 2019), it is important to understand how to improve the destination image through online marketing strategies and tools, such as eWOM. Therefore, it is necessary to investigate the relationship between eWOM and destination image.

Destination image plays a vital role in tourism development because it is important to economic growth for local communities (Fourie and Santana-Gallego, 2011). According to Fakeye and Crompton (1991), image is one of the most important factors in tourists’ decision-making process, and it is also closely related to their visiting behaviors. As a result, it is essential to know how the image is perceived by tourists (Baloglu and McCleary, 1999; Tapachai and Waryszak, 2000). Destination image also plays a key role toward influencing tourists’ perceived behavior control, attitudes, decision-making and behavioral intentions from a marketing perspective (Bigne et al., 2001; Chen and Tsai, 2007; Chi and Qu, 2008). For instance, a positive destination image will increase tourists’ preferences for a specific destination (Lin et al., 2007, Ryu et al., 2012), and tourists’ revisit intention will be positively affected by the destination image (Court and Lupton, 1997). Therefore, destination image can be used to predict tourists’ future travel preferences.

Building upon the above analysis, we proposed the following hypotheses:

H₆: eWOM credibility has a positive influence on destination image (DI).

H₇: Destination image (DI) has a positive influence on tourist’s perceived behavioral control (PBC).

H₈: Destination image (DI) has a positive influence on tourists’ attitudes (ATT).

H₉: Destination image (DI) has a positive influence on travel intention (TI).

The conceptual model

Following the purpose of this study and literature review, a theoretical conceptual model was designed to better understand U.S. residents’ decision-making process and to predict their travel intentions to Beijing. This study added eWOM and destination image as the additional complementary variables to the traditional theoretical model of TPB. Compared with the original TPB model (attitudes, subjective norm and perceived behavior control), this revised model, which includes six constructs (eWOM utilitarian function, eWOM credibility, destination image, perceived behavioral control, attitude toward destination, and tourists’ future visit intentions), is expected to provide a more comprehensive explanation of tourists’ travel intentions. The conceptual model is shown in Figure 2.

Figure 2.

Proposed conceptual model of this study.

Method

Measures

Measurement items were adopted from previous research and the questions were revised based upon the suggestions of three professionals who are scholars in the field of hospitality and tourism. eWOM Utilitarian Function was measured with five items adopted from Jalilvand et al. (2012); a sample item is ‘I often read other tourists’ online travel reviews to know what destinations make good impressions’. eWOM Credibility was measured with two categories: trustworthiness and expertise. The item ‘The online travel information or reviews of Beijing are trustworthy’ for eWOM trustworthiness was adopted from Jalilvand et al. (2012). The three items for electronic eWOM expertise were from Park et al. (2016), and a sample item is ‘The online reviewers seem to have a good sense about Beijing’.

Perceived Behavioral Control was measured with four items from Jalilvand et al. (2012); a sample item is ‘I feel nothing will prevent me from traveling to Beijing if I want’. Tourists’ Attitudes were measured with five items from Jalilvand and Samiei (2012), and a sample item is ‘I can do a wide variety of things in Beijing’. Travel Intention was measured with five items from Jalilvand et al. (2012); one sample item is ‘I predict I will visit Beijing in the future’. Destination Image was measured with six items from Chi and Qu (2008), and one sample item is ‘Beijing’s natural and historic attractions’.

To ensure the quality of the questionnaire, a pilot test was conducted using 50 Qualtrics panel members, all qualified U.S. residents for this study. The main purpose of the pilot test was to determine whether the survey questions could be clearly understood by the targeted online participants. The response rate was 100 percent because forced response function had been utilized. Also, this pilot study was able to ensure the measurement reliability. Cronbach’s alpha coefficient was used in the study in order to find out the reliability of constructs. Based upon related theories, the acceptable reliability coefficient should be higher than .70. Results show that all of the Cronbach’s alpha coefficients of the indicators in the pilot test were highly acceptable (ranging from .849 to .951). According to the results of the pilot test, the survey questionnaire was considered reasonable and acceptable, and the data collection was able to continue.

Sampling and data collection

The respondents were selected by following the two screening questions: 1. U.S. residents who have never been to Beijing before; 2. U.S. residents who have read online travel information or reviews in the past. To ensure the representativeness and sufficiency for SEM analysis, a 400-person sample was designed in accordance with the rule of one item to five or more respondents (Hair et al., 2011) (there are 30 items in this study).

The data collection was conducted through Qualtrics.com. Qualtrics provides a web-based survey and data collection tool for creating and conducting online surveys that can be used to conduct research, departmental surveys, academic surveys, and more. A total of 413 valid responses were collected.

Data analysis

Statistical software programs SPSS 21.0 and Mplus 7.0 were used to analyze the data. SPSS 21.0 was used to analyze demographic statistics, correlations between constructs, and reliabilities such as Cronbach’s alpha coefficient (α). Mplus 7.0 was used to conduct the Confirmatory Factor Analyses (CFA) and Structural Equation Modeling (SEM) analyses. The purpose of SEM is to estimate the relationships among constructs. In addition, it allows the use of multiple measures to represent constructs. Previous guidelines indicated that approximately 20 participants per item would result in a sufficient sample, and a minimum sample size of 200 for an SEM is acceptable (Sideridis et al., 2014). A typical SEM analysis would include a two-stage procedure: the first stage is to use CFA to evaluate the relationship of the latent variables with the items, and the second stage is to assess the structural model fit to determine whether or not the data supports the proposed model. As for the model fit indices, studies show that a non-significant χ2 is acceptable, CFI should be greater than 0.90 (Hu and Bentler, 1995), RMSEA should be less than 0.10 with a maximum upper bound of the 90% CI of 0.10 (Browne and Cudek, 1993), and SRMR should be less than 0.10 (Bentler, 1995).

Results

Demographic and behavioral information

Table 1 shows the participants’ demographic information regarding their gender, age, ethnicity, marital status, education and annual household income level. Approximately two thirds of the respondents were female (69.0%). Most respondents were between 25 and 34 years old (43.0%); between 35 and 44 years old ranked second place. In terms of ethnicity, Caucasians account for 74.3%, followed by black respondents (15.0%), which is reasonable for the population structure of U.S. residents (United States Census Bureau, 2018). Almost all the respondents had a high school or equivalent education (99.5%), and most respondents had a college level education (70.0%). Most of the respondents (57.1%) had an annual family income between $30,000 and $74,999, and respondents with an extreme annual family income (too high or too low) were not significant. In general, the significant characteristics of online searchers are female, age between 25–44 years old, Caucasian, with an annual family income between $30,000 and $74,999.

Table 1.

Demographic profile of the respondents (n = 413).

Variable	Frequency/Percent	Variable	Frequency/Percent
Gender		Education
Male	125/30.3	Less than high school	2/0.5
Female	288/69.7	High school or equivalent	75/18.2
Age		Some college	95/23.0
18–24	67/16.3	Two years college	55/13.3
25–34	179/43.3	Four years college	139/33.7
35–44	83/20.1	Professional degree	39/9.4
45–54	51/12.3	Doctorate	8/1.9
55 and above	33/8.0	Annual Family Income
Ethnicity		Less than $10,000	37/9.0
White	307/74.3	$10,000–$19,999	24/5.8
Black	62/15.0	$20,000–$29,999	38/9.2
American Indian	5/1.2	$30,000–$39,999	48/11.6
Asian	24/5.8	$40,000–$49,999	50/12.1
Other	15/3.6	$50,000–$59,999	53/12.8
Marital Status		$60,000–$69,999	30/7.3
Married	210/50.8	$70,000–$79,999	55/13.3
Widowed	4/1.0	$80,000–$89,999	22/5.3
Divorced	27/6.5	$90,000–$99,999	16/3.9
Separated	6/1.5	$100,000–$149,999	28.6.8
Never Married	166/40.2	More than $150,000	12/2.9

Table 2 presents statistics on the online searching behavior in terms of frequency of online searching and websites used by participants. 71.7% of respondents used online reviews very frequently (33.2%) and frequently (38.5%). As for the online travel websites used by respondents, Expedia (34.6%), TripAdvisor (29.8%), and Travelocity (14.3%) ranked as the first three main websites that account for 78.7% of all websites listed; the first two websites were very popularly used for U.S. online travel searching.

Table 2.

Frequency of using websites and websites used in by participants (n = 413).

Variable	Frequency/Percent	Variable	Frequency/Percent
Frequency of use		Websites
Very frequently	137/33.2	Expedia	143/34.6
Frequently	158/38.5	TripAdvisor	123/29.8
Occasionally	101/24.5	Travelocity	59/14.3
Rarely	13/3.1	Priceline	37/9.0
Very rarely	4/1.0	Orbitz	17/4.1
Never	0/0	Kayak	12/2.9
		Hotwire	11/2.7
		Other (OneTravel, CheapOair, etc.)	11/2.7

Reliability and validity test

Table 3 presents the constructs’ reliability. Cronbach’s alpha is used for analyzing the constructs’ consistency and reliability. The acceptable reliability coefficient should be higher than .70. Based upon the results, all the Cronbach’s alpha coefficients of the constructs were highly acceptable (i.e., ranging from .853 to .957). There was sufficient internal consistency of each construct. Table 3 also presents the correlation matrix among the six different constructs (i.e., eWOM credibility, eWOM utilitarian, destination image, tourists’ attitude, perceived behavioral control, and travel intention). The correlation coefficients ranged from .431 to .773. In addition, it shows that perceived behavioral control and tourist travel intention have the strongest correlation.

Table 3.

Means, standard deviation, and construct inter-correlations.

Variable	Mean	SD	CRE	UTI	DI	ATT	PBC	INT
CRE	2.36	.80	0.853
UTI	2.22	.85	.605**	0.895
DI	2.33	.78	.680**	.665**	0.957
ATT	2.19	.96	.514**	.553**	.761**	0.917
PBC	2.98	1.37	.500**	.431**	.619**	.500**	0.871
INT	3.00	1.43	.519**	.462**	.645**	.564**	.773**	0.949

Note: **p < .01; Numbers at diagonal are Cronbach’s alphas.

A confirmatory factor analysis (CFA) was conducted with Mplus 7.0, and the factor loadings of each item, composite reliability, and overall model fit, convergent and discriminant validity were tested in this section (see Table 4 and Table 5). Results show that all factor loadings were acceptable, and the overall model fit indices present that the model fits the data well. χ² (390) = 1285.172, p < .000, χ²/df = 3.29, CFI = .910, TLI = .900, RMSEA = 0.075 (90% CI .07–.079), SRMR = .055. Table 5 shows the convergent and discriminant validity indicators. Average variance extracted (AVE) was calculated to measure the convergent and discriminant validity. According to Anderson and Gerbing (1988), the cut-off value of AVE should not be less than .50. In this research the AVEs range from .545 to .764, and these values are acceptable. In addition, it is suggested that the discriminant validity can be tested by comparing squared correlation coefficients with AVEs (Fornell and Larcker, 1981). According to Table 5, all of the squared correlation coefficients values are lower than the AVE values, presenting good discriminant validity of the constructs.

Table 4.

Result of CFA analysis with standardized, unstandardized loadings, and P-values.

Construct	Indicator	B	S.E	β	Est./S.E	p-value
eWOM Credibility	EXP1	1.000	0.023	0.782	33.580	<0.001
	EXP2	1.002	0.023	0.786	34.012	<0.001
	EXP3	1.032	0.023	0.790	34.867	<0.001
	TRU	0.888	0.023	0.786	34.289	<0.001
eWOM Utilitarian Function	UTI1	1.000	0.674	0.674	22.018	<0.001
	UTI2	1.033	0.646	0.646	19.979	<0.001
	UTI3	1.212	0.826	0.826	40.521	<0.001
	UTI4	1.107	0.024	0.773	32.291	<0.001
	UTI5	1.206	0.025	0.757	30.320	<0.001
Destination Image	ENV	1.000	0.025	0.743	30.238	<0.001
	ENT	0.901	0.018	0.837	46.929	<0.001
	P&V	0.989	0.019	0.810	41.622	<0.001
	INF	0.882	0.021	0.800	38.927	<0.001
	ACC	0.961	0.017	0.838	48.214	<0.001
Attitude	ATT1	1.000	0.027	0.704	26.197	<0.001
	ATT2	1.186	0.017	0.835	48.412	<0.001
	ATT3	1.308	0.016	0.856	54.306	<0.001
	ATT4	1.309	0.018	0.826	45.828	<0.001
	ATT5	1.138	0.023	0.769	34.023	<0.001
	ATT6	1.327	0.017	0.847	51.104	<0.001
Perceived Behavioral Control	PBC1	1.000	0.023	0.781	34.658	<0.001
	PBC2	1.151	0.020	0.817	40.629	<0.001
	PBC3	0.891	0.025	0.742	29.255	<0.001
	PBC4	1.018	0.019	0.834	44.481	<0.001
Travel Intention	INT1	1.000	0.015	0.849	56.356	<0.001
	INT2	1.010	0.020	0.783	38.708	<0.001
	INT3	1.026	0.011	0.900	83.537	<0.001
	INT4	1.065	0.009	0.915	96.767	<0.001
	INT5	1.100	0.010	0.910	91.185	<0.001
	INT6	1.034	0.012	0.833	72.265	<0.001

Note: 1. Fit indices: χ² (390) = 1285.172, p < .000, χ²/df = 3.29, CFI = .910, TLI = .900, RMSEA = 0.075 (90% CI: .07–.079), SRMR = .055;

2. Please refer to the Appendix 1 to see the meanings of the indicators.

Table 5.

Convergent/discriminate validity of constructs.

Variable	CRE	UTI	DI	ATT	PBC	INT
CRE	.606
UTI	.366	.545
DI	.462	.442	.650
ATT	.264	.306	.579	.653
PBC	.250	.186	.383	.250	.631
INT	.2694	.213	.416	.318	.598	.764

Note: Numbers at diagonal are average variance extracted (AVE), numbers below diagonal are squared multiple correlations.

Hypotheses test

With good convergent validity and discriminant validity, SEM analysis was conducted to test the research hypotheses. The overall model fit indices are: χ² (396) = 1327.297, p < .001, χ²/df = 3.35, CFI = .907, TLI = .897, RMSEA = .075 (90% CI .071–.080), SRMR = .062. The results show very good model fit to the data of this study (Bentler, 1995; Browne and Cudek, 1993; Hu and Bentler, 1995). Table 6 presents the unstandardized and standardized coefficients with estimated standard error and p value. Six of the nine hypotheses were supported; Figure 3 presents the final model with standardized path coefficients.

Figure 3.

Results of the model.

Table 6.

Results of SEM analysis and hypotheses test.

Hypothesis	Path	B	β	Est/S.E.	p	Result
H₁	UTI → CRE	.820	.769	26.998	.000	Supported
H₂	CRE → PBC	.131	.079	.944	.345	Not Supported
H₃	CRE → ATT	−.030	−.030	−.391	.695	Not Supported
H₄	PBC → INT	.763	.715	15.592	.000	Supported
H₅	ATT → INT	.336	.192	3.409	.001	Supported
H₆	CRE → DI	.879	.795	31.654	.000	Supported
H₇	DI → PBC	.959	.642	8.132	.000	Supported
H₈	DI → ATT	.732	.804	11.753	.000	Supported
H₉	DI → INT	.045	.028	.404	.686	Not Supported

Note: UTI (eWOM Uilitarian function), CRE (eWOM Credibility), DI (Destination Image), ATT (Tourists’ attitude), PBC (Perceived Behavioral Control), INT (Travel Intention).

As for the hypotheses, H₁ (eWOM utilitarian function has a positive influence on eWOM credibility) was supported (β value was .769 and p value is less than .001); H₂ (eWOM credibility has a positive influence on tourists PBC) was not supported (β = .079, p > 0.05); H ₃ (eWOM credibility has a positive influence on tourists’ attitudes) was not supported (β = −.03, p > .05); H ₄ (PBC has a positive influence on travel intention) was supported (β = .715, p < .001); H₅ (tourists’ attitude have a positive influence on travel intention) was supported (β = .192, p < .01); H₆ (eWOM credibility has a positive influence on destination image) was supported (β = 0.795, p < .001); H₇ (destination image has a positive influence on tourists PBC) was supported (β = .642, p < .001); H₈ (destination image has a positive influence on tourists’ attitude) was supported (β = .804, p < .001), and H₉ (destination image has a positive influence on travel intention) was not supported (β = .028, p > .05).

Study results show that there is no direct influence of eWOM utilitarian function, eWOM credibility, and destination image on travel intention; while PBC and tourists’ attitude have direct influence on travel intention, and PBC has greater influence than does tourists’ attitude (0.715 and 0.192 respectively). Consequently, there are two paths to influence travel intention: one is from eWOM utilitarian function, to eWOM credibility, to destination image, to PBC, and to travel intention; and the other is from eWOM utilitarian function, to eWOM credibility, to destination image, to tourists’ attitude, and to travel intention.

It is noticeable that destination image linking eWOM utilitarian function, and eWOM credibility to PBC or tourists’ attitude is a key factor in the paths. Still, the path through PBC has a significant influence on travel intention. Therefore, the path consists of external factors with eWOM utilitarian function, eWOM credibility, and destination image as one part, and the internal factors of PBC (or tourists’ attitude) and travel intention as the other. For tourism marketing, external factors are very important to stimulate tourists’ internal motivation; these external factors are eWOM and destination image.

Discussions and implications

Discussion

The findings of this study provide a foundation for meaningful discussions for sustainable tourism development in Beijing. This research attempted to seek strategies to increase U.S. inbound travel to China. Based upon the hypothesis results, we were able to conclude that eWOM utilitarian function and credibility, mediated by destination image, perceived behavioral control and tourists’ attitudes, showed the most substantial impact on U.S. residents’ future travel intentions. In addition to confirming the findings of previous studies on the effect of eWOM on perceived behavioral control and tourists’ attitudes (e.g. Abubakar et al., 2017; Goh, 2015; Jalilvand and Samiei, 2012; Sen and Lerman, 2007; Xia and Bechwati, 2008), this study also analyzed the eWOM in detail by confirming that eWOM utilitarian function has a positive influence on eWOM credibility, which was seldom discussed in previous studies. Most importantly, the overall findings of this study expressed that U.S. residents’ intentions to travel to Beijing were heavily dependent on the perceived behavioral control and their attitudes, and this finding is consistent with previous studies such as Perugini and Bagozzi (2001). In addition, destination image was confirmed to have a strong impact on tourists’ perceived behavioral control and their attitudes (e.g. Bigne et al., 2001; Chen and Tsai, 2007; Chi and Qu, 2008), while the direct impact of destination image on travel intention was not supported, and this is contrary to findings of Lin et al. (2007), and Ryu et al. (2012). This result reflected the important role of the TPB. Specifically, the findings of this study highlighted the influence of eWOM, and the importance of destination image, in attracting international tourists. More importantly, the findings will help strengthen the Chinese government’s awareness of using eWOM and destination image as a marketing strategy for inbound tourism.

Theoretical implications

As for the theoretical implications, this study extended the well-known TPB by adding the three new constructs of eWOM utilitarian function, eWOM credibility and destination image into the TPB model. This new model extends the original TPB and offers a new perspective of PBC and tourists’ travel intention for researchers. With the good fitness indices of the model to the data, it is verified that the new model is more appropriate for tourist behavior studies. In addition, this study divided the general concept of eWOM into eWOM utilitarian function and eWOM credibility, which is seldom investigated by previous studies. It was found that eWOM utilitarian function was a crucial factor that affects eWOM credibility, which further influences the destination image (its path coefficient was .795). Thus, eWOM credibility would significantly influence destination image. This finding is in line with prior study results (e.g. Feick and Higie, 1992; Lin et al., 2007; Ryu et al., 2012).

Utilitarian function influences credibility in online environments. In order to gain consumers’ trust, providing utilitarian functionality is important. Previous research reveals that utilitarian beliefs influence individuals’ integrity beliefs (Liao et al., 2006). Psychologists denote that utilitarian beliefs influence the beliefs in integrity in face-to-face social relationship evaluations. Our results confirm that this also applies to online environments. People associate the useful elements of a technology with the beliefs of integrity (McKnight et al., 2002). They will trust online reviews if they find a utilitarian value. If consumers believe that the eWOM has high utilitarian function, they will be more likely to possess trusting beliefs about the eWOM. The resulting eWOM credibility, in return, influences the perceptions of the destination image. Destination image influences both attitude and PBC positively.

Practical implications

Several practical implications could be inferred from this study; these implications are related to online travel agencies, travel and hospitality industry operators, and local governments. The first implication is based on the causal result from eWOM utilitarian function to eWOM credibility, and to destination image. Destination image is a central concept to understand the tourist destination choice. Studies show that the high influence of WOM in the destination image formation (González-Rodríguez et al., 2016). It is important to identify various online travel reviews or comments about Beijing to gain a better understanding of visitor demands. Because travelers tend to link online reviews to their purchase decisions, it is very important to use eWOM as a marketing and promotional tool in the international travel segment to attract potential travelers, and to create great opportunities for the travel and tourism industry. The local government could work with celebrities and post travel information online to attract target potential travelers. Consumers will form a destination image in their minds if they perceive the reviews and comments as credible. Second, it is crucial for destination marketers and local governments to build a positive destination image of Beijing through social media or travel intermediates by promoting positive online reviews. In addition, the local government should cooperate with online travel agencies such as Expedia, TripAdvisor, Priceline, and Travelocity, which are mostly used by U.S. residents (see Table 2) to promote destination image by using eWOM as a strategy. Finally, the main effect of destination image on travel intention is mediated by perceived behavioral control rather than tourists’ attitude. Our findings indicated that the strongest influence on U.S. residents’ travel intentions was their perceived behavioral control. It implied that this specific construct was considered the most important element when U.S. residents tried to plan to travel abroad. Perceived behavioral control is dependent on the person’s self-confidence in his or her ability to go to the place, and it is connected with the opportunities for visiting the destination (Jalilvand et al., 2012). Therefore, it is wise for the local government and destination marketers to manage eWOM effectively to ensure that potential travelers feel it is easy to visit. Online information could include details about the visa application process, well-developed infrastructures, public security, friendly environment, and high-quality services.

Conclusions

This study investigated the influences of eWOM and destination image of Beijing on U.S. residents’ travel intention based on a model representing the causal relationships among eWOM utilitarian function, eWOM credibility, destination images, tourists’ attitudes, perceived behavioral control and travel intention. Nine hypotheses were proposed in order to have a better understanding of the factors that influence tourists’ travel intention. Based upon the results of this study, six of the nine hypotheses were supported. The overall findings of this study show that eWOM utilitarian function and credibility, destination image, PBC, and tourists’ attitudes have substantial impacts on U.S. residents’ future travel intentions. Most importantly, U.S. residents’ intentions to travel to Beijing were heavily dependent on their PBC (β = 0.715) but lightly on their attitudes (β = 0.192). There was no direct significant influence of destination image on tourists’ travel intentions, while destination image plays a mediating role in the relationship between eWOM credibility and perceived behavioral control and tourists’ attitudes; meanwhile, eWOM utilitarian function has a strong impact on eWOM credibility.

Limitations and future research

This study exhibited several limitations that might have affected the results. One limitation of this study was related to sampling issues. This study employed a convenience sampling method by using the Qualtrics platform for data collection. The major drawback of this convenience sampling technique may limit the application of the findings, so findings should be applied cautiously. In addition, this study only focused on U.S. residents who had not visited China and had read online reviews, and the participants were collected from Qualtrics field panelists online. Therefore, there was a limitation from a demographic perspective because the study did not examine U.S. residents who had visited China or U.S. residents who had not read online reviews. Also, this study did not examine international tourists from other countries. Therefore, more empirical studies need to be conducted to assess how U.S. inbound tourists differ from tourists from other countries. Moreover, there are different types of online travel reviews about Beijing: positive, negative or neutral reviews. This study did not clearly categorize the different types of online reviews. Future research on the influence of negative online reviews on the destination image of Beijing, international tourists’ perceived behavioral controls, attitudes and future travel intentions should be continued to help extend related theories on eWOM and destination image.

Footnotes

Author's note

Fevzi Okumus is also a Visiting Professor at WSB University, Wrocław, Poland.

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

Anil Bilgihan

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