1. Introduction
The development of information and communication technology has eliminated immense temporal and spatial constraints on the Internet, which has, in turn, created new consumption and business markets. In effect, the distribution structure of travel agencies, airlines, and hotels began to install reservation systems through the Internet (Yoon et al., 2008). Travel agencies could now expect certain corporate growth from reduction of marketing costs and real-time communication with customers, while consumers gained easy access to travel packages and information about travel destinations or accommodations (Parasuraman, Zeithaml, & Malhotra, 2005). According to a recent survey by the Consumer Rights Forum and the Consumer Research Institute (2019) on online travel agencies (OTA) consumer experience and satisfaction, “easy reservation and payment process‖ made up 37.4%, which was the highest percentage for reasons why consumers utilize OTA, followed by 27.1% for ―price comparison feasibility” and 22.8% for “cheaper price than offline“. In short, consumers increasingly favored OTA over offline agencies for convenience and low cost and competition among existing offline companies, online companies, and companies operation in online-offline links has intensified.
Although such intensification of competition created a plethora of services to attract customers, consumers are increasingly dissatisfied. Among the reasons, most customers have answered that online travel agencies (OTA) are unreliable (Consumer Rights Forum and the Consumer Research Institute, 2019).
OTA does not provide face-to-face service with customers. Therefore, purchases are often determined through detailed descriptions of products listed through websites, as well as product reviews and travel reviews from consumers (Chun, 2011). However, consumers often determine purchase based on the reliability of other consumers with preceding experience rather than from information on the company websites. Particularly, consumers depend on anecdotal information in cases of travel products, which include subjective and emotional experiential assessments (Litvin et al., 2008; O`connor, 2010).
As such, since anecdotal information on travel agencies contains appraisal information which consumers have experienced first-hand, the quality of service from the OTA inevitably influences the quality of consumer information. Given that tourism is an information-intensive industry, OTA services are heavily dependent on the quality of service provided through websites and the formation of reliability between companies and customers. While low cost was a driving force in OTA‘s prior growth, recent trends point that consumers‘ anecdotal information is a vital component to occupy a competitive edge in the heighted competition of OTA market, and companies now recognize the significance of quality in services provided online (Parasuraman, Zeithaml, & Malhotra, 2005). This research aims to extract what components dictate the quality of service from the OTA. Based on the results, this research identifies how travel agencies in terms of company reputation as well as transactional stability influence consumers‘ anecdotal response in their intention to share practical information.
2. Literature Reviews
2.1. Online Travel Agencies (OTA)
In the early days when travel agencies used websites, agencies had only provided ticketing services. On the other hand, recent developments in information and communication technology have led to the emergence of OTAs that allow offline travel agencies to do tasks once only possible through offline travel agencies, from checking and booking schedules to making payments as well as printing electronic ticket, on an online platform. In other words, OTA refers to a company that makes reservations or purchases of travel packages and services via the Internet, as a travel agency that provides services through direct communication with customers online (Niu, 2018).
Unlike previous information offered by offline agencies, OTA has the advantage of delivering information quicker and cheaper, quicker and easier access to information for both business and users has contributed greatly in its rapid growth ((Koufairs & Hampton-Sosa, 2004). As the size of the Korean OTA market expanded, global OTA companies such as Expedia, Cititrip, Airbnb, Hotels.com and Agoda entered the market. It has become essential for travel agencies to provide quality service for the survival of the company and accordingly, related studies have been conducted to accommodate the rising importance.
2.2. Online Travel Agencies (OTA) Quality of Service
Service quality in travel agencies refers to perceived quality by comparing what travelers expect beforehand with what they actually experience in travel packages and services provided by travel agencies. With the advent of OTA, recent studies have begun to include the concept of quality for online services (Kim & Lee, 2012). However, a service quality assessment model, which consists of factors that make up the general service quality, has yet to adequately reflect the characteristics of online services. Therefore, it has become imperative to derive new service quality components that do reflect such characteristics.
Barnes and Vidgen (2001) relied on ServQual to develop an online service quality assessment model WebQual, which has developed to version 4. Online service quality, according to Web-Qual 4.0, consists of three types: usability, information, interaction. Kaynama and Black (2000) found that the dimensions of accessibility, search, design and expression, content and purpose, responsiveness, interactivity, customerization, personalization, safety and reputation are important factors in OTA service quality assessment in the E-Qual model. Sigala and Sakellaridis (2004), which was a study on people who use websites of travel agencies, concluded that reliability, responsiveness, usefulness, interactivity, and site design are important service quality components for OTAs.
In the domestic cases, Hong and Baek (2006), applying WebQual 4.0 to online bookstores, similarly concluded on usability and informality, but found dissimilarity in cases of security and individuality in interactivity. Also, according to Jindal (2012), usability, informality, security, design, interactivity consisted the online service qualities. Yoon(2006) suggested eight qualities such as ease of use, reliability, accessibility, price, website, design, personalization service, and recognition, transaction safety in the study of OTA service quality and customer loyalty.
Relying on previous works, this research extracts five components in the OTA service quality assessment: Information quality, convenience quality, price quality, customer service quality, and quality of customer relationship management.
Information quality encompasses a plethora of quality components that customers and companies produce and, depending on the form and use of information application, may evaluate its quality with components such as understandability, reliability, timeliness, and usefulness (Poddar & Wei, 2009). Such information quality would affect customers‘ purchase decision, which, as a result, would influence customer satisfaction, business viability, decision level and corporate management performance (Lu and Lee, 2014). Secondly, convenience quality, which refers to the degree of ease users experience in the use or purchase of products, is often cited as a significant factor in raising business performance in the likes of customer satisfaction and willingness to purchase products while using online websites (Niu, 2018). Evaluating such convenience is an act of convenience quality and is applicable in evaluating quality components like inaccessibility, website design, ease of use, when customers use websites of travel agencies.
Thirdly, how customers compare and evaluate prices to purchase at a low cost determine price quality is another key component. Customers dig through information in hope to reduce uncertainty about a product. Therefore, this research defines price quality as ―benefits of product and service quality at reasonable prices‖ (Lee, 2018).
Next, we derived concept of customer service quality from comprehensive consideration of the meaning of components from preceding studies such as personalization, customization, purchase safety and interactivity. Lastly, customer relationship management (or eCRM) can have similar applications to the aforementioned quality of customer service, and it has become urgent for travel agency websites to adopt eCRM, which grants customer maintenance, or the CRM concept to encapsulate higher profits and competitive edge in a highly competitive market. Recently, customer satisfaction has had a greater emotional impact on customer management than eCRM in functional and social terms (Byun, 2011).
2.3. Transaction Safety and Reputation
Formation of online trust between corporations and consumers has played an important role in the recent rise of e-commerce in the business sector. Particularly, transaction safety has become more important online where there are various uncertainty in obtaining information (Oh & Song, 2014). In short, trust is now a vital factor in OTA management.
According to Doney and Cannon (1997), trust forms with a five-step process of calculation-anticipation-capability-intention-transference. In other words, customers compare prices and product contents online, anticipating about a product or an agency‘s competency from word-of-mouth experiences. When the customers‘ experience intersects their expectations, customers would then recommend the product or service to other potential customers. However, customers have become anxious about the safety of their personal information or transaction information when making payments. Therefore, transaction stability is an important variable for OTA companies in building trust with customers (Kim, Kim, & Yang, 2009). In short, if transaction stability of e-commerce is guaranteed in OTA, consumers have shown to be more satisfied than offline transactions because of the convenience of e-commerce (Devaraj, Fan, & Kohi, 2002).
Reputation is also another key variable in determining customers‘ purchase (Kim, Hwang, & Kim, 2007). Reputation, unlike transaction safety, is a direct reflection of the assessment of companies, or company image formed by internal and external stakeholders like customers and employees (Bordon, 2010). For OTA, company reputation is indisputably linked with word-of-mouth information formed online.
Crowther and Bacchus (2004) argued that a company with a good reputation can be more of a benefit to consumers. They also argued that the good reputation would ultimately lead to the establishment of corporate brand assets, generating a continued interest of consumers which would lead to consumer purchasing activities. Graham and Bansal (2007) studied the willingness of consumers to pay for the reputation of an airline, which is one of the sectors of the tourism industry. They discovered that consumers were willing to pay higher costs for companies that had a good corporate reputation.
In other words, reputation occupies a strategically important position at the business level while also serving as an excellent tool to differentiate the business from other competing online travel agencies.
2.4. Word-of-Mouth Intention
Anecdotes, or 'word-to-mouth,' refer to a process of circulating information about a product or service among consumers (Cheung & Anitsal, 2007). Consumers exchange opinions and word-ofmouth experiences to other potential consumers, performing immense influential power in determining product consumption (Fong & Burton, 2006). In other words, considering that acquaintance recommendations are often determining factors in making purchase decision, word-of-mouth information is a noteworthy concept for corporate management (Godes & Mayzlin, 2004). On the other hand, scholars have offered varying understandings of 'word-of-mouth intention' since a definitive answer about the concept has yet to be established. However, scholars do agree that word-of-mouth intention is an oral transference of information about a product or service. As defined by Park (2014), this research views word-of-mouth intention closer to intention than to activity since anecdotes often manifest into an intention to share their experience with the product or service with others via Social Network Service (SNS).
3. Data and Research Methodology
3.1. Sampling and Measurement Scales
The survey in this research targeted people who have experience in using OTA to purchase travel product at Incheon International Airport from November 1 to December 28, 2019. Out of 280 questionnaires that were handed out to people waiting for the check-in time for flights or had just finished checking in, 260 questionnaires were returned, yielding a response rate of 92.8 percent. From the pile, 249 (88.9%) questionnaires were utilized in the analysis. Prior to the main survey, a pilot interview was conducted for about a week by 15 tourism industry officials, from travel agency, airline, and hotel, to assess the content validity of measurement scales. The survey questions, which were selected from previous major researches (refer to Table 1), consisted of 45 items (30 items for measuring OTA service quality, 4 for transaction safety on OTA website, 3 for OTA reputation, 3 for Word-of-mouth Intention of user, and 5 for demographic characteristics of respondent). This study analyzed the collected data using a statistical package 'SPSS 22', and used descriptive analysis, factor analysis and regression analysis as its statistical analysis method.
Table 1: Composition of Questionnaire
3.2. Research model and hypotheses
Through an in-depth literature review, this study established underlying research model shown in Figure 1, and arrived upon the following three hypotheses, which assesses the effect relationships between OTA service quality and word-of-mouth intention, as well as the moderating effects of both transaction safety and OTA reputation on them
Figure 1: Research model
H1: OTA (online travel agency) service quality (information, convenience, price, customer support, and customer relationship management) will have significant impacts on the word-of-mouth intention of users.
H1a: Information quality will have a significant impact on word-of-mouth intention of users.
H1b: Convenience quality will have a significant impact on word-of-mouth intention of users.
H1c: Price quality will have a significant impact on wordof-mouth intention of users.
H1d: Customer support quality will have a significant impact on word-of-mouth intention of users.
H1e: Customer relationship management quality will have a significant impact on word-of-mouth intention of users.
H2: Transaction safety will have moderating effects on the relationship between OTA service quality (information, convenience, price, customer support, and customer relationship management) and word-of-mouth intention of users.
H2a: Transaction safety will have a moderating effect on the relationship between information quality and word-ofmouth intention of users.
H2b: Transaction safety will have a moderating effect on the relationship between convenience quality and word-ofmouth.
H2c: Transaction safety will have a moderating effect on the relationship between price quality and word-of-mouth intention of users.
H2d: Transaction safety will have a moderating effect on the relationship between customer support quality and word-of-mouth intention of users.
H2e: Transaction safety will have a moderating effect on the relationship between customer relationship management quality and word-of-mouth intention of users.
H3: Reputation of OTA will have a moderating effect on the relationship between OTA service quality (information, convenience, price, customer support, and customer relationship management) and word-of-mouth intention of users.
H3a: Reputation will have a moderating effect on the relationship between information quality and word-ofmouth intention of users.
H3b: Reputation will have a moderating effect on the relationship between convenience quality and word-ofmouth intention of users.
H3c: Reputation will have a moderating effect on the relationship between price quality and word-of-mouth intention of users.
H3d: Reputation will have a moderating effect on the relationship between customer support quality and wordof-mouth intention of users.
H3e: Reputation will have a moderating effect on the relationship between customer relationship management quality and word-of-mouth intention of users.
4. Results
4.1. Respondents Profile
Table 2 presents demographic characteristics of respondents. Out of the 249 respondents, almost 76 percent (190) were female and the remaining 24 percent (59) were male. Regarding age group, 37.8 percent of the respondents were in the range of 20-29; 39.8 percent, 30-39; 17.7 percent, 40-49; 4.4 percent, 50-59; and 0.4 percent, 60-69. Also, 50.2 percent (125) of the survey respondents were unmarried persons and the others were the married.
Table 2: Demographic Characteristics of the Sample
4.2. Factor analysis
To examine the factorial validity and the consistency of the scale developed, this study conducted factor analysis and reliability test by using Cronbach‘s alpha. From the result of factor analysis, which were rotated by Varimax and extracted by Principal Component Analysis(PCA), this study adopted the factors which had eigenvalue greater than 1, and the scale items which had factor loading and communality higher than 0.5 respectively.
The criteria was to satisfy the requirements of convergent and discriminant validity. Finally, 1 (from transaction safety) out of 40 scale items which did not meet the above conditions were eliminated in the process of the analysis, and eight factors (five independent variables, one dependent variable, and two moderating variables) were extracted. The results of reliability tests for the extracted eight factors showed that the coefficient alphas of the factors were all above 0.7, which is considered to be moderate in studies of social sciences.
Table 3 presents the result of factor analysis of independent variables regarding OTA service quality. Five factors were generated and accounted for 70.163 percent of the total explained variance. And five items converged on three out of the five factors (information, customer support, customer relationship management) respectively, while four items converged on a factor, price, and six items did on a factor, convenience. Meanwhile, as the results of KaiserMeyer-Olkin measure of sampling adequacy and Bartlett‘s test of sphericity, which assesses the factorability of the scales, this study obtained a sampling adequacy measure of 0.929 and sphericity test value of 4417.222 with an associated significant level of 0.000. The given set of data is adequate for factor analysis with the empirical evidence like the high value obtained for sampling adequacy and the significant level of the test of sphericity.
Table 3: Factor Analysis of Independent Variables
Table 4 shows the result of factor analysis of moderating variables regarding transaction safety on OTA website and reputation of OTA. The two factors were extracted and accounted for 68.8 percent of the total explained variance. Four items converged on transaction safety and three items did on reputation. The research verified that the given set of data is appropriate for factor analysis in accordance with the results of KMO measure of sampling adequacy(0.848) and Bartlett‘s test of sphericity (approximate chi-square value is 897.702, significant level is 0.000).
Table 4: Factor Analysis of Moderating Variables
Finally, the results of factor analysis of dependent variable regarding word-of-mouth intention of users are summarized in Table 5. One factor was extracted and three items converged on it. In accordance with the results of KMO measure of sampling adequacy (0.723) and Bartlett‘s test of sphericity (approximate chi-square value is 346.541, significant level is 0.000), this study also confirmed that the given set of data is proper for factor analysis.
Table 5: Factor Analysis of Dependent Variable
4.3. Regression analysis
The first hypothesis of the research was to assess the impacts of OTA service quality (information, convenience, price, customer support, and customer relationship management) on the word-of-mouth intention of users. To examine the hypothesis, simultaneous multiple regression analysis was conducted. The five factors, OTA service quality, as the independent variables and the word-of-mouth intention of users as the dependent variable were put into the regression equation. The results of the analysis are summarized in Table 6 below. According to the evaluation result of overall model fit for regression equation, it is estimated by F statistics, the regression model was statistically significant at p < 0.001. In addition, the regression equation explained 29.9 percent of the variance of the dependent variable (F = 12.037, p <0.001). Regarding the effect relationship between variables, two independent variables, 'convenience' and 'price' had positively significant relationships with the dependent variable ('price' was statistically significant at p < 0.05 and 'convenience' was significant at p < 0.01). The convenience was the most significant independent one with the biggest beta coefficient (0.272), followed by price (0.217). According to the result of the analysis, the two sub-hypotheses, H1b and H1c, were supported. Hypothesis 1 of the study therefore, was partially supported.
Table 6: The impact of OTA service quality on word-of-mouth intention of users
* P<0.05 ** P<0.01
Hypothesis 2 and 3 were to verify moderating effects of both ‘transaction safety’ and ‘reputation’ on the influential relationship between OTA service quality (information, convenience, price, customer support, and customer relationship management) and the word-of-mouth intention of users. In order to confirm the effects, hierarchical multiple regression analyses were conducted. First of all, based on the findings of the above simultaneous regression analysis where the ‘information’, ‘customer support’, and ‘customer relationship management’ were not significant, the six sub-hypotheses, H2a, H2d, H2e and H3a, H3d, H3e were not supported. To examine H2b, the researchers inputted the independent variable, ‘convenience’, into the regression equation with the dependent variable, the word-of-mouth intention of users, as Step 1. Then we inputted the moderating variable, 'transaction safety' into the equation as Step 2, and lastly, as Step 3, inputted the 'interactive item of convenience and transaction safety' into it. The result of the analysis showed that at the final step, the amount of R2 increased (ΔR2 = 0.017), and the interactive item of convenience and transaction safety (convenience x transaction safety) was statistically significant at p < 0.05. The sub-hypothesis H2b was accordingly supported. For the verification of H2c, ’price’, ‘transaction safety’, and their interactive item (price x transaction safety) were inputted into regression equation with the word-of-mouth intention of users in sequence. As the result, the amount of R2 increased (ΔR2 = 0.014), and the interactive item of price and transaction safety was statistically significant at p < 0.05 at the final step. Therefore, the sub-hypothesis H2c was supported. Finally, hypothesis 2 of the research was partially supported. Table 7 presents the results of the analyse.
Table 7: Moderating effects of transaction safety on the effect relationship between OTA service quality (convenience and price) and the word-of-mouth intention of users
* P<0.05 ** P<0.01
To verify H3b and H3c, the researchers performed hierarchical multiple regression analysis in the same way as the above analyses for H2b and H2c. And got the results like at Step 3 of H3b case, even though the amount of R2 increased very slightly (0.002), the interactive item was insignificant at p < 0.05, while in case of H3c, no increase of R2 was made at the stage. Therefore, Hypothesis 3 of the study was not supported (refer to Table 8).
Table 8: Moderating effects of reputation on the effect relationship between OTA service quality (convenience and price) and the wordof-mouth intention of users
* P<0.05 ** P<0.01
5. Conclusions
This paper analyzed the relationship and degree of relationship between online travel agency (OTA) service quality and the anecdotal intention targeting the subjects who used OTA services, and verified the moderating effects of 'transaction safety' and 'reputation' each on the process. The findings would have important implications not just only for OTA but also for tourism-related enterprises and service providers. The major findings of the study are as follows:
First, the two factors of OTA service quality (convenience and price) had positive effects on the anecdotal intention of users, and convenience had the most significant impact. These results partially support findings of the previous studies that verified the relationships among website, travel agency, and tour-information service quality and users‘ behavior intention (Mcknight et al., 2002; Hor, 2014; Lee, 2018).
Next, this research also verified the moderating effects of the transaction safety and the reputation on the effect relationships between above two factors, convenience and price, and the anecdotal intention of users.
The results showed that some statistically significant differences among the effect degrees of the two independent variables on the dependent variable do exist according to the safety level of transaction on OTA website, which means that the variable, transaction safety, has moderating effects on the above relationships. The moderating variable, reputation, however, didn‘t have any effect on the relationships differently from the case of transaction safety.
And the results showed that there exist some statistically significant differences among the effect degrees of the two independent variables on the dependent variable according to the safety level of transaction on OTA website, which means that the variable, transaction safety, has moderating effects on the above relationships.
Based on the findings of the study, this paper offers the following practical suggestions to OTA and tourism-related enterprises and service providers.
Firstly, price competitiveness was a major factor in service quality when customers use OTA. Accordingly, it is necessary to explore reduction measures for distribution costs based on prices. In order to meet such needs, OTA must ensure price competitiveness through promotions and design systems such as recommendation system for high-end products to attract customers.
Secondly, convenience emerged as another factor in improving word-of-mouth intention of OTA customers. Services based on devices such as laptops, tablet PCs, and mobile devices need to be regularly improved through big data analysis. For example, there is a growing need to deploy a user interface that focuses on quick-response actions that may arise due to the untact services, immediate accessibility, and ease in membership registration, purchase and refund procedures. In addition, various tour products and tourism information are crucial to enhance convenience for product selection. In turn, users of online travel agencies manage their reviews since users value the number of purchases or review information of other users.
Finally, transaction safety transpired as an adjustment variable in the relationship between service quality and word-of-mouth intention of OTA. Therefore, it is important to ensure stability of the platform itself. Confidence in the system is imperative due to the heightened sensitivity over sharing personal information during online transactions. In turn, OTAs have to select valid system providers to ensure customer confidence in online transactions and conduct periodic system inspections. Broadly, transaction safety and stability would be guaranteed through public-private partnership in development of systemic regulations and programs that ensure customers to purchase goods more safely online.
This study also presents certain intellectual implications.
This study extracted five measurable components (information, convenience, price, customer service, customer relationship management) that dictate service quality in Online Travel Agencies, building upon comprehensive analyses presented and verified in the previous studies. Such components were reassessed and recomposed based on the nature of previous factors in service quality and that of in Online Travel Agencies. As a result, such measurement scale may offer fundamental materials to formulate feasible marketing strategies in various forms.
Despite the implications above, this study has shown its limits; hence, this study proposes the following for research hereafter.
Firstly, the response target of this study showed limitations in the lack of sample diversity in nationality. Because of the nature of online use, OTA does not limit its users based on nationality. Therefore, this study proposes that samples of various nationalities be obtained in subsequent studies.
Next, while this study did succeed in verifying transaction stability and effects of reputation adjustment within the relationship between OTA service quality and word-of-mouth intention, this study showed some limitations in answering the question of differences according to OTAs business type. OTA focuses in specific businesses such as aviation, accommodation, tour & ticket, which suggests that its users‘ intentions would reflect such variation as well.
Finally, this study sought to add depth to the studies of traditional word-of-mouth intentions but also on future studies of online word-of-mouth intentions, building on Kang (2018) who suggests that customers actively search and obtain online information and their willingness to transmit information greatly increases in online space.
Funding for this paper was provided by Namseoul University
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