Identifying the relationship between Travel Agent’s Web Service Quality and E-brand Reputation

Abstract

E-travel websites have progressively played a significant role in the tourism industry because travelers can easily book a holiday trip anywhere at their fingertips. However, studies related to the relationship between e-service quality and e-reputation of travel agency websites are limited. This study investigates the components of e-service quality of travel agency websites and explores how e-service quality may influence e-reputation. Based on hierarchical factor analysis, website security, special deal giving, the website’s convenience, and booking flexibility were assigned as the second-order factors of e-service quality. A sample of 444 online users was collected using online questionnaires that were distributed via a social media platform (Facebook) and by email. Several analytical methods were performed to evaluate the associations among the hypothesized variables, including factor analysis and structural equation modeling (SEM). The empirical research results revealed that website convenience, booking flexibility, special deal, and website security all significantly influence e-brand reputation. Among those four factors, website convenience and booking flexibility were found to be the most influential factors. In terms of convenience, the results of our study suggest that a website should provide user-friendly environments and functions to help customers search for what customers need easily and quickly. Additionally, we found that e-travel agents may enhance booking flexibility by providing flexible returns or exchange policies to allow customers to change their itineraries. The adoption of these practices will enhance travel websites, and the yield will be improved in both service quality and e-brand reputation.

Keywords:

• E-brand reputation
• Website quality
• Travel agent

1. Introduction

During the last two decades, the internet has become the fastest-growing communication tool in history. It cannot be denied that this rapid growth has changed businesses in a variety of ways. In the business field, the internet can create value for consumers in terms of transactions. For instance, they conveniently deliver products or services, give details and reviews from existing consumers, enable consumers to compare prices of the same product from different websites, and allow consumers to use and enjoy shopping anywhere and any time (Stewart & Zhao, 2000). In many service industries, they view the internet as an opportunity that can be used to promote their businesses or to communicate with their customers (Kaynama & Black, 2000). The travel industry has been affected by the growth of e-commerce businesses (Kim et al., 2007). Hence, online travel industry competition has also increased following the growth of the e-commerce market (Nusair & Kandampully, 2008) and the introduction of specialized tourism-related services that can encourage more customers within the smart marketplace (Werthner & Ricci, 2004). In general, online travel suppliers provide reservation systems and destination guides, including accommodation, transportation, sightseeing activities, and foods (Naruetharadhol et al., 2020) to their customers who can choose their reservations, or customers can compare fares across airlines and hotel reservations (Kaynama & Black, 2000).

In the era of the internet, the travel industry has become a more interesting area for researchers and marketers to study because of the increasing number of travel websites. Nowadays, online travel websites have almost totally replaced traditional brick and mortar or mall-based travel agencies (Nusair & Kandampully, 2008). In terms of service providers for the tourism market, it cannot be denied that service quality is an essential strategy towards a successful business and is a key element to survival in a highly competitive environment (Abd-El-Salam et al., 2013; Phonthanukitithaworn et al., 2019). The growth of online services has altered the interaction between businesses. The emergence of service quality models demonstrates how the process of providing services has shifted from traditional to IT-based customers. Service quality is the strategy that an organization uses to maintain a positive relationship between customers and the company. In general, service quality is simply required to fulfill a customer’s satisfaction, and is also used as a means for a customer to judge the quality of an organization (Little & Little, 2009). In the internet era, it is very crucial for travel websites to continuously improve web service quality to survive in this competitive business environment. On the other hand, reputation is another vital component of the market strategy that researchers have emphasized. Sarstedt et al. (2013) defined reputation as the image of an organization that can identify the organization’s aim to attract new customers, keep existing customers, neutralize competitors’ actions, and gain more market share, profits, and survival in the market.

During the last two decades, the e-travel industry has become a much more powerful tool for travelers because they can easily book a holiday trip from the comfort of their own home. Moreover, travel websites are improving their marketing strategies to influence customers to book a holiday on their website. Travel websites are providing holiday information, including hotels in the area, local landmarks, and weather forecasts, that can motivate customer to book a holiday on the internet. Understanding consumer behavior on e-commerce will help online travel agency services build a good strong brand reputation (Wong & Law, 2005). According to Abd-El-Salam et al. (2013), service providers act as their companies’ ambassadors because they represent the absolute standard of service-related quality in a customer’s mind. Hence, online travel agents need to understand how web service quality is related to e-brand reputation, which impacts the firms’ survival in the long run.

Nevertheless, empirical studies on the influence of travel agents’ web service quality and e-reputation are scarce. Explicitly, a research question about how travel agents’ web service quality may influence e-reputation remains unaddressed. Recent studies aimed to investigate the attributes of e-service quality (Muzakir et al., 2021; Pourabedin, 2021) and their effects on customer satisfaction, but they neglected to address the important issue of e-reputation. Muzakir et al. (2021) examined data gathered from online customers using the second-order structural equation modeling technique and discovered that e-service quality attributes included site design, fulfillment, customer support, security, efficiency, and incentives. Other recent empirical research suggested that e-service quality influences customer satisfaction, trust, and repurchase intention (or loyalty) (Anser et al., 2021; Vatolkina et al., 2020; Wan Jasni et al., 2020). On the other hand, several studies in online services investigated the roles of e-reputation, but they failed to identify the connection with e-service quality. Most recently, Moccia et al. (2021) discussed many big data analytic methods for monitoring, tracing, improving, and raising e-reputation in the fintech sector, as well as effective mechanisms for improving e-reputation. Additionally, e-reputation was regarded as a intangible asset and was used as an independent variable to influence merger and acquisition value-creation (Chalençon et al., 2017). E-reputation has also been investigated at the individual level. In the sports industry, Castellano and Khelladi (2016) investigated the effect of social networks on athletes’ e-reputation. Hence, further investigation is needed to uncover the relationship between web service quality and e-brand reputation in the travel industry.

In this study, the theoretical guidelines by Nusair and Kandampully (2008) regarding six dimensions of travel website quality will be adapted to the e-travel agent setting. Derived from content analysis, the dimensions of the travel website consist of navigability, playfulness, information quality, trust, personalization, and responsiveness (Nusair & Kandampully, 2008). This research paper poses two primary objectives. The first objective is to investigate what determinants influence the travel agent’s web service quality. The second objective is to explore how web service quality impacts the e-brand reputation of e-travel agent websites. Employing 444 e-travel agent users in Thailand, a higher-order (or second-order) factor approach based on the structural equation modeling analysis was applied to capture the stated structural relationships among different variables. The results of this research will be very beneficial to internet travel agencies. Our proposed model may be utilized as a guideline for managing travel agency websites in order to achieve e-reputation.

This paper is organized, as follows: section 2 begins with a review of the literature on the demographic characteristics of the travel website customers used as a study sample; section 3 outlines the research method involving sample and data gathering, as well as statistical models and testing; section 4 presents the analyses and findings; and, section 5 summarizes this research article by discussing major conclusions drawn from this study, as well as suggesting policy implications.

2. Literature review

Despite the importance of service quality to the service sector, evaluating service quality presents challenges to internet travel agencies due to the specific features of services. The elements of e-service quality are broadly defined, including all stages of consumer contact through web services and sales (Vatolkina et al., 2020). Hence, services providers require a specific framework for understanding and assessing service quality (Phonthanukitithaworn et al., 2020). On the other hand, reputation is the most important measure of the reliability of information sources. E-reputation may influence search engine results, increases customer confidence in e-banking services, and influences consumer decisions, especially in the tourism industry (Duradoni et al., 2021). However, the relationship between travel agent’s web service quality and e-brand reputation remains unclear and requires further investigation. This section discusses the theoretical background, model selection, and hypothesis development of this study, encompassing the association between e-service quality and e-reputation.

2.1. Theories related to web service quality

Studies related to e-service quality have concentrated on either the general e-service or a particular kind of e-service, such as e-travel, e-commerce, or digital platforms (Vatolkina et al., 2020). The concept of E-service quality involves the relationship between customer expectations and evaluations of service experiences. E-service quality is expressed through customer confidence, performance efficiency, and other web services that a business offers to its customers online (Mou et al., 2017). E-service quality can be measured by the quality of the virtual community website, defined by a website’s success and customer satisfaction with the e-commerce industry. In general terms of business, e-service quality is mainly influenced by consumption decisions (Lee & Lin, 2005). When consumers judge an organization’s website to be trustworthy, they perceive e-service (Kim et al., 2016). E-service quality can benefit businesses in many ways. Roger-Monzó et al. (2015) found that e-service quality has been used as a strategic factor by travel websites to increase their competitiveness. Abd-El-Salam et al. (2013) mentioned that the benefit of high levels of e-service quality is increasing market share and profitability for a company. This is supported by the study of Ali and Raza (2017) that found long-term business performance to be dependent on good quality products and services. Also, Chan et al. (2017) indicated that service quality is a critical factor that influences customers to switch brands. Equally, if not more important, service quality also helps companies maintain existing customers.

Recent advances in e-service quality modeling have been characterized by two established fields of study, 1) technology acceptance theories, and 2) services quality theories (Vatolkina et al., 2020). In technology acceptance models, quality attributes are related to behavioral factors originally derived from the theory of planned behavior (TPB), and from the theory of reasoned action (TRA). Examples of these factors include perceived usefulness, perceived ease of use, intention to use, and usage behavior (Gbongli et al., 2019; Lin et al., 2019; Naruetharadhol et al., 2021). Nevertheless, service quality models involve quality attributes and customer experience variables, such as customer expectation, satisfaction, and loyalty (Phonthanukitithaworn et al., 2020; Swar & Panda, 2021; R. Zhou et al., 2019). Most of these studies are based on and modified from the SERVQUAL model (Parasuraman et al., 1998; R. Zhou et al., 2019). The following discusses the significance of technology acceptance theories and service quality theories, leading to the adoption of our research model.

Technology acceptance models have extensively evolved based on the concept of diffusion of innovation (DOI), describing technology attributes that cause an individual’s technology adoption (Vatolkina et al., 2020). According to Rogers (1983), the DOI theory suggested the five perceived characteristics may be used to assess an individual’s attitudes towards an innovation: relative benefit, compatibility, complexity, trialability, and observability. Then, Goodhue and Thompson (1995) proposed the task technology fit (TTF) model, which explains the critical nature of technology’s conformance that improves the probability of adopting e-services. Based on the DOI and TPB theories, the technology acceptance models have included two additional crucial variables: perceived usefulness and perceived ease of use. The subsequent models involve TAM (Davis, 1989), TAM2 (Venkatesh & Davis, 2000), TAM3 (Venkatesh & Bala, 2008), UTAUT, and UTAUT2 (Chang, 2012), which confirmed that various technology attributes influence behavioral intention to use e-services. Such technology acceptance models are effective in explaining and predicting a consumer’s decision about using an e-service. According to Vatolkina et al. (2020), technology attributes from such technology acceptance models are called “starting quality”, which may be adapted to research in travel agent’s web service quality. However, no evidence was found regarding the relationship between e-service quality and e-reputation based on technology acceptance models. Hence, the authors decided that the technology acceptance models may be partly modified and used as a guideline in developing the study’s research framework, not the whole.

Theories of service quality are based on the expectation-conformation (ECT) theory, which posits that satisfied customers would continue using the product or service, whereas dissatisfied customers will discontinue usage (Oliver, 1980). Then, Parasuraman et al. (1998) created a popular service quality assessment scale called the SERVQUAL model, which was created in 1985, and then distilled down to five dimensions in 1998 (tangibles, reliability, responsiveness, assurance, and empathy). In previous years, many researchers have investigated the service quality-related dimensions of online travel websites that can contribute to customer satisfaction. Kaynama and Black (2000) introduced seven dimensions of the EQUAL model to analyze the quality of the websites based on customer perspectives. These seven dimensions have been used to assess service quality, including composed content and purpose, accessibility, navigation, design and presentation, responsiveness and feedback, background information, and personalization and customization. Moreover, the information system success (ISS) theory that evolved from the ECT theory suggests that e-service quality (system quality, information quality, and quality of services) determines customer satisfaction and intention (Petter et al., 2008).

Although the SERVQUAL scale has been utilized in the context of e-service before, this adaption has been challenged since e-service features differentiate it from conventional service (Herington et al., 2009). In the e-travel industry, Nusair and Kandampully (2008) investigated the service quality-related features of online travel websites that most contribute to online customer satisfaction. Expanding from the EQUAL model, Nusair and Kandampully (2008) studied six dimensions consisting of the navigability of the website, playful features, information quality influencing an online reservation, trust from the customers, meeting customers specific needs, and willingness to help customers who run into problems. This study suggested the trust, responsiveness, and navigability dimensions to be the three main dimensions that enhance customer satisfaction. On the other hand, Wen (2009) focused on the online purchase of travel products, including three factors: customer trust, customer satisfaction, and customer perception based on the theory of planned behavior. Wen (2009) mentioned that customers perceived that e-commerce websites should have high-quality information, systems, and service. Additionally, Mohd-Any et al. (2015) focused on identifying the six dimensions that capture the value experience of customers when using travel websites, including utilitarian value, emotional value, social value, perceived control and freedom, value for money, and user’s cognitive effort. In the area of online travel websites, Ajay Kaushik and Potti Srinivasa (2017) studied the effect of website quality on customers’ purchase intention. They focused on the four main areas of website quality, including service quality, system quality, information quality, and website design, that have a positive impact on customer satisfaction and that can lead to customer purchase intention. Additionally, 3 attributes of e-service quality (web design, interface functionality, and support system) were used to evaluate the user’s satisfaction with air cargo management system among 3 different geographical areas: Asia, Europe, and North America (Naruetharadhol & Ketkaew, 2018). Recently, Jou and Day (2021) investigated the attributes of service quality of hotel online booking using the Kano model and suggested the following five attributes: functionality, fulfillment, customer relationship, content, and security. However, these attributes are not novel because they were involved in the study of Nusair and Kandampully (2008). Also, the Kano model provides limited application regarding causal relationships among the variables of interest.

The aforementioned studies and their findings indicate that studies in diverse web service environments yield different results. From the literature, Nusair and Kandampully (2008) demonstrated the dimensions of travel agent web service quality using online service features, which help illustrate explicit business activities occurring around the travel websites. However, the attributes of service equality proposed by Wen (2009), Mohd-Any et al. (2015), and Ajay Kaushik and Potti Srinivasa (2017) are abstract and broader in scope. In this study, we decided to apply the service quality attributes from Nusair and Kandampully (2008). However, this service quality model was derived using the qualitative approach (content analysis). It remains vague what and how many indicators are needed to statistically explain the travel agent’s web service quality based on Nusair and Kandampully (2008). Therefore, further quantitative study is needed.

2.2. Four factors of Travel Agent’s Web Service Quality

This paper proposed 4 factors describing the travel agent’s web service quality derived from Nusair and Kandampully (2008), suggesting that website quality consisted of 6 dimensions. To discover the 6 dimensions of website quality, Nusair and Kandampully (2008) employed content analysis, which is a qualitative research method. Those dimensions involved navigability, playfulness, information quality, trust, personalization, and responsiveness (Nusair & Kandampully, 2008). However, this paper proposed that there were only 4 factors relating to the travel agent’s web service quality, which included security, special deal, convenience, and booking flexibility. These 4 factors would be redefined quantitatively using a quantitative method (Factor Analysis). The results of factor analysis are shown in the results section. Table 1 describes the connection among the 6 dimensions of website quality (Nusair & Kandampully, 2008) and the 4 factors of travel agent’s web service quality (proposed by the authors 2020). The questions relating to the travel agent’s web service quality were created using the theories and concepts derived from the following literature.

Table 1. The connections between the six dimensions of website quality (Nusair & Kandampully, 2008) and the four factors of Travel Agent’s Web Service Quality (the authors’ proposed model 2020)

Six Dimensions of Website Quality (Nusair & Kandampully, 2008)	Questions	Four Factors of Travel Agent’s Web Service Quality (Authors 2020)
1. Trust	The website has high-security levels in terms of personal data.	1. Security of Website
	The website has a trustworthy and secure payment method.
	The website has antivirus protection.
2. Personalization 3. Responsiveness	The website offers you good value for money.	2. Special Deal
	The website offers you extra benefits, such as a free room upgrade and breakfast.
	The website offers midweek and senior citizen breaks.
	The website offers half-price kids rates or kids go free deals.
	The website offers repeat customers a reward system.
4. Navigatibility 5. Playful Features 6. Information Quality	The website is user-friendly and easy to make a reservation.	3. Convenience of Website
	The website provides a user-friendly application for your smartphone.
	The website provides hotel and package experience pictures.
	The website provides clear information on local attractions, amenities, and estimated journey time.
N/A	The website provides flexibility if it is necessary to change or cancel your travel plans without incurring an extra fee.	4. Booking Flexibility (New Factor)
	The website provides you with options for making a reservation and paying later.

Source: Adapted from authors, 2020

2.3. Security of website (SOW)

E-commerce system security is the factor that most strongly influences customer hesitation to purchase online because sensitive information, such as credit card detail, personal ID, and contact details, are provided. Thus, this situation creates a barrier to obtaining products and services online (Kim et al., 2007). As Wong and Law (2005) defined, security is commonly a significant concern for online purchasers. The payment method is an essential attribute that affects the overall quality of travel websites. Ranganathan and Grandon (2002) reported that security and privacy are the key factors that are changing online sales. According Spar and Bussgang (1996), security and privacy are directly related to the trust customers have toward an online travel website. Trust is a party’s promise that is reliable and that it will fulfill its obligation in an exchange relationship (Wen, 2009). Trust is a crucial dimension for the success of online commerce because trust in the online business can improve a customer’s willingness to accept the potential threat in an online transaction process based on his positive expectations of the online business behaviors (Nusair & Kandampully, 2008). Shiau and Luo (2012) found that trust plays a vital role in human behavior when customers are confronted with risk and are unable to control the situation. The significance of trust is imperative to the success of new technologies such as e-travel. Ponte et al. (2015) proposed that it is difficult for e-commerce sellers to convince customers that they will meet their commitment to protecting customer privacy and securing sensitive customer information when conducting online transactions. Hence, good faith in online sellers can build trust that will encourage customers to use online transactions. Meanwhile, Ponte et al. (2015) reported the quality of information on the website, website security protection, and protection of customer privacy to be the three most important areas of trust that lead to the purchase of travel packages online. In this respect, the following questions and codes were created and used for factor analysis.

• The website has high-security levels in terms of personal data (SOW9).

• The website has a trustworthy and secure payment method (SOW10).

• The website has antivirus protection (SOW11).

2.4. Special deal (SD)

In the case of an economic downturn, prices will become a more critical factor in purchase decision-making (Wong & Law, 2005). As Law and Wong (2003) studied, price is a crucial factor in customer decision-making when buying hotels and travel services online. Price is the most sensitive factor for consumers in the marketing mix, and is a significant attribute when consumers consider purchasing. Customers are willing to buy when they find the lowest price on a particular website (Kim et al., 2007). Chiang and Dholakia (2003) supported that price can be defined as customers’ perception relevant to the quality of the product they are purchasing. Shen (2018) pointed out that perceived customer value is one of the most significant factors that influence online customers to come back to the website. It was found that pricing and personalization possibilities are the main factors that affect customer value.

On the other hand, customers consider the price level high, low, or fair by comparing the price with the quality of the product. Kaynama and Black (2000) studied that some e-travel websites allow users to compare fares across hotels or airlines, which can yield significant value for price-sensitive travelers. Degeratu et al. (2000, p. 25) found that online price sensitivity is slightly lower than offline price sensitivity. However, a deal offering a free room upgrade or free breakfast are other factors considered when customers decide whether to make a reservation on a particular travel website (Wong & Law, 2005). Moreover, special offers, such as senior citizen breaks, half-price kid’s rates, and repeat customer reward systems, are other means for travel websites to promote themselves and gain more customers. It is very common for senior citizens to be offered discounted prices and special offers from hotels when traveling during midweek. The following questions and codes were generated.

• The website offers you good value for money (SD12).

• The website offers you extra benefits, such as free room upgrade, breakfast (SD13).

• The website offers midweek and senior citizen breaks (SD14).

• The website offers half-price kids rates, or kids go free deals (SD15).

• The website offers a repeated customer reward system (SD16).

2.5. Convenience of website (COW)

When various websites contain information about similar products, the quality of the sites can influence customers to choose one particular website over the rest to make their required purchase (Ponte et al., 2015). Nusair and Kandampully (2008) found that the convenience of a site can be measured by the number of clicks it takes to get relevant information to the user. The navigation of a website is essential to build up a good quality website. First, the website should provide functions to help customers search for what they need easily and quickly. Additionally, the site should have the fewest possible steps so customers can obtain information quickly. Lastly, the website should provide a user-friendly environment that allows customers to control the process by moving back and forth through the pages. Szymanski and Hise (2000) studied that customers’ perceptions of a website directly affect their satisfaction. Kaynama and Black (2000) suggested that travelers reserved through online travel websites for two reasons, convenience, and control, such as that the e-travel company provided a mobile application. Moreover, customers can spend less time on the online booking process because the websites are always providing destination guides, travel tools (maps, area information, currency exchange, transportation, weather conditions), accommodation prices, and online reservation systems all on one website. The convenience of the site is the fundamental element of a successful transaction in the online business what (environment?) (Kim et al., 2007). The questionnaire included the following.

• The website is user-friendly; it is easy to make a reservation (COW17).

• The website provides a user-friendly app for your smartphone (COW18).

• The website provides hotel pictures or package experience pictures (COW19).

• The website provides clear information about local attractions, amenities, and estimated journey time (COW20).

2.6. Booking flexibility (BF)

Booking flexibility refers to how easily a customer can make changes to the itineraries, the travel time, or the number of travelers while making the reservation or after making the reservation, but during the time before traveling (Kim et al., 2007). Booking flexibility was newly proposed to the travel agent’s service quality construct by the authors. Dholakia and Bagozzi (2001) argued that digital media has been affecting consumer behavior in an unprecedented way as a result of the unique attributes of the internet, including the speed of access and flexibility in representing information. Hence, the e-travel agent uses this opportunity to offer customers the ability to make a reservation, but pay at a later date. According to Nusair and Kandampully (2008), when websites provide flexible returns or exchange policies, it encourages customers to purchase, and it makes customers feel that they have a lower risk of something adverse occurring during that online purchase. Law et al. (2004) reported that customers believe that e-travel suppliers are more flexible and offer more alternatives compared to traditional travel agents. Jiang et al. (2013) examined the benefits of online shopping. For example, online shoppers do not usually have to join long queues to pay for their product, they are offered flexible payment methods, and customers do not need to travel to a shop to get their product or service. Instead, customers can stay home while waiting for the product to be delivered to them. The following two questions and codes were derived.

• The website provides flexibility if it is necessary to change or cancel your travel plans without incurring an extra fee (BF24).

• The website provides you with the option of making a reservation and paying later (BF25).

Figure 1. Hypothesized factors contributing to Travel Agent’s Web Service equality using second-order factor model.Source: Figure created by authors, 2020

According to the above literature and Figure 1, the following hypotheses were developed to describe the travel agent’s web service quality using second-order factor model.

Ha1: Security of Website is positively related to Travel Agent’s Web Service Quality.

Ha2: Special Deal is positively related to Travel Agent’s Web Service Quality.

Ha3: Convenience of Website is positively related to Travel Agent’s Web Service Quality.

Ha4: Booking Flexibility is positively related to Travel Agent’s Web Service Quality.

Based on structural equation modeling, the primary construct was named Travel Agent’s Web Service Quality. This construct has 4 sub-constructs, namely: Security of Website, Special Deal, Convenience of Website, and Booking Flexibility. Security of Website involved 3 indicators (SOW9, SOW10, SOW11). Special Deal consists of 5 indicators (SD12, SD13, SD14, SD15, SD16). Convenience of Website related to 4 indicators (COW17, COW18, COW19, COW20) and Booking Flexibility associated with 2 indicators (BF23, BF24). Based on the literature and Figure 2, the following hypotheses were established. Please note that the Security of Website, Special Deal, and Convenience of Website constructs were modified from Nusair and Kandampully (2008). However, the Booking Flexibility construct was newly proposed to the model by the authors.

Figure 2. Hypothesized indicators relating to E-Brand reputation.Source: Figure created by authors, 2020

2.7. E-brand reputation (EBR)

In the internet era, where the number of websites is increasing almost as fast as the population of the world, a website’s reputation has become the strongest factor influencing customer interaction. E-brand reputation is denoted as the covenant facet of a company reflecting a profound promise to its customers, which makes it essential to the success of business performance (Balmer, 2010). E-brand reputation improved brand differentiation and customer loyalty (Morrison & Crane, 2007). As customer expectation about the brand is met, a positive brand image can be generated in the minds of customers (Argenti & Druckenmiller, 2004). Nguyen and LeBlanc (2018) suggests that the reputation of a company is seen as a mirror image of a company’s history of service to its customers, the quality of the information offered, and the performance of staff all in comparison to competitors. Hence, service quality is crucial to a company’s image and reputation.

Referring to Shiau and Luo (2012), reputation on the internet is a social product and a social process that is perceived to increase reputation affected by information sharing from other sources. Reputation is a crucial dimension that significantly influences individual knowledge sharing in a community. Reputation as an outcome of knowledge sharing may be seen as a belief that can be transmitted within a social network. According to Grabner-Kraeuter (2002), reputation is an integral part of determining the willingness of others to enter into an exchange with a given business. In terms of business, firms can use their reputation to encourage customers into a transaction. In the online market, reputation can be easily managed because of the ease of changing identities and the low cost of collecting and distributing information online. According to L. Zhou et al. (2007), an excellent reputation for online websites can make customers more confident in online transactions and can have a positive effect on customers’ online shopping attitudes and behaviors. Biswas and Biswas (2004) suggested that generally, customers tend to rely on a website’s reputation to lower the risk perception when conducting transactions online. The questionnaire included the following.

• The website has recent reviews from people who made a booking through that website (EBR21).

• The website is highly recommended by a friend or family (EBR22).

• You heard about the website through advertisements, such as TV ads, radio, and billboards (EBR23).

Based on the literature and Figure 2, the following hypotheses were established.

Hb1: EBR21 is positively related to E-brand Reputation.

Hb2: EBR22 is positively associated with E-brand Reputation.

Hb3: EBR23 is positively associated with E-Brand Reputation.

2.8. Impact of Web Service Quality on E-brand Reputation

Mohd-Any et al. (2015) explained that with the increasing number of e-travel services, customers have become more experienced and sophisticated and can organize their travel smartly and as cheaply as they can find. Intense competition among travel websites in the e-travel industry means that companies need to understand how their quality of service can affect their reputation. The web quality and brand reputation can also affect whether customers would come back to book travel through their website and whether they can gain new customers.

Abd-El-Salam et al. (2013) found that a company’s image and reputation can play a critical role in a customer’s decision-making when selecting one brand over another. Reputation is an essential factor representing a company’s capacity to honor the promises that it makes to customers (Nguyen & LeBlanc, 2018). In other words, Liu and Lee (2016) suggested that reputation reflects customer perception of service quality, which is linked to the company brand. This means that service quality and reputation are directly connected, and that both are crucial factors that influence whether a company is a success or a failure. Chan et al. (2017) mentioned that positive service quality and a good reputation would create a switching barrier for customers when deciding whether to switch to another service or provider. Hence, the central hypothesis (H1) and the conceptual model for this study are demonstrated below (see Figure 3).

H1: Travel agent’s web service quality positively influences e-brand reputation.

Figure 3. Proposed structural model.Source: Figure created by authors, 2020

3. Method

1. Data collection

The empirical data included and analyzed in this research was obtained using a mixed sampling technique to obtain a total of 450 completed questionnaires from respondents. This mixed sampling approach employed a quota sampling survey (300 respondents) and an online survey (150 respondents) using Google Forms (Ketkaew et al., 2020a). First, the quota sampling approach was utilized to ensure that the data were collected from respondents who are tourists. This approach may be used to obtain representative data from the population of tourists with online travel service experiences. We selected Bangkok and Chiang Mai, Thailand, since they are both densely inhabited regions with a lot of visitors (Ketkaew et al., 2020b, 2019a). The data was collected from 150 respondents from hotels in Bangkok and Chiang Mai, and from 150 respondents encountered at Suvarnabhumi Airport. We assigned a quota of 100 respondents from 10 hotels in Bangkok (10 respondents each), and 50 respondents from 5 hotels in Chiang Mai (10 respondents each). Data from 150 respondents in the airport were collected based on the floor of the airport where they were encountered (50 respondents from each of 3 floors).

For quota sampling, we use the intercept survey technique to collect the data because it was not specific to a group of travelers, but rather to collect data from Thai people who were willing and available to respond to the survey (Ketkaew et al., 2019b; Phonthanukitithaworn et al., 2020). Finally, the online survey developed using Google Forms was employed to gather data from the remaining 150 respondents. An online survey is a non-probabilistic sampling method. Though, it is straightforward to access by the participants because it depends on who voluntarily took part in the survey (Kitchenham & Pfleeger, 2002). According to Evans and Mathur (2005), online surveys have several major strengths. First, the global reach of the study to obtain data from respondents living in different parts of a country or of the world. Second, an online questionnaire is flexible and can be expanded into several formats, including social networks or emails with a link to the survey. Another major strength is simply the low cost involved compared to circulating paper surveys. Moreover, social media (Facebook) was another distribution tool used to publish surveys and collect data in Thailand. Kuhn (2017) mentioned that Facebook is a costless channel to collect survey data; it can save time, and can be specific to a group of participants.

Of the 450 expected respondents, 294 were finally obtained from the onsite quota sampling approach, and 150 were fully attained online. We excluded six questionnaires due to incomplete information. Hence, the data was collected from a total of 444 respondents.

According to Table 2, most of the participants are female, which accounts for 59.5%, 38.7% of them are male, and 1.8% preferred not to say. 42.5% of respondents are aged between 18–25 years. People who are aged from 26–35 years old, which takes up 22.5%; 27.9% of respondents were aged from 36–55 years of age; and, the smallest group was the over 56’s, which accounts for 7.2%. For the marital status of respondents, 61.3% are single, 36% are married, 1.8% are divorced, and 0.9% are widowed. Concerning education, most participants (35.1%) reported having a postgraduate degree, 32.4% have an undergraduate’s degree, 24.3% have a learning certificate, and 5.4% have achieved secondary school attendance. Respondents reporting having achieved a certificate or professional qualification and diploma each accounted for 0.9%, respectively. The majority of respondents reported being employed (62.2%), 23.4% are students, 8.1% are self-employed, 4.5% are unemployed, and 1.8% of participants are retired. With respect to annual income, 43.2% have an income less than ฿240,000, 41.4% make ฿240,001—฿480,000, 9.95% make ฿480,001—฿720,000, and 5.45% have an annual income more than ฿720,001. For demographic information, most participants are single, aged between 18–25 years old, and employed. Most respondents’ income is considered to be at the low to the middle level of income on the scale.

1. Development of Measures

Table 2. Demographic information of respondents (n = 444)

Measure	Items	Frequency	Percentage
Gender	Female	264	59.5
	Male	172	38.7
	Prefer not to say	8	1.8
Age	18–25 years old	188	42.3
	26–35 years old	100	22.5
	36–55 years old	124	27.9
	56+ years old	32	7.2
Marital status	Single	272	61.3
	Married	160	36
	Divorced	8	1.8
	Widowed	4	0.9
	Separate	0	0
Education background	Secondary school	24	5.4
	Leaving certificate	108	24.3
	Undergraduate	144	32.4
	Postgraduate	156	35.1
	Certificate	4	0.9
	Professional qualification	4	0.9
	Diploma	4	0.9
Occupation	Student	104	23.4
	Employed	276	62.2
	Self-employed	36	8.1
	Unemployed	20	4.5
	Retired	8	1.8
Annual income	0–240,000 ฿	192	43.2
	240,001–480,000 ฿	184	41.4
	480,001–720,000 ฿	44	9.9
	More than 720,001 ฿	24	5.4

Source: Adapted from authors, 2020

To assess e-service quality factors that have an impact on travel website reputation, the research methods used were data collection via a survey using questionnaires, and data analysis using quantitative methods. Leung (2015) argued that quantitative research is completed according to primary numerical data and statistical interpretations under a reductionist, logical, and rigidly objective paradigm. Thus, this study uses a questionnaire to explore numeric data to identify the main e-service quality factors that influence a travel website’s reputation. Quantitative research involves the collection of numerical data, and presentation of the relationship between theory and research as deductive. In this study, an online survey was used to perform data collection of Thai customers.

The data in this study were processed primarily in Thailand to assess e-service quality impact on e-reputation. The collected data is information from Thai internet users selected based on an online questionnaire survey distributed via social networks, such as Thai Facebook groups and emails. The draft survey questionnaire followed the four identified factors of e-service quality on travel websites. A total of 32 measurement items were generated, and those items were divided into three sections. The first section focuses on customer experience on travel websites, and all questions are related to customer behavior related to online travel websites that may lead to the influence factor of e-service quality on the travel website’s reputation. In the second section, the survey provided a linear scale of the five factors of e-service quality to allow individual participants to describe their view of an e-service ranging from not important to very important. The linear scale was composed of five levels of importance (1 = not important, 2 = less important, 3 = Neutral, 4 = important, and 5 = very important). The last section consists of demographic profile questions in the form of multiple-choice questions, including gender, age, marital status, education, occupation, and income. The demographic profiles were also used as a nominal variable to classify the scale.

1. Data Analysis

The research’s data analysis employed the structural equation modeling (SEM) approach. SEM contains various statistical procedures, such as path analysis, confirmatory factor analysis (CFA), casual modeling with latent variables, and analysis of variance (Byrne, 2001). The researchers performed the SEM analyses using Amos (Byrne, 2001). The SEM procedure was utilized to scrutinize the model’s assessment in two steps. The first step is to testify the validity of the second-order CFA model to test the association between each indicator and its factor, whether it is rational and trustworthy (Byrne, 2001). This step relates to the assessment of goodness of fit (GOF), convergent validity, and discriminant validity (Byrne, 2001). The second step is to scrutinize the interior structural model to test whether the complete structure is trustworthy, comprising the assessment of GOF (Byrne, 2001). Lastly, structural model analysis is performed, concluding the third step of the SEM method.

4. Results and discussion

4.1. Measurement development and factor analysis

1. Factor Analysis

Factor analysis was used to examine the validity of constructs, which allows researchers to minimize large sets of information into small factors that are important and interpretable. According to Tabachnick and Fidell (1996), factor analysis can be useful for investigating a theory of the underlying process of nature.

Tabachnick and Fidell (1996) recommended ignoring factor loadings with an absolute value of less than 0.32. According to Table 3, nearly all of the values of factor loading are higher than 0.3. There are only two items that are slightly lower than 0.3, SD14, and SD15, which have a factor loading of 0.289 and 0.191, respectively, which suggests that these two variables have minimal impact on the Special Deals factor based on the factor analysis results. However, the researchers decided to retain these two variables in the model and further perform the reliability analysis to validate the results.

Table 3. Result of Factor Analysis

Constructs	Items	Mean	Std. Deviation	Factor loading
Security of website	SOW 9	4.487	0.841	0.605
	SOW 10	4.712	0.743	0.558
	SOW 11	4.234	0.953	0.457
Special Deals	SD 12	4.523	0.761	0.521
	SD 13	4.081	0.945	0.574
	SD 14	2.856	1.361	0.289
	SD 15	2.802	1.583	0.191
	SD 16	3.487	1.182	0.482
Convenience of Website	COW 17	4.451	0.76	0.685
	COW 18	3.676	1.259	0.505
	COW 19	4.478	0.773	0.61
	COW 20	4.162	0.93	0.703
Booking Flexibility	BE 24	4.135	1.083	0.635
	BE 25	3.964	1.16	0.632
Reputation	RP 21	4.297	0.782	0.652
	RP 22	3.847	1.072	0.678
	RP 23	2.865	1.29	0.599

Source: Adapted from authors, 2020

Table 4. Result of Kaiser Meyer Olkin

Kaiser Meyer Olkin Measure of sampling adequacy	KMO	0.776
Bartlett’s Test of Sphericity	Approx. Chi-Square	806.46
	Sig.	0

Source: Adapted from authors, 2020

From Table 4, the result of Kaiser Meyer Olkin’s (KMO) result of sampling adequacy is 0.776, which, according to KMO, is a middling, but acceptable result (Kaiser, 1974). The significance of Bartlett’s test of sphericity is 0.00, which is less than 0.05, confirming that factor analysis is desirable in this research (Cerny & Kaiser, 1977).

1. Reliability Analysis

Reliability tests are imperative to data validation of variables that are suitable for use in the data analysis process.

From Table 5 above, the data were analyzed using a high-reliability analysis of survey questions by Cronbach’s alpha. Pallant (2010) suggested that Cronbach’s alpha is the most popular method used to measure reliability, and that it is typically used if there are many Likert scale questions in a questionnaire to investigate which questions are reliable enough for analysis. From Cronbach’s alpha analysis, the extent to which items in a survey are correlated with each other is shown in number format. Hence, nearly all of the results of the total correlated items are positive and higher than 0.5, and there are only five items with less than 0.5, which are SD 12, SD 13, SD 15, COW 18, and RP 21. These variables should be interpreted with caution when performing the confirmatory factor analysis (CFA) approach. Moreover, for Cronbach’s alpha, only booking flexibility is more than 0.8, which shows that booking flexibility has higher reliability than the other variables used. In other words, booking flexibility has more covariance than any of the other variables. However, all the other variables are over 0.6 for Cronbach’s alpha, which is acceptable. According to the results of Cronbach’s “if item deleted section,” it shows that booking flexibility is zero, which means that when a variable section has less than two items, the result will always be zero. Pallant (2010) argued that Cronbach’s alpha is sensitive on shorter scales. Hence, the smaller the number of “if items deleted” will have an impact on the result of Cronbach’s alpha. Cronbach’s alpha will work better if a variable has three or more items when compiling measurements.

Table 5. Results of reliability analysis

Constructs	Items	Corrected Item Total Correlation	Cronbach’s If item deleted	Cronbach’s Alpha
Security of website	SOW 9	0.714	0.588	0.778
	SOW 10	0.63	0.695
	SOW 11	0.528	0.814
Special deals	SD 12	0.087	0.685	0.637
	SD 13	0.341	0.608
	SD 14	0.588	0.468
	SD 15	0.428	0.577
	SD 16	0.523	0.517
Convenience of Website	COW 17	0.641	0.609	0.722
	COW 18	0.4	0.78
	COW 19	0.572	0.64
	COW 20	0.555	0.635
Booking Flexibility	BE 24	0.692	0	0.817
	BE 25	0.692	0
Reputation	RP 21	0.368	0.686	0.656
	RP 22	0.556	0.432
	RP 23	0.532	0.486

Source: Adapted from authors, 2020

4.2. Structural equation modeling analysis

There are two primary steps to perform a statistical test on structural equation modeling (SEM): measurement model (confirmatory factor analysis) and structural modeling (Anderson & Gerbing, 1988).

1. Measurement Model (Confirmatory Factor Analysis)

The measurement model was tested using confirmatory factor analysis (CFA). In this respect, the model was assessed for its internal consistency, reliability, convergent validity, and discriminant validity. CFA is performed by connecting all constructs with covariances (Hair et al., 1998). All constructs must involve their manifest variables before testing. CFA suggested the SD12, SD13, COW18, COW19, and RP21 should be removed from the measurement model to increase the goodness of fit. This result was also consistent with the results from the reliability analysis derived from Cronbach’s alpha test in the previous section.

1. Goodness of Fit

The results demonstrated that all the goodness of fit indices passed the thresholds according to Hair et al. (1998)—CMIN/df = 2.024 < 3; GFI = 0.882 ≈ 0.90; IFI = 0.903 > 0.90; CFI = 0.898 ≈ 0.90; RMSEA = 0.098 < 0.10.

1. Convergent Validity

This section was performed by comparing the model results with the fit indices and thresholds. AVE stands for average variance extracted (Fornell & Larcker, 1981), and CR stands for composite reliability (Hair et al., 1998). According to the Table 6, the suggested thresholds of the convergent validity measures and the calculated indicators are, as follows:

Table 6. Result of convergent validity

Construct	Indicator	Estimate	AVE	Threshold	CR	Threshold
Security of Website	SOW9	0.81	0.57	>0.50	0.8	>0.70
	SOW10	0.79
	SOW11	0.67
Special Deal	SD14	0.85	0.53	>0.50	0.8	>0.70
	SD15	0.66
	SD16	0.65
Convenience of Website	COW17	0.59	0.39	>0.50	0.6	>0.70
	COW20	0.65
Brand E-Reputation	RP22	0.82	0.57	>0.50	0.7	>0.70
	RP23	0.69
Booking Flexibility	BE24	0.9	0.71	>0.50	0.8	>0.70
	BE25	0.78

Source: Adapted from authors, 2020

All measures exceeded the thresholds except for the Convenience of the Website construct. Therefore, convergent validity was acceptable, but COW should be treated with caution. We decided to maintain the COW construct due to the literature review and the research framework.

1. Discriminant Validity

Discriminant validity is the degree to which two or more conceptually similar constructs are different. This section was examined by comparing the square root AVEs (on diagonal) with the correlations in the associated matrices (Fornell & Larcker, 1981). According to the Table 7, all constructs passed these criteria.

1. Structural Model

Table 7. Result of discriminant validity

	BE	RP	COW	SD	SOW
BE	0.842
RP	0.594	0.757
COW	0.479	0.797	0.621
SD	0.29	0.502	0.274	0.726
SOW	0.492	0.285	0.463	0.013	0.756

Source: Adapted from authors, 2020

2.1)Goodness of Fit

The results demonstrated that all the goodness of fit indices exceeded the thresholds suggested by Hair et al. (1998)—CMIN/df = 2.10 < 3; GFI = 0.867 ≈ 0.90; IFI = 0.883 ≈ 0.90; CFI = 0.878 ≈ 0.90; RMSEA = 0.101 ≈ 0.10.

Figure 4. Results of structural model.Source: Figure created by authors, 2020

2.2) Hypothesis Test Results

Table 8 reports the results of the standardized coefficient estimates and significant values for the hypothesized relationships (also see Figure 4). The results show that all factors of e-service quality in the survey have a positive and significant impact on e-reputation for travel websites. Referring to the coefficient value of reputation, it is the highest standing at 0.921, which proves the theory that e-service quality impacts e-reputation. The past empirical research (Abd-El-Salam et al., 2013; Chan et al., 2017; Liu & Lee, 2016; Nguyen & LeBlanc, 2018) also suggested that the overall level of service quality is related to and impacts upon reputation. The four variables of e-service quality used in this research that affect e-reputation the most are hereafter listed. Firstly, navigation of the website stands at a coefficient value of 0.817. Booking flexibility has the second biggest impact on e-reputation with a coefficient value of 0.667. Special deal giving has the third biggest impact on a travel website’s reputation with a coefficient value of 0.463. Lastly, the security of the website is the fourth key variable of e-service quality impacting e-reputation with a coefficient value of 0.409. According to the results above, all the hypotheses have been confirmed as accepted.

Table 8. Final hypothesis test results

Exogenous Var.	Endogenous Var.	Standardized Estimate	P	Decision
SOW	E-service quality	0.409	0.006**	Accept Hypothesis
SD	E-service quality	0.463	0.008**	Accept Hypothesis
COW	E-service quality	0.817	***	Accept Hypothesis
BF	E-service quality	0.667	***	Accept Hypothesis
RP	E-service quality	0.921	0.002**	Accept Hypothesis
SOW9	SOW	0.859	***	Accept Hypothesis
SOW10	SOW	0.736	***	Accept Hypothesis
SOW11	SOW	0.666	***	Accept Hypothesis
SD14	SD	0.852	***	Accept Hypothesis
SD15	SD	0.651	***	Accept Hypothesis
SD16	SD	0.656	***	Accept Hypothesis
COW17	COW	0.538	***	Accept Hypothesis
COW20	COW	0.712	***	Accept Hypothesis
RP22	RP	0.835	***	Accept Hypothesis
RP23	RP	0.676	***	Accept Hypothesis
BE24	BE	0.874	***	Accept Hypothesis
BE25	BE	0.802	***	Accept Hypothesis

***significance at 0.001, **significance at 0.01

Source: Adapted from authors, 2020

5. Discussion

In this research, the aim was to investigate the factors that influence e-service quality that directly impact a travel website’s reputation, and to measure e-service quality factors that affect a travel website’s reputation. A survey questionnaire was been sent and modified to fit the hypothesis, which encompasses a total of 32 item questions. The survey used 444 respondents who were living in Thailand. The data analysis of this research used structural equation modeling mainly to analyze the determinants of e-service quality and their effect on e-brand reputation. For the data analysis process, this study applied various statistical tests to examine the result of e-service quality that affects website reputation. The research framework of this study was based on Nusair and Kandampully (2008). The study of Nusair and Kandampully (2008) employed a qualitative research approach and suggested 6 service quality attributes, including the navigability of the website, playful features, information quality influencing an online reservation, trust from the customers, meeting customers’ specific needs, and willingness to help customers who run into problems. However, this research reappraised Nusair and Kandampully’s e-service quality model using quantitative approaches, including 1) factor analysis and 2) structural equation modeling, to gain a better understanding and insights of the e-service quality model for the online travel industry. From the quantitative approach, this paper found 4 attributes of travel agent web service quality. The results showed the convenience of the website, booking flexibility, special deal giving, and security of the site as having a positive and significant impact on e-service quality. The research can also confirm another novel finding that superior service quality has an effect on a firm’s e-brand reputation. This finding has been overlooked by recent technology acceptance models (Gbongli et al., 2019; Lin et al., 2019; Naruetharadhol et al., 2021) and service quality models (Phonthanukitithaworn et al., 2020; Swar & Panda, 2021; R. Zhou et al., 2019).

The findings from this research article demonstrate that the travel agent’s web service quality positively influenced E-brand reputation with a very strong standardized loading of 0.921. Besides, the previous study provided evidence to suggest that online travel agencies were underperforming in terms of delivering web service quality dimensions that boosted customer satisfaction (Nusair & Kandampully, 2008). Furthermore, reputation reflects customer perception of service quality, which is linked to the company brand (Liu & Lee, 2016). This means that service quality and reputation are directly connected, and that both are crucial factors that influence whether a company is a success or a failure.

The convenience of the website was related positively to e-service quality and e-brand reputation. This finding is consistent with the 6 dimensions of website quality (Nusair & Kandampully, 2008) and (Mohd-Any et al., 2015). The theories suggested that the convenience of a website is essential to build up good quality. For instance, it provides functions to help customers to search for what they need easily and quickly. Moreover, the site should have the fewest possible steps so customers can obtain information quickly. Besides, the website should provide a user-friendly environment that allows customers to control the process by moving back and forth through the pages. On the contrary, E-service quality positively influenced the convenience of a website with a very strong standardized loading of 0.817. Also, the convenience of a website, such as a user-friendly environment (COW17) and provide clear information (COW20), for example, local attractions, amenities, and estimated journey time, would greatly enhance E-service quality with a standardized factor loading of 0.538 and 0.712, respectively. Similarly, the navigability dimension of website quality provided a site map, last-minute deals, and frequently asked questions (FAQs) for the hotel and the airline webpages (Nusair & Kandampully, 2008). Additionally, the information quality dimension of website quality. For instance, company information, press release, travelers review, hotel features, room features, and flight status (Nusair & Kandampully, 2008). Besides, the playfulness dimension of website quality. For example, the website displayed hotel or room pictures (Nusair & Kandampully, 2008). Explicitly, this study found that the convenience of a website can influence customers to choose one particular website over the rest to make their required purchase (Ponte et al., 2015).

Booking flexibility greatly enhanced E-service quality with a standardized loading of 0.667. The result implied that when websites provide flexible return or exchange policies, it will encourage customers to purchase and make customers feel they have a lower risk of something adverse occurring during an online purchase (Nusair & Kandampully, 2008). This study demonstrated that the website allows customers to change or cancel their travel plans without incurring an extra fee (BF24), positively influencing booking flexibility with a very strong standardized loading of 0.874. Additionally, the website provides customers with the option of making a reservation and paying later (BF25), which positively influences booking flexibility with a very strong standardized loading of 0.802.

The special deal significantly affected E-service quality with a standardized loading of 0.463. Empirical results from this study revealed that a special deal is an essential method for a travel website to promote an E-brand reputation and gain more customers (Law & Wong, 2003). For instance, the website offers midweek and senior citizen breaks (SD14) positively boosted special deals with a very strong standardized factor loading of 0.852. Moreover, the website offers half-price kids rates, or kids go free deals (SD15) greatly improved special deals with a standardized factor loading of 0.651. Furthermore, the website offers a repeated customer reward system (SD16) significantly enhanced special deal with a standardized factor loading of 0.656. Likewise, the personalization dimension of website quality offered personalized recommendations (Nusair & Kandampully, 2008). Additionally, the websites provided get deals via email on their homepages (Nusair & Kandampully, 2008). Moreover, the websites offered group hotel or flight reservations (Nusair & Kandampully, 2008). Besides, the responsiveness of website quality accomplished good progress in terms of homepage load time, link load time, and clear cancellation/refund policy (Nusair & Kandampully, 2008). Also, price is a crucial factor in customer decision-making when purchasing hotel reservations and travel services online (Law & Wong, 2003).

The security of the website essentially influenced E-service quality with a standardized loading of 0.409. This positive relationship explained that the security of a website could build up the quality of information on the website (Ponte et al., 2015). Moreover, a website with high-security levels in terms of personal data (SOW9) positively affected the security of the website with a very strong standardized factor loading of 0.859. This study indicated that security and privacy are the essential factors that influence online sales (Ranganathan & Grandon, 2002). Furthermore, websites with a trustworthy and secure payment method (SOW10) significantly boosted the security of the website with a standardized factor loading of 0.736. The payment method is the most considerable attribute that impacts the overall quality of the travel website (Wong & Law, 2005). Also, websites with antivirus protection (SOW11) greatly improved the security of the website with a standardized factor loading of 0.666. Also, the trust dimension of website quality provided privacy policy, terms of use, and security certification.

E-brand reputation positively related to the website comes highly recommended by a friend or family (EBR22) with a very strong standardized loading of 0.835. This outcome implies that reputation on the internet is a social product and a social process that is perceived to increase reputation, and that is affected by information sharing from other sources (Shiau & Luo, 2012). Moreover, reputation is a crucial dimension that significantly influences individual knowledge sharing within a community (Shiau & Luo, 2012). Likewise, E-brand reputation significantly influenced customers to hear about the website via advertisements, such as TV ads, radio, and billboards (EBR23) with a standardized loading of 0.676. This research demonstrated that reputation as an outcome of knowledge sharing might be seen as a belief that can be transmitted within a social network (Shiau & Luo, 2012). Additionally, an excellent reputation for online websites can make customers more confident in online transactions and can have a positive effect on customers’ online shopping attitudes and behaviors (L. Zhou et al., 2007). Especially, customers tend to rely on a website’s reputation to lower the risk perception when conducting transactions online (Biswas & Biswas, 2004).

In conclusion, E-service quality is the most crucial approach to enhance E-brand reputation. The 4 factors of the travel agent’s web service quality consist of the convenience of the website, booking flexibility, special deal, and security of the website. Travel companies can integrate these 4 factors into their website to enhance the quality and reputation of their company.

6. Conclusion

Based on the combination of the theoretical framework and final results of this research, a conceptual framework and hypotheses were established upon the previous theories of service quality and reputation. This research used the SEM approach to find the relationship between service quality and reputation of online travel websites, and also the four factors of service quality that impact reputation. Explicitly, this research found that the security of the website, special deal giving, the convenience of the website, and booking flexibility had a positive impact on service quality that leads to the improvement of reputation. This research has corroborated the theory that service quality influences reputation. The results of this research can be indicators that online travel websites can used to develop service quality and increase the reputation of their company, which can lead to more efficient marketing strategies in the online travel agency platform.

6.1. Implications

The online travel industry can use these research results as a significant foundation for the general framework of e-service quality. This research found several useful managerial implications to improve service quality in the online travel industry. The study results indicated that e-service quality positively affects the e-reputation of online travel websites. There are four factors of online travel service quality, which are: security of the website, special deal giving, the convenience of the website, and booking flexibility. The primary four factors of service quality in this research can influence the travel website to increase its reputation. Hence the online travel industry can develop those factors to improve customer service quality, which can lead to a good reputation.

In terms of consumer behavior, this research showed that nearly all participants in this survey visited more than one website before making reservations. Hence, marketing on travel websites should develop customer service to build a positive relationship with customers, which can increase the chance of customers returning to the website, which will lead to an increase in the company’s reputation in the market.

The findings from this research article clearly demonstrate that to enhance a travel agent’s web service quality, travel agencies should provide the convenience of a website. This is to ensure that customers can easily use online platforms, such as mobile applications and official websites. A travel agent’s website navigation structure should allow customers to get relevant information quickly by requiring only a few clicks. When customers need to land on any page of the website, they should take a short period to land on the next page. Furthermore, the travel agent’s website should provide an excellent user-friendly environment. Thus, customers can control the process by moving back and forth through the pages. Travel agencies need to ensure that online travel applications can be used with both mobile platforms like IOS and Android efficiently. The travel agent’s website should provide hotel pictures or package experience pictures on the website to enhance customer satisfaction. Therefore, customers can see pictures and make their own decisions before booking hotels. Besides, the travel agent’s website should provide clear information about local destinations, amenities, accommodation prices, and estimations of journey time so that customers can spend less time on online booking processes.

Moreover, to boost a travel agent’s web service quality, travel agencies should provide booking flexibility. This is to assure that customers can make changes to the itineraries. The website should encourage customers to purchase, and make customers feel they have a lower risk of something unfavorable occurring during an online purchase. Additionally, the site should offer flexible returns or exchange policies to allow customers to change or cancel their travel plans without charging an extra fee. Furthermore, customers may be offered an alternative to making a reservation and paying later. Consequently, it can enhance customer satisfaction by using the travel agent’s website and travel agent’s reputation.

This study indicated that to improve a travel agent’s web service quality, travel agencies should provide a special deal. This is to ensure that customers get the lowest price while making a reservation via the website. Furthermore, the travel agent’s website should offer customers extra benefits, such as a free room upgrade or free breakfast, to help them to make decisions before making a reservation. Besides, the website should provide special offers, such as senior citizen breaks, half-price kid’s rates, and repeat customer reward systems. Thus, the travel website will be able to gain more attention from customers.

Likewise, this study revealed that to develop a travel agent’s web service quality, travel agencies should provide website security. The travel website should protect the privacy of customers and secure their data while processing online transactions. Moreover, the travel agent’s website should have secure online payment methods and antivirus protection to build up trust for customers to conduct online transactions.

6.2. Limitations and suggestions for future research

The participants of this study are only from Thailand, which represents a narrow and limited sample. Future research should target a broader range of participants. The sample size in this research is relatively small, which may have limited the statistical power of this study to identify all significant differences and associations between and among groups. Hence, further study in a much larger multiethnic sample is needed to shore up the findings of this study. This research provided only four factors that influence e-service quality, which relates to website reputation. Future study should add more variable factors. This research focused on the e-service quality factors that impact the reputation of a website. Future study could alternatively focus on customer satisfaction, customer loyalty, or customer values that affect e-reputation, or use e-reputation as a mediating variable. This study investigated online travel agency services, including hotels, flight tickets, car rental, spa packages, and excursions. Therefore, future research could be developed to examine the specific items of online travel services. Moreover, the prospective study could compare e-service quality factors between online travel websites and a hotel or airline company’s website.

Acknowledgements

The authors gratefully acknowledge the study participants that responded to our study questionnaire. We appreciate the International College, Khon Kaen University, Thailand for providing a research grant and research facilities. Additionally, we sincerely appreciate all the valuable comments on the language from Assistant Professor Kevin Jones, International College, Khon Kaen University, Thailand

Disclosure statement

All authors hereby declare no personal or professional conflicts of interest relating to any aspect of this study.

Additional information

Funding

This study was financially supported by the International College, Khon Kaen University 2021, Khon Kaen, Thailand.

Notes on contributors

Chavis Ketkaew

Chanchai Phonthanukitithaworn, Mahidol University International College, Mahidol University, Thailand. His research interests cover many areas, such as sports marketing and technology adoption.

Phaninee Naruetharadhol is an assistant professor of management and a researcher at EXPEDITE Global Management Consulting Center, International College, Khon Kaen University, Thailand. Her research interests involve organizational behavior and innovation management.

Sasichakorn Wongsaichia is a research assistant at ITIM Research Unit, International College, Khon Kaen University, Thailand.

Natrada Mahajak is a researcher at Kemmy Business School, University of Limerick, Ireland

Chavis Ketkaew is an assistant professor of management and a researcher at EXPEDITE Global Management Consulting Center, International College, Khon Kaen University, Thailand. His research interests involve entrepreneurship and consumer research. He is the principal investigator and corresponding author of this research project.

Appendices

Questionnaire

This questionnaire is designed to explore e-service quality factors that influence the e-reputation of travel agency websites. Besides, it aims to investigate the influential factors of service quality for travel websites.

Note: Please respond to all of the items on the questionnaire and follow the instructions carefully. Your response to this survey is kept anonymous.

Thank you for agreeing to take part in this important survey.

1. Have you ever used online travel site services? (Booking.com, Expedia.com, etc.)

Mark only one oval.

• Yes, Skip to no experience using a travel website.

• No Skip to have experience with online travel websites.

No experience using a travel website

2. Have you ever searched online for relevant information before going on holiday?

Mark only one oval.

• Yes

• No

3. How do you usually make a reservation on holiday trips?

Mark only one oval.

• Traditional travel agency shop

• Contact the hotel/airline directly

• Ask the younger generation to make a reservation for you

• Other:

4. How many times do your holiday a year? (weekend away or trip abroad etc.)

Mark only one oval.

• 1–4 times

• 5–10 times

• More than 10 times

5. How do you find information about your holidays?

Mark only one oval.

• Travel brochures

• Travel agencies

• Friends or Family recommendations

• Internet

• Other:

6. What type of reservations do you make the most?

Mark only one oval.

• Hotel Reservation

• Flight Tickets

• Car Rental

• Spa Packages

• Excursions

• Other:

7. What usually is your reason for traveling?

Mark only one oval.

• Business

• Leisure

• Other:

8. How long before you travel do you make a reservation?

Mark only one oval.

• Less than 1 week

• 2–3 weeks

• 1–3 months

• More than 3 months

Skip to Online travel agency service experiences.

Have experience with online travel websites

2. How often do you visit the online travel site in a year?

Mark only one oval.

• Less than 5

• More than 5

3. How often do you make reservations online in a year?

Mark only one oval.

• 1–4 times

• 5–10 times

• More than 10 times

4. How many online travel agency websites do you visit when buying a holiday package online?

Mark only one oval.

• Only 1 website

• 2–3 websites

• 4 or more websites

5. What type of reservations do you make the most?

Mark only one oval.

• Hotel Reservation

• Flight Tickets

• Car Rental

• Spa Packages

• Excursions

• Other:

6. What usually is your reason for traveling?

Mark only one oval.

• Business

• Leisure

• Other:

7. How long before you travel do you make a reservation?

Mark only one oval.

• Less than 1 week

• 2–3 weeks

• 1–3 months

• More than 3 months

8. Do you usually book your hotel and flight tickets on the same website?

Mark only one oval.

• Never

• Yes

• No

Online travel agency service experiences

Please rate your online experience when booking through travel websites

1 = not important, 5 = very important

Security of The Website

9. The website has high-security levels in terms of personal data.

Mark only one oval.

Not Important12345Very Important

10. The website has a trustworthy and secure payment method.

Mark only one oval.

Not Important12345Very Important

11. The website has antivirus protection.

Mark only one oval.

Not Important12345Very Important

Special Deals

12. The website offers you good value for money.

Mark only one oval.

Not Important12345Very Important

13. The website offers you extra benefits such as free room upgrade, breakfast included, free parking, reduced price for spa treatments, etc.

Mark only one oval.

Not Important12345Very Important

14. The website offers midweek and senior citizen breaks.

Mark only one oval.

Not Important12345Very Important

15. The website offers half-price kids rates/kids go free deals.

Mark only one oval.

Not Important12345Very Important

16. The website offers a repeat customer reward system.

Mark only one oval.

Not Important12345Very Important

Convenience of website

17. The website is user-friendly and easy to make a reservation (it provides a clear detail of the hotel or flight).

Mark only one oval.

Not Important12345Very Important

18. The website provides a user-friendly app for your smartphone.

Mark only one oval.

Not Important12345Very Important

19. The website provides hotel pictures or the packages experience pictures.

Mark only one oval.

Not Important12345Very Important

20. The website provides clear information on local attractions, amenities, and estimated journey time.

Mark only one oval.

Not Important12345Very Important

Reputation

21. The website has recent reviews from people who made a booking through that website (such as on trip advisor etc.)

Mark only one oval.

Not Important12345Very Important

22. The website is highly recommended by friends or family.

Mark only one oval.

Not Important12345Very Important

23. You heard about the website through advertisements (such as TV ads, radio, billboards).

Mark only one oval.

Not Important12345Very Important

Booking Flexibility

24. The website provides flexibility if it is necessary to change/cancel your travel plans without incurring an extra fee.

Mark only one oval.

Not Important12345Very Important

25. The website provides you with the option of making a reservation and paying later. (at the hotel)

Mark only one oval.

Not Important12345Very Important

26. What is the travel website that you visit the most?

Mark only one oval.

• Expedia.com

• Booking.com

• Priceline.com

• Travelocity.com

• Kayak.com

• Tripadvisor.com

• Hotels.com

• Skyscanner.com

• Ebookers.ie

• Other:

Personal information

27. Gender

Mark only one oval.

• Female

• Male

• Prefer not to say

28. Age

Mark only one oval.

• 18–25 years old

• 26–35 years old

• 36–55 years old

• 56 + years old

29. Marital Status

Mark only one oval.

• Single

• Married

• Divorced

• Widowed

• Separated

30. Highest level of educational attainment

Mark only one oval.

• Secondary School

• Leaving Certificate

• Undergraduate

• Postgraduate

• Other:

31. Occupation

Mark only one oval.

• Student

• Employed

• Self—Employed

• Unemployed

• Retired

32. Annual Income

Mark only one oval.

• ฿0–240,000

• ฿240,001–480,000

• ฿480,001–720,000

• More than ฿ 720,001