Online travel agencies (OTAs) such as Expedia, Travelocity, and Orbitz have continually expanded as a result of the large volumes of online bookings (Vinod, 2011). It was reported on the website Statistic Brain (2014) that each year over 148.3 million people in the USA (57% of all travel reservations) reserve their travel products and services via the Internet. According to findings reported on the travel comparison and trip planner website Amadeus (2013), among all the people who are traveling for the purpose of leisure and business worldwide, 60% and 41%, respectively, commonly make their travel reservations via the Internet. Every OTA needs to develop a successful website in order to ensure a high level of customer satisfaction with online reservation systems. If customers visit an OTA website, then they will be able to provide them with greater customer satisfaction due in part to their serviceability. Thus the goal of an OTA is to increase customer visits to their website. However, online reservation systems have both positive and negative characteristics, are generally ambiguous, and have numerous obscure elements (McKnight, 2005). Even though the use of online travel reservation systems by e-consumers has been growing steadily, the travel industry continues to face challenges as there are no frameworks or standards for OTAs and because the companies use customer satisfaction or serviceability as their only framework for website evaluation.

Despite the significance of website serviceability for an OTA, the focus in previous research has been predominantly on examining the functionality of these websites. The specific goals in the current research were to explore (a) the differences among five OTAs in terms of the serviceability of items on their website, (b) subdimensions of a website serviceability scale, (c) the effect of website serviceability on customer satisfaction, and (d) the influence of customer satisfaction on brand attachment and loyalty in regard to an OTA.

Literature Review

Online Travel Agencies in the U.S. Market

An online travel agency is an online provider of travel information and options for booking products and services (Kim, Kim, & Han, 2007). OTAs provide a user-friendly interface experience, which allows the user to interact with the software in a natural and intuitive way, thus creating ease of access to travel-related information (Kaplanidou & Vogt, 2004). According to Forbes Magazine (2014), Expedia, Priceline, Orbitz, and Travelocity were the top four OTA competitors in the U.S. OTA market, with a total market share for the four companies of approximately 95%. In addition, according to Ali (2014), in research reported on the website Skift, Hotwire was ranked as a major OTA in the USA according to the number of visits that were recorded during January 2014, 87.94% (13.2 million) of whom were from the USA. According to Gratzer (2003), e-consumers often use a variety of websites to purchase a myriad of travel products or services. For example, an e-consumer may purchase airline tickets with Travelocity and use Expedia for hotel reservations. Therefore, with the increasingly competitive environment in the online travel sector, managers of each OTA need to understand how and what their customers feel about the online service that they provide.

Website Serviceability for the Online Travel Agency

The term website serviceability is defined as how well the website responds to the users’ expectations, so that users can effectively achieve their goals (Kim, Lehto, & Morrison, 2007). In order for OTA managers to be able to identify the level of serviceability of their website, they first need to identify the serviceability dimensions (Caro & Martínez García, 2008) and how website serviceability is measured in order to develop a reliable and effective website serviceability scale (Schmidt, Cantallops, & dos Santos, 2008). In this study, we adopted the service quality criteria set out by Cheyne, Downes, and Legg (2006) as the standard for assessing serviceability, because these authors showed that service quality is a key predictor of satisfaction. These criteria are accessibility, assurance, empathy, good deals, handling complexity, information, reliability, responsiveness, security, and unbiased advice. We assessed the importance of website service quality and its positive effect on the e-satisfaction of customers of five different OTAs, in order to establish clear dimensions for website evaluation.

Effect of Website Serviceability of the Online Travel Agency on Customer Satisfaction

A customer’s experience with a website greatly determines his or her overall satisfaction (Mills, 2002). For example, Kim, Chung, and Lee (2011) investigated shopping online for tourism products and services with 340 online panel respondents. They found that navigational functionality and perceived security played important roles in enhancing the customer satisfaction of the respondents in their study. Given the evidence from this previous study, the following hypothesis was proposed:
Hypothesis 1: The website serviceability of an online travel agency will have a positive influence on customer satisfaction.

Effects of Customer Satisfaction on Brand Attachment and Loyalty

Grisaffe and Nguyen (2011) suggested that a strong attachment to a particular brand is more likely to develop when customers are satisfied with the brand. That is, customers who have a high level of satisfaction with a certain brand tend to establish psychological ties with the brand. Dolbec and Chebat (2013) investigated the antecedents of brand attachment in the retail industry. They found that customers were more likely to have high levels of brand attachment when they had a positive experience with the brand. Based on this theoretical argument, the following hypothesis was postulated:
Hypothesis 2: Customer satisfaction with an online travel agency will have a positive influence on their brand attachment.

Findings reported in many previous studies have shown that satisfied customers are more likely to be loyal to a particular brand or company. For example, Jani and Han (2013) analyzed data collected from hotel guests and suggested that customer satisfaction was the key factor affecting their behavioral intentions. Thus, we proposed the following hypothesis:
Hypothesis 3: Customer satisfaction will have a positive influence on their loyalty.

Effect of Brand Attachment on Loyalty

Brand attachment brings about a positive intention to act emotionally in relation to a particular brand (Fedorikhin, Park, & Thomson, 2008). Previous researchers have attempted to identify the relationship between brand attachment and loyalty in various fields. For example, Hyun and Kim (2014) examined the impact of attachment on customers’ dedicational behaviors, such as advocacy and enhancement. Their results revealed a strong relationship between the customers’ attachment and their dedicational behaviors. Therefore, it can be inferred that when users of an OTA have a strong brand attachment, they might also be loyal to that brand.
Hypothesis 4: Brand attachment will have a positive influence on customer loyalty to an online travel agency.

Method

Measurement

For this study we modified multi-item scales that had already been validated and widely adopted to fit the tourism industry. First, to measure the website serviceability of the OTAs, we adapted 10 items developed by Cheyne et al. (2006). The items have been recognized as being able to gauge the serviceability of an OTA website by overcoming the weaknesses of the SERVQUAL or SERVPERF quality management models (Adil, Ghaswyneh, & Albkour, 2013; Carrillat, Jaramillo, & Mulki, 2007; Jain & Gupta, 2004; Ladhari, 2009; Parasuraman, Zeithaml, & Berry, 1988) through covering a range of a number of items in regards to service quality. Customer satisfaction was measured with two items used by Montoya-Weiss, Voss, and Grewal (2003). Brand attachment was assessed with three items from Fournier (2009), and we assessed loyalty with three items from Ho and Lee (2007). Respondents to the survey rated the items on a 5-point Likert-type scale, anchored with strongly disagree (1) and strongly agree (5).

Data Collection

We handed out self-administered survey forms to travelers who had reserved travel-related products or services through OTAs within the previous 12 months.

Data were collected at San Francisco and Dallas International Airports in the USA from the first week of July 2013 to the last week of August 2013. At each airport, we handed out 200 survey forms. Of the returned forms, 85 (21.3%) were discarded because of incomplete or insincere responses, 21 (5.3%) were discarded because the respondent had not purchased products or services from an OTA within the previous 12 months, and a further 11 (2.8%) were discarded because of invalid answers. Finally, we obtained 283 usable responses for data analysis.

Data Analysis

The sample consisted of 183 female respondents (64.7%) and 100 male respondents (35.3%). The mean age of respondents was 30.57 years, ranging from 19 to 71 years of age. The majority of respondents were Caucasian (42.4%, n = 120), followed by Asian or Pacific Islander (29.3%, n = 83), Hispanic (11.7%, n = 33), African American (9.5%, n = 27), Native American (6.7%, n = 19), and other (0.4%, n = 1). In terms of education, 41.7% of the respondents (n = 118) reported holding an undergraduate degree, followed by a graduate degree (26.5%, n = 75), attended college (23.7%, n = 67), high school (7.4%, n = 21), and other (0.7%, n = 2). Last, as a group the respondents had relatively low income levels, with 27.2% of the respondents (n = 77) reporting an annual income of less than US$20,000, followed by US$20,000–US$39,999 (19.8%, n = 56), US$40,000–US$59,999 (18.7%, n = 53), US$60,000–US$79,999 (15.5%, n = 44), US$80,000–US$99,999 (13.4%, n = 38), US$ 100,000–US$149,999 (3.9%, n = 11), and US$150,000 or above (1.4%, n = 4).

Regarding the most recently used OTA, 21.2% (n = 54) had used Priceline, followed by Hotwire (20.8%, n = 53), Expedia (20%, n = 51), Orbitz (20.0%, n = 51), Kayak (18%, n = 46), and others (9.9%, n = 28). In addition, 41% (n = 116) reported that they had purchased a product or service one or two times through an OTA, followed by 3–4 times (35.7%, n = 101), 5–6 times (16.6%, n = 47), 7–8 times (3.9%, n = 11), and more than 9 times (2.8%, n = 8). In terms of the main purpose of the reservation, 71.4% (n = 202) reported personal travel, 28.3% indicated business travel, and 0.3% who used the service for other purposes. Regarding the most important information source, 43.1% answered that the website was the most important source, and this was followed by acquaintances (e.g. friends, family, 28.3%, n = 80), other (27.9%, n = 79), television (0.4%, n = 1), and magazines/newspaper (0.4%, n = 1). Last, in terms of the service for which the customer had used the website most recently, the highest percentage (46.5%) of respondents had used the website for reserving flights and the next highest percentage (30.0%) had booked accommodation, followed by packages (14.5%, n = 41), car rental (7.1%, n = 20), and other (1.8%, n = 5).

Results

In Table 1 the results are presented of a one-way analysis of variance applied to the 10 website serviceability items across the five OTAs. Statistical differences were found for eight of the items. The significant differences between the means of eight website serviceability items according to the five OTAs suggest variance in each OTA’s merits and demerits.

Principal Component Factor Analysis

We performed principal component factor analysis with varimax rotation to identify the dimensions of website serviceability of the OTAs. A factor loading cut-off point of .40 was used for retaining items in the factor analysis, as this suggests that convergent validity is satisfied. In addition, the reliability of factors as assessed by Cronbach’s alpha exceeded the recommended minimal level of .70 (ranging from .747 to .814). Based on the content of the factors, they were categorized as website functional serviceability (mean value = 4.200, eigenvalue = 3.121, explained variance = 31.207%) and website technical serviceability (mean value = 4.304, eigenvalue = 2.882, explained variance = 28.819%). Items categorized as functional were (a) “I can easily contact the online travel agency by phone or email if necessary,” (b) “It responds to my question/problem in a timely manner,” (c) “I knew that what I arranged through the online travel agency was what I would get,” and (d) “I can receive honest and helpful information that was not influenced by any relationships with specific hotels.” Items categorized as technical were (a) “I felt confident about the knowledge and expertise of the online travel agency,” (b) “It gave me individualized attention,” (c) “It was able to provide a wide range of useful information related to my travel,” (d) “It was able to complete my transaction securely,” (e) “It was able to provide cheaper travel and/or special deals,” and (f) “It was able to handle complex travel arrangements.”

Confirmatory Factor Analysis

We removed one item from further analysis (“It was able to handle complex travel arrangements”) because it had an unacceptable standardized loading below 0.4 (Jöreskog & Sörbom, 1993). The results of a second confirmatory factor analysis demonstrated a satisfactory fit of the measurement model according to calculation of comparative fit index (CFI), incremental fit index (IFI), Tucker-Lewis index (TLI), and root mean square error of approximation (RMSEA) as follows, CFI = .942, IFI = .942, TLI = .928, RMSEA = .098. Table 2 shows the variables used in this study, with their standardized factor loadings. The factor loadings were equal to or greater than .541, and all were significant at p < .001. The average variance extracted (AVE) for all constructs exceeded the threshold value of .50 (Fornell & Larcker, 1981), which confirmed the convergent validity of the measurement scales. The composite reliabilities of the constructs were higher than .70, ranging from .833 to .962, which demonstrated the adequate internal consistency of all constructs in the model (Hair, Black, Babin, Anderson, & Tatham, 2006). Finally, discriminant validity was assessed by comparing the AVE values and squared correlations between the two constructs of interest (Fornell & Larcker, 1981). AVE for each construct was higher than all of the squared correlations (R²) between any pair of constructs, suggesting that discriminant validity was statistically supported.

Structural Model

The proposed model with six constructs was estimated using structural equation modeling analysis. Fit indices provided by analysis of moment structures (AMOS) demonstrated the adequate fit of the proposed model (CFI = .954, IFI = .954, TLI = .943, RMSEA = .087). All five proposed hypotheses were statistically supported at p < .05: Hypothesis 1 (website’s functional serviceability customer satisfaction, β = .639), Hypothesis 2 (website’s technical serviceability customer satisfaction, β = .248), Hypothesis 3 (customer satisfaction brand attachment, β = .920), Hypothesis 4 (customer satisfaction loyalty, β = .787), and Hypothesis 5 (brand attachment loyalty, b = .182).

Discussion

To the best of our knowledge, this is the first study in which significant differences among website serviceability items of five OTAs have been identified. We found statistical differences in eight of the website serviceability items. These results will be useful for management personnel of the five OTAs in developing effective and efficient marketing strategies. According to our findings for the Expedia website, managers should ensure that secure transactions and quick responses are emphasized in advertisements, and should conduct customer surveys to identify their customers’ needs. In addition, with the exception of Expedia, the websites of the other four OTAs did not score well for responding quickly, so in all these companies, managers need to ensure that frequent monitoring of their customer service centers is carried out, in order to become more responsive to customer needs.

Second, website functional serviceability had a positive effect on customer satisfaction, and this effect was stronger than that of website technical serviceability. This suggests that when customers accomplish their primary purpose for visiting the website, they would more likely be satisfied with the overall experience with the OTA website. Previous researchers have also argued that website functional serviceability plays an important role in the formation of customer satisfaction (e.g., Cheyne et al., 2006; Ho & Lee, 2007). In this regard, our findings in this study replicated existing theory by identifying the significant relationship between website functional serviceability and customer satisfaction. Third, the data analysis showed that website technical serviceability positively affected customer satisfaction. In the history of OTA websites, technical serviceability has been considered an important part of the overall website experience (see e.g., Chen & Yen, 2004). Our findings replicated those reported in the existing literature by identifying the critical role of website technical serviceability in the formation of customer satisfaction. In other words, when customers perceive that the website has a high level of technical serviceability, they feel satisfied with their overall experiences.

From a managerial standpoint, there is a need to establish advanced systems because online customers are more likely to switch to other websites when they cannot access the website easily (Ho & Lee, 2007). In addition, OTAs need to ensure the effectiveness of their security system, as users want to be sure that they can complete their transaction securely. Last, customers want to feel confident about the knowledge and expertise they find on the website of the OTA, which necessitates regular employee training to enhance their knowledge and expertise. Customers are then more likely to be satisfied with the OTA website when they inquire about a product or service.

Another important contribution of this study is the clarification of the role of customer satisfaction with OTAs. We found that customer satisfaction was an important predictor of brand attachment, which, in turn, increased the customers’ loyalty. In addition, customers’ satisfaction directly affected their loyalty. That is, when customers are satisfied with the overall experience with an OTA website, they are more likely to have high levels of brand attachment and loyalty. Although the important role played by customer satisfaction is widely known in various industries, the present study is the first in which the role of customer satisfaction in the formation of brand attachment in OTA websites has been identified. In this regard, we have extended the literature by having empirically tested the effects of customer satisfaction on brand attachment.

A limitation of our study is that the data were collected in the OTA industry in the USA, so that caution should be exercised in generalizing the study findings to other industries and countries. A second limitation is that gender plays a crucial role in consumer behavior because men and women act differently (Lee, Hsiao, & Lu, 2015). However, our study sample suffered from a gender imbalance, so future researchers will require a more gender-balanced sample. Last, we focused solely on OTA websites, despite the rapid growth in smartphone utilization for travel and accommodation reservations and information. With the increased number of smartphone users, managers of businesses in the hospitality field should recognize that they need to make every effort to facilitate the use of mobile applications, so that customers can easily access these applications anywhere at any time. Therefore, it would be productive to apply our research methods to smartphone users.

References

Adil, M., Al Ghaswyneh, O. F. M., & Albkour, A. M. (2013). SERVQUAL and SERVPERF: A review of measures in services marketing research. Global Journal of Management and Business Research, 13.

Ali, R. (2014, February 24). The top online travel booking sites for January 2014. Retrieved from http://bit.ly/1U0eLwd

Amadeus. (2013). Amadeus travel insights: The 21st century traveller. Retrieved from http://bit.ly/1PkMfU0

Caro, L. M., & Martínez García, J. A. (2008). Developing a multidimensional and hierarchical service quality model for the travel agency industry. Tourism Management, 29, 706–720. http://doi.org/ckg6bk

Carrillat, F. A., Jaramillo, F., & Mulki, J. P. (2007). The validity of the SERVQUAL and SERVPERF scales: A meta-analytic view of 17 years of research across five continents. International Journal of Service Industry Management, 18, 472–490. http://doi.org/bgsh46

Chen, K., & Yen, D. C. (2004). Improving the quality of online presence through interactivity. Information & Management, 42, 217–226. http://doi.org/c4h6q2

Cheyne, J., Downes, M., & Legg, S. (2006). Travel agent vs. Internet: What influences travel consumer choices? Journal of Vacation Marketing, 12, 41–57. http://doi.org/b3s4wt

Competitive landscape of the U.S. online travel market is transforming. (2014, April 8). Forbes Magazine. Retrieved from http://onforb.es/1PD27qz

Dolbec, P.-Y., & Chebat, J.-C. (2013). The impact of a flagship vs. a brand store on brand attitude, brand attachment and brand equity. Journal of Retailing, 89, 460–466. http://doi.org/bgfj

Fedorikhin, A., Park, C. W., & Thomson, M. (2008). Beyond fit and attitude: The effect of emotional attachment on consumer responses to brand extensions. Journal of Consumer Psychology, 18, 281–291. http://doi.org/cgsv7c

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. http://doi.org/cwp

Fournier, S. (2009). Lessons learned about consumers’ relationships with their brands. In D. J. MacInnis, C. W. Park, & J. W. Priester (Eds.), Handbook of brand relationships (pp. 5–23). Amonk, NY: M. E. Sharpe.

Gratzer, M. (2003). Changes in the travel and tourism industry caused by the internet–competitive advantage for the SME accommodation sector in Austria? (Doctoral thesis). University of Vienna, Austria. Retrieved from http://bit.ly/1tdYnlF

Grisaffe, D. B., & Nguyen, H. P. (2011). Antecedents of emotional attachment to brands. Journal of Business Research, 64, 1052–1059. http://doi.org/c83kt4

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.

Ho, C.-I., & Lee, Y.-L. (2007). The development of an e-travel service quality scale. Tourism Management, 28, 1434–1449. http://doi.org/fdgb5b

Hyun, S. S., & Kim, I. (2014). Identifying optimal rapport-building behaviors in inducing patrons’ emotional attachment in luxury restaurants. Journal of Hospitality & Tourism Research, 38, 162–198. http://doi.org/bgfk

Jain, S. K., & Gupta, G. (2004). Measuring service quality: SERVQUAL vs. SERVPERF scales. Vikalpa, 29, 25–37.

Jani, D., & Han, H. (2013). Personality, social comparison, consumption emotions, satisfaction, and behavioral intentions: How do these and other factors relate in a hotel setting? International Journal of Contemporary Hospitality Management, 25, 970–993. http://doi.org/bgfm

Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Lincolnwood, IL: Scientific Software International.

Kaplanidou, K., & Vogt, C. (2004). Destination marketing organization websites (DMOs): Evaluation and design. What you need to know. Paper published in collaboration between Travel Michigan and Michigan State University, Department of Community, Agriculture, Recreation, and Resource Studies. Retrieved from http://bit.ly/1Yg0KPP

Kim, M.-J., Chung, N., & Lee, C.-K. (2011). The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea. Tourism Management, 32, 256–265. http://doi.org/fck7kj

Kim, D. J., Kim, W. G., & Han, J. S. (2007). A perceptual mapping of online travel agencies and preference attributes. Tourism Management, 28, 591–603. http://doi.org/bj5q2x

Kim, D.-Y., Lehto, X. Y., & Morrison, A. M. (2007). Gender differences in online travel information search: Implications for marketing communications on the Internet. Tourism Management, 28, 423–433. http://doi.org/bp9x42

Ladhari, R. (2009). A review of twenty years of SERVQUAL research. International Journal of Quality and Service Sciences, 1, 172–198. http://doi.org/dpmgwf

Lee, C.-L., Hsiao, K.-L., & Lu, H.-P. (2015). Gender differences in antecedents and consequences of trust in an enterprise’s travel blogs. Social Behavior and Personality: An international journal, 43, 269–286. http://doi.org/7zd

McKnight, D. H. (2005). Trust in information technology. In G. B. Davis (Ed.), The Blackwell encyclopedia of management (Vol. 7, pp. 329–331). Malden, MA: Blackwell.

Mills, J. E. (2002). An analysis, instrument development, and structural equation modeling of customer satisfaction with online travel services (Unpublished doctoral dissertation). Purdue University, West Lafayette, IN, USA. Retrieved from http://bit.ly/1UDhBcS

Montoya-Weiss, M. M., Voss, G. B., & Grewal, D. (2003). Determinants of online channel use and overall satisfaction with a relational, multichannel service provider. Journal of the Academy of Marketing Science, 31, 448–458. http://doi.org/bjfh64

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64, 12–40.

Schmidt, S., Cantallops, A. S., & dos Santos, C. P. (2008). The characteristics of hotel websites and their implications for website effectiveness. International Journal of Hospitality Management, 27, 504–516. http://doi.org/fd54b6

Statistic Brain. (2014). Internet travel hotel booking statistics. Retrieved from http://bit.ly/1mzk5ea

Vinod, B. (2011). The future of online travel. Journal of Revenue and Pricing Management, 10, 56–61. http://doi.org/bhxqm4

Adil, M., Al Ghaswyneh, O. F. M., & Albkour, A. M. (2013). SERVQUAL and SERVPERF: A review of measures in services marketing research. Global Journal of Management and Business Research, 13.

Ali, R. (2014, February 24). The top online travel booking sites for January 2014. Retrieved from http://bit.ly/1U0eLwd

Amadeus. (2013). Amadeus travel insights: The 21st century traveller. Retrieved from http://bit.ly/1PkMfU0

Caro, L. M., & Martínez García, J. A. (2008). Developing a multidimensional and hierarchical service quality model for the travel agency industry. Tourism Management, 29, 706–720. http://doi.org/ckg6bk

Carrillat, F. A., Jaramillo, F., & Mulki, J. P. (2007). The validity of the SERVQUAL and SERVPERF scales: A meta-analytic view of 17 years of research across five continents. International Journal of Service Industry Management, 18, 472–490. http://doi.org/bgsh46

Chen, K., & Yen, D. C. (2004). Improving the quality of online presence through interactivity. Information & Management, 42, 217–226. http://doi.org/c4h6q2

Cheyne, J., Downes, M., & Legg, S. (2006). Travel agent vs. Internet: What influences travel consumer choices? Journal of Vacation Marketing, 12, 41–57. http://doi.org/b3s4wt

Competitive landscape of the U.S. online travel market is transforming. (2014, April 8). Forbes Magazine. Retrieved from http://onforb.es/1PD27qz

Dolbec, P.-Y., & Chebat, J.-C. (2013). The impact of a flagship vs. a brand store on brand attitude, brand attachment and brand equity. Journal of Retailing, 89, 460–466. http://doi.org/bgfj

Fedorikhin, A., Park, C. W., & Thomson, M. (2008). Beyond fit and attitude: The effect of emotional attachment on consumer responses to brand extensions. Journal of Consumer Psychology, 18, 281–291. http://doi.org/cgsv7c

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. http://doi.org/cwp

Fournier, S. (2009). Lessons learned about consumers’ relationships with their brands. In D. J. MacInnis, C. W. Park, & J. W. Priester (Eds.), Handbook of brand relationships (pp. 5–23). Amonk, NY: M. E. Sharpe.

Gratzer, M. (2003). Changes in the travel and tourism industry caused by the internet–competitive advantage for the SME accommodation sector in Austria? (Doctoral thesis). University of Vienna, Austria. Retrieved from http://bit.ly/1tdYnlF

Grisaffe, D. B., & Nguyen, H. P. (2011). Antecedents of emotional attachment to brands. Journal of Business Research, 64, 1052–1059. http://doi.org/c83kt4

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.

Ho, C.-I., & Lee, Y.-L. (2007). The development of an e-travel service quality scale. Tourism Management, 28, 1434–1449. http://doi.org/fdgb5b

Hyun, S. S., & Kim, I. (2014). Identifying optimal rapport-building behaviors in inducing patrons’ emotional attachment in luxury restaurants. Journal of Hospitality & Tourism Research, 38, 162–198. http://doi.org/bgfk

Jain, S. K., & Gupta, G. (2004). Measuring service quality: SERVQUAL vs. SERVPERF scales. Vikalpa, 29, 25–37.

Jani, D., & Han, H. (2013). Personality, social comparison, consumption emotions, satisfaction, and behavioral intentions: How do these and other factors relate in a hotel setting? International Journal of Contemporary Hospitality Management, 25, 970–993. http://doi.org/bgfm

Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Lincolnwood, IL: Scientific Software International.

Kaplanidou, K., & Vogt, C. (2004). Destination marketing organization websites (DMOs): Evaluation and design. What you need to know. Paper published in collaboration between Travel Michigan and Michigan State University, Department of Community, Agriculture, Recreation, and Resource Studies. Retrieved from http://bit.ly/1Yg0KPP

Kim, M.-J., Chung, N., & Lee, C.-K. (2011). The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea. Tourism Management, 32, 256–265. http://doi.org/fck7kj

Kim, D. J., Kim, W. G., & Han, J. S. (2007). A perceptual mapping of online travel agencies and preference attributes. Tourism Management, 28, 591–603. http://doi.org/bj5q2x

Kim, D.-Y., Lehto, X. Y., & Morrison, A. M. (2007). Gender differences in online travel information search: Implications for marketing communications on the Internet. Tourism Management, 28, 423–433. http://doi.org/bp9x42

Ladhari, R. (2009). A review of twenty years of SERVQUAL research. International Journal of Quality and Service Sciences, 1, 172–198. http://doi.org/dpmgwf

Lee, C.-L., Hsiao, K.-L., & Lu, H.-P. (2015). Gender differences in antecedents and consequences of trust in an enterprise’s travel blogs. Social Behavior and Personality: An international journal, 43, 269–286. http://doi.org/7zd

McKnight, D. H. (2005). Trust in information technology. In G. B. Davis (Ed.), The Blackwell encyclopedia of management (Vol. 7, pp. 329–331). Malden, MA: Blackwell.

Mills, J. E. (2002). An analysis, instrument development, and structural equation modeling of customer satisfaction with online travel services (Unpublished doctoral dissertation). Purdue University, West Lafayette, IN, USA. Retrieved from http://bit.ly/1UDhBcS

Montoya-Weiss, M. M., Voss, G. B., & Grewal, D. (2003). Determinants of online channel use and overall satisfaction with a relational, multichannel service provider. Journal of the Academy of Marketing Science, 31, 448–458. http://doi.org/bjfh64

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64, 12–40.

Schmidt, S., Cantallops, A. S., & dos Santos, C. P. (2008). The characteristics of hotel websites and their implications for website effectiveness. International Journal of Hospitality Management, 27, 504–516. http://doi.org/fd54b6

Statistic Brain. (2014). Internet travel hotel booking statistics. Retrieved from http://bit.ly/1mzk5ea

Vinod, B. (2011). The future of online travel. Journal of Revenue and Pricing Management, 10, 56–61. http://doi.org/bhxqm4

Jinsoo Hwang is now at the College of Hospitality and Tourism Management

Sejong University. This publication was supported by Daegu University Research Grants in 2013.

Jinsoo Hwang, College of Hospitality and Tourism Management, Sejong University, 98 Gunja, Gwanjin, Seoul, Republic of Korea. Email: [email protected]