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Factors affecting online hotel reservation intention between online and non-online customers
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Hospitality Management 23 (2004) 381–395
Factors affecting online hotel reservation intention between online and non-online customers Woo Gon Kim*, Dong Jin Kim School of Hotel and Restaurant Administration, Oklahoma State University, 210 Human Environmental Science West, Stillwater, OK 74078, USA
Abstract This study examined the differences between demographic and behavioral characteristics of customers who purchased products online and customers who did not. Additionally, this study investigated determinants that explain a customer’s online reservation intention. To meet the purpose of this study, the researchers surveyed customers from eight hotels in Korea. The data were analyzed using w2 analysis, factor analysis, and multiple regression analysis. The two types of respondents differed in regard to their age, educational background, weekly browser usage, and the number of years of Internet use. Furthermore, the results showed that the determinants affect the respondents’ online reservation intentions differently according to their past online purchasing experience. r 2004 Elsevier Ltd. All rights reserved. Keywords: Online reservation; Online purchase experience; Information technology; Information search; Hospitality industry
1. Introduction Experts anticipate that the Internet, with its recent noticeable increases in users and functions, will greatly transform hospitality organizations. For example, Olsen and Connolly (2000) argued that hospitality firms will experience significant transformations because of the increased customer base available on the Internet. Dev and Olsen (2000) discussed the role of information *Corresponding author. Tel.: +1-405-744-8483; fax: +1-405-744-6299. E-mail addresses: [email protected] (W.G. Kim), [email protected] (D.J. Kim). 0278-4319/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2004.02.001
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technology (IT) and suggested that the Internet will provide great opportunities for future sales. Rayman-Bacchus and Molina (2001) stated that information and computer technology, especially the Internet, have changed the socioeconomic context of tourism and, furthermore, that it will stimulate further changes. Though electronic commerce (e-commerce) is in its infancy, purchasing via the Internet is one of the most rapidly growing forms of shopping (Levi and Weitz, 2001). Forrester Research (2001) reported that Internet sales by consumers totaled $48.3 billion in 2000, while online hotel sales represented $3.7 billion in the same time period. Poel and Leunis (1999) revealed that hotel reservations were ranked second after concert tickets in terms of the average consumers’ propensity to buy a specific product or service. Hospitality corporations have responded to the opportunities offered by e-commerce by developing web sites to take full advantage of the practical and creative business uses of the Internet. The Internet is an alternate distribution channel that can be compatible with existing channels (Rayman-Bacchus and Molina, 2001). The use of web sites in hospitality organizations goes beyond simply promoting and selling products to consumers. The adoption of web sites also provides the hospitality firms with important business opportunities and competitive edges. Using the Internet as a reservation method can benefit the hospitality firms and also the customers by reducing costs and providing real-time information to both parties. According to Cobanoglu (2001), business travelers still use travel agents as their favorite hotel reservation resource followed by toll free reservation numbers, and then calling the hotel directly. Use of online hotel reservation system follows the previous three media in terms of favor. However, experts in IT predict that within several years the Internet will be one of the most important sources for hotel reservations and services (Cline and Warner, 2001). The number of online hotel reservations in 2001 accounted for 4.9% of total reservations made, and this percentage is expected to more than triple over the next 3 years. While the proportion of online reservations is increasing, only 64% of hospitality firms currently handle such transactions (Cline and Warner, 2001). Because an explosive increase in the number of online hotel reservations is expected, hotel marketers need to understand the determinants of customers’ online hotel reservation intentions. Despite the recent growing use of the Internet as a new reservation method, to the best of our knowledge, the factors that affect online hotel reservation intention have not yet been investigated. Considering the above information, this study aims to: 1. Examine the differences in demographic and behavioral characteristics between customers with previous online purchase experience and customers without any such experience. 2. Investigate the factors that affect hotel reservation intentions of online and nononline customers.
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Based on the above objectives, research questions are as follows: Research Question 1: Do customers who make online reservations differ from non-online customers with regard to demographic and behavioral characteristics (i.e., gender, age, income, education, weekly browser usage, and years of the Internet use)? Research Question 2: Do the determinants affect the customer’s online reservation intention differently according to one’s past online purchase experience?
2. Literature review As the importance of the Internet for the hospitality organizations grew and became more broadly accepted, researchers tried to find sociodemographic and behavioral characteristics of online customers. Bonn et al. (1998) argued that individuals who purchased travel online were likely to share sociodemographic characteristics. They investigated differences in the propensity to use the Internet based on the subjects’ sociodemographic and behavioral characteristics and found that gender did not significantly affect the customer’s information search behavior via the Internet, but age, income, and education level did. Similarly, Weber and Roehl (1999) provided a profile of people who used the Internet to search for travel information or to purchase travel arrangements. Their study found that the respondents who searched for travel information or who purchased travel products online reported higher incomes, higher status occupations, and more years of experience with the Internet than those who did not search or purchase online. The findings of these studies suggest that managers of hospitality organizations should understand the differences between sociodemographic and behavioral characteristics between online and non-online customers before implementing their promotion strategies. Previous researchers have also examined what current and potential online customers like to see from hospitality and travel web sites. In an earlier study, Murphy et al. (1996) found 32 common features in the hotel reservation sites found through search engines; those features were then divided into four categories: promotion, service, interactivity, and management. Jarvenpaa and Todd (1997) used a conceptual approach to examine consumer attitudes toward early features of online shopping. The shopping factors identified by online customers were compiled into four categories: product perceptions, shopping experience, customer service, and consumer risks. Chu (2001) conducted focus group interviews to identify Internet users’ needs and expectations toward airline/travel web sites. The results revealed that consumers were more willing to purchase low-involvement products via the Internet than high-involvement products. When asked what they expected to see from an airline/travel web site, the respondents identified informative, interactive, and attractive factors. Jeong et al. (2001) investigated consumer perceptions of hotel web sites. They found that potential online customers only moderately liked online reservation web
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sites. They concluded color combination, ease of use, navigation quality, information completeness, accuracy, and currency were crucial factors for increasing sales via the Internet. Out of the six key drivers of online transaction intentions, information completeness turned out to be the most critical for online customers’ satisfaction with web site information. Also, they discovered that color combination, information currency, accuracy, completeness, and navigation quality did have significant influences on customer’s perception of the hotel product. Yoon (2002) investigated the antecedents and consequences of trust and satisfaction in online purchase decisions. The antecedents included in the study were transaction security (i.e., security warranty phrases and clarity of refund policy), web site properties (i.e., adequacy of product description and width of product selections), navigation functionality (i.e., usefulness of help functions and overall operational efficiency) and personal variables (i.e., familiarity with e-commerce and previous satisfaction with ecommerce). The author found that the four antecedent dimensions influenced online customer’s purchase intentions, whereas web site trust and satisfaction operate as the mediating variables. The above studies focused on the online behavior of customers in the hospitality; however, studies that are more generally focused on consumer online behavior also inform this study. To better understand online purchasing behavior, theoretical foundations were developed from Internet-related studies of consumer’s perspectives. Jeong and Lambert’s (2001) empirical results showed that consumers’ perceived quality of information about products and services on the web was most crucial in predicting their decision-making. In their study of the four constructs of information quality (i.e., perceived usefulness, perceived ease of use, perceived accessibility, and attitudes), perceived usefulness and attitudes were powerful indicators in predicting the customers’ purchase behavior. In another study, Shim et al. (2001) proposed an Online Prepurchase Intentions Model based on the Interaction Model of the prepurchase consumer information search (Klein, 1998) and the Theory of Planned Behavior (Ajzen, 1985, 1991). They concluded that consumers’ intentions to use the Internet for purchasing were influenced by their attitudes (i.e., payment security, privacy, safety, etc.), perceived behavioral control, and Internet purchase experience. Based on empirical findings, Shim et al. (2001) argued that the information search was the single most crucial element leading to purchase via the Internet. In yet another study, Liang and Huang (1998) tested the ability of a transaction cost model to explain online consumers’ purchasing decisions. The researchers included search, comparison, examination, negotiation, order and payment, delivery, and post-service into the online transaction process. Results showed that transaction costs determined the consumer’s acceptance toward Internet shopping. The authors also argued for a learning effect in electronic shopping based on their finding that the determinants of customer acceptance for the online web shoppers were different from those of the non-online ones. According to their findings, uncertainty was the most significant construct for online shoppers, while asset specificity was the most significant for non-online shoppers.
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Finally, Szymanski and Hise (2000) determined that consumer’s perceptions of online convenience, product information, site design, and financial security were the dominant factors in consumer assessments of satisfaction. After examining web sites of retailers, Kagan et al. (2000) concluded that consumers wanted such advanced functions as checking real-time availability and tracking completed orders although current web sites that had only basic transaction functions. The results also indicated that consumers wanted greater assurances of confidentiality and privacy. These findings are consistent with other studies that showed that issues such as privacy (Kelly and Rowland, 2000) and return policies (Wood, 2001) substantially influenced customer’s perceptions of safety regarding online shopping. In summary, the review of the literature revealed online customer decision-making is an important area of study for the hospitality industry and, furthermore, that a body of knowledge exists in the area of consumer online decision-making. However, no study was found that described the differences in the factors that affect hotel reservation intentions of online and non-online customers.
3. Methods To answer the research questions, survey data were collected from the customers in eight hotels in Korea and analyzed. The following sections describe the measurement and analysis, and the subjects for this study. 3.1. Measurement and analysis In this study, a questionnaire comprising 19 determinants was designed to measure the online shopper’s perceived importance toward each determinant. The determinants pertaining to online hotel reservation were developed from previous studies by Jarvenpaa and Todd (1997) and Weber and Roehl (1999), as well as from a focus group interview. The focus group consisted of five hotel managers in charge of online reservations, and five hotel guests who primarily reserved their rooms online. The determinants of online hotel reservation intention used in the study are shown in Table 1. The questionnaire consisted of three sections. The first section measured customer’s perceived importance of online hotel reservation. The respondents were asked to rate the importance of each determinant using a 5-point Likert scale (1=least important; 5=most important). The second section measured respondent’s past online purchase experiences (1=yes, 2=no) and online reservation intention of hotel products and services using a 7-point Likert scale (1=most unlikely, 7=most likely). The third section was designed to obtain the respondent’s demographic and behavioral characteristics: gender, age, income, education level, browser use per week, and number of years of Internet use. According to a recent study by Shim et al. (2001), past purchase experience via the Internet was one of the most important factors (i.e., attitude, behavioral control, and purchase experience) in predicting Internet purchase intention. Liang and Huang
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Table 1 Determinants of online hotel reservation intention used in the study Determinants
Jarvenpaa and Todd (1997)
Freedom from hassles 24-h accessibility Easy payment procedures Appropriate product/service information Real-time location of available rooms Ease of comparing hotels Ease of contacting hotels Ease of finding what I want Variety of choices Ease of acquiring hotel-related information Ease of understanding policy Ease of placing orders Credibility of online transaction Reliability of provided pictures Reliability of products/services information Reduced purchase-related costs Discounted price Ease of canceling Security of sensitive information
O O
O O O O O O O O O O O O O
Weber and Roehl (1999) O O O
O O O O O O O O
This study O O O O O O O O O O O O O O O O O O O
(1998) indicated that non-online and online consumers had different considerations while purchasing electronically. Until recently, the percentage of hotel reservations made online was extremely low in Korea, so limiting the subjects of a study exclusively to guests who made online hotel reservations would produce insufficient data for quantitative analysis. Therefore, for the purpose of this study, respondents who had any past online purchase experience related to airlines, hotels, or time-share resorts were considered to have past online purchase experience. Online purchase experiences with airlines and time-share resorts were included due to the similarity of hotel products in terms of the characteristics of high risk, relatively high price, and intangibility. Online reservation intention was used as the best proxy of real reservation behavior. The collected data were analyzed using Statistical Package for Social Science (SPSS) version 10.0. First, w2 analysis was conducted to find out the differences of demographic and behavioral characteristics of customers who had purchased products online and those who had not. Factor analysis was utilized to determine the underlying structure of the original 19 determinants toward online reservations. Finally, multiple regression analysis was employed to investigate the causal effect of extracted factors on the online reservation intention. 3.2. Sampling Fifty-five questionnaires were distributed for the purpose of pre-testing in May 2001. Based on the comments collected during the pre-testing period, a complete
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questionnaire was designed. A quota-sampling method was adopted for data collection. The number of questionnaires distributed in each hotel was based on the number of rooms in that hotel. The questionnaires were distributed to Korean hotel guests who stayed in eight five-star hotels in Seoul when they checked in at their hotels. The front desk clerks encouraged the guests to complete the form with a prepared speech. The guests were asked to either return the completed questionnaires to the front office or leave them in their room for housekeeping personnel to collect. The questionnaires were collected from May 11 through May 25, 2001. Most hotels were business hotels, mainly due to their location in the city of Seoul, therefore, most respondents were business clients. Out of 500 questionnaires that were distributed, a total of 262 (52.4%) questionnaires were returned. Of the returned questionnaires, 7 were eliminated because of an excessive amount of missing data. After elimination, 255 questionnaires (51.0%) were coded and analyzed for the empirical investigation.
4. Results 4.1. Sample characteristics The w2 analyses were conducted to investigate differences in the respondent’s demographic and behavioral characteristics (i.e., gender, age, income, education level, browser use per week, and the number of years of Internet use) between the online and non-online groups. Table 2 provides the results of w2 analyses. Among the 255 respondents, 135 respondents (52.9%) indicated that they had purchased airline, hotel, or time-share services via the Internet and 120 respondents (47.1%) indicated that they had not. Neither the respondent’s gender nor their income showed statistical significances, suggesting that the online purchase experience was not significantly different in terms of their gender and income. On the other hand, the respondent’s age revealed significant results (w2 ¼ 7:125; p ¼ :028), suggesting that the online purchasers and non-online purchasers differ according to the respondent’s age. Furthermore, the two groups varied in regard to their educational backgrounds (w2 ¼ 6:748; p ¼ 0:080). Similarly, the two groups differed in terms of the respondents’ browser use per day (w2 ¼ 18:290; p ¼ 0:000) and the years of Internet use (w2 ¼ 18:625; p ¼ 0:000). Approximately 64% of the respondents who used a browser 5 days or more per week had past online purchase experience, and 76% of those who had used the Internet for more than 3 years also had past online purchase experience. On the other hand, approximately 66% of the respondents who used browsers 2 days or less per week did not have any past online purchase experience, and 64% of those who had used the Internet for less than 1 year did not have any online purchase experience. Overall, respondents with past online purchase experience showed higher browser usage and more years of Internet use. Most of the results of the w2 analyses are consistent with the results of Bonn et al. (1998) and Weber and Roehl (1999). However, Weber and Roehl’s (1999) empirical
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Table 2 Respondent profiles and the results of w2 analysis Variable
Past online purchase experience Yes (n ¼ 135)
No (n ¼ 120)
Gender Male Female
65 (55.1%) 69 (51.1%)
53 (44.9%) 66 (48.9%)
Age 20–29 30–39 40 or over
59 (44.7%) 53 (60.2%) 20 (64.5%)
73 (55.3%) 35 (39.8%) 11 (35.5%)
Annual income Less than $18,500 $18,501–$37,000 $37,001 or more
72 (52.2%) 39 (56.5%) 23 (48.9%)
66 (47.8%) 30 (43.5%) 24 (51.1%)
Education High school 2-year college 4-year college Graduate school
5 61 53 15
8 70 32 10
Browser use per week Less than 2 days 3–4 days 5 days or more
29 (34.1%) 43 (60.6%) 63 (63.6%)
56 (65.9%) 28 (39.4%) 36 (36.4%)
Internet use Less than 1 year 1–2 years 2–3 years More than 3 years
28 39 32 34
49 36 22 11
(38.5%) (46.6%) (62.4%) (60.0%)
(36.4%) (52.0%) (59.3%) (75.6%)
w2
P
0.399
0.528
7.125
0.028
0.686
0.709
6.748
0.080
18.290
0.000
18.625
0.000
(61.5%) (53.4%) (37.6%) (40.0%)
(63.6%) (48.0%) (40.7%) (24.4%)
results found that online purchasers and non-online purchasers differed in income, while this study found that income was not a significant variable. The limited number of income categories in this study compared to the study by Weber and Roehl (1999) could partially explain why income was not found to be a significant variable in this study. In this study, income was categorized into smaller number categories because of sample size. Additionally, customers had less access to the Internet and online shopping was not widely accepted by the average consumer some years ago. At that time, income could be an important factor that differentiated online purchasers and non-purchasers. However, at present, online shopping is widely accepted and gaining popularity among almost all consumers regardless of income level, so online purchase behavior now correlates with technological familiarity (e.g., browser use per week and the years of Internet use).
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Table 3 Results of factor analysis Factora
Mean
SD
Factor loading
FACTOR 1: Convenience (0.71)b Freedom from hassles 24-h accessibility Easy payment procedures Appropriate product/service information Real-time location of available rooms
3.70 3.69 3.23 3.45 3.46
0.97 1.00 0.94 0.96 0.96
0.756 0.742 0.587 0.494 0.415
FACTOR 2: Ease of information search (0.74) Ease of comparing hotels Ease of contacting hotels Variety of choice Ease of finding what I want Ease of acquiring hotel-related information
3.53 3.35 3.51 3.53 3.20
1.07 1.08 1.03 1.01 0.95
0.823 0.775 0.525 0.501 0.479
FACTOR 3: Transaction (0.71) Ease of understanding policies Ease of placing orders Credibility of online transaction
3.00 3.20 3.02
0.89 0.92 0.90
0.729 0.724 0.613
FACTOR 4: Information credibility (0.65) Reliability of provided pictures Reliability of products/services information
2.94 3.12
0.96 0.91
0.843 0.714
FACTOR 5: Price (0.70) Reduced purchase-related costs Discounted price
2.98 2.77
0.75 0.69
0.853 0.797
FACTOR 6: Safety (0.61) Ease of canceling Security of sensitive information
2.92 2.85
0.96 0.94
0.792 0.570
Eigen value
Variance explained %
5.57
13.53
1.64
13.06
1.52
12.80
1.45
8.98
1.19
8.90
1.05
8.06
Total variance explained a b
65.33
Principal component factors with iterations: Varimax rotation. Reliability score (Cronbach’s a) for each factor grouping is shown in parentheses.
4.2. Factor analysis Prior to multiple regression analysis, the 19 determinants were factor analyzed using principal component analysis with orthogonal varimax rotation in order to identify the structure of determinants related to online hotel reservation. Table 3 presents the results relevant to the question of which determinants are important to explain the total variances in all the variables. The number of factors was determined by retaining only the factors with an eigenvalue of 1 or higher.
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The first factor, convenience, included five items: freedom from hassles, 24-h accessibility, easy payment procedures, appropriate product/service information, and real-time location of available rooms. The second factor, ease of information search consisted of five items: ease of comparing hotels, ease of contacting hotels, variety of choice, ease of finding what the customer wants, and ease of acquiring hotel-related information. The third factor, transaction included: ease of understanding policies, ease of placing orders, and credibility of online transaction. The fourth factor was related to information credibility, and the fifth factor was related to price. Finally, the sixth factor was related to safety which included ease of canceling and security of sensitive information. As seen, all factor loading scores were higher than 0.40 and the six extracted factors accounted for 65.33% of the variation in the original 19 items. Cronbach’s a coefficients of all factor dimensions were higher than 0.60 and were found to be reliable (Hair et al., 1995). 4.3. Multiple regression analysis To determine the importance of each factor to online reservation intention, two multiple regression analyses were conducted based on the earlier findings of the w2 analysis that online and non-online groups are heterogeneous. Online reservation intention was the dependent variable, while the six determinant factors were the independent variables. All variables were entered at the same time. Table 4 reports the results of the multiple regression analyses. For the online group, five factors such as convenience, ease of information search, transaction, price, and safety significantly influenced online reservation intention. Convenience turned out to be the most important factor followed by transaction, safety, ease of information search, and price. Overall, the regression results explained
Table 4 Regression results: factors affecting online reservation intention Past online purchase experience Factor
FACTOR FACTOR FACTOR FACTOR FACTOR FACTOR
Yes (n=135)
1: 2: 3: 4: 5: 6:
Convenience Ease of information search Transaction Information credibility Price Safety
Std. b
t
Std. b
t
0.317 0.236 0.244 0.076 0.211 0.241
3.622 2.708 2.850
0.245 0.136 0.160 0.151 0.178 0.200
2.428 1.378 1.630 1.488 1.820 2.010
0.871 2.450 2.812
Adjusted R2 ¼ 0:492 F ¼ 6:294 po0.10, po0.05, and po0.01.
No (n=120)
Adjusted R2 ¼ 0:426 F ¼ 3:626
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49.2% (adjusted R2 ) of the variance in online reservation intention. On the other hand, only three factors such as convenience, price, and safety were the significant factors for the non-online group. The adjusted R2 was 0.426, suggesting that 42.6% of the variation of online reservation intention was explained by the regression equation. As the results show, convenience, price, and safety were significant factors for both groups. It is essential to note the significant differences in determinant factors between the two groups. The results of the regression analyses indicated that online and non-online groups did not give the same importance to each determinant relative to making an online reservation. Unlike the non-online group, the online group considered ease of information search and transaction to be more important factors than price. Among the five significant factors, price was the least important factor for the online group. Hence, we conclude that price was a more important factor than ease of information search and transaction for first time online reservation users. However, as customers become more familiar with online reservations, price becomes a less important factor than ease of information search and transaction.
5. Conclusion This study empirically investigated the differences in demographic and behavioral characteristics of customers who purchased products online and those who had not. Online purchasers and non-online purchasers did not differ by gender and income. In terms of age and education level, people over the age of 30 and/or people who were highly educated were more likely to make reservations using the Internet. The results of this research also verified that online purchasers and non-online purchasers differed in their weekly browser usage and in the number of Internet use. Respondents with past online purchase experience reported higher weekly browser usage and more years of Internet use than those who did not have any experience with online purchasing. A major objective of this study was to identify different determinants that explain a customer’s online reservation intention for the online group and the non-online group. As the multiple regression results suggested, the significant factors that affected online reservation intention in both the online group and the non-online group were convenience, safety, and price. Ease of information search and transaction were the significant factors affecting online reservation intention only in the online group. On the other hand, the online group considered ease of information search and transaction to be more important than price. Based on the study findings, online hotel marketers need to take into consideration two different strategies depending on the life-cycle stage of the online reservation site. When online reservation systems are designed for the first time users (e.g., the introductory stage), convenience, price, and safety factors should be emphasized. However, when the online customer base reaches the growth stage, information search and transaction functions should be highlighted and integrated into the structure of online hotel reservation systems. As the number of hotel guests who
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have previous online purchase experience increases, most hotel online reservation systems should strengthen and emphasize ease of information search and transaction function. In other words, when online hospitality marketers focus on retaining existing online hotel reservation guests rather than creating new online guests, information search and transaction functions should be emphasized. Ease of information search may include the following: customers should be able to find important contact information within two clicks on the hotel web site. Frequently requested information should be displayed on the first page with hyperlinks. Eventually, online reservation systems should offer sufficient information for customer’s reservation decision-making. The availability of a virtual property tour would enable customers to better understand the hotel facilities. In terms of transaction features, ordering functionality should be designed simply and clearly. Improving the credibility and reliability of online transaction can include establishing a fast and stable reservation system and providing accurate information about hotel products, services, and facilities. Cancellation, refunds, and other general policies should be clearly explained and easily understood by online customers. An effective relationship between marketing and information systems departments should be established. Jeong and Lambert (2001) addressed the need for web researchers to pay special attention to customer’s web behavior patterns and their attitudes toward online reservation systems before and after using them. The results of this study suggest that important factors affecting hotel reservation intentions for the customers who have purchased online differ from those customers who have not purchased online. Over time, information that is frequently requested by the customers is likely to change. Consequently, to better meet the changing needs of the customers, the marketing department should establish a close working relationship with the information systems department. To acquire a competitive advantage, methods of data collection, analysis, interpretation, and implementation must be taken into consideration. Each time hotel guests make online reservations, they are asked to enter the number of rooms, duration of their stay, their address, name, phone number, credit card number, etc. Online hotel reservation systems facilitate the collection of information on customer characteristics and online behavior. Consequently, hospitality marketers can build guest databases easier than ever before and implement customized promotional activities that meet specific needs and wants of respective customers. Examples of such promotional activities include introducing packaged products, sending electronic hotel newsletters, and sending catalogs and direct mail to appropriate market segments. The physical distance between the customer and organization in online shopping makes online customers more concerned about the security of their sensitive information and the safety of using their credit cards. To alleviate customers’ privacy concerns, a focus should be placed on determining and mediating the major issues. Marketing managers must develop strategies to assure customers of the security and safety of online transactions. In addition, hotel employees who are building and
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maintaining a guest database should receive formal training on protecting the privacy of sensitive guest information.
6. Limitations and suggestions for future research The following limitations should be taken into consideration in interpreting the findings of this study. The first limitation is associated with the sampling method. The sampling method used in this study was a quota-sampling method, which is a non-probability sampling method. Thus, the study was restricted to generalization rather than specific application of the findings. The second limitation regards the attributes that were studied. Some seemingly important attributes of online purchasers and non-online purchasers were not included in this study, such as motivations, attitudes, and benefits. Hence, these other important attributes should be examined in future research. The third limitation is in regards to ‘intention’ itself. According to Banks (1950) and Katona (1960), more than 60% of the subjects who said they would buy actually did so. The results of their studies revealed that, while a relationship does exist between intent and action, buying intention does not always accurately reflect actual behavior. Future research should investigate the factors that affect actual online reservation behavior instead of reservation intention. Since the number of online hotel reservations was extremely low in Korea at the time of the survey, past online purchase experience in this study included airlines, hotels, and time-share resorts. However, future research should focus exclusively on customer experiences and behaviors pertaining to online hotel reservations. Additional suggestions for future research should include a ‘‘technology glossary’’. To the non-online customers, ‘‘technology glossaries’’ such as ‘‘freedom from hassles’’ and ‘‘appropriate information’’ might be perceived differently. Hence, the term ‘‘technology glossary’’ should be chosen carefully with detailed explanation when a survey instrument is designed, and particularly when the respondents are unfamiliar with the terms, which non-online customers are. Since online reservation is a relatively new phenomenon, many issues need to be addressed. First, there is a need for additional research on the physical components of online reservation systems, such as color combination and pictorial attributes. According to the study by Jeong et al. (2001), aesthetic quality such as color combination is an important component that affects customer’s perceptions of overall web site quality. Additional research is needed to explore in more detail the physical components of online reservation web sites. Thus, future researchers should investigate which color combinations improve customers’ overall perceptions of web site quality, and which pictorial attributes increase customers’ attention while viewing an online reservation web site. This study investigated determinants affecting online reservations. However, those factors could vary according to property characteristics such as size, type, and location. Those determinants could also vary according to the customer’s cultural background. Therefore, future research should investigate if determinants differ
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depending on the characteristics of property and cultural characteristics of respondents. In addition, some researchers (e.g., Shim et al., 2001; Jeong and Lambert, 2001) investigated online customer’s behavior patterns. However, it would be useful to further investigate online customer’s behavior patterns. Finally, the indication from this study that customers’ intentions to purchase online and the characteristics of income and experience differ depending on the level of Internet acceptance has implications for developing more precise theories on customer use of the Internet. In conclusion, noticeable increases in Internet users and functions provide opportunities for those in the hospitality to transform how they interact with customers. This study provides information that can be useful in the transformation process and offers implications for future practice and research. In today’s changing hospitality industry such information can provide the basis for sound decisionmaking and success.
References Ajzen, I., 1985. From intention to action: a theory of planned behavior. In: Kuhl, J., Beckman, J. (Eds.), Action Control: From Cognitions to Behaviors. Springer, New York, NY. Ajzen, I., 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50 (2), 179–211. Banks, S., 1950. The relationship between preference and purchase of brands. Journal of Marketing 15 (4), 145–157. Bonn, M.A., Furr, H.L., Susskind, A.M., 1998. Using the Internet as a pleasure travel planning tool: an examination of the sociodemographic and behavioral characteristics among Internet users and nonusers. Journal of Hospitality and Tourism Research 22 (3), 303–317. Chu, R., 2001. What online Hong Kong travelers look for on airline/travel websites? International Journal of Hospitality Management 20 (1), 95–100. Cline, R.S., Warner, M., 2001. Hospitality e-business: the future. The Bottomline [Online]. Available: http://www.hftp.org/Members/BottomLine/current/ebusiness.htm [July 16, 2001]. Cobanoglu, C., 2001. Analysis of business travelers’ hotel selection and satisfaction. Ph.D. Dissertation, Oklahoma State University. Dev, S.C., Olsen, M.D., 2000. Marketing challenges for the next decade. The Cornell Hotel and Restaurant Administration Quarterly 41 (1), 41–47. Forrester Research, 2001. NRF/Forrester Online Retail Index. Forrester Research, Inc., Cambridge, MA. Available: http://www.forrester.com/NRF/1,2873,0,00.html [October 15, 2001]. Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 1995. Multivariate Data Analysis 4th Edition. Prentice-Hall International, London. Jarvenpaa, S.L., Todd, P.A., 1997. Consumer reactions to electronic shopping on the World Wide Web. International Journal of Electronic Commerce 1 (2), 59–88. Jeong, M., Lambert, C.U., 2001. Adaptation of an information quality framework to measure customers’ behavioral intentions to use lodging Web sites. International Journal of Hospitality Management 20 (2), 129–146. Jeong, M., Oh, H., Gregoire, M., 2001. An Internet Marketing Strategy Study for the Lodging Industry. American Hotel & Lodging Foundation, Washington, DC. Kagan, A., Post, G., Noel, M.C., 2000. Selling the web: web features used by retailers. The Journal of Applied Business Research 16 (1), 15–26. Katona, G., 1960. Powerful Consumer: Psychological Studies of the American Economy. McGraw-Hill, New York, NY, pp. 80–83.
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Kelly, E.P., Rowland, H.C., 2000. Ethical and online privacy issues in electronic commerce. Business Horizons 43 (3), 3–12. Klein, L.R., 1998. Evaluating the potential of interactive media through a new lens: search versus experience goods. Journal of Business Research 41 (3), 195–203. Levi, M., Weitz, B.A., 2001. Retailing Management 4th Edition. McGraw-Hill, New York, NY. Liang, T., Huang, J., 1998. An empirical study on consumer acceptance of products in electronic markets: a transaction cost model. Decision Support Systems 24 (1), 29–43. Murphy, J., Forrest, E.J., Wotring, C.E., Brymer, R.A., 1996. Hotel management and marketing on the Internet: an analysis of sites and features. The Cornell Hotel and Restaurant Administration Quarterly 37 (3), 70–82. Olsen, M.D., Connolly, D.J., 2000. Experience-based travel: how technology is changing the hospitality industry. The Cornell Hotel and Restaurant Administration Quarterly 41 (1), 30–40. Poel, D.V.D., Leunis, J., 1999. Consumer acceptance of the Internet as a channel of distribution. Journal of Business Research 45 (3), 249–256. Rayman-Bacchus, L., Molina, A., 2001. Internet-based tourism services: business issues and trends. Futures 33 (7), 589–605. Shim, S., Eastlick, M.A., Lotz, S.L., Warrington, P., 2001. An online prepurchase intentions model: the role of intention to search. Journal of Retailing 77 (3), 397–416. Szymanski, D.M., Hise, R.T., 2000. E-satisfaction: an initial examination. Journal of Retailing 76 (3), 309–322. Weber, K., Roehl, W.S., 1999. Profiling people searching for and purchasing travel products on the World Wide Web. Journal of Travel Research 37 (3), 291–298. Wood, S.L., 2001. Remote purchase environments: the influence of return policy leniency on two-stage decision processes. Journal of Marketing Research 38 (2), 157–169. Yoon, S., 2002. The antecedents and consequences of trust in online-purchase decisions. Journal of Interactive Marketing 6 (2), 47–63.
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Factors affecting online hotel reservation intention between online and non-online customers Woo Gon Kim*, Dong Jin Kim School of Hotel and Restaurant Administration, Oklahoma State University, 210 Human Environmental Science West, Stillwater, OK 74078, USA
Abstract This study examined the differences between demographic and behavioral characteristics of customers who purchased products online and customers who did not. Additionally, this study investigated determinants that explain a customer’s online reservation intention. To meet the purpose of this study, the researchers surveyed customers from eight hotels in Korea. The data were analyzed using w2 analysis, factor analysis, and multiple regression analysis. The two types of respondents differed in regard to their age, educational background, weekly browser usage, and the number of years of Internet use. Furthermore, the results showed that the determinants affect the respondents’ online reservation intentions differently according to their past online purchasing experience. r 2004 Elsevier Ltd. All rights reserved. Keywords: Online reservation; Online purchase experience; Information technology; Information search; Hospitality industry
1. Introduction Experts anticipate that the Internet, with its recent noticeable increases in users and functions, will greatly transform hospitality organizations. For example, Olsen and Connolly (2000) argued that hospitality firms will experience significant transformations because of the increased customer base available on the Internet. Dev and Olsen (2000) discussed the role of information *Corresponding author. Tel.: +1-405-744-8483; fax: +1-405-744-6299. E-mail addresses: [email protected] (W.G. Kim), [email protected] (D.J. Kim). 0278-4319/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2004.02.001
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technology (IT) and suggested that the Internet will provide great opportunities for future sales. Rayman-Bacchus and Molina (2001) stated that information and computer technology, especially the Internet, have changed the socioeconomic context of tourism and, furthermore, that it will stimulate further changes. Though electronic commerce (e-commerce) is in its infancy, purchasing via the Internet is one of the most rapidly growing forms of shopping (Levi and Weitz, 2001). Forrester Research (2001) reported that Internet sales by consumers totaled $48.3 billion in 2000, while online hotel sales represented $3.7 billion in the same time period. Poel and Leunis (1999) revealed that hotel reservations were ranked second after concert tickets in terms of the average consumers’ propensity to buy a specific product or service. Hospitality corporations have responded to the opportunities offered by e-commerce by developing web sites to take full advantage of the practical and creative business uses of the Internet. The Internet is an alternate distribution channel that can be compatible with existing channels (Rayman-Bacchus and Molina, 2001). The use of web sites in hospitality organizations goes beyond simply promoting and selling products to consumers. The adoption of web sites also provides the hospitality firms with important business opportunities and competitive edges. Using the Internet as a reservation method can benefit the hospitality firms and also the customers by reducing costs and providing real-time information to both parties. According to Cobanoglu (2001), business travelers still use travel agents as their favorite hotel reservation resource followed by toll free reservation numbers, and then calling the hotel directly. Use of online hotel reservation system follows the previous three media in terms of favor. However, experts in IT predict that within several years the Internet will be one of the most important sources for hotel reservations and services (Cline and Warner, 2001). The number of online hotel reservations in 2001 accounted for 4.9% of total reservations made, and this percentage is expected to more than triple over the next 3 years. While the proportion of online reservations is increasing, only 64% of hospitality firms currently handle such transactions (Cline and Warner, 2001). Because an explosive increase in the number of online hotel reservations is expected, hotel marketers need to understand the determinants of customers’ online hotel reservation intentions. Despite the recent growing use of the Internet as a new reservation method, to the best of our knowledge, the factors that affect online hotel reservation intention have not yet been investigated. Considering the above information, this study aims to: 1. Examine the differences in demographic and behavioral characteristics between customers with previous online purchase experience and customers without any such experience. 2. Investigate the factors that affect hotel reservation intentions of online and nononline customers.
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Based on the above objectives, research questions are as follows: Research Question 1: Do customers who make online reservations differ from non-online customers with regard to demographic and behavioral characteristics (i.e., gender, age, income, education, weekly browser usage, and years of the Internet use)? Research Question 2: Do the determinants affect the customer’s online reservation intention differently according to one’s past online purchase experience?
2. Literature review As the importance of the Internet for the hospitality organizations grew and became more broadly accepted, researchers tried to find sociodemographic and behavioral characteristics of online customers. Bonn et al. (1998) argued that individuals who purchased travel online were likely to share sociodemographic characteristics. They investigated differences in the propensity to use the Internet based on the subjects’ sociodemographic and behavioral characteristics and found that gender did not significantly affect the customer’s information search behavior via the Internet, but age, income, and education level did. Similarly, Weber and Roehl (1999) provided a profile of people who used the Internet to search for travel information or to purchase travel arrangements. Their study found that the respondents who searched for travel information or who purchased travel products online reported higher incomes, higher status occupations, and more years of experience with the Internet than those who did not search or purchase online. The findings of these studies suggest that managers of hospitality organizations should understand the differences between sociodemographic and behavioral characteristics between online and non-online customers before implementing their promotion strategies. Previous researchers have also examined what current and potential online customers like to see from hospitality and travel web sites. In an earlier study, Murphy et al. (1996) found 32 common features in the hotel reservation sites found through search engines; those features were then divided into four categories: promotion, service, interactivity, and management. Jarvenpaa and Todd (1997) used a conceptual approach to examine consumer attitudes toward early features of online shopping. The shopping factors identified by online customers were compiled into four categories: product perceptions, shopping experience, customer service, and consumer risks. Chu (2001) conducted focus group interviews to identify Internet users’ needs and expectations toward airline/travel web sites. The results revealed that consumers were more willing to purchase low-involvement products via the Internet than high-involvement products. When asked what they expected to see from an airline/travel web site, the respondents identified informative, interactive, and attractive factors. Jeong et al. (2001) investigated consumer perceptions of hotel web sites. They found that potential online customers only moderately liked online reservation web
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sites. They concluded color combination, ease of use, navigation quality, information completeness, accuracy, and currency were crucial factors for increasing sales via the Internet. Out of the six key drivers of online transaction intentions, information completeness turned out to be the most critical for online customers’ satisfaction with web site information. Also, they discovered that color combination, information currency, accuracy, completeness, and navigation quality did have significant influences on customer’s perception of the hotel product. Yoon (2002) investigated the antecedents and consequences of trust and satisfaction in online purchase decisions. The antecedents included in the study were transaction security (i.e., security warranty phrases and clarity of refund policy), web site properties (i.e., adequacy of product description and width of product selections), navigation functionality (i.e., usefulness of help functions and overall operational efficiency) and personal variables (i.e., familiarity with e-commerce and previous satisfaction with ecommerce). The author found that the four antecedent dimensions influenced online customer’s purchase intentions, whereas web site trust and satisfaction operate as the mediating variables. The above studies focused on the online behavior of customers in the hospitality; however, studies that are more generally focused on consumer online behavior also inform this study. To better understand online purchasing behavior, theoretical foundations were developed from Internet-related studies of consumer’s perspectives. Jeong and Lambert’s (2001) empirical results showed that consumers’ perceived quality of information about products and services on the web was most crucial in predicting their decision-making. In their study of the four constructs of information quality (i.e., perceived usefulness, perceived ease of use, perceived accessibility, and attitudes), perceived usefulness and attitudes were powerful indicators in predicting the customers’ purchase behavior. In another study, Shim et al. (2001) proposed an Online Prepurchase Intentions Model based on the Interaction Model of the prepurchase consumer information search (Klein, 1998) and the Theory of Planned Behavior (Ajzen, 1985, 1991). They concluded that consumers’ intentions to use the Internet for purchasing were influenced by their attitudes (i.e., payment security, privacy, safety, etc.), perceived behavioral control, and Internet purchase experience. Based on empirical findings, Shim et al. (2001) argued that the information search was the single most crucial element leading to purchase via the Internet. In yet another study, Liang and Huang (1998) tested the ability of a transaction cost model to explain online consumers’ purchasing decisions. The researchers included search, comparison, examination, negotiation, order and payment, delivery, and post-service into the online transaction process. Results showed that transaction costs determined the consumer’s acceptance toward Internet shopping. The authors also argued for a learning effect in electronic shopping based on their finding that the determinants of customer acceptance for the online web shoppers were different from those of the non-online ones. According to their findings, uncertainty was the most significant construct for online shoppers, while asset specificity was the most significant for non-online shoppers.
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Finally, Szymanski and Hise (2000) determined that consumer’s perceptions of online convenience, product information, site design, and financial security were the dominant factors in consumer assessments of satisfaction. After examining web sites of retailers, Kagan et al. (2000) concluded that consumers wanted such advanced functions as checking real-time availability and tracking completed orders although current web sites that had only basic transaction functions. The results also indicated that consumers wanted greater assurances of confidentiality and privacy. These findings are consistent with other studies that showed that issues such as privacy (Kelly and Rowland, 2000) and return policies (Wood, 2001) substantially influenced customer’s perceptions of safety regarding online shopping. In summary, the review of the literature revealed online customer decision-making is an important area of study for the hospitality industry and, furthermore, that a body of knowledge exists in the area of consumer online decision-making. However, no study was found that described the differences in the factors that affect hotel reservation intentions of online and non-online customers.
3. Methods To answer the research questions, survey data were collected from the customers in eight hotels in Korea and analyzed. The following sections describe the measurement and analysis, and the subjects for this study. 3.1. Measurement and analysis In this study, a questionnaire comprising 19 determinants was designed to measure the online shopper’s perceived importance toward each determinant. The determinants pertaining to online hotel reservation were developed from previous studies by Jarvenpaa and Todd (1997) and Weber and Roehl (1999), as well as from a focus group interview. The focus group consisted of five hotel managers in charge of online reservations, and five hotel guests who primarily reserved their rooms online. The determinants of online hotel reservation intention used in the study are shown in Table 1. The questionnaire consisted of three sections. The first section measured customer’s perceived importance of online hotel reservation. The respondents were asked to rate the importance of each determinant using a 5-point Likert scale (1=least important; 5=most important). The second section measured respondent’s past online purchase experiences (1=yes, 2=no) and online reservation intention of hotel products and services using a 7-point Likert scale (1=most unlikely, 7=most likely). The third section was designed to obtain the respondent’s demographic and behavioral characteristics: gender, age, income, education level, browser use per week, and number of years of Internet use. According to a recent study by Shim et al. (2001), past purchase experience via the Internet was one of the most important factors (i.e., attitude, behavioral control, and purchase experience) in predicting Internet purchase intention. Liang and Huang
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Table 1 Determinants of online hotel reservation intention used in the study Determinants
Jarvenpaa and Todd (1997)
Freedom from hassles 24-h accessibility Easy payment procedures Appropriate product/service information Real-time location of available rooms Ease of comparing hotels Ease of contacting hotels Ease of finding what I want Variety of choices Ease of acquiring hotel-related information Ease of understanding policy Ease of placing orders Credibility of online transaction Reliability of provided pictures Reliability of products/services information Reduced purchase-related costs Discounted price Ease of canceling Security of sensitive information
O O
O O O O O O O O O O O O O
Weber and Roehl (1999) O O O
O O O O O O O O
This study O O O O O O O O O O O O O O O O O O O
(1998) indicated that non-online and online consumers had different considerations while purchasing electronically. Until recently, the percentage of hotel reservations made online was extremely low in Korea, so limiting the subjects of a study exclusively to guests who made online hotel reservations would produce insufficient data for quantitative analysis. Therefore, for the purpose of this study, respondents who had any past online purchase experience related to airlines, hotels, or time-share resorts were considered to have past online purchase experience. Online purchase experiences with airlines and time-share resorts were included due to the similarity of hotel products in terms of the characteristics of high risk, relatively high price, and intangibility. Online reservation intention was used as the best proxy of real reservation behavior. The collected data were analyzed using Statistical Package for Social Science (SPSS) version 10.0. First, w2 analysis was conducted to find out the differences of demographic and behavioral characteristics of customers who had purchased products online and those who had not. Factor analysis was utilized to determine the underlying structure of the original 19 determinants toward online reservations. Finally, multiple regression analysis was employed to investigate the causal effect of extracted factors on the online reservation intention. 3.2. Sampling Fifty-five questionnaires were distributed for the purpose of pre-testing in May 2001. Based on the comments collected during the pre-testing period, a complete
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questionnaire was designed. A quota-sampling method was adopted for data collection. The number of questionnaires distributed in each hotel was based on the number of rooms in that hotel. The questionnaires were distributed to Korean hotel guests who stayed in eight five-star hotels in Seoul when they checked in at their hotels. The front desk clerks encouraged the guests to complete the form with a prepared speech. The guests were asked to either return the completed questionnaires to the front office or leave them in their room for housekeeping personnel to collect. The questionnaires were collected from May 11 through May 25, 2001. Most hotels were business hotels, mainly due to their location in the city of Seoul, therefore, most respondents were business clients. Out of 500 questionnaires that were distributed, a total of 262 (52.4%) questionnaires were returned. Of the returned questionnaires, 7 were eliminated because of an excessive amount of missing data. After elimination, 255 questionnaires (51.0%) were coded and analyzed for the empirical investigation.
4. Results 4.1. Sample characteristics The w2 analyses were conducted to investigate differences in the respondent’s demographic and behavioral characteristics (i.e., gender, age, income, education level, browser use per week, and the number of years of Internet use) between the online and non-online groups. Table 2 provides the results of w2 analyses. Among the 255 respondents, 135 respondents (52.9%) indicated that they had purchased airline, hotel, or time-share services via the Internet and 120 respondents (47.1%) indicated that they had not. Neither the respondent’s gender nor their income showed statistical significances, suggesting that the online purchase experience was not significantly different in terms of their gender and income. On the other hand, the respondent’s age revealed significant results (w2 ¼ 7:125; p ¼ :028), suggesting that the online purchasers and non-online purchasers differ according to the respondent’s age. Furthermore, the two groups varied in regard to their educational backgrounds (w2 ¼ 6:748; p ¼ 0:080). Similarly, the two groups differed in terms of the respondents’ browser use per day (w2 ¼ 18:290; p ¼ 0:000) and the years of Internet use (w2 ¼ 18:625; p ¼ 0:000). Approximately 64% of the respondents who used a browser 5 days or more per week had past online purchase experience, and 76% of those who had used the Internet for more than 3 years also had past online purchase experience. On the other hand, approximately 66% of the respondents who used browsers 2 days or less per week did not have any past online purchase experience, and 64% of those who had used the Internet for less than 1 year did not have any online purchase experience. Overall, respondents with past online purchase experience showed higher browser usage and more years of Internet use. Most of the results of the w2 analyses are consistent with the results of Bonn et al. (1998) and Weber and Roehl (1999). However, Weber and Roehl’s (1999) empirical
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Table 2 Respondent profiles and the results of w2 analysis Variable
Past online purchase experience Yes (n ¼ 135)
No (n ¼ 120)
Gender Male Female
65 (55.1%) 69 (51.1%)
53 (44.9%) 66 (48.9%)
Age 20–29 30–39 40 or over
59 (44.7%) 53 (60.2%) 20 (64.5%)
73 (55.3%) 35 (39.8%) 11 (35.5%)
Annual income Less than $18,500 $18,501–$37,000 $37,001 or more
72 (52.2%) 39 (56.5%) 23 (48.9%)
66 (47.8%) 30 (43.5%) 24 (51.1%)
Education High school 2-year college 4-year college Graduate school
5 61 53 15
8 70 32 10
Browser use per week Less than 2 days 3–4 days 5 days or more
29 (34.1%) 43 (60.6%) 63 (63.6%)
56 (65.9%) 28 (39.4%) 36 (36.4%)
Internet use Less than 1 year 1–2 years 2–3 years More than 3 years
28 39 32 34
49 36 22 11
(38.5%) (46.6%) (62.4%) (60.0%)
(36.4%) (52.0%) (59.3%) (75.6%)
w2
P
0.399
0.528
7.125
0.028
0.686
0.709
6.748
0.080
18.290
0.000
18.625
0.000
(61.5%) (53.4%) (37.6%) (40.0%)
(63.6%) (48.0%) (40.7%) (24.4%)
results found that online purchasers and non-online purchasers differed in income, while this study found that income was not a significant variable. The limited number of income categories in this study compared to the study by Weber and Roehl (1999) could partially explain why income was not found to be a significant variable in this study. In this study, income was categorized into smaller number categories because of sample size. Additionally, customers had less access to the Internet and online shopping was not widely accepted by the average consumer some years ago. At that time, income could be an important factor that differentiated online purchasers and non-purchasers. However, at present, online shopping is widely accepted and gaining popularity among almost all consumers regardless of income level, so online purchase behavior now correlates with technological familiarity (e.g., browser use per week and the years of Internet use).
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Table 3 Results of factor analysis Factora
Mean
SD
Factor loading
FACTOR 1: Convenience (0.71)b Freedom from hassles 24-h accessibility Easy payment procedures Appropriate product/service information Real-time location of available rooms
3.70 3.69 3.23 3.45 3.46
0.97 1.00 0.94 0.96 0.96
0.756 0.742 0.587 0.494 0.415
FACTOR 2: Ease of information search (0.74) Ease of comparing hotels Ease of contacting hotels Variety of choice Ease of finding what I want Ease of acquiring hotel-related information
3.53 3.35 3.51 3.53 3.20
1.07 1.08 1.03 1.01 0.95
0.823 0.775 0.525 0.501 0.479
FACTOR 3: Transaction (0.71) Ease of understanding policies Ease of placing orders Credibility of online transaction
3.00 3.20 3.02
0.89 0.92 0.90
0.729 0.724 0.613
FACTOR 4: Information credibility (0.65) Reliability of provided pictures Reliability of products/services information
2.94 3.12
0.96 0.91
0.843 0.714
FACTOR 5: Price (0.70) Reduced purchase-related costs Discounted price
2.98 2.77
0.75 0.69
0.853 0.797
FACTOR 6: Safety (0.61) Ease of canceling Security of sensitive information
2.92 2.85
0.96 0.94
0.792 0.570
Eigen value
Variance explained %
5.57
13.53
1.64
13.06
1.52
12.80
1.45
8.98
1.19
8.90
1.05
8.06
Total variance explained a b
65.33
Principal component factors with iterations: Varimax rotation. Reliability score (Cronbach’s a) for each factor grouping is shown in parentheses.
4.2. Factor analysis Prior to multiple regression analysis, the 19 determinants were factor analyzed using principal component analysis with orthogonal varimax rotation in order to identify the structure of determinants related to online hotel reservation. Table 3 presents the results relevant to the question of which determinants are important to explain the total variances in all the variables. The number of factors was determined by retaining only the factors with an eigenvalue of 1 or higher.
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The first factor, convenience, included five items: freedom from hassles, 24-h accessibility, easy payment procedures, appropriate product/service information, and real-time location of available rooms. The second factor, ease of information search consisted of five items: ease of comparing hotels, ease of contacting hotels, variety of choice, ease of finding what the customer wants, and ease of acquiring hotel-related information. The third factor, transaction included: ease of understanding policies, ease of placing orders, and credibility of online transaction. The fourth factor was related to information credibility, and the fifth factor was related to price. Finally, the sixth factor was related to safety which included ease of canceling and security of sensitive information. As seen, all factor loading scores were higher than 0.40 and the six extracted factors accounted for 65.33% of the variation in the original 19 items. Cronbach’s a coefficients of all factor dimensions were higher than 0.60 and were found to be reliable (Hair et al., 1995). 4.3. Multiple regression analysis To determine the importance of each factor to online reservation intention, two multiple regression analyses were conducted based on the earlier findings of the w2 analysis that online and non-online groups are heterogeneous. Online reservation intention was the dependent variable, while the six determinant factors were the independent variables. All variables were entered at the same time. Table 4 reports the results of the multiple regression analyses. For the online group, five factors such as convenience, ease of information search, transaction, price, and safety significantly influenced online reservation intention. Convenience turned out to be the most important factor followed by transaction, safety, ease of information search, and price. Overall, the regression results explained
Table 4 Regression results: factors affecting online reservation intention Past online purchase experience Factor
FACTOR FACTOR FACTOR FACTOR FACTOR FACTOR
Yes (n=135)
1: 2: 3: 4: 5: 6:
Convenience Ease of information search Transaction Information credibility Price Safety
Std. b
t
Std. b
t
0.317 0.236 0.244 0.076 0.211 0.241
3.622 2.708 2.850
0.245 0.136 0.160 0.151 0.178 0.200
2.428 1.378 1.630 1.488 1.820 2.010
0.871 2.450 2.812
Adjusted R2 ¼ 0:492 F ¼ 6:294 po0.10, po0.05, and po0.01.
No (n=120)
Adjusted R2 ¼ 0:426 F ¼ 3:626
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49.2% (adjusted R2 ) of the variance in online reservation intention. On the other hand, only three factors such as convenience, price, and safety were the significant factors for the non-online group. The adjusted R2 was 0.426, suggesting that 42.6% of the variation of online reservation intention was explained by the regression equation. As the results show, convenience, price, and safety were significant factors for both groups. It is essential to note the significant differences in determinant factors between the two groups. The results of the regression analyses indicated that online and non-online groups did not give the same importance to each determinant relative to making an online reservation. Unlike the non-online group, the online group considered ease of information search and transaction to be more important factors than price. Among the five significant factors, price was the least important factor for the online group. Hence, we conclude that price was a more important factor than ease of information search and transaction for first time online reservation users. However, as customers become more familiar with online reservations, price becomes a less important factor than ease of information search and transaction.
5. Conclusion This study empirically investigated the differences in demographic and behavioral characteristics of customers who purchased products online and those who had not. Online purchasers and non-online purchasers did not differ by gender and income. In terms of age and education level, people over the age of 30 and/or people who were highly educated were more likely to make reservations using the Internet. The results of this research also verified that online purchasers and non-online purchasers differed in their weekly browser usage and in the number of Internet use. Respondents with past online purchase experience reported higher weekly browser usage and more years of Internet use than those who did not have any experience with online purchasing. A major objective of this study was to identify different determinants that explain a customer’s online reservation intention for the online group and the non-online group. As the multiple regression results suggested, the significant factors that affected online reservation intention in both the online group and the non-online group were convenience, safety, and price. Ease of information search and transaction were the significant factors affecting online reservation intention only in the online group. On the other hand, the online group considered ease of information search and transaction to be more important than price. Based on the study findings, online hotel marketers need to take into consideration two different strategies depending on the life-cycle stage of the online reservation site. When online reservation systems are designed for the first time users (e.g., the introductory stage), convenience, price, and safety factors should be emphasized. However, when the online customer base reaches the growth stage, information search and transaction functions should be highlighted and integrated into the structure of online hotel reservation systems. As the number of hotel guests who
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have previous online purchase experience increases, most hotel online reservation systems should strengthen and emphasize ease of information search and transaction function. In other words, when online hospitality marketers focus on retaining existing online hotel reservation guests rather than creating new online guests, information search and transaction functions should be emphasized. Ease of information search may include the following: customers should be able to find important contact information within two clicks on the hotel web site. Frequently requested information should be displayed on the first page with hyperlinks. Eventually, online reservation systems should offer sufficient information for customer’s reservation decision-making. The availability of a virtual property tour would enable customers to better understand the hotel facilities. In terms of transaction features, ordering functionality should be designed simply and clearly. Improving the credibility and reliability of online transaction can include establishing a fast and stable reservation system and providing accurate information about hotel products, services, and facilities. Cancellation, refunds, and other general policies should be clearly explained and easily understood by online customers. An effective relationship between marketing and information systems departments should be established. Jeong and Lambert (2001) addressed the need for web researchers to pay special attention to customer’s web behavior patterns and their attitudes toward online reservation systems before and after using them. The results of this study suggest that important factors affecting hotel reservation intentions for the customers who have purchased online differ from those customers who have not purchased online. Over time, information that is frequently requested by the customers is likely to change. Consequently, to better meet the changing needs of the customers, the marketing department should establish a close working relationship with the information systems department. To acquire a competitive advantage, methods of data collection, analysis, interpretation, and implementation must be taken into consideration. Each time hotel guests make online reservations, they are asked to enter the number of rooms, duration of their stay, their address, name, phone number, credit card number, etc. Online hotel reservation systems facilitate the collection of information on customer characteristics and online behavior. Consequently, hospitality marketers can build guest databases easier than ever before and implement customized promotional activities that meet specific needs and wants of respective customers. Examples of such promotional activities include introducing packaged products, sending electronic hotel newsletters, and sending catalogs and direct mail to appropriate market segments. The physical distance between the customer and organization in online shopping makes online customers more concerned about the security of their sensitive information and the safety of using their credit cards. To alleviate customers’ privacy concerns, a focus should be placed on determining and mediating the major issues. Marketing managers must develop strategies to assure customers of the security and safety of online transactions. In addition, hotel employees who are building and
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maintaining a guest database should receive formal training on protecting the privacy of sensitive guest information.
6. Limitations and suggestions for future research The following limitations should be taken into consideration in interpreting the findings of this study. The first limitation is associated with the sampling method. The sampling method used in this study was a quota-sampling method, which is a non-probability sampling method. Thus, the study was restricted to generalization rather than specific application of the findings. The second limitation regards the attributes that were studied. Some seemingly important attributes of online purchasers and non-online purchasers were not included in this study, such as motivations, attitudes, and benefits. Hence, these other important attributes should be examined in future research. The third limitation is in regards to ‘intention’ itself. According to Banks (1950) and Katona (1960), more than 60% of the subjects who said they would buy actually did so. The results of their studies revealed that, while a relationship does exist between intent and action, buying intention does not always accurately reflect actual behavior. Future research should investigate the factors that affect actual online reservation behavior instead of reservation intention. Since the number of online hotel reservations was extremely low in Korea at the time of the survey, past online purchase experience in this study included airlines, hotels, and time-share resorts. However, future research should focus exclusively on customer experiences and behaviors pertaining to online hotel reservations. Additional suggestions for future research should include a ‘‘technology glossary’’. To the non-online customers, ‘‘technology glossaries’’ such as ‘‘freedom from hassles’’ and ‘‘appropriate information’’ might be perceived differently. Hence, the term ‘‘technology glossary’’ should be chosen carefully with detailed explanation when a survey instrument is designed, and particularly when the respondents are unfamiliar with the terms, which non-online customers are. Since online reservation is a relatively new phenomenon, many issues need to be addressed. First, there is a need for additional research on the physical components of online reservation systems, such as color combination and pictorial attributes. According to the study by Jeong et al. (2001), aesthetic quality such as color combination is an important component that affects customer’s perceptions of overall web site quality. Additional research is needed to explore in more detail the physical components of online reservation web sites. Thus, future researchers should investigate which color combinations improve customers’ overall perceptions of web site quality, and which pictorial attributes increase customers’ attention while viewing an online reservation web site. This study investigated determinants affecting online reservations. However, those factors could vary according to property characteristics such as size, type, and location. Those determinants could also vary according to the customer’s cultural background. Therefore, future research should investigate if determinants differ
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depending on the characteristics of property and cultural characteristics of respondents. In addition, some researchers (e.g., Shim et al., 2001; Jeong and Lambert, 2001) investigated online customer’s behavior patterns. However, it would be useful to further investigate online customer’s behavior patterns. Finally, the indication from this study that customers’ intentions to purchase online and the characteristics of income and experience differ depending on the level of Internet acceptance has implications for developing more precise theories on customer use of the Internet. In conclusion, noticeable increases in Internet users and functions provide opportunities for those in the hospitality to transform how they interact with customers. This study provides information that can be useful in the transformation process and offers implications for future practice and research. In today’s changing hospitality industry such information can provide the basis for sound decisionmaking and success.
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