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Article

Price or Convenience: What Is More Important for Online and Offline Bookings? A Study of a Five-Star Resort Hotel in Taiwan

by
Yi-Man Teng
1,
Kun-Shan Wu
2,* and
Che-Yi Chou
2
1
College of Commerce, Yango University, Fuzhou 350015, China
2
Department of Business Administration, Tamkang University, Taipei 25137, Taiwan
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(10), 3972; https://doi.org/10.3390/su12103972
Submission received: 12 March 2020 / Revised: 29 April 2020 / Accepted: 8 May 2020 / Published: 12 May 2020
(This article belongs to the Special Issue Explore Online Hospitality Management: Price and Reputation)
Versions Notes

Abstract

:
Low price or convenience? What is more important for online and offline bookings? For the sustainable development of the hospitality industry, it is necessary to know the opinions of customers about their online/offline hotel booking experiences. This study aimed to predict the customers’ online/offline booking behavior toward a resort hotel in Taiwan by adapting the marketing mix elements. We first designed and executed a detailed questionnaire involving approximately 300 respondents from a five-star hotel in Taiwan, used hypothesis testing to extract important factors from the data, and finally used logistic regression of these factors to predict the customer choices effectively. The study results show that the majority of the customers believe that booking hotel services online provides for a broader choice, offers more discounts and more privacy than available while booking offline, and is cheaper, faster, and more reliable and convenient. The outcomes of logistic regression confirm that “broad choice,” but not “low price” has the most significant impact on the customers booking a resort accommodation online or offline. Furthermore, the results of the analysis of variance (ANOVA) show that the younger customers (under the age of 45) and those with university degrees are significantly more likely to make the reservations online for a resort. The findings and the recommendations can provide valuable inputs to the resort industry practitioners in Taiwan for improving the online booking services.

1. Introduction

Nowadays, innovative development and a wide array of access to information have changed the way people travel [1]. Scholars have indicated that the tourism industry was one of the first to be affected by the Internet [2]. Syed and Suroso [3] have also asserted that tourism has been a sector that has recently progressed rapidly with the use of e-commerce or m-commerce. With the growth of Information and Communication Technologies (ICT) and the Internet, tourism products can now be directly purchased on the Internet, more specifically, through Online Travel Agencies (OTAs) or provider-owned websites [4]. The technology most influential in changing the behavior of tourists has been ICT [5], which has transformed the tourism industry globally [6]. According to Statista 2019, in 2016 the global online travel sales worldwide amounted to 564.87 billion U.S. dollars. It was projected to be 755.94 billion U.S. dollars in 2019 [7].
For decades, more and more consumers have been booking hotel accommodations on hotel websites directly [8] or through OTAs [9]. Among travel products, the top purchases that consumers make online are hotel accommodation bookings [10]. The easy access to the Internet makes reserving hotel accommodation possible with the press of a button on a person’s electronic device [9].
Mills and Law [11] have asserted that the Internet has changed tourist service users’ behavior. Consumers seek information on hotels, travel, and other facilities on the Internet rather than depending on traditional travel agencies. [5]. When consumers book hotel accommodation online, they now have access to several booking channels, namely the OTAs and the hotels’ websites. OTAs play a main role in the vast Internet market of hospitality transactions [9]. Hotels try to utilize the Internet to its full potential, including for online marketing, regardless of the type of hotel [12]. Research shows that websites alone do not attract potential or current guests for sure [13]. Hotels relying on booking opportunities through their websites find it hard to attract potential customers worldwide. In recent years, the coexistence of hotels and OTAs has fallen on hard times. Along with the rising market share of OTAs, the commissions of OTAs have increased too, leading to decreased profitability for hotels; OTAs have become a double-edged sword for hotels [14]. The relationship is now both of a competitive and a cooperative spirit, even though hotels still expect to increase their exposure through multiple channels of OTAs. Worldwide, tourists browse OTA websites first and visit the hotel websites later on, looking for more specific information. This worked fairly well when the hotels paid large commissions (15–30%). From the Statista 2019 report on the revenue of online gross travel bookings worldwide, revenues have risen from 340 billion U.S. dollars in 2011 to 567 billion U.S. dollars in 2017, an increase of almost 66.7% [15].
Hotel website quality is a strong predictor of eTrust and consumers’ online booking intentions [3,16]. While the proportion of online hotel bookings is increasing, hotel marketers need to understand the determinants of customers’ online hotel booking intentions. There is an increasing number of papers dealing with consumers’ intentions to book hotel accommodations online, emphasizing their trust and commitment [16,17,18,19,20], ease of use and interactive ability of the websites [21], website features and updates [19,22,23,24], hotel response times [25,26,27], online hotel ratings [28,29], price fairness perception [30,31], and the consumer demographics and their behavioral trends [32,33]. Most research studies on online hotel bookings also deal with social influence, reviews, and the phenomenon of e-word of mouth [34,35,36,37,38,39,40,41,42].
The results of previous studies concerning price and cost savings prove that lack of additional booking fees and better pricing are benefits of online bookings [17]. Many hotels have been adopting promotional strategies to help in shaping customers’ choices [43]. Lien et al. [44] asserted that customers tend to believe that “price” positively affects their online hotel booking intentions. In the competitive environment, “price” is referred to as a heuristic cue that positively influences purchase intentions [45]. Parallel to this view, price discounts enhance the probability that a customer will consider and ultimately book hotel accommodation [46,47]. Overall, price discounts may be a key factor when choosing to book online or offline.
While there are many studies related to online hotel bookings, there is a scarcity of studies looking at the price factor and the integration of e-marketing models in analyzing customers’ online hotel booking behavior. Hoteliers compete to offer the best in delivering their services in all of the seven realms of the service marketing mix, comprising the 7Ps—product, price, place, promotion, people, process, and physical evidence [48]. The 7Ps marketing mix is a conceptual framework that highlights the principal decisions marketing managers make while configuring their offerings to the customers’ needs [48]. Prior studies have adopted part of the 7Ps marketing mix elements to evaluate customers’ intentions or preferences on online hotel booking, such as product [3], price [31], promotion [49], place [50], people response [26,27,28], process [27], and physical evidence [51]. However, there is a lack of studies using the 7Ps marketing mix elements to better understand the complete picture of the changes in the hotel accommodation reservation journey. Therefore, this study used the 7Ps marketing mix elements to classify customers’ online/offline booking considerations based on the type of hotel known as resorts. As far as we could find, no literature has discussed the online booking behavior intentions of customers for resorts.
To fill the gap in the literature, this study aimed to (1) study whether the 7Ps marketing mix elements could significantly influence the customers to book a resort online or offline; (2) investigate how the differences in customer demographics could influence online hotel reservation behavior.

2. Literature Review

2.1. Booking Hotel Online and Offline

Booking online and offline are both necessary channels in booking hotel methods. Some customers rely on offline booking hotel, such as walk-in, hotel telephone, fax, reservation center, etc. Some customers rely on online booking hotel, such as OTA, Internet, etc.
Online booking facilities enable establishments to sell hotel rooms both through direct distribution (hoteliers) and intermediaries (OTAs). Hazbar, Mohanned, and Patwary [52] have asserted that the Internet is a useful tool for consumers and suppliers for purchasing products and communicating information in the tourism industry. Due to the several advantages offered to the customers, such as convenience, access to a good comparison of the prices [3], low cost [53], and the provision of latest and accurate information [53], the number of online bookings has grown dramatically during recent decades. This new model of sales is focused on reaching many customers, at any day and time, without geographical limits [4]. Many hotels are focusing on increasing their market share through cooperating with OTAs, which shows how important such cooperative ventures are for hotels to leverage their sales [4]. According to Feinstein [54], with 52% of OTA visitors clicking on hotel websites to receive more information on the hotel and what it offers, hotels could take advantage of the opportunity to convert some of the traffic into direct bookings.

2.2. The Influence of Demographic Variables on Booking Online and Offline

Bonn, Furr, and Suskind [55] have pointed out that while higher household incomes and higher education levels showed significant effects on the online booking behavior of customers, gender did not have any considerable impact. Kim and Kim [56] suggested that highly educated people over the age of 30, who recorded more weekly Internet usage, were more likely to make reservations using the Internet, from their study of eight hotels in Korea. They verified that the demographic profiles of the visitors, such as their educational backgrounds, incomes, and ages, and the results showed different correlations related to their online booking intentions for five-star hotels in Macau [32]. The authors also found that those who continued to make more offline bookings tended to be male, older, less educated, and to have lower incomes. Besides that, the study asserted that online and offline users were two different groups of consumers. Further, the study analyzed the effect of gender on brand image, price, trust, and value for online hotel booking, although the results showed no significant differences between the males and the females regarding their purchase intentions [57]. According to the above discussion, the following hypothesis is proposed.
Hypothesis 1 (H1).
Customers’ decision to book hotel accommodation online or offline differs according to demographics variables.
Hypothesis 1 (H1a).
Customers’ decision to book hotel accommodation online or offline differs according to customers’ gender.
Hypothesis 1 (H1b).
Customers’ decision to book hotel accommodation online or offline differs according to customers’ age.
Hypothesis 1 (H1c).
Customers’ decision to book hotel accommodation online or offline differs according to customers’ income.
Hypothesis 1 (H1d).
Customers’ decision to book hotel accommodation online or offline differs according to customers’ level of education.

2.3. Hotel Booking Behavior Based on the 7Ps Marketing Mix Elements

Hotel marketing product and service planning, communication, and delivery are generally based on the marketing mix elements of the 7Ps (product, price, place, promotion, personnel, process, and physical evidence) [58,59]. Therefore, adoption of marketing mix elements for hotel booking would be relevant and useful for top management and marketers in the hotel industry.
  • Product refers to the product or service that enterprises hope to sell. The rooms provided to the travelers to stay for a while is the main product of a hotel [60]. The purchase of a hotel room includes the guest room, fitness center, pool, restaurants, valet, concierge, housekeeping services, etc. [61]. Hotels cater to different market segments and each of these segments has different requirements. For example, a leisure guest on a family trip looks for recreational and wellness facilities of the hotel. In contrast, a business traveler gives importance to factors like proximity to business centers, and facilities like video conferencing and good in-room Internet connectivity [62]. Thus, the broad choice of hotel rooms, hotel brands, and other available travel products has led to an increased trend to attract potential customers [3,63]. These studies also demonstrated that, if customers wanted a broad selection, they would typically book online. As the above literature [3,63] discussed, with the significance of hotel broad choice, it is assumed that broad choice is the representative factor concerning the product in this study.
  • Price refers to the amount a consumer pays for the good or service [60]. The hotel rate codes and packages are defined keeping in mind attracting or impressing customers [62]. The reference prices of the hotel will be revealed to the customers while shopping online [64]; they could then form a perception of product prices by comparing the prices offered with the reference price on other shopping websites. When measured on a comparative basis, price has a direct and positive effect on the customers’ overall satisfaction or their intention to return and purchase a product online [65]. In addition, research indicated that low price was the most significant factor determining whether customers book hotel accommodation online or offline [53,66]. They also demonstrated that, if customers wanted lower prices, they would typically book online. In this study, it is also assumed low price is an important factor whether customers book a hotel online or offline.
  • Place (distribution) refers to the accessibility of the products to the consumers. For the hotel sector, place refers to how the service is provided to travelers. Generally, the place to build a hotel is selected for strategic reasons, such as near a tourist attraction, city, or business center. Usually, online booking provides easy accessibility at any time and any place that may influence consumer purchasing behavior. Alipour, Hajaliakbari, and Javanbakht [67] confirmed that a company website was more like a counter in a retail store, giving people access to the products or services they wanted. This allowed the customers to have the convenience of finding the various elements related to their trips on the same website they booked their hotel accommodations on online [68]. To echo the literature [68], concerning the prominence factor of “convenience” while booking a hotel, we put forward “booking convenience” to represent place (distribution) in this study.
  • Promotion is the way companies communicate to the target customers. The promotion mix includes advertising, public relations, sales promotion, word-of-mouth promotion, and personal selling and telemarketing [61]. Using advertisements on travel websites related to the location of a hotel is a kind of promotion strategy for hotels. Promotion could also mean directly advertising to past clients using social media and press releases [60]. Nevertheless, generally speaking, the discount amount remained the most attractive factor for the customers. In line with previous views, in this study, we put forward “discount allowance” to represent the factor of promotion.
  • People refer to those individuals involved in the delivery of services directly or indirectly [60]. People belonging to hotels interact with the customers and include those in the job roles of receptionist, doorkeeper, concierge staff, manager, recreation center staff, gym staff, waiter, spa staff, etc. Customer satisfaction is directly linked to the experiences of these people who provide the service to the customers. Lee and Cheng [53] and Chow [63] indicated that “reliable service” was one of the significant factors determining whether customers booked hotel accommodations online or offline. In line with previous views, in this study, we put forward “reliable service” to represent the factor of people.
  • Process refers to the flow of activities, mechanisms, and procedures that happen when the consumers and the establishment interact with each other [60]. When the process is more convenient, the customer is more likely to be attracted and retained [69]. Several of the existing studies demonstrated that the customers perceived the hotel or OTA website to be easy to use, and this had a significant impact on their intention to book offline or online [18,61,70]. In addition, Chow [63] also indicated that the “speed of booking process” was an important factor determining whether customers booked the resort services online or offline. Chow [63] also demonstrated that, if the customers wanted the reservations to be made quickly, they would typically book online. To reply to the above scholars’ view, we put forward “process speed” to represent the factor of process.
  • Physical evidence has a profound impact on customers’ impressions [71]. It refers to the external environment while customers are using the purchased services [72]. Physical evidence is also called the service environment, and is related to the appearance and style of the physical surroundings and other elements encountered by the customer while being served from the establishment [73]. Physical evidence is also important for online booking websites because it conveys to the customer an external image of the website [23,74,75]. Furthermore, Chow [63] indicated that privacy protection was a significant factor determining whether customers booked the resort accommodations online or offline. To echo the literature [63], concerning the prominent factor of “privacy protection” while booking a hotel, we put forward “privacy protection” to represent physical evidence in this study.
Based on the above discussion, the following hypotheses are proposed.
Hypothesis 2 (H2).
The 7Ps marketing mix elements are as important to the customers who booked the services mostly offline as to those who booked them mostly online.
Hypothesis 2 (H2a).
Broad choice of product is as important to the customers who booked the services mostly offline as to those who booked them mostly online.
Hypothesis 2 (H2b).
Low price is as important to the customers who booked the services mostly offline as to those who booked them mostly online.
Hypothesis 2 (H2c).
Booking convenience of place (distribution) is as important to the customers who booked the services mostly offline as to those who booked them mostly online.
Hypothesis 2 (H2d).
Discount allowance of promotion is as important to the customers who booked the services mostly offline as to those who booked them mostly online.
Hypothesis 2 (H2e).
Reliable service of people is as important to the customers who booked the services mostly offline as to those who booked them mostly online.
Hypothesis 2 (H2f).
Process speed is as important to the customers who booked the services mostly offline as to those who booked them mostly online.
Hypothesis 2 (H2g).
Privacy protection of physical evidence is as important to the customers who booked the services mostly offline as to those who booked them mostly online.

3. Methodology

3.1. Sample and Procedure

An empirical study was conducted using purposive sampling of customers who visited the five-star resort hotel in Taipei City, Taiwan. The questionnaires were distributed by the researchers together with the hotel reception staff in the resort lobby. Researchers followed the advice of Roscoe [76] and Sekaran and Bougie ([77] pp: 296–297) based on rules of thumb in determining the sample size. Sample sizes larger than 30 and less than 500 are appropriate and the sample size should be several times (preferably 10 times or more) larger than the number of variables in multivariate study (including multiple regression analysis). Based on this consideration and the rule of thumb, the minimum sample size of the current study is more than 22 (22 × 10) or equal to 220.
A total of 350 questionnaires were distributed, after excluding 50 responses, which answered “no” to the filtering question “Have you stayed at a resort in the last year?” From the 300 recovered samples, after deducting 34 invalid samples, 266 were found to be valid samples, with an adequate sample recovery rate of 88.7%.

3.2. Measurement Instruments

The research instrument was reviewed with reference to previous studies and revised and supplemented according to the objectives of this study. For this study, the questionnaire involved three parts. The first part covered questions related to the 7Ps marketing mix elements as generalized from a study by Lee and Cheng [53] and tried to assess the participants’ perceived degrees of importance with regard to the seven elements, such as broad choice, low price, booking convenience, discount allowance, reliability of the services, process speed, and privacy protection. All the 7Ps marketing mix elements were measured using a 7-point Likert-type scale (where 1 = extremely unimportant and 7 = extremely important). The questions in the second part were again generalized to include the 7Ps marketing mix elements to assess the perceived degrees of satisfaction of the participants when booking hotel accommodations online or offline. All the 7Ps marketing mix elements were measured using a 7-point Likert-type scale (where 1 = strongly disagree and 7 = strongly agree) (See Appendix A). The third part covered questions related to the demographic variables, including gender, age, monthly income, education level, and occupation, to gather the respondents’ demographic profiles.

3.3. Data Analysis Process

Firstly, this study used the analysis of variance (ANOVA) and a post-hoc testing procedure to investigate the differences in demographic influences on online hotel reservation behavior. Secondly, it applied a paired-samples t-test to explore the mean difference of satisfaction when booking hotel accommodations online as against offline. Lastly, it used a logistic regression model to help the resort predict whether a customer would book the accommodation online or offline based on his/her evaluation of the seven predictive variables.

4. Results and Discussions

4.1. Respondent Profiles

The demographic profiles of the respondents are shown in Table 1. The distribution included 68.4% female and 31.6% male respondents; the majority of the respondents were aged between 36 and 45 (48.5%), or below 35 (33.9%); 48.9% had monthly incomes of 30,001–50,000 NT$; about 45.1% of the respondents held an undergraduate degree, and 80.1% were employed full-time.

4.2. The Importance of Seven Predictors While Choosing between the Booking Options

According to the explanation of marketing mix in Section 2.3 above, the elements of 7Ps marketing mix have an impact on whether customers book a hotel online or offline. To identify whether a customer would use an online or an offline channel to book a resort accommodation, we considered the importance given to the seven factors (broad choice, low price, booking convenience, discount allowance, reliability of the service, process speed, and privacy protection) by the customers as predictor variables. Their mean, standard deviation, and Cronbach’s alpha values are shown in Table 2. It is found that, while the most important factor is “reliability of the services,” the least important one is “low price.” In addition, it is noted that the Cronbach’s alpha value is 0.757, indicating the reliability of the results.

4.3. Deviation in Online Booking Behavior Intentions by Demographic Characteristics

One of the objectives of this study was to determine if there were significant differences across the respondents’ demographic characteristics with respect to their online hotel accommodation reservation intentions. The results of the ANOVA and post-hoc (multiple comparison) tests on the demographic variables are presented in Table 3.
The results show that gender did not have any significant effect on the customers’ online booking behaviors/intentions (T-value = 1.494, p > 0.05) from Table 3. As a result, H1a is not supported. These findings echo those of Bonn et al. [55] and Lien et al. [44]. Next, they display that age has a significant effect on customers’ online booking behaviors/intentions (F-value = 9.605, p < 0.01). Accordingly, the results support H1b. In addition, by performing the post-hoc test, it can be seen that the respondents under the age of 45 were significantly more likely to make reservations online. This implies that the consumers who booked their accommodations online tended to be younger, a finding similar to that of Qi et al. [32]. Moreover, Table 3 shows that the level of monthly income has a significant effect on the customers’ online booking behaviors/intentions (F-value = 12.347, p < 0.01). As a result, H1c is supported. Besides, after the post-hoc test, it can be found that the respondents with a monthly income between 50,001 and 100,000 NT$ were significantly more likely to make the reservations online. Lastly, Table 3 also indicates that education level has a significant effect on customers’ online booking behaviors/intentions (F-value = 9.208, p < 0.01). As result, H1d is supported. By performing the post-hoc test, it was found that respondents with a university degree were significantly more likely to make the bookings online.
As the above results, which support H1b, H1c, and H1d, show, customers’ decisions to book hotel accommodation online or offline differ according to customers’ age, monthly income, and educational level. However, the results do not support H1a; as the above results show, H1 was only partially supported.

4.4. Mean Difference of Satisfaction When Booking Hotel Accommodations Online as against Offline

For each of the factors of broad choice, low price, convenience, discount, reliability of the services, speed, and privacy, each respondent provided his/her ratings for online and offline bookings, and a paired samples t-test was conducted. The analysis of the paired-samples t-test identified a statistically significant difference between the satisfactions of booking hotel online and offline, with the confidence level of 0.01 (Table 4), which confirms the difference between online and offline customer satisfaction in 7Ps mixed marketing. Among them, the gap in satisfaction of border choice was the largest, and that of privacy protection was the smallest. In addition, the research has shown that customers tended to agree that online booking provided a broader choice, more discounts, and more privacy, was cheaper, faster, and more convenient, and offered more reliable services than offline booking.

4.5. Predicting the Customer’s Booking Channel

The study used logistic regression to predict whether customers would book their accommodations online or offline, based on how they valued the seven aforementioned variables. It was also noted that how and what the customers perceived/expected about the characteristics and functions of online versus offline booking could influence their choices of a booking channel in the future [78].
Firstly, Table 5 provides details of the correlation between the variables and the percentages of online purchases. It is found that “broad choice” has the strongest positive correlation to purchasing online (r = 0.676, p < 0.001), and also that purchasing online is positively related to factors like booking convenience, process speed, reliability of the services, low prices, and discount allowance (coefficients ranging from 0.109 to 0.405), separately (based on the significance level α = 0.01). However, purchasing online is not related to the factor “privacy protection” (r = 0.120, p > 0.05). Among the predictors, the strongest correlations are with two factors, namely, “low price” and “discount allowance” (r = 0.570, p < 0.01). However, there appears to be no multi-collinearity as we do not see any substantial correlation between predictor variables based on the criterion that none of the Pearson’s correlations (r) are over 0.9 [79]. This implies that the set of variables used in this study can reasonably predict online resort accommodation bookings.
Secondly, it is found from Table 6 that broad choice (Wald value = 46.872, p < 0.01), reliability of the services (Wald value = 6.387, p < 0.05), process speed (Wald value = 3.990, p < 0.05), and privacy protection (Wald value = 7.785, p < 0.01) are the four significant factors determining whether customers book online or offline. That means the hypotheses H2a, H2e, H2f and H2g are supported. Among these, “broad choice” has the most significant impact on customers’ booking decisions. That result is similar to the prior study that greater diversity of hotel rooms, hotel brands, and other available travel products results in an increased tendency of attracting potential consumers [22,68]. “Broad choice” plays a prominent role in customer online and offline booking hotel. However, that result is quite different with Kim and Kim [56]; their study suggested the significant factors that affected online reservation intention in both the online group and the non-online group were convenience, safety, and price. Next, process speed also has a significant influence on consumers’ intentions toward online hotel reservations. These findings echo those of [18,61,63,70]. Moreover, privacy protection has a significant influence on consumers’ intentions toward online hotel reservations. The findings of this study are in line with the findings of [63].
On the one hand, other predictor variables do not play a significant role in customers’ selection of an online booking channel. That means hypotheses H2b, H2c, and H2d are not supported. This implies that price does not significantly influence consumers’ intentions toward online hotel reservations. The finding is inconsistent with prior studies [53,66]. The reason may be that the study subjects are five-star resort hotel guests; they may not pay so much attention to the price factor when booking online or offline. Moreover, booking convenience does not significantly influence consumers’ intentions toward online hotel reservations. This finding is inconsistent with [68]. Further, discount allowance does not significantly influence consumers’ intentions toward online hotel reservations. The findings of this study are in line with the findings of [63]. The reason may be that the five-star resort hotel with the object of this study has little difference in discount allowance whether online or offline.
In addition, Table 7 depicts the predicted numbers of respondents who might book more online or offline as against the observed numbers. The overall accuracy of this model to predict whether or not potential customers would book online is 81.2%. As the above results, the H2 was partially supported.

5. Conclusions

This study is an analysis of customers’ online/offline booking behavior toward a resort and can be used in system analysis in developing online booking facilities for the hospitality industry. While there is a significant increase in the number of users who book their hotel accommodations directly on the hotel websites or OTAs now, for the sustainable development of the hospitality industry, it is necessary to know the opinions of travelers about their online hotel booking experiences and behaviors.
The results of this study highlight the implications for improving online booking facilities in the resort market context. Firstly, it is evident that “broad choice” is an important factor helping customers determine whether to book a resort accommodation online or offline. However, according to Lee and Cheng [53], “broad choice” is not significant for determining booking channel selection behavior in the airline industry. While the rooms are the core products for the hotel industry, the resorts offer much more than rooms, for example, multi-style restaurants, bars, and cafes, spa and fitness, conference, speakerphone with voicemail service, etc. Thus, the broad choice of hotel rooms along with other available travel products could result in an increased tendency of attracting potential customers [3]. Therefore, the resort hoteliers, generally catering to different market segments, would need to take care of the different requirements of these segments to influence the booking choices positively.
Secondly, from the logistic regression between the online bookings and the 7P dimensions, it is found that broad choice, reliability of the services, process speed, and privacy protection are the four significant factors determining whether customers book resort accommodations online or offline. “Low price” does not necessarily stimulate online bookings, and it is found that customers considered promotional price cuts analogous to low service quality [80,81]. As a result, customers are more likely to avoid a hotel offering promotional pricing but providing good quality, reliable service, and broad choice while booking online.
Thirdly, the results of the demographic effect analysis show that gender did not have any significant effect on customers’ online booking behavior/intentions. Younger customers (<45 years of age) and those with a university degree were more significantly found to make online bookings. This could imply developing market segment strategies accordingly. The resort hoteliers may consider selecting middle and younger aged customers with higher education as their target demographic groups for online promotions.
As a whole, this study reflects the importance of 7Ps marketing mix elements to help the hoteliers gain a more significant competitive advantage in the market. To acquire a competitive advantage, while customer booking online, hotel reservation systems facilitate the collection of information on customer characteristics and online behavior. That information provides hoteliers with the ability to build customer databases and implement customized promotional activities that meet different groups’ specific needs in order to delight and incentivize customers, while also protecting the privacy of sensitive guest information, such as direct mail and the sending of electronic hotel newsletters, promotional hotel package products and greeting cards for different holidays and birthdays for different market segments.
Several limitations are present in this study that also provide fertile grounds for further research. To begin with, one limitation is that only one resort was reviewed in this study, leaving scope for a future survey with a broader area of examination. There may be other factors related to consumers’ decisions to use online and offline hotel booking for different types of resorts. In a future study, the survey should include at least three resorts so that it can recognize and indicate more factors that influence consumers’ decisions for different resort types, such as all-inclusive resort, beach and lake resort, golf resort, luxury resort, and mountain resort. Secondly, this research did not consider cross-cultural issues. Referring to suggestions of Liu et al. [82], there will be potential differences in attitude between Chinese and Western tourists towards various hotel room reservation policies. In future studies, comparative studies from different countries could make a valuable contribution to the body of knowledge. Third, as a prior study [83] evidenced, guests’ reviews show a positive effect on hotel purchasing intention; the stream of research on word-of-mouth cannot be ignored in future hotel booking research. Lastly, as the analysis focuses only on the study of online and offline hotel bookings in one resort in Taiwan, generalization of the study results may not be applicable.

Author Contributions

K.-S.W., Y.-M.T., and C.-Y.C. formulated the study, analyzed the collected data and wrote the article. All authors confirmed the final manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare that there is no conflict of interest.

Appendix A

Table
Table A1. Variable descriptions.
Table A1. Variable descriptions.
DescriptionsResponses
Q1. What percentage of air tickets do you purchase online?Please use a 10-point scale, where 1 means “0–10%”, 2 means “11–20%”, and 10 means “90–100%.”
Q2. How important is the broadest choice (i.e., displaying full rates and related information from all nearby hotels) to you?Possible answers ranging from [1]-Extremely unimportant to [7]-Extremely important.
Q3. How important is finding the lowest price to you?
Q4. How important is booking convenient to you?
Q5. How important is discount allowance to you when booking the hotel?
Q6. How important is reliable service to you?
Q7. How important is the speed of the booking process to you?
Q8. How important is privacy protection when booking the hotels?
Q9.Do you agree that you are provided with all possible choices when booking online?Possible answers ranging from [1]-Strongly disagree to [7]-Strongly agree.
Q10.Do you agree that you are provided with all possible choices when booking offline?
Q11. Do you agree that you are getting the lowest price when booking online?
Q12. Do you agree that you are getting the lowest price when booking offline?
Q13.Do you agree that you are getting the more convenient when booking online?
Q14. Do you agree that you are getting the more convenient when booking offline?
Q15. Do you agree that you are getting the more discount when booking online?
Q16. Do you agree that you are getting the more convenient when booking offline?
Q17. Do you agree that online booking provides reliable service?
Q18. Do you agree that offline booking provides reliable service?
Q19. When booking online, how satisfied are you with the speed of the booking process?
Q20. When booking offline, how satisfied are you with the speed of the booking process?
Q21. When booking online, how satisfied are you with the privacy protection?
Q22. When booking offline, how satisfied are you with the privacy protection?

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Table
Table 1. Demographic profile of respondents.
Table 1. Demographic profile of respondents.
VariablesFrequencyPercentage (%)
Gender
Male 8431.6
Female 21668.4
Age
≤359033.9
36–4512948.5
46–553212.0
≥56155.6
Monthly income
<=30,000 NTD72.6
30,001–50,000 NTD13048.9
50,001–100,000 NTD6323.7
>=100,001 NTD3212.0
I’d rather not tell3412.8
Education level
Junior college or below3914.7
University 12045.1
Graduate school10740.2
Occupation
Self-employed269.8
Full-time employed21380.1
Part-time employed62.3
Other217.9
Table
Table 2. Mean and standard deviation of the seven predictors.
Table 2. Mean and standard deviation of the seven predictors.
Factors of BookingMeanSDCronbachs’α
Broad choice6.201.0030.757
Low price6.161.106
Booking convenient6.660.644
Discount allowance6.290.858
Reliable services6.710.764
Process speed6.500.793
Privacy protection6.670.713
Table
Table 3. Variation in online booking behavior intentions by demographic characteristics.
Table 3. Variation in online booking behavior intentions by demographic characteristics.
Variables Booking Online Intentions
Gender
Male7.75
Female7.07
t-test1.494
Age
≤358.02 c
36–457.60 b
46–555.22 a
≥564.47 a
F-test9.605 **
Monthly income
<=30,000 NTD4.43 a
30,001–50,000 NTD6.35 b
50,001–100,000 NTD9.11 c
>=100,001 NTD6.22 b
F-test12.347 **
Education level
Junior college or below6.63
University 8.16 b
Graduate school6.11 a
F-test9.208 **
Note: ** denotes p < 0.01; a,b,c: The mean difference is significant at the p, 0.01 level in post-hoc tests.
Table
Table 4. Paired samples t-test for mean difference.
Table 4. Paired samples t-test for mean difference.
VariablesPaired Sample Difference (Online–Offline)tp-Value
MeanSD95% Confidence Interval
LowerUpper
Broad choice2.0982.3051.8202.37614.8460.000 **
Low price1.7412.4581.4442.03711.5490.000 **
Booking convenient1.8272.6211.5112.14311.3700.000 **
Discount allowance1.7262.2051.4591.99212.7610.000 **
Reliable services0.7142.0400.4680.9615.7120.000 **
Process speed1.3802.5791.0681.6918.7250.000 **
Privacy protection0.3422.1300.0850.5992.6200.009 **
Note: ** denotes p < 0.01.
Table
Table 5. Correlation matrix.
Table 5. Correlation matrix.
Percentage Purchased OnlineBroad ChoiceLow PriceBooking Convenient Discount AllowanceReliable ServicesProcess SpeedPrivacy Protection
Percentage purchased online1
Broad choice0.676 ***1
Low price0.235 ***0.168 **1
Booking convenient0.405 ***0.404 ***0.428 **1
Discount allowance0.169 **0.142 *0.570 **0.455 ***1
Reliable services0.301 ***0.174 **0.203 **0.412 ***0.182 **1
Process speed0.367 ***0.372 ***0.303 **0.576 ***0.342 ***0.441 ***1
Privacy protection0.120 #0.240 ***0.250 **0.344 ***0.147 *0.336 ***0.463 ***1
Note: *** denotes p < 0.001; ** denotes p < 0.01; * denotes p < 0.05; # denotes p < 0.1.
Table
Table 6. Logistic regression between online booking and 7P dimensions.
Table 6. Logistic regression between online booking and 7P dimensions.
VariablesBS.E.Waldp-ValueExp (B)
Broad choice1.6440.24046.8720.000 **5.174
Low price0.2150.1861.3330.2481.239
Booking convenient0.2490.3510.5050.4771.283
Discount allowance0.1990.2560.6050.4371.220
Reliable services0.6010.2386.3870.011 *1.825
Process speed0.6080.3043.9900.046 *1.836
Privacy protection−0.8070.2897.7850.005 **0.446
−2 Loglikehood = 200.962
Cox & Snell R-square = 0.408
Nagelkerke R-square = 0.565
Note: ** denotes p < 0.01; * denotes p < 0.05.
Table
Table 7. Model discrimination.
Table 7. Model discrimination.
Predicted
ObservedMore OfflineMore OnlineTotal
More offline622890
More online22154176
Classification accuracy68.9%87.5%266
Overall accuracy81.2%
Note: Cut value = 0.5 to determine more online or more offline.

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MDPI and ACS Style

Teng, Y.-M.; Wu, K.-S.; Chou, C.-Y. Price or Convenience: What Is More Important for Online and Offline Bookings? A Study of a Five-Star Resort Hotel in Taiwan. Sustainability 2020, 12, 3972. https://doi.org/10.3390/su12103972

AMA Style

Teng Y-M, Wu K-S, Chou C-Y. Price or Convenience: What Is More Important for Online and Offline Bookings? A Study of a Five-Star Resort Hotel in Taiwan. Sustainability. 2020; 12(10):3972. https://doi.org/10.3390/su12103972

Chicago/Turabian Style

Teng, Yi-Man, Kun-Shan Wu, and Che-Yi Chou. 2020. "Price or Convenience: What Is More Important for Online and Offline Bookings? A Study of a Five-Star Resort Hotel in Taiwan" Sustainability 12, no. 10: 3972. https://doi.org/10.3390/su12103972

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