The influence of online reviews to online hotel booking intentions

Abstract

Purpose – This study aims to investigate the impacts of online review and source features upon travelers’ online hotel booking intentions.

Design/methodology/approach – This study developed a research model and empirically examined the model by collecting data from business travelers in the Mainland China. Factor analysis was adopted to identify features of online reviews content and source attribute. Regression analysis was used to examine impacts of these attributes upon travelers’ online booking intention.

Findings – Six features of online reviews content and one source attribute were identified, namely, usefulness, reviewer expertise, timeliness, volume, valence (negative and positive) and comprehensiveness. Regression analysis results testified positive causal relationships between usefulness, reviewer expertise, timeliness, volume and comprehensiveness and respondents’ online booking intentions. A significantly negative relation between negative online reviews and online booking intentions was identified, whereas impacts from positive online reviews upon booking intentions were not statistically significant.

Research limitations/implications – The major limitation of this study is that interrelationships among features of online reviews, which were discussed in other similar studies, were not considered. Still, this study benefited researchers from scrutinizing features of online reviews, rather than several of them. As such, it offered more comprehensive suggestions for practitioners in how to better utilize online reviews as a marketing tool.

Practical implications – Hospitality practitioners could enhance consumer review management by applying the six underlying factors of online review in the present study to find out the ways of increasing consumers’ booking intentions in the specific hotel contexts.

Originality/value – A major theoretical contribution of this paper is its comprehensiveness in examining features of review content as well as its source simultaneously. This study also offered areas worthy of more research efforts from perspectives of practitioners and researchers.

Full text

The inuence of online
reviews to online hotel
booking intentions
Xinyuan (Roy) Zhao
Business School, Sun Yat-Sen University, Guangzhou, China
Liang Wang
School of Hotel and Tourism Management,
The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Xiao Guo
School of Economics and Commerce,
South China University of Technology, Guangzhou, China, and
Rob Law
School of Hotel and Tourism Management,
Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Abstract
Purpose – This study aims to investigate the impacts of online review and source features upon
travelers’ online hotel booking intentions.
Design/methodology/approach – This study developed a research model and empirically
examined the model by collecting data from business travelers in the Mainland China. Factor
analysis was adopted to identify features of online reviews content and source attribute.
Regression analysis was used to examine impacts of these attributes upon travelers’ online
booking intention.
Findings – Six features of online reviews content and one source attribute were identied, namely,
usefulness, reviewer expertise, timeliness, volume, valence (negative and positive) and
comprehensiveness. Regression analysis results testied positive causal relationships between
usefulness, reviewer expertise, timeliness, volume and comprehensiveness and respondents’ online
booking intentions. A signicantly negative relation between negative online reviews and online
booking intentions was identied, whereas impacts from positive online reviews upon booking
intentions were not statistically signicant.
Research limitations/implications – The major limitation of this study is that interrelationships
among features of online reviews, which were discussed in other similar studies, were not considered.
Still, this study beneted researchers from scrutinizing features of online reviews, rather than several of
them. As such, it offered more comprehensive suggestions for practitioners in how to better utilize
online reviews as a marketing tool.
Practical implications – Hospitality practitioners could enhance consumer review management
by applying the six underlying factors of online review in the present study to nd out the ways of
increasing consumers’ booking intentions in the specic hotel contexts.
Originality/value – A major theoretical contribution of this paper is its comprehensiveness
in examining features of review content as well as its source simultaneously. This study
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0959-6119.htm
Online hotel
booking
intentions
1343
Received 6 December 2013
Revised 10 February 2014
7 May 2014
13 September 2014
Accepted 28 September 2014
International Journal of
Contemporary Hospitality
Management
Vol. 27 No. 6, 2015
pp. 1343-1364
© Emerald Group Publishing Limited
0959-6119
DOI 10.1108/IJCHM-12-2013-0542
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also offered areas worthy of more research efforts from perspectives of practitioners and
researchers.
Keywords Online review, Word of mouth, Hotel, E-WOM, Online booking intentions,
Online social media
Paper type Research paper
Introduction
In recent years, the rise of new technologies like the broadband Internet and Web 2.0
applications have rapidly increased the numbers of consumer-generated media
platforms, leading to word-of-mouth (WOM) communications be transformed into
various types of electronic communities and virtual networks (Lee et al., 2008;Ye et al.,
2011). A wealth of opinions on hotels, travel destination and travel services are often
articulated in the form of online consumer reviews (Sigala, 2009). At the same time,
searching for information relevant to their plans, from ights to hotel booking, has
become a dispensable step in travelers’ decision-making process (Guillet and Law, 2011;
Ip et al., 2011;Litvin et al., 2008;Ye et al., 2011).
The underlying belief is that consumers tend to rely on information about hotel
products and services provided by fellow customers (Senecal and Nantel, 2004),
indicating the power and persuasiveness of online product reviews (Litvin et al., 2008).
Kardon (2007) has shown that consumers tend to rely more on peer reviews than
information provided by business entities because peer customers are more independent
and trustworthy (Wilson and Sherrell, 1993). Furthermore, consumers are believed to
have no vested patterns when posting a review online, and there is no structured pattern
for them to post their experiences on the Web (Park and Kim, 2008). More importantly,
there are two main types of reviews on the Internet: review by consumers and reviews
by professional editors (Chen and Xie, 2008). These two types of product reviews do not
offer the same information online, and consumer reviews may include critical
information that hotels are reluctant to reveal to the public (Bickart and Schindler, 2001;
Lee et al., 2008).
The efcacy of online reviews as a good proxy for overall WOM is well-established,
and they are shown to inuence consumers’ purchasing decisions (Zhu and Zhang, 2010;
Lee et al., 2008;Bansal and Voyer, 2000;Duan et al., 2008), customer satisfaction and
their revisit intentions (Berezina et al., 2012) and sales (Liu, 2006;Zhang et al., 2011;Zhu
and Zhang, 2010). Besides hoteliers’ own efforts, new eMediaries including Web-based
travel agents and Internet portals also provide online reviews of hotels in prominent
destinations (Buhalis and Licata, 2002).
Against this background, online reviews have become an important resource for
travelers to evaluate product quality, service excellence and consumption
experiences (Dickinger, 2011;Litvin et al., 2008;Ye et al., 2011). A recent survey
conducted by eMarketer, a market research company on digital media and Internet
marketing, found that in the USA alone nearly two-thirds of Web users relied on
assorted digital channels for travel information in 2013 (eMarketer, 2013).
Accordingly, alert hospitality rms are taking advantage of online reviews as a new
tool to attract information searchers and, ultimately, bookers (Dickinger, 2011).
They distribute travel-related information on online travel communities and review
sites (Xiang and Gretzel, 2010), proactively encourage virtual interactions among
consumers (Litvin et al., 2008), publish travel reviews and comments, and
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sometimes, they allow review functions on their ofcial Web sites in forms of edited
testimonials (Ayeh et al., 2013;Xiang and Gretzel, 2010). More importantly, the
Internet has been regarded as an effective channel to directly market hotels’
environmental initiatives to customers (Chan, 2013;Hsieh, 2012).
However, as reviews gain in popularity, the problem of information overload
occurred, which makes it tougher for hoteliers in utilizing online reviews. As a
consequence, the use of more signaling cues to help users diagnose relevant reviews will
help hoteliers utilize this marketing tool more efciently. Some researchers have
examined indicators that consumers used to evaluate online reviews (Ye et al., 2009)
from either the perspective of source credibility or review characteristics. However,
hardly any prior research has been conducted in examining both perspectives
simultaneously.
To ll this research gap, the current research aims at gaining a more
comprehensive understanding of impacts from online hotel reviews attributes and
source feature (reviewer expertise) upon travelers’ booking intentions. It is generally
accepted that travel reviews have either positive or negative impacts on a hotel’s
reputation and, consequently, to enhance or detract potential customers from a hotel
(Sparks and Browning, 2011;Sparks et al., 2013). As such, many studies are devoted
to examining the causal relationship between online reviews and travelers’
intentions and behaviors. For example, Ye et al. (2011) studied the impacts of
user-generated reviews on online sales. Their ndings showed that a 10 per cent
increase in travel review ratings would increase online bookings by more than 5 per
cent. Vermeulen and Seegers (2009) found that exposure to online reviews would
enhance consumers’ consideration of a hotel. These studies mainly studied how
online reviews as a whole inuence travelers’ attitudes toward tourism products,
and then traveling intentions/behaviors (Lee et al., 2008;Ye et al., 2009). As content
and forms of consumer reviews may vary considerably across products and
services, it would be more practically benecial for hoteliers to gain a better
understanding of how individual aspects inuence consumers’ decision-making.
Besides similarities to traditional WOM, online reviews contain several additional
characteristics. In the online environment, both positive and negative reviews can be
presented to potential consumers simultaneously (Chatterjee, 2001;Herr et al., 1991).
There have been considerable research efforts in comparing the effects of negative and
positive reviews on consumer actions in terms of strength and diffusion speed (Lee et al.,
2008). Another characteristic of online reviews is measurability. Online reviews also
enable customers to intuitively measure the quality and volume of online review
content, as most of them are published in written form. This enables researchers to
estimate the extent to which online reviews can inuence consumers’ attitudes and
subsequent sales (Chevalier and Mayzlin, 2003). In addition, for hotels, especially those
renowned or infamous ones, different customers may comment about them in different
time periods. As such, volume and timeliness of online reviews could inuence
consumers’ purchasing decisions as well. Furthermore, it is reasonable to argue that
consumers’ reputation and reliability of reviews content could inuence consumer
choice. The current study sets out to explore impacts of the aforementioned six
attributes of online reviews content and one feature of its source upon customers’ hotel
booking intentions in the following section.
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Conceptual background
This paper seeks to identify how individual attributes of online review inuence
potential travelers’ hotel booking intentions. While it is well-acknowledged that a
myriad of factors exists in this context, it is only through testing selected factors that
researchers could learn more about the effectiveness of specic aspects that will allow
for practical implementations and research dissemination. The current study focuses
specically on six attributes of online reviews and empirically testies their respective
impacts upon travelers’ online purchase intentions. The current study complements the
results of previous investigations, which examined one or several factors proposed in
the current study. For example, the work of Vermeulen and Seegers (2009) suggests that
factors like rating systems, anonymity and framing of online reviews should be
considered in future research.
Usefulness of online reviews
Usefulness of online reviews is “the degree to which consumers believe that online
reviews would facilitate their purchase decision-making process” (Park and Lee, 2009.
p. 334). Willemsen et al. (2011) suggested that the usefulness of a review is the primary
aspect for users to gauge online reviews. One of the main reasons for traveler to search
hotels’ information online is to plan their trip, and it would be reasonable to argue that
usefulness of online hotel reviews will no doubt inuence consumer expectations. More
importantly, the technology of Web 2.0 has introduced a platform which enables
information aggregation from a huge cluster of disparate individuals (Goodman, 2007).
This development facilitates an unlimited number of people to potentially join virtual
networks by posting and gaining marketing intelligence about hotels of interest.
Confronting the tremendous amount of information, only those valuable comments and
opinions would inuence consumers’ decision-making.
Usefulness of online reviews have been suggested as an effective predictor of
consumers’ intent to comply with a review (Cheung et al., 2008;Park and Lee, 2009).
Several other researchers have shown that usefulness of online reviews could also
determine the frequency of usage (Davis, 1989;Wöber and Gretzel, 2000;Wöber, 2003).
Chen et al. (2008) found that the quality of a review, as measured by the number of
helpfulness votes, positively inuences consumer decision-making.
Hence, the current study proposes the following:
H1. The usefulness of reviews will positively inuence hotel online bookings.
Reviewer expertise
Another distinctive feature of online reviews is that they are provided by anonymous
individuals (Lee et al., 2008). In fact, information sharing is not a genuinely random
behavior, as there exists market “mavens” who have a particular propensity to post
messages about shopping and the marketplace messages (Feick and Price, 1987).
Consumers can identify such market mavens and follow them in the process of making
purchasing decisions. As such, the characteristics of communicators, both senders and
receivers, play a critical role in information persuasiveness (Dholakia and Sternthal,
1977). More importantly, in the online context, people who made postings tend to search
for travel information from others who engage in similar activities (Akehurst, 2009).
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To what extent an information source can be regarded as a “market maven” is
decided by his or her expertise in a certain topic of interest. As suggested by Bristor
(1990), p. 73, expertise is:
[…] the extent to which the source is perceived as being capable of providing correct
information and expertise is expected to induce persuasion because receivers have little
motivation to check the veracity of the source’s assertions by retrieving and rehearsing their
own thoughts.
Individuals who are highly ranked in expertise are also likely to have more knowledge
of alternative products and services (Mitchell and Dacin, 1996). Such reliance on experts
is mainly because the performance of a product can be assessed from the information
provided (Bansal and Voyer, 2000).
In a reduced and altered cues environment, it is difcult for information seekers to
evaluate the knowledge and competence of a reviewer because of the limited access to
personal attributes and background. However, a Web site takes the duty to evaluate a
reviewer by rating him or her. Based on the aforementioned statement, the following
hypothesis is, therefore, proposed:
H2. Reviewer expertise will positively inuence hotel online bookings.
Timeliness of online reviews
During the information search process, consumers may encounter a large amount of
relevant information which is associated with a particular time stamp, which leads to
the research concept of timeliness. Timeliness refers to “whether the messages are
current, timely, and up-to-date” (Cheung et al., 2008, p. 465). Despite its generally agreed
importance, timeliness is frequently ignored in online reviews research (Ives et al., 1983).
Madu and Madu (2002) pointed out that a Web site needs to be updated consistently to
deliver value-added information to users. Its inuence may be even stronger if
comments are labeled as “spotlight reviews” because these are shown before other
reviews on the comments page (Chen et al., 2008). From consumers’ perspective, as time
elapses, the average helpfulness of reviews declines (Liu, 2006). In a similar vein, Jindal
and Liu (2008) found that in the e-commerce environment, more recent product reviews
would get more user attentions. As such, another hypothesis is proposed:
H3. The timeliness of online reviews will positively inuence hotel online bookings.
Volume of online reviews
Volume is another important attribute of WOM, and it measures the total amount of
interactive messages (Liu, 2006). Variations in the volume of online customer reviews
provide evidence that not all hotels are treated equally, and hence, it is reasonable that
not all reviews are treated equally. It has been regarded as a key antecedent of the WOM
effect (Bone, 1995). In online settings, volume of reviews is the number of comments
from reviewers about a specic product or service (Davis and Khazanchi, 2008). Several
studies demonstrate that volume signicantly correlates with consumer behaviors like
customer-initiated contacts with manufacturers (Bowman and Narayandas, 2001) and
market performance in terms of sales (Amblee and Bui, 2007;Liu, 2006;Zhu and Zhang,
2010). This effect is moderated by the increase of customer awareness. Before
consumers decide to buy a product about which they have little information, some
awareness has to be built (Mahajan et al., 1984). Higher volumes of comments, either
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positive or negative, in online communities are more likely to attract information seekers
and then increase product awareness (Davis and Khazanchi, 2008). The number of
online comments also signals the level of agreement among consumers (Elliott, 2002).
However, Davis and Khazanchi (2008) argued that an increase in volume of online
reviews alone has no signicant impact on book sales in e-commerce multiproduct sales.
Godes and Mayzlin (2004) reported that the volume of consumer reviews does not have
signicant explanatory power in terms of weekly box ofce revenues. Nevertheless,
considering the information asymmetry present and the unique features of tourism
products such as intangibility and integration of production and consumption (Litvin
and Ng, 2001;Taylor, 1980), this study argued that a high volume of online reviews may
induce a perception of lowered risk, and hence, the following hypothesis is proposed:
H4. Volume of online reviews will positively inuence hotel online bookings.
Valence of online reviews
Message valence focuses on either the positive (benets gained) or negative (benets
lost) product attributes (Maheswaran and Meyers-Levy, 1990). Online views can be
either negative or positive within the same location, and impacts of each type have been
continuously compared for a better marketing mix. Negative messages are more
diagnostic, which implies low-quality products, whereas positive information may be
connected to high-, average- and even low-quality products (Herr et al., 1991). As a
decision-making process focuses on the message content, consumers place more weight
on negative information in making product evaluations (Mizerski, 1982;Richins, 1983;
Weinberger and Dillon, 1980). In addition, negative information spreads faster than
positive, as angry customers are more likely than satised ones to tell relatives and
friends about their experiences (Hart et al., 1990;Richins, 1983). When the proportion of
negative online consumer reviews increases, consumers’ attitudes towards the product
would become more unfavorable (Lee et al., 2008).
Nevertheless, some scholars argue that positive information is more persuasive.
Levin and Gaeth (1988) presented consumers with descriptions of ground beef framed
either as 75 per cent lean or 25 per cent fat, and showed that the product was more likely
to be favorably evaluated when described as the former.
The ndings of previous work on the effects of message valence are inconsistent. In
Maheswaran and Meyers-Levy’s (1990) series of studies, some results indicate that
positively framed messages are more persuasive, whereas others suggest the reverse.
This lack of consistency may be attributable to the degree to which consumers are
involved in detailed message processing. From the perspective of information
recipients, Westbrook (1987) showed that both positive and negative information can
inuence consumers’ loyalty, product evaluation and purchase decision. Therefore, it
would be more logical to examine the impacts of negative and positive reviews,
respectively.
Negative comments are mainly generated as a response to dissatisfaction and can be
harmful to business retailers and manufacturers by having an adverse effect on
business (Charlett et al., 1995). The action of spreading negative information could be
even more harmful than simply complaining, which is mostly invisible (Charlett et al.,
1995). In contrast to negative comments, positive reviews mainly focus on extolling a
company’s quality orientation, such as making recommendations to others (Brown et al.,
2005). Positive online reviews are generally recognized as a valuable vehicle for
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promoting a rm’s products and services (Gremler et al., 2001). More particularly,
previous studies highlight the importance of customer recommendations in a service
context, as it has been empirically illustrated that a single recommendation can be
convincing enough to persuade someone to try a particular service provider (Gremler,
1994). Clemons et al. (2006) found that strongly positive ratings would lead to a
signicant growth in product sales. Both positive and negative online reviews can
inuence consumers’ attitudes towards a given company. Applying the elaboration
likelihood model, Lee et al. (2008) found that consumers’ attitudes become more
unfavorable as the proportion of negative online consumer reviews increases.
Vermeulen and Seegers (2009) noticed that negative online reviews lower consumers’
attitudes towards a hotel in which they are interested, even though it would increase
their awareness of it. In summary, the following two hypotheses are proposed:
H5a. Positive online reviews will positively inuence hotel online bookings.
H5b. Negative online reviews will negatively inuence hotel online bookings.
Comprehensiveness of online reviews
The Internet contains diverse types of messages ranging from simple recommendations
with several evaluative key points to more complex comments and factual descriptions.
This relates to comprehensiveness, which is a measure of how detailed and complete the
messages are (Cheung et al., 2008). In unfamiliar situations, consumers need detailed and
specic knowledge to make decisions (Anderson, 1996;Money et al., 1998). Money et al.
(1998) also suggested that personal references are the most efcient source of
comprehensive information, highlighting the role played by WOM. According to
Gremler et al. (2001), detailed and extensive knowledge implies, to a certain extent, a
connection between reviewers and information seekers. The comprehensiveness of
reviews could, therefore, be a key factor for consumers considering whether to buy a
product in the uncertain online environment. Previous studies have consistently
identied a relationship between the comprehensiveness of online reviews and
consumer behavior. Sullivan (1999) found that the more comprehensive the information
is on a Web site, the more varied the user categories are, which are closely related to
the likelihood of user acquisition and retention. Cheung et al. (2008) showed that the
comprehensiveness of online reviews is one of the most effective elements of online
postings in terms of the extent to which people are willing to accept and adopt online
reviews, as well as the factors encouraging adoption. Thus, the following hypothesis is
proposed:
H6. The comprehensiveness of online reviews will positively inuence hotel online
bookings.
Method
This study seeks to extend current knowledge by integrating six attributes of online
reviews and empirically testifying their effects upon travelers’ online purchase
intentions. In an empirical research, the development of measurement scales which
reect the meanings of constructs of interest is the crucial determinant of the whole
research. One of the seminal works of measurement scale development is conducted
by Churchill (1979), in which a procedure consisting eight steps was recommended.
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These eight steps can be generally divided into two stages: initial generation of
measurement items and items conrmation. As to identifying measurement items,
Churchill (1979) recommended three steps which include literature search, data
collection and measurement purication. As for measurement conrmation, more
data are needed for validity and reliability check. At the same time, Churchill (1979)
suggested that certain exibilities are allowed, and researchers can selectively use
some steps or replace recommended techniques. For example, Echtner and Ritchie
(1993) used four steps out of eight to develop a measuring scale of destination image.
In a similar manner, Hung and Petrick (2010) adopted Churchill’s (1979) work by
incorporating expert panel and examination of composite reliability/validity. As
suggested by Echtner and Ritchie (1993) that the adoption of multiple techniques is
more reliable in producing a complete list of measurement items. As such, this study
follows Hung and Petrick (2010) by adding in-depth interviews to generate sample
of items. The development of measurement scales had two steps: rst, relevant
literature was extensively reviewed and an initial list of measurement items was
identied. The usefulness of online reviews were measured in terms of importance
levels with the following four statements adapted from Park and Lee (2009) as well
as Papathanassis and Knolle (2011):
(1) “Online reviews are useful”.
(2) “Online reviews are genuine”.
(3) “Online reviews are neutral”.
(4) “Online reviews are relevant to products”.
Measurements of reviewer expertise were adapted from Dou et al. (2012):
• “Reviewers have hotel-related knowledge”.
• “Reviewers are people of some prominence”.
• “Reviewers have good credit record”.
Measures of timeliness of online reviews in terms of agreement level were adapted from
Bailey and Pearson (1983):
• “Instantly posted reviews are important”.
• “Recently posted reviews are important”.
• “Most recent reviews can reect the up-to-date information of products/services”.
Measurement of volume of online reviews in terms of agreement level with the following
statement was adapted from Duan et al. (2008):
• “I pay more attentions to hotels having larger volume of online reviews”.
• “Volume of online reviews relates to attentions a hotel gets”.
• “Larger volume of online reviews reects that many people are interested in a
hotel”.
Measures of valence of online reviews in terms of agreement level were adapted from
Sparks and Browning (2011) as well as Vermeulen and Seegers (2009):
• “I pay more attentions to positive reviews”.
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• “Positive reviews are more valuable”.
• “I pay more attentions to the hotels which have larger volume of positive reviews”.
• “The volume of negative reviews is important”.
• “An abundance of positive reviews will make you dislike a hotel”.
• “Negative reviews will terminate your booking intentions”.
• “I will not book from a hotel if any negative reviews about it are spotted”.
Measures of comprehensiveness of online reviews in terms of agreement level were
adapted from Sullivan (1999) as well as Cheung et al. (2008):
• “Summarized reviews are as valuable as detailed ones”.
• “Detailed reviews will attract more attentions”.
• “Detailed reviews are more valuable”.
Measurement items of purchase intentions in terms of agreement level were adapted
from Ye et al. (2009):
• “I only book branded hotels”.
• “I always pay close attention to hotel reviews when I book hotels”.
• “Online reviews are my main information channel”.
After the literature reviews, interviews were conducted with e-commerce experts and
Web users. Based on the interview results, the list was revised accordingly, and several
additional items were added to the initial list of the measurement items. These items
were as follows:
• “Online reviews are reliable” was added to the construct of usefulness of online
reviews.
• “Posting negative reviews require more professionalism in reviewers”.
• “Reviewers are experienced web users (e.g. senior members, forum master etc.)”
were added to “reviewer expertise”.
Three items of “larger volume of online reviews mean more equally distributed negative
and positive reviews”, “larger volume of online reviews will increase my booking
intentions” and “I will read all available reviews about a hotel”.
A nal draft instrument comprising 32 items emerged from this phase of the study
(Table I). All the measure items were based on a seven-point Likert scale (1 ⫽very
unimportant/strongly disagree, 2 ⫽unimportant/disagree, 3 ⫽somewhat unimportant/
somewhat disagree, 4 ⫽neutral, 5 ⫽somewhat important/somewhat agree, 6 ⫽
important/agree, 7 ⫽very important/strongly agree).
To purify the measures, a pretest was conducted at a university in mainland China.
Some researchers used students for pilot test (Hung and Petrick, 2010;Chen and Tsai,
2007), and similarly, the current study also conducted the pilot test among students.
Responses from a convenience sample of 109 undergraduate students were used to test
the wording and internal reliability of the 29 proposed items. A reliability analysis was
undertaken and the Cronbach’s alpha for each construct checked. A low alpha
coefcient indicates that the item makes a low contribution to the measurement of the
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Table I.
Initial measurement
scale
Constructs Measurement items Cronbach’s alpha
Usefulness of online reviews Review contents are relevant to products 0.874
Review contents are genuine
Review contents are reliable
Review contents are neutral
Online reviews are useful
Reviewer expertise Reviewers have hotel-related knowledge 0.702
Reviewers are people of some prominence
Reviewers have a good credit record
Reviewers are experienced web users (e.g.
senior members, forum master, etc.)
Posting negative reviews requires more
professionalism in reviewers
Timeliness of online reviews Instantly posted reviews are important 0.710
Recently posted reviews are important
Most recent reviews can reect the up-to-date
information of products/services
Volume of online reviews I pay more attentions to hotels having larger
volume of online review
0.776
Volume of online reviews relates to attentions a
hotel gets
Larger volume of online reviews reects that
many peo0ple are interested in a hotel
Larger volume of online reviews mean more
equally distributed negative and positive
reviews
Larger volume of online reviews will increase
my booking intentions
I will read all available reviews about a hotel
Positive online reviews I pay more attentions to positive reviews 0.644
Positive reviews are of more values
I pay more attentions to hotels which have
Larger volume of positive reviews
Negative online reviews The volume of negative reviews is important 0.782
An abundance of positive reviews will make
you dislike a hotel
Negative reviews will terminate your booking
intentions
I will not book from a hotel if any negative
reviews about it are spotted
Comprehensiveness of
online reviews
Summarized reviews are as valuable as detailed
ones
0.620
Detailed reviews would attract more attentions
Detailed reviews are more valuable
Note: Items in italic were deleted in the pretest
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construct of interest (Churchill, 1979). Thus, factors with a Cronbach’s alpha lower than
0.6 are normally considered for elimination (Ha et al., 2007). The results of the reliability
analysis suggest that the alpha coefcients of all constructs were above 0.6, indicating
acceptable internal reliability. However, further analysis found that the items “larger
volume of online reviews will increase my booking intentions” and “I will read all
available reviews about a hotel” were irrelevant to the factors of “review volume” and
“negative review”, respectively. In addition, the item “I will not book this hotel if any
negative reviews are spotted” was regarded as irrelevant and dropped from future
analysis. The alpha coefcient increased accordingly, from 0.530 to 0.782.
A questionnaire was then compiled based on the measurement scales derived from
pilot test, and data were collected from an onsite survey as well as an online survey.
Businesspeople were targeted, as they travel more often because of work, hence making
online hotel booking more possible. Questionnaires were delivered to 313 people, and
303 responses were obtained. Of the 303 collected, 34 questionnaires were either
incomplete or the answers were found to be unreliable, leaving 269 questionnaires
retained for further data analysis (giving a valid response rate of 86 per cent).
Results
The objective of this research was to identify individual impacts of six attributes of
online reviews upon hotel booking intentions. To achieve this objective, a model was
developed, and data were collected to statistically examine characteristics of online
reviews including its usefulness, reviewer expertise, timeliness, volume, valence
(negative/positive) and comprehensiveness.
Respondents’ proles
Approximately 60 per cent of respondents were male (58.6 per cent) and fell in the age
group of 25-30 years (60.1 per cent). In terms of educational background, the
overwhelming majority held a bachelor degree (93.6 per cent). About 70 per cent
reported a monthly salary of RMB 2,001-8,000 (66.6 per cent), indicating that most of
the respondents were nancially sufcient for traveling. More than 90 per cent of
respondents were savvy Internet users (90.8 per cent), but less than 40 per cent had ever
booked a hotel online (36.8 per cent). Such a large discrepancy implies that these
respondents were still reluctant to adopt the Internet as a purchasing tool, but also
suggests that the potential market for e-commerce in mainland China is signicant.
Factor analysis of online reviews
The data were rst checked for the suitability of factor analysis. Bartlett’s test value of
3,458.1 and signicance level of 0.01 were obtained using Bartlett’s test of sphericity.
Based on the criteria level of 0.05, this indicated sufcient correlations among the
variables to make factor analysis appropriate (Hair et al., 2010); In addition, the Kaiser–
Meyer–Olkin was 0.861, showing that each variable could be well predicted by the
others (Hair et al., 2010).
A varimax rotation was applied, which converged 25 iterations. Following Hair et al.
(2010), this study set 0.3 as the cut-off point for identifying signicant factor loadings,
and this level is commonly adopted in similar tourism studies (Özgener and I
˙raz, 2006).
Coincided with prior anticipation, seven factors were extracted as the main attributes of
online reviews in the context of the hotel industry. Reliability was then evaluated by
assessing the internal consistency of the items using Cronbach’s alpha. All the
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Cronbach’s alpha values were signicantly high, ranging from 0.730 to 0.889 across the
seven factors. Table II lists the factor loadings, eigenvalues, variance explained,
accumulated variance explained and Cronbach’s alpha of each construct.
Correlation and regression analyses
Descriptive statistics of the variables and the correlations among the seven attributes
are shown in Table III. The correlation analysis showed that there was a signicant
relationship between the random two variables at the 0.01 level.
A regression test was conducted to examine the impacts of online reviews upon
online hotel booking intentions. Before embarking on such an analysis, it is a common
practice to check for multicollinearity. Following suggestion from Hair et al. (2010), this
study adopted the tolerance value to measure multicollinearity, and the results are
presented in Table IV.Hair et al. (2010) suggested that the larger the tolerance value is,
the higher possibility that a variable will be predicted by other independent variables.
As the cut-off points for the tolerance value vary according to different studies, this
research followed previous work in tourism (Kim and Kim, 2005;Özgener and I
˙raz, 2006)
and set a value of 0.10 as the threshold. Table IV shows that all tolerance values obtained
are well above 0.10, demonstrating no signicant multicollinearity among the
independent variables.
A regression analysis was then conducted to examine the relationship between the
seven attributes and respondents’ online booking intentions (Table IV). It can be seen
that except for “positive online reviews”, all other attributes have signicant positive
relationships with booking intentions (R
2
⫽0.322). The interrelations of the seven
factors were considered, and the R
2
(0.322) was signicant at the 0.01 level (F⫽18.492).
This means that 32.2 per cent of the variance in online hotel booking intentions could be
explained by the independent variables. Of these variables, “negative online reviews” is
the most important in terms of explaining power, as it has the highest regression
coefcient (beta value) of 0.305. The second-ranked variable is the comprehensiveness of
online reviews, with a beta of 0.295. Based on the results of the regression analysis, six
out of the seven hypotheses are supported.
Discussion
Online reviews are a useful information source for most travelers to generate their
intentions and make trip decisions (Gretzel and Yoo, 2008). Understanding how online
reviews affect travelers’ online booking intentions is vitally important for hotels to
optimize e-WOM as a marketing tool. Previous studies mostly investigated features of
either information channel or review itself and rarely had a more comprehensive
perspective of e-WOM. The current study extends the existing knowledge by unfolding
the roles of the specic features of both online reviews’ content and source. The present
ndings demonstrate that impacts of online reviews on travelers’ actions depend on six
characteristics/features, including usefulness, reviewer expertise, timeliness, volume,
valence and comprehensiveness. These features play identical roles in manipulating
traveler intentions and decisions.
Specically, as for review valence, the current results are consistent with previous
ndings that negativity effect is more important than other features in predicting
consumers’ booking intentions, as Willemsen et al. (2011, p. 31) said that “negativity
effect was present only for experience products”. In their study, experience products
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Table II.
Results of factor
analysis of attributes
of online reviews
Factors Factor loading Eigen values Variance explained
Accumulated variance
explained
␣
Factor 1: usefulness of online reviews
Review contents are relevant to products 0.875
3.711 14.274 14.274 0.889
Review contents are genuine 0.857
Review contents are reliable 0.810
Review contents are neutral 0.728
Online reviews are useful 0.599
Factor 2: reviewer expertise
Reviewers have hotel-related knowledge 0.830
2.933 11.281 25.555 0.808
Reviewers are people of some prominence 0.757
Reviewers have a good credit record 0.714
Reviewers are experienced web users (e.g. senior members,
forum master, etc.) 0.694
Posting negative reviews requires more professionalism in
reviewers 0.532
Factor 3: negative reviews
The volume of negative reviews is important 0.770
2.465 9.483 35.038 0.773
An abundance of positive reviews will make you dislike a hotel 0.757
Negative reviews will terminate your booking intentions 0.675
Factor 4: timeliness of online reviews
Instantly posted reviews are important 0.784
2.386 9.178 44.216 0.835
Recently posted reviews are important 0.724
Most recent reviews can reect the up-to-date information of
products/services 0.715
(continued)
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Table II.
Factors Factor loading Eigen values Variance explained
Accumulated variance
explained
␣
Factor 5: volume of online reviews
I pay more attentions to hotels having larger volume of online
review 0.756
2.374 9.132 53.348 0.780
Volume of online reviews relates to attentions a hotel gets 0.726
Larger volume of online reviews reects that many people are
interested in a hotel 0.665
Larger volume of online reviews mean more equally
distributed negative and positive reviews 0.619
Factor 6: positive reviews
I pay more attentions to positive reviews 0.833
2.055 7.905 61.253 0.788
Positive reviews are of more values 0.828
I pay more attentions to hotels which have larger volume of
positive reviews 0.504
Factor 7: comprehensiveness of online reviews
Summarized reviews are as valuable as detailed ones 0.788
1.881 7.236 68.488 0.730
Detailed reviews would attract more attentions 0.657
Detailed reviews are more valuable 0.637
Notes: Extraction method: principal component analysis; rotation method: varimax with Kaiser normalization; rotation converged in 25 iterations
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“are dominated by intangible attributes that cannot be known until purchase, and for
which performance evaluations can be veried only by sensory experience or
consumption” (p. 23). In the hotel industry, Ye et al. (2009) suggested that hotels should
allocate more resources in managing the valence of reviews, which could lead to
increases in bookings/sales. As such, hoteliers may benet from handling customer
complaints more strategically and dealing effectively with service recovery, as at least
5-10 per cent of dissatised customers choose to complain (Tax and Brown, 2012).
In addition to review valence, comprehensiveness signicantly inuences people’s
online booking intentions. This nding extends previous studies suggesting that people
are cognitive misers, as they tend to rely on heuristic cues like easy-to-process graphic
information (e.g. numerical or star ratings) to make evaluations or decision (Macrae and
Bodenhausen, 2001). Holding a similar stance, Ye et al. (2009) found that hotels with
higher star ratings would receive more online bookings. While it is acknowledged that
consumers rely on categorical information because it is simple and easy to understand,
this study found that comprehensiveness has high predictive power of their booking
intentions. A possible explanation for this is the fact that in virtual communities, the
mere presence of arguments and anonymity on the Internet lead people to require more
cues to judge information based on the rigor of arguments.
Also, this study illustrates the positive impacts of timeliness and volume of online
reviews on booking intentions. Thanks to technologies like Web 2.0, consumers can
access and create product/service reviews, rather than solely relying on advertisements
Table III.
Means, standard
deviations and
correlations of scales
Characteristics of online reviews Mean SD 1 2 3 4 5 6
1. Usefulness of online reviews 5.55 1.28
2. Reviewer expertise 4.88 1.28 0.269**
3. Negative online reviews 5.22 1.31 0.343** 0.245**
4. Timeliness of online reviews 5.43 1.29 0.546** 0.466** 0.377**
5. Volume of online reviews 5.22 1.11 0.366** 0.392** 0.498** 0.461**
6. Positive online reviews 4.87 1.30 0.388** 0.412** 0.426** 0.391** 0.427**
7. Comprehensiveness of online reviews 5.30 1.20 0.481** 0.322** 0.431** 0.507** 0.404** 0.344**
Note: **p⬍0.01 (two-tailed)
Table IV.
The results of
regression analysis
of hotel booking
intentions
Characteristics of online reviews
Standardized regression
coefcients (beta)
Tolerance
value FR
2
Adjusted R
2
(Constant) 5.115 18.492** 0.322 0.305
1. Usefulness of online reviews 0.197** 0.619
2. Reviewer expertise 0.275** 0.698
3. Negative online reviews 0.305** 0.648
4. Timeliness of online reviews 0.230** 0.527
5. Volume of online reviews 0.300** 0.614
6. Positive online reviews 0.112 0.664
7. Comprehensiveness of online
reviews 0.295** 0.626
Notes: Dependent variable: online hotel booking intentions; * p⬍0.01
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prepared by marketing professionals. However, this may cause some confusions, as
consumers are bombarded with information from a variety of media channels, creating
a challenging business environment for hotel marketers. Online reviews are a valuable
channel of asynchronous information, which serves as predictive indicators of
consumers’ attitudes. As such, more provision of up-to-date information would arouse
potential consumers’ attentions. At the same time, consumer awareness has been
regarded as a key variable in describing consumer choice, which will nally lead to
purchase (Vermeulen and Seegers, 2009). With more exposure to a hotel brand, there
would be a higher chance for consumers to include a hotel into their awareness set.
Therefore, more efforts could be devoted to increasing the quantity of online reviews
about a hotel.
Furthermore, this study found a positive relation between usefulness of online
reviews and online purchase intentions. As mentioned above, consumers are currently
in an information overloading situation. Therefore, Web sites, especially hotels’ own
Web sites, need to invest resources in enabling Web site visitors diagnose the usefulness
of available reviews. For example, peer-rating systems can be installed for customers to
vote on those reviews they think useful, making lter relevant opinions more efciently.
In addition to features of online reviews, this study also found a positive relationship
between reviewer expertise and people’s booking intentions. This is consistent with
previous studies discussing effects of source expertise upon respondents’ perceptions
(Tan et al., 2008). Biswas et al. (2000) suggested that expertise refers to relevant
intelligence to the object of discussion and a reviewer needs to possess knowledge on a
specic topic. In the hotel industry, this expertise includes good reputation, greater hotel
knowledge and good credit record, all of which are typical features of opinion leadership
(Bloch et al., 1989). Opinion leaders are individuals who can inuence the opinions and
behaviors of others positively and frequently (Jamrozy et al., 1996). Although the
motives for opinion leadership are still mysterious, a substantial body of research
conrms its importance in various areas. Jamrozy et al. (1996) empirically examined the
relationship between involvement and opinion leadership in tourism and suggested that
opinion leaders are identiable. It would, therefore, benet hotels to seek out and obtain
more specic information about opinion leaders, such as how they diffuse their personal
experiences of consuming hotel products and services.
Limitations and future research directions
This study’s limitations provide directions for future study. One of the major ndings is
that the interrelationships among features of online reviews, which were discussed in
other similar studies, were not considered. As such, future studies look at this in their
efforts. Additionally, future research could investigate rms’ online and ofine
marketing strategies and compare their effectiveness. It is suggested that consumers
often make ofine decisions based on online information in the context of movies (Lee
et al., 2008). However, Zhu and Zhang’s (2010) study of information goods like books and
movies showed that ofine promotions may reduce the efcacy of online reviews.
However, in the tourism industry, travelers rely on both online and ofine modes for
information (Gronaten, 2009). As such, it would be worthwhile for tourism scholars
and practitioners to empirically examine different information channels to optimize
their promotional efforts and adjust the resources allocation accordingly. Further, future
work could compare the impact of online reviews across different tourism sectors. While
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the current study focused on hotels only, the results may be applicable to other market
segments. For example, it is reasonable to suggest that online reviews may have a
greater inuence on products that are more likely to be purchased online (such as ight
tickets) than on those sold mainly ofine (such as entrance tickets for scenic spots).
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About the authors
Xinyuan (Roy) Zhao, PhD, is Associate Professor of Department of Hospitality & Service
Management, Business School, Sun Yat-Sen University, China.
Liang Wang, is PhD candidate of the School of Hotel & Tourism Management at The Hong
Kong Polytechnic University, China. Liang Wang is the corresponding author and can be
contacted at: liang.wang@connect.polyu.hk
Xiao Guo, is Project Manager at Creative Research Institute of China.
Rob Law, PhD, is Professor of the School of Hotel and Tourism Management at the Hong Kong
Polytechnic University.
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