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Predicting mobile hotel reservation adoption: Insight from a perceived

value standpoint

Hsiu-Yuan Wang a,*, Shwu-Huey Wang b

a Department of Hospitality Management, Chung Hua University, No. 707, Sec. 2, WuFu Rd., Hsinchu 300, Taiwan, ROC b Department of Business Administration, Far East University. No. 49, Chung Hua Rd., Hsin-Shih, Tainan County 744, Taiwan, ROC

1. Introduction

In order to further support the overwhelming demand for

reservations, a few hospitality corporations have launched services

by introducing a mobile phone-based location technology into

their hotel reservation services in the recent years. With a single

click of a button, users are able to find a favorable hotel nearby, as

the services automatically consider both their approximate

location and personal preferences. The convenience and flexibility,

combined with the available technology like wireless Internet,

mobile phone-ID location, global navigation satellite system

(GNSS), geographic information system (GIS) and global position-

ing system (GPS), have enabled hoteliers to meet customers’

mobile hotel reservation (MHR) demand by delivering time-

critical, location based services (LBS).

In today’s highly competitive environment, hotels have

responded to the opportunities offered by the Internet by

reducing costs and providing real time information to promote

andsell theirproducts to consumers(Connolly et al., 1998;Kim et

al., 2006; Pernsteiner and Rauseo, 2000). However, the era of m-

commercehas beencomingand themajor differencesbetweenm-

commerce and other forms of e-commerce are mobility and

accessibility. To stay competitive and increase revenues, hospi-

tality practitioners start to develop m-commerceby offering MHR

to bring the world’s hotels to our palms. MHR refers to ‘‘a location

based online distribution information system that enables customers

worldwide to reserve hotelroomsanytime, anywherethroughthe use

of the wireless Internet, GNSS, GIS, GPS and mobile phones/devices.’’

Yet, acceptance of MHR by individuals is indispensable to the

successful implementation of MHR.

User acceptance of MHR cannot be entirely explained by the

existed technology adoption models, like the Technology Accep-

tance Model (TAM) (Davis, 1989), the Theory of Reasoned Action

(TRA) (Fishbein and Ajzen, 1975), the Theory of Planned Behavior

(TPB) (Ajzen, 1991) and the Unified Theory of Acceptance and Use

of Technology Model (UTAUT) (Venkatesh et al., 2003) since each

of them has their limitations in explaining the adoption of new

information and communication technology (ICT). Furthermore,

most adopters and users of traditional technologies are either (1)

employees in an organization where they use the free-of-charge

technology daily for work purposes, or (2) users in a context where

they paya one-time fee during the periodof usage. However,in the

context of MHR, most leisure travelers may adopt and use MHR for

International Journal of Hospitality Management 29 (2010) 598–608

A R T I C L E I N F O

Keywords:

Mobile hotel reservationPerceived value

Perceived benefits

Perceived sacrifice

A B S T R A C T

With the attempt of further supporting the overwhelming demand for reservation, a few hospitality

corporations have launched mobile hotel reservation (MHR) services. For the acceptance of MHR by

individuals is indispensable to the successful implementation of MHR, it is critical for practitioners and

academics to understand the factors influencing theadoptionof MHR. This study examines theadoption

of MHR from the value perspective by proposing and examining a new research model that can capture

both gain and loss elements influencing individual value perceptions on behavioral intention to adopt

MHR. Data from 235 usable questionnaires, collected in Taiwan, were tested against the research model

using the structural equation modeling approach. The results indicated that perceived value was a

predictor in explaining the customer’s adoption of MHR. From the benefits point of view, perceptions of

information quality and system quality were the two critical components significantly influencing

perceived value of MHR. On the sacrifice side, the effects of technological effort and perceived fee on

perceivedvalueweresignificant.This study will be helpful to researchers in developing andtesting MHR

related theories, as well as to hospitality firms in understanding individual value perceptions of utilizing

MHR and implementing successful MHR system to attract more customers. Theoretical and managerial

implications of our results are discussed.

2009 Elsevier Ltd. All rights reserved.

* Corresponding author. Tel.: +886 3 5186873.

E-mail addresses: [email protected] (H.-Y. Wang),

[email protected] (S.-H. Wang).

Contents lists available at ScienceDirect

International Journal of Hospitality Management

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i j h o s m a n

0278-4319/$ – see front matter 2009 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ijhm.2009.11.001

mailto:[email protected]:[email protected]://www.sciencedirect.com/science/journal/02784319http://dx.doi.org/10.1016/j.ijhm.2009.11.001http://dx.doi.org/10.1016/j.ijhm.2009.11.001http://www.sciencedirect.com/science/journal/02784319mailto:[email protected]:[email protected]

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personal purposes, such that the usage fee of voluntary adoption is

charged by the quantity of data transferred over the Internet and

borne by the individuals. On the business travelers’ side, although

firms may be responsible for their MHR expenditure for business

purpose, providing them with convenient, efficient and persona-

lized service through LBS should be a goodway to attract this most

profitable group of customers of hotels to adopt MHR. Hence,

additional factors should be considered in understanding the

adoption of MHR.

In this study, we supposed that an overall judgment of value

was a critical determinant of behavioral intention to use MHR.

Based on Prospect Theory (Kahneman and Tversky, 1979), we

understand that value is defined over perceived gain or loss

relative to a reference point. Some studies have indicated that

behavioral intention to use technology needs to consider

consumers’ perception of value (Chen and Dubinsky, 2003; Cheng

et al., 2009; Chi et al., 2008; Soltani and Gharbi, 2008). From

consumers’ perspective, they tend to choose the behavior by

assessing what to maximize thevalue such that themajorprinciple

used in the concept of value should be the trade-off between total

benefits and total sacrifice when making decision in terms of using

MHR; hence, the purpose of this study is to propose and examine a

new research model that can capture both gain and loss elements

influencing individualvalue perceptions on behavioral intention toadopt MHR. It is followed by a review of important theories.

2. Theoretical foundations

2.1. ICT & service industries

The increasing use of ICT in services has radically changed the

interactions between customers and service providers. This trend

makes the practitioners fasten the speed to introduce new

technologies to promote productivity and efficiency (Walker et

al., 2002; Zeithaml and Gilly, 1987), and to provide customers with

services by new and convenient channels (Meuter et al., 2003),

thus better meeting customers’ needs and increasing satisfaction

(Bitner et al., 2002). Prior research has shown evidence thatairlines are early adopters of information technology and have a

long history of accepting innovative technology (Buhalis, 2004).

Automatic reservation systems and much more comprehensive

universal distribution systems are examples of information

technology employed by airlines to offer value-added services.

The emergence of the Internet and the development of customer

relationship management (CRM) systems also provide opportu-

nities for service industries to develop their marketing strategies.

Additionally, earlier research in the restaurant industry confirms

that the use of information technology provides a corporation with

the competence to understand customer preferences and support

personalized service, which, in turn, augment customer satisfac-

tion (Ansel and Dyer, 1999). Other examples of technology-

enabled service delivery, including electronic retailing, onlinebanking, scanning purchases in supermarkets, and paying public

transportation tickets by mobile phone are definitely popular

among customers. Thus, services industries employing ICT to

anticipate and meet customers’ expectation is a prerequisite to

providing satisfactory service (Gilbert and Wong, 2003), which

reminds hotels of not lagging in effective implementation of

advanced ICT in management, marketing and distribution of their

products.

2.2. OHR & MHR

OHR basically refers to using the Internet as a reservation

method. In the past decade, due to the less expensive accom-

modation prices and the efficiency of information offering, hotel

industries have been taking advantage of the enormous opportu-

nities generated by the Internet for online promotions and

purchases to differentiate themselves from their competitors in

the competitive market environment (Connolly et al., 1998; Kim

and Kim, 2004; Kim et al., 2006; Pernsteiner and Rauseo, 2000;

Wong and Law, 2005). The Internet provides the hospitality

corporations a favorable distribution channel that enables

customers worldwide to reserve hotel rooms. Equipped with

powerful information search capabilities offered by the Internet,

potential customers would conduct more extensive online

searches for hotel-related information such that they can compare

the alternatives and make the best purchase decision.

Recently, with the tremendous growth of demands of mobile

phones/devices, rapid development of wireless Internet, GNSS

and GPS, hoteliers have started turning to MHR to deliver new

services to existing customers and attract new ones. Based on

the definition in this study, a MHR system is a location based

online distribution information system that is designed to

provide hotel reservation support for portable devices such as

iPhone, Blackberry, etc. As mobile phones/devices have limited

screen size, users might be guided through the search and

booking procedure in just a few clicks. In this way, typing efforts

could be saved when compared to traditional OHR. To help book

a hotel room as quickly and in as few steps as possible, MHR may allow potential users to use specific mobile applications to

search for hotels nearby. So, aside from traditional features such

as browsing websites and listing hotels from hotel service

providers, specific applications in relation to mobility and

accessibility as well as competence of anticipating customers’

demands will be the point to make MHR different from the

traditional OHR (Report from Travel Distribution Report, 2007).

By making good use of the nature of mobility and accessibility,

MHR can enable hoteliers to deliver LBS from wireless Internet

to customers.

LBS consist of three components: (1) the mobile positioning

system, (2) the mobile telephonynetwork to deliver the services to

users, and (3) the LBS application (Sadoun and Al-Bayari, 2007).

Generally, for the purpose of keeping track of the position of userson thenetwork,one of themost importanttechnologies behindLBS

is positioning. Based on accurate, real time positioning, LBS can

connect users and notify them of the current conditions, such as

weather and traffic conditions, or provide routing and tracking

information through mobile devices/phones (Sadoun and Al-

Bayari, 2007). Meanwhile, by combining wireless communications,

GNSS and GIS, LBS enable managers to offer professional

personalized services and improve theiruser friendly info-mobility

services to customers (Lohnert et al., 2001). Murphy and Schegg

(2006) put research efforts on a specific commercial LBS

application to develop a prototype location based device that will

replace the traditional hotel concierge by a virtual one. The results

of their study show that the most requested information by hotel

guests is about restaurants, local activities and transportation.Thus, MHR embedded with powerful LBS applications (e.g., a

virtual concierge), potential hotel customers with mobile phones

are now not only able to reserve hotel rooms whenever and

wherever they want, but they arealso able to gain timelybeneficial

information they need. Moreover, with highly innovative location

based GPS search function, hotel corporations are now able to

allow all the mobile phone users to search, book, view and cancel

their hotel room reservations. Nevertheless, for facilitating the

implementation of MHR by hoteliers, the problem of customers’

privacy and permissions has to be resolved since ensuring that

customers provide permission to be located at the time and by the

LBS application is very important, and that things related to

protection of the consumers’ location information and location

history are crucial as well.

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2.3. Perceived value

Customer perceived value has gained much attention in the

fields of economics and marketing at both academic and

practitioner levels because it plays an important role in predicting

purchase behavior and achieving sustainable competitive advan-

tage (e.g., Bolton and Drew, 1991; Cronin et al., 2000; Dodds et al.,

1991; Kerin et al., 1992; Zeithaml, 1988). In the recent years,

perceived value has been emphasized by the researchers of

information technology field to explore and understand users

adoption of emerging technology associated with the Internet or

the mobile Internet, and the findings indicated that customer

perceived value is crucial in attracting and retailing customers

(Chen and Dubinsky, 2003; Cheng et al., 2009; Chi et al., 2008; Lee

and Overby, 2004; Soltani and Gharbi, 2008).

On the other hand, several earlier research studies interpret

perceived value from the viewpoint of quality and monetary price

(Chen and Dubinsky, 2003; Grewal et al., 1998), however, which

ignores the multi-dimensionality of value perception. For example,

traditionally, when consumers have to make a decision, they

usually decide by comparing the difference between the sacrifice/

costs (e.g., price) and benefits (e.g., product quality); if the benefit

is greater than the sacrifice, there comes ‘‘consumer surplus’’,

which maylead to a purchase decision. In other words,the quality/price ratio has an influence on if they will buy the product.

However, viewing value as a trade-off between merely quality and

price is too simplistic (e.g., Bolton and Drew, 1991; Schechter,

1984). In modern business society, the seeable cost may not be the

main determinant for consumers to make the final decision, while

some other intangible costs like convenience, time, security and

effort spent would be the more crucial factors (Hoyer and

Maclnnis, 2003). Holbrook (1999) proposes that perceived value

includes eight types of value: convenience, quality, success,

reputation, fun, beauty, virtue and faith. This conception is

comprehensive; yet, it fails to take into account the costs

associated with consumption.

Kotler and Keller (2006, p.133) have defined perceived value as

‘‘the difference between the prospective customer’s evaluation of all the benefits and all the costs of an offering and the perceived

alternatives.’’ Others assert that value denotes consumers believe

that their choices arebetter than any other options andwill choose

what they think is best for them (Holbrook, 1999; Solomon, 2004).

Perceived value could be considered to be the feeling of trade-off

between benefits and costs (Kotler, 2003; Parasuraman et al.,

1988). In the context of MHR, we define perceived value as ‘‘a

customer’s overall value perception of MHR based on the

comparison of its benefits and sacrifice factors when using it.’’

For example, suppose that we have flown to Rio de Janeiro for the

carnival and arrived at 10 pm, as well as we are urgent to find a

hotel. Compared to other factors, time and convenience should be

the first consideration, and booking the hotel ‘‘in a few clicks’’

through mobile phone would be the most effective alternative to

solve the problem. Therefore, consistent with behavioral theories,

such as the TRA (Fishbein and Ajzen, 1975), perceived value is a

context-specific perception that may drive customers’ attitudes

and behaviors. That is, under different contexts, people decide

their usage based on a value trade-off at the moment, at the place

they are. It is followed by describing the research model and

hypotheses.

3. Research model and hypotheses

Taking into account of our definition of perceived value,

customers assess the value of adopting MHR by considering all the

relevant benefits and sacrifice factors. For the sake of covering

some important features that can account for most of the variance

in adoption intention of MHR, we proposed and tested a new

research model, as shown in Fig. 1. In this model, we chose

behavioral intention, a person’s subjective probability to perform aspecified behavior, as the dependent variable for theoretical and

practical reasons. According to prior studies (Ajzen and Fishbein,

1980; Legris et al., 2003; Taylor and Todd, 1995a; Venkatesh and

Davis, 2000; Venkatesh and Morris, 2000), intention has a major

influence on actual behavior in mediating the effect of other

determinants on behavior. Also, even though mobile phones are

now more commonly used, there are very few people having

experienced using MHR, as it is still in its early infancy. Thus, the

choice of intention instead of actual usage as a dependent variable

was desirable, which allows a timely investigation of customers’

acceptance in this early stage of MHR and seems to be more

meaningful. We expected that this parsimonious model could be

useful and helpful in understanding and predicting individual’s

MHR adoption.From customer choice perspective, prior research has indicated

that users’ perceived value could be a predictor of behavioral

intention to accept wireless short messaging service (Turel et al.,

2007), Internet retailing (Cheng et al., 2009), mobile value-added

services (Chi et al., 2008), website browsing (Soltani and Gharbi,

2008), Internet shopping (Lee and Overby, 2004) and e-commerce

(Chen and Dubinsky, 2003). Therefore, in the MHR settings, we

Fig. 1. Research Model.

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expect that high evaluation of perceived value will cause an

increase in the salience of intention to use MHR such that the

following hypothesis was tested:

H1. The overall perceived value of MHR has a positive effect on

behavioral intention to use MHR.

3.1. Perceived benefits

Operationally, the major measure of product benefits is product

quality (Kerin et al., 1992). Product quality refers to the customer’s

cognitive appraisal of the excellence or superiority of a product

(Zeithaml, 1988). Prior research (Ahn et al., 2004) asserts that the

quality of an Internet system could be assessed by information

system (IS) quality measures including information, system and

service quality. Pitt et al. (1995) introduce a model that includes

information, system and service quality as independent variable

for IS success. To understand user technology acceptance of

Internet shopping malls, Ahn et al. (2003) have justified that

information, system, service quality have significant impacts on

user attitude.

According to our definition of MHR, first, it is a location based

online distribution IS in nature. Second, it is obtained by using a

palm-sized mobile device, wireless Internet, GNSS and GPS, andcould be viewed as a natural extension of Internet IS. Hence, we

assume that the users’ total perceived quality of MHR includes

information, system and service quality since MHR is still system-

based. It is followed by a brief illustration and review of

information, system and service quality.

3.1.1. Information quality

Information quality captures the quality of content that the

system presents. Delone and Mclean (2003) have defined that

the content of a web system should be personalized, complete,

relevant, easy to understand and secure if we expect prospective

buyers to initiate transactions via the Internet. So, in a MHR

setting, information quality in this study is defined as the degree

to which utilizing MHR can help customers to get complete,detailed, timely, accurate, reliable and selective information to

compare hotel alternatives, make booking and routing things

more convenient, and make better purchase decisions. Prior

research in hotel reservation has found that information needs is

a crucial factor to increase the number of Internet sales ( Jeong

et al., 2001; Kim et al., 2006; Perdue, 2001; Ranganathan and

Grandon, 2002).

3.1.2. System quality

In the settings of the Internet, system quality was defined by

measuring the desired features of a typical web system,

including usability, availability, reliability, adaptability and

response time (Delone and Mclean, 2003). From the viewpoint

of entire system operation, a system’s quality implies theoperation efficiency in the function of an information system.

Some have argued that the measure of system quality was often

reflected by technical adequacy (Aladwani and Palvia, 2002),

response rate (Seddon, 1997), navigation functionality (Palmer,

2002), and hypermedia presentation (Selz and Schubert, 1997).

In this study, system quality means the degree to which using

MHR appears to have met customers’ needs in several aspects

such as instant connection with web, fast response, good

functionality, error-free transactions, and appropriate hyperme-

dia presentation. Past research about online hotel purchase has

found that essentials of system quality, like 24-h availability

(Kim et al., 2006), searching response time (Au Yeung and Law,

2003; Wong and Law, 2005), are very important in determining

customers’ purchase decisions.

3.1.3. Service quality

In a traditional IS environment, service quality typically refers

to availability of multiple mechanisms for processing customers’

complaints, assisting customers in using a product, suggesting

complementary product or services, and problem solving (Bhat-

tacherjee, 2001); however, in a web setting, due to the lack of face-

to-face contact, service quality has become very crucial. Delone

and Mclean (2003) assertthat the service quality of a webIS should

encompass the measures of assurance, empathy and responsive-

ness since the users are customers and poor user support will

translate into lost customers and lost sales. In a MHR environment,

here, we define service quality as the degree to which using MHR

can provide customers with prompt, promised, follow-up, and

professional personalized service. Hotel-related research (Ho et

al., 2000) has found that whether the online purchase service is

good or not has a significant impact on customers’ Internet hotel

purchase intention.

Researchers have confirmed that product quality has a positive

effect on perceived value (Dodds et al., 1991). As a result, in this

study, we infer that the customers’ total perceived quality of MHR,

including three constructs: (1) information, (2) system, and (3)

service quality, has a positive effect on perceived value. This leads

to the hypotheses as the following:

H2. Information quality has a positive effect on perceived value.

H3. System quality has a positive effect on perceived value.

H4. Service quality has a positive effect on perceived value.

3.2. Perceived sacrifice

Perceived sacrifice consists of not only actual perceived

price but other non-monetary costs, including not limited to the

effort expended in a product’s acquisition and use ( Kerin et al.,

1992). Prior work has identified technological effort is a

significant barrier to mobile Internet adoption (Kim et al.,

2007), and the worry and fear of divulging personal sensitive

information has shown significant influence on individual onlinehotel purchase decision ( Jeong et al., 2001; Kim et al., 2006).

Hence, this study proposes that technological effort , perceived fee,

and perceived risk of using MHR to be the sacrifice components

of perceived value.

3.2.1. Technological effort

Technological effort in this study is defined as the degree to

which an individual believes that using MHR would expend

physical and mental effort. Though the advantages provided by

MHR are competitive from today’s standpoint, there are some

disadvantages compared to other systems due to its running on

mobile devices with small screen size. In general, although most of

mobile phones today have improved a lot by adding more smart

functions, powerful data processing ability and providing instantconnection with wireless Internet to help users start to use quickly,

potential MHR adopters still hope that MHR can offer simple and

easy application system interfaces to make booking things more

convenient for them. In TAM (Davis, 1989) and UTAUT (Venkatesh

et al., 2003), effort is considered to be a component of cost; hence,

technological effort is a sacrifice in using MHR. Prior studies

suggest that a technological effort-oriented factoris expected to be

more significant in the early stages of a new behavior (Davis et al.,

1989; Szajna, 1996; Venkatesh, 1999). The more technological

effortusershave to expend, theless likelihood they will have a high

value perception. Therefore, this study tested the following

hypothesis:

H5. Technological effort has a negative effect on perceived value.


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3.2.2. Perceived fee

Perceived fee in this study means the monetary transaction

costs when customers conduct hotel purchase through using MHR,

including actual expenses of hotel accommodation and mobile

Internet subscription-based pricing. MHR enables customers to

book hotel accommodation directly on their mobile phones by

search, comparison and examination of prices offered by various

grades of hotels. In addition to price for hotel accommodation, in

the situation of MHR, customers may probably correlate the fee of

mobile Internet usage with previous prices of mobile phone calls

and stationary Internet access, and the outcome of this correlation

constructs a part of the customers’ perception of the fee as well.

Prior research has indicated that price is always a key factor for

users when purchasing hotel accommodations (Law and Chung,

2003; Liang and Law, 2003) and tourism products online (Law,

2003). It has been contended that perceived fee directly influences

perceived value (Chang and Wildt, 1994; Dodds et al., 1991;

Zeithaml, 1988), and that higher fee perceptions are related with

lower value perceptions (Chang and Wildt, 1994). So, this study

hypothesized as the following:

H6. Perceived fee has a negative effect on perceived value.

3.2.3. Perceived risk

Based on Forsythe andShi (2003), perceivedrisk in this research

refers to certain types of financial, product performance, psycho-

logical, physical, security and privacy risks when customers make

booking transactions over wireless Internet through MHR. For the

penetration rate of wireless Internet applications is increasingly

high, peoplemay worry about diverse types of risks when engaging

in MHR activities, and the risks mainly include (1) exposing credit

rating, bank balances, financial data, and other personal privacy

information (e.g., personal location information and location

history), (2) unsafe wireless network security and immature

mobile software development technology, and (3) unexpected

product performance. Research has applied perceived risk to

explain customer’s behavior in decision making since 1960s

(Taylor, 1974). Law and Leung (2002) found that the security of the

payment system has a great effect on the overall quality of a travel

website. Among the factors of a successful travel website, Law and

Wong (2003) identified secure payment methods as one of the

most important attributes. Ranganathan and Grandon (2002) also

found security and privacy to be the two key elements affecting

online sales. In another study of m-commerce, Wu and Wang

(2005) considered that perceived risk significantly affected users’

behavior intention.From customer’s point of view, perceivedrisk is

a kind of mentaleffort andcouldbe viewedas a sacrifice.Therefore,

in a MHR setting, this study proposed the following hypothesis:

H7. Perceived risk has a negative effect on perceived value.

4. Research methodology

4.1. Measures

To ensure the content validity of the scales, the items selected

must represent the concept about which generalizations are to be

made. Therefore, validated instruments adapted from prior studies

were used to measure the constructs of information quality,

system quality, service quality, technological effort, perceived fee,

perceived risk, perceived value and behavioral intention. Those for

information quality, system quality, and service quality were

adapted from Ahn et al. (2004), Barnes and Vidgen (2001), Palmer

(2002) and Ranganathan and Ganapathy (2002). The items for

technological effort were adapted from the study of Wu and Wang

(2005). The measures for perceived fee were based on Voss et al.

(1998). Perceived risk was captured from the studies of Kim and

Kim (2004), and Wu and Wang (2005). The construct of perceived

value was derived from Sirdeshmukh et al. (2002). Finally, the

items of behavioral intention were mainly based on Venkatesh et

al. (2003). To make sure that important aspects arenot overlooked,

we performed experience surveys on the measures with three

professionals in m-commerce field and five graduate students who

had experience in using the mobile Internet. They were asked to

comment on list items that corresponded to the constructs,

including scales wording, instrumentlength, questionnaire format,

and ambiguous question. After careful examination, the items

were slightly revised so the wording is more precise to constitute a

complete scale for this study. Consistent with prior research on

social and human behavior, the questionnaire also contained

demographic questions. Likert scales (1–7), with anchors ranging

from ‘‘strongly disagree’’ to ‘‘strongly agree,’’ were used for all

construct items. Furthermore, the original question items were in

English; however, we invited a bilingual expert to translate them

into Chinese to ensure the validity of the questionnaire. The final

list of items for each construct is provided in Appendix A.

4.2. Data collection

To make the results generalizable, we gathered data via an

Internet survey between November 2008 and January 2009. A

questionnaire was designed and placed on a web site. To

increase the response rate of potential respondents, we put

survey messages on several mobile Internet/phones forums for 8

weeks such as Mobile Phone Bus, Taiwan RD Innovation Forum,

Metro Forum, Flash Forum, Plays Life Forum, and Heyzu Forum.

At any time during the 8 weeks, participants could respond to

the online questionnaire by clicking the URL provided on the

message, which also included (1) the purpose of this study, (2) a

reminder that specified potential respondents should not take

the survey if they had no experience in or were regular users of

the mobile Internet, for only users with limited mobile Internet

experience would be appropriate for MHR adoption study, and(3) illustration about 50 NT$100 gift coupons as reward in a

drawing at the completion of this survey. In order to effectively

eliminate repeat responses to the survey, we removed responses

with duplicate IP addresses from our data sample. The

respondents were also requested to offer their mobile phone

numbers in the questionnaire, such that we could check if they

had opened connection to the mobile Internet by asking them to

reply a mobile email.

A sample of 235 usable end-user responses was obtained from

a variety of respondents withdifferent computer, Internet, mobile

phone and mobile Internet experiences. The respondents had an

average of 6.94 years of computer experience (S.D. = 3.37), 6.25

years of Internetexperience (S.D. = 2.86), and 4.38years of mobile

phone experience (S.D. = 2.33). The detailed demographic attri-butes of the respondents are shown in Table 1. Moreover,

surprisingly, we found students and males become the large

portion of our participants. The possible reasons might be the

following. In Taiwan, there are 111 mobile phone subscribers per

100 inhabitants (Report from III FIND center, 2009). About 70% of

mobile Internet users are between 16 and 35 years old (e.g.,

studentsfallingin this age range), andthe numberof male users is

reported tobe higherthan that offemaleby 25% (Liang, 2006).This

might be the major reason why about 48% of our subjects were

students. This study conducted an online field survey, and all the

participants were self selected. Although the survey result

showed thatstudents become thelarge proportionof participants,

most of the participants have university/college educational

background. Prior research (Yang, 2005) stated that university


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students are likely to be the first customer segment to adopt

mobile commerce because of their high educational level and

income potential. Age also makes university/college students

more open to new information technology (Lighter et al., 2002;

Pijpers et al., 2001). In addition,based on Yi et al.’s (2006) review

of prior studies on technology acceptance, we found thatdifferent

level of students had been recruited by much earlier research as

research subjects, including undergraduate students (Agarwal

and Karahanna, 2000; Mathieson, 1991; Yang, 2005), MBA

students (Agarwal and Prasad, 1997, 1998), undergraduate and

MBA students (Taylor and Todd, 1995a,b), undergraduate and

graduate students (Kim et al., 2007) and so on. Therefore, weconsidered that highly educated students are welcomed and

appropriate for participating in research about MHR adoption as

they are potential customers who are most likely to book hotels

through MHR in the near future.

5. Data analysis and results

5.1. Assessment of measurement model

A confirmatory factor analysis via AMOS 7.0 was conducted to

test the measurement model. Six common model-fit measures

were employedto assess themodel’s overall appropriateness of fit:

the ratio of x2 to degrees-of-freedom (df.), goodness-of-fit index(GFI), adjusted goodness-of-fit index (AGFI), normalized fit index

(NFI), comparative fit index (CFI), and root mean square residual

(RMSR), and to attain a better model fitness, eight items, item 1, 4,

9, 14, 17, 18, 22 and 26 (see Appendix A), were eliminated due to

cross factor loadings. As shown in Table 2, all the model-fit indices

exceeded their respective common acceptance levels suggested by

previous research, thus demonstrating that the measurement

model revealed a fairly good fit with the data collected. We could

therefore go forward to evaluate the psychometric properties of

the measurement model in terms of reliability, convergentvalidity,

and discriminant validity.

Reliability and convergent validity of the factors were

calculated by composite reliability, and by the average variance

extracted (see Table 3). The composite reliabilities can be

calculated as follows: (square of the summation of the factor

loadings)/{(square of the summation of the factor loadings) +(summation of error variables)}. The interpretation of the

resultant coefficient is similar to that of Cronbach’s alpha.

Composite reliability for all the factors in the measurement

model was above 0.90, thus demonstrating all were greater than

the bench mark of 0.60 suggested by Bagozzi and Yi (1988). The

average extracted variance was all above the recommended 0.50

level (Hair et al., 1992), which implied that more than one-half

of the variances observed in the items were accounted for by

their hypothesized factors. Convergent validity can also be

evaluated by observing the factor loadings and squared multiple

correlations from the confirmatory factor analysis (see Table 4).

Based on Hair et al.’s suggestion (1992), factor loadings greater

than 0.50 were deemed as very significant. All of the factor

loadings of the items in the research model were greater than0.85. Also, squared multiple correlations between the individual

items and their a priori factors were high. Thus, all factors in the

measurement model had adequate reliability and convergent

validity.

Table 1

Demographic attributes of the respondents.

Frequency Percentage Cumulative

Gender

Female 79 33.6 33.6

Male 156 66.4 100.0

Age

51 2 0.80 100.0

Education level

Under senior high school 2 0.9 0.9

Senior high school 21 8.9 9.8

College 172 73.2 83.0

Graduate (or above) 40 17.0 100.0

Job

Student 112 47.7 47.7

Professional 67 28.5 76.2

Self-employed 16 6.8 83.0

Others 40 17.0 100.0

Mobile Internet experience

1–2 times 91 38.7 38.7

3–4 times 119 50.7 89.45 times 25 10.6 100.0

Table 2

Fit indices for measurement and structural models.

Goodness-of-fit measure Recommended value Measurement model Structural model

x2/degree of freedom 23.00 1.100 1.111

Goodness-of-fit index (GFI) 30.90 0.906 0.904

Adjusted goodness-of-fit index (AGFI) 30.80 0.880 0.879

Normed fit index (NFI) 30.90 0.970 0.969

Comparative fit index (CFI) 30.90 0.997 0.997

Root mean square residual (RMSR) 0.10 0.021 0.022

Table 3

Reliability, average variance extracted, and discriminant validity.

Factor CR 1 2 3 4 5 6 7 8

1. Information quality 0.976 0.911

2. System quality 0.955 0.331 0.809

3. Service quality 0.971 0.114 0.211 0.918

4. Technological effort 0.982 0.182 0.084 0.030 0.949

5. Perceived fee 0.909 0.023 0.027 0.027 0.023 0.769

6. Perceived risk 0.954 0.003 0.001 0.016 0.013 0.013 0.839

7. Perceived value 0.959 0.386 0.314 0.073 0.086 0.017 0.007 0.856

8. Intention to use MHR 0.982 0.450 0.506 0.171 0.172 0.053 0.005 0.419 0.947

CR = composite reliability; diagonal elements are the average variance extracted. Off-diagonal elements are the shared variance.


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To test discriminant validity, this study compared the shared

variance between factors with the average variance extracted of the

individual factors. This analysis exhibited that the shared variances

between factors were lower than the average variance extracted of

the individual factors, thus confirming discriminant validity (see

Table 3). In brief, the measurement model demonstrated adequate

reliability, convergent validity, and discriminant validity.

5.2. Structural model estimation and hypotheses testing

A similar set of model-fit indices was used to examine the

structural model (see Table 2). As estimated, the six common

model-fit measures of the structural model were also above the

individual recommended value. This provided firm evidence of a

good model-data fit. Thus, we could proceed to investigate the

determinants of perceived value and understand if high perceived

value would increase the salience of intention to use MHR.

Fig. 2 presents the significant structural relationship among the

research variables and the standardized path coefficients. As

expected, most of the hypotheses were supported except for

hypotheses H4 and H7. First, perceived value (b = 0.645, p< 0.001)

is significantly related to behavioral intention (R2 = 0.379). Thus,

H1 is supported. Next, the four antecedents are found to be

significantly related to perceived value (R2 = 0.435): informationquality (g = 0.311, p< 0.001), system quality (g = 0.356, p < 0.001), technological effort (g = 0.089, p < 0.05), and per-ceived fee (g = 0.–0.080, p < 0.05). Table 5 summarizes the resultsof hypotheses testing.

6. Discussion

From the customers’ perceived benefits point of view, two

constructs derived from Internet system-based concept (informa-

tion quality, and system quality) had a significant, positive

influence on perceived value. The absolute magnitude of the

estimated standardized path coefficients showed that information

quality had the greatest impact on perceived value of MHR.

Previous studies supported the concept that information quality

plays a dominant role in increasing the number of Internet hotel

sales ( Jeong et al., 2001; Kim et al., 2006; Perdue, 2001;

Table 4

Factor loadings and squared multiple correlations of items.

Factor Loadings Square multiple correlations

Information quality

IQ2 0.939 0.882

IQ3 0.970 0.941

IQ5 0.962 0.925

IQ6 0.946 0.895

System quality

SQ1 0.865 0.748SQ2 0.855 0.731

SQ4 0.900 0.998

SQ5 0.925 0.900

SQ6 0.950 0.938

Service quality

SVQ1 0.965 0.931

SVQ3 0.982 0.964

SVQ4 0.927 0.859

Technological effort

TE2 0.983 0.967

TE3 0.980 0.976

TE4 0.959 0.921

Perceived fee

PF2 0.843 0.711

PF3 0.900 0.945PF4 0.887 0.930

Perceived risk

PR2 0.929 0.863

PR3 0.856 0.733

PR4 0.948 0.898

PR5 0.929 0.864

Perceived value

PV1 0.902 0.814

PV2 0.959 0.920

PV3 0.856 0.733

PV4 0.978 0.957

Behavioral intention to use MHR

BI1 0.976 0.953

BI2 0.988 0.976

BI3 0.955 0.913

IQ, information quality; SQ, system quality; SVQ, service quality; TE, technological

effort; PF, perceived fee; PR, perceived risk; PV, perceived value; BI, behavioral

intention.

Fig. 2. Results of structural modeling analysis.


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Ranganathan and Grandon, 2002). Thus, it is believed that an MHR

IS with high information quality is more likely to have high

evaluation of perceived value by customers. To boost the usage of

MHR, MHR system designers should make efforts on how to help

customers to get complete, detailed, timely, accurate, reliable and

selective hotel information to fulfill potential customers’ needs. For

example, MHR can provide variety of hotel booking information

that are already provided by traditional OHR, including providing

flexible packages to different types of customers. Moreover, by

utilizing flexible MHR system, hotels can provide customers withadditional timely information, like mapping, routing, tracking,

traffic information, to make it more convenient for travelers to find

maps and directions to the hotel of their choice, local attractions or

their next destination. In the near future, if these sorts of

information could be increasingly added to MHR, then adopters

are supposed to increase radically.

In line with prior research on online hotel purchase (Au Yeung

and Law, 2003; Kim et al., 2006; Wong and Law, 2005), the results

revealed that system quality had a significant influence on

individual value perception. This means that the majority of

customers concern about the technical adequacy, response rate,

navigation functionality and hypermedia presentation when using

it. If its usage entails lengthy connecting procedures, long

searching response time, low availability, error transactions, thenits benefits would be decreased greatly. Thus, MHR solution

providers should improve the availability, reliability and response

time of MHR systems so as to increase the evaluation of perceived

value to attract more customers to use MHR in the early stages of

adoption.

In contrast to our expectation, we found that one of the

perceived quality constructs, service quality, did not have a

significant influence on perceived value in the context of MHR.

This is inconsistent with the prior hotel-related research (Ho et

al., 2000). The possible reason might be that the Internet allows

potential customers to make hotel arrangements electronically

without personally contacting a hotel’s sales representative

(Connolly et al., 1998; Pernsteiner and Rauseo, 2000) and the

driving force behind information technology is customers’insistence on convenience and hassle-free service (Connolly

and Olsen, 2000; Ho et al., 2000). Hence, this may be why

potential users do not care too much about whether or not

hospitality firms can provide prompt and follow-up services.

However, justifying and validating our explanations/proposi-

tions need further investigations in future studies. Additionally,

to improve the effect of service quality on usage intention,

except for traditional online booking service items, hoteliers can

identify where customers are by offering highly targeted

services. For example, when powering on your mobile phone

in a specific area, the hotel would send a text message asking if

you would like to begin the check-in process and this powerful

location awareness will help to improve customers’ service

quality. On the other side, the point ‘‘where’’ is the only one

element for location services. If firms also knew the ‘‘who’’,

understanding details about customers at any time: their

availability, interests, and personal information. Putting the

‘‘who’’ and ‘‘where’’ together would be a valuable combination

for any hotel firms. It would enable true customer targeting

based on the customer’s exact preferences at the place and time

the consumer is ready to be served. That way, those hotels

which promote service quality by offering MHR service will

certainly attract more customers.

When considering the effect of perceived sacrifice, we found

that technological effort had a significant, negative influence on

perceived value, which is consistent with the findings of prior

studies about a new behavior in the early stages (Davis et al.,

1989; Szajna, 1996; Venkatesh, 1999). This means that the

majority of customers think MHR systems should be not difficult

to use. Although most of mobile phones today possess friendly

user interfaces and smart functions to help users to start using

quickly, potential MHR adopters still hope that MHR can offer

simple and easy application system interfaces to make booking

things more convenient for them. To promote potential

customers’ intention to use, it is important to make them to

realize how to use MHR systems quickly. Thus, MHR providers

should attempt to develop the user friendliness system

interfaces for MHR so as to lower customers’ mental effort,which, in turn, promote customers perceived value and inspire

customers’ usage intention.

Similarly, as expected, perceived fee was found to have a

significant negative effect in predicting customers’ value percep-

tion, and this is in line with the previous research on consumer

perceptions of price and value (Chang and Wildt, 1994; Dodds et

al., 1991; Zeithaml, 1988). So, excluding better accommodation

prices offered by hotels, telecommunication corporations still have

to decrease usage fees of mobile Internet connection to facilitate

potential customers’ high value perception as customers are

usually sensitive to monetary cost.

As shown in our findings, perceived risk did nothave significant

negative influence on perceived value. This contradicts prior

research on online sales (Law and Leung, 2002; Ranganathan andGrandon, 2002). The possible reasons might be the following. With

the rapid development of the Internet, more and more customers

have online shopping experiences, and this might imply either

they areawareof theexistence of potentialrisk andmay avoid high

risk associated with MHR or they have strong confidence in online

payment procedures and course about giving/withdrawing per-

mission to be located because online security and privacy

problems have been gaining much improvement in the past years.

In addition, a number of advantages in using MHR (e.g., cheaper

accommodation prices, diverse hotel choices, convenience, time

saving and timely LBS) still entice customers to have value

perception so as to ignore the mental sacrifice of MHR though they

perceive some potential risk. However, justifying and validating

our explanations/propositions need further investigations infuture studies.

Furthermore,in thecontext of MHR, excluding monetary cost, it

is difficult to measure information quality, system quality, service

quality, technological effort and perceived risk quantitatively

because they are intangible. Therefore, the real value of adopting

MHR for consumer relies on consumer’s personal cognitive

psychology and interpretative process toward the MHR system

because technology acceptance is individually different (Turel et

al., 2007).

7. Limitations and conclusions

Even though rigorous research procedures were used, this

study has some limitations that could be addressed in future

Table 5

Summary of testing results.

Relationship Hypothesis Testing result

H1 PV-BI Positive Supported

H2 IQ-PV Positive Supported

H3 SQ-PV Positive Supported

H4 SVQ-PV Positive Not supported

H5 TE-PV Negative Supported

H6 PF-PV Negative Supported

H1 PR-PV Negative Not supportedH, hypothesis; IQ, information quality; SQ, system quality; SVQ, service quality; TE,

technological effort; PF, perceived fee; PR, perceived risk; PV, perceived value; BI,

behavioral intention.


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studies. First, the sampling method, an online survey without

using any qualifying questions in the questionnaire, possesses

potential bias since a sample of self selected respondents may

not be generalizable. Data collection was geographically limited

to Taiwan, and about 48% of our subjects were students who are

the most potential mobile Internet adopters in Taiwan ( Liang,

2006); if future researchers wish to make generalizations from

the data, they should randomize their sample to include other

nationalities and geographical areas outside of Taiwan. Addi-

tional research is needed to make the findings of this study

generalizable. Second, the model is cross-sectional, in that it

measures perceptions and intentions at a single point of time.

However, perceptions change over time as individuals obtain

experience. When the MHR technology becomes more mature,

researchers interested in usage of advanced information

technology are able to investigate this issue more thoroughly

by adding use behavior, facilitating conditions, and some

moderating variables (e.g., gender, age, experience and so on)

into the model to evaluate the validity of our findings. Third, our

research model accounted for 44% of the variance in perceived

value and this indicate that there must be some other possible

antecedents of perceived value in terms of benefits and

sacrifices, like perceived playfulness, perceived enjoyment,

and time costs and so on. However, for keeping the researchmodel to achieve parsimony, we strive to capture a small

number of factors that account for most of the variance in value

perception. Future research can place emphasis on discovering

more factors that can directly augment the amount of the

explained variance of perceived value in using MHR.

Combining viewpoints from both marketing and IS field,

this research has served to broaden our understanding of the

factors influencing MHR adoption from the perspective of

customers. The major contributions of this study can be

described as below. First, the proposed research model was

different from other adoption models because it included both

benefits and sacrifice measures that seemed to be highly

relevant to the acceptance of MHR. Second, the results has

shown that the importance of perceived value in explaining theadoption of MHR by customers. This suggested that the existing

marketing literature may be adapted and extended to the

investigation of new technology adoption. Third, from the

perspective of benefits, perceptions of information quality and

system quality were the two critical components significantly

influencing perceived value of MHR. On the sacrifice side, the

effects of technological effort and perceived fee on perceived

value were significant. Finally, the significant antecedents of

perceived value, such as information quality, system quality,

technological effort and perceived fee, explained about 44% of

the variance in customer perceived value. This demonstrated

that they do relatively and adequately describe the phenomenon

of MHR adoption. The findings of this study will not only help

MHR practitioners in understanding the perceptions of potentialadopters, but also provide insights into research on MHR

acceptance.

Appendix A. The final items list used in the study

Information quality

1. IQ1: I think MHR provides complete information.

2. IQ2: I think MHR provides detailed information.

3. IQ3: I think MHR provides timely information.

4. IQ4: I think MHR provides reliable information.

5. IQ5: I think MHR provides selective information for purchase.

6. IQ6: I think MHR provides comparative information between

hotel accommodations.

System quality

7. SQ1: I think MHR could be connected instantly.

8. SQ2: I think MHR provides fast response and transaction

processing.

9. SQ3: I can use MHR when I want to use it.

10. SQ4: I think MHR provides a good functionality relevant to

hotel choices.

11. SQ5: I think MHR provides error-free transactions.

12. SQ6: I think MHR provides an appropriate video-audio

presentation.

Service quality

13. SVQ1: I think MHR could anticipate and respond promptly to

user request.

14. SVQ2: I think MHR could be depended on to provide whatever

is promised.

15. SVQ3: I think MHR could understand and adapt to the user’s

specific needs.

16. SVQ4: I think MHR could provide follow-up service to users.

17. SVQ5: I think MHR could give a professional and competence

image.

Technological effort

18. TE1: I think MHR provides a difficult navigation interface.

19. TE2: I think finding what I want via MHR is difficult.

20. TE3: I think becoming skillful at using MHR is difficult.

21. TE4: It is difficult to use MHR.

Perceived fee

22. PF1: I think the access fee is expensive of using MHR.

23. PF2: I think the transaction fee is expensive of using MHR.

24. PF3: I think I cannot get a better price by using MHR.

25. PF4: The fee that I have to pay for the use of MHR is too high.

Perceived risk

26. PR1: I think using MHR in monetary transactions has potential

risk.

27. PR2: I think using MHR could notinstill confidence in users and

reduce uncertainty.

28. PR3: I think using MHR could not keep personal sensitive

information from exposure.

29. PR4: I think using MHR puts my privacy at risk.

30. PR5: Comparing with other methods, using MHR has more

uncertainties.

Perceived value

31. PV1: Compared to the fee I need to pay, the use of MHR offers

value for money.

32. PV2: Compared to the effort I need to put in, the use of MHR is

beneficial to me.

33. PV3: Compared to the potential risk I need to bear, the use of

MHR is worthwhile to me.

34. PV4: Overall, the use of MHR delivers me good value.

Behavioral intention to use MHR

35. BI1: I intend to use MHR in the future.


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36. BI2: I predict I would use MHR in the future.

37. BI3: I plan to use MHR in the future.

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Hsiu-Yuan Wang is an assistant professor in the Department of Hospitality Manage-ment at Chung Hua University, Taiwan. She has a master degree in computer sciencefrom National Chiao Tung University and a PhD in information management fromNational Changhua University of Education, Taiwan. Her current research interestsinclude customers’ behavior, travel information searching, post-adoption of informa-tion technology, Internet shopping and e-learning. Her research has appeared inComputers in Human Behavior, Cyberpsychology & Behavior, British Journal of Educa-tional Technology and several conference proceedings.

Shwu-Huey Wang is a Lecturer in the department of business administration at FarEast University and a PhD student in the department of businesseducationat NationalChanghua University of Education, Taiwan. Her current research focuses on VR adop-tion and IT adoption.


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