Drivers and Inhibitors of Mobile Payment

50 International Journal of E-Business Research, 8(3), 50-67, July-September 2012

Drivers and Inhibitors of
Mobile-Payment Adoption
by Smartphone Users
Pavel Andreev, University of Ottawa, Canada
Nava Pliskin, Ben-Gurion University of the Negev, Israel
Sheizaf Rafaeli, University of Haifa, Israel

ABSTRACT
The widespread penetration of smart mobile devices has facilitated rapid growth of mobile location-based
services (LBS), which provide users with a variety of beneits and are attractive from a marketing perspective.
However, mobile-payment (M-Payment) adoption by users has been below expectations. For better understanding of drivers and inhibitors of the willingness to M-Pay for mobile LBS, this study contributes by conceptual
modeling and empirical assessment of user willingness to M-Pay. To test the proposed conceptual research
model, data from 122 valid responses were analyzed by employing the Partial Least Squares (PLS) technique.
The indings show that Perceived Risk is the main inhibitor of user willingness to M-Pay for LBS and that the
magnitude of this inhibitor’s negative impact is at least twice the magnitude of any driver’s positive impact.
Keywords:

Conceptual Model, Location-Based Services (LBS), Mobile Payments (M-Payment), Mobile
Services, Partial Least Square (PLS), Perceived Risk

INTRODUCTION
The smartphone, now a valuable and critical
business tool for mobile delivery of products and
services, has been investigated by academics,
professionals, and the media (Bauer, Barnes,
Reichardt, & Neumann, 2005; Gao & Küpper,
2006; Hsu & Kulviwat, 2006; Leppäniemi &
Karjaluoto, 2005; Varshney & Vetter, 2002).
Industry experts predict that the range and extent of mobile products and services available
through smart mobile devices will increase
DOI: 10.4018/jebr.2012070104

exponentially in the coming months and years,
as more and more commercial entities realize
their profit potential.
The widespread penetration of smart
mobile devices facilitates the rapid growth of
mobile location-based services. A locationbased service (LBS) provides services based on
the user’s geographical location. It is possible

to classify LBSs based on the target market:
business-to-customer (B2C) and business-tobusiness (B2B), the service type: infotainment,
navigation, information provision, games,
emergency response, supply chain management and tracking (Giaglis, Kourouthanassis, &

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International Journal of E-Business Research, 8(3), 50-67, July-September 2012 51

Tsamakos, 2003) or, as classified in this study,
the delivery mode: pull and push (Paavilainen,
2002). The Pull services are sent to the user
upon request while the Push services are
non-request based (Unni & Harmon, 2007).
According to industry analyses of the current
mobile LBS market, the main drivers of this
market’s rapid growth include success of new
mobile business models, expansion of mobile
advertising, expanding of network coverage
and increasing of high speed mobile Internet

(Pyramid Research, 2011).
Another factor related to this growth is
mobile payments (M-Payments). Leading players in the mobile market provides a variety of
solutions facilitating of M-Payments. Google
proposes, for instance, smartphones with builtin NFC-powered digital wallets (http://www.
google.com/wallet/ retrieved on November
24th, 2011). While the share of NFS-enabled
smartphones is predicted to reach 30% to 50%
of the market by 2014, mobile network operators, banks, and third parties have provided
other technological and business solutions. For
example, billionaire Richard Branson invested
in the startup Square, which proposes an innovative M-Payment solution by integrating
the existing technologies of smartphone and
credit card (https://squareup.com/ retrieved on
November 24th, 2011).
Despite visible M-Payment advantages and
regardless of the noticeable agiotage around expectations for M-Payment boom, the status quo
shows that there are still many factors inhibiting
user willingness to M-Pay. Indeed, a study by
the Portio Research (2010) demonstrated that,

in 2009, 81.3 million people worldwide M-Paid
(2% of mobile subscribers) and forecasted the
rise to nearly 490 million (8% of mobile subscribers), by the end of 2014, raising interest
in investigating factors driving and inhibiting
the willingness to M-Pay.
The objective of this study is thus to increase understanding of M-Payment drivers and
inhibitors, through modeling and empirically
assessing the willingness of users to M-Pay.
The study’s scope is limited to Push-LBS for
which users exercise less control over their

interaction with the service provider (Xu,
Hock-Hai, Tan, & Agarwal, 2010) and since
behavioral attitudes and intentions regarding
Push-LBS remain blurred and hardly addressed
by literature. Moreover, while adoption of advanced mobile devices facilitates new business
opportunities for mobile commerce sector, the
future of the Push-LBS boom depends on user
willingness to M-Pay.
The next section explores the theoretical

grounding for the development of conceptual
model described in the third section. Then
we outline the methods of data collection and
analysis, followed by description of the results
obtained via empirical assessment using the
partial least square (PLS) approach to structural
equation modeling (SEM). The last section is
devoted to the discussion and the conclusions.

THEORETICAL BACKGROUND
Since, according to Kim and Zhang (2009) the
modeling of an individual’s rationale for adopting smartphone services is under-investigated
in the extant literature, one must look into
theories developed in the literature about user
adoption of other technologies upon investigating acceptance of smartphone applications:
Technology Acceptance Model - TAM (Davis,
1989), Diffusion of innovations - DoI (Rogers, 1995, 2002), perceived characteristics of
innovations - PCI (Moore & Benbasat, 1991).
TAM is a widely cited model for predicting and
explaining user behavior and technology usage

through focusing on perceived usefulness and
perceived ease of use. A thorough review of the
literature reveals a small number of researchers
who employed TAM to explore M-Payment acceptance. Dahlberg, Mallat, and Öörni (2003)
focused on the TAM to understand and explain
issues related to the adoption of mobile payment adoption intentions. Viehland and Leong
(2007) and Dahlberg and Oorni (2007) examined perceived usefulness and perceived ease
of use in the context of consumer willingness
to use M-Payment services for retail point-ofsale payments.

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52 International Journal of E-Business Research, 8(3), 50-67, July-September 2012

Past research mostly explored drivers of
technology adoption leaving inhibitors out of
the model. The model development in this study
considers, in addition to perceived ease of use
and perceived usefulness, two drivers discussed
already in the M-Payment context, two trust

drivers and perceived risk as an inhibitor.
Vendor Trust. In buyer–seller relationships,
trust is defined as the willingness of the buyer to
be vulnerable to a seller based on the belief that
the seller will transact in a manner consistent
with the buyer’s confident expectations (Pavlou
& Gefen, 2004). Consumer trust in an online
vendor has significant effects on their decisions
to purchase from the vendor’s website (Chau,
Hu, Lee, & Au, 2007). In general, consumers
find it substantially more difficult to judge the
trustworthiness of a vendor in an online setting than in the conventional business context
(Reichheld & Schefter, 2000). Consequently,
vendor trust can significantly affect customer
intention to purchase online (Gefen & Straub,
2003). Applying this concept to mobile commerce, trust is crucial given the anonymous
buyer-seller interactions and the lack of formal
contractual agreements (Lie, Fang, & Pavlou,
2010). Andreev, Duane, and O’Reilly (2011)
found that Vendor Trust is a key factor explaining the willingness of smartphone users

to M-Pay for mobile media services.
Mechanism Trust. Mechanism trust pertains to the infrastructure and regulation norms
that enables transactions between users and
LBS providers (Treiblmaier & Chong, 2007).
Dinev (2006) examined trust in the Internet as
a mechanism and found that many users are
well aware that there is a certain amount of risk
that data are captured during transmission even
if the vendor is trustworthy. Cheung and Lee
(2003) recommend that in the highly impersonal
domain of Internet commerce, an objective
third party and the government should play an
important role with regard to the underlying
mechanism. Duane, O’Reilly, and Andreev
(2011) found that dividing the trust construct
into vendor trust and mechanism trust is essential to revealing the real different impact of
the trust domain on the willingness to M-Pay.

Specifically in the context of mobile commerce,
they proposed that an independent objective

third party and the government would play
roles in establishing legislation and standards
of service. Mechanism trust is thus critical in
the context of this study as it imposes a risk
factor of its own.
Perceived Ease of Use. Following TAM research (Davis, 1989; Venkatesh & Davis, 1996),
and given the technical limitations of mobile
devices, ease of use becomes an imminent factor
in acceptance of mobile applications (Schierz,
Schilke, & Wirtz, 2010). Schierz et al. (2010)
noted that ease of use becomes even more important for M-Payment services which need to
provide benefits when it comes to ease of use.
Perceived Usefulness. TAM research has
illustrated that both perceived ease of use and
perceived usefulness and determine the consumer’s attitude toward use (Viehland & Leong,
2007). Dahlberg (2008) demonstrated the role of
perceived usefulness in explaining acceptance
of mobile payments and more recent research
(Xu et al., 2010) found perceived usefulness
as a critical driving factor in LBS adoption.

Perceived Risk. Featherman and Pavlou
(2003) validated empirically the direct and
indirect negative impact of perceived risk on
user adoption intention, deriving the definition
of perceived risk from Bauer (1960) as combination of uncertainty and seriousness of outcome.
M-Payments pose security risks and uncertainty
for smartphone users similar to those associated
with the non-mobile Internet technology (Guo,
Wang, & Zhu, 2004; Gururajan, 2006). Indeed,
according to previous research (Chen, 2008;
Im, Kim, & Han, 2008; Suh & Han, 2002),
consumers are concerned with security and
privacy when using their smartphones to M-Pay.
Authentication, confidentiality, data integrity
and non-repudiation are also mentioned in the
literature as key risk issues (Chen, 2008).

MODEL DEVELOPMENT
This conceptual model proposed in this study,
which is based upon a thorough review of

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International Journal of E-Business Research, 8(3), 50-67, July-September 2012 53

the literature on conceptual models regarding technology adoption, trust, and behavior
and expands conceptual models developed
in previous research on willingness to M-Pay
(Andreev et al., 2011; Duane et al., 2011), is
depicted in Figure 1. The model contains the
five constructs presented in the previous section as theoretically crucial for explaining the
willingness to M-Pay: four drivers - Vendor
Trust (VT), Mechanism Trust (MT), Perceived
Usefulness (PU), and Perceived Ease of Use
(PEU) as well as one inhibitor – Perceived Risk
(PR). Two additional constructs, willingness
to engage in Social Push-LBS and willingness
to engage in Commercial Push-LBS are also
included in the proposed model. Initially, the
Willingness to engage in Push-LBS construct
was developed for this study during modeling
but, based on factor analysis, was divided into
the two social and commercial Push-LBS constructs. This split makes sense since people use
their smartphones to communicate with their
social network (Noll, 2006). They use Social
Push-LBS as unsolicited personalized LBS
of a social nature where public information
or localized emergency or notification alerts
are pushed, and Commercial Push-LBS as
unsolicited personalized LBS of commercial
nature where targeted marketing, promotions, discounts, and special offers are pushed
(O’Reilly & Duane, 2010; Xu et al., 2010). The
rest of this section is devoted to defining 19
research hypotheses, which represent relationships between the research constructs depicted
in Figure 1. In operationalising the constructs
in this study, indicators from the literature were
adopted in developing survey questions for the
data-collection phase and the construct items
along with their associated survey statements
are depicted in Table 1. Since the main goal
of the conceptual model is exploring impacts
on willingness to M-Pay and for the sake of
model parsimony, some relationships between
presented in the model constructs mentioned in
the literature (e.g., relation between PEU and
PU) were omitted from the model.

Willingness to M-Pay. The widely accepted three dimensions of intention (MPay1),
and safety (MPay2) and (MPay3) were employed to measure this variable.
Vendor Trust. The widely accepted three
dimensions of competence, integrity and benevolence were employed in six (two for each
domain) reflective indicators to measure the
Vendor Trust construct, and the following four
hypotheses were derived:
Hypothesis 1: User trust in mobile LBS vendors
positively impacts upon their willingness
to engage in Social Push-LBS.
Hypothesis 2: User trust in mobile LBS vendors
positively impacts upon their willingness
to M-Pay.
Hypothesis 3: User trust in mobile LBS vendors
positively impacts upon their willingness
to engage in commercial Push-LBS.
Hypothesis 4: User trust in mobile LBS vendors
decreases perceived risk.
Mechanism Trust. Four indicators reflecting perceptions of smartphone users regarding the legal framework and regulatory-body
legislations were employed to measure the
Mechanism Trust construct in this study and,
by analogy with Vendor Trust, four similar
hypotheses were derived:
Hypothesis 5: Mechanism trust decreases
perceived risk.
Hypothesis 6: Mechanism trust positively
impacts upon user willingness to engage
in social Push-LBS.
Hypothesis 7: Mechanism trust positively
impacts upon user willingness to M-Pay.
Hypothesis 8: Mechanism trust positively
impacts upon user willingness to engage
in commercial Push-LBS.
Perceived Risk. Four indicators with respect
to privacy and security concerns were used to

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54 International Journal of E-Business Research, 8(3), 50-67, July-September 2012

Figure 1. Developed model of willingness to M-Pay

measure the Perceived Risk construct, and three
hypotheses were derived:

aspects of LBS usability, and three hypotheses
were derived:

Hypothesis 9: Perceived risk negatively impacts
upon user willingness to engage in social
Push-LBS.
Hypothesis 10: Perceived risk negatively
impacts upon user willingness to M-Pay.
Hypothesis 11: Perceived risk negatively impacts upon user willingness to engage in
commercial Push-LBS.

Hypothesis 12: Perceived usefulness positively
impacts upon customer willingness to
engage in Social Push-LBS.
Hypothesis 13: Perceived usefulness positively
impacts upon customer willingness to make
an M-Payment.
Hypothesis 14: Perceived usefulness positively
impacts upon customer willingness to
engage in Commercial Push-LBS.

Perceived Usefulness. Six manifest indicators were employed to measure reflections of
the Perceived Usefulness construct on different

Perceived Ease of Use. Three indicators
were employed to measure reflections of the

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International Journal of E-Business Research, 8(3), 50-67, July-September 2012 55

Table 1. Items and survey statements

Commercial_
Push-LBS

Social_PushLBS

MT

PEU

PR

PU

Item

Survey Statement

PushC1

I think that receiving on my mobile device unsolicited advertisements specific to business
product/service offerings near my location is useful

PushC2

I think that receiving on my mobile device solicited advertisements customized to my
specific location/interests/preferences is beneficial

PushC3

I think that receiving on my mobile device solicited coupons based on my specific location/interests/preferences is beneficial

PushC4

I think that receiving on my mobile device unsolicited discount coupons for products/
services is positive

PushS1

I think that receiving unsolicited emergency alerts based on my location on my smartphone
is beneficial (e.g., alerts about traffic accidents ahead, adverse weather conditions, road
conditions, etc.)

PushS2

I think that receiving unsolicited public interest information based on my location on my
smartphone is beneficial (e.g., information on traffic jams, vacant parking spaces, etc.)

PushS3

I think that receiving unsolicited community centered information based on my location
on my smartphone is useful (e.g., information on local community events, activities,
fundraising, etc).

MT1

Compliance with legal frameworks for mobile LBS is sufficiently enforced to protect
consumers

MT2

Legal frameworks for provision of mobile LBS are sufficiently robust to protect consumers

MT3

Regulatory bodies for provision of mobile LBS are sufficiently authoritative to regulate
providers

MT4

Regulatory bodies for provision of mobile LBS are sufficiently independent to regulate
providers

PEU1

I find it easy to get mobile LBS to do what I want them to do.

PEU2

Use of mobile LBS does not require a lot of knowledge.

PEU3

Use of mobile LBS does not require a lot of technical skills.

PR1

I am not afraid to transmit personal data using mobile LBS.

PR2

I don’t feel confident that the use of mobile LBS will not lead to my personal information
being disclosed to unauthorized parties.

PR3

I don’t feel safe about the privacy control of mobile LBS vendors.

PR4

I don’t believe that SMMS mechanisms will not lead to the disclosure of personal data
to unauthorized parties.

PU1

Use of mobile LBS services is becoming more essential for my work/study activities

PU2

Use of mobile LBS services improves the quality of my work/study activities

PU3

Use of mobile LBS services increases my connectivity with my work/study colleagues

PU4

Use of mobile LBS services is becoming more essential for my personal activities

PU5

Use of mobile LBS services improves the quality of my personal activities

PU6

Use of mobile LBS services increases my connectivity with my friends/family

continued on the following page

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56 International Journal of E-Business Research, 8(3), 50-67, July-September 2012

Table 1. Continued

VT

Willingness
to M-Pay

VT1

Based on my experience with mobile LBS vendors in the past, I know they care about
customers

VT2

Based on my experience with the mobile LBS vendors in the past, I know they have sufficient expertise and resources to do business on the Mobile Internet

VT3

Based on my experience with mobile LBS vendors in the past, I believe them to be honest

VT4

Based on my experience with mobile LBS vendors in the past, I know they have adequate
knowledge to manage their business on the Mobile Internet.

VT5

Based on my experience with mobile LBS vendors in the past, I know they are not opportunistic

VT6

Based on my experience with mobile LBS vendors in the past, I believe that they are
trustworthy

MPay1

I intend to use mobile LBS to M-Pay

MPay2

I consider it safe to M-Pay when using mobile LBS

MPay3

Based upon my experience of mobile LBS vendors, I consider it safe to M-Pay for goods/
services

Perceived Ease of Use construct, and three
hypotheses were derived:

The next section elaborates upon the
method employed in this study.

Hypothesis 15: Perceived ease of use positively
impacts upon customer willingness to
engage in Social Push-LBS
Hypothesis 16: Perceived ease of use positively
impacts upon customer willingness to make
an M-Payment.
Hypothesis 17: Perceived ease of use positively
impacts upon customers’ willingness to
engage in Commercial Push LBS.

METHOD

Willingness to engage in Social and Commercial Push-LBS. Three indicators (PushC1 to
PushC3) were employed to measure the Social
Push-LBS construct, four indicators (PushS1 to
PushS4) were used to measure the Commercial
Push-LBS construct, and two hypotheses were
derived:
Hypothesis 18: User willingness to engage in
Social Push-LBS positively impacts upon
customer willingness to M-Pay.
Hypothesis 19: User willingness to engage in
Commercial Push-LBS positively impacts
upon customer willingness to M-Pay.

Data Collection
A survey questionnaire instrument was used to
test the developed model (Figure 1). Following
generation of an initial version of the instrument
according to Hair et al. (2006), the authors
conducted a pre-test by asking veteran users
of mobile devices to respond to the questionnaire in order to assess the semantic content of
construct items and to facilitate refinement of
the items to be included in the survey. At the
end of the pre-test process, best-fitting questionnaire items that reflected the definitions of the
constructs were retained (Table 1). Data were
collected in March to April 2011 by posting
the final survey questionnaire on “Attentive”,
a feedback-management solutions from Ransys
Feedback Technologies that facilitated a webbased survey, inviting members of the target
population by mail, Facebook, or forums to
respond online.

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International Journal of E-Business Research, 8(3), 50-67, July-September 2012 57

Figure 2. Respondent socio-demographics

The study targeted Israeli university students who were ahead of the general public in
adopting smartphones since the three leading
mobile network operators have chosen students and soldiers as a target population prior
expanding their marketing campaigns to the
entire population. One operator went as far as
to sign an agreement with the Student Union
which dramatically increased the growth of
the student sector among the community of
smartphones users. According to the agreement, students received significant benefits in
both purchasing smartphones and subscribing
to unlimited data plans. Another reason for
targeting the Israeli student population is that
recent research shows (e.g., Scevak, 2010)
that the age group is a very critical factor in
the adoption of advanced mobile devices and
the usage of mobile media services, including
location-based ones and that people under the
age of 30 are significantly more willing to MPay than people in younger or older age groups.
Hence college and university students form the
best target population, especially since they
are more financially independent than teenagers. This is especially true in Israel where the

average age of undergraduate students is 25,
older than in other countries. Despite offering
legitimate justifications for targeting Israeli
students, further research should target a wider
population to reveal and explore more diverse
patterns than observed in this study.

Data Analysis
The data collected were statistically analyzed
mainly using a component-based Partial Least
Squares (PLS) structural equation modeling
(SEM) model-testing tool with reflective
constructs for model evaluation. The PLS
SEM approach is appropriate since it allows
simultaneous exploration of both the measurement (outer) model, relating the measurement
variables (MVs) to their latent variables (LVs),
and the structural (inner) model, relating the
LVs to each other (Chatelin, Vinzi, & Tenenhaus, 2002; Diamantopoulos, 2006; Tenenhaus,
Vinzi, Chatelin, & Lauro, 2005). In addition,
the component-based PLS approach allows for
testing of the relationships in the model with less
restrictive requirements than the covariancebased SEM approach. Another reason for

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58 International Journal of E-Business Research, 8(3), 50-67, July-September 2012

Figure 3. PLS results of measurement and structural models

choosing the PLS tool is that it is considered
appropriate for testing models at an early stages
of development (Fornell & Bookstein, 1982),
as is the case in this study.

RESULTS
Descriptive Statistics
The survey data were collected during March to
April 2011at Ben-Gurion University of Negev
(BGU), Israel. The request to participate in the
survey was sent to the students via mail, Facebook, and BGU student forums. One hundred
and twenty two valid responses were received
from smartphone users. Figure 2 presents the
socio-demographic segmentation of the respon-

dents, revealing that a typical respondent is an
undergraduate unmarried student, 22 to 30 years
old, who holds a part-time job. The Internet is
accessed via a mobile phone for less than one
hour per day by 77% of respondents, while 50%
talk on their mobile phone for less than an hour
per day. More than 10 text messages per day
are sent by 42% of the respondents, but 73%
never send an MMS message, while 28% never
send a mail message from their smartphones
and 68% never M-Pay via their smartphones.
Thus the mobile technology profile of a typical
respondent is someone who accesses the Internet
via their mobile phone for less than an hour
per day, regularly uses their mobile phone for
talking, SMS and email but rarely for sending
MMS messages or for M-Paying.

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International Journal of E-Business Research, 8(3), 50-67, July-September 2012 59

Table 2. Construct cross-correlation matrix and AVE analyses
AVE

Construct

LBSPushC

0.764

LBSPushC

0.874

0.742

LBSPushS

0.731

0.861

0.803

MT

0.187

0.225

0.710

PEU

0.158

0.016

0.182

0.842

0.704

PR

-0.231

-0.185

-0.649

-0.201

0.839

0.679

PU

0.267

0.303

0.275

0.273

-0.226

0.824

0.697

VT

0.243

0.210

0.708

0.288

-0.624

0.334

0.835

0.714

WMpay

0.305

0.319

0.516

0.394

-0.590

0.415

0.553

LBSPushS

Model Evaluation
SmartPLS 2.0 M3 was employed for evaluating
the PLS model. Although a sample size of 122
is relatively small, it is sufficient to get reliable
PLS results because it meets the generally accepted rule of thumb that defines the minimum
sample size as 10 times the most complex
relationship within the research model (Chin,
1998). In the studied research model, the most
complex construct has six reflective indicators,
leading to a minimum necessary sample size
of 60 respondents.
PLS models with reflective constructs have
a well-defined and widely accepted evaluation
technique. The list of assessment criteria, first
summarized and proposed by Chin (1998), was
accepted and further adopted by researchers
from different research fields (e.g., Gefen,
Straub, & Boudreau, 2000; Henseler, Ringle,
& Sinkovics, 2009; Tenenhaus et al., 2005).
Step 1 of the evaluation process for the PLS
path model involves testing the quality of the
measurement (outer) models. If Step 1 is successful and the latent constructs are reliable and
valid, Step 2 is pursued to assess the structural
(inner) model (Henseler et al., 2009).

Assessment of
Measurement Models
To assess the measurement model, we performed
tests showing that the manifest variables (indicators) in the research model are reliable and valid.

MT

PEU

PR

PU

VT

WMpay

0.896

0.845

Reliability. The estimated values of the
Cronbach’s α for all constructs are above 0.8,
indicating that all items are equally reliable.
Composite reliability for all constructs at
above 0.88 demonstrates internal consistency.
As depicted in Figure 3, the magnitude of all
indicators is higher than the minimum required
value of 0.707, indicating individual reliability
(Chin, 1998; Gefen et al., 2000; Henseler et al.,
2009). The test results indicate that all indicators are reliable.
Validity. For validating constructs, we
examined the convergent and discriminant
validities. Sufficient convergent validity is
indicated since the first column in Table 2 shows
that the average variance extracted (AVE) for
all constructs is higher than 0.5. Discriminant
validity refers to two appropriate patterns of
the construct inter-indicators. First, the variance
of a construct should be aligned more with their
own indicators than with other constructs. For
this purpose, we compared construct crosscorrelation and the square root of each construct’s AVE. Table 2 shows that all constructs
have sufficient discriminant validity since the
square root of each latent construct’s AVE (along
the diagonal) is larger than the correlation of
the specific construct with any other reflective
construct (under the diagonal) in the research
model. Second, the results of the cross-loading
test in Table 3 demonstrate that an indicator of
any specific construct has a higher loading on
its own construct than on any other construct.

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60 International Journal of E-Business Research, 8(3), 50-67, July-September 2012

Table 3. Discriminant validity - cross loadings
Construct

LBSPushC

LBSPushS

MT

PEU

PR

PU

VT

Wmpay

Item

LBSPushC

LBSPushS

MT

PEU

PR

PU

VT

Wmpay

PushC1

0.873

0.615

0.200

0.151

-0.239

0.319

0.223

0.283

PushC2

0.906

0.620

0.125

0.232

-0.223

0.211

0.222

0.303

PushC3

0.857

0.687

0.141

0.106

-0.135

0.168

0.196

0.215

PushC4

0.859

0.658

0.186

0.037

-0.189

0.209

0.205

0.248

PushS1

0.557

0.885

0.211

0.029

-0.099

0.229

0.185

0.247

PushS2

0.707

0.911

0.187

0.043

-0.173

0.291

0.213

0.317

PushS3

0.612

0.783

0.186

-0.037

-0.203

0.258

0.138

0.253

MT1

0.124

0.174

0.883

0.187

-0.626

0.281

0.638

0.472

MT2

0.141

0.212

0.888

0.181

-0.591

0.305

0.623

0.482

MT3

0.207

0.217

0.904

0.153

-0.539

0.208

0.637

0.442

MT4

0.199

0.203

0.910

0.131

-0.570

0.194

0.640

0.454

PEU1

0.131

0.050

0.170

0.737

-0.191

0.324

0.320

0.272

PEU2

0.118

0.005

0.158

0.906

-0.184

0.190

0.203

0.414

PEU3

0.155

-0.013

0.130

0.875

-0.129

0.182

0.212

0.294

PR1

-0.170

-0.158

-0.468

-0.192

0.789

-0.244

-0.474

-0.636

PR2

-0.177

-0.118

-0.571

-0.053

0.817

-0.125

-0.521

-0.319

PR3

-0.162

-0.143

-0.610

-0.263

0.872

-0.220

-0.579

-0.438

PR4

-0.260

-0.195

-0.534

-0.156

0.874

-0.164

-0.522

-0.565

PU1

0.176

0.261

0.242

0.234

-0.123

0.829

0.240

0.305

PU2

0.199

0.197

0.215

0.184

-0.154

0.858

0.297

0.309

PU3

0.335

0.347

0.252

0.175

-0.195

0.842

0.290

0.313

PU4

0.170

0.240

0.235

0.261

-0.206

0.801

0.272

0.428

PU5

0.190

0.165

0.209

0.267

-0.246

0.791

0.286

0.273

PU6

0.215

0.243

0.201

0.245

-0.194

0.820

0.267

0.403

VT1

0.235

0.193

0.619

0.227

-0.472

0.301

0.845

0.387

VT2

0.226

0.184

0.570

0.209

-0.536

0.261

0.854

0.514

VT3

0.140

0.093

0.598

0.205

-0.531

0.240

0.836

0.404

VT4

0.227

0.251

0.533

0.188

-0.536

0.323

0.834

0.499

VT5

0.148

0.100

0.547

0.260

-0.450

0.278

0.719

0.384

VT6

0.223

0.196

0.685

0.353

-0.589

0.271

0.910

0.547

MPay1

0.274

0.306

0.407

0.368

-0.339

0.398

0.473

0.797

MPay2

0.219

0.246

0.394

0.309

-0.592

0.360

0.420

0.876

MPay3

0.278

0.257

0.503

0.322

-0.562

0.297

0.505

0.861

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International Journal of E-Business Research, 8(3), 50-67, July-September 2012 61

Table 4. Effect size test
Construct

Wmpay
R excl
2

R incl
2

PR

Effect

f

2

LBSPushC

0.510

0.524

0.03

small

PU

0.504

0.524

0.04

small

R2excl

R2incl

f2

Effect

PEU

0.479

0.524

0.09

small

PR

0.464

0.524

0.13

small

VT

0.521

0.524

0.01

negligible

0.419

0.475

0.11

small

NA

0.388

0.475

0.17

medium

MT

Assessment of the Structural Model
To assess the structural model, we performed
tests regarding explanatory power and predictive power as well as predictive relevance.
Explanatory power. The research model
explains 52.4% of the variance of the dependent construct, Willingness to M-Pay (Figure
3). According to Chin’s criteria (Chin, 1998),
the model explains the dependent construct at
a moderate explanation level, since cut-off R2
values of 0.67, 0.33, or 0.19 for endogenous
latent variables are indicative of substantial,
moderate, or weak explanation levels respectively (Chin, 1998, p. 323). Vendor Trust and
Mechanism Trust explain Perceived Risk also
at a moderate level with R2 =0.475. Perceived
Usefulness is the only significant factor influencing willingness to engage in Commercial
Push-LBS and explains 11.1% of its variance.
Then Perceived Ease of Use and Perceived Usefulness constructs explain 12.4% of willingness
to engage in Social Push-LBS. We explored
changes in R2 to investigate the substantive
impact of each independent construct on the

dependent constructs, carrying out the effect size
technique by re-running seven PLS estimations
and excluding in each run one of the explaining latent constructs. Chin (1998) proposed to
use the effect size f2 of PLS constructs which,
similar to Cohen’s implementation for multiple
regression, the cut-offs f2 levels for small,
medium, and large effects are 0.02, 0.15, and
0.35. Table 4 represents a summary of the quantitative results of the effect-size test, showing
that Vendor Trust has hardly any direct impact
on the Willingness to M-Pay (f2=0.01). The
effects of all other constructs were found to be
small. We also found that, on Perceived Risk,
Vendor Trust has a small effect (f2=0.11) and
Mechanism Trust has a medium effect (f2=0.17)
Predictive power. The t-test for each path
coefficient was conducted with 300 subsample
repetitions, employing the bootstrapping resampling procedure for testing the statistical
significance of the path coefficients.. The
evaluation of the structural model showed that
nine paths coefficients are statistically insignificant (see dotted arrows in Figure 3).

Table 5. Blindfolding test for predictive relevance
Construct

SSO

SSE

Q2

LBSPushC

488

448.67

0.08

LBSPushS

366

332.80

0.09

PR

488

323.61

0.34

MT

366

230.52

0.37

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62 International Journal of E-Business Research, 8(3), 50-67, July-September 2012

Predictive relevance. We also performed
the Stone and Geisser Q2 test for evaluation of the
structural model’s predictive relevance. According to Chin (1998), Q2 reflects an index of goodness of reconstruction by model and a negative
Q2 indicates absence of predictive relevance,
while a positive Q2 provides evidence that the
omitted observations were well-reconstructed
and that predictive relevance is achieved. As
demonstrated in Table 5, all values of Q2 are
positive, indicating predictive relevance.
Table 6 illustrates that 10 of the 19 research
hypotheses are supported by the findings of the
model estimations and data analyses in this
study.

DISCUSSION AND
CONCLUSION
Understanding user willingness to M-Pay has
become an important issue for researchers and

practitioners. While Pull-LBS has been the
focus of much research, Push-LBS remains
under-investigated despite its commercial
potential. The goal of this study is to increase
the understanding of M-Payment drivers and
inhibitors through modeling and empirically
assessing the willingness of users to M-Pay
via smartphones for Push-LBS.
Past research has demonstrated that trust
is a key factor influencing the Willingness to
M-Pay (Andreev et al., 2011), with Vendor Trust
revealed as the critical aspect and Mechanism
Trust found statistically insignificant (Duane
et al., 2011). Our study adds further empirical
evidence of the insignificance of Mechanism
Trust in explaining user willingness to M-Pay.
The empirical analysis presented in this
study reveals that the direct impact of Vendor
Trust on Willingness to M-Pay is rather small,
with Vendor Trust being strongly mediated by
Perceived Risk. Indeed, Perceived Risk is the

Table 6. Summary of hypothesis testing
→

LBSPushSoc

H1

+

Not Supported

-

-

→

WillToMPay

H2

+

Supported

0.130

0.000

→

LBSPushCom

H3

+

Not Supported

-

-

→

PerceivedRisk

H4

-

Supported

-0.331

0.000

→

PerceivedRisk

H5

-

Supported

-0.415

0.000

→

LBSPushSoc

H6

+

Not Supported

-

-

→

WillToMPay

H7

+

Not Supported

-

-

→

LBSPushCom

H8

+

Not Supported

-

-

→

LBSPushSoc

H9

-

Not Supported

-

-

→

WillToMPay

H10

-

Supported

-0.352

0.000

→

LBSPushCom

H11

-

Supported

-0.135

0.000

→

LBSPushSoc

H12

+

Supported

0.277

0.000

→

WillToMPay

H13

+

Supported

0.163

0.000

→

LBSPushCom

H14

+

Supported

0.198

0.000

→

LBSPushSoc

H15

+

Not Supported

-

-

→

WillToMPay

H16

+

Supported

0.23

0.000

→

LBSPushCom

H17

+

Not Supported

-

-

LBSPushSoc

→

WillToMPay

H18

+

Supported

0.183

0.000

LBSPushCom

→

WillToMPay

H19

+

Not Supported

-

-

Vendor Trust

Mechanism Trust

PerceivedRisk

PerceivedUsefulness

PerceivedEaseOfUse

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International Journal of E-Business Research, 8(3), 50-67, July-September 2012 63

Figure 4. PLS assessment of structural model

main inhibitor of Willingness to M-Pay. In other
words, Perceived Risk has the highest negative
impact on Willingness to M-Pay. Moreover, the
magnitude of this inhibitor’s negative impact
is at least twice the magnitude of any driver’s
positive impact (Figure 4). Consequently, to
significantly reduce any fears that smartphone
users may harbor which inhibit user willingness
to M-Pay, the main challenge for practitioners
with regard to M-Payments is to facilitate
reduction of Perceived Risk by evaluating the
potential risks as well as establishing, monitoring, and reviewing appropriate data protection
and privacy safeguards.
The findings also reveal that Perceived
Ease of Use has a strong positive impact on
Willingness to M-Pay. In terms of impact magnitude, this factor is second after Perceived
Risk. Interpreting this finding for practitioners,

the key point is that issues pertaining to usability are more important than those pertaining
to usefulness in terms of impacts on Willingness
to M-Pay.
The respondents were asked a series of
questions regarding their attitudes towards
various Push-LBS services. The analysis of
responses shows that the context of the service
in question mattered, with social preferred to
commercial and strong positive attitude found
toward emergency Push-LBS services. Thus,
our findings reveal that consumers are willing to
pay for Social Push-LBS but not for Commercial
Push-LBS. Practitioners of Commercial PushLBS might therefore consider incorporating
some social context into their service portfolio
to attract potential users.
In addition to meeting the central goal of
clarifying the impacts of a variety of factors on

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64 International Journal of E-Business Research, 8(3), 50-67, July-September 2012

Willingness to M-Pay, the empirical findings
of the study reveal some interesting patterns.
The analysis explains 47.5% of the variance associated with Perceived Risk and suggests that
the main risk factors are related to Vendor and
Mechanism Trust, with the role of Mechanism
Trust being critical in explaining Perceived Risk.
This suggests that respondent risk perception
of M-payments is perhaps elevated by their
concern with the weakness of existing legal
framework and regulatory-body legislations
with regard to M-Payments.
The study shows that both Vendor and
Mechanism Trust have no impact on the willingness to engage in Social and Commercial
Push-LBS. Thus, to understand Push-LBS drivers, practitioners need to explore factors other
than trust. The finding that respondents willing
to engage in Social Push-LBS are also indifferent to Perceived Risk, demonstrates that Social
Push-LBS smartphone users acknowledge and
overcome privacy and security concerns. In
addition, Perceived Risk negatively impacts
those smartphone users who are not in favor of
participation in Commercial Push-LBS.
Another key finding of this study for
practitioners in charge of prioritizing services
is that Perceived Usefulness has positive impacts on the willingness to engage in Social
and Commercial Push-LBS, and smartphone
users value Social Push-LBS benefits more
than Commercial Push-LBS benefits.
Perceived Ease of Use was found not to
affect the willingness to engage in both Social
Push-LBSs and Commercial Push-LBSs. One
explanation for this finding is that Ease of Use
is a non-issue for students who are very experienced with mobile devices. However, previous
research conducted in another country (Andreev
et al., 2011) demonstrated that Perceived Ease
of Use has positive impact on the willingness
to engage in mobile services. This contradiction points to this study’s main limitation, i.e.,
its focus on a specific demographic segment in
one country. For the sake of generality, further
research should collect data from a wider user
population around the world.

The finding of this study, that Perceived
Risk is the main inhibitor of user willingness
to M-Pay is consistent with past research. The
finding that the magnitude of this inhibitor’s
negative impact is at least twice the magnitude
of any driver’s positive impact, is the study’s
main added value compared to previous work.
Based on this finding, it is safe to suggest that the
tangible benefits of this service must outweigh
the intrusion, privacy, and security concerns of
users before the full potential of M-Payments
would be realized.

ACKNOWLEDGMENT
We acknowledge with deep appreciation and
much gratitude the great work of Shani Assaraf and Osnat Nahmias who assisted during
their senior year as students in the Department
of Industrial Engineering and Management at
Ben-Gurion University of the Negev.

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