The Moderating Effect of Espoused Cultural
Dimensions on Consumer’s Intention to Use
Mobile Payment Devices
Completed Research Paper
Khaled A. Alshare
Qatar University
P. O. Box 2713, Doha, Qatar
[email protected]
Abeer A. Mousa
Qatar University
P. O. Box 2713, Doha, Qatar
[email protected]
Abstract
Prior research in cross-cultural studies in technology adoption has mainly focused on
national cultural level and conducted in western countries. This paper examines the
impact of espoused national cultural values on consumer’s intention to use mobile
payment devices using the UTAUT and Hofstede’s cultural dimensions as the basis for
the research framework. The moderating effect of espoused uncertainty avoidance,
espoused power distance, espoused collectivism, and espoused masculinity were
examined. A Structural Equation Model using a convenience sample from Qatar
population was used to test the model. Performance expectancy, social influence, and
perceived information security have direct significant effects on consumer’s behavioral
intention to use the MPD. The espoused national cultural values of uncertainty
avoidance, masculinity, and collectivism have moderating effects on the hypothesized
relationships. For example, social influence is a stronger predictor of consumer’s
intention for individuals who espouse collectivism cultural values.
Keywords: Culture, technology acceptance, UTAUT, espoused cultural values, mobile payment
devices.
Introduction
The technology studied in this paper is the mobile payment devices. Mobile payment is relatively a new
alternative payment method in which the consumer uses a mobile phone or a mobile payment device to
pay for his/her purchases. According to MobiThinking.com (2014) mobile payments are growing strongly
and Gartner predicts that in 2016 there will be 448 million users of mobile payment in a market worth of
$617 billion. It is expected to be the most common way of payment. The Mobile Payment Device (MPD), a
special small electronic device when it connects to mobile phones or computers, it works as a card reader,
which enables the merchant to receive payment from the consumer by swiping his/her credit card (or
debit card) in the MPD at home. It will save time of entering the card information compare to payment
online. The MPD reads the data and converts it into an audio signal. The microphone picks up the audio
signals, then send them through the processor which routed to a software application on the mobile
phone. The encrypted data is transmitted using Wi-Fi or 4G internet connection to back end servers,
which communicate with the payment networks to complete the transaction (Zhou 2011). There will be no
information stored on the mobile devices which ensure security and trust for users. Advocates of mobile
technologies highlight that the mobile payment is a form of payment that simplifies transactions, makes
payment more convenient at home or at the store, and increases loyalty programs. Mobile payments have
the potential to significantly grow the number of electronic transactions. It is more convenient, needs less
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effort and faster. It is expected to be the most common way of payment. The device will be free or for a
very nominal price (e.g., 10 USD).
Since its induction by Venkatesh, Morris, Davis, and Davis (2003), UTAUT has been used for
investigation users’ intention/usage of many technologies in different settings. These applications have
contributed to the applicability and generalizability of the model (Neufeld, Dong, and Higgins 2007).
According to Venkatesh, Thong, and Xu (2012), there have been three main streams of UTAUT extension
which include: 1) Applying UTAUT in new contexts such as new technologies, new user populations, and
new cultural settings; 2) The addition of new constructs to the original model to enhance its
comprehensiveness and theoretical framework; and 3) the inclusion of exogenous predictors of the
UTAUT variables. There is no doubt that all above extensions have added insights to our understanding of
technology adoption. However, Venkatesh, Thong, and Xu (2012) argue that there are still aspects and
issues need further investigation and there is a room for theoretical and practical contributions. “Thus,
while the various studies contribute to understanding the utility of UTAUT in different contexts, there is
still the need for a systematic investigation and theorizing of the salient factors that would apply to a
consumer technology use context.” (Venkatesh, Thong, Xu 2012, p. 158). Moreover, Johns (2006) and
Alvesson and Kärreman (2007) as cited by (Venkatesh, Thong, Xu 2012) contend that employing an
existing theory in a new context provides a rich understanding of a specific phenomenon and ultimately
will lead to the extension of the existing theory by rendering originally hypothesized relationships to be
non-significant, changing the direction of the relationships, or creating new relationships.
Based on the above discussion we expect that this study will make contributions to the extension of the
UTAUT model by investigating the moderating effect of the espoused national cultural values on key
relationships in UTAUT using consumers in a non-Western country (a developing country) as the target
population and by investigating a new technology (mobile payment device). Moreover, adding a construct
(perceived information security) to the UTAUT and altering the original model, in addition to above
mentioned incorporations, could further the applicability of the model to different contexts such as
consumers, a developing country, and a new technology (mobile payment device) which is considered as
an essential step in advancing a theory according to Johns (2006) and Alvesson and Kärreman (2007) as
cited by Venkatesh, Thong, and Xu (2012). The practical importance of the study rely on the fact that
knowing the factors that influence consumers’ adoption of mobile payment device would benefit
businesses in making the necessary modifications on developing and marketing such technologies,
especially in non-western countries.
The paper is organized as follows: The first section provides a brief background perspective on related
literature, and it introduces the research hypotheses. In the second section, the research methods that
include instrument development and data collection, and statistical procedures are discussed. The third
section reports the results. Finally, the last section provides discussion and implications.
Background Perspective
This study utilizes the technology acceptance model (UTAUT) introduced by Venkatesh et al. 2003. The
UTAUT model was built on the premise that an individual’s intention to use a particular technology is
influenced by some beliefs such as performance expectancy, effort expectancy, social influence, and
facilitating conditions. The proposed model in this study includes three main factors as part of the UTAUT
model; these factors are effort expectancy (EE), performance expectancy (PE), and social influence (SI).
The “facilitating conditions” factor which is related to the resources and support needed for the usage of
the technology, was not included in the proposed model because the mobile payment device is provided as
a free from the merchants or for a very nominal price (e.g., 10 USD), and therefore, this factor becomes of
less important to the users. Additionally, the usage of the mobile payment device does not require any
special device other than a mobile device such as a mobile phone and the mobile device provided by the
merchants.
According to the technology acceptance literature, performance expectancy and effort expectancy have
been consistently shown as significant factors in predicting user’s behavioral intention (Venkatesh et al.
2003 and Venkatesh, Thong, Xu 2012). Thus, it expected that these two factors will still be significant in
predicting consumer’s intention to use mobile payment device. Since the effort expectancy factor deals
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with effort that the user expects to spend while using MPD and the performance expectancy represents
the benefits from the usage of such technology, it is a valid assumption that consumers will still consider
these two features as important ones in deciding whether, or not, to use the mobile payment device. Thus,
effort expectancy and performance expectancy were included in the proposed model as predictors for
consumer’s intention.
The security issue has been one of the most important factors that influence consumer’s intention to use
online payment (Khraim et al. 2011). One can argue that it is expected to be even more important in the
case of mobile payment device since the usage of such technology is self-administered and is fully
completed by the consumer and there is no direct interaction with other human; thus, the security issue in
this case becomes very essential, compared to other forms of online payment where there might be many
parties involved in the transaction process. Finally, the social influence was included as part of the
proposed model because it has been shown that the effect of social norms on user’s intention vary by
culture (Srite and Karahanna 2006; Venkatesh, Thong, and Xu 2012), and therefore, it was included in
the proposed model.
It should be noted that in the original UTAUT, the moderating effect of the demographic variables
(gender, age, experience, and voluntarism) were investigated. They were not included in this study due to
the inclusion of the cultural dimensions as moderators since the focus of this study was on the impact of
the espoused cultural values. Additionally, the sample size sample and profile did not support the
inclusion of some of these factors, for example there were only 15% of the respondents who have
experience with MPD. As defined by Venkatesh, Thong, and Xu (2012, p.161) “experience reflects an
opportunity to use a target technology and is typically operationalized as the passage of time from the
initial use of a technology by an individual.” As such, it is not feasible to measure it since the technology
still has not been used in the targeted society. Moreover, these demographic factors have been intensively
investigated by researchers (Srite and Karahanna 2006; Venkatesh et al. 2003). For example Morris,
Venkatesh, and Ackerman (2005) investigated the moderating effect of age and gender on key
components of the technology acceptance models; Morris and Venkatesh (2000) studied the moderating
effect of experience; and Venkatesh and Morris (2000) and Venkatesh and Davis (2000) examined the
effect of gender and voluntariness, respectively.
Behavioral Intention (BI)
Behavioral intention is defined as the degree of “certainty” of the individual’s intention to use a particular
technology. Behavioral intention is one of the most significant factors that influence the actual behavior
(Ajzen 1991; Venkatesh, Thong, and XU 2012). The UTAUT model, which is utilized in this study, includes
three major factors that influence user intention to use mobile payment devices. These factors are effort
expectancy, performance expectancy, and social influence (Venkatesh et al. 2003). In this study, the
perceived information security factor is included in the proposed model as well as the espoused cultural
dimensions. A discussion of these factors is reported below.
Effort Expectancy (EE) and Performance Expectancy (PE)
Effort expectancy (EE) is defined as “the degree of ease associated with the use of the system.” (Venkatesh
et al. 2003, p. 450). Performance expectancy (PE) is defined as “the degree to which an individual
believes that using the system will help him or her to attain gains in job performance.” (Venkatesh et al.
2003, p. 447). Consumers who perceive the mobile payment device as easy to use and helpful in
completing the payment process, compared to the traditionally methods of payments, will most likely
have a strong intention to use the technology. Prior research found that EE has an indirect impact on
intention through PE (Alshare et al. 2011; Luo et al. 2010; Venkatesh and Morris 2000), thus, a link was
established between EE and PE. In this study, the author posits the following hypotheses:
H1: Performance expectancy (PE) has a positive effect on behavioral intention (BI) to use mobile
payment device.
H2: Effort expectancy (EE) has a positive effect on performance expectancy (PE) of mobile payment
device.
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Social Influence (SI)
Social influence is defined as the perceived social pressure from close members to the individual to
perform or not to perform the behavior in question (in this study, the use of mobile payment device).
Many researchers have used social influence as a predictor for behaviors in various contexts
(Bandyopadhyay and Bandyopadhyay, 2008; Brown et al. 2002; Venkatesh, Thong, and Xu 2012; Yu
2012). For example, Bandyopadhyay and Bandyopadhyay (2008) found that social influence had a direct
impact on consumer intention to use prepayment metering systems. The following hypothesis was
developed:
H3: Social influence has a positive effect on behavioral intention to use mobile payment device.
Perceived Information Security (PIS)
Information security has been a major concern for online consumers. Information security is defined as
“the protection of information and its critical elements, including the systems and the hardware that use,
store, and transmit that information.” (Whitman and Mattord 2009, p. 8). It demands the means that are
necessary to minimize unauthorized access to personal information. Information security and trust are
interrelated and considered to be extremely important for the success of any mobile or online transaction
(Khraim et al. 2011; Lee and Turban, 2002). Since consumer needs only to slide his/her card in the mobile
payment device that is connected the mobile phone to make a payment, he/she would be concerned about
where and how the information would be saved. Thus, the security factor was included in the proposed
model as a predictor for consumer’s intention to use mobile payment device.
H4: Perceived information security has a positive effect on behavioral intention to use mobile payment
device.
The Moderating Effect of Espoused National Cultural Values
One of the relatively recent facets of research in the area of technology acceptance has been investigating
the impact of cultural dimensions on the models and theories that have been used in addressing this area
of research. However, few studies have used technology acceptance models outside North America, and
even less studies have tested their applicability in non-Western countries. It has been reported that these
models and theories may not hold equally across countries of different cultures (Alshare et al. 2011; Choi
et al. 2013; Huang et al. 2003; Straub et al. 1997). For example, the results of the study by Straub et al.
(1997) in which they employed the technology acceptance model (TAM) in three countries (USA, Japan,
and Switzerland) revealed that TAM holds for both USA and Switzerland but not for Japan. There were
two approaches used when addressing the impact of cultural dimensions on the components of the
technology acceptance models. The first approach was the employment of Hofstede’s country scores to
explain differences in technology acceptance models among different countries (see Alshare et al. 2011;
Mao and Palvia 2006; Lippert and Volkmar 2007; Straub et al. 1997). This approach was criticized since
it did not establish a link between cultural dimensions and the technology acceptance factors. Thus, and
to establish such a linkage, researchers have adopted an individual level of analysis for cultural factors.
For example, Srite and Karahanna (2006) employed Hofstede’s cultural dimensions at the individual level
to test the moderating effect of espoused cultural values on the technology acceptance model. McCoy et al.
(2007) used Hofstede’s cultural dimensions at the individual level to investigate students’ acceptance of
particular software and found that TAM does not hold for certain cultural orientations and suggest the
need for caution when applying technology acceptance models across countries. Thus, there is still a need
for more investigation of the impact of cultural dimensions on technology acceptance models and
theories.
It should be noted that there are cultural definitions and measures other than the Hofstede’s cultural
dimensions such as the ones introduced by (Triandis and Gelfand 1998); Schein (1990); House et al.
(2004) “GLOBE”, and Trompennaars (1994). Notwithstanding the importance of Hofstede’s work on
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culture, other researchers state the culture may and should also be examined at the personal and
organizational levels. In particular, Triandis and Schein are the major proponents for such examinations
as the dimensions proposed by Hofstede might be too generalized and are not fine-tuned enough to lead
to meaningful relationships in conceptual and empirical models that usually review behaviors at the
individual level such as the technology acceptance models. At the personal level, an individual may exhibit
various levels of individualism and collectivism horizontally (i.e., across similar groups) and vertically
(i.e., across dissimilar groups) (Triandis and Gelfand 1998). At the organizational level, Schein (1990)
proposes that culture can be divided into three factors: (1) the level of its artifacts; (2) the level of its
espoused beliefs and values; and (3) the level of its basic underlying assumptions. While all views of
culture are important, Hofstede’s definition is the most common acceptable definition of culture (Srite
and Karahanna 2006; Straub et al. 2002). Moreover, “Hofstede's model remains popular and continues to
be one of the most cited works in the Social Science Citation Index” (Alshare et al. 2011, P. 34). This study
aimed to extend the previous research on culture and technology acceptance by investigating the
moderating effects of the espoused national cultural values (espoused power distance, espoused
uncertainty avoidance, espoused collectivism, and espoused masculinity) on consumer’s intention to
adopt mobile payment device in a non-Western country.
Effect of Uncertainty Avoidance (UA)
According to Hofstede (1997), uncertainty avoidance (UA) is the extent to which the members of a culture
feel threatened by uncertain or unknown situations. Based on Hofstede’s definition, Srite and Karahanna
(2006) defined espoused uncertainty avoidance as to the degree of risk accepted by the individual and the
extent to which he/she feels threatened by uncertain conditions. People from culture with high score of
uncertainty look for formal rules and well-structured instruction to feel comfortable when using a new
technology. Therefore, when the usage of a particular technology does not involve complicated instruction
and the associated risk is minimal, then individuals with high score of uncertainty avoidance would be at
ease in adopting such technology. The usage of mobile payment device, compared to other forms of online
payment, does not involve much of uncertainty since it includes simple process (sliding a card through the
device) and does not require much of effort. Thus, consumers who espouse cultural values of uncertainty
avoidance would have a strong intention to use the mobile payment device. Reducing the uncertainty of
the usage of the mobile payment device would also reduce the fear from information security breaches.
Therefore, the effect of the information security on consumers’ intention would be stronger in societies
where their people espouse cultural value of uncertainty avoidance. Moreover, the relationship between
performance expectancy and effort expectancy was found to be stronger for people from high uncertainty
avoidance cultures (Alshare et al. 2011). Based on the above arguments, we posit the following
hypotheses:
H5: The espoused national cultural value of uncertainty avoidance (UA) positively moderates the
relationship between perceived information security (PIS) and behavioral intention (BI).
H6: The espoused national cultural value of uncertainty avoidance (UA) positively moderates the
relationship between EE and PE.
Effect of Masculinity (MA)
Srite and Karahanna (2006) defined espoused masculine/femininity value as “the degree to which to
which gender inequalities are espoused by an individual” Srite and Karahanna (2006, p. 682). While
persons who espouse masculine values would concentrate on work goals such as earnings, advancement,
performance, and competitiveness, persons who espouse femininity values would emphasize quality of life
goals such as cooperation, a friendly atmosphere, and comfortable work environment (Hofstede 1984;
Srite and Karahanna 2006). Srite (2006) reported that individuals scoring low on masculinity might be
more concerned with the ease of use of a technology, since individuals from these cultures place less
emphasis on instrumental goals and more on the quality of life. On the other hand, individuals who
espouse masculine cultural values are more likely to adopt technology as they are more task-oriented and
many organizational tasks today require the use of technology. Venkatesh and Morris (2000) assert that,
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in masculine cultures, greater emphasis is placed on whether the computer will do the job given
favoritism towards the technology. Since mobile payment devices support the ego goals of persons who
espouse masculine cultural values by achieving the task that is making the payment in most efficient way.
Therefore, the effect of performance expectancy on consumer’s intention to use mobile payment device
would be stronger on people who espouse masculine cultural values. Thus, we posit the following
hypothesis:
H7: The espoused national cultural value of masculinity (MA) positively moderates the relationship
between performance expectancy (PE) and behavioral intention (BI).
Effect of Collectivism (CO)
Individualism is the degree to which people in a culture prefer to act as individuals rather than members
of groups (collectivism). The two extremes of the individualism-collectivism continuum can be contrasted
as the “me” society versus the “we” society. Individuals scoring high on collectivism (CO) consider
themselves part of a group and tend to have high level of conformity (Srite and Karahanna 2006). As
reported by Srite and Karahanna (2006), people from collectivism culture are more closely linked to the
society norms, compared to people from individualism culture who are more closely to attitudes.
Individuals who espouse collectivism cultural values would respect and conform to opinions of their close
groups. Thus, the social influence will have a stronger impact on user’s intention for individuals scoring
high on collectivism, compared to those individuals scoring low on collectivism. Therefore, if consumers
who espouse cultural values of collectivism see their close influential people use the mobile payment
device, they would be affected by their behavior and influence their behavioral intention. Thus, the
following hypothesis is proposed:
H8: The espoused national cultural value of collectivism (CO) positively moderates the relationship
between social influence (SI) and behavioral intention (BI).
Effect of Power Distance (PD)
According to Hofstede (1997), power distance (PD) is the extent to which the less powerful members of
institutions and organizations within a country expect and accept that power is distributed unequally. In
low power distance cultures, individuals have less pressure from their peers. On the other hand, in
societies having high power distance, people are more likely to be affected by closer personal relations
with others mainly through compliance effect. People who espouse power distance cultural values will be
concerned about complying with their supervisors’ opinions and will be hesitant to disagree with them
(Srite and Karahanna 2006). For example, if a person, who espouse cultural values of power distance sees
his/her supervisor utilizes mobile payment device, it is most likely that person would be considering using
such device since he has the confidence in his supervisor in the sense that whatever he/she is doing is
likely right. Therefore, one can expect the relationship between social influence and behavioral intention
to be stronger for a high power distance culture. The following hypothesis is proposed:
H9: the espoused national cultural value of power distance (PD) positively moderates the relationship
between social influence (SI) and behavioral intention (BI).
Based on the above-mentioned literature review and hypotheses, we proposed the following research
model depicted in Figure 1.
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Figure 1. The Proposed Research Model
Research Methods
Instrument Development and Data Collection
The survey questionnaire employed to collect data had three sections. The first section requests various
types of demographic information such as gender, age, educational level and background. The second
section includes consumers’ perceptions regarding factors that influence their behavioral intention (BI) to
adapt mobile payment device (MPD) such as: performance expectancy (PE), effort expectancy (EE), social
influence (SI), and perceived information security (PIS). Items that measure BI, PE, EE, and SI variables
were adapted and modified from Venkatesh et al. (2003), while PIS variable was adopted and modified
from Roca and Vega (2009). The third section includes items related to the cultural dimensions which are
adopted from Srite and Karahanna (2006) and Akour et al. (2006). The survey questionnaire was
available in both languages Arabic and English. Back translation procedure (Brislin 1986) was used to
ensure that the meaning of the questions is not changed during the translation process. The survey
instrument then was pilot tested. As a result, a few minor modifications were made. Participants
responded to statements on a seven-point Liker scale, which ranged from strongly disagree (1) to strongly
agree (7). The list of scale items is included in Appendix 1.
Statistical Procedure
SPSS and AMOS software packages were used to carry out the analysis. SPSS was used to compute
frequencies, means, standard deviation, reliability coefficients, and principle component analysis. A
confirmatory factor analysis (CFA) approach was taken with AMOS to validate the factor loadings
identified in the principle component analysis. A structural model was then run testing the research
model and hypotheses. A summary of the steps of statistical analysis are reported below:
1.
Running initial descriptive statistics (range, maximum, minimum, mean, standard deviation,
frequencies, and percentages) for all scale-item variables;
2.
The data were inspected for missing and invalid values;
3.
Reliability and validity assessments were performed using Cronbach’s alpha, corrected item-total
correlations, and exploratory factor analysis. According to Nunnally (1978) the standard
reliability value of a scale should be above 0.7 and for the minimum acceptable value for corrected
item-total correlations is 0.5 (Hair et al. 2006). Only items with loadings of at least 0.50 were
retained (Hair et al. 2006);
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4.
The measurement items were finalized; and
5.
AMOS was used to carry out the analysis. Several goodness-of-fit indices were used to assess the
validity of the constructs.
The Sample and Data Collection
The link to the survey was sent mainly through announcement email to students, staff, and faculty in a
university at Qatar. In addition to that the link was sent to a scattered sample from the society in the State
of Qatar. A request was made to all recipients to forward the survey link to their friends. Since the total
sample which refers to persons who presumably were contacted could not be determined, the response
rate was not reported; and thus, tests like the ones reported by Armstrong and Overton (1977) and
Harman’s single factor for common method variance (Podsakoff et al. 1986) could not be performed. A
total of 169 usable responses were received.
Data Analysis
Characteristics of Respondents
Approximately, sixty percent of the sample is females. The average age of the respondents was
approximately 34 years. Approximately, about forty-seven percent have completed their undergraduate
studies. Approximately, 35 percent have graduate degree. Additionally, about one-quarter of the
respondents their educational background was in business. All respondents have a mobile phone. Fiftytwo percent indicated that they have poor knowledge about mobile payment device and 22 percent have
little knowledge. Only 15 percent indicated that they had experience with MPD.
Reliability and Validity Assessment
As shown in Table 1, all constructs, met the minimum value for Cronbach’s alpha coefficient (0.70), as
suggested by Nunnally (1978). The Cronbach’s alpha values ranged from 0.760 to 0.963. A few cultural
items (PD4, UA1, CO4, and MS1) were dropped to improve the reliability values. Additionally, the values
of corrected item-total correlations for all items showed high correlation which is an indication of high
convergent validity. The values for the vast majority of the items were above 0.750. Additionally, all items
loaded on their intended constructs with exception of PE3 and EE4 where they loaded on two factors with
a loading value greater than 0.45, they were dropped from further analysis. Most the other items had
factor loading greater than 0.70 as shown in Table 1.
Table 1. Reliability and Validity Evaluation
Construct /
8
Factor Loadings
Corrected ite
m-Total corre
lations
Items
PIS
PIS3
.857
.892
PIS4
.854
.915
PIS2
.849
.920
PIS1
.739
.817
SI
BI
PE
EE
PD
UA
CO
MS
SI2
.846
.861
SI1
.820
.828
SI3
.789
.834
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SI4
.694
.743
BI1
.858
.912
BI2
.851
.926
BI3
.823
.924
PE2
.833
.753
PE1
.737
.754
PE4
.592
.799
EE2
.718
.827
EE1
.703
.749
EE3
.585
.860
PD1
.743
.643
PD2
.734
.651
PD3
.851
.550
UA2
.802
.701
UA3
.825
.799
UA4
.800
.776
CO1
.711
.719
CO2
.781
.766
CO3
.852
.628
MS2
.655
.595
MS3
.819
.647
MS4
.788
.566
Cronbach’s
Alpha
.953
.920
.963
.885
.893
.777
.872
.838
.760
Table 1. Reliability and Validity Evaluation
Study Results
Model Estimation
The parameters for the structural equation model illustrated in Figure 1 were estimated by the maximum
likelihood method using AMOS 21.0. The model fit indices for the structural equation model are reported
in Table 2.
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Table 2. SEM Fit
Parameter
Estimate
Recommended
Value
Chi-square/Degree of freedom
2.6
<= 3.0
Goodness-of-fit index
0.93
>= 0.9
Adjusted goodness-of-fit index
0.90
>= 0.8
Normal fit index
0.95
>= 0.9
Comparative fit index
0.93
>= 0.9
Standardized root mean square
residual
0.072
<=0.08
Table 2. SEM Fit
All fit indices met the recommended values. An evaluation of the standardized factor loadings for each
construct found all of them above the 0.7 level. Following the guidelines recommended by Comrey and
Lee (1992), this represents a good fit of the data.
Hypotheses Testing
As shown in Figure 2, hypotheses H1, H2, H4, H5, H6, and H8 each achieved a significance level less than
0.01. H7 and H3 were significant at 0.05 and 0.1, respectively. On the other hand, H9 (the moderating
effect of power distance on the relationship between social influence and behavioral intention) was not
significant. The explanatory power of the model is examined using the R2 value for behavioral intention.
The combination of performance expectancy (PE), effort expectancy (EE), social influence (SI), and
perceived information security (PIS) accounted for 57% of the variances observed in consumer’s intention
to use a mobile payment device. Performance expectancy had the strongest direct impact on behavioral
intention, followed by perceived information security. With respect to the moderating effect of the
espoused cultural values, the results revealed that the effects of performance expectancy and perceived
information security were moderated by masculinity and uncertainty avoidance, respectively. The effect of
performance expectancy would be stronger on consumer’s intention in societies with high score on
masculinity such as Greece and Arab countries. Likewise the impact of perceived information security
would be stronger on consumers’ intentions who espouse cultural values of uncertainty avoidance such as
in Korea and Japan. Additionally, the impact of social influence on consumer’s intention is stronger in
countries with high score on collectivism such as China and Arab countries. Moreover, the impact of effort
expectancy on performance expectancy was moderated by uncertainty avoidance such that it is stronger
for consumers with higher espoused uncertainty avoidance cultural value such as in Korea and Japan.
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EFFECT OF ESPOUSED CULTURAL VALUESS ON CONSUMER’S INTENTION
*. P<0.1
**. P<0.05
***. P<0.01
Figure 2. Model Results
Discussion
This study used UTAUT and the espoused national cultural values based on Hofstede’s cultural
dimensions as a basis for developing and testing a model that investigates the factors that affect
consumer’s intention to use mobile payment devices in a non-Western country (Qatar). It is important to
understand the factors that influence consumers’ intention to use mobile payment devices which
ultimately impact their actual usage so that businesses considered such factors when they develop and
market such technologies.
Variables considered in this study were performance expectancy, effort expectancy, social influence, and
perceived information security. Additionally, four espoused national cultural values (espoused power
distance, espoused uncertainty avoidance, espoused collectivism, and espoused masculinity) were used as
moderators in the research model. Among these factors, performance expectancy and perceived
information security were the most influential factors. Thus, businesses need to clearly communicate to
their consumers the benefits and the advantages of using such devices. Additionally, they need to ensure
their customers that their personal information and transactions are well secured while making payment
using the mobile payment device. The security issues are still of concern for the consumers. There are two
main aspects of security issues. The first one is the technical aspect which includes the usage of the latest
technologies in the security field, and the second one is the human aspect which includes security policies
and awareness. The suppliers of such devices need to consider the effort needed by the consumers in
learning and using such devices since the effort expectancy (e.g., ease of use) had an impact on
consumer’s intention through performance expectancy. In communicating with consumers, businesses
should rely on the word of mouth because social influence had a positive impact of consumer’s intention.
It should be noted that the impact of the above factors on consumer’s intention varies from one culture to
another. For example and as the results revealed, businesses need to pay more attention to security and
the ease of use aspects of the mobile payment devices in countries where their citizens espouse national
cultural values of uncertainty avoidance. Another observation regarding the impact of the cultural
differences is the impact of the masculinity on the relationship between performance expectancy and
behavioral intention. As reported by Alshare et al. (2011), people from masculine culture are more prone
to be more task-oriented and technology adopters as long the adoption of such technology would help
them in achieving their tasks. Thus, businesses should evidently highlight the benefits from the use of the
mobile payment devices for people who espouse national cultural values of masculinity. On the other
hand and as reported by Srite (2006), more attention is needed for effort expectancy when introducing a
new technology in societies with low espoused national cultural values of masculinity (e.g., Chile).
Moreover, the results indicated that the impact of social influence on behavioral intention is stronger in
countries with high score on collectivism such as Arab countries and China. Finally, the espoused national
Thirty Fifth International Conference on Information Systems, Auckland 2014
11
Global and Cultural Issues in IS
cultural value of power distance was not significant as a moderator in this study as was the case in a study
conducted by Srite and Karahanna (2006). An explanation for this finding could be the fact that a person
might be more influenced by opinions of his/her supervisors than being affected by the opinions of
his/her close relatives and friends since this espoused cultural values reflect the situation where the less
powerful members of organizations within a country expect and accept that power is distributed
unequally. As such and as reported by Srite and Karahanna (2006, P. 697) when studying the effect of
power distance on behavioral intention one needs to differentiate between the effects of “authority social
norms” like supervisors and “non-authority or in-group social norms” like friends and family.
Conclusion
The primary objectives of this study were (i) to explore the factors that influence consumer’s intention to
use mobile payment device; (ii) to investigate the moderating effect of espoused national cultural values
on the proposed relationships; and (iii) to test the applicability of UTAUT framework in a non-western
countries. By achieving these objectives, this study makes a contribution to the cross-cultural stream of
research. The results of this study confirmed that the main components of UTAUT model which include
performance expectancy, social influence, and effort expectancy were significant in predicting consumer’s
intention to use mobile payment device. However, one should pay attention when applying this
framework since the impact of each factor in the model varies from culture to another as it was evident
from the results of the study. Our empirical research findings suggest that espoused cultural values as
represented by espoused masculinity, espoused collectivism, and espoused uncertainty avoidance,
moderate four relationships in the research model. Toward these ends, the study achieved its objectives.
Therefore, a future research direction could be applying this study in multiple countries that represent
different set of cultures. Another plausible future research is to investigate the moderating effect of
demographic such as gender, age, experience, among other on the hypothesized relationships.
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Appendix 1. List of Scale Items
Construct
Performance
Expectancy
α = 0.885
Effort
Expectancy
α = 0.893
Social Influence
Item
Information
Behavioral
Std.
I think that MPD is useful for me
5.10
1.39
PE2
MPD would save time as compared to traditional payment systems
5.72
1.29
PE3
Using MPD can enhance my effectiveness in managing my payme
nts securely
4.79
1.50
PE4
Overall I will find MPD useful in my payment transactions
5.23
1.29
EE1
I would find it easy to use MPD system to accomplish my payments.
5.33
1.29
EE2
My interaction with MPD system would be clear and understandable
4.92
1.352
EE3
I would find MPD system to be flexible to interact with
5.10
1.33
EE4
Overall I believe that MPD systems would be easy to use
5.20
1.35
SI1
People who are important to me would support my using MPD rather than
traditional payment methods.
4.99
1.55
SI22
I think that those people who are important to me would want me to use
MPD
4.79
1.56
SI3
My co-workers/classmates would encourage me to use MPD
4.67
1.44
SI4
People who influence my behavior would think I should use MPD
4.52
1.45
PIS1
I would consider the MPD system to be trust worthy.
4.55
1.56
PIS2
I think that the MPD system would have sufficient technical capacity to
protect my private information.
4.47
1.53
PIS3
I would have a trust in the security measures used by MPD system to
protect my personal and financial information
4.49
1.61
PIS4
I would be confident with the security system adopted by MPD.
4.52
1.63
BI1
I intend to use MPD when it becomes available to me.
4.89
1.59
BI2
I plan to use the MPD system when it is available in the market.
4.80
1.59
Security
α = 0.91
Mean
PE1
α = 0.92
Perceived
Description
Intention
α = 0.96
14 Thirty Fifth International Conference on Information Systems, Auckland 2014
EFFECT OF ESPOUSED CULTURAL VALUESS ON CONSUMER’S INTENTION
BI3
Given that I had access to the MPD, I predict that I would use the MPD
system
4.93
1.59
PD1
Managers should make most decisions without consulting subordinates
2.54
1.61
PD2
Managers should not ask subordinates for advice because they might
appear less powerful
2.26
1.46
PD3
Employees should not question their manager’s decisions
2.62
1.55
PD4
Decisions making power should stay with top management in the
organization and not be delegated for lower level
3.61
1.92
UA1
like to work in a well-defined job where the requirements are
clear
5.68
1.45
Uncertainty
Avoidance
UA2
It is important for me to work for an organization that provides high
employment stability
6.17
1.19
α = 0.87
UA3
Clear and detailed rules / regulations are needed so employees know what
is expected of them
6.20
1.05
UA4
Order and structure are very important in a work environment
6.06
1.20
CO1
It is better to work in a group than as individuals
5.44
1.45
CO2
Being accepted as a member of a group is more important than being indep
endent
5.20
1.46
CO3
Group success is more important than individual success
5.15
1.60
CO4
Individual rewards are not as important as group welfare
4.49
1.76
MS1
It is important for me to have a job that provides an opportunity
for advancement
6.35
1.00
MS2
It is important for me to work in a prestigious and successful organization
5.97
1.29
MS3
It is important for me to have a job that has an opportunity for high
earnings
5.93
1.11
MS4
It is important that I outperform my coworkers
4.99
1.53
Power
Distance
α = 0.77
Collectivism
α = 0.84
Masculinity
α = 0.76
*. Items in bold were deleted.
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