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Mobile
Mobile payment adoption process: payment
a serial of multiple mediation and adoption
process
moderation analysis
Wanny Oentoro 225
International Business Management, Assumption University, Bangkok,Thailand
Received 3 September 2020
Revised 14 November 2020
Accepted 20 November 2020

Abstract
Purpose – Global digital payment transactions increase continuously. Due to the inconsistencies that
occurred across the research findings, past researchers have called for further investigation to verify
and empirically test the mobile payment acceptance model. The purpose of this paper is to develop an
integrative model that is derived from the multiple technology acceptance models (TAM)’s a theoretical
framework and past literature to understand how consumers decided to adopt mobile payment. By
simultaneously testing mechanisms, namely, ease of use, usefulness and risk, the current study will be
able to advance scholarly knowledge of the underlying consumer’s attitude and behavior that link social
influence to intention to use.
Design/methodology/approach – A total of 370 valid responses were collected using self-
administered questionnaires distributed via online platforms, a representative for Thai consumers. An
ordinary least square regression and bootstrap analyzes were conducted through PROCESS Macro to
analyze the moderated serial-multiple mediation model in the consecutive inducing of social influence,
perceived ease of use, perceived usefulness and perceived risk toward the consumer’s intention to use
mobile payment.
Findings – Within the context of consumers evaluating a mobile payment, statistics significant were found
for the hypothesized direct and indirect effects of perceived ease of use and perceived usefulness on an
intention to use. The results showed that Thai consumers’ intention to use mobile payment was significantly
affected by their attitudes in terms of usefulness and the less complication in using the applications. It is
confirmed that social influence indirectly affects intention to use via the increase of perceived ease of use and
perceived usefulness. The study also found a significant interaction between perceived risk and perceived
usefulness toward intention to use.
Practical implications – It is recommended to service providers to continue improving the user-
friendliness, navigation, integrity and furnish the system with more value-added activities within the
mobile payment application. It is also essential for the company to deliver tutorials and clear and easy-
to-follow instructions to customers. At the same time, the marketer should develop marketing strategies
to promote the usefulness and simplicity of using the applications to the consumers. When consumers
experienced the easiness and usefulness of the applications, these could overcome the resistance feeling
to use due to the concern on any potential risk.
Originality/value – The study contributes to the existing body of knowledge on consumer usage behavior
and TAM by integrating all important variables and developed a parsimony framework to explain
consumers’ usage adoption on mobile payment. Moreover, the current study was the very first that proposed
and tested a serial of multiple mediations of perceived ease of use and perceived usefulness, moderated by
perceived risk, in the relationship between social influence and consumers’ intention to use mobile payment
and discovered a moderating role of perceived risk toward the relationship between perceived usefulness and
mobile payment usage intention.
The Bottom Line
Keywords Intention to use, Perceived risk, Social influence, Mobile payment, Perceived ease of use, Vol. 34 No. 3/4, 2021
pp. 225-244
Perceived usefulness © Emerald Publishing Limited
0888-045X
Paper type Research paper DOI 10.1108/BL-09-2020-0059
BL Introduction
34,3/4 In 2020, the total global transaction value in the digital payment segment reaches US$4.7bn
and it is expected, by 2023, to show an annual growth rate of 12% resulting in the annual value
of US$6.7bn (Statista, 2020). According to the Global Consumer Insights Survey 2019, Thailand
has ranked as one of the fastest-growing mobile payments in Southeast Asia. Similarly to the
global trend, the largest segment in the Thailand market is Digital Commerce with a total
226 transaction value of US$15,629m while it is US$127m in the mobile payments segment with an
average value per user of $15.3. The number of mobile payments a user is expected to extend to
9.9 million by 2023 (Statista, 2020). However, online payment security is still a major concern
for Thai consumers. BOT (Bank of Thailand) reported that the digital payment had been used
by just a limited group of users. According to the BOT survey, 80% of all digital payment
transactions were derived from only 30% of the registered users and 93% of all payment
transactions are still made in cash. Particularly, the major concern among Asian consumers is
data security and privacy (Chung, 2019).
This paper is motivated by the recent increase in digital payment markets, in which
many innovative and alternate mobile payment products have been launched together with
the government’s strategy to stimulate the economy. Nevertheless, the behavior of mobile
payment users is still unclear. Although technology acceptance model (TAM) has proven to
be a powerful theoretical model in explaining how technology acceptance attitude, behavior
and the attempt to extend TAM have gradually arisen by either introducing additional
factors or examining antecedents and moderators by the past researcher; however, the
findings from these studies are often inconsistent and require further investigation. A meta-
analysis study conducted by Lui et al. (2019) found a high level of consensus among the past
research studies on key factors affecting consumers’ adoption in mobile payment including
perceived usefulness, perceived ease of use, perceived risk and social influence. However, the
authors addressed the inconsistency in the findings and suggested the importance of the
further study to verify and empirically test the mobile payment acceptance model.
In responding to the call for verification from past researchers and to understand the
consumer’s intention to use mobile payment, the current study proposed and examined a
parsimony framework drawing from TAM, UTAUT and cognitive dissonance theory
(Venkatesh and Davis, 2000; Venkatesh et al., 2003) grounded with empirical studies together
with a meta-analysis conducted from prior researchers (Liu et al., 2019). A series of multiple
mediations of perceived ease of use and perceived usefulness, moderated by perceived risk, in
the relationship between social influence and consumers’ intention to use mobile payment was
tested. The findings showed that Thai consumers intended to use the mobile payment if they
perceived that the applications are useful and easy for them to use, in which induced by the
power of social influence. Consistent with the prior research, the study found a negative
influence of perceived risk but also found the interaction effect between usefulness and
perceived risk toward consumers’ intention to use.
In this paper, the study makes essential theoretical and empirical contributions to the
literature on technology adoption. First, the study contributes to the literature on consumer
usage behavior and TAM by integrating all significant variables and develop a parsimony
framework to explain consumers’ usage adoption on mobile payment. The study investigated
consecutive factors that influence consumer’s intention of usage, starting from social influence,
perceived ease of use and perceived usefulness, respectively. Beyond the on-going linkage, the
study also contributes to the emerging study of risk. The study extends from the past study by
discovering an interaction effect of perceived risk toward the relationship between perceived
usefulness and intention to use. The findings from the current study implied the importance of
usefulness and ease of use that may overcome the resistance among the customers who are
worried about any potential risk in using mobile payment applications. The service providers Mobile
should continue promoting the usefulness and simplicity in their products that may trigger payment
more customers to adopt and use mobile payment applications.
In the next section, the study provides a revision of key theories and models about
adoption
technology adoption to understand and investigate factors that influence consumers’ process
attitude and behavior intention toward mobile payment. By understanding and empirically
testing the pattern of consumers’ attitude and behavior of how they adopt the new mobile
transaction, the results would contribute to the inconsistent findings in the technology 227
adoption literature and enlighten mobile payment service providers on how to attract new
customers and retain the current consumers. Furthermore, the discussion of theoretical
background, conceptual framework, hypotheses, methodology, the findings, implications
and limitations are presented.

Literature review
Mobile payment is defined as an application of new information technology that facilitates
financial transactions through mobile devices. Mobile payment behavior refers to the
adoption and the usage behaviors of consumers that substitute the traditional means of
payments such as cash and/or credit card by using mobile payment (Trütsch, 2016). The
fundamental for the consumer to use mobile payment is the adoption of technology that
encourages the consumer’s intention to pursue the technology. Intention to use or behavioral
intention refers to the probability that a person will take certain behavior following his/her
willingness and determination (Ajzen, 2002). The term “intention to use” and “behavioral
intention” were used interchangeably in the literature. Intention to use is advocated to be an
instantaneous predecessor of actual behavior (Davis, 1989).
In the information technology adoption literature, TAM plays an important role in
providing a theoretical groundwork for most of the studies in technology adoption. TAM,
which emerged from the diffusion of innovations theory, posits that innovation is
communicated over time by participants in a social system (Rogers, 2003). Moreover, TAM
is proposed based on social psychology models, namely, the theory of reasoned action (TRA)
and the theory of planned behavior (TPB). The premise of TRA is that one’s behavior (i.e.
consumer behavior) must be rational and voluntary, while TPB explains the link between
one’s belief and behavior (Ajzen, 1991). After the first introduction of the TAM model in
1989, past researchers contributed to the model by aggregating more determinants to
explain behavior intention such as social influence, performance expectance, effort
expectancy and facilitating condition (Venkatesh and Davis, 2000; Venkatesh et al., 2003).
TAM has proven to be a powerful theoretical model in explaining how technology
acceptance attitude and behavior were developed. Numerous studies have underlined the
importance of perceived usefulness and perceived ease of use for mobile payment
acceptance and studied the factors that may influence the consumers’ behavior intention.
The attempts to extend TAM have gradually arisen by either introducing additional factors
or examining antecedents and moderators; however, the finding from these studies are often
inconsistent and require further investigation. The inconsistencies that occurred across the
studies are statistically significant and insignificant of the factors. Some studies have shown
that perceived usefulness and perceived ease of use determine the actual use behavior
toward mobile payment (Lee et al., 2019; Wong and Mo, 2019), while other studies failed to
support the significant effect of perceived ease of use on consumers’ behavior intention
(Kalinic et al., 2020; Sharma et al., 2019; Shaw and Kersharwani, 2019). The meta-analysis
conducted by Lui et al. (2019) found a high level of consensus among the past research
studies on key factors affecting consumers’ adoption in mobile payment including perceived
BL usefulness, perceived ease of use, perceived risk and social influence. However, they also
34,3/4 addressed the inconsistency in the findings and suggested the importance of the further
study to verify and empirically test the mobile payment acceptance model.
In response to the call from prior studies to develop an empirical comprehensive mobile
payment acceptance model, the current study focuses on the key findings from a meta-
analysis that reviewed 61 papers from 22 countries covering five continents (Liu et al., 2019).
228 The current study follows the guideline by integrating the key factors summarized by the
meta-analysis from Liu et al. to explain consumers’ mobile payment behavior. These key
factors that explain the existence of an intention to use among the consumers include
perceived usefulness, perceived ease of use, social influence and perceived risk.

Perceived usefulness as a mediator between perceived ease of use and intention to use mobile
payment
The behavioral model, TAM, postulates that two beliefs which are:
(1) perceived ease of use and
(2) perceived usefulness determines the individual’s attitude and intention to use.

Perceived usefulness (PU) is defined as the degree to which an individual believes that using
a particular system would enhance his/her performance, whereas perceived ease of use
(PEOU) refers to the degree to which using a particular system would be free of effort
(Davis, 1989). The original TAM suggests that both constructs influence the consumer’s
attitudes toward system usage and behavioral intention to use a system. Perceived ease of
use is also conceptualized as the predictor of perceived usefulness which contributes to the
attitude and behavioral intention (Davis et al., 1989). Afterward, based on the empirical
findings, the TAM model omitted attitude as it did not fully mediate the effect of perceived
ease of use and perceived usefulness toward intention (Figure 1).
In 2000, Venkatesh and Davis included two additional processes into TAM, namely, the
social influence process and cognitive instrumental process. TRA and Cognitive instrumental
process are ground theories for the second version of TAM. Unfortunately, the findings of
Venkatesh and Davis (2000) failed to fully support the proposed model and admitted that the
original model still holds for the modern time. Three years later, the unified theory of
acceptance and use of technology (UTAUT) was developed by means of integrating
eight theories including TAM, innovation diffusion theory, TRA, the motivational
model, TPB, TAM, PC utilization model and social cognitive theory (Venkatesh et al.,
2003). It has been stated by prior researchers that these theories are suitable to analyze
the technology adoption process at the individual level such as employee or customer
(Molinillo and Japutra, 2017). In the UTAUT model (Figure 2), four predictors of users’
behavioral intention are performance expectancy, effort expectancy, social influence
and facilitating conditions in which performance expectancy and effort expectancy
capture the notions of perceived usefulness and perceived ease of use in the original

Figure 1.
Final version of the
technology
acceptance model by
Venkatesh and Davis
(1996)
 Mobile
payment
adoption
process

229

Figure 2.
Unified theory of
acceptance and use of
technology
(Venkatesh et al.,
2003)

TAM model. As its initiation, UTAUT has been adopted by researchers to explain user
adoption of technologies, including social network (Xu et al., 2019), mobile learning (Chao, 2019)
or mobile banking (Bhatiasevi, 2016). Despite the comprehensiveness of UTAUT which
represents eight separate models, some scholars stressed its limitation in terms of the weakness
in practicality and complicated analysis (Alwahaishi and Snasel, 2013; van Raaij and Schepers,
2008). Therefore, original TAM is the most used framework in technology adoption literature in
explaining users’ behavioral intention; however, it is also argued that the model should be able
to analyze more factors affecting behavioral intentions beyond perceptions of usefulness and
convenience (Lai, 2017).
Up to this point, through the arm’s length of the author’s literature review, the current
study adopted and applied the most powerful technology adoption models, TAM and
UTAUT, to measure consumers’ behavioral intention to use mobile payment. As mentioned
earlier, the study focuses on the key findings and the call from the meta-analysis (Liu et al.,
2019), to construct and test a comprehensive framework for the mobile payment acceptance
model. The key factors that explain the existence of behavioral intention to use mobile
payment among the consumers include perceived ease of use, perceived usefulness and
social influence. Based on TAM, perceived usefulness and perceived ease of use are the
antecedents of behavioral intention leading to actual behavior in the model; whereas social
influence is one of the determinants proposed in the UTAUT model to predict behavioral
intention. Due to the inconsistent findings in past studies on the prediction of perceived
usefulness and perceived ease of use toward behavioral intention in the TAM model (Shaw
and Kersharwani, 2019), it is worthy to empirically reexamine the model in the voluntary
environment in Bangkok, Thailand.

Perceived usefulness, perceived ease of use and intention to use mobile payment
TAM posits that an individual’s initial intention to adopt the technology (i.e. mobile
payment application) is determined by one’s attitude toward using that system if it is useful
and easy to use (Davis, 1989). The model theories that people mentally assess the match
BL between their important task goals and the consequences as a basis for judgments about the
34,3/4 user-performance contingency. As such, if consumers perceive that using the mobile
payment application will benefit them and satisfy their needs, they will be willing to adopt
and use the application. Another key cognitive factor that influences a person’s adoption
regarding the TAM is perceived ease of use. Perceived ease of use is the sense to the degree
of which a person believes that technology will require less effort for consumption
230 (Venkatesh and Davis, 2000). That is, if a person believes that using a mobile payment
application is easy, they will be more willing to try the application. Therefore, the greater
inclination to use mobile payment derives from the expectation that it is easier and more
beneficial to the user. Across the literature, several empirical studies have depicted the
positive attitudinal effect of digital convenience and usefulness toward the behavioral
intention or intention to use. For instance, Rivera et al. (2015) found that perceived
usefulness and perceived ease of use explained timeshare owner’s intentions to use a mobile
application. Moreover, studies also validate that perceived ease of use is considered the most
important factor for technology adoption (Bonn et al., 2016). The argument and findings
support the TAM model in which Davis has suggested an indirect relationship between
perceived ease of use and behavioral intention (Davis, 1989). The phenomenon is expected
that will apply in the same way to the Thai consumer’s intention to use mobile payment as
well. Consequently, the ease of use for mobile payment should be an important part of the
consumer’s perception of its usefulness and their intention to use it. Therefore, the study
proposes the following hypothesis:

H1a. Perceived ease of use exhibits a positive relationship with the intention to use.
H1b. Perceived usefulness exhibits a positive relationship with the intention to use.
H1c. Perceived ease of use exhibits a positive indirect relationship with intention to use
via perceived usefulness.

The serial multiple mediations of intention to use mobile payment

Social influence (SI) is a construct derived from the UTAUT model proposed by Venkatesh
et al. (2003). It is defined as the degree to which a person values the importance of how others
believe that he/she should perform a particular behavior. This notion derives from TRA
proposed by Ajzen (1985). The opinion of referent groups about how one should act has been
referred to as a subjective norm. Interchangeably, subjective norms are social influences that
are concerned about how a person stresses what other people would think of him/her
performing the behavior (Ramos de Luna et al., 2019). Besides, not only behavior but social
influence can potentially enhance users’ view of the technology’s value. This premise is
anchored on the principle of Social Information Process Theory (Salancik and Pfeffer, 1978)
that explains the complex linkages between information, social contexts, perceptions, attitudes
and behaviors. It is believed that the social environment provides a clue to a person in
constructing or interpreting events and situations. Zubair et al. (2019) found the positive
influence of social outcome toward the individual’s subjective knowledge and behavior
intention. Particularly in mobile users, it is stated, “mobile users are often in social situations
and it influences their perceived usefulness and perceived ease of use when they find other
societal group members using mobile application”(Sathye et al., 2016, p. 3). Empirical studies
have confirmed that perceived ease of use and perceived usefulness derive from social influence
(Beldad and Hegner, 2018; Bonn et al., 2016; Qin et al., 2011; Sathye et al., 2016).
Past research also suggested further study to add social influence or subject norms into
the model to enhance the understanding of factors that impact mobile payment adoption
(Bailey et al., 2017). Therefore, social influence can significantly affect consumers’ appraisal Mobile
of technology’s ease of use and usefulness. As forefended, this notion has been empirically payment
confirmed by recent studies that found a positive significant impact of social influence toward
user’s perceived usefulness and perceived ease of use (Beldad and Hegner, 2018; Sathye et al.,
adoption
2016). Drawing on the model of TAM, UTAUT and Social Information Process Theory together process
with past empirical studies, the current study hypothesizes that social influence will enhance the
consumer’s perception toward using mobile payment in terms of ease of use and consequently
enhance the user perception and behavioral intention. Accordingly, the study hypothesizes that:
231

H2. Social influence exhibits a positive indirect relationship with intention to use via
increased perceived ease of use and consequently increased perceived usefulness.

Perceived risk as a moderator in intention to use mobile payment processes

Perceived risk is commonly observed as a feeling of uncertainty about possible negative
consequences in using a product or service. It is formally defined as a combination between
uncertainty and seriousness of outcome involved and the expectation of losses that can
discourage consumer’s purchase behavior (Peter and Ryan, 1976). Cognitive dissonance theory
(CDT) explains the situation why consumers may be reluctant to pursue mobile transactions
(Festinger, 1962). The reluctance occurs because of the contradicting belief in the outcomes of
using mobile payment. CDT posits that a person holds two or more contradicting beliefs, ideas
or values that go against one another. In essence, when two actions or ideas are not
psychologically consistent, people would decide to take appropriate action that helps justify
this stressful cognitive dissonance by either adding new cognition to ease the contradiction or
drop the situation to avoid uncomfortable circumstances. In many cases, consumers
consciously perceive risk when evaluating the adoption of information technology systems
such as using mobile payment to purchase products and services. Some consumers perceive
mobile payment as a dangerous and insecure medium of transaction. Although the product
utility and benefit sound promising, the feelings of uncertainty, discomfort and/or anxiety
would also raise concern to the consumers. These combinations make up the construct of
perceived risk and are shown to hinder product adoption (Featherman and Pavlou, 2003).
Likewise, viewing mobile payment using the TAM would lead to the same expectations
to the effects of perceived risk. The amplified feeling of psychological discomfort and
anxiety caused by increased risk perceptions would cause the consumer to devalue the user
perception that may reduce the consumer’s intention to use toward mobile payment usage.
This leads to the following hypothesis:

H3a. Perceived risk exhibits a negative relationship with the intention to use.
H3b. Through the serial indirect relationship of social influence with the intention to use
via increased perceived ease of use and perceived usefulness, perceived risk
moderates the positive influence of perceived usefulness toward intention to use.
Based on the above background, Figure 3 graphically summarizes and illustrates the
hypothesized relationships among all the research constructs.

Methodology
Research design
The current study conducted quantitative research to investigate Thai consumers’
intentions to use mobile payment in their transactions. The online survey method was used
BL as it can reach a large sample to describe consumer opinion on certain issues rapidly and is
34,3/4 cost-efficient (Bethlehem, 2010; Jason, 2012). This cross-sectional study was conducted for
four weeks and examined at one specific time. Self-administered questionnaires were
distributed and collected via Google Form. Convenience sampling was used due to the
unknown sampling frame, cost-effectiveness and efficiency. The researcher shared a link to
the online survey with the condition addressing that the survey requires the respondents
232 who know or currently using the mobile payment. The link is sharing through social media
and instant communication applications on electronic devices such as LINE, messenger and
WeChat. By doing this, the respondent voluntarily agreed to participate in the study by
themselves at their most convenient time. To eliminate the chance of common method bias,
the survey was designed to follow procedures recommended by Podsakoff (2003). The
online survey was simple, concise with less abstract wording and easy for the respondent to
answer. The confidentiality and anonymity of the respondent were emphasized at the
beginning of the survey (Podsakoff et al., 2012). A total of 370 valid and completed responses
were obtained. The sample includes 52% female and 48% male respondents. In terms of age,
37% of the respondents were 20–30 years old, 50% were 31–40 years old and 13% were
older than 41 years. When it comes to their educational background, 10% received a high-
school diploma, 47% gained a college degree and 43% were graduated. In total, 70% of the
respondents are corporate employees and 17% are business owners.

Instruments
In total, five-Likert scales with anchors ranging from “strongly disagree” to “strongly agree”
were used to measure the respondent’s belief toward all items measured. The items of the
questionnaire were adapted from existing and well-tested scales offered by prior studies. All
items were translated and completed back-translated to Thai, following the method
suggested by Brislin (1970). The initial version of the questionnaire used in the study was
pretested with a convenience sample to identify the item formulation and any language-
related issues. In particular, the measurement scales for PEUO, PU, SI and BI were adapted
from the original TAM and UTAUT instruments (Davis, 1989; Venkatesh et al., 2003).
However, due to the different context in a prior study that focused on the adoption of
software package and of the current study which focuses on mobile payment, the contents of
the TAM items were substantially different.
Intention to use was assessed with three items (e.g. “Given the change, I predict I will use/
continue using mobile payment services in the future.”) adapted from Davis (1989). Perceived
usefulness was assessed with three items (e.g. “Using mobile payment services will increase my
productivity.”); whereas Perceived ease of use was assessed with four items (e.g. “Learning to

Figure 3.
Proposed research
model for intention to
use mobile payment
process
operate mobile payment services is easy for me.”) adapted from Venkatesh and Davis (2000). Mobile
Social influence was measured with four items that were primarily inspired by the scale of payment
Venkatesh et al. (2003). Examples of items used to measure are “People who influence my
behavior think that I should use mobile payment services” and “People who are important to
adoption
me think that I should use mobile payment service.” Finally, the Perceived risk was assessed process
with four items adapted from Kalinic et al. (2019) (e.g. “Other people can know information
about my transaction if I use mobile payment” and “There is a high potential that my money
would be lost if I make purchases via mobile payment services”). 233
With regard to the sample size, the rule of thumb, item-to-response ratio should range
from 10:1 for each set of variables (Hair et al., 2014, p. 100). As 19 items were used in the
questionnaire, 190 – 370 samples are sufficient for the inferential statistics testing.

Control variables
The study controlled for the consumers’ age, gender and income because these variables
have been found to be theoretically and empirically related to intention to use (Kalinic et al.,
2020; Giovanis et al., 2012; Venkatesh and Davis, 2000).

Validity and reliability

To test the construct validity and reliability of variables in the current study, first, the value of
Cronbach’s alpha coefficient was calculated to test the reliability of the model variables. All the
variables’ alpha values reveal greater than 0.70 as recommended by Nunnally (1978). Next, the
researcher conducted confirmatory factor analysis (CFA) with AMOS to verify the model’s
validity. CFA was used to test the convergent and discriminant validity and to test whether all
the measurement items are consistent with the understanding of the nature of their constructs.
It is aimed to test if the data fit a hypothesized measurement model. All five variables, namely,
perceived ease of use, perceived usefulness, social influence, perceived risk and intention to use
were placed into a confirmatory factor analysis and found that the data fit the proposed five-
factor model well ( x 2/df = 425.125/122, TLI = 0.940, CFI = 0.950, NFI = 0.930, RMSEA = 0.08).
The study also ran confirmatory factor analysis fitting a single common-factor. The result
indicated that all item parcels generated poor fit ( x 2/df = 2,669.062/20.220, TLI = 0.511, CFI =
0.578, NFI = 0.567, RMSEA = 0.228) relative to the measurement model with five first-order
factors presented in Table 1. In addition, the study compared alternate models with the four-
factor model, by combining perceived ease of use, perceived usefulness ( x 2/df = 752.899/126,
TLI = 0.873, CFI = 0.896, NFI = 0.878, RMSEA = 0.116) and three-factor model (x 2/df =
1,589.672/129, TLI = 0.712, CFI = 0.757, NFI = 0.742, RMSEA = 0.175) that merged perceived
ease of use, perceived usefulness and social influence into the same factor. Nevertheless, it is
found that the five-factor model revealed the best fit with the data.
The measurement model showed a good fit of the hypothesized five-factor model, all
values of fit indices exceeded the threshold of 0.9 (Table 1). Composite reliability (CR),
average variance extracted (AVE) and maximum shared squared variance (MSV) were
calculated. The results show that the AVE of all the variables is higher than 0.5, CR values
are greater than 0.7 which confirmed the convergent validity of the model (Hair et al., 2010).
To test discriminant validity, the AVE of each variable were compared with the appropriate
MSV and the squared roots of AVE were all greater than the inter-construct correlations
(Table 2). In addition, due to the collection of all data are through a self-report online survey,
this might induce the threat of common method bias. As such, the most widely used
approach, Harman’s single-factor, was conducted to test the presence of the common method
effect. The total variance extracted by one factor equal to 39.77%, which is less than the
recommended threshold of 50% (Podsakoff et al., 2003). Although the results of these
BL analyzes do not impede the possibility of common method variance; however, it does not
34,3/4 appear that the common method variance would likely confound the interpretation of
results. Furthermore, the satisfaction level of convergent and discriminant validity (Tables 1
and 2) provides further evidence against the presence of common method bias.

234 Confirmatory factor analysis Loadings

Constructs and items

Perceived ease of use (PEUO) (Cronbach’s alpha = 0.864)
Learning to operate mobile payment services is easy for me 0.91
I expect it would be easy for me to become skillful at mobile payment services 0.92
Learning to operate mobile payment services will be easy for me 0.92
I expect that my interactions with mobile payment services would be clear and understandable 0.88
Perceived usefulness (PU) (Cronbach’s alpha = 0.928) 0.91
I expect mobile payment services will be useful in my life 0.87
Using mobile payment services will increase my productivity 0.86
Using mobile payment services will enable me to accomplish transactions more quickly 0.73
Social influence (SI) (Cronbach’s alpha = 0.881)
People who influence my behavior think that I should use mobile payment services 0.91
People who are important to me think that I should use mobile payment services 0.92
The service provider has been helpful in the use of mobile payment services 0.80
In general, the service provider has supported the use of the mobile payment system 0.80
Perceived risk (PR) (Cronbach’s alpha = 0.90 )
In general, I think that making purchases with mobile payment is risky 0.85
Other people can know information about my transaction if I use mobile payment 0.89
There is a significant risk in making purchases by using mobile payment 0.92
There is a high potential for lost money if I make purchases via mobile payment services 0.85
Intention to use mobile payment (IU) (Cronbach’s alpha = 0.933 )
I will use/continue using mobile payment services in the future 0.95
Given the change, I predict I will use/continue using mobile payment services in the future 0.93
It is likely that I will use/continue using mobile payment services in the future 0.94
Table 1. Notes: n = 370; five-factor model fit for measurement model: CMIN/df = 425.125/122; normed fit index
Factors loading and (NFI) = 0.93; comparative goodness-of-fit (CFI) = 0.95; Tucker-Lewis index (TLI) = 0.94; incremental fit
Cronbach’s alpha index (IFI) = 0.95; root mean square error of approximation (RMSEA) = 0.08

Descriptive statistics and Pearson correlation coefficient values

Variables Mean SD CR AVE MSV IU PU PEOU PR SI

IU 4.44 0.64 0.934 0.825 0.629 0.909

PU 4.47 0.65 0.882 0.714 0.450 0.620 0.845
PEUO 4.28 0.80 0.931 0.771 0.629 0.793 0.671 0.878
PR 3.50 0.95 0.895 0.684 0.143 20.378 20.186 20.298 0.827
Table 2. SI 3.71 0.97 0.875 0.648 0.244 0.367 0.334 0.494 0.181 0.805
Descriptive statistics, Notes: n = 370, numbers in bold indicate p < 0.05 within Pearson correlations. The square root of AVE is
correlation and shown in italics at the diagonal. IU = intention to use; PU = Perceived usefulness; PEOU = Perceived ease
validity of use; PR = Perceived risk; SI = Social influence
Data analysis Mobile
Table 2 presented descriptive statistics result that include descriptive data and correlation payment
coefficient for all the variables in the study. The statistical significance of the model was
tested through the SPSS add-on software developed by Hayes (2018). The approach is based
adoption
on an ordinary least-squares regression by the bootstrap method. Prior to the hypotheses process
testing, all assumptions were tested to ensure that the data set does not violate any
assumptions of OLS regression. This includes normality, linearity, homoscedasticity and
independence. Upon the bootstrap method, the indirect mediating effects of variables are 235
evaluated based on if the point estimate of the mediating variable is zero within a 95% bias-
corrected and accelerated confidence interval. In other words, a mediating variable is
statistically significant with a no-point estimate within the zero-internal. Bootstrap analyzes
in this study were conducted through PROCESS Macro with the moderated serial-multiple
mediation model 87. Data obtained from 5,000 bootstrap samples were used in the current
study and the significance level was set at 95% confidence level. The data analysis was
conducted through SPSS version 25.

Results
Descriptive statistics data presented in Table 2 indicate that a positive significant
relationship was found among the variables, namely, social influence, perceived ease of use,
perceived usefulness and intention to use. On the opposite, negative relationships were also
found between the aforementioned variables and perceived risk. To determine the serial-
multiple mediation of perceived ease of use, perceived usefulness in the relationship between
social influence and intention to use, the regression-based approach and bootstrap method
was used. The obtained findings are presented in Figure 4, Tables 3 and 4.
Figure 4 illustrated the total effect of social influence on intention to use. The statistics
analysis was started by testing the direct effect between social influence and intention to
use. The total effect of social influence was at a significant level ( b = 0.194, SE = 0.033, t =
5.976, p < 0.001). In addition, the direct effects of social influence on perceived ease of use
( b = 0.337, SE = 0.037, t = 9.168, p < 0.001) and perceived usefulness ( b = 0.151, SE =
0.034, t = 4.427, p < 0.001) were at significant levels. A review of the direct effects of
mediating variables on the intention to use also showed statistically significant effects of
perceived ease of use ( b = 0.499, SE = 0.038, t = 13.29, p < 0.001) and perceived usefulness
( b = 0.211, SE = 0.044, t = 4.83, p < 0.001). However, when social influence and all
mediation variables, namely, perceived ease of use and perceived usefulness, were
simultaneously entered into the equation, the relationship between social influence and
intention to use was not at a significant level ( b = 0.004, SE = 0.025, t = 0.140, p = 0.888).
While the effects of moderating variables on the intention to use still showed statistics
significant (PEUO, b = 0.412, SE = 0.042, t = 9.782, p < 0.001 and PU, b = 0.286, SE =
0.044, t = 6.443, p < 0.001). Consequently, the mediating variables were observed to mediate
the relationship between social influence and intention to use, H2 is supported. Furthermore,
the model overall was seen to be at a significant level (F = 85, p < 0.001) and explained 65%
(R2 = 0.654) of the total variance of intention to use.
Figure 4 and Table 3 also support the hypotheses H1a – H1c which illustrated the direct
and indirect effects between perceived ease of use and intention to use via perceived
usefulness. Figure 4 showed the direct effect of perceived ease of use toward perceived
usefulness ( b = 0.577, SE = 0.028, t = 15.091, p < 0.001); and the positive significant
relationship between perceived usefulness and intention to use ( b = 0.286, SE = 0.044, t =
6.443, p < 0.001). In addition, the researcher also conducted a further analysis using the
PROCESS Marco Model no. 4 testings mediating effect of perceived usefulness on the
BL Bootstrapping
34,3/4 95% confidence
Product of coefficients interval
Variable Point estimate SE Lower Upper
2
Predicting perceived ease of use (R = 0.337)
Gender
0.242*** 0.069
0.379
0.106
236 Age
0.272*** 0.051
0.373
0.171
Income 0.048ns 0.038
0.027 0.122
Social influence (SI) 0.343*** 0.046 0.251 0.434
Predicted perceived usefulness (R2 = 0.462)
Gender
0.047ns 0.051
0.148 0.055
Age 0.035ns 0.039
0.041 0.112
Income
0.010ns 0.028
0.065 0.045
SI
0.043ns 0.030
0.102 0.015
Perceived ease of use (PEOU) 0.577*** 0.038 0.502 0.652
Predicted intention to use (R2 = 0.654)
Gender
0.122** 0.042
0.204
0.041
Age 0.0.87** 0.032 0.024 0.150
Income 0.037ns 0.022
0.007 0.080
Social influence 0.004ns 0.025
0.046 0.053
Perceived ease of use 0.412*** 0.042 0.330 0.494
Perceived usefulness 0.286*** 0.044 0.198 0.373
Perceived risk
0.078*** 0.022
0.122
0.034
Table 3. Perceived usefulness X perceived 0.164*** 0.033 0.100 0.229
Hypotheses test: risk
results of regression
analysis Notes: n = 370, k = 5,000. **p < 0.05, ***p < 0.001, ns = not significant

Figure 4.
Research model for
consumers’ intention
to use mobile
payment

relationship between perceived ease of use and intention to use as proposed by TAM theory.
The indirect relationship was found at significant level ( b = 0.182, SE = 0.068, 95% CI
[0.066, 0.335]). Thus, H1a – H1c are supported.
H3a and H3b predicted that perceived risk would exhibit a negative influence on the
intention to use and would moderate the relationship between perceived usefulness and
intention to use. It is expected that the serial indirect effects of social influence on intention
to use via perceived ease of use and perceived usefulness would be contingent upon perceived Mobile
risk. To test the prediction, interaction terms and indices of moderated mediation for the serial payment
indirect relationships were calculated (shown in Table 3). The negative relationship between
perceived risk and intention to use was found at a significant level ( b =
0.078, SE = 0.022, t =
adoption
5.021, p < 0.001) and the interaction term between perceived usefulness and the perceived risk process
was found significant ( b = 0.164, SE = 0.033, t = 5.021, p < 0.001). However, the interaction
term shifts the negative influence of perceived risk on the intention to use. Rather than reducing
in salience, the relationship between perceived usefulness and intention to use became
237
significantly stronger when perceived risk was presented. This can be interpreted that for this
sample of consumer, perceived usefulness remained strong regardless of perceived risk.
Moreover, in Table 4, the indices of moderated mediation confirmed the moderating effect of
perceived risk on the serial indirect relationships of intention to use (b = 0.32, SE = 0.008, 95%
CI [0.015, 0.047]). As a result, the result of the bootstrapping analysis supported the proposed
hypotheses H3a and H3b.
To interpret the interaction between perceived usefulness and perceived risk on the
intention to use is to notice the slope steepness. Figure 5 illustrates the interaction of
perceived usefulness and perceived risk on the intention to use. It means that an enhanced
effect of intention to use resulted from the interaction between perceived usefulness and
perceived risk. The positive relationship between perceived usefulness is stronger when the
perceived risk is high. Surprisingly, consumers who perceived high usefulness with a high

Total indirect effects Point estimate SE Lower Upper

PEOU ! PU ! intention to use 0.182*** 0.068 0.066 0.335

SI ! PEOU ! intention to use 0.256*** 0.042 0.176 0.340
SI ! PU ! intention to use
0.014 ns 0.010
0.037 0.002
SI ! PEOU ! PU ! intention to use 0.062*** 0.024 0.021 0.114

Index of Perceived risk

moderate mediation Low (
1 SD) Average High (þ1 SD)
SI ! PEOU ! PU ! intention to use 0.032*** 0.024 0.055 0.087 Table 4.
Notes: n = 370, k = 5,000. **p < 0.05, ***p < 0.001, ns = not significant. SI = Social influence; PEOU = Indirect effects
Perceived ease of use; PU = Perceived usefulness analyze

Figure 5.
The interaction effect
between perceived
risk and perceived
usefulness on the
intention to use
BL perception of risk have the highest level of intention to use mobile payment. Simple slopes
34,3/4 yielded the result that perceived high risk manifested a significant positive slope, t-values =
7.11 ( b = 0.452, p < 0.01). Besides, the positive relationship for low perceived risk was
significantly related to the level of intention to use among the consumers, t-values 2.64 ( b =
0.12, p < 0.01). By examining the interaction plot in Figure 5, it shows that the slope for high
perceived risk is steeper than low perceived risk ( b High PR = 0.452 > b LowPR = 0.12).
238 Hence, H3b was supported.

Discussion
In the present research, the researcher proposed and examined indirect relationships via
perceived ease of use and perceived usefulness that underlie the relationships between social
influence and intention to use. Moreover, this serial of indirect relationships was also
expected to moderate by perceived risk. Drawing on TAM, UTAUT and cognitive
dissonance theory (CDT), the researcher proposed a serial process to explain how social
influence strengthens consumers’ intention to use mobile payments via perceived ease of use
and perceived usefulness; then how perceived risk might alter the positive effect of
perceived usefulness. By simultaneously testing mechanisms derived from multiple TAM’s
theoretical frameworks in an integrative model, the current study was able to advance
scholarly knowledge of the underlying consumer’s attitude and behavior that link social
influence to intention to use.
Within the context of consumers evaluating a mobile payment, statistics significant were
found for the hypothesized direct and indirect effects of perceived ease of use and perceived
usefulness on the intention to use. Hypothesis set 1 focused on measuring the extent to
which perceived ease of use and perceived usefulness impede consumer mobile payment
valuations and usage decisions as postulated in the TAM model (Davis, 1989). Despite the
inconsistent findings from past studies, the current study found support for the original
model of TAM theory replicating previous findings regarding the links of perceived ease of
use, perceived usefulness and intention to use mobile payment (Bailey et al., 2017; Chi, 2018;
Khayer and Bao, 2019; Shankar and Datta, 2018). The results showed that Thai consumers’
intention to use mobile payment was significantly affected by their attitudes in terms of
usefulness and the less complicated in using the applications. Thai consumers are more
likely to use mobile payments when the application is simple to use and would enhance their
personal productivity.
Results also confirmed the hypothesized effects that social influence indirectly affects
intention to use via the increase of perceived ease of use and perceived usefulness. Based on
the findings from the serial-multiple mediation tested, the serial-multiple mediation of
perceived ease of use and perceived usefulness and the separate mediation of a single
mediating variable were found statistically significant in the relationship of mobile payment
adoption and social influence. This positive effect of social influence mirrors the results from
the past studies in which demonstrated that social influence affects consumers’ adoption
intention through the mediating role of perceived usefulness (2011). However, the findings
from the current study extended to the literature arguing that the power of social influence
on behavior intention is mediating through multiple factors which are perceived ease of use
and perceived usefulness. The importance of social influence was also highlighted in social
psychology theories. For instance, the social learning theory developed by Bandura (1977)
indicated that people communicate with their friends and learn from one another. Aligning
with TRA suggested that social norms predict and influence a person’s behavior (1985).
Although in the earliest version of TAM, Davis et al. (1989) did not account for social
influence in the adoption model due to the weak prediction but the researchers noted the
importance of social influence and called for further study (p. 998). The past empirical Mobile
studies supported the notion that social influence plays an important role in user behavior in payment
different technologies/applications such as an online social network (Qin et al., 2011), mobile
wallet (Singh et al., 2020), automated vehicle (Zhang et al., 2020) and mobile application
adoption
(Vahdat et al., 2020). On the effect of perceived usefulness to intention to use in the current process
study, the direct effect of perceived ease of use and indirect effect of social influence are
found as significant antecedents to perceived usefulness (R2 = 0.462). This indicates that
consumers perceive mobile payment to be beneficial due to its simplicity and, perhaps, this 239
perception was encouraged by the people and environment around them. Indeed, social
influence should be noted as a significant factor in determining consumer’s usage attitude.
Consistent with prior studies, the study found a negative significant relationship between
perceived risk and intention to use (Ariffin et al., 2018; Marafon et al., 2018). Conversely, some
studies failed to support the significant relationship between perceived risk and behavior
intention (Sohn et al., 2016; Xia and Hou, 2016); beside the positive reinforcement of perceived
risk toward usage adoption was found(Singh et al., 2020; Wu and Wang, 2005). Surprisingly,
the current study found a positive influence of the interaction between perceived risk and
perceived usefulness toward intention to use. These positive conjoining effects may be because
consumers in the 21st century have a better understanding of technology and they are highly
aware of the existing and potential risks(Singh et al., 2020). In particular, although the risk was
found negative significant in predicting consumers’ intention to use mobile payment; however
when it was associated with the usefulness, the negative effect did not appear but instead
induce a positive significance to consumers’ usage intention. With respect to perceived risk, all
of the respondents in the current study are experienced in using mobile transactions. Therefore,
the findings could imply that users who perceived the usefulness of mobile payment systems
were also aware of the existence of potential risk. However, a number of advantages in using
mobile payment are more attractive to consumers to make online transactions though they
acknowledge that there are some levels of risk.

Theoretical contributions
This research contributes to the literature on consumer usage behavior and TAM. Drawing
upon TAM, UTAUT and cognitive dissonance theory, the study integrated all the important
variables and developed a parsimony framework to explain consumers’ usage adoption on
mobile payment. While the majority of the previous empirical studies only investigated a
direct relationship or a single mediation and/or moderation effect, the current study was the
very first that proposed and tested a serial of multiple mediations of perceived ease of use
and perceived usefulness. In turn moderated by perceived risk, in the relationship between
social influence and consumers’ intention to use mobile payment. The findings contribute to
the technology adoption literature by showing that the original TAM model is still
comprehensive and powerful for modern times (Venkatesh et al., 2003).
Beyond the linkage between the perception of usefulness and convenience, the findings
also contribute to the nascent study of risk. Since 2000, there has been increasing attention
given by scholars to the effects of consumer’s perception of risk. Past research studies have
investigated an interaction effect of perceived risk within TAM and UTAUT models.
Featherman and Hajli (2016) found a positive significant interaction effect of perceived risk
with social influence on intention to use but not for the interaction effect between perceived
risk and perceived ease of use. The current study extended from Featherman s) by
investigating the interaction effect between perceived risk and usefulness, which was not
tested in their study. Results from the current study revealed a consistency with prior
research that perceived risk is negatively associated with intention to use; however, the
BL study has also discovered a positive significant interaction effect between perceived risk and
34,3/4 perceived usefulness toward usage intention. A possible explanation could be the change in
the user’s awareness and acceptance of risk and their user experience (Marafon et al., 2018).
When consumers experienced more from online transactions, their awareness and
understanding of any potential risk improved. Certain factors such as financial risk and
privacy risks may be less pronounced (Pelaez et al., 2019). Future study is recommended to
240 explore and empirical test if user’s risk acceptance would affect the influence of perceived
risk toward intention to use.

Managerial implications
The findings indicate that the effects of Thai consumers’ evaluation of using mobile
payment were induced by social influence, perceived ease of use, perceived usefulness and
perceived risk. Although the effect of perceived risk was negatively related to consumer’s
intention to use, the significant level of risk was not strong enough to depress the adoption
attitude when they value the mobile payment to be useful and beneficial to their transaction.
Particularly, the perceived usefulness was induced directly and indirectly by social influence
and perceived ease of use. Therefore, the companies that develop mobile payment
application should continue to improve the user-friendliness, navigation, integrity and
furnish the channel with more value-added activities within the mobile payment application.
It is also essential to the mobile payment service providers to deliver tutorials with clear and
easy-to-follow instructions to customers. By promoting usefulness and simplicity, this could
overcome the resistance of the consumer even if they have concerns about the potential risk.
Therefore, the service providers should continue to improve their product to progress
perceived ease of use and perceived usefulness. At the same time, the marketer should
develop marketing strategies to communicate this usefulness, ease of use to the consumers
and high-security features are emphasized in the mobile payment application to protect their
personal information such as personal identification or bank account information. By
reducing the perception of riskiness and increase the perception of usefulness and
simplicity, there will be more uptake for the application. Consequently, consumers who
experienced the security platform, easiness and usefulness of the mobile payment
application will convey the message to others. This would arouse word-of-mouth and trigger
a new customer to adopt and use mobile payments, resulting in more digital transactions,
boosting the company’s sales revenue and economics.
There are limitations in this study that can present future research opportunities. A
primary limitation is the generalization that the study focuses only on Thai consumers. The
sample is drawn by using convenience sampling that the consumers have voluntarily
participated in this research via social media platforms. While all participants should have a
certain level of knowledge on technology and are more attuned to alternate technological
transaction channels such as mobile payments; however, the findings from this group
cannot be generalized to the entire users’ population. Moreover, the current study did not
investigate the impact of environmental or situational variables. Factors such as culture,
government regulations and promotional strategies could also enhance consumers’ attitudes
and behavior. Additionally, cross-cultural studies across different regions could be
conducted to determine the cultural impact on technological acceptance.

Conclusion
This study focuses on how social influence, perceived ease of use, perceived usefulness and
perceived risk determine consumers’ intention to use. The findings show that social
influence indirectly affects intention to use via the increasing of perceived ease of use and
perceived usefulness. Although perceived risk could have a negative influence on customer Mobile
attitude, through the value-added of easiness and usefulness, consumers still have a strong payment
intention to use the mobile payment applications. Therefore, service providers should
emphasize developing the new technology transaction that promotes user-friendliness and
adoption
easy-to-use to their product to attract new customers by conveying a message from process
experienced customers or through social influence.
241
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Corresponding author
Wanny Oentoro can be contacted at: muaymaa@gmail.com

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