Journal of Open Innovation:

Technology, Market, and Complexity

Article
E-Money Payment: Customers’ Adopting Factors and
the Implication for Open Innovation
Widayat Widayat 1, * , Ilyas Masudin 2 and Novita Ratna Satiti 1
1 Department of Management, Faculty of Economics and Business, University of Muhammadiyah Malang,
Malang 65145, Indonesia; satiti@umm.ac.id
2 Department of Industrial Engineering, Faculty of Engineering, University of Muhammadiyah Malang,
Malang 65144, Indonesia; masudin@umm.ac.id
* Correspondence: widayat@umm.ac.id




Received: 11 June 2020; Accepted: 19 July 2020; Published: 29 July 2020


Abstract: This investigation was carried out on the adoption of the e-money payment model with
the application of a quantitative and qualitative approach (mixed methods). Online questionnaires,
which included closed-ended questions on a Likert five-point scale and open-ended questions,
were distributed through social media chat groups. Respondent samples were drawn from the
population of adolescent customers in Indonesia. The collected questionnaires were verified for
accuracy, reliability, and validity before the data were analyzed. Adequate data were used to test the
relationship model between latent variables, and the relationship of latent variables in the model
was tested using partial least squares by employing Smart-PLS 3.0 software and NVIVO 12 plus.
The final analysis shows that the reasons for adopting e-money are practicality and convenience.
The main reasons that customers adopt electronic money are its practicality, ease of use, efficient
transaction time, faster payment, and the simplicity of the payment process. The final modeling
formed good-fit inner and outer models. This model verifies the significant influence of social
factors, effort expectancy, and facilitation conditions on e-money attitudes. Additionally, social factors,
effort expectancy, and attitudes have a significant influence on e-money behavior.

Keywords: adoption; e-money; modern style; payment decision; customer

1. Introduction
In the recent cashless era, the proliferation of mobile technology and the digitalization of financial
services have developed significantly, marked by the birth of electronic money as an alternative
mode of payment that is seen as part of a new and modern lifestyle [1–4]. These developments
are forcing customers to deal with technology-based payment modes that are relatively unfamiliar
to them. Such methods enable users to choose to pay in cash or use electronic money for their
transactions [5]. Persuasive marketing is flooding various forms of media with the aim of convincing
customers to use electronic money. Various advantages and conveniences are explained by the media
to inspire customers to adopt the electronic money platform. Furthermore, support from authorities,
the provision of facilities by merchants, the ease of obtaining application devices, social–economic
factors, and the widespread use of smartphones encourage customers to adopt these high-technology
payment methods [6–12]. There are many advantages and conveniences associated with making
payments electronically, submitting mobile payments, or using e-money. However, studies on electronic,
online, and mobile payments have revealed some typical security problems, which are one of the
main obstacles to the adoption of e-money. Customers may be faced with the risk of failure to make
payments due to inadequate infrastructure, the risk of misuse of personal data, the risk of fraud
committed by malicious parties, and other risks [13]. Customers are faced with problems related to

J. Open Innov. Technol. Mark. Complex. 2020, 6, 57; doi:10.3390/joitmc6030057 www.mdpi.com/journal/joitmc

J. Open Innov. Technol. Mark. Complex. 2020, 6, 57 2 of 14

risk, a lack of e-money, and ease of possession; therefore, customers have not necessarily been using
e-money in transactions.
E-money payment, as a meaningful new business model [14] innovation in business and economic
life, has attracted great interest from academics and practitioners from multiple perspectives. There are
several important points related to the emergence of electronic money as an innovation in the era
of economic capitalism with limited capital. The platform or application is presented as a new
technology [15], the result of engineering, or a creation by an entrepreneur in a company or through
collaboration between entrepreneurs, that assumes that innovation is based on firms’ need to produce a
successful innovation that creates added value for the firm [16,17]. The success of entrepreneurs creating
innovations that create added value (in this case, e-money payment) is determined by at least three
important parties, namely, innovators who are entrepreneurs, partners such as financial institutions
or providers of internet facilities, and the user in the open innovation ecology [18]. Ecosystems that
involve various parties can create an environment that supports and influences the success of open
innovation. The use of technology is a crucial component that determines the success of innovation
in society. What is thought, perceived, and done by innovators and entrepreneurs in the form of a
platform design (for example, e-money) is not always in harmony and able to fulfill what the user
wants. According to the innovator, some aspects of e-money (practicality, ease of use, and time-saving
potential) are not necessarily the same as what is experienced and felt by the user. Customers are users
of e-money technology, and are a determinant of the success of innovation in finance. Their behavior to
be willing to use and continue to use a Financial Technology (Fintech) innovation is a determinant of its
success or failure. Therefore, this paper is designed to show open innovations in finance from the user
side, namely, customers. The results have implications for open innovation stakeholders and can be
used as dimensions or indicators to measure the success of e-money payment application innovations.
The lack of customer willingness to adopt e-money as a medium of payment in transactions is a
very interesting social business phenomenon that requires deeper study. Some previous research has
addressed the adoption of e-money as a medium for payment. However, previous studies, generally
using the technology adoption model, have not used theories in specific contexts. The traditional
models—the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and the
Unified Theory of Acceptance and Use of Technology (UTAUT), as well as their extensions—form
a framework of approaches that are considered appropriate [19], which continue to be studied and
developed [20]. Some relevant studies have used these approaches to test the effect of attitudes on the
intention to use smartphone-based e-money [21], examine the explanatory factors for the adoption
of financial technology [22], explain the intentions of the electronic payment system (cashless) using
UTAUT [23–27], and study electronic payment adoption specifically in Indonesia [28]. Studies have
used TPB to explain the intention to use e-money [5] and the TAM model to test the factors that
influence someone to buy sports tickets online [29] and to analyze mobile payment systems [30–32].
Other studies have combined TAM with TPB in internet banking [33]. The adoption of e-money as
a transaction payment tool, from the perspective of TPB, is an action that is preceded by intention.
The intention arises as a result of the attitude towards e-money and subjective norms that are supported
by behavioral control. However, in this theory, the fulcrum lies in the actors whose behavior is
being studied and external actors in the form of subjective norms and behavior control. Meanwhile,
the TAM model illustrates that the adoption of the use of technology, as well as the e-money platform,
is predicted by the perceived usefulness and perceived ease of use of technology. This means that
whether someone is willing to adopt new technology, including a form of payment based on financial
technology, is determined by their acceptance of the benefits and the ease of use associated with
the technology.
The use of e-money in transactions can be seen as an adoption of new technology by the
public. In this case, the customer operates the technology’s application system, which requires both
hardware- and software-based devices on the payer’s side and receiving devices on the merchant’s side.
Additionally, e-money is a relatively new financial service product, especially in certain regions, such as
J. Open Innov. Technol. Mark. Complex. 2020, 6, 57 3 of 14

Indonesia. The theoretical perspectives and approaches used by previous researchers are relevant
for explaining the e-money phenomenon, but further development studies are still required [34].
The behavior of someone using or willing to use a technological device, which includes hardware and
software, is determined not only by the attributes inherent in the device system but also contextual
factors and personal factors that exist in the individual. Therefore, integrating parts of the previous
model is challenging work [20,35,36] but will be very useful theoretically and practically. Departing from
the imperfections of the approach used by previous researchers, the study in this paper integrates TPB,
TAM, and UTAUT adjusted to the e-money object. Furthermore, this study also explores the reasons
that customers use these devices. Starting from these conditions, an intriguing question raised in this
paper explores the main reasons that customers use e-money and the degree to which factors inherent
in the actors, attributes inherent in e-money application devices, external conditions that facilitate the
use of e-money, and social factors contribute to the adoption of e-money behavior in the transaction.
Previous studies related to electronic payment have not fully investigated both external and
internal factors that influence customers’ intentional behavior to adopt and continue to use various
e-money apps in a single study, approach, and relevant context. The significance of this paper is
that academic empirical research using both qualitative and quantitative approaches is a relatively
new method for studying electronic payment, and that there is a scarcity of published literature
that explores electronic payment adoption in Indonesia from the customer behavioral perspective.
Using a model based on TPB, TAM, and UTAUT, this study contributes to the research by assessing
the relevance and effects of three independent variables, namely, social factors, effort expectancy,
and facilitating conditions, on influencing the customer to use e-money in Indonesia, mediated by
the customer’s attitudes towards e-money. This study assesses the reasons that adolescent customers
use e-money as well. There are two basic questions that needed to be addressed: What are the
factors that influence adolescent customers to use e-money? How do social factors, effort expectancy,
and facilitating conditions influence the behavioral intentions of customers to adopt e-money in
Indonesia, mediated by attitudes towards e-money? This paper is systematically arranged so that the
reader can easily understand its content, starting from the introduction, which explains the necessity
of the research and the issues to be studied. In the next section, the methodology is described and
includes the approach, population, sample, data gathering technique, data analysis, and evaluation
of the goodness-of-fit of the model. In the last part, the data processing results, the output data
analysis, and discussion of results are presented. At the end of this paper, conclusions, limitations,
and recommendations are presented.

2. Methodology
In this study, quantitative and qualitative survey approaches were used to gather data from
adolescent customers with an age range of 15 to 25 years old. Primary data were obtained using
a points-based questionnaire (closed-ended) on a Likert five-point scale (Strongly Agree, Agree,
Neutral, Disagree, and Strongly Disagree), and some question items were open-ended, distributed
through a social media chat group. The respondents were selected using non-probability convenience
sampling. Respondent samples were drawn from the population of adolescent customers in Indonesia.
The collected questionnaires were verified for completeness and validity by using the imputation
technique, and respondents whose data were incomplete were excluded from the analysis stage.
After the verification, 160 cases (96.96%) were retained. The amount of data has sufficient statistical
power (0.99), which was calculated using G-Power software. The data from open-ended questions
were analyzed by using NVIVO 12 Plus software, and demographic data were analyzed by using
Jeffrey’s Amazing Statistics Program (JASP) software.
The Theory of Planned Behavior (TPB), as an extension of the Theory of Reason Action (TRA),
has been widely applied to explain the interrelationship between attitude and behavior. In the theoretical
model, the real behavior of a person is influenced by the intention of the behavior, while the intention
to behave in a certain way is influenced by one’s attitude towards subjective objects and norms. On the
J. Open Innov. Technol. Mark. Complex. 2020, 6, 57 4 of 14

other hand, the Technology Acceptance Model (TAM) was developed to explain how new technologies
and various inherent aspects are accepted and used by users. Although many models have been
proposed previously in the Information Systems field to describe relationships, this model has been
widely recognized and used. In this model, the acceptance of new technology by users is based on two
factors, namely, perceived usefulness, which refers to how much the user believes that technology
will help improve performance/efficiency, and perceived ease of use, i.e., the extent to which users
feel comfortable using technology features. These factors then determine the user’s attitude towards
the use of technology. This model goes on to say that the perceived usefulness will also influence
behavioral intentions to use. A person’s attitude will determine his or her behavior and, in turn,
affect actual acceptance. The Unified Theory of Acceptance and Use of Technology (UTAUT) theoretical
framework is widely used to predict behavioral intentions for technology adoption. The intention to use
something is predicted by performance expectations (PE), business expectations (EE), social influence
(SI), and facilitating conditions (FC). Some researchers, as mentioned in the introduction, have modified
the model. Departing from various methods in the literature and from the three approaches, the three
models were compiled or modified in this study by applying variables adjusted to the object of study,
namely, e-money. The author deliberately employs the social influence variable, which is commensurate
with the subjective norm and with the social factor terminology. Adequate data related to social factors,
effort expectancy, and facilitating conditions in e-money attitude and behavioral intention were used to
build structural models and measurements using Partial Least Square-Structural Equation Modeling
(PLS-SEM) by employing Smart-PLS 3.0 software. The significance of the correlation between variables
in the structural model (inner model) was tested by comparing the T-statistic values with the T-critical
value (2.00). If the T-statistic value was greater than or equal to 2.00, the relationship of the variable
was declared to be significant. Meanwhile, the significance of the indicators forming latent variables
was tested in the same way. If the T-statistic value was greater than or equal to the T-critical indicator,
it was deemed to be significant.

3. Construct and Indicator

The structural model illustrates the relationship between the influence of social factors,
effort expectancy, and facilitating conditions on e-money attitude and the influence of e-money
attitude on e-money intention behavior. Each of these latent variables is unobservable and measured
by valid indicators. The results of validity testing of the measurement model show that the construct
of latent variables is composed of valid indicators: the T-statistic value is greater than the critical value
(1.96), and the loading value is greater than 0.60, indicating that all the construct indicators are valid.
Indicators as a measure of each latent variable are detailed in Table 1.
J. Open Innov. Technol. Mark. Complex. 2020, 6, 57 5 of 14

Table 1. Variables and Measurement.

Operationalization and
Latent Variable
Measurement Item (Code)
The open-ended question, “What are the advantages and
E-Money Usage reason The reason that customers use the e-money payment in the transaction.
disadvantages, and why use e-money in your transaction?”
 Availability of facilities at the shop visited (FC_1)
The degree to which the customer believes that technical infrastructure  Adequate internet network (FC_2)
exists to support the adoption of the e-money payment, measured by  Smartphone owned supports (FC_3)
Facilitating Conditions [25,35,37,38] the perception of being able to access required resources, as well as to  Having the ability/knowledge about e-money (FC_4)
obtain knowledge and the necessary support to use e-money.  Skillful in using e-money (FC_5)
Assessed using closed-ended five-point-scale questions.  Financial institutions support the use (FC_6)
 Experience of failing to pay with e-money (FC_8)

 Very easy to practice or apply (EE_1)

 Procedures that must be used are easy (EE_2.)
The degree of ease associated with the use of the e-money payment
 Practically no hassle in providing money (EE_3.)
system, measured by the perceptions of the ease of use of e-money
Effort Expectancy (EE) [25,39]  Easy-to-use features (EE_4.)
services, as well as the ease of learning how to use these services.
Assessed using closed-ended five-point-scale questions.  Payment methods are very easy to learn (EE_5)
 Installing applications is easy (EE_6)
 Topping up e-money is easy (EE_7)

 Your friends recommend using e-money (SF_1)

The degree to which peers influence the use of the system,
 Your family recommends using e-money (SF_2)
whether positively or negatively, measured by the perception of how
Social Factors (SF) [23,25,38]  Your neighbor recommends using e-money (SF_3)
peers affect customers’ use of e-money payment. Assessed using
closed-ended five-point-scale questions.  Media encourage using e-money (SF_4)
 Merchants recommend e-money (SF_5)

 Very good idea (A-Att1)

Attitude is a mental or neural state of readiness, organized through
 Fun transactions (A-Att2)
experience, exerting a directive or dynamic influence on the
E-Money Attitude  Have sufficient knowledge insights (A-Att4)
individual’s response to e-money and related matters. Assessed using
closed-ended five-point-scale questions.  Transactions are more modern, sophisticated (A-Att5)
 Transaction feels high-class (A-Att6)

 Continuing to use e-money (BIH-1)

 Planning to use e-money in the future (BIH-2)
Actions to continue to use e-money, recommend it to other parties,
E-Money Intention Behavior  Getting used to using e-money (BIH-3)
and maintain features of the associated technology on devices.
[23,24,38,40]  Not reinstalling from the system (BIH-4)
Assessed using closed-ended five-point-scale questions.
 Topping up immediately if it runs out (BIH-5)
 Preferring merchants that accept e-money (BIH-6)
J. Open Innov. Technol. Mark. Complex. 2020, 6, 57 6 of 14

4. Model Evaluation and Discussion

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Figure 1. Word tree NVivo result for e-money usage by customers.
Figure 1. Word tree NVivo result for e-money usage by customers.

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4.2. Model Evaluation

Figure 2. Word cloud NVivo result for the reasons for e-money usage.

4.2. Model Evaluation

J. Open Innov. Technol. Mark. Complex. 2020, 6, 57 7 of 14

4.2. Model Evaluation

The purpose of this investigation was to test the relationship model of the influence of latent
variables (e-money behavior and e-money attitude), as assessed through social factors, effort expectancy,
and facilitating conditions. These variables were taken from several models, namely, TPB, TAM,
and UTAUT, and each factor was measured by valid indicators. The outer model is a formative
measurement of latent variable models of the first order. The measurement model needs to be assessed
for the reliability and validity of each latent variable. The validity can be assessed by using convergent
validity, which describes the level of confidence in the goodness of the measurement of each indicator.
Furthermore, the model needs to be assessed by using discriminant validity, which illustrates the
differences or discrepancies between indicators in latent variables. Convergent validity was assessed
by Average Variance Extracted (AVE) and Composite Reliability (CR). AVE measures the level of
construct variation compared with the level of measurement error. AVE values above 0.70 indicate
excellent measurements, and AVE values that can be accepted are at least 0.50. CR is a measure of
reliability whose value is lower than Cronbach’s alpha; an acceptable CR value is at least 0.70 [41].
Table 2 shows the AVE, CR, Rho-A, and Cronbach’s alpha for the latent variables (e-money attitude,
e-money behavior), effort expectancy, facilitating conditions, and social factors. All latent variables
were found to be constructively valid according to Cronbach’s alpha, Rho-A, and composite reliability
values, with values above the critical value (0.70). Likewise, with the AVE value, all latent variables were
greater than 0.50. Additionally, discriminant validity was also measured using Heterotrait–Monotrait
Ratio (HTMT) criteria. Many authors have suggested that a latent variable construct is valid if the
HTMT value is below 0.90, and some authors even recommend that it be below 0.85. A HTMT value of
1 indicates that the variable is invalid [42,43]. In Table 3, we can see the HTMT value of e-money attitude
with e-money behavior is 0.877, while others are below 0.85, and that of the facilitating conditions
variable with social factors is 0.570. If the cut-off is 0.90, it can be said that the latent construct in the
model meets the requirements.

Table 2. Indicators of latent construct validity.

Average Variance Extracted

Variables Cronbach’s Alpha Rho-A Composite Reliability
(AVE)
E-Money Attitude 0.821 0.834 0.875 0.585
E-Money Behavior 0.867 0.877 0.901 0.605
Effort Expectancy 0.912 0.915 0.930 0.656
Facilitating Conditions 0.881 0.885 0.910 0.630
Social Factors 0.835 0.843 0.876 0.542
Sources: Smart-PLS output.

Table 3. Heterotrait–Monotrait Ratio (HTMT).

E-Money E-Money Effort Facilitating

Latent Variable Social Factors
Attitude Behavior Expectancy Conditions
E-Money Attitude - - - - -
E-Money Behavior 0.877 - - -
Effort Expectancy 0.770 0.739 - - -
Facilitating Conditions 0.779 0.663 0.740 - -
Social Factors 0.704 0.646 0.567 0.570 -
Sources: Smart-PLS output.

Cross-loading was used to detect discriminant validity. An indicator has a higher correlation
with itself compared with other variables. Table 4 shows the cross-loading values. All indicators that
use a latent variable indicator in the cross-loading value model of each indicator are greater than
the latent variable itself (bolded numbers) compared with other variables (smaller and non-bolded
numbers). For example, in the first row, A-Att1 is an indicator measuring the variable e-money attitude,
J. Open Innov. Technol. Mark. Complex. 2020, 6, 57 8 of 14

and written in the second column is 0.822, which is greater than the values in the other columns
(0.589, 0.686, 0.625, and 0.465). This indicates that A-Att1 is a valid indicator as a measure of the e-money
attitude variable compared with its effectiveness as a measure of other variables. For indicators of
other variables, the value is greater than the variable itself compared with other variables. This also
indicates that the indicators measuring these latent variables are valid.

Table 4. Cross-loading latent variable indicators.

E-Money E-Money Effort Facilitating

Measurement Item Social Factors
Attitude Behavior Expectancy Conditions
A-Att1 0.822 0.589 0.686 0.625 0.465
A-Att2 0.835 0.619 0.617 0.600 0.480
A-Att4 0.756 0.577 0.466 0.496 0.529
A-Att5 0.760 0.582 0.473 0.488 0.398
A-Att6 0.635 0.474 0.309 0.318 0.485
BIH-1 0.650 0.751 0.520 0.414 0.482
BIH-2 0.616 0.834 0.560 0.554 0.444
BIH-3 0.544 0.806 0.538 0.431 0.447
BIH-4 0.548 0.755 0.452 0.414 0.448
BIH-5 0.647 0.855 0.586 0.558 0.490
BIH-6 0.438 0.646 0.406 0.321 0.361
EE_1 0.567 0.513 0.845 0.569 0.443
EE_2 0.613 0.565 0.834 0.578 0.505
EE_3 0.507 0.508 0.727 0.483 0.351
EE_4 0.510 0.498 0.810 0.524 0.475
EE_5 0.594 0.559 0.855 0.532 0.454
EE_6 0.486 0.517 0.761 0.556 0.391
EE_7 0.579 0.580 0.827 0.520 0.396
FC_1 0.486 0.445 0.478 0.707 0.402
FC_2 0.487 0.472 0.420 0.717 0.510
FC_3 0.535 0.432 0.511 0.840 0.378
FC_4 0.548 0.472 0.591 0.856 0.372
FC_5 0.594 0.513 0.587 0.869 0.437
FC_6 0.546 0.447 0.556 0.757 0.383
SF_1 0.485 0.460 0.379 0.414 0.784
SF_2 0.297 0.312 0.244 0.211 0.733
SF_3 0.283 0.316 0.194 0.208 0.705
SF_4 0.603 0.512 0.585 0.501 0.688
SF_5 0.409 0.409 0.428 0.422 0.711
SF_6 0.477 0.437 0.370 0.396 0.791
Sources: Smart-PLS output.

Table 5 shows the values of R-squared and adjusted R-squared, which describe the ability of
the social factors, facilitating conditions, and effort expectancy to explain the e-money attitude and
e-money behavior variables. For the e-money attitude, R-squared is 0.603, which means that the
e-money attitude variable is explained by the two independent variables at 60.3%, and the rest (39.7%)
is influenced by other variables. The R-squared of e-money behavior variables is 0.611, which means
that the influence of social factors and effort expectancy in e-money attitude on e-money behavior is
61.1%, while 39.9% is influenced by other variables.

Table 5. Multiple correlation endogenous variables.

Latent Variables R-Squared Adjusted R-Squared

E-Money Attitude 0.603 0.596
E-Money Behavior 0.611 0.604
Sources: Smart-PLS output.
J. Open Innov. Technol. Mark. Complex. 2020, 6, 57 9 of 14

Multicollinearity occurs when two or more independent variables in a model correlate, resulting in
redundant information and responses. Multicollinearity is measured by variance inflation factors
(VIFs) and tolerance. If the VIF value exceeds 4.0, or if it has a tolerance of less than 0.2, this indicates
that there is a multicollinearity problem in the model. Table 6 shows that the values of VIFs for the
independent variables on e-money attitude and e-money behavior are smaller than 0.4. This indicates
that the tested model is free of multicollinearity problems [41].

Table 6. Inner Variance Inflation Factor (VIF) Values.

Latent Variable E-Money Attitude E-Money Behavior

E-Money Attitude 2.233
Effort Expectancy 1.959 1.952
Social Factors 1.499 1.665
Facilitating Conditions 1.918
Sources: Smart-PLS Output.

Figures 3 and 4 show the structural and measurement models. The measurement model shows
the validity of the construct of latent variables composed of valid indicators, where the T-statistic
value is greater than the critical value (1.96), and the loading value is greater than 0.60, indicating
that all the construct indicators are valid. In the structural model (Table 7), which describes the
path of the relationship between the latent variables, the T-statistic values from 2.591 to 4.758 are
greater than T-critical (1.96) at a significance of 5%, except for the social factor pathway to e-money
behavior, for which the T-statistic is only 1.861, significant at the 10% level. The coefficients of all
paths in the inner model (original sample) range from 0.144 to 0.483, with a standard deviation from
0.078 to 0.101. The coefficient indicates the magnitude of the effect of latent variables on other latent
variables. All coefficients are positive, which means that the relationship between these variables is
unidirectional. If the independent latent variable changes, then the dependent latent variable will
increase. For example, the e-money attitude to e-money behavior path coefficient is 0.483, reflecting
the magnitude of the change that will occur if the e-money attitude changes. The interpretation of the
meaning of changes in variables depends on the measurement and scale used. Not every change can
be interpreted
J. Open quantitatively.
Innov. Technol. Mark. Complex. 2020, 6, x FOR PEER REVIEW 11 of 16

Figure 3. Structural model with weights (coefficients).

Figure 3. Structural model with weights (coefficients).

More specifically, the e-money behavioral intention domain is a construct that is measured
through the following indicators: application installation, continuity of use, plan to use in the short
term going forward, familiarization, the user’s intentions to not reinstall, immediately top up, and
recommend that others use e-money for payment transactions, as well as whether it is the main means
of payment when making transactions. The composite intention and behavior are explained by the
subjects’ attitudes towards e-money, as measured by the following indicators: good ideas, fun,
knowledgeable, modern, and top class. This means that customers intend to use and actually use e-
money in transactions because of their attitude about it. For example, customers will continue to use
and get used to e-money for payment transactions because they feel that using e-money is a good
idea, modern, not old-fashioned, and fun. This indicates that the attitude towards a particular object
is a prediction
J. Open of aMark.
Innov. Technol. person’s behavior
Complex. 2020, 6, 57related to the object, consistent with the TPB framework
10 and
of 14
the previous research that forms the basis of this paper.

Figure 4. Measurement model of latent variable (t-statistics).

Figure 4. Measurement model of latent variable (t-statistics).
Table 7. Path Coefficient Mean, Standard Deviation (STDEV), T-Values, and p-Values.
Besides being influenced by attitude, one’s behavior in using e-money is significantly influenced
Standard
by effort expectancy Original Sample T-Statistics
Pathand social factors. The two predictors are domains
Deviationcommensurate with social
p-Values
Sample (O) Mean (M) (|O/STDEV|)
(STDEV)
E-Money Attitude → E-Money Behavior 0.483 0.494 0.101 4.758 0.000
Effort Expectancy → E-Money Behavior 0.255 0.249 0.098 2.591 0.010
Social Factors → E-Money Behavior 0.144 0.140 0.078 1.861 (*) 0.064
Effort Expectancy → E-Money Attitude 0.329 0.326 0.080 4.106 0.000
Facilitating Conditions → E-Money Attitude 0.313 0.315 0.085 3.674 0.000
Social Factors → E-Money Attitude 0.274 0.276 0.078 3.525 0.001
Source: Smart-PLS Output (*). Significant at alpha 10%.

The basic framework of the model includes one or several TPB, TAM, and UTAUT domains.
The focus of the model is the integration of the three models lying in the domain that is positioned
at the far right of the model, namely, e-money behavioral intention. Within various associated study
contexts, the model has been widely applied, either portraying intention as a mediator or without
mediation. The results show that behavior towards an object is consistently predicted by attitude.
Likewise, the results of data analysis show that the intentions and behavior regarding the use of
e-money are significantly predicted by attitudes towards e-money. These results support previous
research applied in a variety of contexts. For example, in the case of online shopping, it was stated that
shopping behavior online or through the internet was influenced by a person’s attitude towards the
shopping system [44,45] in the context of mobile banking adoption [46], use of non-cash systems [27],
and adoption of smart home technology [47]. More specifically, it supports research carried out in
the context of the use of electronic payments, mobile payments, the use of e-money, and similar
topics [48,49].
More specifically, the e-money behavioral intention domain is a construct that is measured through
the following indicators: application installation, continuity of use, plan to use in the short term going
forward, familiarization, the user’s intentions to not reinstall, immediately top up, and recommend that
others use e-money for payment transactions, as well as whether it is the main means of payment when
making transactions. The composite intention and behavior are explained by the subjects’ attitudes
towards e-money, as measured by the following indicators: good ideas, fun, knowledgeable, modern,
and top class. This means that customers intend to use and actually use e-money in transactions because
of their attitude about it. For example, customers will continue to use and get used to e-money for
payment transactions because they feel that using e-money is a good idea, modern, not old-fashioned,
J. Open Innov. Technol. Mark. Complex. 2020, 6, 57 11 of 14

and fun. This indicates that the attitude towards a particular object is a prediction of a person’s behavior
related to the object, consistent with the TPB framework and the previous research that forms the basis
of this paper.
Besides being influenced by attitude, one’s behavior in using e-money is significantly influenced
by effort expectancy and social factors. The two predictors are domains commensurate with social
influences taken from the UTAUT framework [50] and subjective norms in the TPB framework [51].
This indicates that one or several domains from an established and widely applied basic framework, in
this case, TPB and UTAUT, still provide consistent results. Analysis in the context of the use of e-money
as a means of payment transactions consistently supports previous research results, i.e., that social
factors and effort expectancy are predictors of behavior [26,27,44,47,48,52]. This means that customers
will continue to use e-money, make it the first choice of payment, and keep the installed e-money
application on smart devices because of external persuasion such as shops that they visit and close
friends or family as social factors.
Analysis based on available data shows statistically significant results, indicating that the behavior
and intention to use e-money are positively influenced by the existence of outsiders who provide
useful and practical assessments of their attributes in a meaningful way. The significance of these
findings supports previous research that applied TPB and UTAUT as a whole in the context of
non-cash transaction payments [26,27,46,53]. Attitudes towards e-money are the closest explanatory
variable in the hypothesis model tested in this paper. The attitude domain is taken from the TPB
framework [51,54,55] as an explanation of intentions of behavior. As an explanation, the formation
of attitude is influenced by external factors, namely, factors external to the performer. In this
study, the attitude that is being optimized is influenced by social factors and facilitating conditions.
External factors are parties, close people, or sellers that exist externally to the customer, while facilitating
conditions are more focused on the available infrastructure that allows customers to make transactions
using e-money. This concept is taken from the UTAUT framework, whereas the TPB framework is more
directed towards behavior control. Some valid measures of these variables include the availability
of facilities at merchants, internet connection support, adequate smartphone, user skills, financial
institution support, and the possibility of payment default. The analysis shows that customer attitudes
towards e-money are significantly predicted by these two domains. That is, positive customer attitudes
related to the measures used are caused by the condition of infrastructure as a support and also by the
persuasion of social factors. The existence of internet connection, support by adequate devices, and user
skills, coupled with the encouragement of external parties such as shops, financial institutions, and the
people closest to the individuals, will make users of e-money have a positive attitude. The positive
attitude is reflected by feeling happy, feeling unworried about personal data being misused, and feeling
up to date. This finding does not contradict and instead strengthens previous studies that tried to
take part of the domain or apply the TPB and UTAUT approaches completely in various contexts,
for example, the context of payment transactions that do not use cash [50,55–58].

5. Conclusions and Recommendation

From the findings above, it can be concluded that the frameworks of the TPB, TAM, and UTAUT
models are consistent and can be extended in various contexts, specifically in explaining the social
phenomenon of the adoption of e-money as a means of payment by customers in Indonesia. Constructing
an integrated model by applying one or more domains taken from several theoretical frameworks can
be more useful for explaining recent social phenomena. Reconstruction of a model that is considered
well-established by using a domain that might apply is not the same as the basic model. For example,
in this paper, the attitude variable, without applying the intention variable before the behavior variable,
enriches the existing model.
This paper examines subjects who are mostly in the same age group in Indonesia and who have
relatively similar characteristics, so if the tested model is applied in a different population context,
it will produce different results but become more meaningful. Therefore, there is a great opportunity
J. Open Innov. Technol. Mark. Complex. 2020, 6, 57 12 of 14

for future researchers to apply this model to different social contexts. Although the sample in this study
can be considered statistically adequate, the model should be tested on a larger number of samples
so that the results are more robust. This study was conducted on samples taken from populations
using non-probability techniques, which have low generalizability, compared with using probability
sampling techniques. The next researchers should take their sample using probability techniques so that
their results have a high generalization power. As practical managerial recommendations, especially for
electronic money-based service providers, if companies want to increase the penetration of electronic
money users, they can pursue this goal by strengthening social factors through e-money education,
providing incentives for merchants, educating family and close friends as social factors, and increasing
the availability of adequate infrastructure. Furthermore, in regard to the implications of this study,
the factors that influence e-money payment adoption, such as practicality, time efficiency, and ease
of operation supported by supporting facilities from supporting stakeholders, the banking sector,
and internet service providers, can be used by the entrepreneur and open innovation stakeholders as
dimensions or indicators to measure the success of e-money payment application innovations.

Author Contributions: W.W. conceived, designed and reviewed the survey, managed the literature design and the
online questionnaire, prepared and analyzed data, and drafted the paper; I.M. verified the results of the analysis,
and reviewed the draft and final paper; N.R.S. collected the data through the online questionnaire, and validated
collected data. All authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by the Directorate of Research and Community Service, University of
Muhammadiyah Malang, Indonesia, through the Development of Scientific Work scheme, 2020 fiscal year budget.
Acknowledgments: With much appreciation, I would like to thank all those who supported me during my
journey with this research. First, I’d like to thank Allah for giving me the ability, strength, and guidance for the
successful completion of this manuscript. Thank you to the chancellor and vice-chancellor, the Director of the
Directorate of Research and Community service Dean of the faculty of economics and business, the University of
Muhammadiyah Malang, and those who helped with the research.
Conflicts of Interest: The authors declare that there was no conflict of interest.

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