Published at : 31 Oct 2023
Volume : IJtech
Vol 14, No 6 (2023)
DOI : https://doi.org/10.14716/ijtech.v14i6.6636
Caroline Fe-Yen Chen | Faculty of Applied Communication, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia |
Tak Jie Chan | Faculty of Applied Communication, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia |
Nor Hazlina Hashim | Department of Management and Marketing, Faculty of Business and Economics, Universiti Malaya, 50603, Wilayah Persekutuan Kuala Lumpur, Malaysia |
Fintech adoption has risen significantly in its use
and acceptance in Malaysia, as 84.2% out of the total population of 32.7
million in Malaysia are currently Internet users. The Fintech system has been
providing greater benefits to users more effectively and efficiently in this
fast-paced era, especially with the collaboration of three enormous e-wallet
companies (e.g., Touch’n Go, Boost, and Grab). However, numerous studies have
indicated that perceived technology security is a potential determinant that impacts
continuation intention due to the uncertainties and trust issues of using a
particular technology. Therefore, this study aims to investigate the factors
that contributed to the continuation intention of using Fintech applications
from the user’s perspective. The research uses the Unified Theory of Acceptance
and Use of Technology (UTAUT2) to guide the study by including perceived
technology security to expand the UTAUT2 theory. The study applied a
quantitative (survey) design and 366 valid fintech users were secure as the
respondents through purposive sampling. The results of the study indicated that
performance expectancy, facilitating conditions, hedonic motivation, and habit
have a positive and significant relationship with the continuance intention of
using the Fintech applications. However, social influence and perceived
technology security were not the determinants that contributed to the
continuance intention of Fintech applications. Conclusion, implications, and
future research suggestions were also discussed.
Continuation intention; Fintech applications; Perceive technology security; Unified Theory of Acceptance and Use of Technology 2; User’s perspective
Natural Financial technology (Fintech) has grown
spontaneously in recent years, leading to a fast-paced environment that allows
convenient, safe, and quick online financial services (Efimov, Koroleva, and Sukhinina 2021; Kang, 2018). According to Bommer, Rana, and Milevoj (2022),
Fintech is defined as the term used to describe any technology that delivers
financial services through software, such as online banking, mobile payment
apps, or cryptocurrency. Apart from that, Anifa et
al. (2022) mentioned that Fintech is about the latest technology
that tries to simplify the process of transactions and the use of monetary
services.
Fintech News Malaysia (2022) reported in their 2022 report that the total
population of Malaysia is 32.7 million, with a current Internet user
penetration rate of 84.2%. Consequently, in 2022, over 7.2 billion electronic
payment (e-payment) transactions were recorded in Malaysia, representing a 30%
increase compared to 2021 (Fintech News Malaysia,
2021; Fintech News Malaysia, 2022).
However, Ismail (2021) mentioned
that the continuity of e-wallet usage in Malaysia is still low and
unsatisfying. Moreover, mobile-based payment methods adoption and use are
rather slow in both developed and emerging countries (Talwar
et al., 2020). There have been various studies that focus on the
intention of users or consumers to the adoption of the Fintech system from the
Technology Acceptance Model (TAM) perspective (Ngo
and Nguyen, 2022; Phuong et al., 2022; Singh and Sharma, 2022; Candra, Nuruttarwiyah, and Hapsari, 2020). However, few of the
researchers recommended that future study to be conducted with the perspectives
of the Unified Theory of Acceptance and Use of Technology on predicting the
continuation intention of Fintech among users as the previous studies did not
cover this (Albugami and Zaheer, 2023; Bommer,
Rana, and Milevoj, 2022; Moorthy et al., 2022).
Based on the current literature, the researchers have
found out that the predicting factors that influence the continuation intention
of users toward the fintech system have become arguable, as Abbasia et al. (2022) stated that perceived
technology security has a significant relationship with the continuation
intention of users due to the uncertainties and trust issues of using a
particular system. Hence, Ghaisani, Kannan, and Basbeth (2022) found
that there is a significant relationship between perceived security and
cryptocurrency m-wallets continuation intentions. However, there are limited
existing studies that focus on perceived technology security as a determinant
in UTAUT2, which urged researchers to re-examine the study.
Besides, researchers have also found out that most of
the studies were conducted on the intention and behavior of the users (Ngo and Nguyen, 2022; Phuong et al., 2022; Singh and
Sharma, 2022; Leong,
Kwan, and Ming, 2021). However, there are
limited studies that were conducted focusing on the continuation intention of
Fintech among the users from the UTAUT perspective (Hassan
et al., 2023; Moorthy et al., 2022). Based on the discussion,
therefore, this study aims to test the predicting factors of the Unified Theory
of Acceptance and Use of Technology 2 (Performance Expectancy, Effort
Expectancy, Facilitating Conditions, Social Influence, Hedonic Motivation,
Habit) and Perceived Technology Security on the continuation intention of using
FinTech applications.
2. Literature Review
2.1. Relationship Between
Performance Expectancy and Continuation Intention
According to Gupta and
Arora (2020), performance expectancy suggests that using a specific
application can enhance productivity, and facilitate transaction activities
quickly. Additionally, various researchers have tested and found that
performance expectancy is a factor that positively influences users’ intention
to use a particular technology or service, as it can motivate individuals to
enhance their work performance and achieve their goals (Ngo
and Nguyen, 2022; Ahmad, Urus, and Nazri, 2021; Rahman, Ismail, and Bahri, 2020). Therefore, this study formulates the hypothesis:
H1: There is a positive relationship between performance expectancy and continuation intention.
2.2. Relationship Between Effort Expectancy and Continuation Intention
Past studies (Ahmad, Urus, and
Nazri, 2021; Leong, Kwan, and Ming,
2021; Winata and Tjokrosaputro, 2021) found that effort expectancy is
positively and significantly related to intention to use the Fintech system.
Furthermore, Razak, Bakar, and Abdullah (2017) findings showed that effort expectancy is a strong contributor to the
continuation intention of users. Therefore, based on the above discussion, it
formulated a hypothesis as below:
H2: There is a
positive relationship between effort expectancy and continuation intention.
2.3. Relationship Between Facilitating
Conditions and Continuation Intention
Additionally,
facilitating conditions are referred to as the system that individuals use when
they need it. Based on the existing literature, it was predicted that there is
a possibility of influence toward the continuation of Fintech among users.
Various existing studies mentioned that the relationship between facilitating
conditions and the intention of users was tested significantly (Kamarozaman and Zaidi, 2021; Ambarwati, Harja, and Thamrin, 2020). Likewise, Xie et al. (2021) stated that facilitating
conditions have a strong correlation with the adoption intention of users on
Fintech platforms, which hypothesized that:
H3: There is a positive relationship between
facilitating conditions and continuation intention.
2.4.
Relationship Between Social Influence and Continuation Intention
Besides, various results showed that
there is a significant relationship between social influence and the intention
to use Fintech (Leong,
Kwan, and Ming, 2021; Rahman, Ismail, and Bahri, 2020). Individuals positively influence their family members
and friends to use e-wallets during the pandemic (Angusamy
et al., 2023). Likewise, as supported by Chua,
Lim, and Khin (2020), it positively influences and increases public
awareness as e-wallet is compatible with user’s need and lifestyles. Therefore,
the below hypothesis is formed:
H4: There is a positive relationship
between social influence and continuation intention.
2.5. Relationship Between Hedonic
Motivation and Continuation Intention
Hedonic motivation has become one of the
factors that determine the technology acceptance of use as it is a pleasure
that the individual receives from using a particular system or technology.
Based on the existing findings of the researchers, it showed that hedonic
motivation and the intentions of the users are significantly related (Leong, Kwan, and Ming, 2021; Khatimah,
Susanto, and Abdullah, 2019). It was then tested that it was a highly
positive relationship between hedonic motivation and the intention of users on
e-wallets (Leong,
Kwan, and Ming, 2021). Therefore, the study
hypothesized that:
H5: There is a positive relationship
between hedonic motivation and continuation intention.
2.6. Relationship Between Habit and
Continuation Intention
Various past research mentioned the
positive relationship between habit and the intention of users (Chan et al., 2021; Nikolopoulou, Gialamas, and
Lavidas, 2021). Hence, it was shown by Nikolopoulou
et al. (2021) that the users’ experience and habit of using
mobile technologies in daily life have a significant relationship with the
adoption and continuous use of the system or technology. Indrawati and Putri (2018) mentioned that habit is
the most significant factor that influences the continuance Intention to use
Go-pay. Therefore, the hypothesis is formed:
H6: There is a positive relationship
between habit and continuation intention.
2.7. Relationship Between Perceived
Technology Security and Continuation Intention
Literature also showed that perceived
technology security has a significantly positive relationship with the
intention of using the Fintech system (Abbasia et
al., 2022; Ghaisani, Kannan, and Basbeth, 2022). Therefore, the
researchers predicted that perceived technology security is a determinant that
positively influences the continuation intention of Fintech. Rahman, Ismail, and Bahri (2020) stated that the
more comfortable the users felt, the faster they adopted a cashless payment
system. Therefore, this study hypothesized that:
H7: There is a positive relationship
between perceived technology security and continuation intention.
Figure
1 Proposed
conceptual framework
3.1. Research
Design
This research was conducted through a
quantitative method. Ahmad et al. (2019) highlighted
that quantitative research needs to be conducted with a structured
questionnaire and objective manner to obtain data to test for validity and
reliability.
3.2. Sampling
Technique
Researchers utilized
purposive sampling, which is selective, judgemental, or subjective sampling (Sharma, 2017), which depends on the judgment of
the researchers when it comes to specific criteria. Hence, to filter the valid
response, researchers have incorporated a screening question “Do you have
experience using the Fintech system?”. Those respondents who answered “No” will
be excluded from this study, as the study focused on continuance intention.
Additionally, since the researchers were not able to get the sampling frame for
the entire population, thus, G-power software was utilized. Therefore, the
minimum sample size with seven predictors, 0.15 effect size, and alpha (0.95)
is 153, but this study has 366 valid respondents. Hence, it is sufficient for
statistical analysis.
3.3. Measurement
A structured
questionnaire was utilized in this research, and it was divided into four
sections. Section A is the demographic questions of the respondents, such as
gender, nationality, age, education qualification, and race.
Subsequently, the
performance expectancy, facilitating conditions, social influences, hedonic
motivation, and perceived technology security instruments are adapted from the
studies of Boonsiritomachai and Pitchayadejanant
(2019). The facilitating conditions and effort expectancy items were
adapted from Venkatesh et al. (2003).
Followed by the social influences, habit and continuation intention instruments
are adapted from Venkatesh, Thong, and Xu (2012). Last but not least,
the items on perceived technology security were adapted from Salimon, Yusof, and
Mokhtar (2017).
The researcher used the 5-points Likert-type scale, which categorized with 1=
Strongly Disagree, to 5= Strongly Agree (Vagias,
2006).
3.4. Data Collection Procedures
This particular study was
conducted using an online questionnaire/survey via Google Forms. All
participant's information in this research was fully confidential and always
kept anonymous, and it will only be used for the research purpose. The data
collection was conducted from 14th December 2022 to 31st
March 2023, and a total of 390 responses were received. After filtering, there
are 366 valid responses to be used.
More
than half of the respondents are female (64.5%) and male respondents (35.5%).
The majority of the respondents are Malaysian (97.5%). Most of the respondents
are of age 20-29 years old (79.5%). This was followed by respondents of the age
below 20 years old (13.7%), which indicated that the respondents are young
adults and technologically savvy. Not to mention, Chinese respondents’
percentage is 53.3%, followed by Malay (26.8%) and Indian (15.8%).
Additionally, more than half of the respondents have a Bachelor’s degree
(65.3%), which showed that they are educated and able to make wise judgments.
4.1. Measurement Model
The convergent validity of the model is
verified by the factor loading, Composite Reliability (CR), and Average Variance
Extracted (AVE). To test the reliability and validity of the constructs, this
research utilized Cronbach’s alpha and CR. Tentama
and Anindita (2020) stated that CR needs to be higher than the
recommended value of 0.700. As shown in Table 2, all constructs have Cronbach’s
alpha values exceeding 0.700. Thus, the convergent validity is deemed
acceptable, with the AVE needing to be higher than the recommended value of
0.500 (Hair et al., 2022; Tentama &
Anindita, 2020). Hence, the criteria for the measurement model were
established.
This research assessed the discriminant validity using the Heterotrait-Monotrait Ratio of Correlations (HTMT) to check the discrimination validity of the constructs (Henseler, Ringle, and Sarstedt, 2015). According to Tian et al. (2023) and Kline (2015), the HTMT value between construct should not exceed 0.85 or 0.90. The results in Table 3 showed that all values of HTMT did not exceed the value of 0.85. Therefore, the discriminant validity was granted.
Table 2 Assessment
of measurement model
Construct |
Item |
Loadings |
Cronbach’s Alpha |
CR |
AVE |
Performance Expectancy (PE) |
PE1 |
0.829 |
0.842 |
0.894 |
0.679 |
|
PE2 |
0.858 |
|
|
|
|
PE3 |
0.789 |
|
|
|
|
PE4 |
0.818 |
|
|
|
Effort Expectancy (EE) |
EE1 |
0.807 |
0.879 |
0.917 |
0.734 |
|
EE2 |
0.893 |
|
|
|
|
EE3 |
0.885 |
|
|
|
|
EE4 |
0.838 |
|
|
|
Facilitating Conditions (FC) |
FC1 |
0.799 |
0.814 |
0.878 |
0.643 |
|
FC2 |
0.848 |
|
|
|
|
FC3 |
0.828 |
|
|
|
|
FC4 |
0.728 |
|
|
|
Social Influence (SI) |
SI1 |
0.775 |
0.866 |
0.908 |
0.713 |
|
SI2 |
0.806 |
|
|
|
|
SI3 |
0.894 |
|
|
|
|
SI4 |
0.895 |
|
|
|
Hedonic Motivation (HM) |
HM1 |
0.844 |
0.888 |
0.922 |
0.747 |
|
HM2 |
0.882 |
|
|
|
|
HM3 |
0.901 |
|
|
|
|
HM4 |
0.827 |
|
|
|
Habit (HB) |
HB1 |
0.869 |
0.858 |
0.902 |
0.697 |
|
HB2 |
0.869 |
|
|
|
|
HB3 |
0.755 |
|
|
|
|
HB4 |
0.842 |
|
|
|
Perceived Technology Security (PTS) |
PTS1 |
0.863 |
0.869 |
0.908 |
0.711 |
|
PTS2 |
0.832 |
|
|
|
|
PTS3 |
0.846 |
|
|
|
|
PTS4 |
0.831 |
|
|
|
Continuation Intention (CI) |
CI1 |
0.929 |
0.945 |
0.96 |
0.858 |
|
CI2 |
0.945 |
|
|
|
|
CI3 |
0.940 |
|
|
|
|
CI4 |
0.889 |
|
|
|
Table
3 Discriminant validity using HTMT criterion
|
CI |
EE |
FC |
HB |
HM |
PE |
PTS |
SI |
CI |
|
|
|
|
|
|
|
|
EE |
0.602 |
|
|
|
|
|
|
|
FC |
0.699 |
0.835 |
|
|
|
|
|
|
HB |
0.702 |
0.615 |
0.697 |
|
|
|
|
|
HM |
0.602 |
0.592 |
0.678 |
0.732 |
|
|
|
|
PE |
0.748 |
0.866 |
0.800 |
0.644 |
0.573 |
|
|
|
PTS |
0.397 |
0.388 |
0.489 |
0.640 |
0.684 |
0.367 |
|
|
SI |
0.410 |
0.349 |
0.569 |
0.528 |
0.669 |
0.384 |
0.648 |
|
4.2. Structural Model
Assessment
The structural model was performed using a bootstrapping procedure with a
resample of 5,000 to enhance the accuracy level of the estimation (Tian et al., 2023). The outcome of the path coefficients of the
PLS-SEM shows that the performance expectancy has a positive significant
relationship with the continuation intention of users on Fintech applications
(?1 = 0:401, t= 7.301, p < 0:05). Therefore, Hypothesis 1 is accepted.
Additionally, users’ continuation intention of Fintech is positively influenced
by effort expectancy (?2 = -0.112, t= 1.981, p < 0:05), thus maintaining
hypothesis 2. Likewise, facilitating conditions have a significantly positive
relationship with the continuation intention of the user on Fintech
applications (?3 = 0.179, t = 2.873, p < 0:05). Thus, supporting hypothesis
3. In addition, results showed that social influence has a non-significant
relationship with the continuation intention of users on Fintech applications
(?4 = -0.009, t = 0.196, p > 0:05). Therefore, hypothesis 4 was not supported.
Moreover, the habit of user’s continuation intention toward Fintech application
has a significantly positive relationship (?5 = 0.330, t = 6.395, p < 0:05).
Hence, hypothesis 5 is supported. Furthermore, users’ continuation intention of
Fintech application is significantly influenced by hedonic motivations (?6 =
0.179, t= 2.873, p < 0:05). Thus, hypothesis 6 is accepted. Likewise,
perceived technology security has a non-significant relationship with the
users’ continuation intention on Fintech applications (?7 = -0.057, t= 1.177, p
> 0:05). Therefore, hypothesis 7 was rejected.
Table 4
Hypothesis testing for direct path
Path |
Std. beta |
Std. errors |
T - value |
P |
LLCI (5%) |
ULCL (95%) |
D |
R2 |
f2 |
VIF |
H1: PE -> CI |
0.401 |
0.055 |
7.301 |
0.000** |
0.308 |
0.488 |
S |
0.590 |
0.152 |
2.575 |
H2: EE -> CI |
-0.112 |
0.057 |
1.981 |
0.024* |
-0.201 |
-0.016 |
S |
|
0.010 |
2.914 |
H3: FC -> CI |
0.179 |
0.062 |
2.873 |
0.002** |
0.077 |
0.280 |
S |
|
0.030 |
2.631 |
H4: SI -> CI |
-0.009 |
0.046 |
0.196 |
0.422 |
-0.085 |
0.068 |
NS |
|
0.000 |
1.808 |
H5: HB -> CI |
0.330 |
0.052 |
6.395 |
0.000** |
0.246 |
0.417 |
S |
|
0.152 |
2.575 |
H6: HM -> CI |
0.141 |
0.049 |
2.887 |
0.002** |
0.057 |
0.218 |
S |
|
0.020 |
2.471 |
H7: PTS -> CI |
-0.057 |
0.049 |
1.177 |
0.120 |
-0.137 |
0.024 |
NS |
|
0.004 |
1.898 |
** p-value < 0.01;
* p-value < 0.05; S= Supported; NS = Not Supported
LLCI= Lower Limit Confidence Interval;
ULCI = Upper Limit Confidence Interval
Figure 2 Path diagram of structural model
According to Shmueli et al. (2019), PLS-SEM is used to solve the apparent dichotomy between explanation and prediction. Moreso, Hair (2021) mentioned that variables can be replaced as the study continues to evolve by assessing out-of-sample prediction ability by retaining the sample. Therefore, Shmueli et al. (2019) stated that PLSpredict was performed to investigate the out-of-sample predictive power to assess the model's practical relevance. As shown in Table 5, Q² predicts that all indicators exceeded 0, and all of the RMSE in PLS-SEM analysis for users’ continuation intention on Fintech applications are more than the naïve LM value. Thus, these results showed that the model has weak predictive power.
Table 5
PLSpredict assessment
|
Q²predict |
PLS-SEM_RMSE |
LM_RMSE |
PLS-SEM-LM |
Interpretation |
CI1 |
0.517 |
0.661 |
0.669 |
-0.008 |
|
CI2 |
0.510 |
0.687 |
0.675 |
0.012 |
Weak |
CI3 |
0.441 |
0.729 |
0.702 |
0.027 |
|
CI4 |
0.466 |
0.751 |
0.740 |
0.011 |
|
4.3. Academic Implications
There is a limited study that focuses on
the continuation intention of users in UTAUT2 perspectives (Hassan et al., 2023; Moorthy et al., 2022),
which makes it rarely explored. The main contribution of this research is the
expansion of UTAUT2 by adding a new variable, which is perceived technology
security as a factor. Although the current study found it not significant, the
researcher believes this construct can be tested again in different settings,
which is strongly urged in numerous studies (Abbasia
et al., 2022; Ghaisani et al., 2022) and contributes to
information technology management scholarship.
4.4. Practical Implications
This research provides useful information and applications for the
government. Especially the Ministry of Finance Malaysia. As they mentioned, the
Malaysian government is currently collaborating with three enormous e-wallet
companies in Malaysia. Thus, the government could utilize the current trends by
motivating the older generation to adopt Fintech applications to generalize
Fintech applications in Malaysia.
Besides, there are a few implications that small and medium enterprises
(SMEs) or international companies could take into consideration on Fintech
applications. This research found that the majority of respondents agree and
are willing to use E-wallets such as Touch N Go to improve their pleasure (e.g.
Hedonic Motivation). By using discounts from the Fintech application, customers
are more willing to purchase the items that SMEs provide as it promotes the
satisfaction of users. Secondly, users' perspectives show a strong intention to
continue using Fintech in the near future due to the habit of using Fintech
applications daily. Therefore, SMEs should consider utilizing the benefits of
Fintech applications and follow the new trends by adopting Fintech applications
into their business strategy to enhance the efficiency and effectiveness of
doing transactions.
4.5. Limitations and
Suggestions for Future Research
This study has several limitations. Firstly, the questionnaire used in
this research was primarily answered by Malaysians. As a result, individuals
who are non-Malaysians but currently residing in Malaysia were not extensively
studied. Future research could consider including samples of non-Malaysians
living in Malaysia to further investigate user behavior regarding Fintech
acceptance. Such comparative studies could later help distinguish differences
in the intention to continue using Fintech applications between Malaysians and
non-Malaysians.
This study extended the UTAUT2 constructs and only focused on the
continuance intention to adopt the technology. Even with the addition of
perceived technological security, the framework was only able to explain the
variance by 59%. Thus, other variables can be incorporated in the future, such
as the design of the Fintech applications, technological self-efficacy,
technological stress, and demographic variables by testing the mediating and
moderating effects and contributing to the information technology management scholarship.
This
study provided insight into examining the influence of the Unified Theory of
Acceptance and Use of Technology 2 on the continuation intention of using
Fintech. This research finding shows that performance expectancy, effort
expectancy, facilitating conditions, hedonic motivation, and habit have a
significant and positive relationship with the continuation intentions of users
on Fintech applications. However, social influence and perceived technology
security do not have a significant relationship with the continuation intention
of the users to use Fintech applications.
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