• International Journal of Technology (IJTech)
  • Vol 14, No 6 (2023)

Factor Influencing Continuation Intention of Using Fintech from the Users’ Perspectives: Testing of Unified Theory of Acceptance and Use of Technology (UTAUT2)

Factor Influencing Continuation Intention of Using Fintech from the Users’ Perspectives: Testing of Unified Theory of Acceptance and Use of Technology (UTAUT2)

Title: Factor Influencing Continuation Intention of Using Fintech from the Users’ Perspectives: Testing of Unified Theory of Acceptance and Use of Technology (UTAUT2)
Caroline Fe-Yen Chen, Tak Jie Chan, Nor Hazlina Hashim

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Cite this article as:
Chen, C.F.-Y., Chan, T.J., Hashim, N.H., 2023. Factor Influencing Continuation Intention of Using Fintech from the Users’ Perspectives: Testing of Unified Theory of Acceptance and Use of Technology (UTAUT2). International Journal of Technology. Volume 14(6), pp. 1277-1287

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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
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Abstract
Factor Influencing Continuation Intention of Using Fintech from the Users’ Perspectives: Testing of Unified Theory of Acceptance and Use of Technology (UTAUT2)

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

Introduction

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

Experimental Methods

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.

Results and Discussion

    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. 

Conclusion

    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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