




































INDIAN JOURNAL OF FINANCE AND BANKING 13(1) (2023), 39-47 

39 

 

                       FINANCE AND BANKING 

                                                                  IJFB VOL 13 NO 1 (2023) P-ISSN 2574-6081  E-ISSN 2574-609X 
                                                  

        Available online at https://www.cribfb.com 

                                                                                                                                           Journal homepage: https://www.cribfb.com/journal/index.php/ijfb 
                                                                                                                                                                                                   Published by CRIBFB, USA 

EXAMINING CONVENIENCE, WEBSITE DESIGN & SOCIAL 

INFLUENCE AS DETERMINANTS OF USERS’ INTENTION TO USE 

FINTECH SERVICES        

          
 V Shunmugasundaram (a)   Shanu Srivastava (b)1    

 

(a) Professor, Faculty of Commerce, Banaras Hindu University, India; E-mail: sundaram@bhu.ac.in 
(b) Research Scholar, Faculty of Commerce, Banaras Hindu University, India; E-mail: shanusrivastava1234@gmail.com 
 

 
A R T I C L E I N F O 
 

 

Article History: 
 

Received: 5th November 2022 

Revised: 27th December 2022 
Accepted: 13th February 2023   

Published: 25th February 2023   

 
Keywords: 

 

Convenience, FinTech Services,  
Financial Technology, Social Influence,  

Website Design  

 

 
JEL Classification Codes: 

 

G41, O30, O31, 033  

 

  

 
A B S T R A C T 
 
An amalgamation of the financial sector with information technology has brought a tremendous 

transformation in the financial services sector that resulted in FinTech, which is an invention that makes 

it easier and more convenient for users to conduct financial transactions digitally. So, the present study 

aims to examine Convenience (C), Website Design (WB) & Social Influence (SI) as the determinants of 

users’ Intention to Use (ITU) FinTech Services. For the study, we collected data through a survey 

instrument using the hybrid mode of data collection from 257 FinTech users. Data analysis and 

hypotheses testing was done by using SmartPLS 4 software. The findings of the study concluded that 

determinants namely, Convenience (C), Website Design (WB) & Social Influence (SI) have a significant 

positive influence on users’ Intention to Use (ITU) FinTech Services. Hence, all hypotheses framed in 

the study were accepted. The outcome of this study will facilitate FinTech service providers to design 

more specialized services for their consumers. Further, it contributes to the literature concerned with 

the FinTech service sector and antecedents of ITU FinTech services.  

 
 

© 2023 by the authors. Licensee CRIBFB, USA. This article is an open-access article distributed 

under the terms and conditions of the Creative Commons Attribution (CC BY) license 

(http://creativecommons.org/licenses/by/4.0/).                           

 

                                                                      INTRODUCTION 

The internet has become a significant part of every person's life throughout this fourth Industrial Revolution (IR 4.0). Every 

aspect of human life has been impacted by innovation, technology, and improvements in ICT as they bring significant 

changes to the economy and the nation’s financial industry has undergone a radical transformation as a result of these 

innovations (Setiawan et al., 2021). The financial service sector is now concentrating on the consumer's viewpoint to 

successfully create and present cutting-edge technologies to satisfy consumers' financial needs and demands (Singh et al., 

2020). Therefore, such sudden transformation in the financial ecosystem has resulted in the development of FinTech (Singh 

et al., 2021). The acronym "FinTech," which stands for financial technology, refers to businesses or firms that integrate 

financial services with innovative & advanced technologies (Dorfleitner et al., 2017). FinTech contributes to business 

process improvement by automating its procedures and services, which improves its competitiveness and profitability 

(Dwivedi et al., 2021). And is luring customers away from traditional financial services with an improved and effective 

customer experience (Singh et al., 2020).  

After reviewing prior literature on FinTech services, its determinants & users’ intention, it was found that several 

previous pieces of research have addressed the issues regarding the analysis of various determinants that may influence 

users’ intention towards various technology-enabled services but the majority of them have focussed on the significance of 

determinants like perceived usefulness, perceived risk, trust, quality of service, image, etc. while other characteristics like 

SI, WD & C are analyzed very rarely so it needs to be examined. Further, there is a geographical research gap as well since 

very few works of literature are available relating to FinTech services usage as per our knowledge that is conducted in the 

                                                      
1Corresponding Author: ORCID ID: 0000-0002-1052-4882 
© 2023 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA.  

https://doi.org/10.46281/ijfb.v13i1.1960 

 
To cite this article: Shunmugasundaram, V., & Srivastava, S. (2023). EXAMINING CONVENIENCE, WEBSITE DESIGN & SOCIAL INFLUENCE 

AS DETERMINANTS OF USERS’ INTENTION TO USE FINTECH SERVICES. Indian Journal of Finance and Banking, 13(1), 39-47. 

https://doi.org/10.46281/ijfb.v13i1.1960 

https://orcid.org/0000-0003-0336-8073
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://doi.org/10.46281/ijfb.v13i1.1960
https://orcid.org/0000-0002-1052-4882


Shunmugasundaram & Srivastava, Indian Journal of Finance and Banking 13(1) (2023), 39-47 

 

40 

Indian state of Uttar Pradesh. So, for enhancing greater adoption of FinTech Services in India, there is a need for FinTech 

Companies & service providers to have a better understanding of their users’ regarding their intentions & perception while 

using technology-enabled services as it will facilitate them to provide user-friendly services and design proper strategies for 

attracting & retaining consumers. In this regard, the present research aims at examining the determinants namely 

Convenience (C), Website Design (WB) & Social Influence (SI), that may influence users’ intent towards FinTech Services 

in the Uttar Pradesh district of Noida, Prayagraj, Lucknow & Varanasi. 

The present research starts with the introduction of the study, followed by a review of past literature consisting of 

theoretical background, & developing a research model for examining determinants of users’ intent towards FinTech 

services. The next section describes the hypotheses & methodology used in the study, in which survey instrument 

development, data collection & descriptive statistics are explained. In the next part, the results of the study are presented in 

which the measurement model & statistical model are analyzed. Further, the next section consists of discussions & findings 

followed by the conclusion of the study. 

 

LITERATURE REVIEW 

FinTech Services 

In light of recent advancements in information technology (IT), the continuous digitization process is not only increasing 

process automation but also an important reorganization of the financial services value chain with the emergence of new 

business models & FinTech Companies (Puschmann, 2017). As per (Billore & Billore, 2020), the term FinTech refers to 

the financial technology that utilizes software and a contemporary technical ecosystem for improving, supporting, and 

automating the delivery of financial services to the huge user market. It includes organizations that merely supply technology 

(such as software solutions) to financial service providers and aims at attracting consumers by delivering products & services 

that are highly convenient for users, easy to use & more innovative than conventional services (Dorfleitner et al., 2017). As 

per (Billore & Billore, 2020), there is a significant requirement to understand the determinants that alter users’ intent 

regarding the usage & acceptance of innovative financial services. Hence, in our study, we have considered digitally 

accessed financial services namely, payment gateway, e-wallet, cryptocurrency, digital investment, crowdfunding, digital 

lending, digital trading, digital insurance, and digital banking (Singh et al., 2021).   

 

Convenience (C) 

Convenience is referred to the minimization of time as well as effort a person while utilizing a FinTech service as a cost 

(Zhang & Kim, 2020). It is the extent to which users can access & manage their financial transactions from anywhere at any 

time (Chawla & Joshi, 2018). Nasri (2011) outlined 24*7 services accessibility, a wide range of services, reduced time & 

global access as the main drivers of convenience in internet services. Shankar and Rishi (2020) found that various 

dimensions of convenience have a major impact on the adoption intention of users.  While as per the study of (Khare et al., 

2012), it was found that Indian consumers’ adoption of technology-enabled services is influenced by the Convenience factor. 

Hence, it is a significant attribute considered by consumers in terms of the advantages resulting from FinTech Services 

(Diana & Leon, 2020). So, we hypothesize that: 

 

Web Design (WB) 

Bashir and Madhavaiah (2015a) defines website design as the structure, appearance, functionality, and other elements of 

the FinTech companies’ website. While as per (Sakhaei et al., 2014), WD is the weblinks' aesthetic appeal, well-organized 

custom search features, greater accessibility, and effortless error detection and correction. It plays a significant role in online 

business, as information available on the website regarding various products & services offered by the company acts as a 

salesperson and motivates consumers to use products & services on the website (Rahi et al., 2020). Users could find it 

challenging to find the information they seek on a website with poor design (Nour, 2022). So, a website should clearly 

present its content material so that it is easy to navigate with minimal complexity (Kesharwani & Singh Bisht, 2012). 

Therefore, website design is an important determinant that may alter users’ intent. So, we hypothesize that: 

 

Social Influence (SI) 

Social Influence (SI) is the influence of recommendation or suggestion of family, friends, acquaintances, colleagues, etc. on 

a person’s decision to use FinTech services as it might be beneficial for them. Social norms have a significantly greater 

impact on disruptive technologies since it is anticipated that people consult their social circles when they encounter any new 

technology and can be persuaded by the knowledge they supply (Singh et al., 2020). 

Prior studies have shown that people are mostly influenced by social normative influences while making use of any 

product or services (Kesharwani & Singh Bisht, 2012; Bashir & Madhavaiah, 2015b; Patel & Patel, 2018; & Billore & 

Billore, 2020). But the study by Singh et al. (2020) found that there is a significant negative effect of SI on users’ ITU 

FinTech services. So, it is essential to examine the association of SI with users’ ITU FinTech services, since they change 

their behavior to fit in with others for social validation. Considering the above points, we hypothesize that: 

 

Intention to Use (ITU) 

It is the possibility that the perception & belief of a person will turn into their behavior or the arbitrary probability that the 

perception & belief of a person will turn into their behavior (Zhang & Kim, 2020). As per Alothman and Al-Meshal (2022), 

ITU is the adoption of something based on one's willingness toward a particular object. It is a significant determinant while 

assessing the potential behavior of users towards the adoption or usage of various technology-enabled services. The intention 



Shunmugasundaram & Srivastava, Indian Journal of Finance and Banking 13(1) (2023), 39-47 

 

41 

to use technology has drawn the interest of researchers, and a plethora of theories and frameworks have been suggested to 

analyze behavioral intention (Singh et al., 2021). Therefore, in our study ITU has been used as the outcome variable against 

C, WD & SI as a predictor variable. In table 1, the prior works of literature concerned with the proposed association are 

presented and figure 1 shows the proposed conceptual framework of the study. 

 

Table 1. Effects of the Proposed Association among constructs 

 
Association Prior works of literature 

C > ITU (Zhang & Kim, 2020), (Diana & Leon, 2020), (Chawla & Joshi, 2018), (Nasri, 2011), (Tanoto et al., 2021) 

WD > ITU (Rahi et al., 2020), (Kesharwani & Singh Bisht, 2012), (Bashir & Madhavaiah, 2015a), (Sakhaei et al., 2014) 

SI > ITU (Patel & Patel, 2018), (Billore & Billore, 2020), (Kesharwani & Singh Bisht, 2012), (Singh et al., 2020), (Kim et al., 2015), (Bashir 

& Madhavaiah, 2015b) 

Source: Authors’ 2023        

                   
Figure 1. Proposed Conceptual Framework 

Source: Authors’ 2023 

SI= Social Influence, C= Convenience, WD= Website Design, ITU= Intention to Use 

 

MATERIALS AND METHODS 

Survey Instrument Development 

The present study attempts to empirically examine Convenience (C), Website Design (WD) & Social Influence (SI) as 

determinants of Users’ Intention to Use (ITU) FinTech Services. To assess the users’ intention towards FinTech services 

and to test the hypotheses, we developed a survey instrument that consists of the demographic characteristics of the 

informants, and 20 items were used for assessing various constructs concerned with FinTech services adoption to propose 

association among them. We used a 5-pointer Likert scale where 1 denotes strongly disagree and 5 denotes strongly agree 

as adopted by Singh et al. (2021), Hu et al. (2019), Yee‐Loong Chong et al. (2010), and Patel and Patel (2018) to analyze 

individuals’ behavior towards technology-enabled services. ITU is the outcome variable which was measured through 4 

items while C, WD & SI were the predictor variables that were measured with 5, 4 & 7 items respectively. Table 2 presents 

the sources from which the statements of each construct were taken.  

 

Table 2. Format of survey Instrument 

 
Variable No. of Statements Sources 

C 5 (Diana & Leon, 2020), (Chawla & Joshi, 2018), 

WD 4 (Bashir & Madhavaiah, 2015a), (Rahi et al., 2020), (Alothman & Al-Meshal, 2022) 

SI 7 (Billore & Billore, 2020), (Singh et al., 2020), (Bashir & Madhavaiah, 2015b) 

ITA 4 (Davis, 1989), (Yee‐Loong Chong et al., 2010), (Alothman & Al-Meshal, 2022) 

Source: Authors’ 2023 

 

Hypotheses of the Study 

 Hypothesis 1 (H1): Convenience has a significant positive influence on users’ Intention to Use FinTech Services. 

 Hypothesis 2 (H2): Website Design has a significant positive influence on users’ Intention to Use FinTech Services. 

 Hypothesis 3 (H3): Social Influence has a significant positive influence on users’ Intention to Use FinTech Services. 

 

Data Collection and Descriptive Statistics 
Before conducting the final survey, we conducted the preliminary screening of the survey instrument among 40 informants 

and it was modified as per the feedback of the informants. The target informants of our research were individuals who 

consume FinTech services and reside in the Indian state, Uttar Pradesh districts namely, Varanasi, Prayagraj, and Lucknow 

& Noida. According to (F. Hair Jr et al., 2014), when the population size is unknown the sample size is calculated by 

multiplying the minimum number of indicators used in the study by 5, and in the present study, there were 20 indicators so 

the minimum required sample size of the study is 100 informants. Therefore, we have fulfilled the required samples by 



Shunmugasundaram & Srivastava, Indian Journal of Finance and Banking 13(1) (2023), 39-47 

 

42 

collecting responses from 257 informants through the hybrid mode of data collection. The sampling technique used in the 

study is the Convenience random sampling method. MS Excel & SmartPLS 4 (v.4.0.8.6) were used for statistical analysis. 

Table 3 depicts the demographic profile of the respondents, in which 43.2 percent of the respondents were male while 

56.8 percent were female. 18.3 percent of the respondents were below 25 years, 26.4 percent were between 25-40 years, 

30.4 percent were between 41-55 years and 24.9 percent were above 55 years of age. Educational qualifications show that 

the majority of the respondents were graduated (38.9 percent) and post-graduated (46.7 percent) while 9.3 percent had an 

intermediate degree, 1.6 percent hold a Ph.D. degree & above and 3.5 percent had any Diploma/ Professional Degree. Out 

of 257 respondents, 25.3 percent belong to Prayagraj, 22.6 percent were from Lucknow, 31.5 percent were from Varanasi 

and 20.6 percent were residing in Noida.  

 

Table 3. Demographic Characteristics of the Informants 

 
DEMOGRAPHIC CHARACTERISTICS FREQUENCY PERCENT (%) 

SEX   

Male 111 43.2 

Female 146 56.8 

Total 257 100.0 

AGE   

Below 25 years 47 18.3 

25-40 years 68 26.5 

41-55 years 78 30.4 

Above 55 years 64 24.9 

Total 257 100.0 

EDUCATION   

Intermediate 24 9.3 

Graduation 100 38.9 

Post-Graduation 120 46.7 

Ph.D. & above 4 1.6 

Any Diploma/ Professional Degree 9 3.5 

Total 257 100.0 

RESIDENCE   

Prayagraj 65 25.3 

Lucknow 58 22.6 

Varanasi 81 31.5 

Noida 53 20.6 

Total 257 100.0 

Source: Authors’ 2023 

 

Common Method Variance (CMV) 

As data for the study was self-reported for all constructs- 3 predictor variables & 1 outcome variable (Yoon, 2010), that 

were measured through a common survey instrument so there are chances of the presence of CMV, which means that their 

measured outcomes might contain variance that goes beyond their actual covariance (Malhotra et al., 2016).  We applied 

Harman’s single-factor test for addressing the concern of CMV in the study as used by prior studies (Daragmeh et al., 2021; 

H. S. Yoon & Barker Steege, 2013; & Roy et al., 2016). Under this single factor test, all the items used in the research are 

gone through EFA and it is assumed that there is the presence of CMV if a single item arises from unrotated factor solutions 

or the first item accounts for most of the variance in the constructs (Malhotra et al., 2006). As per (Podsakoff et al., 2003), 

if a single factor value is less than 50 percent of the variance, then there is less possibility of CMV. By conducting Harman’s 

test in our study, it was found that the complete variance explained by a single factor was 44.230 percent which is less than 

the suggested limit. So, there is no problem with CMV in the present data. 

 

RESULTS 

In the present study, a structural equation model was employed for testing the hypotheses. Partial Least Square method was 

applied using the SmartPLS 4 (v.4.0.8.6) software (Rahi et al., 2020) as the parameter estimation method (Daragmeh et al., 

2021). For analyzing data, a two-step process was followed whereby firstly, the suitability & efficacy of the measurement 

model was analyzed using CFA for checking reliability & validity, and further, the structural model was examined through 

SEM for ascertaining the significance of association among various constructs (Patel & Patel, 2018). 

 

Measurement Model 

Scale Reliability 

Reliability is the extent to which measurement outcomes are consistent or stable reflecting the reliability of the research 

instrument items (Hu et al., 2019). We have used Composite Reliability (CR) & Coefficient Alpha (α) for assessing the 

internal reliability of the data. As per (Fornell & Larker, 1981), constructs with CR more than 0.7 & α above 0.8 are 

considered to have good internal consistency reliability. In table 4, it can be seen that the CR & α of all latent constructs are 

more than the threshold values, which means that the present model has attained the required level of internal consistency. 

 

 



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43 

Table 4. Findings of the Measurement Model 

 
Constructs Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) 

C 0.836 0.920 0.890 0.642 

ITU 0.924 0.926 0.946 0.815 

SI 0.875 0.881 0.903 0.572 

WD 0.805 0.891 0.869 0.638 

Source: SmartPLS 4 (v.4.0.8.6) 

 

Scale Validity 

Validity refers to the extent to which the model serves the collected data (Hu et al., 2019). For assessing the validity of the 

present model, we applied the parameters of Convergent Validity (CV) & Discriminant Validity (DV) (Daragmeh et al., 

2021). CV depicts the extent of correlation among various indicators for a construct i.e., determined by the average variance 

extracted (AVE) of the latent construct (Hu et al., 2019). As suggested by Hair et al. (2019), the acceptable AVE values of 

the constructs should be more than 0.50. Table 4 shows that every construct’s AVE is above the threshold limit which 

suggests that all variables have the required CV. Moreover, we applied DV to confirm that there is no association among 

each construct and that each construct’s measures are different from each other (Daragmeh et al., 2021; & Hu et al., 2019). 

As per (Fornell & Larcker, 1981), the AVE of each variable should be larger than its correlation with the other variables. 

As depicted in table 5, every variable measure exceeds the squared inter-scale correlation in all cases, suggesting that all 

constructs are differed from each other, hence, the DV of each construct is satisfactory. We also applied the Heterotrait-

monotrait (HTMT) test for the assessment of the DV of the study. The HTMT value should be less than 0.85 as suggested 

by (Henseler et al., 2014), and in table 6, it can be seen that all construct’s HTMT values are less than the threshold range. 

So, there is no DV issue in the present study. 

 

Table 5. Fornell-Larcker criterion 

 
Constructs C ITU SI WD 

C 0.801    

ITU 0.679 0.903   

SI 0.489 0.538 0.757  

WD 0.651 0.627 0.504 0.799 

Source: SmartPLS 4 (v.4.0.8.6) 

 

Table 6. Heterotrait-monotrait ratio (HTMT) 

 
Constructs C ITU SI WD 

C     

ITU 0.731    

SI 0.551 0.593   

WD 0.705 0.670 0.564  

Source: SmartPLS 4 (v.4.0.8.6) 

 

Structural Model 

SEM is a statistical technique used to analyze the association among variables based on their covariance matrix using 

multiple regression method, path analysis & CFA (Hu et al., 2019). Once the measurement model is analyzed, then the next 

process is to study the structural model, by investigating its explanatory strength and statistical significance of the path 

(Setiawan et al., 2021b). So, firstly we reviewed the collinearity among the constructs, and collinearity problems arise when 

the VIF values are more than 5 (Hair et al., 2019). Table 7 reveals that all constructs VIF values are less than 5 which 

suggests that there are no collinearity-related issues among the constructs. Further, the coefficient of determination (R2) is 

used for assessing the structural model explanatory power. In table 8, it can be seen that our model has moderate explanatory 

strength since the R2 measure for user intention construct is 0.550 (Hair et al., 2011).  

 

Table 7. Collinearity Statistics (VIF) 

 
Constructs VIF 

C1 2.919 

C2 4.182 

C3 3.175 

C4 2.234 

C5 1.084 

ITU1 3.228 

ITU2 3.597 

ITU3 4.351 

ITU4 4.293 

SI1 1.844 

SI2 2.347 

SI3 3.609 



Shunmugasundaram & Srivastava, Indian Journal of Finance and Banking 13(1) (2023), 39-47 

 

44 

SI4 3.762 

SI5 2.090 

SI6 2.191 

SI7 2.252 

WD1 1.186 

WD2 2.106 

WD3 2.268 

WD4 2.743 

Source: SmartPLS 4 (v.4.0.8.6) 

Table 8. R-square 

 
Construct R2 R2 adjusted 

ITU 0.550 0.545 

Source: SmartPLS 4 (v.4.0.8.6) 

 

For testing the hypotheses framed in the study, bootstrapping process with 5,000 samples was applied to obtain the 

statistical significance of the path coefficients. The structural model analysis is shown in Figure 2. 

Table 10 depicts that all three hypotheses framed in the study are accepted. The findings of the present research 

revealed that Convenience (C) has a significant positive influence on users’ ITU FinTech Services since the β value is 0.411 

and the p-value is less than 0.05 (Table 9). So, H1 is accepted. Further, Social Influence (SI) has a significant positive 

influence on users’ ITU FinTech Services (β = 0.208, p < 0.05) so H2 is also supported (Table 9). Similarly, Website Design 

(WD) also has a significant positive impact on users’ ITU FinTech Services since the β value is 0.255 and the p-value is 

less than 0.05 (Table 9). So, H3 is also supported. Therefore, C, WD & SI has a significant positive influence on users’ ITU 

FinTech Services so all hypotheses are accepted.  

 

 
Figure 2. Findings of Structural Model Analysis 

Source: SmartPLS 4 (v.4.0.8.6) 

 

Table 9. Results of Hypotheses Testing 

 
H No. Association Original sample 

(O) 

Sample mean (M) Standard 

deviation 

(STDEV) 

T statistics 

(|O/STDEV|) 

P values 

H1 C -> ITU 0.411 0.407 0.092 4.457 0.000 

H2 SI -> ITU 0.208 0.209 0.062 3.362 0.001 

H3 WD -> ITU 0.255 0.256 0.085 2.982 0.003 

Source: SmartPLS 4 (v.4.0.8.6) 

 

Table 10. Acceptance/ Rejection of Hypotheses 

 
Hypotheses no. Hypotheses Statement Acceptance/ Rejection 

H1 Convenience has a significant positive influence on users’ Intention to Use FinTech Services. Accepted 

H2 Website Design has a significant positive influence on users’ Intention to Use FinTech Services. Accepted 

H3 Social Influence has a significant positive influence on users’ Intention to Use FinTech 
Services. 

Accepted 

Source: Authors’ 2023 

 

DISCUSSIONS 

FinTech is becoming more and more popular with the development of new & innovative technologies, causing disruptive 

changes in the financial service sector, & opening up new prospects for telecom and retail businesses but still the long-term 

success of FinTech services is significantly affected by user intent towards present FinTech services (Singh et al., 2021). 



Shunmugasundaram & Srivastava, Indian Journal of Finance and Banking 13(1) (2023), 39-47 

 

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Therefore, the goal of the present study is to empirically examine the C, WD & SI as the determinants of users’ ITU FinTech 

Services in certain districts of Uttar Pradesh, India namely, Prayagraj, Lucknow, Noida & Varanasi. Results of our study 

revealed that Convenience (C), Website Design (WB) & Social Influence (SI) are the significant factors that positively 

influence users’ intent towards the usage of FinTech Services.  

It was found that Convenience has a significant positive influence on users’ intentions while using FinTech Services 

which means that our H1 is accepted, and results of (Zhang & Kim, 2020; Nasri, 2011; Diana & Leon, 2020; & Chawla & 

Joshi, 2018), validates our findings. So, it is suggested that FinTech services should be designed and delivered in such a 

way that they are more flexible & easier to use in comparison with conventional services to be more competitive in the 

market. And FinTech service providers need to advertise convenience factors to motivate consumers to use FinTech services.  

Consistent with the results of (Rahi et al., 2020), (Alothman & Al-Meshal, 2022) & (Kesharwani & Singh Bisht, 2012), our 

study also found that Website Design significantly affects users’ intent to use FinTech Services which means that our H2 is 

accepted. FinTech websites are a crucial initial point from where people can interact with and gain access to fintech services 

so, these websites' usability and affordability are essential components in bringing FinTech services to a wider range of 

audiences (Nour, 2022). Also, it plays a significant role in turning visitors into consumers (Alothman & Al-Meshal, 2022). 

Therefore, we conclude that FinTech companies & service providers with user-friendly & well-planned website interfaces 

will be able to gather and retain more users. So, it is suggested that website characteristics like simplicity to navigate, risk 

alert flash, clear guidelines & reduced chance of errors will possibly increase users' intention to use FinTech services (Bashir 

& Madhavaiah, 2015a). 

Similar to the findings reported by Billore and Billore (2020), Bashir and Madhavaiah (2015b) and Patel and Patel 

(2018), our study also concluded that Social Influence has a significant positive influence on users’ intentions while using 

FinTech Services, which means that our H3 is accepted. Therefore, it can be said that various reference groups like family, 

friends, colleagues, etc. motivate informants to modify their attitudes & beliefs toward FinTech Services. So, it’s suggested 

that FinTech services providers need to train their consumers to encourage their relatives & acquaintances to adopt FinTech 

Services. And they must render services that are effective & efficient, as each negative perception of the reference group 

can affect the adoption of FinTech services by potential users, who are influenced by them.  

The outcome of the research enhances the understanding regarding determinants of users’ intention to use FinTech 

services bringing forward the forthcoming field of research & practical grasp for latent consumers of FinTech services. 

Moreover, our findings offer several critical insights for investigating the determinants of users’ intention to utilize FinTech 

services. 

In brief, the outcome of our research proposes some points which may be advantageous for various associated 

parties to amplify the users’ intent towards usage of FinTech Services. Firstly, FinTech service providers & regulatory 

bodies can adopt our results as a blueprint for enhancing users’ adoption of FinTech Services. Moreover, FinTech service 

providers should build & deliver users with financial services that are convenient, user-friendly, & have appealing website 

designs capable of attracting users as well as being easy to use. Also, the outcome of this research can be utilized to support 

the results of future studies concerned with FinTech service usage. 

 

CONCLUSIONS 

The Fintech service sector is still in its embryonic stage in India but it is gradually attaining growth & development. It is 

because people are reluctant to adopt despite being aware of its various advantages as they prefer offline financial services 

and find Fintech Services to be complicated & inconvenient.  However, there is a huge opportunity for FinTech companies 

as they can attract and retain consumers by enhancing efficiency and convenience in customer service and thereby fostering 

loyalty. In this context, the present study aims to empirically examine the determinants that may influence users’ intention 

to use FinTech Services. For examining the FinTech services usage, Convenience (C), Website Design (WD) & Social 

Influence (SI) were identified as significant factors based on the literature review that may influence users’ intention towards 

FinTech services. It was found that all constructs have a strong positive influence on users’ intention to use FinTech 

Services. The results of the study contribute theoretically to the field of FinTech services & technology-enabled services 

usage literature by investigating Convenience (C), Website Design (WD) & Social Influence (SI) as determinants of users’ 

intention towards usage of FinTech services. 

The research model used in the study can be applied in future studies conducted in other developing nations to 

examine factors that affect users’ intention to use FinTech services. Further, other variables derived from various models 

like TAM, UTAUT, TRA, etc. can be added to the present model for assessing the users’ intent toward FinTech services. 

Also, future studies can be conducted focussing on any specific type of FinTech services for instance, on Digital payment 

systems, Peer to Peer Lending, blockchain & cryptocurrency, etc. Moreover, future studies can also research larger sample 

sizes and may add various demographic characteristics as moderators to assess their effect. 

 

Author Contributions: Conceptualization, V.S. and S.S.; Methodology, S.S.; Software, S.S.; Validation, V.S. and S.S.; Formal Analysis, S.S.; 
Investigation, V.S.; Resources, S.S.; Data Curation, S.S.; Writing – Original Draft Preparation, S.S.; Writing – Review & Editing, V.S.; Visualization, 

V.S.; Supervision, V.S.; Project Administration, V.S.; Funding Acquisition, V.S. and S.S. Authors have read and agreed to the published version of the 

manuscript. 



Shunmugasundaram & Srivastava, Indian Journal of Finance and Banking 13(1) (2023), 39-47 

 

46 

Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable groups 

or sensitive issues. 

Funding: The authors received no direct funding for this research. 
Acknowledgments: Not applicable. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 
due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest. 

 

REFERENCES 

Alothman, A. I., & Al-Meshal, S. A. (2022). The Impact of Website Design and Customer Support on Customer Experience 

and Its Relation to Fintech Adoption Intention in Saudi Arabia. International Journal of Marketing Studies, 14(1), 

126. https://doi.org/10.5539/ijms.v14n1p126 

Bashir, I., & Madhavaiah, C. (2015a). Consumer attitude and behavioral intention towards Internet banking adoption in 

India. Journal of Indian Business Research, 7(1), 67–102. https://doi.org/10.1108/JIBR-02-2014-0013 

Bashir, I., & Madhavaiah, C. (2015b). Consumer attitude and behavioral intention towards Internet banking adoption in 

India. Journal of Indian Business Research, 7(1), 67–102. https://doi.org/10.1108/jibr-02-2014-0013 

Billore, S., & Billore, G. (2020). Consumption switch at haste: insights from Indian low-income customers for adopting 

Fintech services due to the pandemic. Transnational Marketing Journal, 8(2), 197–

218. https://doi.org/10.33182/tmj.v8i2.1064 

Chawla, D., & Joshi, H. (2018). The Moderating Effect of Demographic Variables on Mobile Banking Adoption: An 

Empirical Investigation. Global Business Review, 19(3_suppl), S90–

S113. https://doi.org/10.1177/0972150918757883 

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–

334. https://doi.org/10.1007/bf02310555 

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS 

Quarterly, 13(3), 319. https://doi.org/10.2307/249008 

Diana, N., & Leon, F. M. (2020). Factors Affecting Continuance Intention of FinTech Payment among Millennials in 

Jakarta. European Journal of Business and Management 

Research, 5(4). https://doi.org/10.24018/ejbmr.2020.5.4.444 

Dorfleitner, G., Hornuf, L., Schmitt, M., & Weber, M. (2017). Definition of FinTech and Description of the FinTech 

Industry. FinTech in Germany, 5–10. https://doi.org/10.1007/978-3-319-54666-7_2 

Dwivedi, P., Alabdooli, J. I., & Dwivedi, R. (2021). Role of FinTech Adoption for Competitiveness and Performance of 

the Bank: A Study of Banking Industry in UAE. International Journal of Global Business and 

Competitiveness, 16, 130–138. https://doi.org/10.1007/s42943-021-00033-9 

F., Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling 

(PLS-SEM). European Business Review, 26(2), 106–121. https://doi.org/10.1108/ebr-10-2013-0128 

Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: 

Algebra and Statistics. Journal of Marketing Research, 18(3), 382–388. 

https://doi.org/10.1177/002224378101800313 

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and 

Practice, 19(2), 139–152. https://doi.org/10.2753/mtp1069-6679190202 

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-

SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/ebr-11-2018-0203 

Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based 

structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–

135. https://doi.org/10.1007/s11747-014-0403-8 

Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption Intention of Fintech Services for Bank Users: An Empirical 

Examination with an Extended Technology Acceptance Model. Symmetry, 11(3), 340. 

https://doi.org/10.3390/sym11030340 

Kesharwani, A., & Singh Bisht, S. (2012). The impact of trust and perceived risk on internet banking adoption in 

India. International Journal of Bank Marketing, 30(4), 303–322. https://doi.org/10.1108/02652321211236923 

Khare, A., Mishra, A., & Singh, A. B. (2012). Indian customers’ attitude towards trust and convenience dimensions of 

internet banking. International Journal of Services and Operations Management, 11(1), 107. 

https://doi.org/10.1504/ijsom.2012.044802 

Kim, Y., Park, Y. J., Choi, J., & Yeon, J. (2015). An Empirical Study on the Adoption of “Fintech” Service: Focused on 

Mobile Payment Services. Advanced Science and Technology Letters. https://doi.org/10.14257/astl.2015.114.26 

Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common Method Variance in IS Research: A Comparison of Alternative 

Approaches and a Reanalysis of Past Research. Management Science, 52(12), 1865–

1883. https://doi.org/10.1287/mnsc.1060.0597 

Malhotra, N. K., Schaller, T. K., & Patil, A. (2016). Common Method Variance in Advertising Research: When to Be 

Concerned and How to Control for It. Journal of Advertising, 46(1), 193–

212. https://doi.org/10.1080/00913367.2016.1252287 

Nasri, W. (2011). Factors Influencing the Adoption of Internet Banking in Tunisia. International Journal of Business and 

Management, 6(8). https://doi.org/10.5539/ijbm.v6n8p143 

https://doi.org/10.5539/ijms.v14n1p126
https://doi.org/10.1108/JIBR-02-2014-0013
https://doi.org/10.1108/jibr-02-2014-0013
https://doi.org/10.33182/tmj.v8i2.1064
https://doi.org/10.1177/0972150918757883
https://doi.org/10.1007/bf02310555
https://doi.org/10.2307/249008
https://doi.org/10.24018/ejbmr.2020.5.4.444
https://doi.org/10.1007/978-3-319-54666-7_2
https://doi.org/10.1007/s42943-021-00033-9
https://doi.org/10.1108/ebr-10-2013-0128
https://doi.org/10.1177/002224378101800313
https://doi.org/10.2753/mtp1069-6679190202
https://doi.org/10.1108/ebr-11-2018-0203
https://doi.org/10.1007/s11747-014-0403-8
https://doi.org/10.3390/sym11030340
https://doi.org/10.1108/02652321211236923
https://doi.org/10.1504/ijsom.2012.044802
https://doi.org/10.14257/astl.2015.114.26
https://doi.org/10.1287/mnsc.1060.0597
https://doi.org/10.1080/00913367.2016.1252287
https://doi.org/10.5539/ijbm.v6n8p143


Shunmugasundaram & Srivastava, Indian Journal of Finance and Banking 13(1) (2023), 39-47 

 

47 

Nour, R. (2022). An Assessment of Accessibility and Usability of Saudi Online FinTech Services for People with 

Disabilities. Computational and Mathematical Methods in Medicine, 2022, 1–

9. https://doi.org/10.1155/2022/8610844 

Patel, K. J., & Patel, H. J. (2018). Adoption of internet banking services in Gujarat. International Journal of Bank 

Marketing, 36(1), 147–169. https://doi.org/10.1108/ijbm-08-2016-0104 

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: 

A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–

903. https://doi.org/10.1037/0021-9010.88.5.879 

Puschmann, T. (2017). Fintech. Business &Amp; Information Systems Engineering, 59(1), 69–

76. https://doi.org/10.1007/s12599-017-0464-6 

Rahi, S., Ghani, M. A., & Ngah, A. H. (2020). Factors propelling the adoption of internet banking: the role of e-customer 

service, website design, brand image, and customer satisfaction. International Journal of Business Information 

Systems, 33(4), 549. https://doi.org/10.1504/ijbis.2020.105870 

Roy, S. K., Balaji, M., Kesharwani, A., & Sekhon, H. (2016). Predicting Internet banking adoption in India: a perceived 

risk perspective. Journal of Strategic Marketing, 25(5–6), 418–

438. https://doi.org/10.1080/0965254x.2016.1148771 

Sakhaei, S. F., Afshari, A. J., & Esmaili, E. (2014). The Impact Of Service Quality On Customer Satisfaction In Internet 

Banking. Journal of Mathematics and Computer Science, 09(01), 33–40. https://doi.org/10.22436/jmcs.09.01.04 

Setiawan, B., Nugraha, D. P., Irawan, A., Nathan, R. J., & Zoltan, Z. (2021b). User Innovativeness and Fintech Adoption 

in Indonesia. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 

188. https://doi.org/10.3390/joitmc7030188 

Shankar, A., & Rishi, B. (2020). Convenience Matter in Mobile Banking Adoption Intention? Australasian Marketing 

Journal, 28(4), 273–285. https://doi.org/10.1016/j.ausmj.2020.06.008 

Singh, S., Sahni, M. M., & Kovid, R. K. (2020). What drives FinTech adoption? A multi-method evaluation using an 

adapted technology acceptance model. Management Decision, 58(8), 1675–1697. https://doi.org/10.1108/md-09-

2019-1318 

Singh, S., Sahni, M. M., & Kovid, R. K. (2021). Exploring trust and responsiveness as antecedents for intention to use 

FinTech services. International Journal of Economics and Business Research, 21(2), 254. 

https://doi.org/10.1504/ijebr.2021.113152 

Tanoto, N., Monica, I., Grasela, & Rahmi, N. U. (2021). The Influence of Convenience, Benefits, Security and a Trust on 

the Interest in Using Financial Technology in OVO Applications as a Digital Payment. Journal of Economics, 

Finance and Management Studies, 04(10), 1829–1834. https://doi.org/10.47191/jefms/v4-i10-03 

Yee‐Loong Chong, A., Ooi, K., Lin, B., & Tan, B. (2010). Online banking adoption: an empirical analysis. International 

Journal of Bank Marketing, 28(4), 267–287. https://doi.org/10.1108/02652321011054963 

Yoon, C. (2010). Antecedents of customer satisfaction with online banking in China: The effects of experience. Computers 

in Human Behavior, 26(6), 1296–1304. https://doi.org/10.1016/j.chb.2010.04.001 

Yoon, H. S., & Barker Steege, L. M. (2013). Development of a quantitative model of the impact of customers’ personality 

and perceptions on Internet banking use. Computers in Human Behavior, 29(3), 1133–

1141. https://doi.org/10.1016/j.chb.2012.10.005 

Zhang, L.-L., & Kim, H.-K. (2020). The Influence of Financial Service Characteristics on Use Intention through Customer 

Satisfaction with Mobile Fintech. Journal of System and Management Sciences, 10(2), 82–

94. https://doi.org/10.33168/jsms.2020.0206 

 

 

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https://doi.org/10.1007/s12599-017-0464-6
https://doi.org/10.1504/ijbis.2020.105870
https://doi.org/10.1080/0965254x.2016.1148771
https://doi.org/10.22436/jmcs.09.01.04
https://doi.org/10.3390/joitmc7030188
https://doi.org/10.1108/md-09-2019-1318
https://doi.org/10.1108/md-09-2019-1318
https://doi.org/10.1504/ijebr.2021.113152
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https://doi.org/10.33168/jsms.2020.0206
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