By applying Cronbach’s Alpha test, EFA, OLS model and other econometrics tests, the dissertation has pointed out that the impacts of Fintech on all five dimensions of SERVQUAL positively
LITERATURE REVIEW
Theoretical framework
Fintech, short for financial technology, refers to the integration of modern technology into the financial sector Initially, in the early 21st century, it pertained solely to the technology employed by financial institutions for their back-end operations However, with advancements in the Internet and digitalization, Fintech has expanded to encompass a wide array of innovations across various sectors, including retail banking, finance, investment management, and cryptocurrencies.
Fintech is defined variably across different sources, with Schueffel (2016) describing it as a financial industry that leverages modern technology to enhance financial activities Leong and Sung (2018) emphasize that Fintech encompasses innovative ideas aimed at improving financial service processes through technological solutions According to PwC (2016), Fintech represents the convergence of finance and technology, enabling both new and established firms to offer financial services at reduced costs compared to traditional methods, while also fostering the creation of novel solutions through technology.
Fintech encompasses the use of technology to innovate and enhance financial services for customers, aiming to improve their quality compared to traditional offerings This sector leverages technology to boost efficiency and convenience while maintaining affordability A diverse range of entities, from startups to established financial institutions, participate in the Fintech landscape, highlighting its growing significance and trend within the finance industry.
Generally, the development of Fintech is divided into three main eras: a Fintech 1.0 (1866 - 1967)
The evolution of Fintech began in 1866 with the installation of the first transatlantic cable, marking the start of financial globalization In 1918, the introduction of Fedwire, the first electronic fund transfer system using telegraph and Morse code, revolutionized transactions The public introduction of credit cards by Diners Club in 1950 further transformed the financial landscape Additionally, the invention of the first automated teller machine (ATM) by Barclays in 1967 represented a significant technological advancement These pivotal developments laid the groundwork for what is now known as Fintech 1.0, setting the stage for the modern Fintech era.
Fintech 2.0 marked a pivotal shift from analogue to digital financial services, highlighted by the establishment of SWIFT in 1973, which enabled global cross-border transactions This era also saw the founding of NASDAQ in 1971, transitioning securities trading from physical to electronic platforms Additionally, the rise of technology in financial operations during this period spurred Fintech innovations, exemplified by Michael Bloomberg's Innovative Market Solution (IMS) launched in 1981, which introduced the Bloomberg terminal, offering financial professionals real-time market data and analytics.
The most significant milestone in the development of the Fintech industry occurred in 1995 with Wells Fargo's introduction of the first internet banking protocols via the World Wide Web This innovation laid the groundwork for online financial services, offering customers their inaugural internet banking experience As a result, individuals gained the ability to conduct financial transactions and monitor account activities from anywhere at any time, eliminating the need for physical bank visits This convenience has significantly increased the popularity and necessity of online financial services in today's banking landscape.
The 2008 financial crisis significantly impacted the global economy, marking a pivotal shift in the Fintech industry from 2.0 to 3.0 According to Arner et al (2015), this transition was driven by two key factors: a declining public perception of traditional banking due to the crisis's roots in the financial sector, necessitating innovation, and the loss of approximately 8.7 million jobs in the U.S., particularly among financial professionals This influx of highly-skilled, unemployed workers, along with fresh graduates struggling to enter the job market, provided a valuable talent pool for the emerging Fintech sector, propelling its growth into a new era.
The third period of Fintech evolution has seen significant innovations that have transformed the global financial industry The establishment of Bitcoin in January 2009 marked the beginning of the cryptocurrency era, making it the most widely used and sought-after digital currency This paved the way for other cryptocurrencies like Ethereum and Tether Additionally, the capital market underwent changes with the introduction of Basel 3, which heightened capital requirements and redirected funding towards SMEs and individuals, leading to the rise of P2P lending to meet their credit needs Moreover, the expansion of the Internet and e-commerce has fueled the popularity of digital payments, enabling consumers to conduct online transactions effortlessly Innovations such as near-field communication, payment gateways, and QR code transfers have further simplified and enhanced the payment process for customers.
Figure 1.1 Number of Fintechs worldwide from 2018 to 2024, by region (Statista,
Fintech 3.0 is also considered the era of successful startups Major developments in Fintech 3.0 have proven that this industry has high potential, hence promoting the emergence of thousands of Fintech companies every year It can be seen from figure 1.1 that the number of Fintech firms has increased by nearly 2.5 times from 12,131 in
From 2018 to 2024, the number of Fintech companies is projected to rise to 29,955, with the Americas leading in this growth While Fintech initially flourished in Western nations, it has seen significant expansion in Asia recently As of February 2024, Asia boasts 82 Fintech unicorns, with India at the forefront, contributing 22 of these companies The rapid advancements in modern technology suggest that this trend will continue to accelerate.
2 Fintech unicorns are private Fintech companies with market valuation from 1
Americas Europe, Middle East, Africa (EMEA) Asia-Pacific (APAC)
The Fintech industry is poised for significant growth in the future, building on over a century of evolution With endless potential ahead, the development of Fintech is set to advance beyond its current phase, promising innovative solutions and transformative impacts on the financial landscape.
Fintech provides a wide range of services, which can be categorized into two groups: the supporting group and the business group (Nguyen, 2020, p.19-22)
The supporting group encompasses essential tools that establish a foundation for Fintech business activities, prominently featuring blockchain technology This secure, distributed ledger system efficiently stores and transmits information, particularly in the payment sector, where it streamlines complex processes and reduces transaction costs Additionally, products like cybersecurity management tools, data analysis software, and customer identification services play a crucial role in simplifying financial service delivery For instance, eKYC technology enables financial institutions to verify customer identities online, expediting the onboarding process while minimizing costs and manual effort.
The business group consists of financial services that are technology-applied and generate revenue: payment, capital mobilization, lending and investment management a Payment:
Fintech provides innovative solutions that process real-time transaction fast and securely, such as money transferring, e-wallets or NFC payment b Capital mobilization:
Fintech platforms provide various financing options, including reward-based fundraising and cryptocurrency issuance, with P2P lending and crowdfunding emerging as the most successful services.
P2P lending is an online financing model that connects individuals and businesses in need of loans directly with lenders P2P platforms evaluate borrowing purposes, risks, and interest rates to match borrowers with suitable investors, allowing lenders to assess borrowers' creditworthiness based on their income and credit history This funding method is increasingly popular due to its convenience and efficiency, significantly reducing the time and effort required compared to traditional lending It caters to various borrowing needs, including home improvement, medical expenses, business funding, and tuition fees, with different borrowing limits and rates of return Leading P2P lending platforms include Lending Club in America, Funding Circle in the United Kingdom, and Faircent in India As of 2022, the global P2P lending market was valued at 147.05 billion USD, with projections to reach 1506.24 billion USD by 2031, according to Skyquest (2024).
Crowdfunding is a method of raising funds for projects or businesses through online platforms, where borrowers showcase their proposals and investors contribute based on perceived potential This model allows numerous individuals to invest small amounts, collectively enabling projects to come to fruition According to Fortune Business Insight (2024), the global crowdfunding market was valued at 1.41 billion USD in 2023 and is projected to grow to 4.50 billion USD by 2032.
Fintech provides two main services to give users advices and help them supervise their investment, which are robo-advisor and social trading
Empirical researches
1.2.1 International researches about impacts of Fintech on customer satisfaction
The rapid advancement of Fintech has led to numerous studies exploring its impact on financial services, covering a variety of topics While direct research on the effects of Fintech on the quality of these services is limited, several international studies have focused on how Fintech influences customer satisfaction For example, research by Choudhary et al highlights these dynamics.
A 2023 survey of 100 respondents in India revealed that Fintech significantly enhances banking activities by offering advantages such as speed, transparency, time-saving, and resource optimization, which in turn boosts customer trust and satisfaction Similarly, Hutapea (2020) found that Fintech information systems positively impacted customer satisfaction in Indonesian coffee shops run by young entrepreneurs, as they streamlined business processes like transaction recording and bank deposits Additionally, Alwi et al (2019) identified five key variables influencing Fintech adoption: ease of use, security and privacy, information presentation, convenience, and overall user experience.
(5) service quality, and found out that all of them have a positive relationship with customer satisfaction in Malaysia
Research consistently shows that Fintech positively influences customer satisfaction Higher service quality typically leads to increased customer satisfaction, especially when it meets or exceeds expectations Therefore, it is reasonable to conclude that Fintech enhances service quality, contributing to improved customer experiences.
In Vietnam, Fintech research is primarily categorized into three key areas: the first focuses on identifying factors that influence customer adoption of Fintech services; the second examines the impact of Fintech on financial inclusion; and the third assesses how Fintech affects banking activities and the financial services offered by banks.
Recent studies by Dao et al (2018) and Nguyen (2020) focus on the growing Fintech landscape in Vietnam, particularly in major cities like Hanoi and Ho Chi Minh City, where most Fintech companies are based Dao et al identified six key factors influencing customers' intention to use Fintech for payment: safety and confidentiality, usefulness, user attitude, user initiative, ease of use, and service convenience, which collectively accounted for 60% of the decision-making process The study emphasized that safety and confidentiality are the most critical determinants, highlighting Fintech's role in data protection and minimizing financial loss during issues Recommendations for enhancing customer acceptance included investing in technological infrastructure and simplifying software interfaces for better usability.
In 2020, a study employed confirmatory factor analysis (CFA) and structural equation modeling (SEM) to identify seven key factors influencing customers' continued use of Fintech payment services, narrowing down from an initial set of 14 variables The primary factors identified included perceived usefulness and perceived ease of use, which play crucial roles in shaping user decisions.
The study identified seven key factors influencing customers' adoption of Fintech: social influence, perceived minimal needs, perceived excitement, security concerns, and personal knowledge, with perceived usefulness having the strongest positive impact However, the research faced limitations due to its reliance on convenience sampling and a small sample size of 251 university students, which may not be representative of the broader population Despite similarities in findings regarding ease of use and usefulness in enhancing financial services, both studies exhibited drawbacks that could be addressed to improve the accuracy of future results.
Fintech plays a crucial role in enhancing financial inclusion by providing individuals and businesses with access to essential and affordable financial services, as defined by the World Bank (2022) According to Mai and Nguyen (2022), Fintech positively impacts financial inclusion by enabling low-income residents to access basic financial services, particularly in rural areas, and offering user-friendly platforms for transactions like bill payments and money transfers This ease of use encourages users to recommend these services, further expanding access Bui (2021) highlights that Fintech reduces transaction costs and collateral requirements for low-income households and SMEs, significantly improving customer experiences Nguyen et al (2023) emphasize that mobile money's perceived usefulness and ease of use foster greater financial inclusion as users become more engaged with various financial services However, challenges remain, such as the potential for financial service providers to prioritize higher-income clients for profit maximization (Mai and Nguyen, 2022), and risks like system errors and fraud that may deter users from engaging with financial services (Nguyen et al., 2023).
Fintech significantly transforms banking activities by introducing innovative services such as QR code payments and digital banking, as noted by Le (2018) This evolution leads to a diverse portfolio of 24/7 financial services; however, it also exposes banks to cybersecurity risks, including data theft and malicious code The rapid advancement of Fintech outpaces existing legal frameworks, contributing to financial fraud Additionally, Le (2023) highlights that technologies like AI and IoT have revolutionized banking operations by improving market trend predictions and user behavior analysis Despite these advancements, banks face challenges in acquiring skilled personnel and must continuously update their systems to meet customer demands and enhance competitiveness While both studies offer valuable insights, they rely solely on secondary data, lacking comprehensive user opinions.
Research gap and research question
Fintech has significantly transformed the financial sector in Vietnam and globally, enhancing customer engagement through its ease of use, safety, and convenience While these advancements suggest a positive impact on the quality of financial services, it is essential to acknowledge the potential downsides that still need to be addressed.
Current research on the impact of Fintech in Vietnam lacks a focused examination of service quality, with most studies addressing broader Fintech effects and often relying solely on qualitative methods, which may not yield comprehensive insights Given the significant transformations in the financial sector post-pandemic, both globally and within Vietnam, there is a pressing need for updated research This study aims to utilize a quantitative approach through the SERVQUAL model to statistically analyze the influence of Fintech on the quality of financial services.
Vietnam, from which recommendations would be provided to enhance the advantages of Fintech towards financial services in this country
This study aims to answer two research questions:
- Does the impact of Fintech on each dimension of the SERVQUAL model have a positive or negative influence on the quality of financial services in Vietnam?
- What are the recommendations to improve the quality of financial services in Vietnam through the application of Fintech?
Research framework
This study investigates the influence of Fintech on the SERVQUAL model dimensions and its subsequent effect on financial service quality It is anticipated that a positive impact of Fintech on any dimension will enhance overall service quality Additionally, the evaluation of financial service quality within the Fintech context can be further clarified through the criteria outlined in Table 1.2.
Factor Description Fintech-related criteria
Customers’ level of trust that Fintech service would be delivered correctly as promised
- Smoothness and transparency of financial services
Customers’ satisfaction with the ability of Fintech service providers to meet their demands
- Offer prompt customer services and service improvements
Customers’ assessment on the ability of Fintech service providers to inspire trust and confidence
Customers’ assessment on physical factors and appearance of financial services
Customers’ assessment on the attention and caring of financial service providers for them
- Understanding of customers’ needs and preferences
The level of user satisfaction when they experience financial services
DATA AND METHODOLOGY
Data
The data in this research was collected using the mixed methods approach, which includes both primary and secondary data
The author conducted a survey using a questionnaire to evaluate the impact of Fintech on the quality of financial services in Vietnam The survey comprises 15 primary questions and 2 additional questions, organized into three distinct sections, including users' information.
This section aims to identify the participants of the survey based on 3 criteria: (1) age, (2) educational level and (3) average monthly income b Users’ behavior of using Fintech
To determine users’ behavior using Fintech, respondents were required to answer
- Types of Fintech services that users are currently using
- Sources of information through which users are introduced to Fintech services
- Users’ monthly expenditure on Fintech services c Users’ evaluation about impact levels of Fintech on financial services quality in Vietnam through five dimensions of SERVQUAL
The influence of Fintech on the quality of financial services in Vietnam is assessed through five SERVQUAL criteria: reliability, responsiveness, assurance, tangibles, and empathy Each criterion aligns with variables in a regression model, suggesting that Fintech positively impacts the overall quality of financial services Participants evaluated these statements using a 5-point Likert scale, ranging from "Completely disagree" to "Completely agree."
This study utilizes secondary data sourced from scientific journals and empirical research on Fintech, drawing from both domestic and international perspectives Additionally, information and statistics from reputable socio-economic organizations, including the WTO and Statista, will be incorporated to provide a comprehensive overview of the Fintech landscape in Vietnam.
2.1.2 Data collection and processing procedure
Data was collected over a four-week period from July 15, 2024, to August 11, 2024, using a questionnaire designed on Google Forms and shared through social media platforms like Facebook and LinkedIn The data collection and processing involved three main steps, illustrated in Figure 2.1.
Figure 2.1 Data collection and processing procedure
Step 2: Checking and cleansing the collected data This step would filter out invalid responses and only keep valid ones A response would be considered valid if all questions are completed thoroughly with on-topic information By cleansing data, a more precise final research result would be ensured
Step 3: Analyzing data, which would be done through several steps:
- Describing characteristics of the sample using graphs converted from raw data
- Encrypting and importing data into SPSS
- Analyzing the data and regression model
Step 2: Checking and cleansing data
The survey focused on randomly selected Vietnamese individuals, with a total of 264 participants Out of these, 257 qualified responses were selected for further analysis As detailed in section 2.1.1.1 on primary data, the participant profile was established based on three specific criteria.
(1) age, (2) educational level and (3) average monthly income
Figure 2.2 Age of participants, by percentage (Author’s survey)
The data presented in Figure 2.2 indicates that a significant 83.66% of respondents, totaling 215 individuals, are under the age of 30, while only 16.34%, or 42 individuals, are aged 30 and above This trend suggests that the majority of Fintech users are young, reflecting their affinity for technology.
Under 30 years oldFrom 30 years old and above
Figure 2.3 Educational level of participants, by percentage (Author’s survey)
The majority of participants, nearly 60%, have attained tertiary education, indicating they have attended university or college Additionally, 34.63% have achieved postgraduate education, while only 5.45% have completed high school, with no respondents falling below this level Despite the varying educational backgrounds, all participants possess sufficient qualifications to effectively utilize technology, including Fintech solutions.
High school educationTertiary educationPost graduate education
Figure 2.4 Average monthly income of participants, by percentage (Author’s survey)
The survey results indicate that most respondents earn between 5 to 30 million VND per month, with 80 individuals in the 5 to 15 million VND bracket and 106 in the 15 to 30 million VND bracket, representing 31.13% and 41.25% of the sample, respectively Approximately 19% earn over 30 million VND monthly, totaling 49 individuals, while only 8.56% earn less than 5 million VND According to the General Statistics Office (GSO) in 2024, the average monthly income for Vietnamese individuals in 2023 is 4.96 million VND, which is lower than the majority of respondents in this study This higher income level suggests that participants are likely to afford technology applications, such as smartphones and internet services, thereby enhancing their access to Fintech solutions.
Overall, it is believed by the author that with the characteristics mentioned above, this sample is qualified enough for further analysis
Methodology
This research employs a quantitative approach to evaluate the effects of Fintech on the quality of financial services, utilizing an OLS multiple regression model Prior to in-depth analysis, the study assesses variable reliability through Cronbach’s Alpha and conducts exploratory factor analysis (EFA) to identify the variables for the regression model Essential tests are performed to validate the model's suitability, including Pearson correlation coefficients, the Durbin-Watson test for autocorrelation, F-test for overall model fit, VIF test for multicollinearity, and residual scatterplot analysis for heteroscedasticity The research sets a 95% confidence interval and a 5% significance level, with SPSS version 25 as the analytical software.
The qualitative method would also be used to further clarify users’ behavior of using Fintech and their opinions on the research topic
Regarding to the quantitative method, the variables and model would be explained in the following section
This research utilizes a regression model grounded in the SERVQUAL framework, incorporating one dependent variable, Customer Loyalty (CL), and five independent variables: Tangibles (TC), Dependability (DU), Responsiveness (DB), Assurance (HH), and Empathy (DC) Additionally, the model includes a control variable, Age (AGE), to enhance the comprehensiveness of the findings, as outlined in Chapter 1.
This study evaluates the quality of financial services (CL) as the dependent variable, utilizing the SERVQUAL model It emphasizes that customer perception plays a crucial role; higher satisfaction levels among customers indicate an implied increase in service quality.
The research model includes 5 independent variables corresponding to 5 dimensions of SERVQUAL Each independent variable includes several observable variables, which shall be listed in Table 2.1 and Table 2.2
Reliability TC Impacts of Fintech on the reliability of customers for financial services (customers’ level of trust that Fintech service would be delivered correctly as promised)
Responsiveness DU Impacts of Fintech on the responsiveness of financial service providers (the ability to meet customers’ demand)
Assurance DB Impacts of Fintech on the assurance of financial services (the ability of financial service providers to inspire trust and confidence)
Tangibles HH Impacts of Fintech on the physical factors and appearance of financial services, including user interfaces and fuctions of financial services
Empathy DC Impacts of Fintech on the empathy of financial service providers (attention and caring for customers)
Fintech provides financial service smoothly with few operational errors
Transactions are recorded fully and accurately
Fintech provides financial service with consistent quality between experiences
Fintech increases information transparency of financial services (E.g: fee, interest rate, are publicly announced on application/website and presented in an easy-to- understand way)
Diverse and easy-to-access customer service channels (E.g: 24/7 chatbot, direct customer service on application/website or through third party chat applications
Fintech helps handle customer problems more quickly and accurately than traditional financial services
Fintech service providers promptly upgrade and modify services according to customer feedbacks
Using Fintech helps customer save more time compared to traditional financial services
Using Fintech helps customer save more cost compared to traditional financial services
Fintech protects private data of customer better
Fintech diversifies security forms in transaction process: Authentication by SMS OTP, Face ID, Soft OTP
Fintech service providers regularly update security policy in accordance with legal regulations and security standards
Customer data processing policy is presented clearly and transparently
Fintech mitigates risks in using financial services
The interfaces of application/website providing financial services are easy to interact
The interfaces of application/website providing financial services are modern, beautiful and highly attractive to customers
Functions on application/website providing financial services are diversed and useful
Fintech allows customers to adjust services and products based on personal preferences (E.g: Naming account by themselves)
Fintech service providers regularly conduct online surveys to understand customers' demands
Fintech service providers create suitable products for each customer group (based on age, job, educational level, )
Fintech service providers understand customers' frequency of using financial services to provide suitable promotion policies
Table 2.2 List of observed variables c Control variable
When doing a research based on customer perception, it is common to include variables that show personal characteristics such as age, gender or place of residence
In this study, customer age (AGE) will be transformed into dummy variables for inclusion as a control variable in the regression model Specifically, the value "0" will denote the group of individuals below 30 years old, while the value "1" will represent those aged 30 years and above.
The proposed model is presented below:
- 𝛽 𝑘 , with k ranging from 1 to 6, indicates how much CL will change if the corresponding dependent variables change by 1 unit
- 𝑒 is the error term which explains the effects of factors not included in the equation on CL
A meaningful research should be done with a suitable sample size Therefore, it is important to determine whether the sample size collected for this research fits to its scope and model
According to Tabachnick and Fidell (2013, p.159), the required sample size for a multiple regression model can be calculated with the formula 𝑁 ≥ 50 + 8𝑚, in which
In a statistical model, the total number of independent variables, denoted as 𝑚, consists of five initial independent variables: TC, DU, DB, HH, and DC, along with one control variable, AGE This results in a comprehensive set of independent variables for analysis.
6, thus, N should be equals to or larger than 50 + 8 × 6 = 98 observations
According to Hair et al (2010), conducting Exploratory Factor Analysis (EFA) necessitates a minimum of 50 observations, with an ideal sample size being at least five times the number of variables analyzed In this study, with 21 observed variables, a minimum of 105 observations is required to ensure robust results.
As the total number of survey responses or observations is 257, the sample size of this research is qualified to perform EFA and multiple regression model analysis
2.2.2 Determine the suitability of independent variables
To assess the suitability of the proposed independent variables and the 5-level Likert scale for the model and research objectives, this study will employ two key methods: Cronbach’s Alpha and Exploratory Factor Analysis (EFA) Further details will be provided in the subsequent sections.
2.2.2.1 Assess the reliability of the scale using Cronbach’s Alpha
Cronbach’s Alpha, as described by Tavakol and Dennick (2011), is a crucial measure of reliability and internal consistency for a scale, reflecting how well the observed variables align with the same concept or independent variable To ensure validity, it is essential to assess internal consistency prior to conducting further analyses The value of Cronbach’s Alpha ranges from 0 to 1, with higher values indicating stronger correlations among the observed variables When testing Cronbach’s Alpha, three main elements should be considered.
- The minimum acceptable value of Cronbach’s Alpha is 0.7 (Nunnally, 1978, p.245)
- The maximum value of Cronbach’s Alpha should not surpass 0.9, as it might suggest that some observed items are duplicated and testing the same question (Tavakol and Dennick, 2011)
- The minimum value of corrected item-total correlation is 0.3 (Cristobal, Flavián and Guinalíu, 2007, p.327)
The analysis reveals that the Cronbach’s Alpha values for all five groups of variables exceed 0.7, with corrected item-total correlations above 0.3, indicating that the observed variables are suitable for further examination (see Appendix B for details).
2.2.2.2 Assess factor structure of the scale using EFA
Exploratory factor analysis (EFA) is a statistical technique used to identify the underlying structure among variables (Hair et al., 2010) It synthesizes observed variables into distinct factors that encapsulate the same concept, effectively assessing whether these observed variables align with the theoretically assumed independent variables.
According to Hoang and Chu (2008, pp.30-31), to apply EFA, the data must meet several requirements:
Bartlett’s test of sphericity assesses the correlation among variables, indicating that those reflecting the same factor should be correlated A significance level of less than 0.05 confirms sufficient correlation among the variables, making them suitable for exploratory factor analysis (EFA).
The Kaiser-Meyer-Olkin (KMO) value is crucial for determining the suitability of factor analysis in research, with an ideal range between 0.5 and 1 A KMO value below 0.5 indicates that factor analysis is not appropriate for the given data.
KMO measure of sampling adequacy 0.882
Table 2.3 Result of KMO and Barlett’s test
The results from Table 2.3 indicate that the KMO value of 0.882 falls within the acceptable range of 0.5 to 1, and the Bartlett’s test significance level of 0.000 is less than 0.05, confirming that Exploratory Factor Analysis (EFA) is appropriate for the dataset To perform EFA, a rotated component matrix will be generated using the VARIMAX rotation method, which includes all observed variables and their corresponding factor loadings Factor loadings reflect the correlation between each variable and its associated factor, as outlined by Hair et al.
In factor analysis, a factor loading of 0.3 is deemed the minimum acceptable value, while a loading of 0.5 indicates practical significance Ideally, a factor loading of 0.7 or higher reflects a well-defined structure, highlighting the importance of these thresholds in evaluating variable relationships.
After rotating the variables, five distinct components—DB, DC, TC, DU, and HH—were identified, each representing independent variables with their respective observed variables The factor loading values for all components exceed 0.5, with the majority surpassing 0.7, indicating a strong correlation between each observed variable and its corresponding independent variable.
IMPACTS OF FINTECH ON THE QUALITY OF FINANCIAL
The situation of Fintech in Vietnam
The Fintech sector in Vietnam has experienced remarkable growth, paralleling global trends According to Mordor Intelligence (2023), the total value of the Vietnamese Fintech market is projected to reach $39.02 billion by 2024 and $72.24 billion by 2029, reflecting a compound annual growth rate (CAGR) of 13.11% This anticipated expansion is driven by various factors contributing to the market's potential.
Figure 3.1 The number of Vietnam Fintech firms in 2022, by segment (Nextrans,
First of all, the number of Fintech firms in Vietnam is increasing at a fast pace, from 144 companies in 2018 to 260 companies in 2022 (Nextrans, 2022) As shown in
P2P lending Blockchain/Crypto Wealth management POS
The rise of Fintech companies in Vietnam has led to significant innovations in financial services, with sectors like other lending, blockchain/cryptocurrency, and wealth management growing at rates of 16%, 12%, and 8%, respectively Key players in the digital payment space, such as MoMo, VNPAY, and ZaloPay, have introduced multifunctional e-wallets that offer greater convenience than traditional banking, allowing customers to easily transfer money and pay for utilities, mobile top-ups, and movie tickets all within a single app In the realm of P2P lending, companies like Fiin, Eloan, Vaymuon, and Tima Lender have emerged, providing diverse lending options for individuals, business households, and SMEs Investment management has also evolved, with platforms like Finhay, Tikop, and SaveNow enabling users to invest in ETFs without requiring extensive market knowledge, thus making investing accessible to those with limited capital Additionally, robo-advisor technologies, such as TCWealth from TechcomSecurities, offer personalized financial advice to investors Overall, the establishment of Fintech firms has enriched the variety of financial services available to consumers, and there is a growing trend of collaboration between startups and traditional financial institutions in Vietnam's digital transformation.
Fintech companies are enhancing and expanding their services, exemplified by MoMo e-wallet's platform that facilitates credit card loan collection for 20 major banks, benefiting 5 to 7 million customers with quick and easy loan payments (MoMo, 2023) This collaboration not only improves customer experience but also reduces operational costs for banks and financial institutions Additionally, banks can leverage Fintech-developed services and technologies, such as big data analysis and eKYC biometric identification, to enhance their offerings (Quynh, 2023) A prime example is the VNPAY e-commerce platform, VnShop, which has been integrated into 22 mobile banking applications, allowing seamless online shopping and payment in a single app This integration is advantageous for customers, banks, and Fintech companies alike, as it encourages customers to deposit money for transactions and enables Fintech firms to reach a broader user base among existing bank customers.
Another growing trend in Vietnam Fintech industry is embedded finance, which is
The integration of financial services into non-financial entities' products is exemplified by Vietnamese ride-hailing and e-commerce applications like Be, Grab, and Shopee, which feature embedded payment functions for seamless in-app transactions This trend is supported by Fintech partners such as Moca, ShopeePay, MoMo, and ZaloPay, enhancing user experience As a result, Vietnam boasts the highest embedded finance adoption rate in ASEAN, reaching approximately 83%.
Vietnam's fintech market is a promising destination for foreign investors, with approximately 70% of startups being foreign-funded, including notable unicorns like VNPAY and MoMo VNPAY achieved unicorn status in 2019 after securing $300 million from SoftBank Vision Fund and GIC, while MoMo followed in 2022 with a $200 million investment from global investors This influx of foreign capital highlights the market's potential Additionally, international companies are actively participating in Vietnam's fintech landscape, exemplified by the introduction of Samsung Pay in 2017 and Apple Pay in 2023, both of which offer convenient contactless payment solutions that have quickly garnered customer support.
The Fintech ecosystem in Vietnam has experienced significant growth in recent years, driven not only by startups but also by established financial institutions and foreign companies This trend indicates a promising future for the development of the Fintech landscape in Vietnam.
It can be said that Vietnam is a country with fast-paced technology development
In 5 years from 2018 to 2022, the proportion of individuals using the Internet out of total Vietnamese population increased by from 70% to 79%, meaning that out of 10 Vietnamese, there are 7 to 8 people having access to Internet (World Bank, 2024) The high Internet penetration rate creates a good opportunity for online services including Fintech to approach more customers in Vietnam An example is that in the same period, the transaction value of digital payment in Vietnam has nearly doubled, from 11,515.09 billions USD in 2018 to 22,674.09 billions USD in 2022, and is predicted to reach 45,344.51 billions USD in 2028 (Statista, 2024) These numbers indicate that the usage of Fintech in Vietnam is rising, showing a great potential of Vietnam Fintech market
This research explores the usage of Fintech services in Vietnam by examining four key aspects: the types of Fintech services currently utilized by users, the sources of information that introduce them to these services, the frequency of their usage, and the monthly expenditure users allocate to Fintech services.
Type of services Number of users
Proportion of total survey participants
Digital payment (e-wallet; contactless payment; electronic fund transfer; etc.)
Lending, capital mobilization (P2P lending, crowdfunding)
Investment management (robo- advisor; automatic investment management)
Table 3.1 illustrates the distribution of Fintech users based on service types, highlighting that all survey participants utilize at least one Fintech service, with 18.29% engaging with two or more Digital payment emerges as the dominant service, used by 97.67% of respondents, while investment management and P2P lending/crowdfunding attract significantly fewer users, at 14.40% and 6.61%, respectively This indicates a high penetration of Fintech in Vietnam, predominantly in digital payments Interestingly, investment management services have double the users compared to lending, contrasting with the broader market where P2P lending ranks second This discrepancy may stem from recent fraud cases in P2P lending, causing apprehension among users, and the survey's focus on young individuals under 30, who may find low-to-moderate-value investment options more appealing than lending services.
Figure 3.2 Source of information through which users are introduced to Fintech services (Author’s analysis)
Consultancy of Fintech companies' employees
Self-research out of the perception that Fintech is convenient
A survey revealed that over 50% of participants learn about Fintech through media advertisements, while nearly 22% conduct their own research due to the perceived convenience of Fintech Additionally, around 17.51% utilize Fintech services based on recommendations from friends and family, compared to only 5.45% who consult Fintech company employees These findings highlight the crucial role of media in shaping the public's perception of Fintech and suggest that users prioritize personal recommendations over corporate advice Consequently, it is vital for Fintech companies to enhance their service quality to build trust and expand their customer base, as satisfied users are more likely to recommend services to their social circles.
Figure 3.3 Frequency of usage, by age (Author’s analysis)
Usage frequency of Fintech varies significantly between age groups Approximately 70% of individuals under 30 engage with Fintech daily, while 23.72% use it several times a week In contrast, the majority of those aged 30 and older utilize Fintech only a few times per week or month, with 33% for each option This observation supports assumptions made by Krupa and others regarding age-related Fintech adoption trends.
Few times/month Few times/year
According to Buskzo (2023, p.1), younger individuals engage with Fintech services more frequently, while older users tend to adopt these technologies primarily when they recognize high service quality Overall, the data indicates that many users utilize Fintech on a daily or weekly basis, suggesting that these services effectively meet consumer demands and encourage regular use.
Figure 3.4 Monthly expenditure on Fintech services (Author’s analysis)
Over 80% of survey participants reported spending less than 1% of their monthly income on Fintech services, while 14.01% allocate between 1% to 5%, and only 5.06% spend 6% to 10% Notably, no respondents exceed a 10% expenditure on Fintech, highlighting the affordability of these services This cost-effectiveness presents a significant advantage of Fintech over traditional financial services, which will be explored in detail in the subsequent sections.
Under 1% income1% to 5% income6% to 10% income
Despite the growth of Fintech in Vietnam over the past several years, the legal framework remains inadequate According to Nguyen (2020, p.77), many Fintech services lack a specific legal basis, leaving them vulnerable to regulatory challenges For example, while cryptocurrencies have gained popularity among Vietnamese investors, their transactions are not officially recognized as legal in the country Similarly, P2P lending, which is closely tied to finance and banking, falls outside the purview of the Law on Credit Institutions due to insufficient regulatory measures (Nguyen, 2024) The absence of detailed regulations for Fintech activities can lead to complex legal issues and serious consequences.
In Vietnam, many P2P lending models are often disguised as black credit, charging exorbitant interest rates and employing illegal debt collection practices, which can severely impact borrowers' finances, reputations, and careers While P2P lending has the potential to benefit both lenders and borrowers, it is crucial to develop a comprehensive legal framework for Fintech to reduce risks, regulate activities, and improve the overall efficiency of the Fintech ecosystem in Vietnam Recognizing this need, the Vietnamese government has progressively introduced various decrees and policies to guide the Fintech sector, including Decision No 328/QD-NHNN issued by the Governor of the State Bank of Vietnam in 2017.
Model analysis and discussion
Table 3.2 Descriptive statistics of variables (Author’s analysis)
In a study assessing six variables on a 5-level Likert scale, five variables exhibited mean values exceeding 4, while the variable DB recorded the lowest mean at 3.2942 Notably, DU achieved the highest mean at 4.4086, with its minimum value of 2.00 also surpassing the others, which ranged between 1.00 and 1.50 All variables had a maximum value of 5.00, and their standard deviations were below 1, indicating a tight clustering of data around the mean This suggests that most values for the variables are concentrated between 3 and 5, except for DB, where values primarily fall within the range of 2 to 4.
The control variable AGE has a mean value of 0.16 and a standard deviation of 0.370, indicating that most survey participants are under 30 years old, as "0" represents this age group This finding is consistent with the sample description in Chapter 2, where out of 257 participants, 215 are below 30, while only 42 are aged 30 and above.
3.2.2 Correlation analysis using Pearson correlation coefficient
Collinearity, as defined by Hill et al (2018), is the systematic relationship between two variables When this occurs between independent variables, it can diminish the precision of the model, as these variables may convey overlapping information Consequently, performing a collinearity analysis is essential to remove insignificant variables and enhance model accuracy.
Collinearity is quantified using correlation coefficients that range from -1 to +1, where -1 indicates a perfect negative correlation, +1 signifies a perfect positive correlation, and 0 suggests no linear relationship between two variables According to Field (2009, p.170), Pearson correlation coefficients are essential for understanding these relationships.
- ± 0.8: Extremely large effect It is also commonly known in statistics that if the correlation between 2 variables are around this level, 1 variable should be eliminated to avoid duplication explanation
Field (2009, p.171) also stated that before checking the value of correlation coefficients, it is necessary to test hypotheses:
H0: There is no linear correlation between 2 variables
H1: There is linear correlation between 2 variables
Variables CL TC DU DB HH DC AGE
Table 3.3 Correlation matrix (Author’s analysis)
Table 3.3 indicates that, with the exception of AGE and DB, the 2-tailed significance values for all other variable pairs are less than 0.05 Consequently, we reject the null hypothesis (H0) and accept the alternative hypothesis (H1), confirming the presence of linear correlations between each pair of variables.
Looking at the Pearson correlation coefficient value, 5 independent variables (TC,
The analysis reveals that DU, DB, HH, and DC exhibit positive correlations with the dependent variable CL, while the control variable AGE shows a negative correlation with CL Notably, HH demonstrates the strongest correlation with CL, recorded at 0.659, indicating that the independent variables significantly contribute to explaining the dependent variable.
The correlation coefficients for the independent variables do not exceed ± 0.8, indicating that all variables are acceptable for inclusion in the model However, several pairs exhibit relatively high correlation coefficients, approximately ± 0.5 Therefore, a Variance Inflation Factor (VIF) test will be performed to confirm the absence of multicollinearity within the model.
3.2.3 Model estimation and hypothesis testings
Standard error of the estimate
Table 3.4 Model summary (Author’s analysis)
R-squared is 0.684, implying that the independent variables in the model explain the variability in the dependent variable CL by 68.4% Adjusted R-square is 0.676, which is only 0.008 lower than R-squared This deduction indicates that if the model were extracted from the population rather than the sample, it would take up about 0.8% less variance in the outcome It is a good sign that R-squared and adjusted R-squared are approximately the same, as adjusted R-squared shows how well the model generalizes and the closer two values are, the better the model is (Field, 2009, p.235)
3.2.3.2 Autocorrelation check using Durbin-Watson test
The value of Durbin-Watson is 1.816, which is between 1.5 to 2.5, therefore, there is no autocorrelation in this model
3.2.3.3 Assessing the overall fit of the model using ANOVA F-test
Table 3.5 ANOVA F-test (Author’s analysis)
Table 3.5 shows that the significance level of the F-test is 0.000, which is less than 0.05 This indicates that the multiple regression model is appropriate for the collected data, demonstrating that the model significantly predicts the dependent variable, CL.
Table 3.6 VIF and tolerance (Author’s analysis)
To test whether multicollinearity happens in this model, VIF and tolerance (which equals 1/VIF) would be used According to Field (2009, p.242), if VIF is larger than
The analysis indicates that multicollinearity is absent in the research model, as evidenced by all Variance Inflation Factor (VIF) values being below 10, with the highest recorded at 2.048 Additionally, the tolerance values exceed 0.2, with the lowest being 0.488, confirming the model's robustness.
Figure 3.5 Scatterplot of regression standardized predicted value and regression standardized residual (Author’s analysis)
Whether heteroscedasticity exists in a model can be checked by using the scatterplot of regression standardized residual and predicted value According to Field
In a model lacking heteroscedasticity, data points should be evenly spread around zero A funnel-shaped distribution of points suggests the presence of heteroscedasticity However, as illustrated in Figure 3.5, the data points are consistently distributed around zero, and no funnel shape is evident, indicating that heteroscedasticity is likely absent in this model.
Table 3.7 Coefficients estimation and t-test (Author’s analysis)
To assess the impacts of the independent variables on CL, use the hypothesis testing:
H0: βk = 0 (The corresponding independent variable has no influence on service quality)
H1: βk ≠ 0 (The corresponding independent variable has an influence on service quality)
Performing the Student’s t-test, if sig < 0.05, the null hypothesis is rejected and H1 is accepted
It can be seen from table 3.7 that all independent variables have sig < 0.05, therefore, they all have influences on service quality (CL) All 5 SERVQUAL variables
(TC, DU, DB, HH, DC) have positive beta, indicating a positive relationship with CL, whereas AGE has a negative beta, showing a negative relationship with CL
The analysis reveals that among the independent variables, TC exerts the greatest influence on the dependent variable CL, with a standardized coefficient of 0.279 This is closely followed by DU at 0.271, while DC has a coefficient of 0.247 HH shows a lower impact at 0.185, and DB has the least influence with a coefficient of 0.094.
> AGE (-0.096) With that, the standardized regression model without β0 is:
To understand the impact of a one-unit change in an independent variable on the dependent variable while keeping other variables constant, we utilize unstandardized coefficients to develop the unstandardized regression model.
It can be seen that when all independent variables stay constant, service quality would drop by 2.210 units Detailed analysis on the independent variables would be given in section 3.2.5
3.2.4 Analysis and discussion of results
Fintech positively impacts customer reliability in financial services, enhancing service quality significantly Specifically, a 1-unit increase in customer reliability correlates with a 0.357-unit rise in service quality This improvement is attributed to several factors: Fintech minimizes operational errors through automation, with 74.32% of respondents noting reduced mistakes from complex processes, 50.19% citing fewer lost transaction papers, and 44.75% observing less information confusion Additionally, the full and accurate recording of transaction data through online databases and automated systems contributes to reliability Fintech also promotes transparency by clearly publishing information on fees and interest rates, allowing customers to access precise details quickly Finally, the reduction of manual work in Fintech services decreases human-related issues, ensuring consistent service quality across different interactions.
The rise of Fintech has significantly enhanced the responsiveness of financial service providers, leading to improved service quality; for instance, a 1-unit increase in responsiveness correlates with a 0.469 unit boost in quality Advances in technology, particularly artificial intelligence, enable Fintech companies to offer diverse and accessible customer service options, such as 24/7 chatbots, which facilitate quicker and more efficient responses to customer inquiries compared to traditional services, ultimately enhancing customer satisfaction Additionally, Fintech allows users to access financial services online anytime and anywhere, saving them both time and money; survey results show that time-saving and cost-saving are highly rated by users Many Fintech companies provide services at low or no cost to attract customers, with most users spending less than 1% of their monthly income on these services A notable example is Vietnam's Napas, which, in collaboration with local banks, launched a free QR code money transfer service in 2021, making it increasingly popular due to its convenience and cost-effectiveness, as users only require an Internet-connected smartphone.
QR payment increased 829.95% and 1,062.01% YoY, respectively, making it the leading cashless payment method in Vietnam market
RECOMMENDATIONS AND CONCLUSION
Recommendations
To improve the quality of financial services and foster the growth of Fintech in Vietnam, this research offers key recommendations for the Vietnamese government, financial institutions, Fintech companies, and users.
The Vietnamese government must urgently establish a comprehensive legal framework for Fintech services to address the current lack of regulation, which poses risks of fraud and instability within the industry Clear and stringent regulations for each Fintech segment will enhance service quality, fostering customer confidence and security when using financial services This framework should outline the conditions for establishing and operating Fintech activities, particularly to prevent deceptive practices like those seen in recent P2P lending cases Additionally, the government should publish guidelines on legal Fintech services and quality standards, empowering consumers to make informed choices and protect themselves from illegal activities Implementing user protection regulations will further bolster trust in Fintech, encouraging greater adoption and driving industry growth Moreover, the timely introduction of a regulatory sandbox is essential for supporting Fintech startups in innovating and refining their technologies, allowing them to adapt to rapid market changes While the initial sandbox proposal covers only three segments, expediting its implementation will enable a broader range of Fintech services to be tested, catering to diverse customer needs and enhancing the overall landscape of the industry.
4.1.2 For Vietnam financial institutions and Fintech companies
Financial institutions and Fintech companies must consistently upgrade their technology systems to ensure minimal disruptions and provide a seamless customer experience Regularly updating security measures, such as data encryption and authentication, is crucial to protect customer data, as privacy remains a top concern for users By enhancing technology systems, these firms can boost the reliability and assurance of their financial services Additionally, continuous development of innovative and user-friendly functions will not only improve the overall quality of financial services but also attract a diverse customer base Gathering frequent customer feedback allows companies to swiftly adapt their services to meet user needs effectively.
Fintech firms should focus on enhancing their existing strengths while developing new features Survey results indicate that users appreciate the time savings and reduced operational errors associated with Fintech solutions compared to traditional financial services Therefore, it is crucial to continuously improve user experience by streamlining processes, simplifying in-app interactions, and leveraging AI for faster and more accurate outcomes.
While Fintech services offer significant advantages to Vietnamese users, there remain untrustworthy options that pose hidden risks To mitigate these risks, users should thoroughly research service providers and their offerings, ensuring they are legally licensed and checking customer reviews This proactive approach can help users avoid deceptive services and enhance their overall safety in the Fintech landscape.
Fintech is an evolving industry that greatly benefits from customer feedback, which helps companies refine their services Many Fintech applications and websites offer features for users to provide in-app feedback and access 24/7 customer support This allows users to promptly report issues or share comments, enabling Fintech firms to enhance the quality of their financial services.
Conclusion
In conclusion, this research effectively utilized a multiple regression model, analyzing primary data from a survey of 257 responses alongside secondary data from organizational statistics and prior studies, to achieve significant results.
The research provides a comprehensive theoretical framework for Fintech, financial services, and their quality, detailing definitions and classifications of these concepts It introduces the SERVQUAL model as a novel approach to evaluating financial service quality within the Fintech context in Vietnam, a topic previously unexplored The study analyzes the Fintech ecosystem, its usage, and the legal framework, employing quantitative analysis to assess the impact of Fintech on financial service quality through the SERVQUAL dimensions Findings indicate that Fintech positively influences all five SERVQUAL dimensions, with the strongest impact on reliability, followed by responsiveness, empathy, tangibles, and assurance Fintech enhances financial service quality by increasing transparency, simplifying processes, improving data security, and catering to diverse customer needs, thus revolutionizing traditional financial services and fostering economic growth However, challenges remain, including the absence of a robust legal framework and customer concerns about risks associated with Fintech services Notably, individuals under 30 exhibit a more favorable perception of Fintech service quality compared to those aged 30 and above.
To enhance the positive impact of Fintech on financial service quality in Vietnam, several key recommendations have been proposed The government should establish a formal legal framework for Fintech and introduce a regulatory sandbox promptly Financial institutions and Fintech companies are encouraged to continuously upgrade their technology, innovate new products and features, and leverage their existing strengths to retain customers Additionally, Fintech users are advised to carefully select services and provide feedback based on their experiences, which will assist Fintech companies in improving their offerings Implementing these strategies is anticipated to significantly elevate the quality of financial services in Vietnam.
Fintech services in Vietnam in the upcoming years, consequently becoming the premise for a stronger Vietnam Fintech industry
Despite the positive outcomes of this research, several limitations should be noted The sample size of 257 participants is relatively small, which may lead to biased results; future studies should aim to expand the survey scale for greater accuracy Additionally, the focus on individual users' assessments of Fintech's impact on financial service quality overlooks the perspectives of service providers, potentially limiting the comprehensiveness of the findings Addressing these issues in future research could enhance the overall quality and reliability of the results.
APPENDIX A SURVEY QUESTIONNAIRE: USERS’ ASSESSMENT ON THE IMPACTS OF FINANCIAL ON THE QUALITY OF FINANCIAL
SERVICES IN VIETNAM SECTION 1: USERS’ INFORMATION
1 Your age: o Under 30 years old o From 30 years old and above
2 Your education level: o Under high school education o High school education o Tertiary education o Post graduate education
3 Your average monthly income: o Below 5 million VND o From 5 to 15 million VND o From 15 to 30 million VND o Above 30 million VND
SECTION 2: USERS’ BEHAVIOR OF USING FINTECH
4 What are the types of Fintech services that you are currently using?
Respondents can select multiple options regarding their preferred Fintech services, including digital payment methods such as E-wallets like Momo and VNPAY, contactless payments through platforms like Apple Pay and QR code transactions, as well as electronic funds transfers Additionally, options for lending and capital mobilization, such as P2P lending and crowdfunding, are available Investment management services, including Robo-advisors and automatic investment management platforms like Finhay and TCWealth, are also offered Participants may specify any other types of Fintech services they utilize.
5 Through which sources are you introduced about Fintech? (Respondents can choose more than 1 options) o Advertisements on the media o Friends and families’ recommendation o Consultancy of Fintech companies’ employees o Self-research out of the perception that Fintech is convenient
6 Your frequency of Fintech services usage: o Everyday o Few times per week o Few times per month o Few times per year
7 Your monthly expenditure on Fintech services: o Under 1% income o 1% to 5% income o 6% to 10% income o Above 10% income
SECTION 3: USERS’ EVALUATION ABOUT IMPACT LEVELS OF FINTECH ON FINANCIAL SERVICES QUALITY IN VIETNAM
Please provide your evaluation about impact level of Fintech on each of the following aspect For questions that require assessment on a Likert scale, the level are defined as follow:
8 The reliability of customers for financial services (TC) (Please answer both questions: 8a and 8b)
8a The reliability of customers for financial services (TC)
TC1.Fintech provides financial service smoothly with few operational errors
TC2 Transactions are recorded fully and accurately
TC3 Fintech provides financial service with consistent quality between experiences
TC4 Fintech increases information transparency of financial services (E.g: fee, interest rate, are publicly announced on application/website and presented in an easy-to-understand way)
Fintech has successfully addressed several errors prevalent in traditional financial services, including operational mistakes stemming from complex processes and reliance on human handlers Additionally, it has mitigated issues related to the loss of transaction paperwork and information confusion, enabling quicker access to data for verification before financial transactions However, some respondents believe that Fintech has not fully overcome these traditional errors, highlighting the need for ongoing improvements.
9 The responsiveness of financial service providers (DU)
DU1 Diverse and easy-to-access customer service channels (E.g: 24/7 chatbot, direct customer service on application/website or through third party chat applications.)
DU2 Fintech helps handle customer problems more quickly and accurately than traditional financial services
DU3 Fintech service providers promptly upgrade and modify services according to customer feedbacks
DU4 Using Fintech helps customer save more time compared to traditional financial services
DU5 Using Fintech helps customer save more cost compared to traditional financial services
10 The assurance of financial services (DB) (Please answer both questions: 10a and 10b)
10a The assurance of financial services (DB)
DB1 Fintech protects private data of customer better
DB2 Fintech diversifies security forms in transaction process: Authentication by SMS OTP,
DB3 Fintech service providers regularly update security policy in accordance with legal regulations and security standards
DB4 Customer data processing policy is presented clearly and transparently
DB5 Fintech mitigates risks in using financial services
Fintech has effectively addressed several risks associated with traditional financial services, including the risk of information breaches and abuse, as well as credit risk related to debt collection challenges Additionally, it has mitigated the risk of losing money for consumers However, some respondents believe that Fintech has not fully overcome these risks, highlighting the ongoing concerns within the industry.
11 Impacts of Fintech on the physical factors and appearance of financial services (HH)
HH1 The interfaces of application/website providing financial services are easy to interact
HH2 The interfaces of application/website providing financial services are modern, beautiful and highly attractive to customers
HH3 Functions on application/website providing financial services are diversed and useful
12 Impacts of Fintech on the empathy of financial service providers (DC)
DC1 Fintech allows customers to adjust services and products based on personal prefences (E.g:
DC2 Fintech service providers regularly conduct online surveys to understand customers' demands
DC3 Fintech service providers create suitable products for each customer group (based on age, job, educational level, )
DC4 Fintech service providers understand customers' frequency of using financial services to provide suitable promotion policies
Table B1 Reliability statistics of TC1, TC2, TC3 and TC4 (Author’s analysis)
Scale mean if item deleted
Scale variance if item deleted
Cronbach’s Alpha if item deleted
Table B2 Item – Total statistics of TC1, TC2, TC3 and TC4 (Author’s analysis)
Table B3 Reliability statistics of DU1, DU2, DU3, DU4 and DU5 (Author’s analysis)
Scale mean if item deleted
Scale variance if item deleted
Cronbach’s Alpha if item deleted
Table B4 Item – Total statistics of DU1, DU2, DU3, DU4 and DU5 (Author’s analysis)
Table B5 Reliability statistics of DB1, DB2, DB3, DB4 and DB5 (Author’s analysis)
Scale mean if item deleted
Scale variance if item deleted
Cronbach’s Alpha if item deleted
Table B6 Item – Total statistics of DB1, DB2, DB3, DB4 and DB5 (Author’s analysis)
Table B7 Reliability statistics of HH1, HH2 and HH3 (Author’s analysis)
Scale mean if item deleted
Scale variance if item deleted
Cronbach’s Alpha if item deleted
Table B8 Item – Total statistics of HH1, HH2 and HH3 (Author’s analysis)
Table B9 Reliability statistics of DC1, DC2, DC3 and DC4 (Author’s analysis)
Scale mean if item deleted
Scale variance if item deleted
Cronbach’s Alpha if item deleted
Table B10 Item – Total statistics of DC1, DC2, DC3 and DC4 (Author’s analysis)
Alwi, S., Alpandi, R.M., Salleh, M.N.M, Basir, I.N and Ariff, F.F.M (2019) An Empirical Study on the Customers’ Satisfaction on Fintech Mobile Payment Services in Malaysia International Journal of Advanced Science and Technology [online] 28
Arner, D., Barberis, J and Buckley, R (2015) The Evolution of Fintech: A New Post-Crisis Paradigm? University of Hong Kong Faculty of Law Research Paper
Binance (2024) Bitcoin price Available from: https://www.binance.com/en/price/bitcoin [Accessed 03 June 2024]
In the context of the Covid-19 pandemic, Bui (2021) explores the significant effects of Fintech on financial inclusion in Vietnam, highlighting how digital financial services have become crucial for enhancing access to finance The study emphasizes the necessity for policy adaptations to leverage Fintech innovations, ensuring that marginalized populations can benefit from these advancements Ultimately, the findings suggest that effective integration of Fintech can promote greater financial inclusivity and resilience in Vietnam’s economy during and after the pandemic.
Banking Review [online] 11 [Accessed 13 June 2024]
Buttle, F (1996) SERVQUAL: review, critique, research agenda European Journal of Marketing [online] 30 (1) pp 8-32 [Accessed 06 July 2024]
Choudhary, U., Dang, K and Kaur, S (2023) A research study on the impact of Fintech on customer satisfaction in banking sector Journal of the Oriental Institute
Hutapea, R.S (2020) The Effect of Financial Technology (Fin-Tech) on Customer Satisfaction Level (A Case Study on SMEs) International Seminar of Science and Applied Technology [online] 198 pp 668-674 [Accessed 10 June 2024]
Cristobal, E., Flavián C and Guinalíu, M (2007) Perceived e-service quality (PeSQ): Measurement validation and effects on consumer satisfaction and web site loyalty Managing Service Quality [online] 17 (3), pp 317-340 [Accessed 17 June
Hoang, T and Chu, N.M.N (2008) Phân tích dữ liệu nghiên cứu với SPSS - tập 2
(Analyzing research data with SPSS - part 2) Hong Duc Publishing House
In her 2018 work, T.N.L Dang explores the evolution of Fintech and its significant impact on the banking and finance sectors The article discusses the transformative changes driven by technological advancements and highlights the future trajectory of Fintech in relation to traditional banking practices Published by UEH Publishing House, this insightful analysis emphasizes the need for adaptation within financial institutions to keep pace with the rapid developments in Fintech For further details, the full text is available online.
Dao, M.H., Nguyen, T.T., Dang, T.H and Nguyen, T.L.T (2018) Factors affecting individual customers’ decision to use fintech services in payment activities in Vietnam
Banking Science & Training Review [online] 194 pp 11-19 [Accessed 12 June 2024]
Field, A (2009) Discovering statistics using SPSS 3rd ed London: SAGE
The Crowdfunding Market is projected to experience significant growth from 2024 to 2032, as detailed in the Fortune Business Insight report This analysis covers various types of crowdfunding, including equity-based, debt-based, and blockchain-based models, catering to diverse end-users such as startups, NGOs, and individuals The report provides a comprehensive overview of market size, share, and regional forecasts, highlighting the evolving landscape of crowdfunding and its implications for various sectors.
The General Statistics Office (GSO) released a press statement detailing the population, labor, and employment situation for the fourth quarter of 2023 This report provides insights into demographic trends, employment rates, and labor market dynamics, highlighting key statistics and analysis relevant to understanding the economic landscape For more information, visit the GSO website at https://www.gso.gov.vn/du-lieu-va-so-lieu-thong-ke/2023/12/thong-cao-bao-chi-ve-tinh-hinh-dan-so-lao-dong-viec-lam-quy-iv-va-nam-.
2023/#:~:text=Quy%20m%C3%B4%20v%C3%A0%20c%C6%A1%20c%E1%BA% A5u,%2C%20chi%E1%BA%BFm%2061%2C9%25 [Accessed 17 June 2024] Hair J.F., Black, W.C., Babin, B.J and Anderson, R.E (2010) Multivariate Data
Analysis 7th ed Pearson Prentice Hall
Hill, R.C., Griffiths, W E and Lim, G.C (2018) Principles of Econometrics 5th ed New Jersey: John Wiley & Sons, Inc
In recent studies, the regulatory sandbox for banking and finance services on technology platforms has been explored by Hoang (2020), highlighting its significance in fostering innovation within the financial sector Additionally, Krupa and Buszko (2023) examined age-dependent differences in the use of FinTech products and services, revealing distinct preferences between young customers and older adults These findings underscore the evolving landscape of financial technology and the necessity for tailored approaches to meet diverse consumer needs.
Le, C.T (2023) Impacts of financial technology on the banking industry Banking
Le, H.N (2018) explores the significant impacts of Fintech on banking operations in Vietnam, highlighting the need for collaboration between traditional banks and Fintech companies The article emphasizes that embracing Fintech innovations can enhance efficiency and customer service in the banking sector It also presents recommendations for banks to adapt and thrive in the evolving financial landscape, fostering a synergistic relationship with Fintech to drive mutual growth and development in Vietnam's financial ecosystem.
The future of Fintech and Banking: Evolution and revolution, November 2018 UEH
Publishing House Available from: https://sob.ueh.edu.vn/wp- content/uploads/2018/11/16.-LE-HUYEN-NGOC.pdf
Leong, K and Sung, A (2018) FinTech (Financial Technology): What is it and How to Use Technologies to Create Business Value in Fintech Way? International
Journal of Innovation, Management and Technology [online] 9 (2), pp 74-78
Mai, X.D and Nguyen, T.H (2022) Công nghệ tài chính: Động lực thúc đẩy tài chính toàn diện (Financial technology: Motivation to promote financial inclusion)
Economy and Forecast Review [online] [Accessed 13 June 2024]
Minh, H (2024) 3 lĩnh vực được thử nghiệm giải pháp Fintech (3 areas where Fintech solutions are tested) Social Republic of Vietnam - Government News [online]
07 March 2024 Available from: https://baochinhphu.vn/3-linh-vuc-duoc-thu-nghiem- giai-phap-fintech-
10224030616364095.htm#:~:text=C%C3%A1c%20gi%E1%BA%A3i%20ph%C3% A1p%20Fintech%20trong,ngang%20h%C3%A0ng%20(P2P%20Lending) [Accessed
MoMo (2023) partners with banks and financial institutions to make financial services accessible to low-income customers, enhancing their financial inclusion This collaboration aims to bridge the gap between traditional banking and underserved communities, ensuring that essential financial resources are readily available to those who need them most For more information, visit MoMo's official announcement.