Acknowledge First of all, I hereby declare that the thesis “DETERMINANTS AFFECTING THE E-BANKING SERVICE QUALITY IN VIETNAMESE COMMERCIAL BANKS DURING COVID-19 EPIDEMIC” is my independe
INTRODUCTION
Research Rationale
Digital transformation is reshaping socio-economic activities, including the banking sector, driven by changing consumer behaviors and emerging technology platforms The rise of modern digital technologies such as artificial intelligence, cloud computing, and mobile applications is altering how individuals access information, conduct transactions, and interact In Vietnam, the increasing internet and mobile application usage has led to new consumption trends, including the sharing economy Terms like "e-banking" and "online transactions" have become integral to the competitive landscape of commercial banks E-banking enables users to perform transactions and access information without visiting ATMs or bank branches, benefiting both customers and the broader economy The Vietnamese government and the State Bank emphasize digital transformation and e-banking through various legal frameworks, including Resolution No 52-NQ/TW, which outlines guidelines for engaging in the Fourth Industrial Revolution, and the 13th Party Congress's focus on developing a digital economy from 2021 to 2030.
The "National Digital Transformation to 2025, Orientation to 2030" initiative, along with Decision No 810/QĐ-NHNN from the State Bank of Vietnam dated May 11, 2021, emphasizes a customer-centric approach in the banking sector's digital transformation This necessitates continuous enhancement and development of e-banking services, with a strong focus on improving service quality, which is a key priority for commercial banks.
Vietnamese commercial banks, supported by the Party, Government, and SBV, have introduced digital banking applications for smartphones and computers, including VCB Digibank, VietinBank iPay, BIDV SmartBanking, VPBank Online, and eBank X.
TPBank, Ebanking of HDBank, Mobile banking of Eximbank, Agribank E-Mobile Banking, Omni-Channel of OCB, SCB Mobile Banking, etc
The COVID-19 pandemic has significantly transformed consumer behavior and decision-making, with anxiety about the virus posing a serious threat to many As a result, online classes for children have become mandatory, and both buyers and sellers are increasingly turning to digital channels The banking sector has seen a marked shift towards online banking, with a surge in e-banking platforms due to the decline of traditional banking methods In Vietnam, e-banking activities have notably increased, with over 8 million new online consumers in the first quarter of 2022, predominantly from non-urban areas A remarkable 97% of these consumers continue to use e-commerce services, and 99% plan to maintain their online banking habits in the future, driven by restrictions on visiting physical bank locations.
In Vietnam's highly competitive market, commercial banks must prioritize the quality of their E-banking services to enhance customer satisfaction.
Research Objectives
Overall Objective: The main objective of this research is to identify and measure the determinants that influence the E-banking service quality of Vietnamese commercial banks during COVID-19 epidemic
To present the situation and status of e-banking services at Vietnamese commercial banks during the COVID-19 period or the process of "digital transformation"
Systematizing the theoretical basis and building a foundation of theoretical concepts related to service quality and customer satisfaction about e-banking services, thereby giving a suitable model in this research
Proposing some solutions and recommendations to improve and enhance the quality of E-banking services and customer satisfaction about these services at Vietnamese commercial banks.
Research Scope
This study examines the various factors influencing the quality of e-banking services in Vietnamese commercial banks during the COVID-19 pandemic Conducted over a period of 3-4 months starting in June 2022, the research gathers data from customers utilizing e-banking services at several prominent banks, including Vietcombank, BIDV, and Vietinbank.
Research Methodology
The study employed a quantitative research approach utilizing a detailed survey questionnaire consisting of 27 statements related to customer satisfaction factors such as tangibles, reliability, responsiveness, assurance, and empathy, measured on a 5-point Likert scale A stratified random sampling method was used, and after three months of data collection, 200 valid questionnaires were analyzed SmartPLS software facilitated data processing and analysis, ensuring the validity and reliability of the results.
Research Structure
This dissertation is structured into six chapters Chapter 2 reviews the existing literature, while Chapter 3 examines the state of digital banking services at Vietnamese commercial banks during the COVID-19 pandemic Chapter 4 outlines the methodology used for the study, followed by the presentation and discussion of empirical results in Chapters 5 and 6 The dissertation concludes in Chapter 7, where limitations are discussed and recommendations for future research in this area are provided.
LITERATURE REVIEW
Theoretical overview of Electronic Banking (E-banking)
Electronic Banking, or "E-banking," refers to the delivery of banking services and products through electronic channels, including electronic payments (To, 2021; Indrasari et al., 2022; Bui et al., 2021) It encompasses the provision of information and services by banks to customers via various platforms accessible through personal computers or smart devices (Daniel, 1999) According to Allen et al (2001) and Nguyen et al (2021), E-banking allows clients to access banking services over computers or televisions Keivani et al describe electronic banking as a comprehensive term for completing financial transactions electronically, eliminating the need to visit physical bank branches.
(2012) The majority of experts concur that e-banking gives accessibility via a type of cutting-edge information system 24 hours a day, 7 days a week
Customers can access the bank's electronic banking services to remotely view account information, make payments, and perform financial transactions Additionally, they can enroll in various supplementary services (Pham and Nga, 2021; Verma and Tanwar, 2022).
E-banking services refer to banking transactions conducted through electronic means and telecommunications networks, allowing customers to access services without visiting a bank branch or using a phone This electronic banking channel enables the distribution of banking services to customers via the Internet and mobile devices.
2.1.2 E-banking services of Vietnamese commercial banks
The Automatic Teller Machine (ATM) is a secure electronic device that enables customers of financial institutions to access their bank accounts directly Users can perform various transactions, including cash withdrawals, cash advances with credit cards, and checking account balances, all without the assistance of a human teller Additionally, many ATMs offer the option to deposit cash, enhancing convenience for users (Tran and Tran, 2019; To, 2021).
5 or cheques, transfer money between their bank accounts, top up their mobile phones’ pre-paid accounts or even buy postage stamps
Modern ATMs require customers to identify themselves by inserting a plastic card with a magnetic stripe or a smartcard with a chip that holds their account number To verify their identity, customers must enter a passcode, commonly known as a PIN (Personal Identification Number).
Number) of four or more digits Upon successful entry of the PIN, the customer may perform a transaction
Mobile banking is generally defined as a service offered by banks that enables users to access online banking services and conduct transactions via a mobile application According to Luong et al (2020) and Dong (2021), this service allows customers to manage their financial information conveniently through their mobile devices, tailored to their specific needs.
This application offers a range of convenient services, allowing users to perform essential transactions without visiting a bank branch Users can easily transfer money, pay utility bills, register for a card, open an online savings account, check interest rates, and monitor their savings and account balances By simply downloading the mobile banking app from their bank onto their smartphones and registering for the service, customers can access these features effortlessly.
Internet Banking is an online service that allows customers to access banking functions through devices like phones, laptops, and desktop computers with an internet connection (Tran and Tran, 2019; Dam and Ta, 2020) To use this service, customers must register directly with their bank, which then provides them with an identification number (ID) and a personal encryption code (password) for secure login Through Internet Banking, customers can conveniently perform various transactions, including money transfers, account inquiries, and bill payments, all without needing to visit a physical bank branch.
According to Nguyen and Nguyen (2019) and Le and Nguyen (2020), customers can access banking services anytime and anywhere using a computer and the internet Internet Banking transactions are secured and authenticated through a one-time password (OTP) sent to the customer's registered phone number.
E-banking services enable customers to conduct transactions using mobile devices, including account inquiries, customer support information, money transfers, and payments Users can send text messages to a designated bank number to access these services and receive updates on account balance changes via SMS to their pre-registered phone numbers.
In 2021, the bank introduced a service in collaboration with telecommunications carriers, requiring customers to register directly with the bank To participate, customers must provide essential information, including their mobile phone number and the personal account designated for payments Once the registration process is completed, customers can access the service seamlessly.
Service Quality of E-banking
Numerous studies have established definitions of service quality According to Parasuraman et al (1988), service quality is characterized as the gap between consumers' expectations and their actual perceptions of the service's outcomes Additionally, Edvardsson, Thomasson, and Ovretveit contribute to this understanding.
(1994) also stated that: “Service quality is a service that meets customers’ expectations and satisfies their needs”
E-service quality refers to customers' overall assessment of excellence in e-service delivery within the virtual market (Santos, 2003) According to Zeithaml et al (2002), it encompasses the efficiency and convenience of websites in facilitating the buying, selling, and receiving of products and services In the context of e-banking, service quality is determined by the comparison between customers' expectations and their actual experiences with e-banking services.
The quality of e-banking services ensures reliability in transactions, enhances customer satisfaction, and modernizes electronic service delivery.
To enhance security and manage risks in e-banking transactions, it is essential to improve the skills and service capacity of staff providing e-banking services Additionally, ensuring a cost-effective relationship in e-banking transactions is crucial for boosting the efficiency of service delivery in the future Ultimately, customer satisfaction is a key indicator of the quality of e-banking services.
Customer Satisfaction
Customer satisfaction is defined as the assessment of a product or service based on whether it meets their needs and expectations (Zeithaml and Bitner, 2000; Tran and Tran, 2021; Shanka, 2012) The level of satisfaction is determined by the gap between expected and actual results: if the actual outcome falls short of expectations, the customer is dissatisfied; if it meets expectations, the customer is satisfied; and if it exceeds expectations, the customer experiences high satisfaction (Nga, 2022; Indrasari et al., 2022; Bala, 2021).
Customer satisfaction is defined as a customer's overall attitude towards a service provider, reflecting their emotional response to the disparity between their expectations and the actual service received It represents the fulfillment of a need, goal, or desire.
Customer satisfaction with e-banking services is influenced by their understanding and subjective assessments of service quality (Nguyen, 2020) Satisfaction arises when e-banking services fulfill customers' usage needs, which is shaped by their prior experiences with traditional banking and the actual e-banking services they encounter (Kumbhar, 2011; Agbor, 2011; Hammoud et al., 2018) After utilizing e-banking services, customers compare their experiences against their expectations, leading to evaluations of satisfaction or dissatisfaction.
Theoretical Models
2.4.1 Gronroos’ Technical-Functional Quality Model
According to the model of Gronroos (1984), service quality is assessed by comparing the value that customers expect before using and the value customers
Gronroos conducted a study to explore the impact of technical quality and functional quality on service delivery and customer perceptions He identified three key criteria for measuring service quality: technical quality, functional quality, and image.
● Technical quality is the value that the customer actually gets from the service provider (what does the customer receive?)
● Functional quality represents the service provider’s way of delivering the service to consumers (how does the customer receive the service?);
● Image plays a very important role for service providers and this factor is built mainly on two components of technical quality and functional quality
Gronroos asserts that customer expectations are shaped by various factors, including traditional marketing activities such as advertising and public relations, as well as external influences like cultural traditions and word of mouth Notably, word of mouth has a greater impact on potential customers compared to traditional marketing methods He emphasizes that the assessment of service quality should be grounded in the perspective of the service user However, a limitation of his model is its failure to clarify how technical and functional quality are measured.
Figure 2.1 – Gronroos’ Technical-Functional Quality Model
2.4.2 Five Service Quality Gap Model
On the basis of the service quality model of Gronroos (1984), Parasuraman et al
(1985) also conducted to build a quality model of the gap between consumers and suppliers at different levels The model presents five service quality gaps:
GAP1 refers to the disparity between a customer's expectations and the service provider's understanding of those expectations This gap arises when there is a lack of clarity regarding service quality characteristics and customer traits, leading to misinterpretations of what customers truly desire.
GAP2 arises when a supplier faces both objective and subjective challenges in converting perceived expectations into specific quality standards and delivering them as anticipated These standards ultimately serve as marketing communications to customers.
GAP3 forms when employees deliver services to customers that do not meet predetermined criteria The direct transaction staff role is very important in creating service quality
GAP4 represents the disparity between the service provided and the information communicated to the client While this information can elevate customer expectations, it may also lead to a decline in perceived service quality if the actual service falls short of what was promised.
GAP5 is formed from the difference between perceived quality and expected quality when customers use the service
According to Parasuraman et al (1985), service quality is identified as the fifth gap, which is influenced by the preceding four gaps, expressed as GAP5 = f(GAP1, GAP2, GAP3, GAP4) To enhance service quality and reduce this fifth gap, service providers must focus on addressing and minimizing the earlier gaps.
According to this model, service quality is a function of sq (service quality) of perceptions and expectations and can be modeled as follows:
● SQ: the overall service quality;
● Eij (Expectation): expected quality for attribute j under the effect i;
● Pij (Perception): the result of performing action i on attribute j
The GAP Gap Model is a valuable analytical tool for managers to systematically identify service quality gaps across various delivery quality variables It aids in recognizing key service quality factors from the customer's viewpoint However, a significant limitation of the model is its lack of clear measurement sequences for assessing distances at different levels.
The five-gap model serves as a theoretical framework for understanding service quality To enhance its practical application, Parasuraman refined this model and introduced the SERVQUAL measurement system, which was detailed in studies conducted in 1988 and 1991 The SERVQUAL model encompasses ten key components that assess service quality effectively.
(1) Reliability: Referring to the ability to perform the service appropriately and on time right the first time
(2) Responsiveness: Expressing the desire and willingness of service staff to provide services to customers
Competence refers to the level of expertise required to deliver services effectively It is demonstrated through employee interactions with customers, the direct execution of services, and the ability to research and gather relevant information essential for meeting customer needs.
Access involves establishing optimal conditions for customers to utilize the service, including reducing wait times and ensuring that the service location and operating hours are convenient for them.
(5) Courtesy: Expressing a warm, respectful and friendly service personality to customers
(6) Communication: Relating to communicating, communicating to customers in a language they understand easily and listening to issues related to them such as explaining services, costs, handling complaints and questions
Credibility is essential for building customer trust in a company This trust is influenced by the company's name and the demeanor of the service staff who interact directly with customers.
(8) Security: Relating to the ability to ensure the safety of customers, expressed through physical and financial safety as well as information security
(9) Understanding/Knowing the customer: Demonstrating through the ability to understand customer needs through understanding customer requirements, paying personal attention to them, and identifying regular customers
(10) Tangibles: Showing through appearance, service staff’s clothing, service equipment
The model of 10 service quality components, while comprehensive, presents challenges in measuring service quality due to its complexity Additionally, many components lack discriminant validity To address these issues, Parasuraman et al (1988, 1991) streamlined the model by combining correlated variables, resulting in five key components.
The SERVQUAL scale consists of 22 statements designed to assess both the expected and perceived service quality from customers The initial section focuses on understanding customer expectations regarding banking services, while the subsequent section evaluates their perceptions of the actual service performance of the bank in question The findings aim to identify the disparity between customers' perceptions of the service quality offered by banks and their expectations for that quality According to the SERVQUAL model, service quality is defined based on these evaluations.
The SERVQUAL model has notable drawbacks, including its lengthy questionnaire and the confusing nature of the expectation concept for respondents These issues can compromise the quality of the collected data, ultimately resulting in decreased reliability and instability of the observed variables.
The SERVPERF model was developed to address the limitations of the SERVQUAL model According to Parasuraman et al (1988), SERVQUAL is a dependable and precise tool for measuring service quality.
VIETNAM COMMERCIAL BANKS' STATE OF ELECTRONIC
Policies and Solutions to facilitate Digital Transformation of Banking Services in
Between 2020 and 2022, Vietnam's economy and society faced significant challenges due to the Covid-19 pandemic In response to these difficulties and the growing trend of digitizing banking services, the State Bank of Vietnam (SBV) proactively conducted research and evaluations The SBV implemented crucial decisions and policies aimed at fostering innovation and promoting digital transformation in the banking sector, effectively leveraging Industry 4.0 technologies to adapt to the evolving landscape.
Resolution No 52-NQ/TW, issued by the Politburo on September 27, 2019, outlines guidelines for engaging in the Fourth Industrial Revolution, emphasizing the development of a digital economy through science and technology and national digital transformation Additionally, Decision No 749/QD-TTg, approved by the Prime Minister on June 3, 2020, identifies the banking and finance sector as a priority for digital transformation, highlighting its potential to rapidly change perceptions, reduce costs, and enhance social efficiency.
The State Bank of Vietnam has issued Decision No 810/QD-NHNN on May 11, 2021, outlining a digital transformation plan for the banking sector aimed at 2025, with a vision extending to 2030 This initiative emphasizes a customer-centric approach, focusing on enhancing utility, customer experience, and product offerings through process automation and business optimization Key objectives include achieving at least 50% of transactions conducted entirely in a digital environment by 2025, ensuring a minimum of 70% of customer transactions occur via digital channels, and digitizing at least 50% of decisions related to disbursement, retail lending, and consumer loans for individual customers.
To enhance digital transformation in banking, it is essential to complete the legal framework by issuing guidelines for opening individual payment accounts and electronic bank card issuance through eKYC Additionally, the Government should approve the pilot implementation of Mobile-Money for small-value transactions, with coordination among ministries to license this initiative for three telecommunications operators A proposal for a Decree on a controlled trial management mechanism for Fintech activities, known as a Regulatory Sandbox, should also be developed, alongside a non-cash payment development scheme Furthermore, it is crucial to promulgate professional guidelines and regulations that ensure security and safety while unifying technical standards, such as QR Codes and chip cards, to facilitate seamless service provision.
The State Bank collaborates with various ministries to enhance the legal framework for electronic transactions, aiming to attract investment and support digital transformation It actively contributes to the development of the Prime Minister’s Decision and Decree regarding electronic identification, authentication, and personal data protection Additionally, the bank works with the Ministry of Public Security to establish mechanisms that enable the banking sector to access and verify customer information against the National Population Database and Citizen Identification Database, including biometric data from chip-mounted identification cards.
The State Bank of Vietnam (SBV) has significantly enhanced the interbank electronic payment system, ensuring safe, efficient, and smooth operations to meet nationwide interbank payment needs This includes the development of an electronic clearing system for retail transactions (ACH) that offers real-time payment capabilities and operates continuously, 24/7 The system supports multi-channel transaction processing and integrates with various industries to deliver banking products and services on digital platforms As of the first nine months of 2021, the interbank electronic payment system saw a 33% increase in transaction volume and an 87% rise in transaction value compared to the same period in 2020 Additionally, the electronic clearing and switching financial transaction system experienced a remarkable growth of 96.63% in quantity and 133.11% in value The market now boasts over 20,000 ATMs and nearly 300,000 POS terminals, reflecting increases of 2.81% and 6.27%, respectively, from the previous year.
The Vietnam National Credit Information Center (CIC) has undergone significant upgrades to enhance its data processing and automatic updating capabilities This improvement also includes an expanded collection of both internal and external data within the industry According to the World Bank, the coverage of credit information in Vietnam now reaches 59.4% of the total adult population, with an impressive 87% automation rate in information provision.
Providing E-banking services and Improving Customer Experience in the context
As banks increasingly prioritize digital transformation for sustainable development, they are integrating digital banking models into their business strategies to address rising customer demands A September 2020 survey by the SBV revealed that approximately 95% of banks and foreign bank branches are actively developing or implementing digital transformation strategies, with over 75% aiming to digitize all products and services, from customer communication channels to internal operations To support this shift, banks are investing in and upgrading their infrastructure, core banking systems, and technology to ensure stable operations, data security, and reduced system risks Notably, 87% of banks reported that their Core Banking systems have either fully or partially met the requirements for digital transformation activities.
Figure 3.1 - Core Banking system meets the needs of digital transformation
Banks are increasingly investing in the research and development of digital banking products and services, particularly for mobile devices This focus on innovation aims to enhance safety and security while improving user experience and boosting customer satisfaction The integration of 4.0 technologies plays a crucial role in this transformation.
The integration of AI, machine learning, and big data has revolutionized banking operations, enabling customers to conduct transactions entirely through digital channels, including payments, savings, and financial management Banks continuously enhance their Smart Banking and Internet Banking applications, incorporating user-friendly features to ensure safety and security in online payments They are also developing automated branch models that facilitate self-service transactions through digital technology With significant advancements in digital databases, many banks now report that over 90% of customer transactions occur via digital platforms.
Since its official implementation in 2002, the interbank electronic payment system has undergone numerous upgrades, playing a crucial role in the innovation and modernization of the banking system By 2021, the system was processing an average of over VND 600,000 billion per day.
Satisfied Partially Responsive Not Responding
Figure 3.2 - Transaction fluctuations in the electronic banking system over the years 2010 - 2021
Vietnam's banking industry is recognized by McKinsey as the fastest in the region for digital banking adoption, with a remarkable increase from 41% in 2015 to 82% in 2021 This growth rate surpasses the regional average of 23% and even exceeds the 33% average growth seen in emerging markets.
Figure 3.3 - McKinsey survey of personal financial services in 15 countries in the Asia-Pacific region
Digital banking services, particularly electronic payment solutions, have experienced significant growth, driven by rapid digitization In 2021, electronic payment channels demonstrated a robust performance compared to 2020, with Internet transactions increasing by 48.76% in volume and 32.59% in value, while mobile phone transactions surged by 75.97% in volume and an impressive 87.5% in value.
Many Vietnamese banks are focusing on enhancing their infrastructure to support digital transformation by investing in innovative and flexible solutions, including the implementation of new Core Banking systems, such as those adopted by the Joint Stock Commercial Bank for Development.
Ho Chi Minh City (HDBank) has adopted the Thought Machine platform, while Kien Long Joint Stock Commercial Bank utilizes Oracle FS's Flexcube platform Additionally, banks like International Joint Stock Commercial Bank (VIB) and Vietnam Technological and Commercial Joint Stock Commercial Bank (Techcombank) have transitioned to cloud computing platforms, specifically Microsoft Azure and AWS, respectively, since September 2021 Vietnam Public Joint Stock Commercial Bank (PVCombank) began integrating on WS in July 2021, and Viet A Joint Stock Commercial Bank has operated its entire Data Center system on a Private Cloud since 2017 Furthermore, many banks are exploring new infrastructure trends, including multi-cloud and hybrid cloud models.
Digital technologies such as Artificial Intelligence (AI), Machine Learning (ML), Big Data, and Robotic Process Automation (RPA) have been extensively adopted by banks, enhancing operational efficiency and customer experience while generating new revenue streams Over 90% of customer transactions are now conducted through digital channels, achieving a cost-to-income ratio (CIR) of just 30-40% Millions of customers regularly utilize digital banking applications, including Vietcombank's Digibank, VietinBank's iPay, BIDV's Smart Banking, TPBank's eBank X, and OCB's Omni-Channel By the end of December 2021, 24 banks reported the implementation of approximately 3.4 million active eKYC payment accounts, with around 1.3 million bank cards opened by March 1, 2022 The successful application of RPA technology has significantly improved processing speed and efficiency in banking operations.
Challenges and Difficulties in Converting the Arguments of Vietnamese
The banking industry's digital transformation has made significant strides, enhancing its development in the current landscape However, it faces substantial challenges that need to be addressed, particularly regarding the synchronization and relevance of existing legal regulations Key areas of concern include electronic transactions, electronic signatures, electronic documents, customer identification and authentication, data sharing, customer information security, and transaction processing on digital platforms.
The outdated technical infrastructure, characterized by legacy systems and a lack of common standards, poses significant challenges for digital transformation and integration within the industry Additionally, the rise in cyber-attacks and rapid technological advancements necessitate enhanced security measures throughout service delivery Consumers in the era of Industry 4.0 demand highly personalized, convenient banking experiences that are accessible anytime and anywhere Furthermore, the industry faces a shortage of qualified personnel, intensifying competition for talent and complicating resource investment for effective digital transformation.
DATA AND RESEARCH METHODOLOGY
Research Design
The research design serves as the framework for the methods and techniques selected by the researcher In this study, a survey strategy was employed to examine the impact of various factors on the quality of E-banking services provided by Vietnamese commercial banks during the Covid-19 pandemic This approach facilitates large-scale data collection, process control, and quantitative analysis, while emphasizing the relationship between service quality attributes—such as tangibles, reliability, responsiveness, assurance, and empathy—and customer satisfaction The effectiveness of this method is supported by previous studies, including those by Tran and Tran (2019), Al-Zatari and Reehan (2021), and Couto et al.
Sampling Method
Selecting the right sampling method is crucial for effective data collection in research In this study, the stratified sampling method was identified as the most suitable approach This probability sampling technique involves dividing the general population into groups based on criteria relevant to the research objectives, followed by the random selection of units from these groups (Etikan, 2016; Singh and Mangat, 1996; Saunders et al.).
The method discussed allows for the selection of a sample population that closely resembles the general population, particularly through proportional selection, resulting in high representativeness and minimal sampling error It is considered more scientific than simple random sampling and systematic random sampling, making it widely applicable, especially for studies involving large populations that cannot be selected mechanically Its effectiveness has been demonstrated in research conducted by Sharma and Halvadia (2015), Agbor (2011), and Saeed et al.
Data Collection
Data collection primarily utilized primary sources, supplemented by secondary sources such as journal articles and websites The primary data was gathered through a structured questionnaire.
In July and August 2022, primary data was collected from approximately 200 participants utilizing E-banking services of Vietnamese commercial banks The researcher employed an online survey method using Google Sheets and Survey Monkey, distributing the survey link to participants via email This study focuses on quantitative data, specifically cross-sectional data, and intends to utilize quantitative methods, including Ordinary Least Squares (OLS) analysis.
Descriptive Analytics
Median The value that lies in the center of data when ordering the collection of data
Min This value that is less than or equal to all other values in set of data
Max This value is the highest observation in set of data
It is sum the values of all observations in the set by the number of observations in the set
Kurtosis Value used to measure extreme values in either tail
Skewness The value used to distinguish extreme values from one tail to the other
The attribute that provides information about how the data set values differ or separate from the mean of data.
Variables Definition
Tangibles are understood as the appearance of physical facilities, equipment, personnel and written materials Researches of Dinh (2009), Chi and Quan (2013) and Couto et al
E-banking services are emphasized for their visual appeal, user-friendly layout, and convenience, ensuring that product and service information is consistently updated Additionally, research by Le and Dao (2019) and Malhotra and Singh highlights the importance of these factors in enhancing user experience.
(2009) also said that tangible means are also indispensable for the dedication of bank staff when they introduce and advise e-banking services to customers
Reliability is crucial in assessing the quality of banking services, as it reflects the dependability and accuracy of service delivery Clients gauge their satisfaction based on their ability to trust the bank's services Research by Siu and Mou (2005) and Sharma and Halvadia (2015) emphasizes that reliability is demonstrated through the consistent accuracy and stability of operations and transactions in electronic banking.
Banking services encompass various offerings, but the reliability of these services is significantly influenced by information security and verification methods (Tran and Tran, 2019; Siu and Mou, 2005) Additionally, the reputation of the service provider plays a crucial role in shaping customer trust (Pham, 2014).
Responsiveness in banking highlights the willingness and readiness of employees to provide services, which can be assessed through the availability of these services Aga (2007) demonstrated that responsiveness is evident in the usage fees and the variety of E-banking utilities offered Furthermore, McNesh (2015) indicated that the application registration process is another indicator of E-banking service responsiveness to customers Additionally, studies by Ali and Raza (2017) and Suleman et al (2012) reveal that responsiveness is also reflected in the comprehensive promotions available to customers.
Assurance denotes the ability of bank to make the clients assured about their deposit and transaction Studies of Couto et al (2013), Agbor (2011), and Sharma and Halvadia
In 2015, it was demonstrated that responsiveness in E-banking services is characterized by features such as immediate system access, the ability to conduct transactions at any time and from any location, significant time and cost savings, and the swift and precise execution of transactions.
The 'Empathy' factor emphasizes the importance of personalized customer service, recognizing that each customer is unique and their needs must be understood This dimension is demonstrated by ensuring that customers' needs and interests are met, providing support during challenges, and the bank's proactive care for its clients (Archakova, 2013; Siu and Mou, 2005; Shanka, 2012; Saeed et al., 2015).
Variables Items Question types Source
Malhotra and Singh (2009); Chi and Quan (2013); Le and Dao
Siu and Mou (2005); Sharma and Halvadia (2015); Pham (2014); Tran and Tran (2019)
Aga (2007); McNesh (2015); Ali and Raza (2017); Suleman et al
Assurance (As) Independent 6 Rating Couto et al (2013); Agbor (2011);
Archakova (2013); Siu and Mou (2005); Shanka (2012); Saeed et al
Regression
Reliability, as defined by Noble and Smith (2015), refers to the consistency of results when a test is repeated To establish reliability, researchers must consider several key parameters, including Cronbach’s Alpha, Composite Reliability, and Outer Loadings.
Cronbach’s Alpha is a key metric for assessing the reliability of scale variables, as noted by Cuu and Boi (2018, pp 26, 27) This coefficient is particularly sensitive to the number of observed variables in the scale, which can lead to an underestimation of consistent reliability.
(2016, pp 64, 65) assumed that Cronbach’s alpha must be greater than 0.7
Composite Reliability (CR) is a key metric for assessing the reliability of values, but it often overestimates internal consistency, leading to inflated reliability estimates Similar to Cronbach’s alpha, a CR value must exceed 0.7 to satisfy reliability standards (Cuu and Boi, 2018, p 27; Garson, 2016, p 63).
Outer Loadings represent the estimated relationships between latent variables and their indicators in reflective measurement models According to Hair et al (2017, p 315), these loadings indicate the extent to which an item contributes to its designated construct, with a threshold of greater than 0.7 required for validity.
Validity, according to Heale and Twycross (2015), is the degree of conceptuality that is precisely measured in quantity research
The Average Extracted Variance (AVE) is calculated by summing the average of the squared factor loadings of the variables associated with the research concept, as noted by Cuu and Boi (2018, p 27) Essentially, it represents the total of the squared load factors divided by the number of variables examined.
An AVE value of 0.5 or higher indicates that the research concept accounts for more than half of the variance in its observed variables In contrast, an AVE value below 0.5 suggests that the errors in the variables exceed the variance explained by the research concept.
Cross-loadings measure the correlation of an indicator with other constructs in a model, as outlined by Hair et al (2017) This method serves as an alternative to Average Variance Extracted (AVE) for evaluating the discriminant validity of reflective models For a model to be deemed appropriate, no indicator variable should exhibit a higher correlation with another underlying variable than with its own.
HTMT, or Heterotrait-Monotrait Ratio, measures the correlations between characteristics and their interrelationships, representing the average correlations of observed variables across different concepts Henseler et al (2015) proposed a threshold of 0.9 for concepts that are closely related in content, while a lower acceptance threshold of approximately 0.85 is recommended when the concepts are assessed to have significant distinctions (Cuu and Boi, 2018, pp 27, 28).
Collinearity, as defined by Fassott et al (2016), occurs when independent variables are closely related, resulting in linear relationships within a regression model.
To understand Variance Inflation Factor (VIF), it is essential to recognize that tolerance measures the variance of a formation index not explained by other indicators within the same block Consequently, VIF is the inverse of the tolerance coefficient In Partial Least Squares Structural Equation Modeling (PLS-SEM), a tolerance value of 0.20 or lower, along with a VIF value of 5.0 or higher, suggests a potential collinearity issue (Garson, 2016; Hair et al., 2017).
Path coefficients, as described by Garson (2016), indicate the impact of a causal variable on an effect variable, normalized from correlations For instance, the workload factor has a direct influence on job satisfaction Additionally, path coefficients reveal the significance and dimensions of relationships between variables, typically represented in SmartPLS based on the original sample parameters.
The R-square is a factor representing the combined effect of the independent variable on the dependent variable, based on Garson
The R square value, which ranges from 0 to 1, measures the predictive power of a model by quantifying the squared correlation between actual values and forecasts A higher R square indicates greater accuracy in predictions, making it a crucial metric for evaluating a model's effectiveness (Cuu and Boi, 2018, p 28).
For a factor to be considered significant in a model, it must meet two key conditions: the p-value should be less than 0.05, indicating statistical significance, while an R square value must fall within the confidence interval range of 2.5% to 97.5% (Hair et al., 2017, p 209).
Total effects encompass both direct effects, represented by path coefficients, and indirect effects This value is presented in the original sample and assessed using the p-value to determine the significance of the variables within the model (Garson, 2016, pp 59, 60).
4.6.3 Model Type and Support Tool
The researcher selected Structural Equation Modeling (SEM) as the suitable framework for the study's topic and objectives, as it effectively estimates measurement models and structures while illustrating the relationships between research variables Additionally, Smart PLS was chosen as the software tool to implement the Partial Least Squares (PLS) method for the SEM model.
FINDINGS
Descriptive Analysis
According to Table / Figure 5.1.1, out of the 200 respondents, 121 people are women, more than 1.5 times higher than the proportion of men
A survey of 200 customers reveals that E-Banking service users are diverse in age, with the highest usage rate among young customers under 25 years old at 41% The second most active group is those aged 31–40, accounting for 24%, followed by the 25–30 age group at 17% Users aged 41–50 represent 8%, while those over 50 have the lowest usage rate This indicates a trend of decreasing E-Banking usage as age increases.
E-Banking services are predominantly utilized by individuals with a College/University education, comprising 52.1% of users This is followed by Postgraduate customers at 33.9%, while those with an Intermediate education account for 14% High School graduates represent 5% of the user base, and a mere 0.5% of users come from Primary and Secondary School backgrounds.
According to Table / Figure 5.1.4, the group of customers with income below 10 million used E-banking services the most with 101 people,
Primary School Secondary School High School Intermediate level University / College Postgraduate
0.05, therefore, H3 is rejected
H4 – Assurance is positively related to Customer Satisfaction
Assurance affects positively and meaningfully on customer satisfaction (B 0.200; p-value = 0.027), therefore, H4 is supported.
H5 – Empathy is positively related to Customer Satisfaction
Empathy has positive and significantly relationship with customer satisfaction (B = 0.376; p-value = 0.007), therefore, H5 is supported
About R-square value , 78.7% of customer satisfaction are explained by tangibles, reliability, assurance and empathy
DISCUSSION
Descriptive Analytics
According to a recent survey, the percentage of women utilizing E-banking services is over 1.5 times higher than that of men This trend can be attributed to women's increased spending on essential goods during the Covid-19 pandemic, leading to a greater reliance on E-banking services Furthermore, data from Table 5.1.2 indicates that the highest frequency of E-banking usage during the Covid-19 outbreak is among individuals under a certain age group.
25 years old, combined with these people are also mostly qualified
Young individuals, particularly university and college students, typically earn around 10 million and are increasingly utilizing smart devices for banking services The Covid-19 pandemic has accelerated this trend, leading to a significant rise in the use of Internet Banking and Home Banking Popular banks like Mbbank, Techcombank, BIDV, and Vietcombank cater to their needs effectively In contrast, individuals aged 41 and above tend to avoid Internet Banking and Home Banking, preferring SMS Banking and ATM services due to their unfamiliarity with new technologies This older demographic primarily relies on the services of major banks, known as the Big 4: BIDV, Vietcombank, Vietinbank, and Agribank.
Table 5.1.7 and the histograms in Appendix B.1 indicate a strong consensus among users regarding the attractiveness and comfort of E-banking services, particularly in terms of image, continuity, and usefulness The values for 'Tg' in Table 5.2.1 demonstrate high reliability and validity, with factor loadings, CR, Cronbach’s alpha, and composite reliability index all exceeding 0.7, and an AVE over 0.5 This suggests that customers are significantly interested in E-banking services, with a notable focus on banks like MBbank and Techcombank.
BIDV and Vietcombank are notable players in the banking sector, although there are some differing opinions regarding the user experience Specifically, Agribank and VIB have received mixed feedback on their interface layout, which is described as simple and easy to understand, as well as the appeal of their e-banking services.
The analysis of Table 5.1.8 reveals that most participants selected the "Agree" option regarding the reliability of E-banking services, indicating that customers perceive the features and promotions as accurate and aligned with the Bank's promises Furthermore, the values of 'Re' presented in Table 5.2.1 demonstrate strong reliability, with factor loadings, CR, Cronbach’s alpha, and composite reliability indices all exceeding 0.7, and an AVE greater than 0.5 Notably, Re5 received the highest mean score, suggesting that customers associate high-quality and reliable E-banking services primarily with banks such as Vietcombank, BIDV, and Vietinbank.
The survey respondents unanimously agreed on the importance of responsiveness, indicating their comfort with the flexibility and customization of the service The factor loadings, Cronbach’s alpha, and composite reliability (CR) values exceed 0.7, while the average variance extracted (AVE) is above 0.5, demonstrating high convergent validity and reliability Notably, Rs2 recorded the highest mean value of 4.328, highlighting the numerous utilities available in the E-banking applications, particularly at BIDV, MBbank, and Vietcombank.
Participants unanimously agree that the service capacity of E-banking services offered by commercial banks is rated as very good The factor loadings, Cronbach’s alpha, and composite reliability (CR) values exceed 0.7, while the average variance extracted (AVE) is above 0.5, indicating that all six assurance items demonstrate high convergent validity and reliability This reflects customers' desire for E-banking services to be accessible anytime and anywhere, especially during the complexities of the Covid-19 pandemic Notably, banks such as Vietcombank, Techcombank, BIDV, MB, and Vietinbank effectively meet these customer expectations.
Table 5.1.11 indicates a strong consensus among customers regarding empathy, as they perceive that the bank actively cares for their needs and ensures essential benefits when utilizing e-banking services Additionally, all indicators, including Cronbach’s alpha, factor loadings, and CR, support this finding.
The 'Em' variable demonstrates a strong contribution to the overall model, with values exceeding 0.7 and an Average Variance Extracted (AVE) greater than 0.5, indicating a high level of convergent validity Notably, Em2 has the highest mean score of 4,471, reflecting customers' perception that banks, particularly BIDV, Vietcombank, and Vietinbank, consistently provide optimal benefits for e-banking services during the Covid-19 pandemic.
The findings indicate a high level of customer satisfaction with E-banking services, as reflected by mean values around 4.5 Additionally, the indicators of the 'Sa' variable exceed the standard thresholds for factor loadings, Cronbach’s alpha, CR, and AVE, demonstrating strong convergent validity and reliability The R-square value of 0.787 suggests that 78.7% of customer satisfaction is accounted for by the SERVPERF model, which includes tangibles, reliability, responsiveness, assurance, and empathy, while other factors remain unexplored Notably, the highest mean value for Sa2 indicates that customers are eager to continue using E-banking services beyond the Covid-19 context.
Relationship Test
The analysis in Table 5.2.3 reveals a p-value of 0.043, indicating a significant relationship between tangibles and customer satisfaction, as it is less than the threshold of 0.05 Furthermore, the confidence interval of 0.204 suggests that for every one-unit increase in tangibles, customer satisfaction rises by 0.204 units, confirming a positive correlation These results align with the findings of Dinh (2009), Chi and Quan (2013), and Couto et al (2013).
Customers prioritize form, interface, and convenience when using E-banking services on smart devices, a trend that intensified during the Covid-19 pandemic Survey results indicate high satisfaction levels with E-banking, particularly when product information is regularly updated, the browser interface is user-friendly, and the service is visually appealing However, opinions on staff support vary between "Neutral" and "Agree," largely due to regulations limiting close contact and staff rotation, resulting in less direct interaction with employees Overall, customer satisfaction with E-banking services is largely driven by tangible aspects.
The results from Table 5.2.3 indicate a significant relationship between reliability and customer satisfaction, as evidenced by a p-value of 0.03, which is less than the 0.05 threshold The confidence interval of 0.179 suggests that for every one unit increase in reliability, customer satisfaction increases by 0.179 units These findings align with the research conducted by Hammoud et al.
Maintaining reliability in E-banking significantly enhances client satisfaction, as accurate and dependable information leads to higher levels of contentment with the service.
As the use of smart devices and technology surges during the Covid-19 pandemic, customer interest in reliability has intensified due to the inherent risks associated with electronic devices and cyberspace Nguyen (2020) highlights several challenges in e-banking, including network and technical issues, confidentiality and security concerns, and customer authentication risks By effectively addressing these risks, banks can significantly enhance the customer experience with e-banking services, ensuring stable systems and the protection of confidential information Furthermore, a bank's reputation plays a crucial role in fostering customer trust.
42 customers in E-banking products Thereby, the reliability of using E-banking services can meet customer satisfaction
The p-value of Rs – Sa is 0.835, indicating no significant relationship between responsiveness and customer satisfaction, as shown in Table 5.2.3 This finding aligns with the research conducted by Leninkumar (2016), Ismail et al (2013), and Agbor (2011).
Many banks are failing to meet customer expectations despite regular updates to their services The lack of diversified tools, such as tuition collection and online bill payments, further exacerbates this issue Additionally, the eKYC online registration process remains complicated due to government restrictions related to Covid-19, limiting direct contact Furthermore, many banks are not offering free services during these challenging times, which negatively impacts customer satisfaction with e-banking services.
With p-value is 0.027 < 0.05(*) and B = 0.200 (Table 5.2.3), so that, assurance has marginal positive impact on customer satisfaction The results are supported by Couto et al (2013), and Sharma and Halvadia (2015)
During the Covid-19 pandemic, customer expectations for electronic applications and services have risen significantly, demanding speed, convenience, accuracy, and accessibility at any time and place This trend is particularly evident in electronic banking, where users seek the ability to conduct transactions quickly and reliably from home or while in isolation Additionally, customers are increasingly focused on saving time and costs due to the economic challenges posed by the pandemic Meeting these heightened expectations leads to a substantial increase in customer satisfaction and fosters ongoing trust in E-banking services.
The p-value of Em – Sa is 0.007(*) < 0.05 and confidence intervals value is 0.376, which means that when increasing 1 unit of empathy, the customer
Empathy has a significant positive relationship with customer satisfaction, with an increase of 0.376 units in satisfaction levels These findings align with the research conducted by Saeed et al (2015) and Shanka (2012).
Customers using E-banking services expect their banks to meet all transaction needs, including shopping and bill payments, while ensuring their rights are protected When banks actively engage with customers through gestures like birthday gifts and preferential vouchers, and provide prompt support during issues, it enhances the feeling of being valued Additionally, during the challenging times of the Covid-19 pandemic, banks have offered interest rate support and loan incentives, further boosting customer satisfaction with E-banking services.
