(Luận văn thạc sĩ) internet banking adoption in ho chi minh city, an application of unified theory of acceptance and use of technology (utaut) model

CHAPTER 1: INTRODUCTION

Research background

Internet banking has revolutionized the way customers manage their finances, enabling them to perform various banking activities online without the need to visit a physical branch This innovative service, driven by advancements in Internet communication technology, offers convenience for users while also serving as a cost-effective solution for banks to deliver customer services efficiently.

Internet banking provides significant benefits, including reduced costs, convenience in location and time, and the quick execution of transactions Research indicates that a 1% increase in the acceptance of Internet banking can lead to an 18% reduction in operating costs (Bhattacherjee, 2001) Consequently, a lack of consumer adoption can lead to substantial financial losses for institutions Thus, the enhancement of Internet banking services is crucial for effective bank management.

Between 2000 and June 2012, global Internet user growth reached an impressive 566.4% Asia accounted for the largest share of Internet users at 44.8%, followed by Europe at 21.5% and North America at 11.4% Notably, Vietnam ranked among the top 20 countries with the highest number of Internet users during this period In 2000, Vietnam had only 200,000 Internet users, but by June 2012, this number surged to 31,034,900, reflecting a remarkable increase of 155 times over 12 years (Internet World Stats, 2012).

Launched in May 2002, Internet banking in Vietnam aimed to provide banking services to account holders However, a 2012 Nielsen Global survey revealed that only 1.02% of banking customers utilized these services, a significantly low figure compared to the country's Internet subscriber base at that time This low adoption rate also pales in comparison to developed Asian nations, indicating that Vietnam's Internet banking sector represents a promising market for future growth Consequently, understanding the factors that influence the use of Internet banking services is essential for development in this area.

Problem statement

Internet banking has transformed the banking industry by integrating information technology and communication networks, as noted by Yasuharu (2003) This shift to electronic banking is essential for banks, providing significant competitive advantages and enabling the establishment of stronger, more lasting relationships with customers.

Despite the numerous benefits of Internet banking, its acceptance in Vietnam remains relatively low compared to Western countries and the United States Most studies in the Asian region have focused on developed nations such as Singapore, Hong Kong, and Taiwan, leaving developing countries like Vietnam underexplored over the past two decades Understanding the factors influencing the acceptance or rejection of new technologies poses significant challenges Therefore, researching the behavioral intention to use Internet banking in Ho Chi Minh City, Vietnam, is essential, serving as the primary motivation for this study.

Many studies have primarily examined the positive aspects of Internet banking services, with notable research by AbuShanab and Pearson (2009) highlighting the roles of trust, relative advantage, and user attitudes Additional factors enhancing the UTAUT model include voluntariness of use (Anderson and Schwager, 2004), perceived credibility, and anxiety (Yeow et al., 2008), along with self-efficacy, perceived trust, perceived risk, personal innovativeness, and locus of control (AbuShanab and Pearson, 2009) Yeow et al (2008) demonstrated that perceived credibility and anxiety are significant in the Malaysian market, which shares similarities with Vietnam’s market The motivation for this study stems from the scarcity of research on Internet banking within Vietnam through the UTAUT lens Notably, Khuu and Nguyen (2011) found that performance expectancy, social influence, perceived credibility, and anxiety significantly influence customers' intentions to adopt Internet banking, while effort expectancy and self-efficacy do not.

This study investigates the impact of the UTAUT model, which includes performance expectancy, effort expectancy, social influence, and facilitating conditions, along with two additional factors—perceived credibility and anxiety—on the intention to use Internet banking in Ho Chi Minh City, Vietnam The findings are significant as they will assist banks in developing effective marketing strategies to promote the adoption of Internet banking among customers in the future.

Research objective

This study aims to explore and validate the factors influencing behavioral intentions to use Internet banking in Ho Chi Minh City, Vietnam, utilizing the UTAUT model for analysis.

- Examine the impact of performance expectancy on behavioral intention to use Internet banking

- Examine the impact of effort expectancy on behavioral intention to use Internet banking

- Examine the impact of social influence on behavioral intention to use Internet banking

- Examine the impact of facilitating conditions on behavioral intention to use Internet banking

- Examine the impact of perceived credibility on behavioral intention to use Internet banking

- Examine the impact of anxiety on behavioral intention to use Internet banking.

Research scopes and methodology

This study was carried out in Ho Chi Minh City, Vietnam's largest economic hub, focusing on customers with bank accounts who are aware of but do not utilize Internet banking services Due to time and cost constraints, the research analyzed data from three banks: Bank for Investment and Development of Vietnam (BIDV), Asia Commercial Bank (ACB), and Military Commercial Joint Stock Bank (MBBank).

The study is structured in two phases: a qualitative phase involving in-depth interviews with ten participants, followed by a quantitative phase with a sample size of 300 respondents Data analysis will be conducted using SPSS 22.0 software, which includes three main steps: first, assessing the reliability of the measurement scale using Cronbach’s alpha; second, validating the measurement scale and reducing data through Exploratory Factor Analysis (EFA); and finally, employing Multiple Linear Regression (MLR) to examine the relationships between variables in the research model.

Thesis structure

This study is structured into five chapters: the first chapter serves as the introduction, while the second chapter presents a literature review and outlines the hypotheses The third chapter details the research methodology employed, followed by the fourth chapter, which focuses on data analysis Finally, the fifth chapter concludes with a discussion of the research findings, implications, and recommendations for future research.

This chapter reflects the current situation of Internet banking services in Vietnam

It leads to proposal for the research problem, research objectives, research scope and significance of this study also presented in this section

This chapter elucidates the variables in the model and the rationale behind their selection Additionally, it provides insights into the model and outlines the hypotheses to be tested in this research.

The study will encompass a detailed design and the creation of survey questions, alongside qualitative research methods It will explicitly identify the measurement scale factors utilized in the study Additionally, the approach for data collection and analysis will be outlined to effectively test the hypotheses introduced in Chapter 2.

This section discussed the data collection methods used to test hypotheses, and analyze the data received, the reliability and multiple regressions

This chapter includes the conclusions and managerial implications The limitations are recognized to direct in the future Lastly, thesis has proposed further research on the subject area h

CHAPTER 2: LITERATURE REVIEW AND HYPOTHESIS

Theoretical foundation

The technology acceptance literature offers a diverse array of models and theories that elucidate the adoption of information technology innovations, as highlighted by Venkatesh et al (2003) Their empirical comparison of eight prominent models—including the Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), Model of PC Utilization (MPCU), Motivation Model (MM), Innovation Diffusion Theory (IDT), Social Cognitive Theory (SCT), and a combined model of TAM and TPB (C-TAM-TPB)—provides valuable insights into technology adoption dynamics Notably, influential models such as TRA, TPB, and TAM have significantly shaped our understanding of user acceptance of technology (El-Gayar, O., Moran, M., & Hawkes, M., 2011).

2.1.1 The Theory of Reasoned Action (TRA)

The Theory of Reasoned Action, developed by Fishbein and Ajzen in the 1970s, evolved from earlier research on attitudes to examine the relationship between attitude and behavior This model predicts behavioral intention by integrating three key constructs: behavioral intention (BI), attitude toward behavior (A), which reflects an individual's positive or negative feelings about engaging in a specific behavior, and subjective norm (SN), which represents the individual's perception of social pressure from important others regarding the behavior in question.

Figure2.1: Theory of Reasoned Action (Ajzen& Fishbein, 1975)

According to the Theory of Reasoned Action (TRA), an individual's intention to engage in a behavior is influenced by their attitude towards that behavior and the subjective norms surrounding it, represented by the formula BI = A + SN Therefore, if a person has a strong intention to perform a behavior, it is highly probable that they will follow through with it.

2.1.2 Theory of Planned Behavior(TPB)

The Theory of Planned Behavior (TPB), developed by Ajzen in 1985, extends the earlier Theory of Reasoned Action (TRA) and effectively predicts deliberate behaviors by acknowledging that actions can be both planned and intentional This well-established theory has been validated in social science and information technology literature, particularly in explaining and forecasting user behavioral intentions (Mykytyn and Harrison, 1993) Key factors influencing these intentions include the individual's attitude toward the behavior, subjective norms, and perceived behavioral control.

Normative Beliefs and Motivation h control (PBC) Perceived behavior control is “the perceived ease of difficulty of performing the behavior” (Ajzen 1991,p.188)

Figure2.2: Theory of Planned Behavior (Ajzen& Fishbein, 1985)

The Theory of Planned Behavior (TPB) posits that intention is influenced by subjective attitudes, social norms, and perceived behavioral control, which encompasses the resources, skills, opportunities, and perceptions necessary for an individual to achieve their goals This model is regarded as a more effective framework than the Theory of Reasoned Action (TRA) for predicting and understanding consumer behavior in various contexts.

The Technology Acceptance Model (TAM), developed by Fred Davis and Richard Bagozzi, is rooted in the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB), specifically within the context of electronic banking This model aims to elucidate the factors influencing a user's decision to accept or reject information technology By concentrating on the behavioral intention to utilize a particular technology or service, TAM has established itself as a prominent framework for understanding user acceptance and usage patterns.

Perceived Behavioral Control Control Beliefs h

Figure2.3: Technology Acceptance Model (Davis et al, 1989)

The Technology Acceptance Model (TAM), introduced by Davis in 1989, is widely utilized to understand the adoption of new technologies Unlike earlier models such as the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB), TAM is particularly effective in explaining user behavior in the realm of E-banking.

- Perceived ease of use: The degree to which a person believes that using a particular system would be free from effort

- Perceived usefulness: The degree to which a person believes that using a particular system would enhance his or her job performance

Research indicates that the Theory of Planned Behavior (TPB) is a more effective predictor of behavioral intention compared to the Theory of Reasoned Action (TRA) (Ajzen & Madden, 1986) Additionally, both the TPB and the Technology Acceptance Model (TAM) demonstrate significant predictive capabilities regarding adoption intentions, with TAM showing a superior ability to forecast attitudes than TPB (Mathieson, 1991) Furthermore, TAM accounts for a greater variance in adoption intentions when compared to TRA (Davis et al., 1989).

The Unified Theory of Acceptance and Use of Technology (UTAUT)

The development of the Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al (2003) builds upon several foundational theories, including the Theory of Reasoned Action (TRA) by Fishbein and Ajzen (1975), the Technology Acceptance Model (TAM) introduced by Davis in 1989, the Theory of Planned Behavior (TPB) by Ajzen (1991), and the combined TAM and TPB model (C-TAM-TPB) proposed by Taylor and Todd in 1995.

Attitude Intention to use Use h

The article discusses several influential theories in understanding technology adoption, including Rogers' Theory of Diffusion of Innovation (1995), Bandura's Social Cognitive Theory (1986), Davis et al.'s Motivational Model (1992), and Thompson et al.'s Model of PC Utilization (1991) Venkatesh et al (2003) identified four key determinants of behavioral intention and usage: performance expectancy, effort expectancy, social influence, and facilitating conditions Notably, empirical findings indicate that the UTAUT model accounts for 70% of the variance in usage intention, highlighting its superiority over other antecedent models.

The Unified Theory of Acceptance and Use of Technology (UTAUT) identifies four key constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions These constructs serve as direct determinants of both usage intention and behavioral intention, as established by Venkatesh et al.

Performance Expectancy (PE) refers to the degree to which an individual believes that utilizing a system will enhance their work performance, as outlined by Venkatesh et al (2003) This concept is closely related to constructs in other models, such as perceived usefulness in the Technology Acceptance Model (TAM) and the combined TAM-Theory of Planned Behavior (TPB).

Effort expectancy refers to the level of ease experienced when using a system, as defined by Venkatesh et al (2003) This concept is similarly represented in other models, such as perceived ease-of-use in the Technology Acceptance Model (TAM) and its extension TAM2.

Social influence refers to the extent to which individuals feel that significant others expect them to adopt a new system, as outlined by Venkatesh et al (2003) This concept aligns with similar constructs found in established models, including subjective norms in the Theory of Reasoned Action (TRA), Technology Acceptance Model 2 (TAM2), Theory of Planned Behavior (TPB), and the combined TAM-TPB framework.

Facilitating conditions refer to an individual's perception of the availability of organizational and technical infrastructure that supports system usage, as defined by Venkatesh et al (2003) This concept encompasses three constructs from established models: perceived behavioral control from the Theory of Planned Behavior (TPB), the Decomposed TPB (DTPB), and the combined Technology Acceptance Model (TAM) with TPB, as well as facilitating conditions from the Model of PC Utilization (MPCU).

Figure2.4: Unified Theory of Acceptance and Use of Technology(Venkatesh et al., 2003)

Development of research hypotheses

The UTAUT model has been empirically validated and shown to outperform other competing models (Venkatesh et al., 2003; AbuShanab & Pearson, 2009; Yeow et al., 2008; Venkatesh & Zhang, 2010; Foon & Fah, 2011), making it the chosen theoretical framework for developing the hypotheses in this study.

This research presents a model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), as illustrated in Figure 2.5, highlighting six key factors that influence the behavioral intention to adopt Internet banking The model incorporates four core elements from the original UTAUT framework: performance expectancy, effort expectancy, social influence, and facilitating conditions, which collectively shape users' acceptance of Internet banking services.

Age Gender h expectancy, social influence, and facilitating conditionsare from Venkatesh et al.(2003) Two additional dimensionsperceived credibility and anxietyadapted from Yeow et al

Performance expectancy refers to the user's belief that a technology enhances productivity (Raman & Don, 2013) This concept encompasses the evaluation of benefits gained from adopting and utilizing new initiatives Internet banking provides numerous advantages to customers, enabling them to conduct various financial transactions efficiently Additionally, accessibility plays a crucial role in shaping the performance expectancy associated with Internet banking (Polatoglu & Ekin, 2001; Raman & Don).

According to Polatoglu & Ekin (2001), the Internet allows users to access online banking services anytime and anywhere, making banking more efficient and convenient than traditional methods This accessibility reduces the need for customers to wait in lines, leading to improved work efficiency and time savings As a result, customers are likely to develop a positive attitude towards Internet banking, recognizing its advantages and utilities.

In the context of this study, performance expectancy is defined as the belief that utilizing Internet marketing can enhance user benefits, including increased productivity, efficiency, and time savings Previous research in Internet banking has demonstrated a significant positive relationship between performance expectancy and the intention to use Internet banking (AbuShanab & Pearson, 2009; Yeow et al., 2008; Foon & Fah, 2011) Therefore, this study hypothesizes that performance expectancy will significantly influence the behavioral intention to use Internet banking, leading to the formulation of Hypothesis H1.

Hypothesis H1: “Performance expectancy has a positive effect on behavioral intention to use Internet banking”

Effort expectancy refers to an individual's perception of how easy or difficult it is to use a particular system, encompassing both physical and mental aspects (Davis, 1989) This perception significantly influences the intention to adopt and utilize the system in the future, as it relates to the overall complexity experienced by the user (Thompson et al.).

Effort expectancy plays a crucial role in the acceptance of Internet banking, as it influences users' perceptions of ease in interacting with the system Research by Calisir and Gumussoy (2008) highlights that when consumers believe they can effortlessly navigate Internet banking, they are more likely to adopt it Studies conducted by AbuShanab and Pearson (2009) in Jordan, as well as Foon and Fah (2011) in Malaysia, further confirm the significant relationship between effort expectancy and behavioral intention This study aims to test the hypothesis that effort expectancy is a significant determinant of the intention to use Internet banking.

Hypothesis H2: “Effort expectancy has a positive effect on behavioral intention to use Internet banking”

Social influence significantly impacts the intention to use Information-Based Systems (IBS), as highlighted in various studies (Venkatesh & Davis, 2000) This concept, prevalent in technology acceptance literature, refers to how individuals are affected by the beliefs, feelings, and behaviors of others (Mason et al., as cited in Ting et al., 2011) Key figures in an individual's life, as well as reference groups like organizations and media, play crucial roles in shaping decisions regarding the adoption of new technologies (Thompson et al., 1991; Mathieson, 1991; Taylor & Todd, 1995).

Social influence has been extensively studied in various contexts, yielding mixed results regarding its impact on behavioral intentions related to technology use While some research, including studies by Deng et al (2011), Gao and Deng (2012), and Wong et al (2013), found insignificant relationships among these constructs, other studies highlight the importance of personal connections—such as family, supervisors, and peers—in shaping user behavior towards Internet banking (Bagozzi and Dholakia, 2002) Additionally, previous research indicates that social influence significantly affects the intention to use Internet banking (AbuShanab & Pearson, 2009; Yeow et al., 2008) This study posits that social influence will positively impact behavioral intentions towards Internet banking, leading to the formulation of specific hypotheses for testing.

Hypothesis H3: “Social influence has a positive effect on behavioral intention to use Internet banking” h

Facilitating conditions play a crucial role in the adoption of new technology, particularly in Internet banking, as they encompass the availability of resources like written documents and technological infrastructure (Raman & Don, 2013) To promote the use of Internet banking, it is essential to ensure easy access, navigation, and effective searching, especially when accompanied by proper guidance Additionally, factors such as associated costs and the prior knowledge required by users significantly influence their ability to engage with Internet marketing Ultimately, facilitating conditions emerge as the most vital factor impacting the use of Internet banking.

Ajzen (1991) proposed that facilitating conditions represent the external environmental influences on an individual's perceived controllability In the context of Internet banking, we define perceived facilitating conditions (PFC) as the external aspects of the system as perceived by customers, which they believe impact their ability to utilize the Internet banking services Although customers do not have control over these conditions, it is anticipated that PFC will positively influence their behavioral intention to use Internet banking in Vietnam Additionally, both facilitating conditions and habit are significant predictors of the intention to adopt Internet banking, highlighting their direct impact on behavioral intention (Ajzen, 1991; Yuen et al., 2010; Foon and Fah, 2011) Consequently, this study tests the following hypotheses.

Hypothesis H4: “Facilitating conditions have a positive effect on behavior intention to use Internet banking” h

Perceived credibility is the degree to which individuals view Internet banking services as trustworthy and secure, making it a crucial predictor of their intention to use these services (Yeow et al., 2008) Enhancing perceived credibility can lead to greater acceptance of Internet banking, highlighting the importance of building customer trust to retain existing bank clients (Kumar, 2013; Mukherjee & Nath, 2003) Distinguishing perceived credibility from perceived risks and trust, Wang et al (2003) emphasized security and privacy as key components of perceived credibility Additionally, research by Luarn and Lin (2005) and Amin et al (2008) demonstrated that perceived credibility significantly influences users' intentions to adopt mobile banking services.

Different scholars approach the concepts of security, risk, trust, and credibility from various perspectives, leading to diverse interpretations based on their disciplines Perceived credibility has been empirically validated in studies related to mobile banking (Luarn & Lin, 2005; Amin et al., 2008) and is also prominent in Internet banking research (Wang et al., 2003; Amin, 2009; Yuen et al., 2010) This study utilizes perceived credibility to address individual concerns regarding security, privacy, risk, and trust in the context of Internet banking adoption Therefore, the study proposes the following hypothesis.

Hypothesis H5: “Perceived credibilityhas a positive effect on behavioral intention to use Internet banking” h

Anxiety, as defined by Liao and Cheung (2003), refers to the nervousness experienced when using new technology, particularly in the context of computer usage This phenomenon has been supported by Compeau and Higgins (1995), who noted that anxiety influences behavior trends related to computer use Research by Venkatesh (2000) and Venkatesh and Bala (2008) indicates that technology anxiety reduces perceived ease of use, which in turn affects individuals' intentions to engage with technology Specifically, Venkatesh et al (2003) describe anxiety as the level of apprehension an individual feels when using information-based systems (IBS), including fears of password theft or making mistakes in online banking Furthermore, Doyle et al (2005) found that individuals with limited computer and Internet experience tend to experience higher levels of anxiety compared to their more experienced counterparts.

This study explores the impact of Internet service anxiety on the intention to use Internet banking services, highlighting that anxiety represents a negative emotional reaction to engaging with such technology Existing literature emphasizes the role of technology anxiety in shaping user intentions Nevertheless, as users gain familiarity with Internet banking, they often overcome their initial anxiety and develop positive perceptions of the service.

Summary of research model and hypotheses

The proposed conceptual framework model, illustrated in Figure 2.5, encompasses six hypotheses (H1 to H6) derived from the literature review The independent and quantitative variables identified in this model directly influence the behavioral intention to use Internet banking, which serves as the dependent variable.

Figure2.5: The proposed research model with hypotheses h

Hypothesis H1: “Performance expectancy has a positive effect on behavioral intention to use Internet banking”

Hypothesis H2: “Effort expectancy has a positive effect on behavioral intention to use Internet banking”

Hypothesis H3: “Social influence has a positive effect on behavioral intention to use Internet banking”

Hypothesis H4: “Facilitating conditions have a positive effect on behavior intention to use Internet banking”

Hypothesis H5: “Perceived credibilityhas a positive effect on behavioralintention to use Internet banking”

Behavioral Intention to use Internet Banking

Hypothesis H6: “Anxiety has a negative effect on behavioral intention to use

Summary

This chapter examines the relationship between performance expectancy, effort expectancy, social influence, facilitating conditions, perceived credibility, anxiety, and the behavioral intention to use Internet banking These models have been effectively utilized in prior research concerning bank customers, providing a solid foundation for developing a research model that measures the factors influencing customer intention to adopt Internet banking services The subsequent chapter will outline the methodology employed to analyze the data and test the hypotheses of the research model.

CHAPTER 3: RESEARCH METHODOLOGY

Research design process

This part shows a flowchart of methods used in this research in Figure 3.1.The current study consisted mainly of two stages: a qualitative phase (pilot study) and a quantitative phase (main survey)

Development of questionnaires

The model's variables were assessed using multiple items developed by prior researchers to effectively capture the constructs' domains Performance expectancy was evaluated with six items from Yeow et al (2008), while effort expectancy utilized four items from Foon & Fah (2011) and AbuShanab & Pearson (2009) Social influence was measured through five items from AbuShanab & Pearson (2009), and facilitating conditions were assessed with four items from Yeow et al (2008) Additionally, perceived credibility and anxiety were each measured using four items from Yeow et al (2008) Finally, behavioral intention to use Internet banking was gauged with three items developed by AbuShanab & Pearson (2009) and Foon.

In the studies by Fah (2011) and Yeow et al (2008), a five-point Likert scale was utilized to assess responses, with options ranging from 1 (strongly disagree) to 5 (strongly agree) A detailed list of indicators used in the measurement model is presented in the table below.

Table3.1: Construct and corresponding items

Construct Code Items Items sources

PE1 IBS is convenient and easy to access

PE2 I can transfer money anytime and any where

With PE3, I can efficiently save time by paying essential bills at the post office PE4 allows me to maintain a comprehensive record of my finances, while PE5 eliminates the need for regular visits to traditional banks Additionally, PE6 provides the convenience of managing my money online at any time.

EE1 Internet banking is easy to learn

EE2 Internet Banking is easy to use EE3 Internet Banking saves me a lot of time

EE4 It would be easy for me to become skillful at using IBS h

Construct Code Items Items sources

SI1 In general, the bank has supported the use of

SI2 The senior management of the bank has been helpful in the use of IBS

SI3 People who are important to me think that I should use IBS

SI4 People who influence my behavior think that

SI5 People using IBS have more prestige than those who do not

FC1 All contents of IBS are easy to read and understand

FC2 Basic system requirements for using IBS are met

FC3 Specific person (or group) is always available for assistance

FC4 The language in which the document is written is easily understood

TR1 I trust in the ability of an IBS to protect my privacy and personal information

TR2 I believe no money will be lost when I transfer

TR3 IBS has enough specialists to detect fraud and information theft

TR4 Other people cannot view my bank account information

AT1 I am afraid of losing information by hitting the wrong key

AT2 I am worried about the inaccessibility of IBS web pages AT3 I am afraid of being charged for IBS

AT4 I am afraid of making mistakes that I cannot correct

BI1 I intend to use IBS in the next few months AbuShanab &

BI2 I predict that I would use IBS in the next few months BI3 I plan to use IBS in the next few months h

The questionnaire, utilizing a five-point Likert scale, was designed to gather data on the factors of the research model, with items primarily adapted from prior studies to ensure content validity The measurement of performance expectancy, effort expectancy, social influence, facilitating conditions, perceived credibility, anxiety, and behavioral intention to use Internet banking was sourced from existing research (refer to section 3.2.1 for measurement scales) The structure of the questionnaire is organized into three main sections.

- Part 1: This section includes a consent form and screening questions to identify exactly the target audience of this survey

In Part 2 of the research, the core information revolves around the measurement scale utilized for data collection The items were assessed using a five-point Likert scale, where responses ranged from 1 (strongly disagree) to 5 (strongly agree), allowing for a nuanced understanding of participant attitudes and perceptions.

- Part 3: Questions on demographics characteristics of the participants including age and gender, income and education

The survey questionnaire was originally designed in English and then translated into Vietnamese by the researcher with the support of some English experts and banking experts.

Pilot study

To ensure the effectiveness of the qualitative research, the Vietnamese version of the survey questionnaire underwent pre-testing through in-depth interviews with 10 participants, including 6 banking experts and 4 customers knowledgeable about Internet banking services These interviews aimed to validate the clarity and relevance of the final questions for measuring observed variables prior to the main survey launch The detailed questionnaire was presented to the interviewees to confirm their understanding, while also assessing the appropriateness of the selected measurement scale for the Vietnamese context Feedback from the respondents was collected to refine the measurement scale, resulting in a slightly modified survey questionnaire that enhanced clarity and comprehension (see appendix B).

After Pilot study, questionnaires were used for survey in large numbers Results of Pilot test proved the good design of questionnaires, and they were used in main survey.

Main survey

Researchers emphasize the necessity of a large sample size for this method, grounded in large sample distribution theory (Raykov and Widaman, 1995) Nonetheless, there is no clear definition for what constitutes a sample size significantly larger than the current requirements.

In EFA, the sample size is usually determined based on (1) the minimum size and (2) the number of variables included in the analysis

Hair et al (1998) suggested that the minimum sample size for statistical analysis is based on the equation below:

N is sample size k is number of variables

The model in this study consisted seven factors with 30 variables so that the minimum sample size for this study should be: 5*30= 150 observations

Tabachnick and Fidell (2001) stated that the minimum sample size in case of multiple regression should be:n = 50+8m (where m is the number of independent variables)

Applying this formula for 6 independent variables, we have the minimum sample size for multiple regression as follow:n = 50 + 8 x 6 = 98

Summarily,30 variables and 6 independent variables,this research needed 150 observations at least for running EFA and Multiple Regression

The author employed convenience sampling via a survey, a widely utilized method for data collection in research, as noted by Tull and Hawkins (1987).

The study focused on participants with bank accounts who are knowledgeable about Internet Banking services but do not utilize them, specifically from three banks in Ho Chi Minh City: BIDV, ACB, and MBBANK A survey was conducted involving 300 clients, employing a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) to assess their perceptions and attitudes towards Internet Banking.

The author utilized her personal connections to reach the target sample for the study Data collection occurred from December 2013 to January 2014, employing both direct methods through paper surveys and indirect methods via online surveys to gather responses from participants.

Participants were invited to complete either an online or paper survey, consisting of friends, clients, colleagues, and partners with a direct connection to the author To facilitate quicker responses, the author employed various methods, including phone calls, chat reminders, and in-person meetings.

Respondents were able to provide their feedback via email, mail, or chat platforms such as Yahoo and Skype, with direct responses sent to the author Additionally, the author conducted phone calls to clarify any questions and assist participants in providing accurate answers.

Data collection took place over a month, encompassing both weekdays and weekends A total of 300 questionnaires were distributed via paper and online surveys, resulting in 278 responses, with 242 deemed usable This quantitative survey was conducted in Ho Chi Minh City from December 2013 to January 2014.

Data analysis method

After cleaning the data and removing invalid questionnaires, the remaining data will be processed using SPSS 22.00 software (Statistical Package for Social Sciences) The analysis will be conducted through a series of systematic steps to ensure accurate results.

The scale serves as a dependable measure of internal consistency, evaluating the reliability of each variable within the measurement scales Observed variables reflect a common construct, with high reliability indicated by strong inter-correlations among items According to Hair et al (1998), reliability analysis utilizes Cronbach’s alpha coefficient and item-total correlation to assess these relationships A Cronbach’s alpha value of 0.7 is generally considered acceptable for reliability, while a threshold of 0.6 may be acceptable in exploratory research Additionally, any variable with a Corrected Item-Total Correlation below 0.4 is deemed unsuitable for inclusion (Hair et al., 1998).

Exploratory Factor Analysis (EFA) was conducted to confirm construct validity by simplifying interrelated measures and examining relationships among interval variables (Leech et al., 2005) This analysis allows researchers to identify how items cluster together In this study, EFA utilized varimax rotation to eliminate items with low loadings, adhering to strict criteria by removing factors with loadings below 0.5 while retaining components with an Eigenvalue greater than 1.0.

Hair et al (2010, p.156) proposed that the multiple regression standardized score equation is as follows (with all the variables measured on the same metric):

Where in: β is called beta weight, standardized regression coefficient, or beta coefficient

X is the predictor entered into the equation in a single step βX represents the score of a predictor and its associated beta weight

Hair et al (2010) highlighted the discrepancy between actual and predicted values of dependent variables, indicating that random errors, known as residuals (ε or e), can arise when forecasting sample data.

Based on these studies, the multiple regression formula will be

Meyers et al (2006) emphasized the significance of R² in representing the variance of the dependent variable explained by the regression model A higher R² value indicates a stronger explanatory power of the regression equation (Hair et al., 2010).

Summary

This chapter outlines the research methodology, detailing aspects such as sample size, data analysis methods, questionnaire design, and the steps undertaken during the main survey, which included 242 respondents Reliability of the measurement scale was assessed using Cronbach’s alpha, while Exploratory Factor Analysis was employed to validate the measurement scale and facilitate data reduction Additionally, multiple linear regression was utilized to test the research hypotheses The subsequent chapter will present the results of the data analysis from the main survey.

CHAPTER 4: DATA ANALYSIS

Descriptive data analysis

The official survey utilized a final questionnaire distributed through both paper and online formats, with a total of 145 questionnaires sent directly and 97 indirectly via online means Out of 278 responses received, only 242 were deemed valid The frequency of responses is summarized in Table 4.1.

Table 4.1: Mode of data collection

Mode of data collection Frequency Percent

The descriptive statistics will describe the characteristics of interviewees, including the gender, age, education and income These main characteristics are showed from table 4.2 to table 4.5 h

As shown in table 4.2,the gender distribution of the survey respondents is 54,1% for males and 45,9% for females

Type of gender Frequency Percent Valid Percent Cumulative

Type of age Frequency Percent Valid Percent Cumulative

In terms of type of age this research divides it to four areas According to Table 4.3, the results also indicated that the highest percentage 54.1% had age between 23and

35 years oldand 40.5% is from 36to 50 years old, 3.3% ofbelow 23 years old and lowest rate above 50 years old with 2.1%

The table 4.4 is about education level, the most respondents are from graduates with rate of 71,9%, the remaining are 15,3% of master, 9,9% of bachelors and 2,9% of others h

Type of education Frequency Percent Valid Percent Cumulative

According to Table 4.5, income levels exceeding VND 20 million account for 23.1%, while those ranging from VND 10 million to VND 20 million represent 34.3% Additionally, income brackets from VND 5 million to VND 10 million and below VND 5 million comprise 22.3% and 20.2%, respectively.

Type of income Frequency Percent Valid Percent Cumulative

Reliability analysis

Cronbach’s alpha was used to test the reliability of the measurement scales, the value of Cronbach’s alpha need to be accepted is over 0.6 and any variables which the h

Item-total correlations below 0.3 will be removed However, after evaluating the initial reliability testing of all proposed factors, no variables were identified for deletion, as indicated in Table 4.6.

The official assessment results closely aligned with the preliminary findings, confirming the validity of all constructs as indicated by Cronbach’s alpha coefficients exceeding 0.7 Notably, social influence achieved the highest coefficient at 901, while effort expectance had the lowest at 757 Additionally, all variables demonstrated corrected item-total correlation values above 0.3, indicating strong components for a summated rating scale.

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Performance Expectancy(PE)– Cronbach's alpha: 0.871

Effort Expectance(EE)– Cronbach's alpha: 0.757

Social Influence(SI)– Cronbach's alpha: 0.901

Facilitating Conditions (FC)– Cronbach's alpha: 0.883

Perceived Credibility (PC)– Cronbach's alpha: 0.899

Behavioral Intention (BI)– Cronbach's alpha: 0.861

The results from Cronbach’s alpha analysis indicate that the behavioral intention dimension achieved a reliability score of 0.861, falling within the desirable range of 0.8 to 0.9 Although the Cronbach's alpha value would slightly increase if the item BI1 were deleted, its significance warranted its inclusion in subsequent reliability tests Therefore, all items were retained for further analysis.

Exploratory factor analysis (EFA)

Exploratory Factor Analysis method was used mostly for data reductions reason and testing validity of measurement scale (discriminatory and convergence) (Nguyen,

In a 2011 study, the author utilized the Principle Axis Factoring (PAF) extraction method combined with Promax rotation, as it provided a more accurate representation of data structure compared to the Principle Component (PC) method with Varimax rotation (Nguyen, 2011).

4.3.1 Exploratory factor analysis results for measurement scales of independentfactors

Table 4.7: KMO and Bartlett's test of independent variables

Kaiser-Meyer-Olkin Measure of Sampling 877

The KMO value of 0.877, exceeding the threshold of 0.7, indicates that the dataset is likely to factor well, as supported by Leech et al (2005) Additionally, Bartlett’s test reveals a significant value below 0.05, confirming that the correlation matrix significantly differs from an identity matrix, suggesting that the correlations among variables are not all zero Consequently, both diagnostic tests affirm the suitability of the data for factor analysis The first six factors collectively account for 62.00 percent of the variance, indicating that these factors explain more than half of the total variance.

Table 4.8: Rotated component matrix of independent

SI4 People who influence my behavior think that I should use IB 817

SI3 People who are important to me think that I should use IB 799

SI2 The senior management of the bank has been helpful in the use of IB 752

SI5 People using IBS have more prestige than those who do not 741

SI1 In general, the bank has supported the use of IB 605

PE6 I can manage my money online at any time 717

PE5 I need not visit traditional banks regularly 714

PE2 I can transfer money anytime and any where 714

PE3 I can save time paying essential bills at the post office 698

PE1 IBS is convenient and easy to access 675

PE4 I can keep a record of my finances 591

FC4 The language in which the document is written is easily understood 824

FC1 Basic system requirements for using

FC3 Specific person (or group) is always available for assistance 710

FC2 Basic system requirement for using IBS are met 498

AN3 I am afraid of being charged for IBS 786

AN4 I am afraid of making mistakes that I cannot correct .760

AN2 I am worried about the inaccessibility of

AN1 I am afraid of losing information by hitting the wrong key 738

PC2 I believe no money will be lost when I transfer 719

PC3 IBS has enough specialists to detect fraud and information theft 643

PC4 Other people cannot view my bank account information 627

PC1 I trust in the ability of an IBS to protect my privacy and personal information 584

EE4 It would be easy for me to become skillful at using IBS 756

EE1 Internet banking is easy to learn 660

EE2 Internet Banking is easy to use 615

EE3 Internet Banking saves me a lot of time 578

Notes: SI: Social Influence;PE: Performance Expectancy;FC: Facilitating Conditions; AN: Anxiety; PC: Perceived Credibility;EE: Effort Expectancy;BI: Behavioral Intention to use Internet Banking

After conducting factor analysis using Principal Axis Factoring, six distinct factors were identified among the independent and dependent variables The Rotated Component Matrix (refer to table 4.8) displays the items and their respective factor loadings, with a threshold of 0.5 for acceptance Notably, item FC2 had a loading of 0.498, which is close to the threshold, leading to its retention in the analysis Consequently, a total of 27 items across six independent variables were successfully grouped into these six components based on their high loadings.

4.3.2 EFA analysis results for measurement scales of dependent factors

(behavioralintention to use Internet banking)

The KMO value reported in Table 4.9, while not exceptionally high, exceeded the acceptable threshold of 0.7, indicating sufficient items to effectively measure each construct Additionally, the Bartlett’s test yielded a significant result, with a significance value below 5%, confirming strong correlations among the variables.

Table 4.9: KMO and Bartlett’s test of dependent variables

Kaiser-Meyer-Olkin Measure of Sampling 710

Initial Eigenvalues Extraction Sums of Squared Loadings

Extraction Method: Principal Axis Factoring

As shown in the Total Variance Explained, the Cumulative % total variance 68,235% of the information contained in dependent variables

Table 4.11: Rotated component matrix of dependent variable

Correlation and regression

Before conducting regression analysis to examine hypotheses and the relationships between independent quantitative factors—such as performance expectancy, effort expectancy, social influence, facilitating conditions, perceived credibility, and anxiety—and the dependent quantitative factor of behavioral intention to use Internet banking, correlation analysis was performed The author utilized the Pearson correlation coefficient, revealing significant correlations at the 0.01 level (2-tailed) and at the 0.05 level (2-tailed) The findings of the correlation analysis are presented in Table 4.12.

At a significance level of 0.01 (two-tailed), the author identified five independent factors—performance expectancy, social influence, facilitating conditions, perceived credibility, and anxiety—that significantly correlate with the dependent factor Following these findings, the author proceeded to conduct a regression analysis to further explore these relationships.

SI PE FC AN PC EE BI

** Correlation is significant at the 0.01level (2-tailed) h

Before running Regression, many assumptions required are testing in Appendix H

The model summary indicates a multiple correlation coefficient and adjusted R square of 539, suggesting that 53.9% of the variance in behavioral intention to use IBS can be predicted by a combination of performance expectancy, effort expectancy, social influence, facilitating conditions, perceived credibility, and anxiety.

The graph shows that there was linearity in the relationship between independentvariables and dependence variables

The scatterplot matrix which shows that the independent variables are generally linearly related to the dependent variable of behavioral intention to use IBS, meeting this assumption

According to Hair et al (1998), the Variance Inflation Factor (VIF) should be maintained below 10 to ensure reliable statistical analysis Appendix H confirms that all independent variables in this study adhered to this guideline, as their VIF values were consistently below the threshold of 10.

In this example, we do not need to worry about multicollinearity because the tolerance values are bigger than 0.1

Assumption 5: Normality of the residuals

The histogram's shape indicated a satisfactory normal distribution for all variables, with a mean close to 0 and a standard deviation of 0.987, confirming that the assumption of normality was upheld.

The graph of normal P-P Plot of regression standardized residual was rather a straight line from the bottom left to top right showed a linear relation between independent and dependent variables

All regression assumptions were satisfied (check more detail at AppendixH), so using multiple linear regressions for regression analysis was acceptable

4.4.3 Multiple linear regression analysis (MLR)

The identifications of relationshipbetween independent factors and behavioral intentionto use Internet banking have conducted by Multiplelinear regression analysis The theoretical model for this relationship is formatted as equation:

BI = β1SI + β2PE + β3AN + β4FC + β5PC + β6EE+ ε

After aggregating the data, a multiple linear regression analysis was performed to examine the impact of various factors on the behavioral intention to use IBS The findings of this analysis are presented in Tables 4.13 and 4.14.

Table 4.13 shows theadjusted R square is 0.539, meaning that 53,9% of the variance in behavioral intention could be explained by the six independent factors

Table 4.13: Model summary of multiple linear regression analysis

Std Error of the Estimate

1 742 a 551 539 64007420 551 19.660 6 235 000 a Predictors: (Constant), effort expectance, perceived credibility, anxiety, facilitating conditions, performance expectancy, social influence b Dependent variable: behavioral intention to use Internet banking

Moreover F test results in table ANOVA in table 4.14 (sig=0,000 which was less than 0,05 level of significant) also confirmed that using MLR model was suitable

Table 4.14: ANOVA of multiple linear regression analysis

Model Sum of Squares df Mean Square F Sig

Total 214.237 241 a Dependent variable: behavioral intention to use Internet banking b Predictors: (constant), effort expectance, perceived credibility, anxiety, facilitating conditions, performance expectancy, social influence

The table 4.15 Coefficients (MLR) showed the results of Coefficients of Regression Analysis h

Table 4.15: Coefficient of multiple linear regression analysis

The study examines the factors influencing behavioral intention to use Internet banking, highlighting key variables such as social influence (SI), performance expectancy (PE), facilitating conditions (FC), anxiety (AN), perceived credibility (PC), and effort expectancy (EE) The results indicate a significant relationship between these factors and the intention to adopt Internet banking services, with effort expectancy showing a notable negative correlation Understanding these dynamics is essential for enhancing user acceptance and promoting the effective use of Internet banking.

Based on that author could write the regression equation as following:

Behavioral intention to use Internet banking = 0.321social influence + 0.417performance expectancy + 0.327facilitating conditions- 0.110 anxiety+ 0.228perceived credibility + ε

The analysis of the coefficients reveals that performance expectancy, perceived credibility, facilitating conditions, and social influence positively impact the intention to use Internet banking Notably, performance expectancy exhibits the strongest correlation with behavioral intention, reflected by a beta coefficient of 0.417, followed by facilitating conditions at 0.372, while perceived credibility shows a lesser effect with a beta coefficient of h.

0.228.However, anxietyhad negative impact on behavioral intention to use Internet banking

Based on the value of beta and Sig in Table 4.15, the testing for hypothesis is conducted one by one as following

Hypothesis H1 suggests that performance expectancy significantly influences the behavioral intention to utilize Internet banking The analysis indicates a strong positive correlation, with a beta value of 0.417, a t-value of 9.503, and a p-value of 0.000, confirming support for this hypothesis at a 1 percent significance level.

Hypothesis H2: Hypothesis H2 exhibited a positive impact of effort expectancy on behavioral intention As (β = -.070, t = -1.601, p = 0.111>0.05), posited that H2 is not supported by the data

Hypothesis H3 posits that social influence positively affects the intention to use Internet banking The findings presented in Table 4.15 indicate a significant outcome (β = 0.321, t = 7.310, p = 0.000), thereby supporting Hypothesis H3.

Hypothesis H4 is supported, indicating a positive impact of facilitating conditions on the behavioral intention to use Internet banking The standardized regression coefficient beta is 0.372, with a t-value of 8.475 (greater than 2) and a p-value of 0.000.

Hypothesis H5:The standardized regression coefficient beta of perceived credibilityon behavioral intention to use Internet banking is 0.228 and value of t is h

At a confidence level of 95%, the statistical analysis shows a significant positive impact of perceived credibility on the behavioral intention to use Internet banking, with a p-value of 0.000 Consequently, this supports hypothesis H5.

Hypothesis H6 indicates that anxiety significantly affects the behavioral intention to use Internet banking, as evidenced by the data (β = -.110, t = -2.510, p = 0.013) This suggests that higher levels of anxiety negatively impact users' intentions to engage with Internet banking services, thus supporting hypothesis H6.

No Hypothesis statement Testing result

H 1 There is a positive impact of performance expectancy on behavioral intention to use Internet banking Supported

H 2 There is a positive impact of effort expectancy on behavioral intention to use Internet banking

H 3 There is a positive impact of social influence on behavioral intention to use Internet banking Supported

H 4 There is a positive impact of facilitating conditions on behavioral intention to use Internet banking Supported

H 5 There is a positive impact of perceived credibilityon behavioral intention to use Internet banking Supported

H 6 There is a negative impact of anxiety on behavioral intention to use Internet banking Supported

Final research model

This research utilizes factor analysis and multiple linear regression analysis to identify a model outlining the factors influencing behavioral intentions toward Internet banking, as illustrated in Figure 4.1.

Summary

This chapter presented the results of data analysis for measurement scales, research models, and hypotheses The reliability and validity tests confirmed that the majority of measurement scales effectively assessed each construct Additionally, multiple regression analysis revealed strong relationships between most independent variables and the dependent factor, with the exception of effort expectancy The upcoming chapter will summarize the discussions, conclusions, implications, and limitations of this study.

Behavioral intention to use Internet banking

CHAPTER 5: CONCLUSIONS AND IMPLICATIONS

Conclusions of research

This study presents a conceptual model identifying six independent factors that influence the behavioral intention to utilize internet banking services: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived credibility, and anxiety.

The multiple linear regression analysis identified five independent factors that positively influence the behavioral intention to use Internet banking, as illustrated in Figure 4.1 The model accounts for 53.9% of the variance in this intention Among the identified factors, performance expectancy has the strongest positive impact, with a standardized beta of 0.417, indicating that changes in performance expectancy will most significantly affect behavioral intention Following this, the factors in descending order of influence are facilitating conditions (β=0.372), social influence (β=0.321), and perceived credibility (β=0.228) Conversely, anxiety negatively impacts the intention to use Internet banking, with a standardized beta of -0.110.

Performance expectancy significantly influences behavioral intention to use Internet banking, as users who believe that Internet banking services enhance their performance are more inclined to adopt these services Customers with a high performance expectancy demonstrate a stronger intention to utilize Internet banking, aligning with findings from Venkatesh et al (2003) and subsequent studies by Yeow et al (2008), AbuShanab and Pearson (2009), and Foon and Fah (2011).

The study highlights the significant impact of social influence, facilitating conditions, perceived credibility, and anxiety on the intention to use Internet banking These findings align with previous research by Venkatesh et al (2003), Yeow et al (2008), AbuShanab and Pearson (2009), and Foon and Fah (2011) Notably, customers experiencing high anxiety exhibited lower intentions to adopt Internet banking services While some respondents expressed confidence in using these services, others were apprehensive about potential information loss from errors and the reliability of Internet Banking System (IBS) web pages, with mean scores of 3.14 (SD = 1.031) and 3.24 (SD = 1.039), respectively.

4 i.e afraid of being charged for IBSand afraid of making mistakes that I cannot correct (mean 3.22/SD = 1.001and mean 3.22/ SD = 1.022 respectively) in appendix H

The study found that effort expectancy did not significantly influence the intention to adopt Internet banking, challenging previous literature that suggested it would be a key predictor of behavioral intention This discrepancy contrasts with earlier findings by Lin, Wu, and Tsai (2005) and Venkatesh et al., indicating the need for further investigation into the factors affecting Internet banking adoption.

In Vietnam, nearly 90% of Internet users access the web more than once a week, with about 60% using it daily, leading to a rich experience that makes the Internet easy to use and learn Consequently, effort expectancy does not significantly influence individuals' intentions to adopt Internet banking services (IBS), as evidenced by research indicating that it was not a key factor in behavioral intention (Yeow et al., 2008; Yang, 2010) This trend suggests that in Ho Chi Minh City, effort expectancy may no longer be a critical determinant for predicting the adoption of IBS.

Research contribution

This study aims to provide valuable insights for the banking industry, particularly for commercial bank leaders and marketing managers looking to enhance the adoption of Internet banking in Vietnam, focusing on the Ho Chi Minh City market By developing Internet banking services, banks can gain a competitive edge and foster greater integration A thorough understanding of consumer behavior towards Internet banking will significantly improve the chances of success for banking managers in this region Ultimately, the findings of this research will also serve as a resource for policymakers in the banking sector aiming to expand their business presence in Ho Chi Minh City.

Chi Minh City and who have already developed in the market and want to maintain the loyalty of their customers.

Managerial implications

Chapter 4 identifies four key factors that positively influence performance expectancy: social influence, facilitating conditions, and perceived credibility These research findings hold significant implications for managers at BIDV and specifically for banking managers.

Internet banking in Vietnam is an innovative solution designed to enhance customer service and reduce operational costs for banks The primary motivator for utilizing Internet banking services is performance expectancy, with users valuing the ability to transfer money anytime and anywhere (mean 3.64/SD = 1.035) and the convenience of not having to visit traditional banks regularly (mean 3.99/SD = 0.085) For busy working parents, effective time management is crucial, and any technology that improves efficiency is highly valued Internet banking services enable these parents to swiftly complete their banking tasks, allowing them more time to focus on their family's needs Thus, the time-saving advantages of Internet banking should be highlighted.

Utilizing Internet Banking Services (IBS) offers significant advantages, including convenience and easy access (mean 3.62/SD = 1.025), time savings on essential bill payments, and the ability to manage finances online anytime Banks should highlight these key benefits—efficiency, convenience, and effectiveness—in their advertising efforts Additionally, by introducing innovative banking products and services that meet consumer needs, such as tax payments and summons through IBS, banks can enhance the value of their offerings Incorporating third-party services like insurance, unit trusts, and stock purchases would create a comprehensive financial service platform, ultimately helping to retain existing customers.

Facilitating conditions are a crucial motivator for Internet Banking Services (IBS), with a mean rating of 3.55 Users find that the basic system requirements are generally met (mean 3.61), and the content is easy to read and understand (mean 3.633) The language used in documents is also accessible (mean 3.65) To enhance user experience, it is vital for banks to offer advice before service registration to avoid unnecessary subscriptions Banks should provide responsive consumer support through various channels, including email, online chat, and face-to-face assistance Additionally, a repository of articles and literature on IBS should be available on banks' official websites for user reference Given the complaints about the lack of knowledge among bank personnel regarding online banking, mandatory training and examinations should be implemented Offering free foundational tutorials in schools and public spaces could also benefit potential users.

Overall, social influencehad the strong effect on behavioral intention to use

Internet banking has gained widespread adoption due to its convenience, appealing to both high and low-income individuals, with 20.2% of users earning less than 5 million VND monthly In Vietnam's highly social society, the use of Internet banking is significantly influenced by social factors, including the recommendations of friends and family Consequently, banks can enhance their marketing efforts by leveraging social influence to promote the benefits of Internet banking Additionally, understanding consumers' behaviors within their social and cultural contexts, along with the role of technology, is crucial for identifying the factors that shape their banking practices.

Perceived credibility in Internet banking is crucial, as customers prioritize security over traditional banking services due to the financial risks involved To enhance trust, it is essential for all domestic banks to adopt industry-wide best security standards, including mandatory two-factor authentication This should involve a combination of a username and password as the first factor, along with a transaction authorization code, identity card, or passport number as the second factor For improved security, banks may also consider implementing three-factor authentication, incorporating biometric methods such as iris or thumbprint recognition for user identification.

To effectively develop a marketing strategy for Internet banking, banks must prioritize enhancing their security systems This involves taking proactive measures to manage and mitigate perceived security risks Key steps include implementing updated security policies, improving internal communication, and aligning services with customer expectations through service recovery programs Additionally, banks should bolster their capacity to manage inherent risks in Internet banking by utilizing advanced security technologies such as encryption, firewalls, and intrusion detection systems to ensure robust protection for their online banking platforms.

The last objective, anxietyshould significant negatively affect the trend of using

Internet banking (IBS) can evoke mixed feelings among users, as highlighted by Yeow et al (2008), where some respondents express confidence while others experience significant anxiety regarding potential charges and accessibility issues Key features of a bank's web design, such as ease of navigation, clarity, and organization, play a crucial role in alleviating these concerns High anxiety levels related to password security and transaction errors can deter customers from utilizing IBS Furthermore, Yeow et al (2008) suggest that increased experience with IBS tends to reduce user anxiety, indicating that banks should consider offering free internet training sessions for novice users to enhance their comfort and confidence in online banking.

Limitations and recommendations for future research

This work still exposes some limitations, based on that future research can be developed

The sample in this study was conveniently selected from a limited number of banks in Ho Chi Minh City, which may not accurately represent the broader population To enhance the reliability of future research findings, it is recommended to increase the sample size and consider expanding the survey to include other cities in Vietnam.

In addition to the existing independent variables, limitations arise from the banking environment, prompting further research to explore additional constructs like perceived self-efficacy, security, and transaction costs.

Next, another area for future investigation is the impact of moderators such as gender, education, income, and age on behavioral intention to use Internet banking

Lastly, the study on behavioral intentions can be extended to corporate customers Comparison can be made between individual customers and corporate customers to identify factors influencing their adoption decisions h

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Appendix A: Summarizes the Unified Theory of Acceptance and Use of Technology (UTAUT) model

Name Major Constructs Major citations

Attitudes, Subjective Norm, Intention and Behavior

Attitudes, Subjective Norm, Perceived Behavioral control, Intention and Behavior

External Factors, Perceived Usefulness, Perceived Ease of Use, Intention to use, and Actual Systems Usage

Davis (1989), Davis et al (1989), Venkatesh and Davis

(2000), Venkatesh and Davis (2000) The Decomposed

Perceived Usefulness, Compatibility, Perceived Ease of Use, Peer’s Influence, Superior’s Influence, Self-efficacy, Resource Facilitation Conditions, Technology

Facilitation Conditions, Attitudes, Subjective Norm, Perceived Behavioral control,

Behavior Intention and Usage Behavior

Intrinsic Motivation, Extrinsic Motivation, Amotivational Style, and Behavior

The Model of PC Long term Consequences of PC, Job Fit, Triandis (1977), and h

Name Major Constructs Major citations

Affect, Social Factors, Complexity, Facilitating Conditions and Utilization of PC

Voluntariness, Image, Relative Advantage, Compatibility, Trialability, Visibility, Result Demonstrability, Ease of Use, and Rate of Adoption

Rogers (1983), and Moore and Benbasat

Encouragement by Others, Other’s Use, Support, Self-efficacy, Outcome

Expectations, Affect, Anxiety and Usage

Performance Expectancy, Effort Expectancy, Social Influence, and Facilitation Conditions

Venkatesh, Morris, Davis and Davis

Appendix B: Guidelines for in-depth interview

Performance Expectancy OBS is convenient and easy to access

All respondents stated that they understood the scale meaning

However, they suggested adding the word “IBS” instead of “OBS”

IBS is convenient and easy to access

Using Internet banking save me a lot of time

With this sentence, for more understandable

Internet Banking saves me a lot of time

It would be easy for me to become skillful at using the system

All respondents stated that they understood the scale meaning

However, they suggested adding the word “IBS” instead of “the system”

It would be easy for me to become skillful at using IBS

In general, the organization has supported the use of the system

They suggested changing the word

“organization “ and “ the system” to make this scale clearer

In general, the bank has supported the use of IBS

The senior management of this business has been helpful in the use of the system

All respondents stated that they understood the scale meaning

However, they suggested changing the word “the bank” instead of “this business” and “IBS” instead of “the system” to make this scale clearer

The senior management of the bank has been helpful in the use of IBS

People who are important to me think that I should use the system

All respondents advocated to change word “the system” into “IBS”

People who are important to me think that I should use IBS h

People who influence my behavior think that I should use the system

All respondents advocated to change word “the system” into “IBS”

People who influence my behavior think that I should use IBS People in my organization who use the systemhave more prestige than those who do not

With this sentence, for easy to understand People using IBS have more prestige than those who do not

All contents of OBS are easy to read and understand

All respondents advocated to change word “OBS” into “IBS”

All contents of IBS are easy to read and understand

Basic system requirements for using OBS are met

Basic system requirements for using IBS are met

I trust in the ability of an online banking to protect my privacy and personal information

All respondents advocated to change word “OBS” into “IBS”

I trust in the ability of an IBS to protect my privacy and personal information

Online bank has enough specialists to detect fraud and information theft

IBS has enough specialists to detect fraud and information theft

I am worried about the inaccessibility of OBS web pages

All respondents advocated to change word “OBS” into “IBS”

I am worried about the inaccessibility of IBS web pages

I am afraid of being charged for OBS

I am afraid of being charged for IBS h

Name Sex Age Banking Major

Nguyễn Thị Lập Female 35 BIDV-HCMC Customer

Lê Thị Thanh Tuyền Female 31 BIDV- HCMC Customer

Nguyễn Thị Hồng Thanh Female 24 BIDV- HCMC Customer

Nguyễn Hoàng Quân Male 38 BIDV-HCMC Customer

Trần Thị Tuyết Nhung Female 52 BIDV- HCMC Banking expert Nguyễn Hoàng Minh Male 28 BIDV- HCMC Banking expert

Trần Minh Huy Male 34 BIDV-HCMC Banking expert

Nguyễn Thành Nhơn Male 41 BIDV- HCMC Banking expert Nguyen Thi Thu Ha Female 39 BIDV- HCMC Banking expert

Le Thi Thu Hanh Female 37 BIDV- HCMC Banking expert h

We are currently studying the program "Master of Business Administration" of International School Of

The University of Economics Ho Chi Minh City is conducting a survey to analyze the factors influencing customer behavioral intentions towards Internet banking (IB) Your participation will greatly assist in enhancing our research and enable banks to better serve their customers Thank you for taking the time to contribute to this important study.

1 Are you using a personal account or banking card? □ Yes □ No

2 Did you know about Internet banking services? □ Yes □ No

2 Have you currently registered to use Internet banking services?

(Continues to survey if answer of question 1, 2 is “Yes” and question 3 is “No”) □ Yes □ No

Please you indicate level of agreement on the following statements by mark X in the appropriate box:

[1] Strongly disagree; [2] Disagree; [3] Neutral; [4] Agree; [5] Strongly agree

Performance Expectancy Level of agreement

1 IBS is convenient and easy to access 1 2 3 4 5

2 I can transfer money anytime and any where 1 2 3 4 5

3 I can save time paying essential bills at the post office 1 2 3 4 5

4 I can keep a record of my finances 1 2 3 4 5

5 I need not visit traditional banks regularly 1 2 3 4 5

6 I can manage my money online at any time 1 2 3 4 5

Effort Expectancy Level of agreement

7 Internet banking is easy to learn 1 2 3 4 5

8 Internet Banking is easy to use 1 2 3 4 5

9 Internet Banking saves me a lot of time 1 2 3 4 5

10 It would be easy for me to become skillful at using IBS 1 2 3 4 5

Social Influence Level of agreement

11 In general, the bank has supported the use of IBS 1 2 3 4 5

12 The senior management of the bank has been helpful in the use of IBS 1 2 3 4 5

13 People who are important to me think that I should use IBS 1 2 3 4 5 h

14 People who influence my behavior think that I should use IBS 1 2 3 4 5

15 People using IBS have more prestige than those who do not 1 2 3 4 5

Facilitating Conditions Level of agreement

16 All contents of IBS are easy to read and understand 1 2 3 4 5

17 Basic system requirements for using IBS are met 1 2 3 4 5

18 Specific person (or group) is always available for assistance 1 2 3 4 5

19 The language in which the document is written is easily understood 1 2 3 4 5

Perceived Credibility Level of agreement

20 I trust in the ability of an IBS to protect my privacy and personal information 1 2 3 4 5

21 I believe no money will be lost when I transfer 1 2 3 4 5

22 IBS has enough specialists to detect fraud and information theft 1 2 3 4 5

23 Other people cannot view my bank account information 1 2 3 4 5

24 I am afraid of losing information by hitting the wrong key 1 2 3 4 5

25 I am worried about the inaccessibility of IBS web pages 1 2 3 4 5

26 I am afraid of being charged for IBS 1 2 3 4 5

27 I am afraid of making mistakes that I cannot correct 1 2 3 4 5

Behavioral Intention to use IBS Level of agreement

28 I intend to use Internet Banking in the next few months 1 2 3 4 5

29 I predict that I would use Internet Banking in the next few months 1 2 3 4 5

30 I plan to use Internet Banking in the next few months 1 2 3 4 5

4 Education □ Bachelors □ Graduate □ Master □ Others h

Chúng tôi là sinh viên chương trình Thạc sỹ Quản trị Kinh doanh tại Viện Đào Tạo Quốc Tế - Trường Đại học Kinh tế TP.Hồ Chí Minh Để đánh giá các yếu tố ảnh hưởng đến quyết định sử dụng dịch vụ ngân hàng trực tuyến (Internet-banking), chúng tôi rất mong nhận được sự hỗ trợ từ quý Anh/Chị thông qua việc hoàn thành bảng khảo sát dưới đây Ý kiến của quý vị sẽ được bảo mật và đóng góp quan trọng vào nghiên cứu của chúng tôi, đồng thời giúp các ngân hàng cải tiến dịch vụ, phục vụ khách hàng tốt hơn.

1 Anh/chị có đang sử dụng tài khoản cá nhân hoặc thẻ qua ngân hàng? □ Có □ Chưa

2 Anh/chị có biết về dịch vụ ngân hàng trực tuyến không? □ Có □ Chưa

3 Anh/chị có đăng ký sử dụng dịch vụ Internet-banking chưa?

(Các anh/chị tiếp tục thực hiện khảo sát nếu câu 1,2 trả lời có và câu 3 là chưa”) □ Có □ Chưa

Vui lòng cho biết mức độ đồng ý của bạn về các phát biểu dưới đây bằng cách đánh dấu (X) vào ô tương ứng: [1] Hoàn toàn không đồng ý; [2] Không đồng ý; [3] Bình thường; [4] Đồng ý; [5] Hoàn toàn đồng ý.

Hiệu quả mong đợi của dịch vụ ngân hàng trực tuyến Mức độ đồng ý

1 Sử dụng ngân hàng trực tuyến thì thuận tiện và dễ dàng truy cập 1 2 3 4 5

2 Sử dụng ngân hàng trực tuyến tôi có thể chuyển tiền bất cứ lúc nào và bất cứ nơi đâu 1 2 3 4 5

3 Ngân hàng trực tuyến giúp tôi tiết kiệm thời gian chi trả hóa đơn tại bưu điện 1 2 3 4 5

4 Ngân hàng trực tuyến giúp tôi lưu trữ các giao dịch tài chính mà tôi đã thực hiện 1 2 3 4 5

5 Sử dụng ngân hàng trực tuyến tôi không cần thường xuyên ra ngân hàng giao dịch 1 2 3 4 5

6 Sử dụng ngân hàng trực tuyến tôi có thể quản lý tiền bạc của tôi bất cứ lúc nào 1 2 3 4 5

Tính dể sử dụng của dịch vụ ngân hàng trực tuyến Mức độ đồng ý

7 Học sử dụng ngân hàng trực tuyến thì dễ dàng 1 2 3 4 5

8 Tôi thấy ngân hàng trực tuyến dễ sử dụng 1 2 3 4 5

9 Sử dụng ngân hàng trực tuyến giúp tôi tiết kiệm thời gian rất nhiều 1 2 3 4 5

10 Tôi dễ dàng có kỹ năng để sử dụng ngân hàng trực tuyến 1 2 3 4 5 Ảnh hưởng xã hội của dịch vụ ngân hàng trực tuyến Mức độ đồng ý

11 Nhìn chung, các ngân hang hỗ trợ việc sử dụng ngân hang trực tuyến 1 2 3 4 5

12 Các nhà quản lý của ngân hàng giúp tôi sử dụng ngân hàng trực tuyến 1 2 3 4 5

13 Người quan trọng với tôi cho rằng tôi nên sử dụng ngân hàng trực tuyến 1 2 3 4 5 h

14 Người có ảnh hưởng đến tôi nghĩ tôi nên sử dụng ngân hàng trực tuyến

15 Sử dụng ngân hàng trực tuyến thể hiện đẳng cấp và uy tín 1 2 3 4 5 Điều kiện hỗ trợ của dịch vụ ngân hàng trực tuyến Mức độ đồng ý

16 Tất cả nội dung hướng dẫn của ngân hàng trực tuyến dễ đọc và dễ hiểu 1 2 3 4 5

17 Thiết bị hỗ trợ cho việc sử dụng ngân hàng trực tuyến thì dễ dàng đáp ứng 1 2 3 4 5

18 Luôn luôn có sẵn người để được hỗ trợ khi bạn sử dụng ngân hàng trực tuyến 1 2 3 4 5

19 Ngôn ngữ sử dụng của ngân hàng trực tuyến dể hiểu 1 2 3 4 5

Sự đáng tin của dịch vụ ngân hàng trực tuyến Mức độ đồng ý

20 Tôi tin tưởng vào khả năng bảo mật thông tin cá nhân của ngân hàng trực tuyến 1 2 3 4 5

21 Tôi tin rằng khi giao dịch qua ngân hàng trực tuyến thì không lo ngại bị mất tiền 1 2 3 4 5

22 Tôi tin tưởng rằng ngân hàng trực tuyến có thể phát hiện ra việc gian lận và trộm cắp thông tin 1 2 3 4 5

23 Những người khác không thể xem thông tin tài khoản của tôi 1 2 3 4 5

Sự Lo lắng của dịch vụ ngân hàng trực tuyến Mức độ đồng ý

16 Tôi cảm thấy lo sợ mất thông tin khi nhấn sai phím 1 2 3 4 5

16 Tôi lo lắng khả năng kết nối trang web khi sử dụng ngân hàng trực tuyến 1 2 3 4 5

16 Tôi sợ bị tính phí khi sử dụng dịch vụ ngân hàng trực tuyến 1 2 3 4 5

Nhiều người ngại sử dụng dịch vụ ngân hàng trực tuyến vì lo sợ mắc lỗi mà không thể sửa chữa Tuy nhiên, việc sử dụng dịch vụ này ngày càng trở nên phổ biến Để giảm bớt lo lắng, người dùng cần nắm vững các bước thực hiện và hiểu rõ về tính năng bảo mật của ngân hàng trực tuyến Mức độ đồng ý với việc sử dụng dịch vụ ngân hàng trực tuyến đang gia tăng, cho thấy sự tin tưởng ngày càng lớn của khách hàng vào công nghệ tài chính.

16 Tôi có ý định sử dụng ngân hàng trực tuyến trong vài tháng tới 1 2 3 4 5

16 Tôi dự định sẽ sử dụng ngân hàng trực tuyến trong vài tháng tới 1 2 3 4 5

16 Tôi có kế hoạch sử dụng ngân hàng trực tuyến trong vài tháng tới 1 2 3 4 5

1 Gi ới tính □ Nam □ Nữ

2 Thu nh ập □ 20 triệu

4 H ọc vấn □ Trung cấp □ Đại học □ Sau đại học □ khác h

Appendix F: Results of EFA testing for independent variables

Kaiser-Meyer-Olkin Measure of Sampling

Extraction Method: Principal Axis Factoring h

Extraction Method: Principal Axis Factoring

Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations h

Appendix G: Results of EFA testing for dependent variables

Kaiser-Meyer-Olkin Measure of Sampling

Initial Eigenvalues Extraction Sums of Squared Loadings

% of Variance Cumulative % Total % of Variance

Extraction Method: Principal Axis Factoring h

PE, SI b Enter a Dependent Variable: Behavioral Intention b All requested variables entered

Std Error of the Estimate Durbin-Watson

1 742 a 551 539 64007420 1.828 a Predictors: (Constant), Effort Expectancy, Facilitating Conditions, Social Influence,

Performance Expectancy, Anxiety, Perceived Credibility b Dependent Variable: Behavioral Intention to use Internet banking

Squares df Mean Square F Sig

Total 214.237 241 a Dependent Variable: Behavioral Intention to use Internet Banking b Predictors: (Constant), Effort Expectancy, Facilitating Conditions, Social Influence,

Performance Expectancy, Anxiety, Perceived Credibility h

Standardiz ed Coefficien ts t Sig

B Std Error Beta Tolerance VIF

Expectancy -.076 047 -.070 -1.601 111 992 1.008 a Dependent Variable: Behavioral Intention to use Internet Banking