CHAPTER 1: INTRODUCTION
Research background
Internet banking is a groundbreaking service facilitated by advancements in Internet communication technology It enables customers to manage their bank accounts and conduct transactions conveniently from their homes or offices Additionally, internet banking offers banks a cost-effective method to deliver customer services efficiently.
Internet banking provides significant benefits, including reduced costs and enhanced convenience in terms of location and time, along with quick and easy transaction processing Research indicates that a one percent increase in customer retention for Internet banking can lead to an 18 percent decrease in operating costs (Bhattacherjee, 2001) Consequently, a lack of consumer interest in adopting Internet banking services can lead to substantial financial losses for institutions Thus, prioritizing the development of Internet banking services is crucial for effective bank management.
Between 2000 and June 2012, global Internet user growth surged by 566.4%, with Asia contributing the largest share at 44.8%, followed by Europe at 21.5% and North America at 11.4% Notably, Vietnam ranked among the top 20 countries for Internet users during this period, experiencing a dramatic rise from 200,000 users in 2000 to 31,034,900 by June 2012, marking an astonishing increase of 155 times over 12 years (Internet World Stats, 2012).
Vietnam launched Internet banking in May 2002 to offer online banking services to account holders However, a 2012 Nielsen Global survey revealed that less than 1.02% of banking customers utilized these services, a stark contrast to the growing number of Internet subscribers in the country This low adoption rate also pales in comparison to that of developed Asian nations, highlighting the untapped potential of the Internet banking market in Vietnam Consequently, understanding the factors that influence the use of Internet banking services is crucial for future growth.
Problem statement
Internet banking is crucial to the banking industry, as it has revolutionized the operations of banks and financial institutions through the implementation of information technology and communication networking (Yasuharu, 2003) The shift to electronic banking is essential for banks, providing significant competitive advantages and enabling them to build stronger, more enduring relationships with their customers.
Despite the numerous benefits of Internet banking, its acceptance in Vietnam remains relatively low compared to Western countries and the United States Most research in the Asian region has focused on developed countries such as Singapore, Hong Kong, Taiwan, and Malaysia, leaving developing nations like Vietnam underexplored Understanding the factors influencing the acceptance or rejection of new technologies is a significant challenge Therefore, this study aims to investigate the behavioral intentions towards Internet banking in Ho Chi Minh City, Vietnam, serving as the motivation for this research.
While many studies have primarily examined the positive aspects of Internet banking services, such as the impact of trust and relative advantage (AbuShanab and Pearson, 2009), additional dimensions have been integrated into the UTAUT model, including voluntariness of use (Anderson and Schwager, 2004) and perceived credibility and anxiety (Yeow et al., 2008) Research indicates that these factors, along with self-efficacy, perceived trust, and perceived risk, are significant in shaping user attitudes towards Internet banking (AbuShanab and Pearson, 2009; Foon and Fah, 2011) Notably, findings from Yeow et al (2008) suggest that perceived credibility and anxiety are relevant in the Malaysian context, which shares similarities with Vietnam's market However, there is a scarcity of studies focusing on Internet banking within Vietnam using the UTAUT model, with Khuu and Nguyen (2011) being one of the few to highlight 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 have a notable effect.
This study investigates the impact of the UTAUT model—comprising 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 crucial for banks as they develop 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 the behavioral intention to utilize Internet banking in Ho Chi Minh City, Vietnam, by employing the UTAUT model Specifically, it seeks to identify the key determinants that impact users' willingness to adopt Internet banking services.
- 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 who possess bank accounts and have knowledge of Internet banking services but do not utilize them Due to time and cost constraints, the research analyzed a database 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 designed in two phases: a qualitative phase involving in-depth interviews with ten participants, followed by a quantitative phase with a main survey of 300 respondents The collected data will be analyzed using SPSS 22.0 software, which involves three key stages Initially, Cronbach’s alpha will assess the reliability of the measurement scale Next, Exploratory Factor Analysis (EFA) will evaluate the validity of the measurement scale and facilitate data reduction Finally, Multiple Linear Regression (MLR) will examine the relationships between variables within the research model.
Thesis structure
This study consists of five chapters: the first chapter serves as an introduction, while the second chapter presents a literature review and outlines the hypotheses The third chapter details the research methodology, followed by data analysis in the fourth chapter Finally, the fifth chapter concludes with discussions of the research findings and offers 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 elaborates on each variable in the model, detailing the rationale behind their selection Additionally, it presents information about the model and outlines the hypotheses that will be tested in this research.
The study will focus on designing a comprehensive survey, including the careful development of survey questions and qualitative research methods It will clearly identify the measurement scale factors utilized in the research Additionally, the approach for data collection and analysis will be established to effectively test the hypotheses outlined 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.
CHAPTER 2: LITERATURE REVIEW AND HYPOTHESIS
Theoretical foundation
The technology acceptance literature presents a diverse array of models and theories that elucidate the adoption of information technology innovations (Venkatesh et al., 2003) In their empirical comparison of eight competing models, including the Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and others, Venkatesh et al (2003) provided valuable insights into technology adoption Notably, influential models such as TRA, TPB, and TAM have significantly shaped understanding in this field (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 explore the relationship between attitudes and behavior This model predicts behavioral intention by focusing on three key components: behavioral intention (BI), which indicates a person's likelihood to engage in a behavior; attitude toward behavior (A), defined as an individual's positive or negative feelings about performing the behavior; and subjective norm (SN), which refers to the individual's perception of social pressure from important others regarding the behavior.
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 specific behavior is influenced by their attitude towards that behavior and the subjective norms surrounding it, expressed as Behavioral Intention (BI) = Attitude (A) + Subjective Norms (SN) When a person has a positive intention to perform a behavior, it significantly increases the likelihood of them actually carrying it out.
2.1.2 Theory of Planned Behavior(TPB)
The Theory of Planned Behavior (TPB), developed from the Theory of Reasoned Action (TRA) by Ajzen in 1985, predicts deliberate and planned behaviors TPB is widely recognized in social science and information technology as an effective framework for explaining and forecasting user behavioral intentions (Mykytyn and Harrison, 1993) Key factors influencing these intentions include an individual's attitude toward the behavior, subjective norms, and perceived behavioral control.
Subjective Norm Normative Beliefs and Motivation 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 individual perceptions necessary for task completion This model is regarded as a more effective alternative to the Theory of Reasoned Action (TRA) in predicting and elucidating consumer behavior within research contexts.
The Technology Acceptance Model (TAM), developed by Fred Davis and Richard Bagozzi, is based on the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB) and is particularly relevant in the context of electronic banking TAM aims to elucidate the factors influencing users' acceptance or rejection of information technology By focusing on behavioral intentions towards specific technologies or services, TAM has emerged as a prominent framework for understanding user acceptance and utilization of technology.
PerceivedBehavioral ControlControl Beliefs
Figure2.3: Technology Acceptance Model (Davis et al, 1989)
The Technology Acceptance Model (TAM), developed by Davis in 1989, is widely utilized to understand the adoption of new technologies Unlike its predecessors, the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB), TAM is the most prevalent model for analyzing user behavior, especially in the context 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 Technology Acceptance Model (TAM) and TPB demonstrate significant predictive capabilities for adoption intentions, with TAM showing a superior ability to predict attitudes (Mathieson, 1991) Furthermore, TAM accounts for a greater variance in adoption intentions when contrasted with 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) by Davis (1989), the Theory of Planned Behavior (TPB) by Ajzen (1991), and the combined TAM and TPB (C-TAM-TPB) proposed by Taylor and Todd (1995).
Attitude Intention to use Use
The article discusses key theories related to 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 primary factors influencing behavioral intention and usage: performance expectancy, effort expectancy, and social influence Additionally, the empirical findings of the UTAUT model indicate that it explains 70% of the variance in usage intention, establishing its dominance over other antecedent models.
The Unified Theory of Acceptance and Use of Technology (UTAUT) identifies four essential constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions These constructs serve as direct determinants of both usage intention and behavioral intention, highlighting their critical role in technology adoption and utilization.
Performance Expectancy (PE) refers to the belief that using a system will enhance an individual's work performance, as established by Venkatesh et al (2003) This concept is closely related to perceived usefulness, which is a key element in various models, including 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 successor 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 is mirrored in various 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 key constructs from various models: perceived behavioral control from the Theory of Planned Behavior (TPB), the Decomposed TPB, and the combined Technology Acceptance Model and TPB, as well as the facilitating conditions outlined in 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 Unified Theory of Acceptance and Use of Technology (UTAUT) 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) Therefore, this study adopts UTAUT as its theoretical framework for formulating the hypotheses.
This research presents a model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), illustrated in Figure 2.5, which identifies six key factors influencing the behavioral intention to use Internet banking Among these, four factors from the original UTAUT model—performance expectancy and effort expectancy—play a crucial role in shaping user acceptance and engagement with online banking services.
AgeGender 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 or utilizing new initiatives Internet banking, in particular, provides numerous advantages, allowing customers to conduct various financial transactions conveniently 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 facilitates easy and efficient access to online banking services anytime and anywhere Unlike traditional banking, Internet banking reaches a broader audience and eliminates the need for customers to wait in lines, significantly enhancing work efficiency through time-saving convenience As a result, customers are expected to recognize the benefits of Internet banking, leading to a positive attitude toward this technology.
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 established 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 hypothesis H1.
Hypothesis H1: “Performance expectancy has a positive effect on behavioral intention to use Internet banking”
Effort expectancy refers to an individual's belief in the ease of using a specific system, encompassing both physical and mental aspects (Davis, 1989) This perception significantly impacts the user's intention to adopt the system in the future Essentially, effort expectancy reflects the consumer's view on the simplicity or complexity of the system, influencing their overall experience and willingness to engage with it (Thompson et al.).
Effort expectancy plays a crucial role in the acceptance of Internet banking, as it reflects the ease of use perceived by consumers When users find a system easy to navigate and understand, they are more likely to feel competent in using it Research by Calisir and Gumussoy (2008) highlights effort expectancy as a key factor in the growth of Internet banking acceptance Additionally, studies by AbuShanab and Pearson (2009) in Jordan and Foon and Fah (2011) in Malaysia confirm a significant relationship between effort expectancy and behavioral intention This study anticipates that effort expectancy will be a significant determinant of the intention to adopt Internet banking, leading to the formulation of specific hypotheses for testing.
Hypothesis H2: “Effort expectancy has a positive effect on behavioral intention to use Internet banking”
Social influence significantly impacts the intention to use Information Behavior Systems (IBS), as highlighted in various studies (Venkatesh & Davis, 2000) This well-established concept, particularly relevant in the field of technology acceptance, 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 individuals in one's life play a crucial role in the decision-making process regarding the adoption of new technologies Additionally, reference groups such as organizations and media also contribute to shaping social dynamics and perceptions (Thompson et al., 1991; Mathieson, 1991; Taylor & Todd, 1995).
Social influence has been examined in various contexts, yielding mixed results regarding its effect on behavioral intention related to technology use Notably, studies by Deng et al (2011), Gao and Deng (2012), and Wong et al (2013) indicated an insignificant relationship among the relevant constructs Conversely, personal connections—including family, supervisors, professors, peers, university administrators, and online communities—have been shown to enhance users' intentions to adopt Internet banking (Bagozzi and Dholakia, 2002) Additionally, research in the realm of Internet banking supports the notion that social influence significantly affects users' behavioral intentions (AbuShanab & Pearson, 2009; Yeow et al., 2008) This study posits that social influence will positively impact the intention to use 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”
Facilitating conditions play a crucial role in the adoption of new technology, particularly in Internet banking, by encompassing the availability of resources like written documents and technological infrastructure (Raman & Don, 2013) To enhance the use of Internet banking, it is essential to ensure ease of access, navigation, and searching, especially when users receive proper guidance Additionally, factors such as associated costs and the prior knowledge required for effective Internet banking usage significantly influence user experience Ultimately, facilitating conditions have been identified as the most critical factor impacting Internet banking adoption.
Ajzen (1991) proposed that facilitating conditions represent the external environmental influences on an individual's perceived controllability In the context of Internet banking, we can define perceived facilitating conditions (PFC) as the external factors related to the system as perceived by customers, which they believe impact their performance and usage of the Internet It is anticipated that PFC will positively influence the behavioral intention to use Internet banking in Vietnam Additionally, facilitating conditions and habit are significant predictors of this intention, with previous research indicating that facilitating conditions directly affect the behavioral intention to use Internet banking systems (Ajzen, 1991; Yuen et al., 2010; Foon and Fah, 2011) Consequently, this study will test the following hypotheses.
Hypothesis H4: “Facilitating conditions have a positive effect on behavior intention to use Internet banking”
Perceived credibility, defined as the extent to which individuals view Internet banking services (IBS) as trustworthy and secure, is a crucial predictor of the intention to use these services (Yeow et al., 2008) Enhancing perceived credibility can lead to greater acceptance of Internet banking among users, making the establishment of customer trust vital for retaining existing bank clients (Kumar, 2013; Mukherjee & Nath, 2003) Building on the work of Wang et al (2003), who differentiated perceived credibility from perceived risks and trust, Luarn and Lin (2005) and Amin et al (2008) identified security and privacy as key dimensions of perceived credibility Their empirical findings further demonstrate that perceived credibility significantly influences individuals' intentions to utilize mobile banking services.
The literature indicates that various scholars approach the concepts of security, risk, trust, and credibility from different perspectives, leading to diverse interpretations based on their respective disciplines Perceived credibility has been empirically validated in studies related to mobile banking adoption (Luarn & Lin, 2005; Amin et al., 2008) and extensively explored in Internet banking research (Wang et al., 2003; Amin, 2009; Yuen et al., 2010) This study aims to utilize perceived credibility as a representation of individual concerns regarding security, privacy, risk, and trust in the context of Internet banking adoption Consequently, the study proposes the following hypothesis.
Hypothesis H5: “Perceived credibilityhas a positive effect on behavioral intention to use Internet banking”
Anxiety, defined as the nervousness associated with using new technology (Liao and Cheung, 2003), significantly impacts computer usage behaviors Research indicates that technology anxiety reduces perceived ease of use and intention to engage with technology (Venkatesh, 2000; Venkatesh and Bala, 2008) Specifically, in the context of Internet Banking Services (IBS), anxiety manifests as fear of password theft or making mistakes while using online banking (Venkatesh et al., 2003) Furthermore, Doyle et al (2005) found that individuals with limited computer and Internet experience exhibit higher levels of anxiety compared to those who are more experienced.
This study highlights that Internet service anxiety significantly impacts users' intention to adopt Internet banking services Anxiety is defined as a negative emotional response to engaging with technology, such as online banking Existing literature underscores the critical role of technology anxiety in shaping user intentions Nevertheless, users often manage to surpass their initial anxieties, leading to more positive perceptions as they gain familiarity with the technology (Hackbarth et al 2003).
Summary of research model and hypotheses
The proposed conceptual framework model, illustrated in Figure 2.5, includes six hypotheses (H1 to H6) derived from the literature review This model identifies independent and quantitative variables that directly influence the behavioral intention to use Internet banking, which serves as the dependent variable.
Figure2.5: The proposed research model with hypotheses
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 applied in previous studies involving bank customers, making them a solid foundation for developing a research model This model aims to measure the factors influencing customer intentions to adopt Internet banking and their decisions regarding the use of these services The following 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 study utilized multiple-item measurements for all variables to effectively capture the constructs involved Performance expectancy was assessed using six items from Yeow et al (2008), while effort expectancy was evaluated through four items from Foon & Fah (2011) and AbuShanab & Pearson (2009) Social influence was measured with five items from AbuShanab & Pearson (2009), and facilitating conditions were gauged using four items developed by Yeow et al (2008) Perceived credibility and anxiety were each measured with four items from Yeow et al (2008) Lastly, behavioral intention to use Internet banking was assessed through three items from AbuShanab & Pearson (2009) and Foon.
The measurement model utilized a five-point Likert scale to assess responses to questions 1 to 30, with values ranging from 1 (strongly disagree) to 5 (strongly agree), as outlined by Fah (2011) and Yeow et al (2008) A detailed list of indicators employed in this model is provided 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 clear 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, enhancing my financial management experience.
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
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
The research utilized a questionnaire employing a five-point Likert scale to gather data on the factors outlined in the research model To ensure content validity, most items were adapted from prior studies The questionnaire assessed performance expectancy, effort expectancy, social influence, facilitating conditions, perceived credibility, anxiety, and behavioral intention to use Internet banking, drawing from established research (refer to section 3.2.1 for measurement scales) The structure of the questionnaire comprised 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, we utilized a measurement scale to gather data, employing a five-point Likert scale ranging from 1 to 5 Participants rated their responses, where 1 indicated "strongly disagree," 2 represented "disagree," 3 was "neutral," 4 signified "agree," and 5 denoted "strongly agree." This structured approach facilitated a comprehensive analysis of the participants' attitudes and opinions.
- 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 was pre-tested through in-depth interviews with 10 participants, including 6 banking experts and 4 customers knowledgeable about Internet banking services These interviews aimed to confirm the clarity and relevance of the final questions in measuring the observed variables prior to launching the main survey The detailed questionnaire was presented to the interviewees for feedback on their understanding, while also assessing the appropriateness of the chosen measurement scale for the Vietnamese context All comments collected from the interviewees were utilized to refine the measurement scale, resulting in slight modifications to the survey questionnaire for improved 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, as it relies on large sample distribution theory (Raykov and Widaman, 1995) However, the specific threshold for what constitutes a "much larger" sample size remains undefined.
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 method widely recognized for data collection in research, as noted by Tull and Hawkins (1987).
The study focused on participants who possess bank accounts and have knowledge of Internet Banking services but do not actively use them, specifically targeting clients from three banks in Ho Chi Minh City: BIDV, ACB, and MBBANK A total of 300 clients were surveyed using a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).
The author utilized her personal connections to reach the target sample for the study Data was collected directly through paper surveys and indirectly via an online survey from December 2013 to January 2014, facilitating the necessary data collection for the research process.
Respondents participated in the survey via online and paper formats, consisting of friends, clients, colleagues, and partners with direct connections to the author To expedite responses, the author utilized various methods, including phone calls, chat messages, and in-person meetings.
Respondents were able to provide their feedback via email, mail, or chat platforms such as Yahoo and Skype, directly communicating with the author Additionally, the author reached out to participants through phone calls to clarify any questions and assist them in providing accurate responses.
Data collection took place over a month, encompassing both weekdays and weekends A total of 300 questionnaires were distributed through paper and online surveys, yielding 278 responses, of which 242 were 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 by removing invalid questionnaires, the processed data will be analyzed using SPSS 22.00 (Statistical Package for Social Sciences) The analysis will follow a systematic approach to ensure accurate results.
The scale serves as a dependable measure of internal consistency for evaluating the reliability of variables within measurement scales Observed variables reflect a common construct, with constructs demonstrating high reliability characterized by strong inter-correlations among items Reliability analysis, including Cronbach’s alpha coefficient and item-total correlation, assesses 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 investigating relationships among interval variables (Leech et al., 2005) This analysis allows researchers to identify how a large set of items cluster together In this study, EFA was performed using varimax rotation to eliminate items with low loadings on the construct, adhering to strict criteria by deleting factors with loadings below 0.5 and 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 can arise when forecasting sample data This discrepancy is referred to as the residual (ε or e).
Based on these studies, the multiple regression formula will be
Meyers et al (2006) emphasized the significance of R² in determining the extent to which the full regression model explains the variance of the dependent variable A higher R² value indicates a stronger explanatory power of the regression equation (Hair et al., 2010).
Summary
This chapter outlines the research methodology, including sample size, data analysis methods, questionnaire design, and the steps taken throughout the study The main survey involved 242 respondents, with Cronbach’s alpha employed to assess the reliability of the measurement scale Validity was tested using Exploratory Factor Analysis for data reduction purposes, while multiple linear regression was utilized to evaluate the research hypotheses The following 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 methods, sending out 145 questionnaires directly and 97 indirectly via online channels A total of 278 responses were received, of which 242 were deemed valid The frequency of responses for each method 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
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
Type of education Frequency Percent Valid Percent Cumulative
According to Table 4.5, individuals with an income exceeding VND 20 million represent 23.1% of the total, while those earning between VND 10 million and VND 20 million account for 34.3% Additionally, 22.3% of individuals fall within the income range of VND 5 million to VND 10 million, and 20.2% earn below VND 5 million.
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
Item-total correlations below 0.3 will be removed Following 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; social influence scored the highest at 901, while effort expectance was the lowest at 757 Additionally, all variables demonstrated corrected item-total correlation values above 0.3, suggesting a robust component for the 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 has a reliability score of 0.861, falling within the acceptable range of 0.8 to 0.9 This suggests that the measurement scale exhibits very good reliability Although the Cronbach's alpha value for the item BI1 was slightly higher than 0.861 when deleted, its significance warranted its inclusion in subsequent reliability testing.
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 opted for the Principle Axis Factoring (PAF) extraction method combined with Promax rotation, as it provided a more accurate representation of the 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 0.7 threshold (Leech et al., 2005), indicates that the dataset is suitable for factor analysis 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 validate the appropriateness of the data for factor analysis Notably, the cumulative variance explained by the first six factors is 62.00%, indicating that these factors account for over 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
Factor analysis was conducted using Principal Axis Factoring, resulting in the identification of six distinct factors The Rotated Component Matrix (see table 4.8) illustrates the items and their corresponding factor loadings, with a threshold set at 0.5 for acceptance Notably, item FC2 displayed a factor loading of 0.498, which is slightly below the threshold; however, it will still be retained in the analysis Consequently, a total of 27 items across six independent variables were categorized 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)
Table 4.9 indicates that the KMO value, while not exceptionally high, exceeds the acceptable threshold of 0.7, suggesting that there are sufficient items to effectively measure each construct Additionally, the Bartlett’s test yielded a significant result (with a significance value below 5%), confirming that the variables are well correlated.
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 test hypotheses and examine relationships between independent quantitative factors—namely performance expectancy, effort expectancy, social influence, facilitating conditions, perceived credibility, and anxiety—and the dependent quantitative factor of behavioral intention to use Internet banking, the author utilized correlation analysis The Pearson coefficient indicated significant correlations at both the 0.01 level (2-tailed) and 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 (2-tailed), the author identified a notable correlation between five independent factors—performance expectancy, social influence, facilitating conditions, perceived credibility, and anxiety—and 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)
Before running Regression, many assumptions required are testing in Appendix H
The model summary indicates that the multiple correlation coefficient and adjusted R square are 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 ideally be below 10 As shown in Appendix H, all independent variables in this study maintained VIF values under this threshold, confirming compliance with this guideline.
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 nearly 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
Following the aggregation of 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
Table 4.15: Coefficient of multiple linear regression analysis
The analysis of the behavioral intention to use Internet banking reveals significant factors influencing user adoption Key variables include social influence (SI), performance expectancy (PE), facilitating conditions (FC), anxiety (AN), perceived credibility (PC), and effort expectancy (EE) The results indicate that while effort expectancy (EE) shows a negative correlation of -.076, its impact is not statistically significant (p = 111) Other factors such as social influence and perceived credibility may play a more crucial role in shaping users' intentions to engage with Internet banking services.
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 indicates that performance expectancy, perceived credibility, facilitating conditions, and social influence positively impact the intention to use Internet banking Among these factors, performance expectancy exhibits the strongest relationship with behavioral intention, evidenced by a beta coefficient of 0.417, followed by facilitating conditions at 0.372, while perceived credibility has a comparatively lower effect.
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 intention to use Internet banking The findings indicate a strong positive relationship, with a coefficient of β = 0.417, a t-value of 9.503, and a p-value of 0.000, confirming the 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 relationship, with a beta value of 0.321, a t-value of 7.310, and a p-value of 0.000, thereby supporting Hypothesis H3.
Hypothesis H4 is supported by the findings, which reveal a standardized regression coefficient beta of 0.372 for facilitating conditions affecting behavioral intention to use Internet banking The t-value of 8.475 (greater than 2) and a p-value of 0.000 indicate a significant positive impact of facilitating conditions on users' behavioral intentions.
Hypothesis H5:The standardized regression coefficient beta of perceived credibilityon behavioral intention to use Internet banking is 0.228 and value of t is
At a confidence level of 95%, the statistical evidence indicates a significant positive impact of perceived credibility on the behavioral intention to use Internet banking, thus supporting hypothesis H5.
Hypothesis H6 is supported, as the analysis reveals that anxiety significantly influences the behavioral intention to use Internet banking The data indicates a negative impact, with a coefficient value of β = -0.110, t = -2.510, and a p-value of 0.013.
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 identifies a model outlining the factors influencing behavioral intention to use Internet banking, as determined through factor analysis and multiple linear regression analysis, illustrated in Figure 4.1.
Summary
This chapter analyzed the results of measurement scales, the research model, and hypotheses Reliability and validity tests confirmed that the measurement scales effectively assessed each construct The multiple regression analysis revealed strong relationships between most independent variables and the dependent factor, with the exception of effort expectancy The following 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 use 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, explaining 53.9% of the variance in this intention Among these factors, performance expectancy emerged as the most significant, with a standardized beta of 0.417, indicating that changes in performance expectancy have the greatest impact on users' intentions 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 affects the behavioral intention to use Internet banking, with a standardized beta of -0.110.
Performance expectancy significantly influences the behavioral intention to use Internet banking, as users who believe that these services enhance their performance are more inclined to adopt them Customers with a strong sense of performance expectancy demonstrate a higher intention to utilize Internet banking This finding aligns with the research of Venkatesh et al (2003) and subsequent studies by Yeow et al (2008), AbuShanab and Pearson (2009), and Foon and Fah (2011).
Significant effects of social influence, facilitating conditions, perceived credibility, and anxiety on the behavioral intention to use Internet banking have been confirmed, aligning with previous studies by Venkatesh et al (2003), Yeow et al (2008), AbuShanab and Pearson (2009), and Foon and Fah (2011) However, customers experiencing high anxiety tend to exhibit low behavioral intentions towards Internet banking While some respondents express confidence in using online banking services, others fear losing information due to mistakes or face concerns regarding the accessibility of Internet banking service 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
This study found that effort expectancy did not significantly influence the intention to adopt Internet banking, contradicting previous literature that suggested it would be a key predictor of behavioral intention The results challenge earlier findings by Lin, Wu, and Tsai (2005) and Venkatesh et al., indicating a need for further exploration of factors affecting Internet banking adoption.
Research indicates that effort expectancy does not significantly influence the intention to use Internet Banking Services (IBS) in Ho Chi Minh City (HCMC), as supported by findings from Wang et al (2003) and Yeow et al (2008) In Vietnam, internet usage is notably high, with nearly 90% of users accessing the internet more than once a week and about 60% using it daily, leading to a wealth of experience that makes the technology easy to use and learn This extensive familiarity may explain the minimal impact of effort expectancy on consumers' intentions to adopt IBS Additionally, a recent UTAUT study by Yang (2010) corroborates these findings, suggesting that effort expectancy is not a significant factor in influencing the adoption of mobile shopping services Thus, effort expectancy appears to be less critical in predicting behavioral intentions toward IBS in HCMC.
Research contribution
This study aims to provide valuable insights for leaders and marketing managers in the banking industry, particularly those looking to enhance the adoption of Internet banking in Ho Chi Minh City, Vietnam By focusing on consumer behavioral intentions, the research highlights how the development of Internet banking services can offer a competitive advantage and foster integration among banks The findings will be instrumental for banking managers seeking success in this market and will also serve as a resource for policymakers aiming to expand their business operations in the region.
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, particularly those in the banking sector.
Internet banking in Vietnam is a cutting-edge technology designed to enhance customer service and lower 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 needing to visit traditional banks regularly (mean 3.99/SD = 0.085) For busy working parents, effective time management is crucial, and any technology that facilitates more efficient banking is likely to be embraced Internet banking services enable these parents to swiftly complete their banking tasks, allowing them more time to attend to family needs Thus, the time-saving advantage of internet banking is a key feature worth highlighting.
Customers believe that Internet Banking Services (IBS) offer significant advantages, including convenience and easy access (mean 3.62/SD = 1.025), time savings for essential bill payments, online money management, and financial record-keeping To leverage these benefits, banks should highlight efficiency, convenience, and effectiveness in their advertising Additionally, introducing innovative banking products and services that cater to consumer needs—such as tax payments and summons through IBS—can enhance the value of these services Incorporating third-party offerings like insurance, unit trusts, and stock purchases would create a comprehensive financial service, helping banks retain existing customers.
Facilitating conditions are crucial motivators for Internet Banking Services (IBS), with users generally finding support readily available (mean 3.55/SD = 994) The basic system requirements are well met (mean 3.61/SD = 891), and users find the content easy to read and understand (mean 3.633/SD = 934), with the language being accessible (mean 3.65/SD = 1.025) It is essential for users to receive guidance from bank personnel before registering for services to avoid unnecessary subscriptions To enhance user experience, banks should offer responsive support through various channels, including email, online chat, call centers, and in-person assistance Additionally, a repository of informative articles and resources related to IBS should be available on bank websites for user reference Given that many customers have reported a lack of knowledge among bank staff regarding online banking, mandatory training and assessments for bank employees are necessary Banks could also provide free foundational tutorials at schools, branches, or shopping centers to educate the public.
Overall, social influencehad the strong effect on behavioral intention to use
Internet banking (IBS) has gained widespread popularity due to its convenience and accessibility, appealing to both high and low-income individuals, with 20.2% of users earning less than 5 million per month In Vietnam's highly social society, the adoption of Internet banking is significantly influenced by social interactions, as decisions to use such services are often swayed by family, friends, and important social circles Consequently, banks can enhance their marketing efforts by leveraging social influence to highlight the benefits of Internet banking Additionally, understanding consumer behavior within the social and cultural context, along with the impact of technology, is crucial for shaping the practices of Internet banking users.
The perceived credibility of Internet banking significantly influences customer trust, as users prioritize security over traditional banking services due to inherent financial risks To enhance this trust, it is essential for all domestic banks to adopt mandatory industry-wide security standards Implementing two-factor authentication, which combines a username and password with a transaction authorization code or identification number, is crucial For improved security, banks should also consider three-factor authentication, incorporating biometric measures like iris or thumbprint recognition for user identification.
To effectively develop a marketing strategy for Internet banking, banks must prioritize security by upgrading their security systems and managing perceived risks This includes implementing new security policies, enhancing internal communication, and evaluating services to align with customer expectations Additionally, banks should establish service recovery programs and strengthen their ability to manage inherent risks in Internet banking Utilizing advanced security measures such as encryption, firewalls, and intrusion detection systems will further safeguard their Internet banking platforms.
The last objective, anxietyshould significant negatively affect the trend of using
Internet banking is associated with varying levels of user anxiety, as highlighted by Yeow et al (2008) While some users feel confident using Internet banking services (IBS), others express concerns about potential charges and difficulties accessing web pages Therefore, website quality—encompassing ease of navigation, clarity, accuracy, and overall design—plays a crucial role in user experience High anxiety regarding security, such as fears of password theft or transaction errors, can deter customers from utilizing IBS However, increasing user experience can alleviate these anxieties To support novice users, banks should consider offering free Internet banking training sessions regularly.
Limitations and recommendations for future research
This work still exposes some limitations, based on that future research can be developed
The study's sample was conveniently chosen from select banks in Ho Chi Minh City, limiting its representativeness of the broader population To enhance the reliability of future research, it is recommended to increase the sample size and consider expanding the survey to include other cities across Vietnam.
In addition to existing variables, future research should explore the inclusion of additional constructs like perceived self-efficacy, security, and transaction costs, as these factors can significantly influence the banking environment.
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
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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
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
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
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
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 study to analyze the factors that influence customer behavior towards Internet banking (IB) We invite you to take part in our survey, which will assist us in completing our research and enable banks to enhance their services for customers Your participation is greatly appreciated and will contribute to a better understanding of customer preferences in the digital banking landscape.
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
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
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 sự lựa chọn của khách hàng khi sử dụng dịch vụ ngân hàng trực tuyến (Internet-banking), chúng tôi kính mong quý Anh/Chị dành thời gian tham gia 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 đánh dấu mức độ đồng ý của bạn đối với các phát biểu dưới đây bằng cách chọn một trong các ô sau: [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
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 cảm thấy ngại khi sử dụng dịch vụ ngân hàng trực tuyến do lo ngại về việc mắc lỗi mà không thể sửa chữa Tuy nhiên, ý định sử dụng dịch vụ này vẫn đang gia tăng, thể hiện qua mức độ đồng ý ngày càng cao của người dùng.
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
Appendix F: Results of EFA testing for independent variables
Kaiser-Meyer-Olkin Measure of Sampling
Extraction Method: Principal Axis Factoring
Extraction Method: Principal Axis Factoring
Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations
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
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
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