OVERVIEW OF THE THESIS
Research background
Commercial banks are continually enhancing their services to support the growing trend of noncash payments, particularly through credit cards This shift towards noncash transactions is evident in the rapid rise of e-commerce, where shopping online has become a daily routine for many consumers, replacing traditional supermarket visits According to WeAreSocial, the average annual transaction value per user surged from $167 in 2020 to $238 in the first half of 2021, highlighting the significant role of credit cards in boosting online spending Additionally, the widespread use of smartphones has made credit card transactions even more convenient, thanks to biometric authentication features.
In light of the escalating COVID-19 pandemic, the State Bank of Vietnam has urged citizens to minimize cash transactions and embrace online payment services to mitigate health risks As a result, non-cash payment methods, particularly credit cards, are anticipated to gain popularity among users This shift presents significant opportunities for the growth of online payment systems, especially as the use of cash poses a heightened risk of virus transmission.
As credit cards gain popularity and offer various benefits, they also present increasing risks for users, particularly among students in Ho Incidents of theft and accidents involving credit card owners have led to serious consequences, prompting authorities to address economic crimes that exploit vulnerabilities in credit card payment systems These issues have raised concerns about the future use of credit cards, influencing users' intentions and behaviors.
Chi Minh City So what type of risks are perceived by customer influencing the intention to use credit card within object who are students in Ho Chi Minh city?
Despite numerous domestic studies examining factors influencing customers' intentions to use credit cards at commercial banks like Vietcombank, Vietinbank, and MBbank, there is a notable lack of research focusing on the role of perceived risk, particularly among university students This gap highlights the urgency of investigating how perceived risks affect the credit card usage intentions of students in Ho Chi Minh City The findings of this study aim to provide valuable insights for credit card issuers and cardholders, enabling them to enhance governance and safeguard their privacy assets effectively.
This research thesis titled "The Impact of Perceived Risks on the Intention to Use Credit Cards Among University Students in Ho Chi Minh City" aims to systematically analyze the effects of potential risks associated with credit card usage The study will encompass theoretical foundations, methodologies, materials, and results, highlighting key risks that influence students' intentions to use credit cards In conclusion, the thesis will provide an overview of the significant findings and explore implications for future research.
Study’s objectives research questions
1.2.1 :The aim of the research:
The main focus of this study is identifying and evaluating perceived risks influencing the intention to use Credit card of students in Ho Chi Minh City
The study has the identified sub-objectives:
Identifying perceived risk factors influencing the intention to use Credit card of University’s student in Ho Chi Minh City
Evaluating the level of impact of each risk influencing significantly intention to use Credit card
Providing management implications for credit card issuers as well as card owners to manage and keep their privacy assets in safety.
Research questions
(1) What are the perceived risks influencing the intention to use Credit card of University’s student in Ho Chi Minh City?
(2) Which risk has the highest impact on the intention to use Credit card of University’s student in Ho Chi Minh City ?
(3) Which management implications can be released by the researcher to solve these problems?
1.4 Object and scopes of study
Perceived risks influencing the intention to use Credit card of University’s students in Ho Chi Minh city
Survey object: University’s Students in Ho Chi Minh city (including Sophomore, Junior and Senior)
This research focuses on universities in Ho Chi Minh City, specifically Hoa Sen University, Banking University, University of Finance and Marketing, and University of Economics The study will be conducted from July 7th to October 2021.
The research was carried out by using the mixture methodology of qualitative and quantitative research in this paper
- The qualitative method is used for literature review, purpose of the study and the scale by consulting experts and the previous researches
The quantitative method is employed to test hypotheses and assess the impact of independent variables on dependent variables Additionally, the author conducts interviews with experts to gather high-quality data for qualitative research.
The priority data collected about the student of Universities were cleaned and would analyzed by the SPSS 20 software through the following specific steps:
- Statistics description of describing the research sample
This research paper highlights the impact of various research models on students' intentions to use credit cards, particularly concerning perceived risks It enhances understanding of the relationships between key variables and serves as a valuable reference for future researchers, enabling them to leverage these findings to support and advance their own studies Ultimately, this work lays a solid foundation for practical applications in the field.
This research explores the perceived risks that influence students' intentions to use credit cards in Ho Chi Minh City The findings provide valuable insights for commercial banks, non-profit organizations, and credit card users, highlighting practical concerns and offering guidance on how to safeguard their assets effectively.
In the first chapter, the background of the study is illustrated, the issues and purposes are discussed Research questions are defined and finally the structure is presented
Chapter 2: Literature Review and Review research models
A review of previous studies related to problems under investigation
Comments and statements of unsolved issues
Chapter 2 outlines the theoretical framework surrounding perceived risks that influence the intention to use credit cards, drawing on findings from prior research The author presents a research model that highlights these relationships, reinforcing the significance of understanding perceived risks in credit card usage.
In Chapter 3, the author examines the variables that affect the intention to use credit cards, focusing on perceived risk factors through both qualitative and quantitative analyses The data collected will be thoroughly analyzed and cleaned using SPSS 20, which includes sample description, scale checks, factor analysis, verification of factor analyses, Cronbach's Alpha, linear regression, ANOVA, and hypothesis testing.
The study utilizes conceptual scales adapted from previous international research to fit the specific context of universities in Ho Chi Minh City These scales have been refined through adjustments and enhancements to align with the local market The research model employs various scientific methods, including Cronbach’s Alpha coefficient, exploratory factor analysis (EFA), linear regression analysis, ANOVA, and Durbin-Watson tests, ensuring that the model is accurately tailored to the statistical sample.
Interpretation and discussion of consequences/findings of the research
Seft-criticism and limitations are given
Practical managerial implications and suggestions for future researches
In Chapter 5, the author presents a comprehensive summary of research findings regarding the factors influencing risk perceptions that affect credit card usage intentions in Ho Chi Minh City The author also offers insightful administrative recommendations for credit card providers to gain a deeper understanding of customer concerns and anxieties associated with credit card usage.
Chapter 1 of the research paper provides a comprehensive overview of the research problem, outlining the rationale for selecting the topic, as well as the research objectives, subjects, and scope Additionally, this chapter details the research methods employed and the structural design of the study, laying the groundwork for the subsequent chapters.
Research method
The research was carried out by using the mixture methodology of qualitative and quantitative research in this paper
- The qualitative method is used for literature review, purpose of the study and the scale by consulting experts and the previous researches
The quantitative method is employed to test hypotheses and assess the impact of independent variables on dependent variables, while expert interviews are conducted to gather high-quality data for qualitative research.
The priority data collected about the student of Universities were cleaned and would analyzed by the SPSS 20 software through the following specific steps:
- Statistics description of describing the research sample
Contribution of research
This research paper highlights the impact of various research models on students' intentions to use credit cards in relation to perceived risks It enhances awareness of research methodologies and clarifies the relationships among key variables The findings serve as a valuable reference for other researchers, enabling them to leverage this study's results to inform and advance their own work Ultimately, it lays a solid foundation for effectively exploring practical applications in this field.
This research identifies perceived risks that influence students' intentions to use credit cards in Ho Chi Minh City The findings offer valuable insights for commercial banks, non-profit organizations, and credit card users, helping them understand these realistic challenges and take proactive measures to safeguard their assets.
Proposed thesis structure
In the first chapter, the background of the study is illustrated, the issues and purposes are discussed Research questions are defined and finally the structure is presented
Chapter 2: Literature Review and Review research models
A review of previous studies related to problems under investigation
Comments and statements of unsolved issues
Chapter 2 outlines the theoretical foundations of perceived risks that influence credit card usage intentions, supported by relevant studies Drawing from previous research findings, the author proposes a comprehensive research model to examine these dynamics.
In Chapter 3, the author examines the variables affecting the intention to use credit cards, focusing on perceived risk factors through both qualitative and quantitative analyses The data collected will be systematically analyzed and cleaned using SPSS 20, which includes sample description, scale checks, factor analysis, verification of factor analyses, Cronbach's Alpha, linear regression, ANOVA, and hypothesis testing.
The study utilizes conceptual scales adapted from previous international research, tailored to the specific context of universities in Ho Chi Minh City These scales are developed through modifications and enhancements to align with local market conditions The research model employs various scientific methods, including Cronbach’s Alpha coefficient, exploratory factor analysis (EFA), linear regression analysis, ANOVA, and Durbin-Watson tests, allowing the author to refine the model for optimal relevance and accuracy with the statistical sample.
Interpretation and discussion of consequences/findings of the research
Seft-criticism and limitations are given
Practical managerial implications and suggestions for future researches
In Chapter 5, the author summarizes the research findings on the factors influencing risk perceptions that affect credit card usage intentions in Ho Chi Minh City The author also offers insightful administrative recommendations for credit card providers to enhance their understanding of customer concerns and anxieties related to credit card usage.
Chapter 1 of the research paper provides a comprehensive overview of the research problem by addressing key elements such as the rationale for selecting the topic, research objectives, and the subjects and scope of the study Additionally, this chapter outlines the research methods employed and the structural design of the study, establishing a foundation for the subsequent chapters.
LITERATURE REVIEW AND REVIEW RESEARCH
The concept of customers
A customer is defined as any individual or entity that engages in the purchase or consumption of goods or services from a business Organizations typically interact with two categories of customers: external customers, who are individuals or groups outside the organization, and internal customers, who are members within the organization itself.
External customers are individuals or entities not directly affiliated with an organization but who influence its processes, as defined by Tennant (2001) The most recognizable external customers are those who purchase goods or services, while suppliers can also be considered customers due to their impact on billing and payment processes In quality initiatives, external customers hold the highest significance Active customers are those who have engaged with a business within a specific timeframe, while non-customers include former, current, and potential customers who have not recently consumed the product or service Understanding non-customers is crucial, as they represent opportunities for future engagement Additionally, internal clients are those directly connected to the organization, often working within it.
Internal consumers, as noted by Tennant (2001), are typically stakeholders due to their vested interest in the company's services and performance Similarly, shareholders focus on the organization's fundamental value, profitability, and return on investment Additionally, employees are committed to addressing daily operational challenges, playing a crucial role in the organization's success.
8 an important role in the overall outcome of every company Other stakeholders may include regulatory entities and creditors with an interest in each
This research specifically targets external customers, defined as university students in Ho Chi Minh City, who are in their sophomore to senior years For the purposes of this study, the term "customer" will refer exclusively to these external university students.
Consumer credit
Credit loans have a rich history, dating back approximately 3000 years to the Babylonians, with consumer lending evolving over the last 750 years through various stages, including the roles of pawnbrokers and gombeen men in the Middle Ages In recent decades, the rise of global awareness and technological advancements has transformed lending practices for the mass market A key factor influencing consumer spending is liquidity, which has been historically constrained The evolution of consumer credit has leveraged businesses' monopoly and status to alleviate these liquidity limitations, although this shift has exerted negative pressure on the commodity market.
Research indicates that purchasing facilities significantly influence the rise in consumer debt In the United States, approximately 66% of new cars are leased or financed, and installment debt accounts for about 20% of an average household's disposable income (Keenan, 1998).
A pioneering study by Mathews and Slcoum (1986) revealed that social class is not the most effective market segmentation variable in consumer behavior related to credit card usage Commercial banks often issue credit cards based on customers' social class, with lower-class members typically using their cards for installment purchases, while higher-class individuals utilize them for convenience These differing usage patterns highlight the underlying social class values, which serve as a foundation for marketing credit services.
Cardholders often prioritize stores that accept their credit cards, reflecting a favorable attitude toward credit usage Convenience users prefer using their cards over cash to avoid overspending and typically refrain from purchasing items that incur additional fees This behavior aligns with sociological concepts of varying satisfactions influenced by social class.
Goyal (2008) explored how complementary services can help manage perceived risks associated with credit card purchases, highlighting their role as a source of non-personal data for consumers The study focused on the effects of these services on both functional and psychological risks, revealing that they play a crucial role in mitigating such risks By offering controllable add-on services, credit card marketers can enhance customer value and increase purchase likelihood Addressing perceived risk is vital in the marketing of financial services, influencing consumer purchasing decisions and presenting a significant challenge for service marketers.
The concept of perceived risk
Risk significantly influences consumer behavior, particularly in understanding customer purchasing decisions and knowledge acquisition It can be viewed through two theoretical lenses: one emphasizes the unpredictability of decision-making outcomes, while the other highlights the associated costs and effects (Barnes et al., 2007) The increasing use of credit cards and cashless payment methods is seen by consumers as beneficial and convenient, even before payment is completed However, this raises the question of whether credit cards and cashless transactions are genuinely safe and effective or merely present potential risks in both online and offline environments.
Bauer (1960) was the pioneering researcher who defined perceived risk (PR) within the context of the procurement process, describing it as consumer behavior influenced by potential negative consequences His analysis identified key factors impacting PR, including time loss, performance, personal, financial, and social risks Perceived risk reflects customer uncertainty in online purchases, where unpredictable outcomes are a concern It is crucial to understand perceived risk in consumer behavior through two main variables: perceived risk in online transactions (PRT) and perceived risk related to the product or service (PRP).
Figure 2.1 Perceived Risk Model (Bauer,1960)
Perceived risk in online transactions encompasses various concerns that customers face during electronic purchases, including privacy, security, authentication, and non-repudiation These factors significantly influence the overall perception of risk associated with online shopping, impacting consumer confidence and decision-making Understanding these risks is crucial for enhancing customer trust and ensuring a secure online purchasing experience.
Perceived risk in marketing refers to the awareness and magnitude of risks that customers associate with making a purchase, encompassing various dimensions such as opportunity risk, financial risk, and time-loss risk According to Taylor (1974), this concept highlights the uncertainty consumers face when considering a product or service The total perceived risk is the cumulative effect of these uncertainties, which includes six key factors: time-loss risk, performance risk, physical risk, security risk, privacy risk, and psychological risk (Kaplan et al., 1974; Cox and Rich, 1964) Understanding these risk perception patterns is crucial for businesses aiming to address consumer concerns and enhance their purchasing experience.
Measure it by six dimensions, according to Stone and Gronhaug (1993), illustrated to six groups of perceived risks following as:
- Psychological risk: concentrating the desire of the customers in the quality of
Perceived risk in an online transaction (PRT)
Perceived risk with a product or service (PRP)
12 a product / service that suits their perception of themselves
Time-loss risk refers to the uncertainty associated with the time invested in acquiring, utilizing, or discontinuing a product or service This risk highlights the potential high costs of time, particularly concerning long-term intentions to purchase, use, or withdraw from goods and services.
- Social risk: potential risk to the social status of a individual when purchasing, using or withdrawing a good or service
- Financial risk: Much more higher if the product or service costs an arm and a leg and potential risk that product is not valuable about financial
- Physical risks: effects on the possible threat to consumer health of goods and services.
The theoretical models of intention and behaviour
2.4.1 Theory of Reasoned Action (TRA) - Behavioral Intention to Use the
Ajzen and Fishbein have developed theory of reasoned action ( TRA) since
The Theory of Reasoned Action (TRA), developed by Fishbein and Ajzen in 1975, posits that behavioral intention is the primary determinant of personal behavior, rather than one's attitude towards that behavior This statistical model emphasizes the significance of intention as a continuation of both attitude and behavior Hogarth (1991) highlighted the importance of understanding client attitudes in the workplace to build a theoretical foundation for analyzing how individuals accept or reject information The interplay between behavioral understandings and the formation of attitudes and intentions has been a focal point for many social psychologists, reinforcing the notion that individuals rely on rational thought and systematically available information in their decision-making processes.
According to rational action theory, an individual's behavior is primarily influenced by their intention to act rather than their attitude This intention is shaped by two key factors, which play a crucial role in determining whether a person will engage in a specific behavior.
Figure 2.2 Theory of Reasoned Action (Ajzen and Fishbein,1975)
According to Figure 2.2., the two basic components of the TRA model are "Attitude toward Behaviour" and "Subjective norm"
Attitude reflects our feelings towards actions, influencing how we engage with products and services It encompasses our investment in these actions, shaping our positive or negative beliefs A consumer's attitude is determined by their awareness of a product's features, emphasizing the importance of understanding attributes that provide essential benefits, each varying in significance.
- Subjective norm: the social environment is seen as having an impact on individual behaviour It is the perception of others (people like friends, family who
14 are important to the individual) that he/she should or should not be doing the behaviour 1991
Behavioral intention serves as a key indicator of an individual's willingness to engage in specific actions As outlined in the Theory of Reasoned Action (TRA), intention is a crucial precursor to actual behavior The model emphasizes that a stronger intention to perform a behavior significantly increases the likelihood of that behavior being executed.
One major limitation of the Theory of Reasoned Action (TRA) is its assumption that behavior is entirely under conscious control and willpower This theory exclusively addresses actions that are premeditated and ignores irrational judgments, habitual behaviors, or impulsive activities that are not consciously contemplated, making it less applicable in certain real-world scenarios.
Theory of Planned Behaviour (TPB)
The Theory of Reasoned Action (TRA) has limitations in predicting customer behavior due to its inability to account for uncontrollable factors influencing attitudes and expectations To address these shortcomings, Ajzen developed the Theory of Planned Behavior (TPB) in 1985, introducing the variable "Perceived Behavioral Control" (PBC) alongside attitude and subjective norms PBC is shaped by control beliefs and perceived ease, enhancing the model's applicability in situations where individuals lack subjective norms and attitudes Ajzen's TPB emphasizes that PBC and intention are crucial for forecasting behavior, acknowledging that the significance of each factor can vary depending on the context.
Figure 2.3 Theory of Planned Behavior (Ajzen,1991)
Defining factors in the TPB model
Attitude toward the behaviour: positive or negative emotions of the individual about the performance of the target behavior
Subjective norms: the opinion of the plurality of individuals who are relevant to him that he/ she should/ should not practice the action in question
Intention: an indicator of an individual's ability to act and is considered to be a prefix immediately before the behavior
Perceived behavioral control: a person feels comfortable in his/ her abilities to execute an action comparable to confidence
Review of previous researches and models
Managing perceived risks for credit card purchase through supplementary services, Anita Goyal (2008)
The research model built in the project “Managing perceived risks for credit card purchase through supplementary services” was conducted by Anita Goyal,
2007 The aim of this study was to analyze the effects of additional services to reduce perceived risk in credit card payments and purchases
Research indicates that supplementary services like ATM access and cash withdrawal enhance perceived risk management for credit card users, providing them with a greater sense of security and increasing the overall value of credit card usage.
Data mining for credit card risk analysis: A Review, S Srivastava & A Garg (2013)
The study "Data Mining for Credit Card Risk Analysis: A Review" by S Srivastava and A Garg (2013) highlights that both credit card holders and banks face three primary types of risks: security risks, operational risks, and financial risks The research aims to analyze the financial risks associated with credit card holders, identify the contributing factors, and explore existing methods for managing these risks effectively.
Inadequate risk management can lead to significant consequences for both companies and individuals, as highlighted by research findings Unexpected financial risks, such as a credit crunch, can disrupt financial markets due to poor credit risk management practices among financial institutions Furthermore, the research emphasizes the importance of risk education in mitigating the adverse effects of these risks.
Fraud Detection in Credit Card Transactions: Classification, Risks and Prevention Techniques, N Sivakumar & Dr.R Balasubramanian (2015)
This article explores the challenges faced by credit cardholders and issuers, highlighting the prevalence of fraud and the latest news on credit card fraudsters It emphasizes that fraud poses the greatest risk to both consumers and businesses, prompting the need for enhanced technology by investigating agencies and issuers to mitigate these risks Additionally, the article offers essential prevention techniques for cardholders to protect themselves against fraudulent activities.
Analysis of factors affecting the intention to use international credit cards of individual customers at Military Commercial Joint Stock Bank (MBBANK) – Hue Branch: Huong (2020)
This study analyzes the factors influencing individual customers' intentions to use international credit cards at Military Commercial Joint Stock Bank (MB) By examining data from MB, the research aims to propose solutions that encourage future usage of this product Findings indicate that the perceived cost significantly impacts customers' willingness to use credit cards in Hue; specifically, the costs must align with the benefits received To enhance customer adoption, MB should consider lowering fees and interest rates, thereby increasing the competitiveness of its international credit card offerings.
Analysis of factors affecting the intention to use international credit cards of individual customers at Saigon Thuong Tin Commercial Joint Stock Bank - Quang Binh Branch: Ngan (2016)
A study conducted at Saigon Thuong Tin Commercial Joint Stock Bank in Quang Binh province revealed that risk perception, alongside subjective norms, attitudes, and behavioral control, significantly influences individual customers' intentions to use credit cards Among various factors, customers identified operational risks and security risks as the most critical influences on their credit card decisions.
Table 2.1 Summary of the previous researches
Name of research Author Research method Related factors
Managing perceived risk for credit card purchase through supplementary services
- Perform analysis tools: reliability test Cronbach's Alpha, coefficient of discovery EFA, regression, correlation
There are 3 influencing factors: o Perceived Risk o Supplementary Services o Credit card marketing
- Perform analysis tools: reliability test Cronbach's Alpha, coefficient of discovery EFA, regression, correlation
There are 3 influencing factors: o Security Risks o Operational Risk o Financial Risk
- Perform analysis tools: reliability test Cronbach's Alpha, coefficient of discovery EFA, regression, correlation
There are 3 influencing factors: o Skimming o Thieves o Counterfeit
Analysis of factors affecting the intention to use international credit cards of individual customers at
- Perform analysis tools: reliability test Cronbach's Alpha, coefficient of discovery EFA, regression, correlation
Five key factors influence the usage of international credit cards: customer reliability, subjective behavioral standards, perceived usefulness, perceived behavioral control, and perceived financial risk Understanding these elements is essential for assessing consumer behavior regarding international credit card transactions.
Analysis of factors affecting the intention to use international credit cards of individual customers at
- Perform analysis tools: reliability test Cronbach's Alpha, coefficient of discovery EFA, regression, correlation
There are 4 influencing factors: o Customer Reliability o Perceived usefulness towards international credit card usage behavior o Perceived behavioral control towards
Branch international credit card usage o Perceived risk towards international credit card usage
The first hypothesis, Security Risk – signed H1
Credit card security risks have become a global concern, primarily due to issues like card misrepresentation stemming from lost or stolen cards and card numbers This allows fraudsters to exploit card information for online or phone transactions, often referred to as card-not-present purchases One common method of fraud is "skimming," where criminals generate card numbers and hack the card verification value (CVV), a three to four-digit code found on credit cards Another prevalent form of credit card fraud is "phishing," where victims are tricked into visiting fraudulent websites masquerading as trusted institutions to update or restore personal account information through deceptive emails.
H1: Security risks have negative impact on the intention to use credit card of students in HCM City
The second hypothesis, Operational Risk – signed H2
Operational risk refers to the potential for loss resulting from inadequate or faulty internal processes, distinct from market and credit risks This type of risk can arise from user errors, system failures, or external events To mitigate operational risk, organizations often implement robust internal audit and control systems Additionally, educating employees at all levels about complex operations can further reduce the likelihood of operational failures.
H2: Operational risks have negative impact on the intention to use credit card of students in HCM City
The third hypothesis, Financial Risk – signed H3
RESEARCH MODEL AND RESEARCH METHOD
Overview of the research process
The article titled "The Impact of Perceived Risks on University Students' Intentions to Use Credit Cards in Ho Chi Minh City" analyzes survey data to evaluate how perceived risks affect students' willingness to use credit cards Based on the research findings, the author proposes solutions and administrative implications aimed at assisting students who are either considering or currently using credit cards These recommendations focus on minimizing risks and enhancing the overall efficiency and user experience for each individual.
Research process
After identifying the research problem, the author created a comprehensive outline for the study This outline served as a framework for exploring relevant documents and studies to identify variables for a suitable survey questionnaire Following the survey, the collected data will be processed using SPSS software for analysis and evaluation Once these steps are completed, the author will provide personal insights and recommend appropriate solutions Finally, the instructor will review and assess the research before the project is finalized A diagram summarizing the research process is included for clarity.
Scale buidling
Through the process of selection and research, the author has selected a research model that includes factors affecting purchase intention as follows:
3.3.1 Perceived Security Risk (signed: PSR)
Credit cards are not secure for financial transactions
Hackers can easily skim your CVV code
You feel the personal details are easily leaked through credit card transactions
Your personal information is easily accessed
Table 3.1: Perceived Security Risk Scale 3.3.2 Perceived Operational Risk (signed: POR)
The issuing banks systems maintenance affect your payment experience
Credit card transactional facilities are often found mistakes
The risk of lack of capital of issuing banks will hinder your capital needs
The lack of uniformity between devices that support credit card payments will affect the experience of using credit cards
Table 3.2: Perceived Operational Risk Scale
3.3.3 Perceived Financial Risk (signed: PFR)
The cost of payment by credit card is higher than other payment methods
You can hardly refund when transacting by credit card
Credit card interest rates are higher than regular rates
You will be charged an additional penalty if you spend over your limit
Late payment fee will be imposed when you do not pay the minimum amount of 5% - 10% required by the issuing banks
Table 3.3: Perceived Financial Risk Scale 3.3.4 Perceived Fraud Risk (signed: PFrR)
You can be impersonated to steal your credit card from the issuing banks
Your credit card may be lost or stolen
Your official card may be takeovered after you lose your valid personal information through email
Your credit card has a chance of being counterfeited
Table 3.4: Perceived Fraud Risk Scale 3.3.5 Intention To Use Credit card (signed: ITUC)
You intend to continue using credit card in the future
ITUC1 You like to use credit card
You are willing to introduce credit card to the others
IUTC4 You are willing to use credit card
Table 3.5: Intention To Use Credit card Scale
In the data collection process, the author employed the Likert scale, developed in 1932, which utilizes a five or seven-point scale to gauge individual agreement or disagreement with specific statements This method offers the advantage of capturing nuanced opinions, allowing respondents to express varying degrees of agreement, disagreement, or neutrality, rather than providing a simple yes or no answer.
Methods of collecting information
Secondary information is collected from the following documents:
Theoretical documents are collected from books and textbooks on Credit card Marketing, Sales
Scientific research works, graduation thesis related to “Intention to use”
To ensure that the research findings are logical, reliable, and accurate, it is essential to gather both secondary information from various sources and primary data through direct collection methods.
To effectively research the factors influencing online shopping intentions, first identify the problem and current situation, along with the target subjects for the survey The questionnaire was crafted to address these relevant issues An online survey was then created using Google Forms, and the link was distributed to customers After collecting the survey responses, the data was analyzed using SPSS 20.0.
On the basis of the proposed model and preliminary research, the author builds from that to measure the influencing factors, including 5 main components, with 22 observed variables, included:
Perceived Security Risk: 4 observed variables
Perceived Operational Risk: 4 observed variables
Perceived Financial Risk: 5 observed variables
Perceived Fraud Risk: 4 observed variables
Intention To Use: 4 observed variables
This study explores the influence of perceived risks on university students' intentions to use credit cards in Ho Chi Minh City By examining the relationship between perceived risks and credit card usage intentions, the research aims to uncover how these risks affect students' decision-making processes regarding credit card adoption The findings will provide insights into the correlation between perceived risks and the willingness of students to utilize credit cards, contributing to a better understanding of consumer behavior in this demographic.
The questionnaire uses questions that are a combination of scales such as nominal and Likert scales including 5 levels: (1) Completely disagree, (2) Disagree,
(3) Normal, (4) agree, (5) Totally agree to measure the values
The questionnaire consists of three parts:
The author introduces the researcher and outlines the purpose and urgency of the study, establishing credibility and fostering cooperation among participants to ensure the collection of accurate data.
The objective of gathering detailed demographic information about respondents, including factors such as gender, school year, occupation, and place of residence, is crucial for comprehensive analysis The data collection utilizes nominal scales to categorize these characteristics effectively.
Part 3: Information about intention to use credit cards
This research investigates the influence of risk perceptions on students' intentions to use credit cards in Ho Chi Minh City Utilizing a 5-point Likert scale, the study aims to gather insights on various factors outlined in the proposed research model.
Survey subjects are students who have been and are intending to use credit cards in Ho Chi Minh City including Sophomores, Juniors and Seniors
For the EFA discovery factor, the minimum sample size ensures the formula: n
≥ 5*x (n: sample size and x: total observed variables) (Hair et al (2014))
The survey questionnaire in the official study includes 17 observed variables
To achieve a minimum sample size of 5/1, the sample size must be at least 105 elements (= 5*17 observed variables) So the author chose 200 as the sample size to serve the research
Survey scope: The scope of the study is students in Ho Chi Minh City and the perceived risk of credit cards
The study employed questionnaires and an online survey to gather data The survey featured a nominal scale to categorize respondents' choices, along with a hierarchical scale to assess the level of interest in the surveyed subject.
In response to the COVID-19 pandemic and the necessity for social distancing, researchers are unable to conduct random sampling in person Instead, convenience sampling will be utilized by distributing surveys and questionnaires via social networks such as Facebook and Google Mail This quantitative approach will assess and measure various factors using a 5-level Likert scale.
Methods of information processing
The statistical method is designed to identify key characteristics of the research sample, utilizing both quantitative and qualitative variables to assess their effects on the research model Descriptive statistics are employed to visually represent data, illustrating the percentage of each variable in relation to the overall sample.
3.5.2 Method of testing Cronbach's Alpha scale
Testing Cronbach's Alpha is essential for assessing the reliability of a scale, determining whether the observed variables consistently measure the same underlying concept This process enables researchers to identify and eliminate unsuitable variables from the research model by utilizing the Corrected Item-Total Correlation coefficient.
Observable variables with an item-total correlation below 0.3 will be excluded from analysis, while the selection criteria for the scale will require a Cronbach's Alpha of 0.6 or higher, as outlined by Hoang Trong et al (2008).
3.5.3 Method of factor analysis EFA
EFA factor analysis is a statistical technique that simplifies a large set of observed variables into a more concise and meaningful group, while retaining the majority of the information from the original data.
In the context of Exploratory Factor Analysis (EFA), a Factor Loading index value exceeding 0.5 is deemed practically significant, as noted by Hair et al (1998) The appropriateness of the EFA method is assessed using the KMO index, with a KMO coefficient between 0.5 and 1 indicating suitability for factor analysis Furthermore, Bartlett's test, as described by Trong & Ngoc (2005), evaluates the hypothesis that there is no correlation among observed variables in the population A statistically significant result, indicated by a significance level (Sig.) of less than 0.05, confirms that the observations are indeed correlated within the population.
Gerbing and Anderson (1988) established that in factor analysis, a stopping criterion is met when the Eigenvalue exceeds 1 and the total variance explained by each factor surpasses 50%.
Regression analysis is a statistical method used to evaluate the relationship between a dependent variable and one or more independent variables This technique enables the formulation of a regression equation that identifies the key factors influencing consumer purchase intentions The one-pass selection method, also known as the Enter method, is employed for this analysis To ensure accurate results, it is essential to adhere to specific principles during the regression analysis process.
- Check the Adjusted R Square coefficient to consider the fit of the model
- Check Sig values < 0.05 and F coefficient in the ANOVA table to verify the fit of the regression model with the sample population
- Check if the variance inflation factor (VIF) is in the range (1, 10) to consider multicollinearity
Evaluate the strong or weak impact of the variables on the satisfaction level through the Beta coefficients in the Coefficient table
3.5.5 Method of testing variance ANOVA
ANOVA, or Analysis of Variance, is a statistical method employed to compare the means of three or more populations This technique is particularly useful for analyzing survey and experimental data, allowing researchers to determine if there are significant differences among the group means.
Based on the results of One-Way ANOVA analysis to compare the difference between groups in qualitative and dependent variables
In the Test of Homogeneity of Variances, a significance level (Sig.) greater than 0.05 indicates that there is no significant difference in variance among the groups Conversely, in the ANOVA table, a significance level less than 0.05 suggests that there is a significant difference in the mean values of qualitative variables between the groups.
In Chapter 3, the author explores the variables influencing the intention to use credit cards, focusing on perceived risk factors through both qualitative and quantitative analyses Utilizing focus group techniques, the research model is revised based on qualitative insights, with comments documented in line with official scale adjustments The quantitative study involves a survey conducted via online interviews using Google Docs, targeting 200 university students Following established procedures, the collected data will be analyzed and cleaned using SPSS 20, which includes sample description, scale checks, factor analysis, verification of factor analyses, Cronbach’s Alpha, linear regression, ANOVA, and hypothesis testing.
RESULTS AND DISCUSSIONS
Overview of the research samples
Surveys for this research were conducted at Banking University, Sai Gon University, Hoa Sen University, and the University of Economics in Ho Chi Minh City using a soft questionnaire rated on a 5-point scale Initially, 206 responses were collected via Google Forms; however, 6 questionnaires were excluded due to incomplete answers or uniform responses throughout Consequently, the final number of valid questionnaires used for the study was 200.
Descriptive statistical analysis
4.2.1 Descriptive statistic for norminal varibles
Table 4.1: Sample characteristics of gender
Table 4.1 reveals that the study includes a total of 200 students, with female participants making up 61% and male participants comprising 39%.
According to Table 4.2, the study reveals that 18 second-year students represent 9%, 32 junior students account for 16%, and 150 senior students make up 75% of the total participants Consequently, this research primarily targets senior students.
According to Table 4.3, the majority of students in this research are from the Business Administration major, comprising 105 students or 52.5% of the total This is followed by the Finance and Banking major with 53 students (26.5%), the Accounting major with 28 students (14%), and other disciplines with 14 students (7%) Consequently, the study primarily emphasizes students from the Business Administration and Finance and Banking departments.
Table 4.4 reveals that a total of 200 students participated in this study, with 167 students (83.5%) from universities indicating they utilize credit cards by answering "Yes."
“No” answer, 33 students (16.5) Therefore, Most of Universites’s student are using credit card
Table 4.5 reveals that a significant majority of university students, 56% (112 individuals), reported using their credit cards "frequently," while only 17.5% (35 students) used them "sometimes," 15% (30 students) "rarely," and 16.5% (33 students) reported not using them at all.
Table 4.6 Sample income per month
A survey conducted among 200 students revealed that 25.5% earn below three million VND per month, totaling 51 students Additionally, 21% of students have an income ranging from 5,100,000 VND to 7,000,000 VND The largest group, comprising 39.5% or 79 students, falls within the income range of 3,100,000 to 5,000,000 VND Furthermore, 14% of students, or 28 individuals, earn above seven million VND monthly Overall, there is little variation in the income levels among the student population.
4.2.2 Descriptive statistic for ordinal variables
Table 4.7 Descriptive statistic of PSR
N Minimum Maximum Mean Std Deviation
The findings presented in Table 4.7 reveal significant diversity in the perceptions of 200 participants regarding Perceived Security Risk, with scores ranging from 1 (Strongly disagree) to 5 (Strongly agree) The survey team anticipates these varied responses will provide valuable insights into participants' security concerns.
The analysis of the PSR1 to PSR4 factors indicates that their average values range from 3 to 5, signifying a transition from normal to strong agreement This reinforces the initial hypothesis of positive signs in the model, as the standard deviation remains within acceptable limits, confirming that the observed data is appropriate.
Table 4.8 Descriptive statistic of POR
N Minimum Maximum Mean Std Deviation
The findings presented in Table 4.8 reveal significant variability in the perceptions of 200 participants regarding Perceived Operational Risk, with scores ranging from 1 (Strongly disagree) to 5 (Strongly agree).
The analysis reveals a positive sign, indicating that the oscillation has resulted in a standard deviation within acceptable limits The average values of the POR1 to POR4 factors consistently range from 3 to 5, reflecting a transition from normal to strong agreement This supports the initial hypothesis of positive indicators in the model, confirming that the observed data is appropriate.
Table 4.9 Descriptive statistic of PFR
N Minimum Maximum Mean Std Deviation
The findings in Table 4.9 reveal significant diversity in the perceptions of 200 participants regarding Perceived Financial Risk, with scores ranging from 1 (Strongly disagree) to 5 (Strongly agree) The survey team anticipates that these varied responses will provide valuable insights.
The oscillation observed in the data has resulted in a standard deviation that remains within acceptable limits The average values of the PFR1 to PFR5 factors consistently range from 3 to 5, indicating a transition from normal to strong agreement This supports the initial hypothesis of positive signs within the model, confirming that the observed data is appropriate.
Table 4.10 Descriptive statistic of PFrR
N Minimum Maximum Mean Std Deviation
The findings presented in Table 4.10 reveal significant variability in the perceptions of 200 participants regarding Perceived Fraud Risk, with scores ranging from 1 (Strongly disagree) to 5 (Strongly agree) Despite this variation, which resulted in a standard deviation, the average scores for the PFrR1 to PFrR4 factors consistently fall between 3 and 5, indicating a general consensus from "Normal" to "Strongly agree." This supports the initial hypothesis of positive indicators within the model, confirming that the standard deviation remains within acceptable limits and validating the appropriateness of the observed data.
Cronbach’s alpha analysis
The Cronbach’s Alpha test serves as a preliminary evaluation of the scale by assessing the Cronbach’s Alpha coefficient and the corrected item-total correlation for each observed variable Selected observed variables must have an item-total correlation exceeding 0.3 and a Cronbach’s Alpha coefficient greater than 0.7 The subsequent analysis focuses on Security Risk, Operational Risk, Finance Risk, Fraud Risk, and the Intention to use credit cards, which will be utilized for exploring factors through Exploratory Factor Analysis (EFA).
The analysis results of Cronbach’s Alpha for the independent variables reveal distinct components of risk The Perceived Security Risk component includes four observed variables: PSR1, PSR2, PSR3, and PSR4 The Perceived Operational Risk component consists of four observed variables: POR1, POR2, POR3, and POR4 Additionally, the Perceived Financial Risk component is represented by five observed variables: PFR1, PFR2, PFR3, PFR4, and PFR5 Lastly, the Perceived Fraud Risk component comprises four observed variables: PFrR1, PFrR2, PFrR3, and PFrR4.
Table 4.11 Cronbach ’Alpha coefficient of the PSR scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
The findings presented in Table 4.11 indicate that the overall Cronbach’s alpha coefficient is 0.898, falling within the acceptable range of 0.8 to 0.9 Furthermore, the correlation coefficients for all component scales exceed the minimum threshold of 0.4 Consequently, the PSR variable scale, consisting of components PSR 1, 2, 3, and 4, demonstrates good reliability and is suitable for subsequent analysis in the following section.
Table 4.12 Cronbach ’Alpha coefficient of the POR scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
The findings in Table 4.12 indicate that the overall Cronbach’s alpha coefficient is 0.866, falling within the acceptable range of 0.8 to 0.9 Furthermore, all correlation coefficients for the component scales exceed the minimum threshold of 0.4 Consequently, the POR variable is comprised of four reliable component scales—POR 1, 2, 3, and 4—making it suitable for further analysis in subsequent sections.
Table 4.13 Cronbach ’Alpha coefficient of the PFR scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
The analysis presented in Table 4.32 indicates that the overall Cronbach’s alpha coefficient is 0.844, falling within the acceptable range of 0.8 to 0.9 Furthermore, all correlation coefficients for the component scales exceed the minimum threshold of 0.4 Consequently, the PFR variable scale, consisting of five component scales (POR 1, 2, 3, 4, and 5), demonstrates strong reliability and is suitable for subsequent analyses.
Table 4.14 Cronbach ’Alpha coefficient of the PFrR scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
The findings from Table 4.14 indicate a total Cronbach’s alpha coefficient of 0.933, exceeding the acceptable threshold of 0.9, while all component scales exhibit correlation coefficients greater than the minimum standard of 0.4 Consequently, the PFrR variable comprises four reliable component scales: POR 1, 2, 3, and 4, making it suitable for subsequent analysis.
Table 4.15 Cronbach’s Alpha coefficient of the Dependent Variable
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
The analysis presented in Table 4.15 indicates that the overall Cronbach’s alpha coefficient for the Intention to Use Credit Card (ITUC) is 0.909, surpassing the acceptable threshold of 0.9 Furthermore, the correlation coefficients for all component scales exceed the minimum standard of 0.4 Consequently, the ITUC variable comprises four reliable component scales—ITUC 1, 2, 3, and 4—making it suitable for further analysis in subsequent sections.
Exploratory factor analysis (EFA)
4.4.1 EFA analysis for Independent Variable Scale
The scales are assessed using the Exploratory Factor Analysis (EFA) method following an analysis of the Cronbach’s alpha reliability coefficient EFA evaluates two key value types: convergence and discriminant values, utilizing criteria such as factor load value, KMO coefficient, Bartlett test, and the percentage of total variance.
The Independent Variable Scale (Perceived risks) consists of 4 components with
17 observed variables After passing the reliability test of the scale using Cronbach’s Alpha coefficient, all variables are conducted in the EFA analysis
Table 4.16 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .799
The results of the factor analysis show that the coefficient KMO = 0.799 > 0.5,
The significance level (Sig.) of 0.000, which is less than 0.5, leads to the rejection of the null hypothesis (Ho) that the observed variables are not correlated overall Consequently, the factor model hypothesis is deemed inappropriate and is rejected This indicates that the data utilized for factor analysis is highly suitable.
Table 4.17 Total Variance Explained of Independent variables
Component Initial Eigenvalues Extraction Sums of
Based on the results table, at the Eigenvalue value = 1.665 (> 1), the total extracted variance is 73.359% (> 50%) This means that these 17 observed variables can explain 73.359% of the data variability
Table 4.18 Rotated Component Matrixa of Independent variables
Using Principal Components extraction method with Varimax procedure rotation, loading coefficient greater than 0.5 is considered to be of practical significance The result shows that all variables are accepted
Table 4.19 Discriminant coefficient of the observed variables
The results in Table 4.19 show that the discriminant values of the observed variables all exceed the minimum allowable limit of 0.3
4.4.2 EFA analysis for Dependent Variable Scale
Table 4.20 EFA analysis for Dependent Variable Scale
Source: SPSS 20 statistics Testing the suitability of the EFA factor analysis model and the correlation between observed variables (Bartlett's Test)
The result of table 4.20 shows that KMO coefficient = 0.797 > 0.5 passes Bartlett’s test at significance level of 0.000 (0% error) Therefore, factor analysis for the research model is appropriate
Test of extracted variance of factors (% Cumulative variance)
The Eigenvalue of 3.266 indicates a strong factor, as it exceeds 1, while the extracted variance of 81.65% surpasses the 50% threshold, confirming the validity of the EFA model and aligning perfectly with the original hypothesis.
Factor Loading Convergence Factor Test
The EFA analysis results indicate that the convergence coefficients of the observed variables meet the criteria for factor analysis, with a factor loading coefficient greater than 0.5 Additionally, the analysis reveals that only one factor is generated.
Testing the quality of the scale for the constituting factors (Cronbach's Alpha)
The observed variables in the factor “Intention to use credit card ” satisfy Cronbach’s Alpha analysis conditions > 0.7, ensuring the requirements for regression analysis.
ANOVA analysis and linear regression
ITUC PSR POR PFR PFrR
** Correlation is significant at the 0.01 level (2-tailed)
The Pearson coefficient analysis reveals a correlation between the variables "Perceived Security Risk," "Perceived Operational Risk," "Perceived Financial Risk," and "Perceived Fraud Risk."
“Intention to use credit card” Because the Sig coefficients of these variables are all
< 0.05 and the correlation coefficients of the variables are all positive, in which,
“Perceived Security Risk” is the factor that has the strongest impact on the variable
The study reveals a strong linear correlation between "Intention to use credit card" and five independent variables, with a correlation coefficient of r = 0.611 Among these, "Perceived Operational Risk" shows the weakest correlation at r = 0.456 The identified factors—Perceived Security Risk, Perceived Operational Risk, Perceived Financial Risk, and Perceived Fraud Risk—are suitable for regression analysis, highlighting their significance in influencing credit card usage intentions.
Std Error of the Estimate Durbin-Watson
1 806 a 650 643 32521 2.053 a Predictors: (Constant), PFrR, PSR, POR, PFR b Dependent Variable: ITUC
Table 4.23 ANOVA a Model Sum of Squares df Mean Square F Sig
Total 58.880 199 a Dependent Variable: ITUC b Predictors: (Constant), PFrR, PSR, POR, PFR
R 2 = 0.650 (F = 90.430; Sig = 0.00
