Introduction
Background
The rapid growth of the Internet has significantly influenced e-commerce, making it a crucial aspect of international trade In this context, TradeCard payment systems have emerged as essential in modern transactions However, Vietnam's banking system continues to be heavily affected by the prevailing use of cash, which remains the dominant payment method Despite advancements, cash transactions account for a substantial portion of personal consumption, leading to operational challenges for businesses, including increased costs and delays associated with handling large cash volumes To address this issue, the State Bank of Vietnam (SBV) initiated a non-cash transaction project in 2006 aimed at reducing cash payment volumes and promoting digital payment solutions.
Between 2006 and 2020, Vietnam experienced a significant positive change in its total liquidity composition The ratio of cash within total liquidity decreased from 17.21% in 2006 to 16.36% in 2007, ultimately reaching 14.6% in 2008 (SBV, 2008).
Since joining the WTO in 2004, Vietnam's economy and banking industry have faced both opportunities and challenges, necessitating a shift in payment transaction behaviors to align with global trends The emergence of new business models like e-commerce and e-business has prompted banks to integrate advanced IT solutions to improve service quality and meet customer expectations With many banks offering similar services, the adoption of new technologies has become crucial for long-term success and customer trust The State Bank of Vietnam has actively worked to reduce cash transactions through a non-cash transaction initiative from 2006 to 2020, while fostering rapid development of bank alliances and diversifying TradeCard payment options for international trade This highlights the importance of enhancing customer awareness and promoting the TradeCard payment system within society.
Previous research on e-commerce and e-banking has predominantly focused on developed countries with advanced IT infrastructure, such as Europe, North America, and Finland, which are leaders in online banking technology and usage More recent studies have expanded to include a diverse range of regions, incorporating both developed and developing countries, with Singapore being a notable example (Tan & Teo).
While previous studies on electronic payments primarily focus on developed countries with advanced IT knowledge, this research aims to analyze TradeCard payment systems in Vietnam, a developing nation This study highlights the significance of TradeCard as a representative model of e-commerce and e-transactions in a country where IT and e-banking systems are still emerging.
Research Purpose, Objectives, and Research Questions
This study aims to explore the adoption of TradeCard payment in Vietnam, focusing on the factors that directly impact its payment process in a developing country context By identifying essential factors influencing TradeCard payment adoption, the research seeks to enhance Vietnamese customers' understanding of electronic payment transactions Additionally, the findings will provide valuable insights for related organizations to develop effective programs that better serve customers The study will specifically examine the key factors affecting the adoption of TradeCard payment in Vietnam.
1 To identify significant factors that influence customer’s adoption of TradeCard payment. The corresponding research question developed was:
Q1 What are main factors influencing adoption of TradeCard in Vietnam?
2 To explore how the factors affect customer’s adoption of TradeCard payment in Vietnam. The corresponding research question developed was:
Q2 How do the factors influencing adoption of TradeCard in Vietnam?
This research is structured into five chapters: Chapter 1 serves as an introduction, offering a general overview of the study Chapter 2 presents a literature review, focusing on the technology acceptance model (TAM) and related theories Chapter 3 outlines the research design, including the proposed research model, hypotheses derived from the previous theories, and the methodologies employed for testing the model Chapter 4 discusses data collection and analysis, validating the relationships between the identified variables Finally, Chapter 5 presents the study's results, discusses their implications, and outlines the limitations and recommendations for future research.
This study investigates the adoption of TradeCard payment in Vietnam by analyzing the factors influencing its usage While electronic payment encompasses a wide range of topics, this research focuses specifically on individuals with experience using TradeCard over the years in two major Vietnamese cities Questionnaires will be distributed to gather essential data for the analysis.
Scope and Limitations
This study aims to investigate the acceptance of TradeCard payment in Vietnam, focusing on the country's electronic commerce and electronic payment markets It examines the Technology Acceptance Model (TAM) as a moderating factor that may significantly influence the adoption of TradeCard The research also formulates and discusses relevant questions and hypotheses to further explore these dynamics.
The three key terms (i) Electronics banking, (ii) Electronics money, (iii) electronics payment, (iv) TradeCard payment (v) electronics payment means are identified according to several previous related studies as followings:
E-banking technology encompasses a wide range of services, including automatic teller machines (ATMs), automatic bill payments (ABP), electronic funds transfers (EFT), and internet banking (IB), also known as home banking (HB) Additional e-banking services include phone banking (PB), credit cards (CCs), and point-of-sale (POS) terminals According to Chou and Chou (2000), the five fundamental services associated with e-banking are account balance viewing, transaction history access, bill payments, fund transfers between card accounts, credit advances, and check ordering.
In 1993, as the development of new payment technologies was just beginning, EU central banks examined what is now known as electronic money, specifically focusing on electronic money stored on chip cards, or electronic purses Electronic money is defined as a digital store of monetary value on a technical device that can be widely used for payments to entities other than the issuer, without necessarily involving bank accounts, functioning instead as a prepaid bearer instrument Unlike traditional cash, which relies solely on physical security features, electronic money offers distinct advantages and functionalities.
Literature Review
Research Model
This chapter outlines the development of a research framework, building upon the discussions from previous sections It specifies the research questions and hypotheses introduced in Chapter II, which will be examined using the research methods detailed later Additionally, the research procedure is presented at the conclusion of this section.
3.1Research Questions and Associated Hypotheses
This section restates the research questions and hypothesis formulated in the previous chapter There are 2 research questions accompanied by 9 associated hypotheses under the following 2 categories of questions:
1 What are factors influencing the use of TradeCard in Vietnam? (Q1)
2 How do the factors influence the use of TradeCard in Vietnam? (Q2)
To answer this question, the study hypothesizes as follows:
H1: Perceived ease of use has a positive effect on TradeCard use
H2: Perceived usefulness has a positive impact on TradeCard use
H3: Perceived risk has a negative effect on TradeCard use.
H3a: Perceived risk with products and services has a negative effect on TradeCard use H3b: Perceived risk in the context of online has a negative effect on TradeCard use.
H4: Information on TradeCard has a positive effect on perceived risk
H4a: Perceived risk in the context of online transaction
H4b: Perceived risk with product/service
H5: Information on TradeCard has a positive effect on TradeCard use.
H6: Computer anxiety has a positive effect on TradeCard use.
The research consisted of two phases: a pilot study to validate and refine measurement tools, followed by a main survey to evaluate the measurement and structural model illustrated in Figure 3.1 of the research flowchart To mitigate potential confounding effects from demographic factors and experience, participants were screened using demographic filters, ensuring a balanced sample for the study.
Research Design
Research Questions and Associated Hypotheses
This section restates the research questions and hypothesis formulated in the previous chapter There are 2 research questions accompanied by 9 associated hypotheses under the following 2 categories of questions:
1 What are factors influencing the use of TradeCard in Vietnam? (Q1)
2 How do the factors influence the use of TradeCard in Vietnam? (Q2)
To answer this question, the study hypothesizes as follows:
H1: Perceived ease of use has a positive effect on TradeCard use
H2: Perceived usefulness has a positive impact on TradeCard use
H3: Perceived risk has a negative effect on TradeCard use.
H3a: Perceived risk with products and services has a negative effect on TradeCard use H3b: Perceived risk in the context of online has a negative effect on TradeCard use.
H4: Information on TradeCard has a positive effect on perceived risk
H4a: Perceived risk in the context of online transaction
H4b: Perceived risk with product/service
H5: Information on TradeCard has a positive effect on TradeCard use.
H6: Computer anxiety has a positive effect on TradeCard use.
Subjects and Sampling
The research consisted of two phases: a pilot study to validate and refine measurement tools, followed by a main survey to test the measurement and structural model outlined in the research flowchart (Figure 3.1) To mitigate potential confounding effects from demographic variables and experience, participants underwent screening using demographic filters The sample was then balanced according to these parameters, with users divided into groups through a random assignment process to prevent selection bias An analysis of demographic factors between the two groups revealed no significant covariant variables, ensuring the integrity of the study's findings.
This study focused on TradeCard users in Hanoi and Ho Chi Minh City, Vietnam's two largest urban centers with significant electronic transaction activity Respondents were selected based on the proportion of TradeCard users in each city, employing a snowball sampling method where each participant identified additional suitable individuals The participants included senior executives from the sampled firms, and further interviews were conducted to gather detailed information about the frequency of TradeCard usage among users with regular access.
Instrument design
The questionnaire, detailed in Appendix A, comprises 35 developed items, with Part II featuring sub-heading variable names that are not included in the formal version Table 3.1 illustrates the constructs of the questionnaire along with their supporting literature.
Construct Indicators Items (From the reference sources)
1 It is easy to become skillful at using the TradeCard
2 I find TradeCard payment easy to what
3 It is easy for me to learn how to use the TradeCard payment.
4 I find TradeCard payment to be flexible to interact with.
5 My interaction with TradeCard payment is clear and understandable
6 Overall, I find TradeCard payment easy to use
7 Using TradeCard payment improve performance of utilizing banking service
8 Using TradeCard payment enhances effectiveness of utilizing banking service.
9 Using TradeCard payment services enables to utilize banking service more quickly
10 Using TradeCard payment for banking service increase productivity.
11 Not the same as the introduced and advertised functions
12 Waste time using TradeCard payment in transactions
13 It may lose the opportunity to use other payment services that have the same quality with lower cost.
14 Users may have to pay other fees when using TradeCard payment.
D Context of online transaction (PRT)
15 It may have problems on transmission line so TradeCard payment trade is failed to complete.
16 Payment information is not secured (account name, password, payment money…)
17 Payment information (account name, password…) may be used by other users.
18 The suppliers may say that they still haven’t got money by TradeCard payment.
19 I have a generally received enough information about TradeCard payment.
20 I have received enough information about the benefits of using the TradeCard
(2004) 21 I have received information about using TradeCard payment service from a bank, a phone bank, the internet, a friend, an advertisement and another source.
22 I am confident that I can learn
23 Computers do not scare me at all.
24 Using Computers makes me feel easy.
25 Working with a computer makes does not make me feel nervous.
26 Computers make me feel comfortable
Feelings toward a particular behavior (Performing behavior)
27- I feel using TradeCard payment is a wise idea.
28- I feel using TradeCard payment is a good idea.
29- I like to use TradeCard payment
Table 3-1 Measure of research variables for designing questionnaire The 35-item self-administered questionnaire is constructed with two parts of questions.
Part 1, Administrative and Classification questions (Question 1-6): The aim of this part is to gather general data from the responses It includes multiple choice questions concerning.
Part 2, Target questions (Question 7-35): The questions are developed based on the literature review, displayed under the forms of affirmative statements, and categorized in ten groups:
▪ Group 1, 6 questions, Q7 – Q12, refer to the Perceived Ease of Use,
▪ Group 2, 4 questions, Q13 – Q16, refer to the Perceived Usefulness,
▪ Group 3, 8 questions, Q17 – Q24, refer to Perceived Risk (including Perceived Risk with Product/Service and Perceived Risk in the Context of Online Transaction),
▪ Group 4, 3 questions, Q25 – Q27, refer to Information on TradeCard payment
▪ Group 5, 5 questions, Q28 – Q32, refer to Computer Anxiety
▪ Group 6, 3 questions, Q33 – Q35, refer to TradeCard Payment Use
Expert Validity
The questionnaire was initially developed based on existing literature and subsequently evaluated for suitability by two experts: one from the TradeCard office in Hong Kong and a Vietnamese professional experienced in TradeCard payment management After their assessment, a few minor adjustments were made to the questionnaire Finally, the revised questionnaires were distributed to the participants for completion.
Pilot Test and Face Validity
Following expert validation, the revised questionnaire was administered to forty users for further evaluation and validation of its items Participants were instructed to review the instructions and questions, answer them, and provide corrections or comments directly in the designated areas of the questionnaire This included feedback on wording, grammar, and any inappropriate content, as well as any additional relevant comments The outcomes of the pilot test are detailed in Appendix B.
Quantitative Research
The research commenced by assessing the current situation regarding the challenges faced in utilizing TradeCard payment systems in Vietnam's two largest cities This initial evaluation led to the identification of the research objectives Following this, a literature review was conducted, emphasizing the factors influencing user acceptance of TradeCard, while incorporating the Technology Acceptance Model (TAM) to establish a theoretical foundation Subsequently, the research framework was developed, utilizing an email survey as the primary research method.
The development of mail survey questionnaires will be informed by literature reviews and consultations with seasoned academic researchers and experienced business professionals A pre-survey phase will assess the validity and reliability of the questionnaires Subsequently, quantitative data will be gathered through self-administered mail surveys The research process will conclude with the formulation of conclusions and recommendations based on the findings.
The research commenced in April 2013, focusing on identifying the research dilemma and objectives After developing and refining questionnaires through pilot testing and expert consultation, they were distributed to respondents in Ho Chi Minh City and Hanoi in early June 2013, with data collection concluding two months later Data analysis took place in October 2013, and the findings were documented within the same month The research process was finalized by the end of November 2013 with the completion of the report writing.
Define actual situation of TradeCard payment in VN
Define research purpose concerning to Vietnam
Literature review on TradeCard method
Design research model and develop hypotheses n = 40 Questionnaire pre-survey
Data Analysis
The statistical analysis commenced with descriptive analysis to outline the demographic characteristics of the research subjects To assess the reliability and validity of the investigative questions, Cronbach’s Alpha coefficients were calculated, and Exploratory Factor Analysis was performed to identify the factors influencing the adoption of TradeCard payment in Vietnam A Cronbach’s Alpha coefficient greater than 0.7 indicates a high level of reliability for the variables.
The assumption of equal variances was verified using Levene’s test Subsequently, both Linear and Multiple Regression analyses were performed to explore the direct and indirect relationships between independent and dependent variables, focusing on those that significantly impacted TradeCard payment Path Analysis was employed to gain deeper insights into these effects Additionally, the relationship between Personal Innovativeness in IT and factors such as Perceived Use, Perceived Usefulness, Information on TradeCard payment, and Perceived Risk was assessed through Multiple Regression analysis.
Figure 3-2 The Structure of the Method approach for this Research
Data Analysis
Data Collection
Data was gathered from users in Ha Noi and Ho Chi Minh City through online surveys and direct questionnaires The demographic variables included in the questionnaire are key factors that influence the development of relevant perceptions.
Out of 300 respondents, nearly 69% (145 individuals) provided complete answers to both demographic and investigative questions Notably, 77% (58 respondents) were from Ha Noi, while 81% (87 respondents) came from Ho Chi Minh City Detailed profiles of the respondents are illustrated in Tables 3-4 and 3-5.
Table 4-1 Response rate in Questionnaire Survey
City Sample size Response rate Valid response rate
Table 4-2 Response rate in Questionnaire Survey among professional groups
Ha Noi Ho Chi Minh Valid response rate in total response/sent out response/sent out response/sent out
Descriptive Analysis
This section includes (i) respondents’ demographics concerning business type, annual revenue, experience in using TradeCard payment, TradeCard payment information sources,
TradeCard payment methods and frequencies in using TradeCard payment; and (ii) respondents’ comments and suggestions concerning the use of TradeCard payment.
Table 4-3 shows the summary of demographic statistics of the research respondents on frequency and percentage resulted from the descriptive analysis.
Table 4-3 Demographic Statistics with Frequency and Percentage
Ha Noi Ho Chi Minh Total
Profession Account payable or receivable 38 65.5% 59 67.8% 97 66.9%
Annual < US$ 1 million 5 8.6% 4 4.6% 9 6.2% revenue US$ 1 - $2.5 million 6 10.3% 6 6.9% 12 8.3%
The attributes of respondents consist of nine major control variables including (1) business type, (2) profession, (3) annual revenue, (4) experience, (5) frequency (6) know about
TradeCard payment from as Table 4-3.
The table shows that the rates of business type respondents are different between Ha Noi and
Ho Chi Minh Most of respondents are wholesale trade and manufacturing industry in Ho Chi
Minh (37.9% and 42.5%) compared with 35.5% and 51.7% in Ha Noi.
More than sixty percent respondents have annual revenue more than US$6 million, while only 6.2% who is less than US$ 1 million The rate with the annual revenue from US$1 to
In Vietnam, 8.3% of respondents reported transactions ranging from $2.5 to $4 million, while 16.6% engaged in transactions between $4 and $6 million Despite the TradeCard payment system being utilized in the country for over eight years, only 15.9% of participants have more than seven years of experience with it.
The majority of TradeCard payment users have utilized the service for 3 to 5 years, while 15.2% have less than one year of experience According to statistics from Table 4.1, most users learned about TradeCard payment and its services through business partners (71%) and conferences (16.6%).
According to the survey, 27.6% of respondents reported making 10 to 20 transactions monthly using TradeCard, while only 7.6% conducted between 5 and 9 transactions Additionally, 20.7% of users engaged in more than 30 transactions each month.
The results of the descriptive statistic which evaluate and present the questionnaire variables of the respondents are shown in Table 4-4 The framework has 29 items, including 6 items of
Perceived Ease of Use, 4 items of Perceived Usefulness, 4 items of Perceived risk with product/service, 4 items of Perceived risk in the context of online transaction, 3 items of
The findings presented in Table 4-4 indicate that respondents generally view the TradeCard payment system positively, as evidenced by mean scores exceeding 3.00 on a five-point Likert scale for most items related to Perceived Ease of Use, with the exception of PEU1 and PEU2 Additionally, the data highlights key aspects of Computer Anxiety, identifying five critical factors, while also outlining three specific uses of TradeCard payment.
Respondents have provided positive evaluations of TradeCard payment, with all metrics scoring above 3.0 The findings indicate that users appreciate the ease-of-use features and the information provided, reflecting a favorable perception of the TradeCard payment system.
TradeCard payment enhances work performance and accelerates payment transactions Customers generally perceive lower risks associated with products and services, as well as online transactions The findings indicate that most items related to the construct of Perceived Risk are centered around the mean, with a few exceptions.
The findings reveal that the PRT1 score is lower than the group mean, indicating a potential area for improvement Additionally, the data on the TradeCard payment system shows that respondents generally hold a positive perception of the information provided, with most mean values exceeding 2.5 While users acknowledge the ease of use and usefulness of the TradeCard payment system for their tasks, they remain cautious and lack confidence in their understanding and knowledge of the system, highlighting the importance of addressing perceived risks.
In term of standard deviation (S.D), most of S.D values are accepted The results show that, these items are very good because the difference among results are not much high.
Table 4-4 Demographic Statistics with Frequency and Percentage
Using TradeCard is straightforward, making it easy to become proficient The payment process is user-friendly and aligns well with my needs Learning to navigate TradeCard payments is simple, and the platform offers flexibility in interactions Overall, my experience with TradeCard payments has been clear and comprehensible.
PEU6 6 Overall, I find TradeCard payment easy to use 3.26 1.249
PU1 7 Using TradeCard payment improve performance of utilizing banking service 3.41 1.184
PU2 8 Using TradeCard payment enhances effectiveness of utilizing banking service 2.67 1.181
PU3 9 Using TradeCard payment services enables to utilize banking service more quickly 2.84 1.121
PU4 10 Using TradeCard payment for banking service increase productivity 3.00 1.237
Perceived risk with product/service
PRP2 12 Waste time using TradeCard payment in transactions 3.58 0.837
PRP3 13 It may lose the opportunity to use other payment services that have the same quality with lower cost 3.60 0.784
PRP4 14 Users may have to pay other fees when using TradeCard payment 3.61 0.858
Perceived risk in the context of online transaction
PRT1 15 It may have problems on transmission line so TradeCard payment trade is failed to complete 2.46 1.133
PRT2 16 Payment information is not secured (account name, password, payment money…) 2.62 1.255
PRT3 17 Payment information (account name, password…) may be used by other users 2.62 1.271
PRT4 18 The suppliers may say that they still haven’t got money by
19 I have a generally received enough information about TradeCard 2.53 0.848 IF2 20 I have received enough information about the benefits of using the TradeCard payment services 2.58 1.225
IF3 21 I have received information about using TradeCard payment service from a bank, a phone bank, the internet, a friend, an 2.64 0.916 advertisement and another source.
CA1 22 I am confident that I can learn computer skills 3.59 1.131
CA2 23 Computers do not scare me at all 3.74 1.157
CA3 24 Using Computers makes me feel easy 3.88 1.105
CA4 25 Working with a computer makes does not make me feel nervous 3.75 1.111
CA5 26 Computers make me feel comfortable 3.60 1.136
TPU1 27 I feel using TradeCard payment is a wise idea 3.12 1.251
TPU2 28 I feel using TradeCard payment is a good idea 2.73 1.331
TPU3 29 I like to use TradeCard payment 2.91 1.156
The Validity and Reliability
This research identifies two key criteria for evaluating measurement instruments: validity and reliability Validity pertains to how accurately a test measures the intended concept, whereas reliability focuses on the consistency and precision of the measurement process.
The validity of the research was confirmed through consultations with three esteemed academic experts in Vietnam, whose insights significantly enriched the topic and content of the study.
To assess the internal consistency reliability of the scale and evaluate how effectively a set of items measures a single uni-dimensional latent construct, Cronbach’s alpha will be calculated for each item group as well as for the overall scale A Cronbach’s alpha value greater than 0.6 is deemed "acceptable" in most social science applications (Hair et al., 2003).
Table 4-5 Reliability of the Developed Questionnaire
Principle components No of Item Cronbach's Alpha
Perceived risk with product/service 4 853
Perceived risk in the context of online transaction 4 863
The author conducted a principal axis factor analysis on variables related to TradeCard payment usage, identifying six key factors: (1) Perceived Ease of Use, (2) Perceived Usefulness, (3) Perceived Risk concerning Product/Service, (4) Perceived Risk in online transactions, (5) Information, and (6) Computer Anxiety To validate these constructs, an Exploratory Factor Analysis (EFA) utilizing Principal Axis Factoring with Varimax rotation was performed on 29 items designed to measure these factors.
Table 4-6 Principal Component Analysis of the Developed Questionnaire
PEU CA PU PRT PRP IF TPU
E21 Information on the procedures/ how to operate the 682
Note: CA = Computer Anxiety; PEU = Perceived Ease of Use;
PU = Perceived Usefulness; PRP = Perceived Risk with Product/Service;
IF = Information; PRT = Perceived Risk in the Context Online Transaction;
The Kaiser-Meyer-Olkin (KMO) value of 804, exceeding the threshold of 0.5, along with Bartlett's Test showing significance at p < 001, confirms the absence of multi-collinearity in the data This suggests that a valid factor analytic solution can be achieved Additionally, the factor loadings, ranging from over 6 to nearly 9, indicate moderate correlation coefficients for each item.
Regression analysis was done for examining the relationships between or among variables
Initially, the analysis involved assessing the correlations between pairs of direct variables to determine the presence of relationships among them The findings from this correlation analysis are detailed in Table 4-7.
Table 4-7 Correlation Coefficients among Independent Variables under Study
PEU PU PRP PRT IF CA TPU
Note: IF = Information PRP = Perceived risk with product/service;
PRT = Perceived risk in the context of online transaction; PEU = Perceived Ease of Use;
PU = Perceived Usefulness; CA = Computer Anxiety;
** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed)
Table 4-7 shows good relationship between factors examined without negative relationship reflected in the chart It’s also indicated the strength of positive relationship between
Perceived Ease of Use (PEU) and Perceived Usefulness (PU); between Computer Anxiety
(CA) and variables of the rest (.326, 393, 227, 215, and 233, respectively) The strength of positive relationship was also found between Information on TradeCard payment (IF) to both
Perceived Usefulness (PU) and Perceived Risk in the Context of Online Transaction (PRT)
(.266 and 247 respectively) A very close positive relationship found between TradeCard payment Use (TPU) and other variables including Perceived Ease of Use (PEU), Perceived
Usefulness (PU), Perceived Risk with Product/Service (PRP) and Perceived Risk in the
Context of Online Transaction (PRT); Information on TradeCard payment (IF) and Computer
Anxiety (CA) are regarded moderate (r = 566, r = 634, and r = 294, r = 363, r = 492 and, r
= 539, respectively) All these three relationships are significant (p< 001 and p < 01, respectively).
4.5.1 Information on TradeCard payment Predicting Perceived Risk with
The initial regression analysis focused on the TradeCard payment variable to assess its impact on the Perceived Risk associated with Products/Services This analysis aimed to explore the relationship between the TradeCard payment predictor and the criterion of Perceived Risk with Products/Services, addressing the second research question regarding the influence of various factors on the use of TradeCard payment in Vietnam while testing the related hypotheses.
H4a: Information on TradeCard payment has a positive effect on Perceived Risk in the
Table 4-8 Regression Analysis for Information on TradeCard payment Variables Predicting
Perceived Risk in the Context of Online Transaction
Table 4-8 shows that Information on TradeCard payment was significant (with F = 9.256, p 0.05
4.5.4 Comparisons between Two City Groups in Regression Analysis
Table 4-12 shows the summary of the regression analysis for the two cities, Ha Noi and Ho
The demographic regression analysis highlights the differing impacts of predictors on criteria between Ha Noi and Ho Chi Minh City, as illustrated in the provided table This study aims to explore the contrasting perspectives of these two cities regarding the use of Trade Cards, providing valuable insights for future research.
H1 Perceived Ease of Use TradeCard payment Use
H2 Perceived Usefulness TradeCard payment Use
Table 4-12 Summary of the Regression Analysis for Separate City, Ha Noi and Ho Chi Minh
Ha Noi Ho Chi Minh
H1 Perceived Ease of Use TradeCard payment Use 3.503 001* 3.683 000*
H2 Perceived Usefulness TradeCard payment Use 1.344 185* 4.655 000*
Perceived Risk in the Context of Online
H6 Computer Anxiety TradeCard payment Use
Note: * Not supported due to p > 0.05
The statistics indicates that (i) Perceived Ease of Use had positive affect on TradeCard payment Use at Ho Chi Minh and Ha Noi (t = 3.683 and 3.503; p < 001); (ii) Perceived
Usefulness had only positive affect on TradeCard payment Use at Ho Chi Minh (t = 4.655; p
< 001); (iii) Perceived Risk with Product/Service had positive affect on TradeCard payment
Use at Ha Noi (t = 2.573, p < 05) in contrast at Ho Chi Minh; (iv) Perceived Risk in the
Context of Online Transaction had no effect on TradeCard payment to Ho Chi Minh and Ha
Noi; (v) Information on TradeCard payment had no effect on Perceived Risk with
Product/Service to both Ha Noi and Ho Chi Minh while Information on TradeCard payment only effects on Perceived Risk in the Context of Online at Ho Chi Minh (t = 3.279, p
