Int. J. Anal. Appl. (2023), 21:23 Received: Dec. 25, 2022. 2020 Mathematics Subject Classification. 91B70. Key words and phrases. E-commerce; awareness; perceived value; online shopping. https://doi.org/10.28924/2291-8639-21-2023-23 © 2023 the author(s) ISSN: 2291-8639 1 An Empirical Research on Customers’ Awareness of E-Commerce in the Context of Vietnamese Developing Economies Nhu-Ty Nguyen1,2, Thanh-Tuyen Tran3,*, Anh-Quan Huynh1,2 1School of Business, International University, Vietnam 2Vietnam National University HCMC, Quarter 6, Linh Trung ward, Thu Duc district, HCMC, Vietnam 3Nguyen Tat Thanh University, HCMC, Vietnam *Corresponding author: tttuyen@ntt.edu.vn ABSTRACT. Many research studies have been conducted in the field of e-commerce, which can contribute for academic background and development of e-commerce around the world. In Vietnam, the awareness of customers toward e- commerce is limited for the emerging of this industry in Vietnam recently. This study tries to figure out research model explains the factors and to study the awareness of customers that influence the behaviors and acceptance of users buying online in the developing economy. According to the research, buying online is quite risky. Therefore, product safety would be the purchasing decision's priority. Online sales providers need to provide a safe way of ensuring consumers ' purchasing process. Through hard qualification process, they need to shortlist the good quality product. The sale would grow dramatically as consumers gained confidence. Not only should they care about the short-term profit in order to get the products of low quality. I. INTRODUCTION Although there are many theories in the world today, the research model explains the factors that affect online buyers ' behavior and acceptance. However, it is difficult to apply the environment in Vietnam due to the differences in the economic model, culture and society, while the country's research models are quite limited ([19]). Therefore, it is urgent to study models in the world that have been based on domestic research in the past to build an appropriate model for Vietnam's https://doi.org/10.28924/2291-8639-21-2023-23 2 Int. J. Anal. Appl. (2023), 21:23 current situation. Consumers in Vietnam are still used to product trials or direct purchases because of low-quality psychology compared to online purchasing lustration images ([22]). An effective marketing strategy should be based on a rigid basis for understanding market segmentation expectations, cognition, decision-making, trends and behaviour. The Internet has become the destination for business and mass media in recent years, leading to a dramatic change in the way people purchasing. In addition to the rapid development and national coverage of Internet service providers, customers are now truly free from the constraints of brick-and-mortar stores and adapt quickly to the new era of online shopping ([21]). Although there are now many theories in the world, the research model explains the factors that influence the behavior and acceptance of users buying online. But the application to the environment in Vietnam is difficult due to differences in economics, culture and society, while the research models in the country are quite small ([20]). II. LITURATURE REVIEW 2.1. The concept of e-commerce E-commerce means the distribution of goods, services, information or payments via computer networks or other electronic means. E - Commerce is an application for technology that automates business transactions. E - Commerce is a tool that helps businesses; customers reduce service costs, improve the quality of the product, and speed up service. In short, e - commerce is a business model that is enabled by information technology. There are two major e-commerce models: B2B (business-to-business) and B2C (business-to- consumer). B2B is an e-commerce model in which participants are businesses or organizations. Currently, the majority of e-commerce is implemented under this model. B2C is the e-commerce model in which the business sells to the consumer directly. In addition to the two main models, the internet also forms many new models: C2C (consumer-to-consumer): Personal sales direct to individuals such as car applications, consumer goods, real estate and software. C2B (consumer-to-business): Individuals can find businesses to sell (goods, software) to businesses. G2C (Government-to-citizens): State-owned organizations purchase and sell goods, services and information to businesses and citizens. 3 Int. J. Anal. Appl. (2023), 21:23 Online banking: access to banking services on individuals or businesses from commercial services online or via the Internet. This thesis shall focus on Business to Customer (B2C) e-commerce. Suggestion Research Model Figure 1: Proposed research model Describe the elements in the proposed research model Variable Definition Reference Perceived Usefulness The online shopping will bring convenience to consumers, they are no longer limited time and place when shopping. [18] PEU Interoperability between web- based and consumer web sites, easily handled when performing searches and transactions. [23] Moon Ji Won et al., 2001 [24] Venkatesh et al., 2003 4 Int. J. Anal. Appl. (2023), 21:23 Perceived Enjoyment Sense of the user when using the online shopping service. [23] Moon Ji Won et al., 2001 Perceived Risk Risks arising during the transaction such as passwords, online fraud, loss of credit card. [25] Joongho Ahn et al., 2001 Reference Group The introduction of the use of friends, relatives and co- worker. [24] Venkatesh et al., 2003 Perceived Price The price of the product on the web compared to the price at the store is a factor of concern consumers over the network. [18] 2.2. Developmental Hypothesis Perceived Usefulness: [18] also stated that consumers find online shopping timesaving, effortless and convenient. As a result, the study posits the following hypothesis: H1: Perceived Usefulness of e-buying affect purchase intention of customers positively (+) Perceived Ease of Use (PEU): PEU is the degree of effortlessness a person believes a particular system could provide (Davis 1985). In this study, the PEU demonstrates that users are easy to get acquainted with, use electronic purchases online and become easy to become a proficient user of the service. Therefore, it can be hypothesized that: H2: User’s PEU of e-commerce will positively (+) influence their intention to purchase. Perceived Enjoyment: is defined as the enjoyment degree of using technology perceived by a person apart from any probable anticipation caused by performance consequences [26] . In a research by Moon and Kim (2001) [23], pleasure perception expresses three components: concentration, curiosity, and enjoyment. They also discovered that pleasure was the premise of the inner motivation of using the world-wide-web, and confirmed that the intrinsic motivation was strongly correlated with the decision to use the Internet-based system. Thus, the expectation that perceptions of pleasure affect the acceptance of e-buying services online. From that, it can be hypothesized that: 5 Int. J. Anal. Appl. (2023), 21:23 H3: Perceived enjoyment positively affects (+) intention to purchase. Perceived Risk: In Risk-Focused E-Commerce Adoption Model, the perception of risk associated with the product or service reflects the consumer's anxiety about the use of the online product. The risks associated with using the online purchase service include personal information loss, account loss, credit card information, the actual product does not conform to the advertisement. H4: Perceived Risk negative effects (-) intention of consumer Reference Group: The concept of social influence is defined as the extent to which users perceive that other important people believe they should use new systems, information technology products. Following to Unified Technology Acceptance and Use Technology model of Venkatesh et al. 2003 [24], Social influence has a positive influence on intention. In this study, social influences are manifested by the perception that people around them, such as family, friends, co-workers or other institutions, influence the intention to use the service online purchases of their electronics. Therefore, it can be hypothesized: H5: Reference Group relate positively (+) to users ‘intention to purchase. Perceived Price: The price is what consumers pay to get the desired product or service. Perceived price is the consumer's perception of what you're going to sell at a cost. Consumers will feel the price on two sides: the cost of money must be spent and the opportunity cost of abandoning the use of that money to buy other products or services. According to the "Factors affecting online consumers" model, [18] mentioned that consumers believe that online purchases will save money and and be comparable in price. H6: Perceived price have a positive (+) effect on the consumer's willingness to buy e-electronic products. III. METHODOLOGY METHODOLOGY RESEARCH Perceived Price Scale Perceived price refers to the extent to which an individual believes that using electronic purchase e- commerce will help them save money and be comparable in price to shopping. According to the model "Factors affecting online consumers," [18] and "A study on the online purchasing behavior of women," Eliasson Malin (2009) [27] used the observation variables to measure the concept of "perceived price." After a qualitative study, the preliminary scale consists of 3 observation variables added: "E-commerce promotions help me save money." 6 Int. J. Anal. Appl. (2023), 21:23 Table 3. 1 Scale of Perceived Price Code Research Question Price 01 The price of electronic goods on e-commerce cheaper than the price at the store Price 02 Using e-commerce services makes it easy for me to compare prices Price 03 Using e-commerce services helps me save on transport expenses to view the goods Price 04 The promotions on e-commerce help me save money Perceived Usefulness Scale Perceived Usefulness refers to the level of an individual who believes that using e-commerce for electronic purchase will help them gain benefits in work and life. Following “Factors affecting Online Consumer Behaviour” model of [18] use the observation variables to measure the concept of "perceived usefullness ". The "Perceived Usefulness" scale initially had four observational variables. This scale has nothing to do with the original. Table 3. 2 Scale of Perceived Usefulness Code Research Question Conve_05 E-commerce service useful, saving time Conve_06 E-commerce shopping service that helped me find information about the product quickly Conve_07 Using e-commerce service helps me buy products anywhere Conve_08 Using e-commerce service helps me buy products anytime Perceived Ease of Use Scale PEU is defined as the level of ease related to the usage of system, IT products. The "PEU" preliminary scale has 4 observation variables. Through a qualitative study to eliminate the variable " Learning how easy it is to use e-commerce" and is replaced by "The features on e-commerce are extremely clear and easy to understand." 7 Int. J. Anal. Appl. (2023), 21:23 Table 3. 3 Scale of PEU Code Research Question PEasy_09 Account registration procedures, e-commerce purchase and payment process is quite simple PEasy_10 Easy to find the product you want to use e-commerce PEasy_11 The features on e-commerce web are extremely clear and easy to understand PEasy_12 Online shopping is easily compare one product to the others and make decision from that Reference Group Scale Social influences reflect the influence and impact of the surrounding people in encouraging and supporting users to use electronic purchase services online. The initial scale consists of four observation variables. This scale has nothing to do with the original. Table 3. 4 Scale of Reference Group Code Research Question SoInf_13 Every member of the family uses e-commerce, so I use it SoInf_14 Friends, colleagues, customers recommend me to use the e- commerce service SoInf_15 The media advertise e-commerce, so I join and try it out SoInf_16 I would recommend that my friends shop online as well Perceived Enjoyment According to research by Moon and Kim (2001) [23], pleasure perception expresses three components: concentration, curiosity and enjoyment. The initial "Perceived enjoyment" scale was initially proposed to include four variables. Through qualitative research, rejecting the statement "Using the Internet every day is my hobby" as it does not focus on the interest of the e-commerce user. Replaced by ” Promotion on e-commerce attracts me a lot”. Referring to the research by Moon and Kim (2001) [23], the preliminary scale and the observed variables for the "Perceived Enjoyment" component are as follows: 8 Int. J. Anal. Appl. (2023), 21:23 Table 3. 5 Scale of Perceived Enjoyment Code Research Question Enjoy_17 I have accounts on multiple shopping sites Enjoy_18 I spend over 2 hours daily use online shopping websites Enjoy_19 Whenever I find a good item on e-commerce I feel very excited Enjoy_20 Promotion on e-commerce attracts me a lot Perceived Risk In Risk-Focused e-Commerce Adoption Model, the perception of risk associated with the product or service reflects the consumer's anxiety about the use of the online product. Use the following observation variables to measure “Perceived Risk” when using e-commerce for electronic purchase. Qualitative research helps to make words easier to understand, so there is no change in the variables. Table 3. 6 Scale of Perceived Risk Code Research Question PRisk_21 The quality of goods received from online purchases is lower than the advertised information PRisk_22 I'm so worry that my personal information would be leaked out to the third party PRisk_23 Online purchases may not receive the item PRisk_24 My credit card information may not be secure Intention to Purchase Intention to use refers to the intention of the user will continue to use or will use e-commerce services. Thus, one additional observation variable "I intend to use”. Finally, the intended scale is as follows: Table 3. 7 Scale of Intention to Purchase Code Research Question Inten_25 I intend to use (or continue to use) e-commerce in the future Inten_26 I will definitely use e-commerce Inten_27 I will learn to use e-commerce in the future Inten_28 I will introduce e-commerce for many people to use 9 Int. J. Anal. Appl. (2023), 21:23 Summary of qualitative research results Qualitative research helped to calibrate the scale for the following research models: • Edit words in scale to make them easier to understand • Add 5 observation variables, remove 2 observation variables. • Finally, the "Research of Factors Affects Purchasing Decisions on E-commerce" model uses six conceptual components that influence the intention to use and a total of 28 observed variables in this model. Quantitative Research Quantitative research was conducted through questionnaires. The results are used to evaluate the reliability and validity of the scale, test the scale, verify the fit of the model. Data Collection Data collection was conducted using open questionnaire interviews. For research person who has a stable job and over 22 years old. The questionnaire survey was conducted as follows: design the questionnaire online and send the link to the survey respondent’s online, information recorded in the database. - Place of research: Ho Chi Minh city - Research time: May. 2019 The variables used for this concept will be measured by the 5 points Likert scale: • Strongly Disagree • Disagree • Neutral • Agree • Strongly Agree Likert type scales or frequencies use fixed-choice feedback formats and are designed to measure attitudes or opinions in the survey. (Bowling, 1997; Burns, & Grove, 1997) Data Analysis The sequence of data analysis is as follows: Step 1 - Prepare information: retrieve the answer sheet, filter the information, encrypt the necessary information in the answer table, input and analyze the data using SPSS 2.0. Step 2 - Statistical: conduct statistics describing the data collected. Step 3 - Estimate the reliability: Cronbach Alpha analysis was performed. 10 Int. J. Anal. Appl. (2023), 21:23 Step 4 - EFA analysic: Scale analysis by EFA analysis Step 5 - Multivariate regression analysis: performed multivariate regression analysis and validated the hypotheses of the model with a significance level of 5%. Analyze data based on demographic variables to analyze the differences between the following groups: male and female; high income and low income; young and old. IV. DATA ANALYSIS DATA DESCRIPTION ANALYSIS Questionnaires have been given to 550 people for the survey. Following the elimination of invalid sa mples with missing information or from participants outside the target age range, 467 remains are up to quantitative analysis. Table 4. 1 Result of respondents. Total samples Valid samples Invalid samples Online survey 330 272 58 Offline survey 220 195 25 Total 550 467 83 4.1. Gender Table 4. 2 Sample analysis by gender Gender Quantity Percent Male 210 44.97% Female 257 55.03% Total 467 The number of women using online electronics purchase is higher than that of men, according to the figures in the sample. Specifically: 44.97% female and 55.03% male. 4.2. Age 11 Int. J. Anal. Appl. (2023), 21:23 Table 4. 3 Sample analysis by age Age group Quantity Percent 22-24 27 5.78% 25-27 208 44.54% 28-30 85 18.20% 31-40 126 26.98% Over 40 21 4.50% Total 467 The most distributed group in the age group is 25-27 (about 44.54%), the age from 31 to 40 accounted for 26.98%, the age group 28-30 accounted for 19.20 % and lowest among the age group of 22-24 and over 40 (following by 5.78% and 4.50% respectively). 4.3. E-commerce access time Table 4. 4 Average time per visit e-commerce Access Time Quantity Percent Never use 46 9.85% Under 10 minutes 58 12.42% 10-30 minutes 287 61.46% Over 30 minutes 76 16.27% Total 467 Although they know about e-commerce, only 9.85% of respondents (46 respondents) have never purchased online. Most observers use online shopping sites over an average of 10-30 minutes at 61.46% and over 30 minutes at 16.27%. 4.4. Internet habits Table 4. 5 Figures Internet habits Internet Habit Quantity Percent Under 3 years 1 0.21% 3-5 years 86 18.42% 5-7 years 182 38.97% Over 7 years 198 42.40% Total 467 12 Int. J. Anal. Appl. (2023), 21:23 Among 467 observed targets, Internet users over 7 years is made up for almost half of number (42.40% of the targets) and only 0.21% that use Internet less than 3 years. 4.5. Reliability test 4.5.1. Evaluation criteria Cronbach's Alpha Analysis is a statistical test of the degree of correlation between the items in the scale. This is a necessary reflection scale analysis that is used to exclude inappropriate variables before analyzing the EFA. Acceptable range for exploration purposes when the Alpha value of Cronbach is 0.6. Variable Coefficient — sum is the variable correlation coefficient with the average of other variables in the same high group. Coefficient of correlation-the sum must be greater than 0.3. Variables with variable correlation-rubbish variable less than 0.3 is considered and removed from the scale. 4.5.2. Cronbach’s Alpha analysis results Table 4. 6 Cronbach's Alpha analysis results Factors Item Average intertem Covariance Standard Deviation Corrected Item- Total Correclation Crombach's Alpha if item deleted P e rc e iv e d p ri c e Price_01 3.52 0.920 0.681 0.731 Price_02 3.94 0.892 0.542 0.799 Price_03 3.57 0.832 0.662 0.743 Price_04 3.49 0.868 0.620 0.761 Crombach's Alpha: 0.808 P e rc e iv e d u se fu ln e ss Conve_05 3.26 1.127 0.531 0.672 Conve_06 3.45 1.035 0.718 0.654 Conve_07 3.91 .843 0.478 0.778 Conve_08 3.56 1.027 0.638 0.699 Crombach's Alpha : 0.781 P E U PEasy_09 3.14 1.025 0.594 0.715 PEasy_10 2.82 1.098 0.587 0.716 13 Int. J. Anal. Appl. (2023), 21:23 PEasy_11 3.21 1.244 0.531 0.748 PEasy_12 3.15 1.185 0.610 0.703 Crombach's Alpha: .744 R e fe re n c e g ro u p SoInf_13 3.16 1.224 0.720 0.745 SoInf_14 3.10 1.170 0.714 0.748 SoInf_15 3.47 1.081 0.594 0.803 SoInf_16 3.33 1.100 0.574 0.812 Crombach's Alpha: 0.825 P e rc e iv e d E n jo y m e n t Enjoy_17 3.79 .990 0.753 0.823 Enjoy_18 3.71 .926 0.743 0.821 Enjoy_19 3.71 1.030 0.793 0.797 Enjoy_20 3.54 1.090 0.614 0.875 Crombach's Alpha: 0.867 P e rc e iv e d R is k PRisk_21 2.81 1.186 0.706 0.738 PRisk_22 2.67 1.357 0.755 0.706 PRisk_23 2.70 1.346 0.721 0.724 PRisk_24 2.96 1.283 0.390 0.874 Crombach's Alpha: 0.815 In te n ti o n t o u se Inten_25 3.46 0.818 0.606 0.779 Inten_26 3.39 0.819 0.696 0.737 Inten_27 3.55 0.869 0.677 0.745 Inten_28 3.51 0.803 0.558 0.800 Crombach's Alpha: 0.814 14 Int. J. Anal. Appl. (2023), 21:23 Comment: The concept of components has a coefficient of Cronbach Alpha higher than 0.6. The lowest is the "PEU" component with a coefficient of 0.774 for the Cronbach Alpha and the highest is "Perceived Enjoyment" (0.867). This shows that in the same concept the variables are closely related to one another. 4.5.3. Exploratory Factor Analysis (EFA) The conceptual scale in the satisfactory model in the reliability assessment will be used in the EFA analysis. 4.5.3.1. Standard analysis This study uses the Principal component extraction method with Varimax rotation and stopping when extracting elements with Eligen Values higher than or equal to 1 for 24 variables of measurement observation. 4.5.3.2. First EFA analysis At the first EFA analysis, remove the SoInf_16 and PRisk_24 variables because of factor coefficients of <0.5. Conducted the second EFA analysis with the remaining 26 variables. Hypothesis H0: The observed variables have no correlation in the whole. Barlett Test: Sig = 0.000 <5%. Rejection of H0, the observed variables in EFA analysis are correlated in overall. • KMO = 0.883> 0.5: Factor analysis is required for analytical data. • There are six factors extracted from the EFA analysis with: Eigenvalues of all factors are> 1: qualified Observed variables have load coefficients > 0.5: qualified Total variance value = 69.364% (> 50%): EFA factor analysis was satisfactory. It can be said that these 6 factors explained 69.364% of the variance of the data. Table 4. 7 The second factor loadings EFA Rotated Component Matrix a Component Code 1 2 3 4 5 6 7 Enjoy_19 .847 Enjoy_18 .816 15 Int. J. Anal. Appl. (2023), 21:23 Enjoy_17 .810 Enjoy_16 .604 Price_01 .842 Price_03 .794 Price_04 .754 Price_02 .736 Inten_26 .774 Inten_25 .750 Inten_27 .730 Inten_28 .658 Conve_06 .811 Conve_07 .738 Conve_08 .664 Conve_05 .596 PEasy_12 .778 PEasy_11 .722 PEasy_10 .680 PEasy_09 .658 PRisk_22 .944 PRisk_23 .934 PRisk_21 .750 SoInf_13 .794 SoInf_14 .758 SoInf_15 .653 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 16 Int. J. Anal. Appl. (2023), 21:23 The results of the second EFA analysis showed that seven factors were extracted from these factors, corresponding to six original independence concepts: Perceived Enjoyment, Perceived Price, PEU, Reference Group, Perceived Risk, Perceived Usefulness and 1 Dependence Concept is Purchasing Intent. Factor analysis results show that these factors ' observed variables have a good factor load factor (0.596 and above) and the Alpha coefficients of Cronbach are higher than 0.7. So the model will still consist of 6 conceptual component elements as the proposed model after calibration. 4.6. Research model after measuring scale There is no change in the composition of the intended use of e-commerce from the results of the analysis. The research model will remain the same as the original proposal: 6 independent variables are the variables that influence the intention to use e-commerce and the intention to buy is one dependent variable. Table 4. 8 Abstract hypothesis in the research model Hypothesis Content H1 Perceived price have a positive (+) effect to users’intention to use e-commerce H2 Perceived Usefulness of e-buying relate positively (+) to users’intention to use e-commerce H3 User’s PEOU of e-commerce will positively (+) to users’intention to use e-commerce H4 Perceived enjoyment positively affects (+) to users’intention to use e-commerce H5 Reference Group relate positively (+) to users’intention to use e-commerce H6 Perceived Risk negative affects (-) to users’intention to use e-commerce 17 Int. J. Anal. Appl. (2023), 21:23 4.7. REGRESSION analysis and hypothesis 4.7.1. Correlation analysis Between INTEN and independent variables such as Perceived Price (PRICE), Perceived Usefullness (CONVE), PEU (PEASY), Perceived Enjoyment (ENJOY), Reference Group (SOINF), Perceived Risk (PRISK), correlational analysis is carried out. At the same time, it is also analyzed the correlation between the independent variables to find the strong correlation between the independent variables. Because such correlations can have a major impact on the result of regression analysis as they result in multi-co linearity. Pearson correlation analysis results as shown below: Table 4. 9 Pearson correlation analysis Correlations INTEN PRICE CONVE PEASY SOINF ENJOY PRISK Pearson Correlation 1.000 .237** .393** .435** .454** 389** -.276** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 467.000 467 467 467 467 467 467 Pearson Correlation .237** 1.000 .052 .026 .104* -.139** -.025 Sig. (2-tailed) .000 .262 .574 .025 .003 .596 N 467 467.000 467 467 467 467 467 Pearson Correlation .393** .052 1.000 .357** .584** .480** -.086 Sig. (2-tailed) .000 .262 .000 .000 .000 .063 N 467 467 467.000 467 467 467 467 Pearson Correlation .435** .026 .357** 1.000 .436** .412** -.156** Sig. (2-tailed) .000 .574 .000 .000 .000 .001 N 467 467 467 467.000 467 467 467 Pearson Correlation .454** .104* .484 .436** 1000 .468** -.108* Sig. (2-tailed) .000 .025 .000 .000 .000 .020 N 467 467 467 467 467.000 467 467 Pearson Correlation .389** -.139** .480** .412** .486** 1.000 -165** Sig. (2-tailed) .000 .003 .000 .000 .000 .000 N 467 467 467 467 467 467.000 467 Pearson Correlation -.276** -.025 -086 -.156** -.108* -.165** 1.000 Sig. (2-tailed) .000 .596 .063 .001 .020 .000 N 467 467 467 467 467 467 467.000 PRISK INTEN PRICE CONVE PEASY SOINF ENJOY Comment: Independent variables have a strong linear correlation with dependent variables and statistically significant correlation coefficients (p<0.01). 18 Int. J. Anal. Appl. (2023), 21:23 4.7.2. Regression analysis Multivariate regression results as shown in Table 4.10 Table 4. 10 The coefficients of the independent variables in the multivariate regression Model Sumary b Model R R Square Adjusted R Square Std.Error of the Estimate Durbin - Watson 1 620 .384 .376 .52246 2.076 a. Predictors: (Constant), PRISK, PRICE, CONVE, PEASY, ENJOY, SOINF b. Dependent Variable: INTEN ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 78.422 6 13.070 47.883 .000 a Residual 125.563 460 .273 Total 203.985 466 a. Predictors: (Constant), PRISK, PRICE, CONVE, PEASY, ENJOY, SOINF b. Dependent Variable: INTEN Coefficients a Unstandardized Coefficients Standardized Coefficients Collinearity Statistics Model B Std.Error Beta t Sig. Tolerance VIF 1 (Constant) 1.289 .205 6.327 .000 PRICE .214 .036 .225 5.991 .000 .939 1.065 CONVE .087 .040 .104 2.199 0.28 .599 1.067 PEASY .163 .032 .217 5.121 .000 .744 1.345 SOINF .118 .033 .177 3.634 .000 .567 1.764 ENJOY .131 .036 .169 3.677 .000 .636 1.572 PRISK -.103 .021 -.181 - 4.843 .000 .962 1.040 a. Dependent Variable: INTEN Comment: Relevance of the model: Thus, the modified R2 model is 0.376, which means that 37.6% of the variance of the intended use (INTEN) is explained by the variation of the 19 Int. J. Anal. Appl. (2023), 21:23 components: Perceived price (PRICE), perceived usefullness (CONVE), PEU (PEASY), perceived enjoyment (ENJOY), reference group (SOINF), perceived risk (PRISK). Test the hypothesis of model fit: Hypothesis H0: β1 = β2 = β3 = β4 = β5 = 0 (all partial regression coefficients = 0) sig(β1), sig(β2), sig(β3), sig(β4), sig(β5), sig(β6) < significance (5%), the independent variables are PRICE, PEASY, SOINF, ENJOY, PRISK with significant statistical significance at 5% significance. Multi-collinearity testing: VIF values <10: Multiplicity phenomena of independent variables do not affect the interpretation of the model. Remainder: From normal normalized frequency histogram (Appendix II. 2.4) with mean value = 1.5 * 10-16≅ 0; Standard deviation = 0.994 ≅1: distribute the remainder of the form near the standard, satisfying the hypothetical assumption of the normal distribution of the residual. The Durbin-Watson coefficient of 2.076 shows that the errors in the model are independent of each other. 4.7.3. Hypothesis verification Perceived price Hypothesis H1: Perceived price have a positive (+) effect to users’intention to use e-commerce The reference standard deviation β1= 0.226, sig (β1)= 0.000 <5%: Support the hypotheis H1 Comment: Survey results show that "perceived price" has a positive (+) effect on the intention of users to use e-commerce. The more about the price, the more interested users intend to use the e- commerce service. Perceived usefulness Hypothesis H2: Perceived Usefulness of e-buying relate positively (+) to users’s intention to use e- commerce. The refercence standard deviation β2 = 0.104, sig (β2) = 0.000 < 5% : Support the hypothesis H2. Comment: Thus, e-buying perceived usefulness is positively (+) related to the intention of users to use e-commerce. The more convenience sellers bring to buyers, the more online shopping services they would use. PEU Hypothesis H3: User’s PEOU of e-commerce will positively (+) to users’intention to use e- commerce. The reference standard deviation β3 = 0.217, sig (β3) = 0.000 <5%: Support the hypothesis H3. 20 Int. J. Anal. Appl. (2023), 21:23 Comment: So that user’s PEOU of e-commerce will positively (+) to users’s intention to use e- commerce that means that when the user realizes that the functionality and operation of e- commerce is easy to use, the intention to use the service for consumers will increase. Perceived enjoyment Hypothesis H4: Perceived enjoyment positively affects (+) to users’intention to use e-commerce. The reference standard deviation β4 = 0.169, sig (β4) = 0.000 < 5% : Support the hypothesis H4. Comment: As the hypothesis mention, perceived enjoyment positively affects (+) to users’intention to use e-commerce. Perceived enjoyment when buying electronic on e-commerce as an internal motive increases the individual's intention. When users find that content and activities on e- commerce websites are interesting, their intentions will increase. Reference Group Hypothesis H5: Reference Group relate positively (+) to users’intention to use e-commerce. The reference standard deviation β5 = 0.117, sig (β5) = 0.000 < 5%: Support the hypothesis H5. Comment: Positively (+) refers to the intention of users to use e-commerce. In other words, in the survey, the impact of the people around them affected consumers. The more influential people (family, family, colleagues, etc.) support and encourage, the greater the intention of consumers to use the e-commerce service. Perceived Risk Hypothesis H6: Perceived Risk negative effects (-) to users’ intention to use e-commerce. The standard deviation β6 = -0.181, sig (β6) = 0.000 < 5%: Support the hypothesis H6. Comment: As we can see Perceived Risk negative effects (-) to users ‘intention to use e- commerce, consumers are more aware of the risk, the less they intend to use. Today, cyber security is a top issue for online shopping sites in Vietnam, which has had a negative impact on consumers' purchasing decisions. Table 4. 11 Summary of hypothesis verify results Hypothesis Content Result H1 Perceived price have a positive (+) effect to users’intention to use e-commerce Support H1 H2 Perceived Usefulness of e-buying relate positively (+) to users’intention to use e-commerce Support H2 21 Int. J. Anal. Appl. (2023), 21:23 H3 User’s PEOU of e-commerce will positively (+) to users’intention to use e-commerce Support H3 H4 Perceived enjoyment positively affects (+) to users’intention to use e-commerce Support H4 H5 Reference Group relate positively (+) to users’intention to use e-commerce Support H5 H6 Perceived Risk negative affects (-) to users’intention to use e-commerce Support H6 4.8. Discriminant analysis in demographic variables Income discrimination Income differences hypothesis: • Hypothesis H3,0: In terms of income, there is no difference in intention to use • Hypothesis H3,1: In terms of income, there is no difference in perceived price • Hypothesis H3,2: In terms of income, there is no difference in perceived usefullness • Hypothesis H3,3: In terms of income, there is no difference in PEU • Hypothesis H3,4: In terms of income, there is no difference in social influence • Hypothesis H3,5: In terms of income, there is no difference in perceived enjoyment • Hypothesis H3,6: In terms of income, there is no difference in perceived risk Homogeneity tests of the PRICE and ENJOY components 0.0%, 0.9% respectively, were higher than 5%, indicating the variance of income was equal to the ANOVA analysis. Homogeneity test of components INTEN = 78.9%, CONVE = 5.5%, PEASY = 39.0%, SOINF = 48.4%, PRISK = 56.6% <5% variance of income is not equal, does not satisfy the ANOVA analysis conditions. Among factors that satisfy the condition of ANOVA can be seen: Sig (INTEN) = 1.9%, Sig (SOINF) = 4.1%< 5% Rejection of H3,0 & H3,4 : There are difference in income group in the intention to use e-commerce and social influence. In particular, the intention to use low-income group e-commerce tends to be lower than that of high-income groups, while low-income groups tend to be more vulnerable than high-income groups to social impact. This may be due to the higher income group being more exposed to using e- 22 Int. J. Anal. Appl. (2023), 21:23 commerce services, so their intentions to buy e-commer are higher. Moreover, the level of impact from their social impact is lower than that of low-income groups due to more exposure to information sources. Sig (CONVE) = 31.2%, Sig(PEASY) = 19.5%, Sig(PRISK) = 23.8% > 5% There is no basis for H3,2, H3,3, H3,6 rejection: meaning there is no evidence of income group differences with PEU, perceived usefulness, and perceived risk. This can be explained by the fact that in recent years only e-commerce service has grown, making online payments one of the key strengths of e-commerce has not yet become widely known. Therefore, there is no difference between income groups in ease of use, usefulness as well as risk. 4.9. Conclusion Section 4 presented information on survey samples, Cronbach’s Alpha and EFA analysis, multivariate regression analysis, and control variables. The information from the observation sample showed that the sample was young, ranging from 25 to 27 years old. Most of them have knowledge of using the Internet, have knowledge of using e- commerce. The Cronbach Alpha reliability and EFA analysis of SoInf_16 and PRisk_24 variables. V. Conclusion The study suggests that e-commerce providers can improve and enhance customer service, depending on the extent to which each factor influences the intention to use electronic purchases. 5.1.1. Perceived Price Service providers therefore need to pay attention to price-related factors in order to attract consumers in order to improve the intention to buy electronic goods through the user network. 5.1.2. Perceived Usefulness Service providers need to improve the purchasing process, simple, convenient, complete installation information and guarantee to improve the intention to use consumer purchasing e-commerce services. There will also be a significant reduction in online purchases if you do not pay online. Moreover, wider advertising is needed to enable consumers to see the convenience of buying electronics online. 5.1.3. PEU This demonstrates that consumers are very keen on ease of use. However, there was not a high level of consumer feeling in the survey (from 2.82 to 3.21). In order to raise awareness of e- 23 Int. J. Anal. Appl. (2023), 21:23 procurement providers ' ease of use, it is important to provide complete user information, displaying instructions in the process at prominent locations; access website. 5.1.4. Perceived Enjoyment The survey results agree with the perceived pleasure statements that the user's approval level for this factor is quite high (3.54 to 3.79 on average). This demonstrates that e-commerce is now a new trend that attracts attention to all ages. Therefore, when implementing advertising programs to promote their services, e-commerce service providers need to pay attention to the aspect of exploring and discerning. 5.1.5. Reference group As friends and relatives, colleagues, partners, media, the audience can influence consumers. The survey results agree with the social impact statements that the user's level of consent to family and family impact is low (from 3.10 to 3.16), while the organization's level of impact is higher (3.47), the data does not support media impact information. E-commerce providers should therefore focus on marketing programs for teams, organizations and referral discounts. Consumers will introduce and invite their friends and colleagues to participate in promotional activities. 5.1.6. Perceived Risk The results show that users do not agree with Perceived Enjoyment (average of observation variables from 2.57 to 2.94) The impact of risk awareness tends to be greater for women, so online e-commerce providers need to have policies tailored to women in order for them to feel secure when purchasing electrical goods. death through the network. 5.2. Contribution of research Research with theoretical and practical contribution in online trading in Vietnam 5.2.1. Contribution in theory Based on the UTAUT model combination (Venkatesh et al., 2003) [24] model "Factors affecting online consumers", E-CAM (Joongho Ahn et al., 2001) [25] and extended TAM for WWW (Moon Ji Won & Kim Young Gul, et al., 2001) [23], this research provided a more comprehensive overview of research and survey compared to a single model. On the other hand, it is designed for developed countries to measure this research. However, through practical data in Ho Chi Minh City and surrounding provinces, this measurement is modified and evaluated to match the Vietnamese 24 Int. J. Anal. Appl. (2023), 21:23 environment. The data will play a role in measurement theory that will help academic and applied researchers gain a better understanding of the Vietnamese market. 5.2.2. Contribution in application This research has opened up a path for service providers to carry on similar research with other products such as: magazines, movies ... And eventually, service providers in both developing countries and Vietnam will be able to improve e-commerce service. 5.3. Proposal for E-commerce Business 5.3.1. Increase spending benefit for consumers. The business needs to concentrate on building up its customer services: The third party would organize fastest delivery activities, COD activities across the country. 5.3.2. Minimize the risk for the purchasers when they use online sale services According to the research, buying online is quite risky. Therefore, product safety would be the purchasing decision's priority. Online sales providers need to provide a safe way of ensuring consumers ' purchasing process. Through hard qualification process, they need to shortlist the good quality product. The sale would grow dramatically as consumers gained confidence. Not only should they care about the short-term profit in order to get the products of low quality. That is the easiest way to kill their business. 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