The survey about factors affecting the online purchase intention electronic devices in ho chi minh city

Knowing that, this study focuses on exploring the influential factors such as product value, perceived risk, website quality, trust and perceived usefulness that affect customers’ online

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ô The main objective of this study is to identify factors affecting the online purchase intention of electronic devices in Ho Chi Minh City

% Position the impact of these factors on buying intention

% From there, propose solutions suitable for online business for businesses.

Subjects of study and scope oŸ studịy - ẶĂc cành Hee 5

This study examines the factors influencing online purchase intentions among consumers in Ho Chi Minh City Utilizing the theory of customer purchasing intention, the research highlights the relationship between independent and dependent variables The findings provide valuable administrative implications based on the identified relationships and their varying degrees of impact.

This research focuses on electronic items, specifically phones, computers, CCTV cameras, data memory devices, televisions, and various accessories It surveys customers aged 18 and over who are internet users and reside in Ho Chi Minh City The study was conducted over a five-month period, from January 2021 to May 2021.

Research methods 1n

The research process was carried out with two methods: qualitative research and quantitative research

Danh Thi Ngoc Anh 5 Lớp DHMDT13A

Qualitative research methods are designed to explore and enhance questionnaires through a two-phase study In Stage 1, the focus is on developing theoretical foundations for models and scales Stage 2 employs qualitative techniques, particularly group discussions, to examine factors influencing the intention to purchase electronic devices in Ho Chi Minh City, including concepts such as intent, risk perception, and service quality This approach ultimately aids in refining the draft model for the research.

The quantitative research method involves formal research using samples to estimate online purchase intentions among customers in Ho Chi Minh City, utilizing a questionnaire adapted from group discussions This approach collects and analyzes survey data while employing various data analysis tools through SPSS software, including descriptive statistical analysis, Cronbach's Alpha scale testing, exploratory factor analysis (EFA), and linear regression analysis.

Structure of the thesy .- cecceseeseeeeeeseseesceseesecesaeeaesseesenaeseeeeenaeeaes 6

Chapter 1: Research Overview Chapter 2: Theoretical Basis Chapter 3: Research Design Chapter 4: Data Analysis Chapter 5: Managerial Implications

THEORETICAL BASIS 7

Purchase intention and online purchase infention

The Theory of Planned Behavior (TPB), developed by Martin Fishbein and Icek Ajzen in 1980, builds upon the earlier Theory of Reasoned Action (TRA) This influential model is widely applied across various fields, focusing on the factors that influence consumer intentions and behaviors, including attitudes, subjective norms, and perceived behavioral control.

Figure 2.1 TPB Model (Ajzen Extract 1991, p.182)

TPB intended behavior behavior subjective norms perceived behavioural boom — — — — — ———— — control

The model illustrated in Figure 2.1 highlights the influence of three key factors on intentional behavior Notably, the perception of behavioral control pertains to individuals' beliefs regarding the ease or difficulty of performing a behavior, which significantly affects their ability to regulate their actions.

Võ Thị Huỳnh Hân emphasizes that attitudes and subjective norms, as outlined in Ajzen's Theory of Planned Behavior (1991), play a crucial role in influencing individual actions Attitudes reflect a person's positive or negative evaluation of a specific behavior, while subjective norms pertain to the social pressures that impact the decision to engage in that behavior Ajzen (1991) asserts that when individuals possess favorable attitudes and subjective norms, along with a strong sense of behavioral control, their intention to perform the behavior in question is significantly heightened.

The Technology Acceptance Model (TAM), introduced by Davis in 1989, predicts the acceptability of information systems by assessing key factors such as perceived usefulness and perceived ease of use This model aims to anticipate the adoption of specific information technologies and suggest necessary modifications to enhance user acceptance.

Figure 2.2 TAM model extracted TAM2 2000, p188

Behavioral Actual System Intention to Use Use

Perceived usefulness refers to the extent to which individuals believe that utilizing a specific system will improve their work performance Additionally, perceived ease of use indicates that interacting with particular systems is straightforward and hassle-free Research by Venkatesh and Davis (2000) emphasizes that the perception of usefulness significantly influences the intention to use a system.

The TAM model is enhanced by incorporating additional variables including subjective norms, voluntaryness, image, work suitability, output quality, and proof of results, leading to the development of TAM2 Subsequently, the TAM3 model was thoroughly researched in 2008 by Venkatesh and Bala.

2.1.3 Intention to buy and intend to buy electronic devices online

The intention to buy reflects the likelihood that a consumer will purchase a product or service Marketers utilize predictive models to evaluate purchasing intentions, drawing on historical data to forecast future outcomes According to Ajzen (1991), intention serves as a key indicator of an individual's willingness to engage in specific behaviors and the frequency with which they attempt to do so This makes intention a crucial factor in predicting actual consumer behavior, as highlighted by Montafio and Kasprzyk (2015).

Online purchase intention refers to the willingness of consumers to buy products from online stores (Pavlou, 2003) It encompasses the tendency of customers to engage in online shopping and their readiness to participate in purchasing activities (Wen & Maddox, 2013) According to Chen et al (2010), purchase intention serves as a crucial predictor of actual buying behavior, reflecting consumers' desires to make purchases through websites.

The related research - - c + xxx TH HH HH ky 10

In 2009, Sam and his associates conducted research on the relationship between website quality and consumer online purchase intentions for air tickets The study highlights that to enhance online purchasing intentions, service providers must deliver empathetic services and foster customer trust The findings reveal a direct correlation between website quality factors and the likelihood of consumers purchasing air tickets online.

The author proposes a model of factors that influence the intention to buy online including variables: Usability, Website design, Information quality, Trust, Perceived risk, Empathy

Figure 2.3 Sam and Tahir Research Model (2009)

Perceived usefulness in online shopping refers to a consumer's belief that utilizing the internet will enhance their purchasing efficiency (Chiu et al., 2005).

Web design encompasses various elements, including text, images, graphics, layouts, sounds, and even scents, highlighting the importance of selecting the right content for effective web design.

Information quality refers to the amount, accuracy and the form of information about the products and services offered on a web site (Nusair et al., 2008)

Trust in online transactions is marked by uncertainty, vulnerability, and dependence, as customers cannot physically inspect products or interact directly with sellers during the purchasing process (Sam et al 2009).

Perceived risk is defined as uncertainty about the possible negative consequences of using a product or service

Empathy is an interaction between a human factor that is not directly involved in providing personal care and interest such as email communication

2.2.2 Research of Hemantkumar P Bulsara, Pratiksinh S Vaghela (2020)

A study conducted by Hemantkumar P Bulsara and Pratiksinh S Vaghela in 2020 examined online shopping intentions for consumer electronic products among 274 university students in India The research identified key factors influencing these intentions, including perceived usefulness, perceived ease of use, perceived behavioral control, e-shopping quality, trust, perceived risk, and subjective norms The authors proposed a conceptual model to illustrate these relationships, as depicted in Figure 2.4.

Figure 2.4 Research model of online shopping intention for consumer

Fig The Proposed Conceptual Model

(T=Trust, SN=Subjective norms, ESQ=E-shopping quality PE=Perceived enjoyment, PR= Perceived risk, PBC =Perceived behavioral control, PEOU=Perceived ease of use, PU= Perceived usefulness, 1=Intention)

Source: Hemantkumar P Bulsara, Pratiksinh S Vaghela 2020 p29[10]

The study revealed that ease of use and usefulness of online shopping platforms, E-shopping quality, perceived behavioral control, trust in retailers and online

Võ Thị Huỳnh Hân platforms, perceived risk and subjective norms were found to be factors that correspondingly influenced consumers’ purchases of electronic consumers online

The perceived ease of use and perceived usefulness significantly impact online purchases of consumer electronics Additionally, the quality of service provided by online platforms plays a crucial role, encompassing information quality, system quality, and after-sales service Furthermore, perceived behavioral control highlights consumers' ability to navigate online shopping efficiently.

Trust and perceived risk significantly influence consumers' intentions to make purchases online Additionally, subjective norms play a crucial role, as shoppers are often swayed by the opinions of those whose views they value To aid their decision-making, consumers actively seek out reviews and comments from others regarding the products they consider.

The six factors identified by the author significantly influence customers' online shopping intentions Additionally, the author offers solutions and highlights key considerations for online retailers to develop effective strategies.

To enhance the reliability of the theoretical foundation for developing a proposed model, the authors' team conducts a comprehensive review of related studies.

Chen et al (2010) conducted a study that identified key properties and features of shopping websites that enhance consumer purchasing intentions Utilizing information technology for data collection, the research gathered insights from over 4,000 university students in Taiwan.

The results of the study show that purchasing intention is influenced by groups of factors such as technological factors, product factors, procurement factors

A Leeraphong and A Mardjo (2013) Research on Trust and Risk in Purchase Intention through Online Social Network: A Focus Group Study of Facebook in Thailand This study used a focus group study among working adults (ages 25 to 34), to explore the preliminary research model and hypotheses gathered from reviews of materials related to trust and risks affecting their online purchasing decisions through online social networks, especially Facebook The results of the study point to factors of trust, risk, procurement experience and subjective norms that have an impact on consumers’ online purchase intention In addition, the study also pointed to relationships between factors

A study by Haryo Bismo Putro and Budhi Haryanto (2015) examined the factors affecting online shopping intentions at Zalora Indonesia, based on data from 150 consumers at Paragon Mall in Surakarta The findings revealed that ease of use, usefulness, and perceived risk significantly influence consumer attitudes toward purchasing fashion products online from Zalora Indonesia.

A study by Rasha Abu-Shamaa and colleagues (2015) explored the factors influencing purchasing intentions from online stores by enhancing the Technology Acceptance Model (TAM) to incorporate trust in Internet technology and online retailers The research examined how consumers' preferred payment methods affect these factors The findings revealed that both the TAM framework and trust are significant predictors of consumers' intention to make purchases online.

Nesha, A.U., Rashed, M.S., & Raihan, T (2018) Identifying the factors that influence Online Shopping Intentions and practices: a case study on Chittagong Metropolitan City Data is collected through online sources and data collected from

352 leads Use reliability and regression analysis was also used to examine the

Võ Thị Huỳnh Hân's study investigates the factors influencing customers' intentions toward online shopping The findings indicate that price, user-friendliness, perceived risk, and perceived web quality significantly impact online shopping attitudes Notably, awareness of risk negatively affects these attitudes Ultimately, the study concludes that positive customer attitudes are strongly linked to their intention to engage in online shopping.

A study by Lee and his partner (2019) investigated the factors influencing Malaysian consumers' intention to purchase electronic air tickets It highlighted that the perceived risks associated with e-tickets overshadowed their perceived usefulness, ultimately leading to a negative impact on consumers' purchasing intentions Data from 231 participants revealed that the disadvantages of e-tickets, particularly concerning risk perception, hindered the acknowledgment of their benefits, making it difficult for consumers to develop a positive online purchasing intention.

RESEARCH DESIGN 23

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Group discussions are an effective data collection method in qualitative research, where trained researchers conduct collective interviews with participants from diverse backgrounds Utilizing phone discussions fosters interpersonal communication, enabling interactions that yield insights unattainable through traditional face-to-face interviews When executed effectively, these phone groups reveal participants' feelings and perspectives on various ideas and policies.

Participants for the study will be chosen based on their online shopping skills and demographics, specifically targeting consumers aged 18 and older To gather extensive comments and feedback, discussion groups will be conducted via phone across various specialties in different areas of Ho Chi Minh City.

Group discussions are a widely used data collection technique in dosing research projects due to their cost-effectiveness and rapid results This method is straightforward to implement and allows for the integration of verbal responses with body language and other non-verbal cues, enhancing the accuracy of the information gathered Additionally, the flexibility of group discussions enables them to be customized to meet specific research needs.

The team's inability to expand the number of groups, or sample size, in the dosing research is attributed to the non-probability selection of samples This limitation means that individuals within the same group can influence one another, potentially skewing the study's results, especially if participants exhibit authoritarian tendencies.

Results from a small sample group discussion can always be generalized into larger quantities

The study was conducted in Ho Chi Minh City with a sample size of 500 internet shoppers A total of 501 responses were collected through an online survey Although the survey data was generally comprehensive, 20 incomplete responses were discarded during the cleaning process.

Based on the concept of the theory and considering the relevant studies Besides, based on the results of the group discussion, a set of 6 conceptual elements and 22 observation variables

Table 3.1 Proposed variables in the research model

Product Value The features of the product meet PV1 Chen & ctg (2010) your requirements

The online store sells good quality PV2 products

Online store selling products at PV3 reasonable prices

The online store sells products of PV4 clear origin and guaranteed Danh Thi Ngoc Anh 27 Lớp DHMDT13A

Võ Thị Huỳnh Hân genuine

Perceived Risk You find it difficult to accurately PR1 Hemantkumar P assess product quality when Bulsara, Pratiksinh shopping online S Vaghela (2020);

Bui Thanh Trang You find it very difficult to PR2 (2013) compare the quality of similar products when shopping online

Personal information such as your PR3 address, email, phone number may be disclosed to others

You find your shopping habits and PR4 process easy to track when shopping online

Website Product information and images WQ1 Hemantkumar P

Quality are detailed and clear Bulsara, Pratiksinh

S Vaghela (2020) The online store has a user- WQ2 friendly interface designed to be Chen & ctg (2010) easy to use

The website has a fast loading WQ3 Pefia Garcia et al., speed to help you find the exact (2020) product in a short time

Trust The online store has a solid TRUI Chen & ctg (2010) confirmation message when you close the purchase Y Hwang, and D J

The website's online reply service TRU2 meets your requirements

Hemantkumar P The terms of the transaction TRU3 Bulsara, Pratiksinh (including payment, shipping, S Vaghela (2020) watranty, return, etc.) are detailed and clear by the online store Chen & ctg (2010);

Y Hwang, and D J The online store is known by TRU4 — Kim (2007) many to be reputable and trustworthy

Perceived An online store that offers a wide PU1 Hemantkumar P Usefulness range of electronic products Bulsara, Pratiksinh information S Vaghela (2020)

Online store and you can easily PU2 Chen & ctg (2010) exchange information back and forth Pefia Garcia et al.,

(2020) Buying online saves customers PU3 shopping time

Online It is likely that you will purchase OPI Pefia Garcia et al., Purchase electronic products through an (2020); Hausnam & Intention online store Siepe (2009)

You will definitely buy electronic products through online stores

You will introduce — other customers to buy _ electronic devices online

You will continue to purchase electronic products through the online store

The questionnaire structure consists of 2 parts:

Part 1: Introduction and general information

Briefly introduce the topic of research, researcher, purpose of the research topic, ensure that the answer increases the cooperation to provide accurate information accurately

This section of the content is added to screen the survey respondent's information related to the subject study Besides, the opening questions are also

OPI4 included to guide the surveyor in the important answer below

In this section, the questions are aimed at gathering information about factors in the proposed research model "Affecting the online purchase intention of electronic

In Ho Chi Minh City, V6 Thi Huynh Han tớp DHMDT13A devices utilize the Likert scale, a widely recognized measurement tool developed by Rensis Likert This ordered scale assesses the level of agreement among survey participants, allowing them to select responses that reflect their consent to specific questions or statements The aggregated scores from these responses provide valuable insights into the overall attitudes of the participants.

The ratio is designed in 5 levels from 1 to 5 in order: 1 - Totally disagree, 2 - Disagree, 3 - Wonder (no opinion), 4 - Agree, 5 - Totally agree [13]

The purpose of this section is to gather more information about the demographic characteristics of the sample including (gender, age, work, income).

DATA ANALYSIS 31 4.1 Statistical methods Ác - + cSn HH2 TH HH1 112111111 T1 HH Hy 31 4.1.1 Cronbach's Alpha tesf Ặ- 2À SnSS SH HH H1 xe, 31 1ơ”) Vẻ

Survey sample distribution - - 5c ssssx si ee 36

In a recent reality survey, a team conducted a group discussion to analyze data and identify any errors or unclear questions before proceeding with an official survey The survey achieved a remarkable response rate of 97.4%, collecting a total of 501 questionnaires This included 301 online responses, of which 288 were valid, alongside a paper-based survey that gathered 200 responses, yielding 193 valid samples.

Research results nh ốố

Variables used in sample characteristics analysis include: gender, age, job, and average monthly income

In the survey, female participants represented 58.2% of the total, significantly outnumbering male participants, who accounted for only 41.8% This indicates that women tend to shop more frequently than men.

Table 4.1 Sample distribution by gender GENDER

Frequency Percent Cumulative Percent Valid Male 201 41.8 41.8

(Source: Aggregated from analytics using SPSS)

The survey results indicate that the majority of customers in Ho Chi Minh City are young, with individuals aged 18 to 22 making up 63.0% of the respondents Those aged 23 to 27 represent nearly 16.0%, while participants aged 28 to 32 account for about 7.5% Additionally, 8.9% of respondents fall within the 33 to 37 age range, and only 3.7% are over 37 years old This data highlights a significant youth demographic among customers in the city.

Table 4.2 Sample distribution by age Age

Percent Valid From 18 to 22 years old 303 63 63

From 23 to 27 years old 81 16.8 79.8 From 33 to 37 years old 36 7.5 87.3 From 21 to 25 years old 43 8.9 96.3

Total 481 100 (Source: Aggregated from analytics using SPSS)

The survey revealed that 62.16% of respondents were students, reflecting the research team's focus on this demographic Additionally, office workers represented 16.01% of the sample, while civil servants and employees accounted for approximately 4.78% Entrepreneurs and managers made up nearly 2.29% of participants, and other professions constituted about 14.76% This diverse range of occupations allowed the study to gain a comprehensive understanding of purchasing intentions for electronic devices.

Table 4.3 Distribution of samples by occupation Job

Officials, civil servants 23 4.78 82.95 Entrepreneurs, Managers = 11 2.29 85.24

In the income distribution, a significant 40.1% of individuals earn less than VND 3 million, highlighting a prevalent low-income segment Following this, 25.4% of the population earns between VND 3 million and 7 million, while those earning between VND 7 million and 15 million account for 25.8% Lastly, only 8.7% of individuals have incomes exceeding VND 15 million, representing the smallest income bracket.

Table 4.4 Sample distribution by income level

Total 481 100 (Source: Aggregated from analytics using SPSS)

4.3.2 Testing the reliability of the scale Cronbach's alpha

To ensure that the scales in the study are reliable enough, each scale will be tested using Cronbach's alpha method The test results are shown in Table 4.5

Table 4.5 Cronbach's Alpha analysis results of survey data

Scale Mean if Scale Corrected Cronbach's

Item Deleted Variance’ if ItemTotal Alpha if Item

Online purchase intention Cronbach's Alpha =.86

The results presented in Table 4.5 indicate that the reliability of all six scales is reflected in a Cronbach's Alpha index ranging from 0.788 to 0.895 Previous research suggests that a Cronbach's Alpha between 0.8 and 1 signifies good reliability (Nunnally and Bernstein, 1994, as cited by Duy et al., 2020), while values from 0.7 to 0.8 are considered acceptable for usability (Peterson).

1994, quoted by Duy et al (2020)) Therefore, we conclude that the scales set out such as website quality, trust, perceived usefulness, perceived risk, product value meet standards and statistical significance

The results presented in Table 4.5 indicate that the correlation of observation variables is greater than or equal to 0.572 According to Duy et al (2020), a total correlation difference of less than 0.3 suggests a need to evaluate the type of observed variable from the scale If the variable type shows an increase compared to Cronbach's Alpha, it should be considered initially However, it is essential to assess the content value expressed by the observation variable on the scale and examine its performance in previous similar studies Consequently, specific observation variables with correlations greater than 0.3 are retained for further analyses, leading to the exploratory factor analysis (EFA).

Following the reliability test, six key elements and 22 observation variables were retained for further analysis To explore the factors influencing online purchase intentions for electronic devices in Ho Chi Minh City, the study will conduct Exploratory Factor Analysis (EFA) This analysis aims to validate the proposed components and uncover additional influencing factors.

The topic will conduct research simultaneously on independent variables, the remaining dependent variable “online purchase intention” will be analyzed separately

4.3.3.1 EFA analysis for independent variables

Verify the converged value of the scale of the independent variable This step will be performed EFA is conducted using the factor analysis performed using the

"Principal Component” with the rotation "Varimax" as an extraction factor ((Anderson et al., 1998) extracted Nesha et al (2018))

The results of the analysis are presented in table 4.6

Table 4.6 EFA analysis results for independent variables

(Source: Compiled from the results of running EFA analysis on SPSS) Danh Thi Ngoc Anh 42 Lớp DHMDT13A

From table 4.6 results we see: KMO value is equal to 0.89 (conditions greater than 0.5 and less than 1), thereby showing that factor analysis is appropriate Sig

The factor analysis results indicate that the data is suitable, with a significance level of 0.00 < 0.05 The extracted variance of 72.417% surpasses the 50% threshold, demonstrating that five extracted factors account for the majority of the observed data variation Additionally, the Eigenvalue coefficient of 1.008 reflects strong informative significance, confirming that all observed variables converged effectively on the scales and were retained for analysis.

4.3.3.2 EFA analysis for dependent variables

After conducting the EFA analysis for the dependent variable "online purchase intention”, the results are aggregated and presented specifically in table 4.7

Table 4.7 EFA analysis turns online purchase intention

(Source: Compiled from the results of running EFA analysis on SPSS)

The KMO value of 0.819 indicates that factor analysis is appropriate, as it falls between the acceptable range of greater than 0.5 and less than 1 Additionally, a significance level of 0.00, which is less than 0.05, confirms the data's suitability for factor analysis Furthermore, the extracted variance of 70.384% exceeds the 50% threshold, demonstrating a strong factor extraction result.

Võ Thị Huỳnh Hân explained 70.384% of the variation of the observed data The Eigenvalues coefficient is 2.815 > 1 , the extracted coefficient has good informative significance

With the hypothetical model, the authors conduct linear regression analysis in turn according to the following steps:

The initial linear analysis evaluates how independent factors—such as website quality, trust, perceived usefulness, perceived risk, and product value—affect the dependent variable, which is the intention to purchase electronic devices online.

Second, regression was performed to test the impact of three independent variables, namely website quality, trust, and perceived risk, with the dependent variable being perceived usefulness

This study investigates the relationship between independent factors—perceived value (PV), perceived risk (PR), trust (TRU), perceived usefulness (PU), and website quality (WQ)—and the dependent variable, online purchase intention (OPI) for electronic devices Using linear regression analysis, the findings reveal significant insights into how these factors influence consumers' intentions to buy electronics online.

Table 4.8: Model summary of factors in which to intention to buy electronic devices online

Model R R Adjusted Std Error of the Durbin-Watson

1 8 16a 0.666 0.663 0.35814 1.992 a Predictors: (Constant), PU, PR, PV, WQ, TRU b Dependent Variable: OPI

Based on the results of 4.8 table, we can give the following analysis:

The R² determination index indicates that 66.6% of the volatility in the OPI variable is explained by five independent factors: PV, PR, TRU, PU, and WQ, as shown in Table 4.8 The remaining 33.4% is attributed to other random factors and errors.

Table 4.9 ANOVA for factors affecting online purchase intention of electronic devices

Model Sum of Squares df MeanSquare F Sig

Residual 60.925 475 0.128 Total 182.63 480 a Dependent Variable: OPI b Predictors: (Constant), PU, PR, PV, WQ, TRU

Table 4.9 shows F = 189,775 and significant Sig = 0.000 (sig < 0.05), which means that the scale model is consistent with the collected data and the included variables have statistical significance with 5%

Danh Thi Ngoc Anh 45 tớp DHMDT13A V6 Thi Huynh Han

Table 4.10 Regression weight table of factors affecting the intention to buy electronic devices online

Model B Std Beta t Sig Collinearity

The analysis presented in Table 4.10 indicates that the Variance Inflation Factor (VIF) ranges from 1.009 to 1.855, suggesting a minimal multi-collinearity issue within the model All variables—Perceived Value (PV), Website Quality (WQ), Trust (TRU), and Perceived Usefulness (PU)—have significance levels below 0.05, confirming their positive impact on consumers' intention to purchase electronic devices online Notably, PV, WQ, TRU, and PU exhibit positive Beta coefficients, reinforcing their favorable influence on buying intentions, while the variable Price (PR) shows a negative effect despite its positive Beta coefficient Consequently, the results yield a comprehensive regression model to understand these dynamics.

OPI = 0.205PV - 0.06 PR + 0.208WQ + 0.272TRU + 0.323PU

The research team conducted a linear regression analysis to examine the relationships between the factors of RRCN, STC, and CLW with the THI variables The findings are summarized in the table below.

Table 4.11 ANOVA of PR, WQ, and TRU factors affecting PU

Model Sum of df Mean F Sig

Total 323.375 480 a Dependent Variable: PU b Predictors: (Constant), PR, WQ, TRU

Table 4.11 shows F = 116.098 and significant Sig = 0.000 (sig < 0.05), which means that the scale model is consistent with the collected data and the included variables have statistical significance with 5%

Table 4.12 Regression weight table of PR, WQ, and TRU factors affecting

Model B Std Beta t Sig Collinearity

The analysis of Table 4.12 reveals that the highest Variance Inflation Factor (VIF) is 1.578, indicating a minimal multicollinearity issue within the model Additionally, the significance indices for the variables Water Quality (WQ) and Trust (TRU) are both below 0.05, demonstrating their significant impact on Overall Performance Index (OPI) In contrast, the variable Perceived Risk (PR) shows a significance level of 0.098, suggesting a weaker influence on OPI.

> 0.05, so the variable PR does not affect the variable OPI Beta of the variables WQ

= 0.368, TRU = 0.353 which shows that both variables are positive So these two variables have the same effect on the dependent variable PU

4.3.5 Inspection of average values of variables observed in TRU, PU, WQ

From the results synthesized and analyzed in section 4.3.4, the team found that

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This research aims to identify the factors influencing online purchase intention for electronic devices in Ho Chi Minh City, focusing on five key elements: product value, perceived risk, website quality, trust, and perceived usefulness The study reveals that the most significant factors impacting consumers' online buying intentions are perceived usefulness, trust, website quality, perceived risk, and product value, with perceived risk negatively affecting purchasing intentions Based on these findings, the study suggests tailored solutions for online businesses, emphasizing the development of effective marketing strategies and business methods to enhance customer satisfaction, boost operational efficiency, and increase revenue, ultimately helping businesses to gain a competitive edge in the market.

Perceived usefulness significantly impacts consumers' intentions to purchase electronic products online, highlighting the need for online entrepreneurs to prioritize customer experience Survey results indicate that customers value effective customer care and prompt communication with sellers, emphasizing their desire for a time-efficient shopping experience.

MANAGERIAL IMPLICATIONS 55

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Perceived usefulness significantly impacts consumers' intentions to purchase electronic products online, highlighting the importance of enhancing customer experience for online entrepreneurs Survey results indicate that customers prioritize effective communication with sellers and value prompt customer care, as they seek to save time while shopping Therefore, managers should focus on improving these aspects to meet consumer expectations.

Võ Thị Huỳnh Hân should develop effective business strategies and ensure a diverse range of quality products while providing accurate, detailed, and timely information that aligns with customer needs Additionally, the perceived usefulness of the site is influenced by its quality, making the service quality of the online platform a crucial aspect that requires careful consideration.

The increasing unpredictability of internet infrastructure has raised consumer concerns about the potential threats posed by hackers and third parties to their financial and personal information To address these concerns, electronics providers must focus on building consumer trust to enhance online purchasing intentions This can be achieved by implementing clear rules and regulations for online trading, ultimately fostering greater confidence and reliability in online transactions.

Enhanced customer care before and after a purchase is crucial in the e-commerce landscape A recent survey indicates that timely responses to online inquiries remain a significant challenge, highlighting an area that customers feel has not yet reached optimal performance in the e-commerce sector.

The analysis conducted by the research team highlights the significant role of website quality in influencing online purchasing intentions for electronic devices in Ho Chi Minh City To foster a consumer-centric e-commerce business, it is essential for administrators to prioritize the quality of information regarding electronic products on their online platforms This entails ensuring that the website content is accurate, comprehensive, clear, and trustworthy By providing essential information, online stores can better assist customers in making informed product choices.

Thang and Doan Thi Mai (2020) emphasize the importance of enhancing website quality and access speed to facilitate seamless shopping experiences for customers This improvement aims to prevent connection issues that could deter potential buyers and disrupt their shopping intentions Additionally, businesses should adjust their strategies based on these findings to better cater to individual customer needs.

This study, like many others, faces several limitations, including time constraints, limited resources, and varying levels of knowledge among team members The restricted timeframe has hindered in-depth analysis of variables, and the brief duration allocated for developing the theoretical framework and conducting surveys may have resulted in some shortcomings in the research.

Recent research conducted in Ho Chi Minh City examined consumers' intentions to purchase electronic devices online, but its generalizability may be limited To enhance the findings, the authors recommend expanding the study to various provinces and comparing key factors influencing online purchase intentions across different countries Additionally, future research should investigate other potential factors or attributes that could affect the intention to buy electronic devices online.

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The results of adjusting the scale according to the comments discussed are as follows:

From the original questionnaire, after discussing the opinion, the authors team made the following correction:

The WQ3 variable has been introduced to the website quality assessment, emphasizing that a fast loading speed is essential for efficiently locating products.

In question the OPI1 observation variable has been replaced “Your intention to buy electronic products online” => “It is likely that you will purchase electronic products through an online store.”

The online purchase intention factor now includes an additional observed variable in the questionnaire, specifically OPI3: "You will recommend other customers to purchase electronic devices online."

The OPI4 variable's question has been revised from "Do you think you will buy electronic devices through the online store?" to "Will you continue to purchase electronic products through the online store?"

Khảo sát về các yếu tổ ảnh hưởng đến ý định mua thiết bị điện tử trực tuyến tại thành phố Hồ Chí Minh

Tiêu đề	The Survey About Factors Affecting The Online Purchase Intention Electronic Devices In Ho Chi Minh City
Tác giả	Danh Thi Ngoc Anh, Vo Thi Huynh Han
Người hướng dẫn	PhD. Nguyen Thi Phuong Giang
Trường học	Industrial University of Ho Chi Minh City
Chuyên ngành	Electronic Commerce
Thể loại	Graduation Thesis
Năm xuất bản	2017-2021
Thành phố	Ho Chi Minh City

Định dạng
Số trang	101
Dung lượng	6,35 MB