STATE BANK OF VIETNAM MINISTRY OF EDUCATION AND TRAINING BANKING UNIVERSITY OF HO CHI MINH CITY NGUYEN DO NGOC TRAN (Student code 030633171432) FACTORS AFFECTING ONLINE SHOPPING BEHAVIOR OF BANKING UN[.]
GENERAL OVERVIEW
Background
Online shopping has surged in popularity due to its convenience and speed, particularly accelerated by the Covid-19 pandemic, which shifted consumer trends significantly The Internet has transformed into a global marketplace, offering a vast array of products available 24/7, especially in developed countries Research indicates that understanding consumer online shopping behavior is crucial, as this process allows easy access to product information from various sources, including social media and price comparison websites Despite the advantages, such as time savings for students, the participation rate of Vietnamese consumers in online shopping remains lower than in other regions To boost online shopping among students, it is essential to identify the factors influencing their purchasing behavior This research aims to explore these factors affecting Banking University students' online shopping habits during the Covid-19 pandemic and propose solutions for online retailers to better connect with consumers.
The urgency of the topic
A recent Nielsen survey presented at the Online Marketing Forum 2020 revealed a significant 25% increase in online shopping, contrasting with modest growth in traditional retail channels like supermarkets (7%), markets (3%), and groceries (6%) Ms Le Minh Trang from Nielsen Vietnam highlighted that the Covid-19 pandemic has accelerated the online shopping trend, presenting new business opportunities as consumers increasingly prefer shopping from home With an annual growth rate in online shopping projected to remain in double digits, this trend is expected to continue in Vietnam The pandemic disrupted global trade and supply chains, impacting developing countries like Vietnam This research aims to provide online retailers with insights into customer behavior, particularly among students, to enhance their online shopping experience during the pandemic Despite numerous global theories on online shopping behavior, specific studies in Vietnam are limited, especially regarding the student demographic during Covid-19 Adapting existing models to fit Vietnam's unique cultural, economic, and social context is crucial for understanding consumer behavior in this evolving landscape.
Research objectives
This article explores the key factors influencing students' online shopping behavior during the Covid-19 pandemic and assesses the impact of these factors It also presents insights from the research team on effective strategies for enhancing online shopping services to attract customers amid the ongoing crisis.
- Systematize the theoretical basis of students' online shopping behavior during the Covid pandemic 19
This study examines the factors influencing the online shopping behavior of students at the University of Banking in Ho Chi Minh City during the Covid-19 pandemic It aims to identify and analyze the impact of each factor on students' purchasing decisions in the context of heightened online shopping due to health concerns Understanding these influences is crucial for businesses targeting this demographic in the evolving digital marketplace.
- Contribute solutions for service providers to purchase online and attract more customers during the Covid 19 pandemic.
Research question
- What factors affecting the online shopping behavior of HCM Banking University students during Covid 19?
- What is the influence of factors affecting online shopping behavior of Ho Chi Minh City Banking University students during Covid 19?
- What improvements or changes are needed from online shopping service providers to attract student customers to use?
Research object and Research scope
This study examines the factors influencing online shopping behaviors among Banking University students in Ho Chi Minh City during the Covid-19 pandemic The focus is on this young demographic, which is well-versed in online shopping, making their purchasing habits indicative of broader consumer trends The research spans from March 2020 to March 2021, providing insights into the evolving landscape of online retail during this period.
- Scope of space: Research conducted at Banking University of Ho Chi Minh City
- Scope of time: Secondary data from 2020-2021, primary data taken from survey samples during February 2021 to March 2021.
Research Methods
The research team selected the observation sample of this topic as "a student of the Banking University of Ho Chi Minh City" for the following reasons:
When conducting research, students must consider time and cost as critical factors Therefore, selecting a topic that minimizes both time and expenses for sampling is essential for efficient investigation.
- Second, "students of Banking University of Ho Chi Minh City" are those who have been using online shopping regularly
Qualitative research aims to investigate the factors influencing students' online shopping behavior during the Covid-19 pandemic and refine the scale of these factors in the proposed research model The findings from this qualitative study will inform the development of questionnaires for subsequent quantitative research, ensuring a comprehensive understanding of the topic.
Quantitative research: used to measure the influence of these factors on students' online shopping behavior during Covid time
- Evaluate the reliability of the scales by testing Cronbach's Alpha
- Analysis of discovery factors EFA by KMO test
Regression analysis and testing hypotheses with F and Sig tests
- T-Test and ANOVA test to find out significant differences in online shopping behavior among student customers
Research content
This article investigates the online shopping behavior of students at the Banking University of Ho Chi Minh City, focusing on cultural, social, personal, and psychological factors influencing their purchasing decisions It evaluates the shift in online shopping patterns during the Covid-19 pandemic, comparing current demands to pre-pandemic behaviors By utilizing both theoretical frameworks and empirical research from various authors, the study aims to provide a comprehensive analysis of this demographic's online shopping trends The findings will highlight the key factors affecting the online shopping behavior of students during the pandemic and propose strategies to enhance online shopping engagement in this segment.
Research contribution
Buying online is no longer a new field in Vietnam, but when buying goods during Covid
This study offers a fresh perspective on online shopping behavior during the pandemic by developing a theoretical model that identifies key factors influencing students' online purchasing decisions It builds on previous research conducted in countries like India, Iran, and Bangladesh, and adapts these findings to the Vietnamese context through survey data collected at Banking University The insights gained aim to enhance research and practical applications in the Vietnamese market, providing online retailers with effective strategies to compete during Covid-19.
After the study is successful, the topic will help the reference to identify the factors affecting online shopping behavior during the time of Covid 19 of the current student of
The study focuses on the impact of various factors on students at Ho Chi Minh City University of Banking, rather than general customers This targeted approach allows researchers to deepen their analysis and utilize the findings for related academic inquiries.
Structure of research
Introducing an overview of the topic, this chapter includes main contents such as research reasons, research questions and objectives, research scope and object, and research methods
This chapter includes the main contents such as detailing research methods, describing research samples, making hypotheses and proposing research models
LITERATURE REVIEW
Basic Concepts
Online shopping is a type of e-commerce that enables customers to purchase products or services directly from sellers via the internet Since the late 20th century, it has gained significant importance in the development of B2C e-commerce This service allows consumers to use electronic devices with internet connectivity to make their purchases.
Online shopping has transformed the market landscape by fostering globalization and advancing a knowledge-based economy Businesses increasingly prefer online sales over traditional methods, enabling them to lower transaction costs, expand their reach, and bridge the gap between buyers and sellers Research by Monsuwe, Dallaert, and Ruyter (2004) highlights that online shopping is more convenient than offline shopping, requiring less time and effort Consumers benefit from easier access to comprehensive product and service information, allowing for effective price and quality comparisons across different manufacturers Overall, the internet empowers customers to effortlessly obtain the data they need.
When shopping online, consumers are unable to physically interact with products before making a purchase However, online services offer extensive information about their offerings, enabling customers to evaluate products and services at their convenience.
Consumer behavior encompasses the actions and decision-making processes of individuals regarding the purchase, use, evaluation, and rejection of products and services aimed at fulfilling their needs It examines how consumers allocate their limited resources—such as time, money, and effort—when deciding on relevant items to consume Key aspects include what consumers buy, their motivations, timing and frequency of purchases, usage patterns, post-purchase evaluations, the impact of these assessments on future buying decisions, and the disposal of products.
Consumer behavior encompasses both the mental decisions and the physical actions stemming from those thoughts, with consumers defined as individuals who purchase or utilize products and services in the market They are categorized into two primary groups: personal consumers and organizational consumers Personal consumers buy goods for personal use, family needs, or as gifts for friends and relatives, often referred to as end-users In contrast, organizational consumers consist of businesses and institutions that acquire products and services to support their operations Research on consumer behavior typically emphasizes individual consumers, as their end consumption reflects various types of consumer behavior relevant to all buyers.
Recent research indicates that consumer purchasing decisions are significantly influenced by family, friends, and advertising, while also being shaped by individual mood, circumstances, and emotions These elements collectively create a holistic model of consumer behavior that encompasses both cognitive and emotional aspects of the decision-making process.
The consumer decision-making process encompasses three key phases: input, processing, and output During the input phase, consumers' perceptions of product needs are shaped by marketing efforts—such as product offerings, pricing, promotions, and distribution—and external social factors like family, friends, and cultural influences The processing phase examines how individual psychological factors, including motivation, perception, and personality, impact consumer decisions and their need for information before making a purchase This evaluation experience influences consumers' inherent psychological attributes The output stage involves purchasing behavior and post-purchase evaluation, where low-cost products may be swayed by promotions and trial purchases Returning to purchase indicates consumer acceptance, especially for durable products like laptops, which may quickly become outdated Research indicates that consumer motivation is affected not only by social influences and advertising but also by mood and circumstances, illustrating a comprehensive model of consumer behavior that integrates both cognitive and emotional aspects of the decision-making process.
The consumer decision-making process involves three key phases: input, processing, and output During the input phase, consumers' perceptions of product needs are shaped by marketing efforts, including product details, pricing, promotions, and distribution channels, as well as external social influences from family, friends, and cultural factors The processing phase examines how individual psychological factors—such as motivation, perception, education, personality, and opinions—affect consumers' need for information and their evaluation of choices This experience influences their inherent psychological attributes Finally, the output stage encompasses purchasing behavior and post-purchase evaluation, where low-cost, non-durable products may be influenced by promotions and trial purchases Satisfied consumers are likely to return for repeat purchases, while for durable products like laptops, acceptance is indicated by the willingness to buy despite rapid obsolescence.
General theories and research models of consumer behavior
2.2.1 Theory of Reasoned Action – TRA
The Theory of Reasoned Action (TRA), formulated by Fishbein and Ajzen, is a key framework for understanding human behavior It posits that an individual's behavioral intentions are shaped by their attitudes towards the behavior and the subjective norms surrounding it Attitudes are characterized as the positive or negative feelings an individual has regarding the performance of a specific behavior, as outlined by Fishbein and Ajzen in 1975.
Studies over the decades show that attitudes do not predict much of behavior Vicker
Fishbein and Ajzen (1980) found that attitudes are generally weakly related to behavior, leading to their development of the Theory of Reasoned Action (TRA), which emphasizes that planned behavior, rather than attitude, is a more accurate predictor of actual behavior Within this framework, Behavioral Intention (I) emerges as the key determinant of a person's actions, shaped by Attitude (A) towards the behavior and the Subjective Norms (SN) surrounding it Ultimately, Intent serves as the closest and most significant predictor of behavior, influenced by both attitudes and subjective norms.
Model 1: Theory of Reasoned Action - TRA
(Source: Fishbein & Ajzen, 1975) 2.2.2 Theory of Planned Behavior – TPB
The Theory of Planned Behavior (TPB), developed by Ajzen in 1991, builds upon the Rational Action Theory model introduced by Fishbein and Ajzen in 1975 TPB posits that consumer intent to act is influenced by attitudes, subjective norms, and perceptions of behavioral control Unlike the Theory of Reasoned Action (TRA), TPB incorporates a cognitive factor that impacts behavioral intention, as well as a belief about facilitation that relates to perceptions of control As noted by Bunchan (2005), TPB was designed to address the limitations of previous behavioral theories.
According to TPB, "behavioral intent" of customers is affected by "attitudes",
The Theory of Planned Behavior (TPB) incorporates "subjective norms" and "perception of behavioral control," making it a valuable framework for predicting individual user intent and behavior Empirical studies demonstrate the effectiveness of TPB in analyzing consumer behavior, particularly in online shopping contexts Research by Hansen et al (2004) indicates that the TPB model provides a more accurate explanation of customer behavior compared to the Theory of Reasoned Action (TRA) This highlights the importance of the research context in understanding consumer decision-making processes.
Vietnam, several studies have shown that TPB is more suitable for predicting consumer online shopping intentions
Model 2: Theory of Planned Behavior – TP
Factors affecting online shopping behavior
Attitude refers to an emotional evaluation that influences how individuals feel and act towards an object or idea, shaping their preferences and perceptions A positive attitude towards a brand can significantly drive consumer behavior, making it crucial for businesses to understand and assess buyers' attitudes in today’s competitive landscape Consequently, companies develop targeted marketing strategies to effectively influence and modify customer attitudes to align with their goals, ultimately enhancing profitability.
Online shopping significantly influences users' attitudes, which in turn play a crucial role in shaping their buying behavior Research indicates that positive online buyer attitudes are strongly linked to increased online purchasing activity (Ariff et al., 2014) The consumer's attitude toward engaging in online shopping serves as a powerful predictor of their actual behavior (Fishbein & Ajzen, 1975) Specifically, attitude reflects the acceptance of online shopping as a viable channel (Olson et al., 2001), and previous studies have consistently highlighted that these attitudes are key indicators of online purchasing decisions (Yang et al.).
Price significantly influences customer buying behavior, as it reflects the amounts charged for products or services that consumers pay to acquire them (Kotler & Armstrong, 2012) According to Satit et al (2012), among the key factors of product, price, location, and promotion, price is often the most critical element affecting consumers' purchasing decisions.
To attract consumers, companies frequently implement competitive pricing and various promotions, as most shoppers prefer convenience stores for their reasonable prices, which significantly influence their purchasing decisions (Andreti et al., 2013) Research by Munusamy and Hoo (2008) highlights that pricing strategies play a crucial role in motivating customers, underscoring the importance of price in the consumer decision-making process Ultimately, consumers prioritize price when considering a product purchase.
Subjective norms, as defined by Ajzen (1991), refer to the perceptions of influencers regarding whether certain behaviors should be performed According to the Theory of Reasoned Action (TRA) by Ajzen and Fishbein (1980), human behavior is driven by intention, which is shaped by consumer attitudes and perceptions influenced by family, friends, co-workers, media, and other factors Positive or negative opinions about a product can significantly impact consumer purchasing behavior Subjective norms represent an individual's perception of social pressures to engage or not engage in a behavior Research indicates a positive correlation between subjective norms and consumer intentions, particularly in online shopping, where Lin (2007) suggested that these norms reflect how group influences affect consumers' online shopping capabilities.
Perceived behavioral control refers to an individual's belief about the ease or difficulty of performing a specific behavior, focusing on the control over the behavior itself rather than its outcomes In online shopping, this concept encompasses consumers' views on the availability of necessary resources and opportunities to engage in shopping activities Research indicates that perceived behavioral control positively affects consumers' intentions to shop online, as it encompasses both internal limitations, like self-efficacy, and external factors such as resource availability Ultimately, these perceptions significantly influence actual online shopping behavior.
2004) and have a strong relationship with online shopping (Khalifa & Limayem 2003)
Risk-aware consumer behavior in IT products, as outlined by Bauer (1960), encompasses two key factors: the perception of risk associated with products/services and the perception of risk related to online transactions The former includes concerns such as functionality loss, financial loss, time consumption, and an overall risk assessment of the product/service Pavlou (2003) identifies specific risks in online shopping, including economic, seller, privacy, and security risks Forsythe et al (2006) highlight four criteria for measuring risk perception: the possibility of not receiving the product, challenges in testing products, lack of physical contact with the product, and the inability to examine it before purchase Additionally, Corbitt et al (2003) emphasize that financial risk and product risk—where the product may not meet customer expectations—are critical in assessing consumer risk perception in online shopping The component related to online transaction risk encompasses potential security and safety issues that arise during e-commerce transactions, underscoring the importance of complete authentication and awareness of online transaction risks.
Overview of research
- Factors affecting online shopping intentions of Vietnamese consumers: Expanded research planning behavior theory ( Ha Ngoc Thang, 2016)
This study examines the factors that influence online buying intentions among Vietnamese consumers through the lens of the theory of planned behavior Over a five-month period, questionnaires were distributed online, yielding 423 valid responses for analysis The data underwent a comprehensive analysis process, including factor analysis, reliability testing, and regression analysis Findings indicate that both attitudes and perceived behavioral control significantly enhance online buying intentions, while perceived risk negatively impacts these intentions.
Opinion of the reference group
Model 3: Research model in research of Thang (2016)
- Analysis of factors affecting online shopping behavior of Can Tho city consumers ( Nguyen Thi Bao Chau and Le Xuan Dao, 2014)
This study aims to identify the factors influencing the online shopping behavior of consumers in Can Tho city Data was collected from 130 participants, including 100 online shoppers and 30 non-shoppers, utilizing factor analysis, multivariate regression, and differentiation techniques The findings reveal that financial and product risks, product variety, trust, website responsiveness, time risk, comfort, convenience, and price significantly affect consumers' decisions to engage in online shopping Notably, the comfort factor emerged as the most influential element impacting online shopping behavior.
Model 4: Research model in research of Chau & Dao (2012)
- Factors affecting online shopping intentions of customers in Vietnam ( Nguyen Le Phuong Thanh, 2013)
This study utilizes a modified Technology Acceptance Model (TAM) to develop a research model examining factors influencing online shopping intentions Employing both qualitative and quantitative methods, the research began with direct interviews of five individuals aged 22-25, all with over two years of online shopping experience A quantitative survey yielded 171 valid responses, with reliability assessed through Cronbach's alpha and exploratory factor analysis (EFA) Hypothesis testing was conducted using multivariate regression analysis via SPSS 20 Findings indicate that perceived usefulness, perceived ease of use, expected price, and reliability significantly influence online shopping intentions, with the price expectation showing the strongest impact Conversely, perceptions of risk associated with online transactions and product/service quality negatively affect online shopping intentions.
- An Analysis of Factors Affecting on Online Shopping Behavior of Consumers (Javadi, Dolatabadi, Nourbakhsh, Poursaeedi, và Asadollahi, 2012)
This study investigates the factors influencing online shopping behavior among consumers in Iran, focusing on cognitive risk, infrastructure variables, and return policies Utilizing a model and analyzing data from 200 randomly distributed questionnaires, the research employs regression to test hypotheses related to consumer attitudes and norms, perceived behavioral control, and innovation Findings reveal that financial and non-delivery risks adversely impact online shopping attitudes, while domain-specific innovation and subjective indicators enhance shopping behavior Ultimately, positive online shopping attitudes significantly influence consumers' online purchasing decisions.
Model 5: Research model in research of Javadi et al (2012)
- Factors Affecting Consumers’ Internet Shopping Behavior During the COVID-19 Pandemic: Evidence From Bangladesh (Neger and Uddin, 2020)
The study explores various factors influencing shopping behavior during the COVID-19 pandemic, including product attributes, pricing strategies, time-saving benefits, payment options, security measures, administrative considerations, and psychological influences on consumer internet usage.
A study conducted in Bangladesh from May 10 to June 10, 2020, involving 230 online consumers, utilized descriptive statistical analysis, reliability analysis, and multivariate regression analysis The findings revealed that all factors, except for price and security, had a positive and significant association with consumers' online shopping behavior during the Covid-19 pandemic in Bangladesh.
Model 6: Research model in research of Neger and Uddin (2020)
- Fluctuating Attitudes and Behaviors of Customers toward Online Shopping in Times of Emergency: The Case of Kuwait during the COVID-19 Pandemic (Alhaimer,
This study explores the risk factors influencing online shopping behavior in Kuwait during the COVID-19 pandemic, utilizing online questionnaires distributed across various social media platforms A total of 385 responses were analyzed using AMOS 21 for structural equation modeling Findings reveal that risk tolerance, the severity of risks, and the threat of legal penalties positively influence consumers' online shopping attitudes, while product risk, financial risk, and non-delivery risk show no significant impact Notably, convenience risk emerges as a negative factor affecting attitudes Additionally, the study indicates that official penalties for violating lockdown measures can positively influence consumer behavior towards online shopping during this period It highlights that the factors shaping user attitudes and behaviors in regular times differ significantly from those in emergencies.
This research draws upon Ajzen and Fishbein's Theory of Reasoned Action (1980) and the Theory of Planned Behavior (TPB) developed by Ajzen in 1991, while also referencing various domestic and international studies on online shopping behavior, including those by Ha Ngoc Thang.
Based on the theoretical framework and insights from previous studies (Nguyen Le Phuong Thanh, 2013; Javadi et al., 2012; Neger and Uddin, 2020; Alhaimer, 2021), a research model has been developed to examine the factors influencing online shopping behavior among Banking University students during the Covid-19 pandemic The proposed model identifies five key factors: (1) Attitude, (2) Price, (3) Subjective Norms, (4) Perceived Behavioral Control, and (5) Perceived Risk.
During the Covid pandemic, students' attitudes significantly influenced their online shopping behavior, fostering a positive experience Conversely, the price of products negatively affected their purchasing decisions, leading to hesitance in online spending Additionally, subjective norms played a crucial role, as the social influence from peers encouraged students to engage more in online shopping activities during this period.
H4 : Perceived behavioral control has a positive impact on online shopping behavior of students during Covid
H5 : Perceived Risk has a negative impact on online shopping behavior of students during Covid
Perceived behavioral control Perceived risk
METHODOLOGY
Research design
In the research design section, the authors will mention how to build a scale, select a sample, collect tools, and collect information
The study has 2 research objectives related to the establishment of the scale, including:
- Measuring the characteristics of students participating in the study from the perspective of being an interviewee
- Ask participants to rate the level of agreement with the main factors affecting students' online shopping behavior during the Covid epidemic
The nominal scale is designed to identify and categorize research subjects, encompassing various aspects such as gender, school year, field of study, income, duration of Internet usage, daily Internet hours, frequently visited shopping sites during the Covid pandemic, and products commonly purchased by participants Its primary advantage lies in its simplicity of setup, specificity, and the valuable insights it offers.
The hierarchical scale is designed to systematically quantify and prioritize issues, measuring attitudes, consciousness, opinions, interests, and perceptions Utilizing a detailed Likert scale with five levels, the study identifies key factors influencing students' decisions when selecting a foreign language center, as outlined in a comprehensive table.
The model has 5 scales of independent factors (with 25 observed variables) and a scale of dependent factors (with 5 observed variables) built on a theoretical basis
Hypothesis H0 has 5 factors including: Attitude factor, Price factor, Subjective norms factor, Perceived behavioral control factor, Perceived risk factor
The dependent factor is the online shopping behavior of students at Banking University of Ho Chi Minh City during Covid
Data sources include primary and secondary data
Primary data for the study was gathered through surveys and interviews, utilizing questionnaires to facilitate easier completion for respondents The questionnaires were distributed to student groups at Banking University of Ho Chi Minh City, with responses being recorded via the researcher's email.
Secondary data is collected from external sources such as books, journals, research articles and internet databases to provide information on theoretical foundations, research models, research methods, the scale…
The scales built in the article:
The scale for the Attitude variable in this study is referenced from the scale in the study of Samar, S., M Ghani, and F Alnaser (2017); Rahi, S., M Ghani, and A Ngah (2018)
A1 I find online shopping to be worth using, especially during Covid's time Samar, S., M Ghani, and
F Alnaser (2017) A2 I think that online shopping during Covid has brought many new experiences
A3 I found it wise to shop online during Covid's time
A4 Online shopping during Covid becomes my habit
A5 Shopping online during Covid's time was fun and enjoyable
Hypothesis H1 : Attitude has a positive impact on online shopping behavior of students during Covid
The scale for the Price variable in this study is referenced from the scale in the study of Cheong and Park (2005) ; Tinne, Wahida Shahan (2011)
P1 I think online shopping is expensive
P2 I am willing to use online shopping even though the price is high
Shipping price is my main consideration when deciding to use or not to use online shopping services
P4 Price is the key factor in online shopping
P5 Choose products with affordable prices when shopping online
Hypothesis H2 : Price has a negative impact on online shopping behavior of students during Covid
The scale for the Subjective Norms variable in this study is referenced from the scale in the study of Nguyen Ngoc Tram (2015) ; Rehman and Ayoup (2019)
SN1 Your opinion is important to me when shopping online
I would have no problem shopping online if my friends and relatives bought it without problems
SN3 Sharing experiences through product reviews will catch my attention
SN4 I shop online because I can get details about products online
SN5 I shop online because I can shop whenever I want
Hypothesis H3 : Subjective Norms have a positive impact on online shopping behavior of students during Covid
The scale for the Perceived behavioral control variable in this study is referenced from the scale in the study of Nguyen Ngoc Tram (2015) ; Hsu and Chang (2006)
PBC 1 I don't shop online without a home computer
PBC 2 I don't shop online if I don't have a credit card
PBC 3 I don't shop online because the network speed is very slow
PBC 4 Whether I use online shopping or not is entirely up to me
PBC 5 I feel like I can control my spending when
Hypothesis H4 : Perceived behavioral control has a positive impact on online shopping behavior of students during Covid
The scale for the Perceived Risk variable in this study is referenced from the scale in the study by Javidi et al (2012) ; Featherman and Pavlou (2003)
PR1 I feel that my credit card information could be compromised and misused if I shop online
I feel that my personal information providing to deal with retailers may be compromised to third parties
I might be overcharged if I shop online because the retailer has my credit card information
When transferring money online, I am afraid that I will lose money due to careless errors such as wrong account number or wrong amount
PR5 When a transaction error occurs, I am worried that I cannot get a refund from the seller
Hypothesis H5 : Perceived Risk has a negative impact on online shopping behavior of students during Covid
The scale for the Online shopping behavior variable in this study is referenced from the scale in the study of Nguyen Ngoc Tram (2015) ; Huang et al (2013)
OSB 1 Online shopping makes it easier for me to shop during social breaks
(2015) OSB 2 I have more choices when I shop online
OSB 3 I can shop whenever I want
OSB 4 I can avoid crowds when shopping online during Covid time OSB 5 I will continue to shop online in the future Huang et al (2013)
Summary of the scales in the research model
1 I find online shopping to be worth using, especially during Covid's time
2 I think that online shopping during Covid has brought many new experiences
3 I found it wise to shop online during Covid's time
4 Online shopping during Covid becomes my habit
5 Shopping online during Covid's time was fun and enjoyable
6 I think online shopping is expensive
7 I am willing to use online shopping even though the price is high
8 Shipping price is my main consideration when deciding to use or not to use online shopping services
9 Price is the key factor in online shopping
10 Choose products with affordable prices when shopping online
11 Your opinion is important to me when shopping online
12 I would have no problem shopping online if my friends and relatives bought it without problems
13 Sharing experiences through product reviews will catch my attention
14 I shop online because I can get details about products online
15 I shop online because I can shop whenever I want
16 I don't shop online without a home computer
17 I don't shop online if I don't have a credit card
18 I don't shop online because the network speed is very slow
19 Whether I use online shopping or not is entirely up to me
20 I feel like I can control my spending when I shop online
21 I feel that my credit card information could be compromised and misused if I shop online
22 I feel that my personal information providing to deal with retailers may be compromised to third parties
23 I might be overcharged if I shop online because the retailer has my credit card information
24 When transferring money online, I am afraid that I will lose money due to careless errors such as wrong account number or wrong amount
25 When a transaction error occurs, I am worried that I cannot get a refund from the seller
26 Online shopping makes it easier for me to shop during social breaks
27 I have more choices when I shop online
29 I can avoid crowds when shopping online during Covid time
30 I will continue to shop online in the future
Choose a sample
Overall of this research topic are students of Banking University of Ho Chi Minh City
This study uses non-probability sampling technique with convenient sampling form to collect survey data for the following reasons:
- Firstly, this study is exploratory, so the non-probability sampling method with convenient sampling form proved to be the most suitable
When conducting surveys, students must prioritize time and cost efficiency, which is why selecting an appropriate sampling method is crucial to minimize both expenses and time spent on the study sample.
- Thirdly, this sampling method helps the researcher to easily approach the survey subjects compared to other sampling methods
However, the non-probability sampling technique method also has its limitation that the research results cannot be representative and generalizable to the study population
According to Hair, Anderson, Tatham, and Black (1998), the sample size for exploratory factor analysis is determined by two key factors: the minimum level required and the number of scales included in the model analysis.
(1) The minimum number of samples is 5 times the total number of observed variables
(2) Number of scales included in the model's analysis
If the model has m scales, n is the number of samples n=5*m
The research topic uses self-answered questionnaires to collect information from subjects to be investigated because this tool has the following basic advantages:
- Help researchers save time, cost and human resources for the survey
- The basic feature of the self-answered questionnaire is that the subject will not have to specify his identity, thus ensuring the confidentiality of personal information
- The response rate to this form of survey is usually very high
The basic steps in the questionnaire design process:
- Step 1: Based on existing theories and research papers to form the original question
- Step 2: The questionnaire is consulted with the instructor to supplement and complete with questions
- Step 3: The questionnaire is completed and the online questionnaire is designed
The study focuses on the students of Banking University of Ho Chi Minh City, utilizing self-administered questionnaires created through Google Forms Due to the extended school break, distributing physical surveys was challenging, leading to the decision to send the online questionnaire via student groups.
Once the participant completes the questionnaire and clicks the "Submit" button, the responses are recorded via the researcher's email The survey will remain open until the necessary number of samples is collected, at which point data collection will conclude.
Data collected from survey questions is coded and entered using SPSS 20.0 data analysis software to facilitate data analysis later.
Statistical data analysis technique
3.3.1 Testing the reliability of a scale
To analyze statistical data, the study used SPSS 20.0 software to test the reliability of the scale along with other inferential statistics
This research explores the reliability of scales measuring factors influencing online shopping behavior among students at Banking University of Ho Chi Minh City during the Covid pandemic, utilizing Cronbach's Alpha coefficient and factor analysis for validation.
Research by Hoang Trong and Mong Ngoc (2010) indicates that a Cronbach's alpha of 0.8 or higher signifies a good scale, while a range of 0.7 to 0.8 is considered usable Some researchers advocate for a minimum of 0.6 in certain contexts For this study, only analytical factors with a Cronbach's coefficient exceeding 0.7 will be deemed reliable and retained Additionally, only variables with total correlation coefficients greater than 0.3 will be included in the model to ensure robust reliability.
3.3.2 Testing the reliability of the model
The study employs Exploratory Factor Analysis (EFA) to validate the original hypothesis, which is derived from various sources and may contain errors EFA aims to confirm the accuracy of the model's variables and to consolidate related variables into fewer, more coherent groups of factors.
3.3.3 Correlation coefficient and regression analysis
Before performing regression analysis, it is essential to calculate the correlation coefficient matrix using SPSS 20.0 software to assess the linear relationship between the independent and dependent variables in the model This matrix not only reveals the degree of association but also identifies potential multicollinearity issues among the independent variables, allowing for model refinement and improvement.
Then, regression analysis was used with qualitative dependent variable as “Online shopping behavior”, expected independent variable as “Attitude”, “Price”, “Subjective norms”, “ Perceived behavioral control”, “Perceived risk”
The t-test is a statistical method used to determine if there is a significant difference between the mean of a single variable and a specified value, starting with the hypothesis that the mean is equal to that particular number.
ANOVA analysis is utilized to evaluate the hypothesis of equal means among sample groups, with a significance level set at 5% If the significance value (Sig) is less than or equal to 0.05, the null hypothesis (Ho) is rejected, indicating a significant difference between groups regarding the dependent variable In contrast, if Sig exceeds 0.05, there is insufficient evidence to assert a difference between the groups for the dependent variable.
Conclusion of chapter 3
This chapter aims to outline the research process and methods utilized in the study, focusing on research design and statistical data analysis techniques The research involved creating an official survey scale and questionnaire Given the exploratory nature of the topic, limited budget constraints, and the need for convenient access to research subjects, a non-probability sampling technique was employed, specifically convenient sampling through an online questionnaire distributed via direct links in group settings Subsequently, the collected data was processed and encrypted for analysis using SPSS 20.0 software.
This article explores various analytical techniques, starting with Cronbach's Alpha coefficient to assess the reliability of scales It employs factor analysis to condense related variables into fewer groups and utilizes ANOVA tests to examine the relationship between qualitative variables and buying behavior Additionally, the correlation coefficient is calculated to evaluate the linear association between independent and dependent variables within the model Finally, regression analysis is applied to quantify the relationship between two variables, enabling the determination of the impact levels of the factors and leading to the development of a proposed model.
FINDING
Data analysis
The survey process, including the distribution of questionnaires and collection of responses, took approximately three weeks Following the closure of the online questionnaire, data entry was performed, allowing subjects to select the relevant observations.
On June 7, 2021, a survey concluded with 165 samples analyzed using SPSS 20.0 software After filtering, 5 samples were excluded due to a lack of online shopping experience on e-commerce sites, resulting in 160 valid samples for further processing and analysis.
The survey participants' gender was coded and received two values (1: male; 2: female) for processing convenience
The survey participant's school year was coded and received five values as (1: year 1; 2: year 2; 3: year 3; 4: year 4; 5: other)
The survey categorized participants' majors into seven distinct values: 1 for Business Administration, 2 for Accounting – Auditing, 3 for Finance – Banking, 4 for International Economy, 5 for Information Systems Management, 6 for Economic Law, and 7 for English Language Additionally, participants' income levels were classified into five categories, ranging from 1 for no income (dependent on family) to 5 for incomes between 7-10 million VND.
Internet usage time is encrypted and gets 4 values (1: 5 years)
How many hours of Internet use in 1 day are encrypted and get four values (1: 3 hours)
Shopping sites that often buy during the Covid epidemic are encrypted and receive five values (1: Shopee, 2: Lazada, 3: Tiki, 4: Sendo, 5: Other)
When shopping online, products are typically categorized into four main groups: clothing and shoes, cosmetics, electronics (such as phones and televisions), and other items To streamline data processing, consumer attitudes, prices, subjective norms, perceived behavioral control, and perceived risk are classified into five distinct groups ranging from "Strongly disagree" to "Strongly agree."
Sample discription
This article analyzes the distribution of a sample based on several criteria, including gender, school year, major, income, and internet usage patterns such as daily hours spent online and preferred shopping sites during the Covid epidemic It also explores frequently purchased products, as well as factors influencing online shopping behavior, including attitudes, price sensitivity, subjective norms, perceived behavioral control, and perceived risk.
Frequency Percent Valid Percent Cumulative Percent
(Source: Author investigated and analyzed)
The results of Table 4.1 show that, in 160 observed samples, the sex "Female" appeared
The study reveals a gender bias, with the term "Female" appearing 93 times (58.1%) compared to "Male," which was mentioned 67 times (41.9%) This discrepancy highlights the predominance of female students at Banking University of Ho Chi Minh City, indicating a significant imbalance in gender representation within the study's sample.
Table 4 2: Statistics for school year
Frequency Percent Valid Percent Cumulative Percent
(Source: Author investigated and analyzed)
The results of Table 4.2 show that in terms of sample structure by academic year, out of
160 survey samples, there are 24 first-year students (accounting for 15%), 31 second- year students (accounting for 16.9%), and 31 third-year students (accounting for 16.9%) accounting for 16.9%), 69 fourth-year students (accounting for 43.1%) The remaining
5 people chose Other (accounting for 3.1%)
(Source: Author investigated and analyzed)
The analysis of the sample structure by discipline reveals that among the 160 survey participants, 30% are students of Business Administration, 15% are enrolled in Accounting - Auditing, 19.4% are majoring in Finance and Banking, 13.8% study International Economy, 6.3% focus on Information Systems Management, 7.5% specialize in Economic Law, and 8.1% are pursuing a degree in English Language.
No income yet, depending on family 36 22.5 22.5 22.5
(Source: Author investigated and analyzed)
The analysis of the sample structure by income reveals that out of 160 students surveyed, 22.5% (36 students) have no income and rely on their families Additionally, 29.4% (47 students) earn between 1-3 million dong, while 28.7% (46 students) have incomes ranging from 3-5 million dong Furthermore, 8.8% (14 students) earn between 5-7 million dong, and 10.6% (17 students) make 7-10 million dong The findings indicate a significant bias towards students earning 1-3 million dong, likely due to the prevalence of part-time work and internships among the student population.
Table 4 5: Statistics for How long have you been using Internet
Frequency Percent Valid Percent Cumulative Percent
(Source: Author investigated and analyzed)
The analysis in Table 4.5 reveals the Internet usage patterns among 160 students, indicating that only 1.3% have used the Internet for less than 12 months, while 7.5% have used it for 1-2 years A significant portion, 24.4%, have been online for 3-5 years, and the majority, comprising 66.9%, have over 5 years of Internet experience This trend suggests that the prevalence of early Internet access among students is a contributing factor to the high percentage of those with more than 5 years of usage.
Table 4 6: Statistics for How many hours in a day do you use the Internet
(Source: Author investigated and analyzed)
The analysis of the sample structure regarding daily Internet usage reveals that out of 160 observed students, only 1.3% (2 students) use the Internet for less than 1 hour, while 14.4% (23 students) use it for 1-2 hours, and 25.6% (41 students) for 2-3 hours Notably, a significant majority, 58.8% (94 students), spend more than 3 hours online, indicating a trend towards extensive use of social networks among students.
Table 4 7: Statistics for shopping sites do you usually buy during Covid?
Frequency Percent Valid Percent Cumulative Percent
(Source: Author investigated and analyzed)
The analysis of sample structure during the Covid epidemic reveals that among 160 students surveyed, 48.8% preferred Shopee, 41.9% chose Lazada, and 8.8% opted for Sendo, with only 0.6% selecting other platforms This indicates a significant preference for Shopee, attributed to its affordability, making it an appealing choice for students.
Table 4 8: Statistics for products do you usually buy when shopping online
(Source: Author investigated and analyzed)
Table 4.8 reveals that among 160 surveyed students, 34.4% selected clothes and shoes, making it the most popular choice, followed by cosmetics at 30.6% and electronics at 29.4% A smaller percentage, 5.6%, opted for other products This data indicates a clear preference among students for purchasing clothes and shoes due to their accessibility.
Table 4 9: Descriptive Statistics for Attitude
N Minimum Maximum Mean Std Deviation
(Source: Author investigated and analyzed)
The results of Table 4.9 show that in 160 survey participants, there is a great fluctuation in opinion about the importance of the ATTITUDE factor with the smallest point being
The analysis reveals that responses ranged from 1 (strongly disagree) to 5 (strongly agree), with the team's expectation being "positive," leading to a standard deviation in the results Despite this fluctuation, the average values for factors A1, A2, A3, A4, and A5 consistently fall between 3 and 5, indicating a trend from neutral to strongly agree This supports the original hypothesis of a positive correlation in the model, as the standard deviation remains within acceptable limits, confirming the appropriateness of the observed data.
Table 4 10: Descriptive Statistics for Price
N Minimum Maximum Mean Std Deviation
(Source: Author investigated and analyzed)
The analysis of Table 4.10 reveals significant variability in the opinions of 160 survey participants regarding the importance of the PRICE factor, with responses ranging from 1 (strongly disagree) to 5 (strongly agree) Despite this fluctuation, the average values for factors “P3, P4, P5” fall between 3 and 5, indicating a consensus from neutral to strong agreement, while factors “P1, P2” hover around an average of 3 This supports the initial hypothesis of a positive correlation in the model, as the standard deviation remains within acceptable limits, suggesting that the observed data is valid and appropriate.
Table 4 11: Descriptive Statistics for Subjective norms
N Minimum Maximum Mean Std Deviation
(Source: Author investigated and analyzed)
The analysis of Table 4.11 reveals significant variability in the opinions of 160 survey participants regarding the importance of the SUBJECTIVE NORMS factor, with responses ranging from 1 (strongly disagree) to 5 (strongly agree) Despite this fluctuation, the team's expectation remains positive, resulting in a standard deviation that indicates the data's reliability The average values for factors SN1, SN2, SN3, SN4, and SN5 cluster around 3 to 5, suggesting a consensus from neutral to strong agreement Consequently, the original hypothesis of a positive correlation in the model is upheld, confirming that the standard deviation falls within acceptable limits and the observed data is valid.
Table 4 12: Descriptive Statistics for Perceived behavioral control
N Minimum Maximum Mean Std Deviation
(Source: Author investigated and analyzed)
Table 4.12 reveals significant variability in the opinions of 160 survey participants regarding the importance of the PERCEIVED BEHAVIORAL CONTROL factor, with responses ranging from 1 (strongly disagree) to 5 (strongly agree) Despite the team's expectation of a "positive" outcome, this variability resulted in a notable standard deviation, indicating diverse perceptions among respondents.
The analysis of variables "PBC4" and "PBC5" indicates that they cluster around an average value of 3-5, reflecting a range from neutral to strong agreement In contrast, "PBC1," "PBC2," and "PBC3" exhibit average values closer to 3 This supports the initial hypothesis of a positive correlation in the model, as the standard deviation remains within acceptable limits, confirming the appropriateness of the observed data.
Table 4 13: Descriptive Statistics for Perceived risk
N Minimum Maximum Mean Std Deviation
(Source: Author investigated and analyzed)
The analysis of Table 4.13 reveals significant variation in opinions regarding the importance of the PERCEIVED RISK factor among 160 survey participants, with responses ranging from 1 (strongly disagree) to 5 (strongly agree) Despite this fluctuation leading to a standard deviation, the average values for factors PR1, PR2, PR3, PR4, and PR5 consistently fall between 3 and 5, indicating a general consensus from neutral to strong agreement Consequently, the original hypothesis of a positive sign in the model remains supported.
Table 4 14: Descriptive Statistics for Online shopping behavior
N Minimum Maximum Mean Std Deviation
(Source: Author investigated and analyzed)
The analysis of Table 4.14 reveals significant variability in the opinions of 160 survey participants regarding the importance of the Online Shopping Behavior (OSB) factor, with ratings ranging from 1 (strongly disagree) to 5 (strongly agree) Despite this fluctuation, which resulted in a standard deviation, the average ratings for OSB factors OSB1, OSB2, OSB3, OSB4, and OSB5 consistently fall between 3 and 5, indicating a general consensus from neutral to strong agreement Consequently, the original hypothesis suggesting a positive relationship in the model remains validated.
Reliability analysis and appropriate scale
The study on "Factors Affecting Online Shopping Behavior of Banking University Students During the Covid-19 Pandemic" identifies five key research factors, each encompassing multiple aspects derived from their definitions and prior research Since different models select various indices for these factors, it is crucial to assess the reliability of the scale used To ensure accuracy, the study evaluates the reliability through two methods: Cronbach's Alpha coefficient and coefficient analysis.
4.3.1 Testing of scale reliability by Cronbach's alpha coefficient
The results of testing the scale of the factors in the research model according to Cronbach's Alpha coefficient are presented in detail as follows:
Table 4 15: Reliability Statistics for Attitude
(Source: Author investigated and analyzed)
The findings from Table 4.15 indicate a total Cronbach's Alpha coefficient of 0.858, falling within the reliable range of 0.8-0.89 Additionally, all component scales exhibit correlation coefficients exceeding the minimum standard of 0.3 Consequently, the ATTITUDE variable, comprised of five component scales (A1, A2, A3, A4, A5), demonstrates high reliability and will be utilized for further in-depth analysis in the subsequent section.
Table 4 16: Reliability Statistics for Price
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
(Source: Author investigated and analyzed)
The analysis presented in Table 4.16 indicates that the total Cronbach's Alpha coefficient is 0.652, which falls below the acceptable range of 0.7-0.79, suggesting low reliability Additionally, the correlation coefficient among the variables is not significant, and removing any scale related to this variable does not increase the Cronbach's Alpha above 0.7 Consequently, the PRICE variable is deemed unreliable and subjective, making it unsuitable for inclusion in the research model, necessitating its removal.
Table 4 17: Reliability Statistics for Subjective norms
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
(Source: Author investigated and analyzed)
The findings from Table 4.17 indicate a total Cronbach's Alpha coefficient of 0.818, falling within the reliable range of 0.8-0.89 Furthermore, the correlation coefficients for all component scales exceed the minimum threshold of 0.3 Consequently, the SUBJECTIVE NORMS variable, comprising five component scales (SN1, SN2, SN3, SN4, SN5), demonstrates high reliability and will be utilized for further analysis in the subsequent section.
Table 4 18: Reliability Statistics for Perceived behavioral control first time
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
(Source: Author investigated and analyzed)
The results of Table 4.18 show that the total Cronbach's Alpha coefficient is 0.705 in the range 0.7-0.79 However, the correlation coefficient of the scale PBC4 = 0.271 and PBC5
The current Cronbach's Alpha coefficient of 0.185 falls below the minimum standard of 0.3 Analysis of the component scale indicates that removing items PBC4 and PBC5 from the PERCEIVED BEHAVIORAL CONTROL variable will enhance the overall Cronbach's Alpha, increasing it from 0.705 to 0.721 and 0.705 to 0.748 Therefore, it is essential to eliminate PBC4 and PBC5 and conduct a reliability test on the revised PERCEIVED BEHAVIORAL CONTROL scale.
Table 4 19: Reliability Statistics for Perceived behavioral control second time
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
(Source: Author investigated and analyzed)
The total Cronbach's Alpha coefficient is 0.872, indicating high reliability, as it falls within the range of 0.8 to 0.89 Furthermore, the correlation coefficients of all component scales exceed the minimum standard of 0.3 Consequently, the PERCEIVED BEHAVIORAL CONTROL variable, derived from the four component scales PBC1, PBC2, and PBC3, is deemed reliable for further in-depth analysis.
Table 4 20: Reliability Statistics for Perceived risk
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
(Source: Author investigated and analyzed)
The analysis presented in Table 4.20 reveals a total Cronbach's Alpha coefficient of 0.928, indicating high reliability, as it falls within the range of 0.9 to 1 Furthermore, all component scales exhibit correlation coefficients exceeding the minimum threshold of 0.3 Consequently, the PERCEIVED RISK variable is comprised of five reliable component scales—PR1, PR2, PR3, PR4, and PR5—which will be utilized for further in-depth analysis in the subsequent section.
Table 4 21: Reliability Statistics for Online shopping behavior
Scale Mean if Item Deleted
Scale Variance if Item Deleted
(Source: Author investigated and analyzed)
The analysis presented in Table 4.21 indicates that the total Cronbach's Alpha coefficient is 0.804, falling within the reliable range of 0.8 to 0.89 Furthermore, all component scales exhibit correlation coefficients exceeding the minimum standard of 0.3 As a result, the ONLINE SHOPPING BEHAVIOR scale, which comprises five components (OSB1, OSB2, OSB3, OSB4, OSB5), demonstrates high reliability and will be utilized for further in-depth analysis in the subsequent section.
Factor analysis for independent variables
Table 4 22: KMO coefficient and 1st Bartlett's test
Kaiser-Meyer-Olkin Measure of Sampling
(Source: Author investigated and analyzed)
The KMO coefficient of the model is 0.776, exceeding the acceptable threshold of 0.5, and the Bartlett test confirms significance at the 0.000 level Thus, the factor analysis conducted for the research model is deemed appropriate, indicating that the selected variables are valuable for study.
Table 4 23: Eigenvalues and % explaining factors first time
(Source: Author investigated and analyzed)
The factor analysis model identified four factors with Eigenvalues exceeding 1, collectively accounting for 70.819% of the variance, which confirms the model's validity in alignment with the original hypothesis Consequently, the research model comprises four independent variables.
Table 4 24: Convergence coefficient of factors forming first time
(Source: Author investigated and analyzed)
Table 4.24 demonstrates that following the Varimax factor rotation, 18 observed variables (scales) have successfully grouped into converging clusters, with all values exceeding the minimum threshold of 0.5, indicating a strong correlation among the variables The observed variables are organized in a manner that reflects the order of their individual values.
Table 4 25: Discriminant value between observed variables first time
(Source: Author investigated and analyzed)
Table 4.25 indicates that the majority of discriminant values for the observed variables surpass the minimum threshold of 0.3, with the exception of variable SN5, which has a discriminant coefficient below this limit and is therefore excluded The analysis will proceed with a second exploratory factor analysis utilizing the remaining 17 variables.
Table 4 26: KMO coefficient and 2 nd Bartlett's test
Kaiser-Meyer-Olkin Measure of Sampling
(Source: Author investigated and analyzed)
The results indicate that the KMO coefficient of the model is 0.776, exceeding the acceptable threshold of 0.5, and the Bartlett test confirms significance at a level of 0.000 This suggests that factor analysis is suitable for the research model, validating the relevance of the selected variables.
Table 4 27: Eigenvalues and % explaining factors second time
(Source: Author investigated and analyzed)
The factor analysis model identified four factors with Eigenvalues exceeding 1, collectively accounting for 71.836% of the variance, which supports the original hypothesis Thus, the research model is validated with four independent variables.
Table 4 28: Convergence coefficient of factors forming second time
(Source: Author investigated and analyzed)
Table 4.28 demonstrates that after applying the Varimax factor rotation method, 17 observed variables have successfully grouped into converging clusters, with all values exceeding the minimum standard of 0.5, indicating strong correlations among the variables The convergence of these observed variables occurred in a sequential manner, reflecting the order of each variable.
Table 4 29: Discriminant value between observed variables second time
(Source: Author investigated and analyzed)
Summary of research results
After conducting quantitative research with a sample of 160 and analyzing the data using SPSS software, we tested the reliability of the scale through Cronbach's Alpha method The variables "Price" (P) and “Perceived Behavioral Control” (PBC4, PBC5) did not meet the required standards and were subsequently disqualified As a result, we retained four significant factors: attitude, subjective norms, perceived behavioral control, and perceived risk, which consist of a total of 18 observed variables.
The exploratory factor analysis revealed that the reliability coefficient for "Subjective Norms 5 (SN5)" was inadequate, leading to its rejection Additionally, the correlation analysis indicated that the variable "Perceived Risk" was also unsatisfactory Consequently, three factors remained: attitude, subjective norms, and perceived behavioral control, comprising a total of 12 observed variables Following this, regression analysis and ANOVA were conducted, yielding the subsequent results.
- The model fits and explains 25.9% of the variation in online shopping behavior of students of Banking University in Ho Chi Minh City
The study identifies that "subjective norms" significantly influences online shopping behavior among Banking University students during the Covid-19 pandemic, followed by attitude and perceived behavioral control The final research model comprises one dependent factor—online buying behavior—and three independent factors: attitude, subjective norms, and perceived behavioral control, measured through twelve observed variables.
Research indicates that three key factors influence the online shopping behavior of Banking University students during the Covid-19 pandemic: attitude, subjective norms, and perceived behavioral control Among these, subjective norms play the most significant role, accounting for 40.3% of the impact on online shopping behavior, as students tend to prioritize reviews and opinions from previous buyers Overall, all three factors—attitude, subjective norms, and perceived behavioral control—positively affect the online shopping habits of students during this period.