Factors affecting the decision to hunt for sales on e commerce platforms of students in hanoi

Factors affecting the decision to hunt for sales on e commerce platforms of students in hanoi Factors affecting the decision to hunt for sales on e commerce platforms of students in hanoi

INTRODUCTION

Research background

In the digital age of 4.0, the rapid advancement of technology has significantly transformed e-commerce, establishing it as a crucial economic sector that profoundly influences global consumer shopping behavior.

Figure 1.1: Retail e-commerce sale worldwide from 2014 to 2024

Global e-commerce retail sales have experienced significant growth from 2014 to 2024, with total revenue rising from approximately 1,336 billion USD in 2014 to an estimated 6,388 billion USD by 2024 This trend reflects a notable shift from traditional shopping to e-commerce, driven by advancements in digital technology and changing consumer behavior Key factors contributing to this increase include the rise of popular e-commerce platforms like Amazon, Alibaba, and Shopee, as well as the convenience of online payment methods and improved logistics services Notably, during the period of 2020-2021, global e-commerce revenue surged from 4,280 billion USD to 4,891 billion USD, largely due to the COVID-19 pandemic, which compelled consumers to embrace online shopping.

E-commerce is experiencing rapid growth in Vietnam, with projections indicating that by 2030, major e-commerce platforms will represent nearly 50% of the nation's total e-commerce revenue, reaching an impressive $60 billion This trend underscores their pivotal role in driving Vietnam's exponential e-commerce expansion.

Figure 1.2:Annual gross merchandise value (GMV) of the internet economy in Vietnam from 2015 to 2023

The internet economy has experienced remarkable growth, increasing tenfold from 2015 to 2023 and projected to rise over fourteen times by 2025 In this dynamic landscape, e-commerce platforms like Shopee, Lazada, TikTok Shop, and Tiki have evolved beyond mere shopping channels to become intense competitive arenas, particularly during major promotional events such as 11/11, 12/12, and Black Friday.

In Vietnam, online shopping behavior has notably evolved, particularly among students who are tech-savvy and keen on finding sales to maximize their spending A report by Kantar Worldpanel (2023) indicates that over 85% of Gen Z, including students, engage in at least one online shopping transaction monthly, with 60% actively seeking deals during major promotional events This trend highlights the significance of promotions not just as a sales tactic but as a key influencer of consumer behavior Despite this, there is a lack of comprehensive research on the factors influencing students' online shopping decisions, especially in Vietnam Hanoi, as the country's primary economic and educational hub, features a high concentration of students and a robust e-commerce landscape, housing 97 universities and 33 colleges, which together represent 40% of the national student population (Ministry of Education & Training, 2023).

In 2023, 75% of Vietnamese students favor online shopping over traditional methods, often waiting for promotional events to make purchases This trend highlights Hanoi as a promising market for e-commerce platforms and a valuable location for studying students' sales hunting behavior Despite the strong allure of promotional programs, not all students engage in sales hunting, prompting an investigation into the factors that influence their decisions to seek out sales on e-commerce platforms in Hanoi.

Problem Statement

Understanding the factors that drive sales hunting is crucial for e-commerce businesses aiming to enhance marketing strategies and boost conversion rates As web technology and digital marketing evolve, consumers gain greater control over their shopping experiences Nonetheless, online shopping behavior remains shaped by various elements, including price perception, risk, usefulness, and trust in the e-commerce platform This is particularly relevant for students, who actively seek discounts while weighing multiple factors before finalizing their purchase decisions.

Despite numerous studies on online shopping behavior, in-depth research on the sales hunting behavior of students in Vietnam remains scarce Hanoi, home to over 40% of the nation's students (Ministry of Education and Training, 2023), represents a significant market that has yet to be thoroughly examined regarding this demographic's motivation for seeking sales Most existing research primarily addresses factors influencing online shopping behavior, with limited focus on students utilizing vouchers Additionally, previous studies have largely concentrated on aspects such as price, convenience, and attitude in e-commerce, neglecting critical factors like social influence, risk perception, and technology that may impact sales hunting decisions Furthermore, the uneven development of e-commerce across different regions highlights a crucial research gap that warrants further investigation (World Bank, 2021).

This study investigates the key factors that influence Hanoi students' decisions to seek sales on e-commerce platforms, emphasizing perceived usefulness, ease of use, price sensitivity, financial motivation, social influences from friends and family, product reviews, and risk perceptions related to counterfeit goods, product quality, and personal information security By analyzing these elements, the research enhances the understanding of online consumer behavior among students and offers valuable insights for e-commerce platforms to develop effective strategies aimed at attracting and retaining young customers in the digital landscape.

Research Objectives

This study aims to explore the significance of students' decisions to seek out sale items and to identify the factors influencing their use of vouchers while shopping on e-commerce platforms in Hanoi.

Measure the level of intention to use vouchers when shopping on e-commerce platforms of Hanoi students

Identify factors affecting Hanoi students' decision to hunt for items on e-commerce platforms

Consumer intention significantly influences the success of e-commerce platforms, particularly in competitive markets like Hanoi To effectively attract students, businesses should implement targeted discount strategies that resonate with this demographic Offering student-specific promotions, loyalty programs, and seasonal discounts can enhance engagement and drive sales Additionally, leveraging social media and online marketing can further amplify these efforts, ensuring that the discounts reach the intended audience effectively.

Research Question

Previous research by Ajzen (1991), Davis (1989), and Venkatesh & Bala (2008) highlights the significance of understanding online consumer behavior This study focuses on identifying the factors that influence students in Hanoi to seek out sales on e-commerce platforms It specifically examines the role of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) in attracting students to these platforms.

Perceived Risk (PR), Price (P), Attitude (AT), and Subjective Norms (SN) play a crucial role in shaping online shopping decisions This study seeks to offer recommendations for e-commerce platforms to enhance their strategies and effectively attract young customers, particularly students in Hanoi.

LITERATURE

Theoretical framework

2.1.1 Concept of sale hunting behavior

Sale hunting, or "bargain hunting," involves consumers actively seeking discounts and promotions to purchase products at lower prices According to Merriam-Webster, a "bargain hunter" is someone who shops specifically for bargains Gentry & Pesendorfer (2021) describe bargain hunters as individuals whose willingness to pay is influenced by the perceived benefits of lower prices, highlighting that this behavior is driven not only by cost savings but also by the positive emotions associated with finding a "good deal." Collins Dictionary further defines bargain-hunting as purchasing items at discounted prices, particularly during major shopping events like Black Friday and Cyber Monday Consumers engaged in sale hunting are motivated to save money and optimize their budgets, actively seeking information on promotions, comparing prices, and assessing product quality The rise of social networks and online shopping apps has made it easier to share information about deals, further encouraging sale hunting behavior Understanding this behavior is crucial for studying online shopping habits, particularly among students interested in sales hunting activities.

2.1.2 Concept of online shopping behavior

Online shopping is a form of electronic commerce that enables consumers to buy products or services directly from sellers via the Internet, utilizing web browsers or shopping applications It involves a process where consumers shop through online stores or e-commerce websites, allowing transactions without visiting physical stores This behavior encompasses the entire journey of searching, selecting, and purchasing items online The study will explore online shopping behavior as influenced by technological, economic, and social factors With the rapid growth of e-commerce, online shopping is particularly popular among students, who often prefer digital platforms to maximize savings and take advantage of incentives Understanding these behaviors can reveal the factors that influence shopping habits on e-commerce platforms, especially among students seeking sales in Hanoi.

The Technology Acceptance Model (TAM), created by Davis in 1989, is grounded in the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB) to forecast users' acceptance and utilization of information technology This widely recognized model elucidates how individuals engage with and implement new technologies in their personal and professional lives TAM highlights the significance of psychological factors that affect the intention to adopt technology, concentrating on the end-user experience to assess the willingness to embrace a technological system.

The Technology Acceptance Model (TAM) identifies two key factors influencing technology adoption: perceived usefulness (PU) and perceived ease of use (PEOU) Perceived usefulness indicates users' belief that technology enhances productivity and efficiency, while perceived ease of use reflects how accessible and manageable the technology is These factors shape users' attitudes, intentions, and actual usage behaviors A study by Bauerová and Klepek (2018) demonstrates that TAM effectively elucidates consumer behavior in online shopping.

The TAM model effectively explains students' shopping behavior on e-commerce platforms, highlighting two key factors that influence their participation in promotions: the perceived usefulness of e-commerce, which relates to their belief that online shopping saves money and enhances their experience, and the perceived ease of use, which pertains to how simple they find searching for products, using coupons, and placing orders A user-friendly e-commerce platform that offers practical value is likely to encourage students to engage more actively in seeking out deals.

2.1.4 Theory of Planned Behavior (TPB)

The Theory of Planned Behavior (TPB), developed by Ajzen in 1991, builds upon the earlier Theory of Reasoned Action (TRA) by Fishbein and Ajzen from 1975, to provide a more comprehensive understanding of the factors influencing behavioral intentions While TPB retains the TRA's focus on attitude toward the behavior and subjective norms, it introduces a crucial third component: perceived behavioral control This factor reflects individuals' beliefs in their ability to perform a behavior, taking into account any barriers or facilitating conditions Ultimately, TPB posits that behavioral intention is the key predictor of actual behavior, shaped by these three essential components: attitude, subjective norm, and perceived behavioral control.

This research adapts the Theory of Planned Behavior (TPB) to examine how students' attitudes toward sales hunting are shaped by cognitive factors such as Perceived Usefulness, Perceived Ease of Use, and Perceived Risk These factors influence students' behavioral beliefs, leading them to evaluate the benefits and drawbacks of engaging in sales hunting When students view sales hunting as cost-effective, accessible, and low-risk, they are more likely to develop positive attitudes and intentions to participate Additionally, the subjective norm reflects the social pressure students experience from peers and the community, which can encourage them to engage in sales hunting when they observe support from those around them Although the study does not directly address behavioral control, price is a significant factor that can facilitate this behavior; students with limited budgets are more inclined to hunt for sales when prices are attractive and promotions appealing.

The Theory of Planned Behavior (TPB) effectively explains students' behavior in seeking promotions on e-commerce platforms By incorporating additional cognitive factors into the TPB, the research model becomes more comprehensive and accurately represents actual behavior in today's digital landscape.

Keys Concept

Perceived usefulness is defined as the extent to which an individual believes that using a particular system will improve their performance or enhance their work efficiency (Davis,

In the realm of online shopping, consumers perceive that it offers significant time savings, minimizes effort, and enables transactions to be conducted at any hour (Hasslinger et al., 2007).

Numerous studies indicate that online shopping provides significant advantages over traditional methods, including quick information searches, online ordering, and home delivery, which collectively save customers valuable time (Darian, 1987; Burke, 1997) Additionally, the online shopping process not only accelerates product searches but also lowers transaction costs, allowing consumers to easily find the right products and explore a wider variety of goods compared to in-store shopping, which can be more time-consuming and expensive (Moshref Javadi et al., 2012).

Research indicates that when individuals recognize the practical advantages of online shopping, their motivation to engage with this platform increases Studies by Koufaris (2002) and Gefen & Straub (2000) highlight that perceived usefulness significantly predicts online shopping intentions Specifically, among Hanoi students interested in purchasing discounted products, the impact of perceived usefulness on their online shopping behavior is evident.

The Technology Acceptance Model (TAM) posits that consumers believe using technology is easy, which positively influences their perceived usefulness and purchase intentions Research indicates that perceived ease of use significantly impacts online shopping behavior, as consumers are more likely to engage with e-commerce platforms that offer user-friendly interfaces and quick adaptability Furthermore, studies reveal that an easy-to-navigate system enhances customer confidence in online shopping, leading to increased service usage and motivating students to make more purchasing decisions on e-commerce sites.

A study by Xiang Yan and Shiliang Dai (2009) identifies two key factors influencing online shopping decisions: perceived benefits and perceived risks While perceived benefits encourage shopping behavior, perceived risks tend to diminish purchase intentions High perceived risks lead consumers to hesitate and reduce their likelihood of engaging in online shopping, whereas lower perceived risks significantly boost purchase intentions.

Previous research indicates that perceived risk encompasses the anxiety and uncertainty associated with online shopping Many consumers remain reluctant to make purchases due to concerns over privacy and financial security, fearing the potential exposure of personal information or theft of credit card details Additionally, the inability to physically inspect product quality before buying further contributes to customer hesitation.

2002) Incomplete product information from the seller further increases suspicion

Customers often face concerns during the delivery process, including slow shipping, challenges with returns, and unexpected extra shipping fees despite existing return policies These issues can result in significant time, effort, and financial costs for consumers, particularly impacting students' willingness to seek out sales on e-commerce platforms.

Research by Pham Thi Thuy Mien (2021) indicates that reasonable pricing and appealing promotions significantly enhance online shopping intentions, particularly during the COVID-19 pandemic Additionally, Thanh and On (2021) highlight that price is a crucial factor influencing online shopping intentions among Generation Z consumers in Vietnam When consumers perceive a product's price as fair relative to its quality, their likelihood of purchasing online increases Students, in particular, actively seek discounts, promotions, and flash sales to optimize their savings, as these incentives not only make products more affordable but also serve as psychological motivators for their purchasing decisions.

Price perception is crucial in shaping consumers' understanding of a product and its value, significantly impacting their purchase decisions, particularly for frequently bought items It also affects consumers' preferences for stores, products, and brands (Faith & Agwu, 2014) Consequently, this study will examine how price positively influences the online purchasing behavior of consumers who are inclined to buy discounted products.

Attitude is a psychological tendency shaped by learning, influencing how individuals consistently respond to specific behaviors (Fishbein & Ajzen, 1975) In the context of consumer behavior, particularly in e-commerce, students' attitudes reflect their overall evaluation of online shopping, including their engagement with promotions and discounts The theory of reasoned action (TRA) and the theory of planned behavior (TPB) highlight the importance of attitude in forming behavioral intentions A positive attitude towards shopping leads students to perceive promotional shopping as cost-effective and rewarding, significantly increasing their likelihood of participating in such activities (Ajzen, 1991).

Attitudes significantly mediate the relationship between technology awareness and shopping behavior Research by Dam & Cuong (2021) indicates that students are more inclined to engage in sales hunting when they have positive experiences with e-commerce platforms, perceiving them as convenient, useful, and safe These favorable evaluations foster a positive attitude towards sales hunting, which directly influences purchasing behavior As young consumers increasingly focus on optimizing their spending, their attitudes towards sales hunting not only play a crucial role in purchasing decisions but also reflect the consumer psychology of students who prioritize meaningful shopping experiences and tangible benefits.

Subjective norm (SN) refers to an individual's beliefs about whether significant others think they should or should not engage in a particular behavior (Fishbein & Ajzen, 1975)

In online shopping, subjective norms refer to the impact of social influences, including family, friends, peers, media, and online reviews, on consumers' purchasing decisions.

Research indicates that influential figures significantly shape consumers' online purchasing intentions According to Senecal and Nantel (2002), reference sources directly impact online shopping behaviors, as individuals often align their actions with the expectations of admired figures or their reference groups (McClelland, 1987) Additionally, Othman and Sudarmin (2022) highlighted a positive and significant relationship between subjective norms and the intention to utilize online shopping platforms Therefore, it is essential to consider subjective norms as a key factor influencing the online shopping behaviors of students seeking sales.

Literature matrix

Conceptual framework

Based on the underlying theories such as Davis's Technology Acceptance Model (TAM)

This study develops a research model to meet its initial objectives by integrating Ajzen's Theory of Planned Behavior (TPB) from 1991 with foundational theories from 1989, alongside insights from previous studies by Nguyen & Pham (2015) and Dam & Cuong (2021), as well as feedback from instructors.

" Factors affecting sales hunting of students on e-commerce platforms in Hanoi."

The research model, as illustrated in Figure 2.2 and sourced from Nguyen & Pham (2015) and Dam & Cuong (2021), highlights six essential factors that can impact the online shopping behavior of students, particularly those interested in utilizing vouchers during their purchases.

The advancement of information technology has led to the development of user acceptance theories, notably the Technology Acceptance Model (TAM) introduced by Davis in 1989 This model highlights perceived usefulness and ease of use as critical factors influencing technology acceptance TAM has been extensively utilized across various fields, including websites and applications, making it a favored framework for assessing consumer acceptance of new technologies In the realm of e-commerce, the concept of usefulness is evident in how platforms assist consumers, particularly students, in finding promotions, comparing prices, gathering discount codes, and efficiently completing orders, ultimately saving both time and money.

Numerous empirical studies indicate a strong positive correlation between perceived usefulness and consumer attitudes Research by Nguyen & Do (2020) and Ha et al (2019) demonstrates that perceived usefulness significantly influences users' attitudes toward online shopping platforms Additionally, Tan & Giang (2023) highlight that usefulness is the most impactful factor affecting consumers' purchase decisions on platforms like Shopee A survey conducted by Nguyen et al (2025) involving 250 consumers who bought electronic goods online revealed that users develop more favorable attitudes when they recognize the practical benefits of a platform Furthermore, students are inclined to adopt positive attitudes toward sale hunting behaviors when they perceive e-commerce platforms as valuable tools for finding deals and managing their budgets effectively Thus, this study posits that perceived usefulness plays a crucial role in shaping consumer attitudes.

H1: Perceived usefulness has a positive impact on students' attitude toward sales hunting on e-commerce platforms

Perceived Ease of Use refers to how effortless a technology is for consumers, as defined by Davis (1989) In his technology acceptance model, ease of use significantly influences users' perceptions of a technology's usefulness In the e-commerce sector, this encompasses user-friendly interfaces, effective search filters, streamlined checkout processes, and promotional support For students, who are tech-savvy yet often impatient with complicated procedures, ease of use fosters a more favorable attitude towards online shopping.

Numerous studies highlight the significant impact of ease of use on online shopping behaviors Jadhav & Khanna (2016) found that a user-friendly platform greatly influences students' shopping attitudes, while Sajid et al (2022) emphasized its importance in driving purchase intentions during the pandemic, when consumers prioritize simplicity and speed Although Vongurai (2020) suggests that ease of use may not always directly affect behavior, it still shapes attitudes, particularly in sales contexts where quick decision-making is crucial When users perceive easy access to deals or discount codes, they tend to develop more positive feelings towards their shopping experiences Therefore, this study hypothesizes that ease of use plays a critical role in shaping consumer behavior.

H2: Perceived ease of use has a positive impact on students' attitude toward sales hunting on e-commerce platforms

Perceived risk in online shopping encompasses consumers' worries regarding transaction security, product quality, and personal data privacy (Bhatnagar et al., 2000) Those who perceive a high level of risk are often hesitant to seek out sales due to fears of scams or purchasing counterfeit or low-quality products (Kim et al., 2008) Additionally, students with limited income and less shopping experience are particularly sensitive to these risks, especially when considering discounted or flash sale items from lesser-known sellers.

Perceived risk significantly negatively affects students' online purchase attitudes and intentions, as highlighted by Suharyati et al (2021) Similar findings by Rachbini (2018), Khairunnisa et al (2018), and Tanadi et al (2015) indicate that when consumers feel unsafe during transactions or question product quality due to steep discounts, they develop negative shopping attitudes In the pursuit of sales, unexpectedly low prices often trigger a cautious mindset among students, leading to concerns about being "trapped" and ultimately diminishing their willingness to engage in sales-hunting behavior.

H3: Perceived risk has a negative impact on students' attitude toward sales hunting on e-commerce platforms

Price significantly impacts purchasing decisions, particularly among price-sensitive groups like students Research by Kotler & Keller (2016) indicates that students often wait for discounts to manage their budgets effectively Furthermore, a study by Ibrahim et al (2023) reveals that price is the most influential factor in young people's online shopping behavior Makhitha and Ngobeni (2021) found that deeply discounted products not only fulfill students' desire to save money but also provide a sense of achievement and intelligence in their decision-making Their research with 250 consumers highlights that successful sale hunting fosters pride and reduces perceived risk Consequently, it is essential to evaluate the influence of price on students' online shopping behavior, leading to the hypothesis of this study.

H4: Price has a positive impact on students’ sales hunting purchase behavior on e- commerce platforms

Attitude is a learned and stable tendency to respond positively or negatively toward a specific object or behavior, as defined by Fishbein and Ajzen (1975) In consumer behavior, an individual's attitude represents their overall evaluation of participating in a particular purchase, including aspects related to promotions and discounts, according to Kotler and Keller.

College students are typically budget-conscious and price-sensitive, leading to a favorable view of price promotions as a means to enhance consumption value (Kotler & Keller, 2016) This perspective is bolstered by their perception of cost savings and the excitement of "getting a bargain," which encourages repeat shopping Furthermore, research by Makhitha and Ngobeni (2021) indicates that students experience satisfaction and happiness while seeking attractive deals, fostering a positive emotional response that increases their likelihood of continued bargain hunting.

Positive attitudes towards online promotions significantly influence consumers' motivation and intention to engage in sales hunting behavior When students view sales hunting as a source of financial benefits, entertainment, or social recognition, they are more inclined to participate in online promotional events Consequently, these positive attitudes serve as a crucial predictor of actual sales hunting behavior on e-commerce platforms This study hypothesizes that these factors play a vital role in shaping consumer engagement in online promotions.

H5: Attitude has a positive impact on students’ sales hunting purchase behavior on e-commerce platforms

Subjective norms refer to the influence of social relationships, including friends, family, and online reviews, on an individual's purchase decision (Ajzen, 1991) Limayem et al.'s

A 2000 study highlighted the significant impact of peer recommendations and social influence on consumer behavior Dam (2023) examined 300 students at Ho Chi Minh City University of Industry and found that subjective norms positively influence online shopping intentions When students perceive support from friends, family, and significant others regarding sales, they are more likely to engage in shopping to feel connected to their social group Similarly, Nguyen et al (2021) studied 385 university students in Hanoi and confirmed that subjective norms positively affect online shopping behavior The findings revealed that students often seek advice from friends about sales and are motivated to participate in shopping to maintain social relationships and a sense of belonging Thus, the influence of peers and relatives plays a crucial role in driving students' purchasing decisions on e-commerce platforms.

H6: Subjective norm has a positive impact on students’ sales hunting purchase behavior on e-commerce platforms.

METHODOLOGY

Research Flowchart

The research process begins by clearly defining the research objectives, which is essential for orienting the study Following this, a thorough investigation of the theoretical framework related to the topic is conducted to establish a robust foundation for the research hypotheses and models A theoretical model is then developed, incorporating factors that influence the research problem Finally, consultations with field experts validate the accuracy and applicability of the theoretical model and its components.

Following the development of the theoretical model, the research will create survey questions that align with the identified factors Crafting precise survey questions is crucial for gathering meaningful data from participants Prior to large-scale data collection, the study will pilot the survey with a small group to verify the clarity and relevance of the questions After refining the survey based on feedback, data collection will commence with the research subjects.

After gathering sufficient data, the study will assess the validity of the variables and their interrelationships The analysis results will enable the research team to draw significant conclusions, address the initial research questions, and offer insights into the research problem This phase wraps up the research process and supplies essential information for practical applications or guidance for future studies.

Research Data

Secondary data is crucial for analyzing the factors influencing students in Hanoi when hunting for sales on e-commerce platforms, serving as a foundation for research knowledge This data, sourced from scientific articles, industry reports, and reputable studies, aids in identifying research gaps and formulating research questions, as highlighted by Saunders, Lewis, and Thornhill (2016) Additionally, it offers a cost-effective means to understand broader market trends and consumer consumption patterns, thereby enhancing the primary research process (Bryman, 2012) Ultimately, secondary data not only provides a comprehensive view of consumer behavior but also enriches subsequent research stages.

Primary data is crucial for gaining insights into specific research areas, as it offers direct answers to research questions In this study, utilizing surveys to collect primary data yields current information on student preferences and consumption behaviors, enabling informed adjustments to strategic decisions (Creswell, 2014).

Utilizing primary data enables a mixed research approach that integrates both quantitative and qualitative methods, offering a holistic perspective on consumer interactions with e-commerce platforms Collecting real-time data is particularly beneficial in today's dynamic market, as it aids businesses in comprehending customer behavior and making informed decisions that align with current consumption trends.

Research method

In researching the factors influencing Hanoi students' decisions to hunt for sales on e-commerce platforms, an online survey was utilized as the primary data collection method This approach enables the gathering of data from diverse groups, leveraging the widespread availability of the internet, which not only saves time but also enhances efficiency.

The survey was meticulously crafted with a pre-testing phase to ensure clarity and appropriateness, following the guidelines of Dillman, Smyth, and Christian (2014) It was distributed through social networks, email, and online forums, utilizing both convenience and chain sampling methods to enhance response rates Ethical considerations were prioritized, ensuring participant anonymity, voluntary participation, and confirmed consent prior to survey completion Data analysis involved statistical software for quantitative questions and qualitative methods for open-ended responses.

This method, drawing on the work of Creswell (2014) and Dillman et al (2014), offers valuable insights into student shopping behavior during promotional searches, enabling businesses to develop more effective marketing strategies tailored to this demographic.

Sampling

In statistical research, a population refers to the complete set of subjects from which data is gathered to meet research goals This population may consist of individuals, events, organizations, or any relevant elements pertaining to the research topic.

In statistics, the term "population" encompasses not only individuals residing in a specific area at a given time but also any group of data relevant for drawing research conclusions.

This study focuses on students in Hanoi who utilize shopping vouchers Hanoi, recognized as a major educational hub in Vietnam, hosts approximately 40% of the nation's student population (Ministry of Education & Training, 2023) This demographic is characterized by their high technological engagement, frequent use of e-commerce platforms, and keen interest in online promotions and discounts.

A sampling frame is a comprehensive list of individuals or elements within a population that researchers aim to analyze, serving as the foundation for selecting a representative sample It may include specific characteristics, such as age or other relevant factors, tailored to the research objectives A well-defined sampling frame is crucial for ensuring that the sample is representative and unbiased, thereby enhancing the generalizability of the research findings The quality of the sampling frame significantly impacts the validity and reliability of the study In this research, the sampling frame consisted of students interested in hunting for sales on e-commerce platforms.

This study utilized a convenience sampling method, selecting participants based on accessibility and willingness rather than employing random or systematic sampling techniques (Leiner, 2014).

This method is efficient and economical, facilitating a swift and convenient data collection process However, the convenience sampling method has limitations, as the data gathered may be biased and may not accurately represent the entire population, which can impact the study's overall representativeness.

This study utilized a random sampling method to evaluate the proposed hypotheses, achieving confidence levels of 90% and 95% through the Yates method (Box et al., 1987) To guarantee that the sample accurately represents the overall student population in Hanoi, it is crucial to establish a sufficiently large sample size (Smith et al., 2016).

This study focuses on the student population in Hanoi, which is projected to reach approximately 800,000 by 2025 Research by La and Le (2022) indicates that around 88% of these students have engaged in online shopping, with the Shopee e-commerce platform being their top choice Notably, 60% of these students actively seek discounted products during significant promotional events, according to Kantar Worldpanel.

The sample size was determined according to the Slovin formula (1960): n = 𝟒𝟐𝟐𝟒𝟎𝟎

In which: n is the minimum number of samples required;

N is the overall research population size (total population in Hanoi); e is the allowable margin of error (5% or 10%)

Based on the assumption that the total study population is about 800000 x 88% x 60% 422400 students in Hanoi and the error level is 5% (e=0.05), we calculate:

This study requires a minimum of 400 survey samples to ensure high reliability and generalizability of the data To enhance the accuracy of hypothesis testing, a total of 500 survey samples will be collected.

Questionnaire Design

A survey, as defined by Young (2015), is a collection of questions aimed at gathering information from research participants This method is user-friendly and does not necessitate advanced technical skills, simplifying the planning, design, and implementation processes compared to other research methods.

This study employed a 5-point Likert scale for its survey, providing a straightforward method for participants to express their level of agreement or disagreement with various statements related to the research factors The scale ranges from 1 (completely disagree) to 5 (completely agree), facilitating systematic data collection and simplifying the analysis process.

Note: PU:Perceived Usefulness, PEOU: Perceived Ease of Use, PR: Perceived Risk, P: Price, AT: Attitude, SN: Subject Norm, SH: Sale Hunting Students’ Purchase.

ANALYSIS

Respondent Analysis

4.1.1 Gender Analysis of Survey Participants

Based on Table 4.1, the gender ratio of survey participants was evenly divided with 52% male (red) and 48% female (blue) from a total of 458 valid responses

Table 4.1: Descriptive statistics about gender

Source: Result in Google Form

The pie chart illustrates a balanced gender distribution among survey participants, indicating a diverse range of responses from both men and women This diversity enhances the study's ability to accurately reflect the factors influencing online shopping decisions for each gender.

The study's balanced gender distribution allows for precise insights into the online shopping behaviors of both men and women, particularly regarding the factors that influence purchasing decisions on e-commerce platforms.

4.1.2 Analysis of Age of Survey Participants

Based on Table 4.2, the age group from 18-25 accounts for the highest proportion with 84.5%, followed by the age group from 25-30 with 8.5% The age group over 30 accounted for lower proportions at 7%

Table 4.2: Descriptive statistics about age

The primary respondents in this study are individuals aged 18-25, highlighting the focus on young students as the target audience This age group demonstrates a significant interest in online shopping and promotions available on e-commerce platforms.

This study primarily targets students aged 18-25, aiming to uncover key insights into their shopping behavior and identify the factors that significantly influence their purchasing decisions.

4.1.3 Analysis of Survey Participants' Income

According to Table 4.3, a significant 81.9% of survey participants earn less than 5 million VND, while smaller income brackets of 5-7 million VND, 7-10 million VND, and over 10 million VND represent only 9%, 7%, and 2% of respondents, respectively.

Table 4.3: Descriptive statistics about income

The chart indicates that most respondents have low incomes, primarily due to their status as students with limited financial resources Consequently, these low-income students are likely to prioritize deals, discounts, and promotions when shopping online, seeking opportunities to reduce costs while fulfilling their consumption needs This behavior aligns with Maslow's hierarchy of needs, highlighting the importance of addressing safety and social needs within a constrained financial context Retailers should consider these factors when developing marketing and sales strategies aimed at attracting and retaining student customers.

Data Analysis

Prior to evaluating the structural model, the research utilized SmartPLS software to examine the reliability and convergent validity of the scales within the model This assessment ensures that the observed variables accurately represent the underlying latent concepts, thereby enhancing the precision of the subsequent analyses.

The internal reliability of each scale was assessed using Cronbach's Alpha coefficient, which must be 0.7 or higher for consistency among variables within the same factor, as per George and Mallery (2003) The study's results indicated that all scales met this criterion, with Cronbach's Alpha values ranging from 0.808 to 0.875 Specifically, the Attitude scale scored 0.808, Price 0.815, Perceived Ease of Use 0.875, Perceived Risk 0.866, Perceived Usefulness 0.864, Subjective Norm 0.815, and the sales hunting behavior scale achieved 0.871 These findings demonstrate that the measured variables within each concept are closely linked and reliable.

The study evaluated overall reliability using Composite Reliability (CR – rho_c), with a threshold of 0.7 or higher indicating scale reliability in the SEM model, as per Hair et al (2010) The findings revealed that all scales achieved high CR values between 0.874 and 0.914, significantly surpassing the minimum requirement and enhancing measurement stability.

The study assessed convergent validity using the Average Variance Extracted (AVE) index, with values exceeding 0.5 indicating that the measured variables adequately reflect the variance of the latent concept The findings revealed that all scales achieved AVE values between 0.635 and 0.726, demonstrating that the observed variables effectively explain the latent concepts they represent, thus confirming convergence in line with Fornell & Larcker's (1981) criteria.

The test results indicate that all scales in the research model satisfy the criteria for reliability and convergent validity, enabling the reliable use of measurement variables in subsequent structural model analysis.

Table 4.4: Construct reliability and validity

Note: PU:Perceived Usefulness, PEOU: Perceived Ease of Use, PR: Perceived Risk, P: Price, AT: Attitude, SN: Subject Norm, SH: Sale Hunting Students’ Purchase

4.2.2.1 EFA analysis for independent variables

The study assessed the data's suitability for factor analysis using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity The KMO index of 0.902 exceeds the minimum threshold of 0.5, confirming the data set's appropriateness for analysis Additionally, Bartlett's Test yielded a p-value of less than 0.001, indicating significant correlations among the factors The exploratory factor analysis (EFA) revealed that factors such as Ease of Use (PEOU) and Usefulness (PU) have high factor loadings between 0.764 and 0.861, highlighting their substantial impact on students' online shopping behavior Furthermore, factors related to Perceived Risk (PR) and Social Norms (SN) also exhibited high factor loadings, underscoring the influence of social factors and perceived risk on students' shopping decisions.

The findings indicate that the model demonstrates high reliability, with key factors such as Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Perceived Risk (PR), and Social Norms (SN) significantly influencing students' online shopping behavior This reinforces the relevance of these factors in research and validates the hypotheses concerning students' online consumption patterns.

Table 4.5: KMO and Bartlett's test for independent variables

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .844

Table 4.6: Pattern Matrix for independent variables

Extraction Method: Principal Component Analysis

Rotation Method: Promax with Kaiser Normalization a Rotation converged in 5 iterations

Note: PU:Perceived Usefulness, PEOU: Perceived Ease of Use, PR: Perceived Risk, P: Price, SN: Subject Norm

4.2.2.2 EFA analysis for mediating variable

The EFA analysis results indicate that the KMO index is 0.795, surpassing the minimum threshold of 0.5, which confirms the suitability of the data sample for factor analysis Additionally, Bartlett's Test shows a significance value of 0.000, which is below the 0.05 threshold, indicating a significant correlation between the factors in the data and confirming that the correlation matrix is not a unit matrix.

The Pattern Matrix table indicates that the "Attitude" (AT) group variables, including AT2, AT4, AT3, and AT1, exhibit high factor loadings of 0.823, 0.790, 0.789, and 0.786, respectively These values demonstrate a strong interrelationship among the variables, significantly enhancing the understanding of students' shopping behavior within the model.

The EFA analysis model for the mediating variable demonstrates suitability and stability, supported by a high KMO index and a satisfactory Bartlett's Test, enabling further exploration of the relationships within the model.

Table 4.7: KMO and Bartlett's test for mediating variable

Table 4.8: Component Matrix for mediating variable

Extraction Method: Principal Component Analysis a 1 components extracted

4.2.2.3 EFA analysis for independent variables

The Exploratory Factor Analysis (EFA) results indicate that the KMO (Kaiser-Meyer-Olkin) index is 0.745, surpassing the acceptable threshold of 0.5, which confirms the data's suitability for factor analysis Furthermore, Bartlett's Test of Sphericity yielded a significance value of less than 0.001, demonstrating that the correlation matrix is not a unit matrix and that the variables are interrelated, thereby validating the use of factor analysis.

The Component Matrix reveals that the variables associated with "Sale Hunting Students’ Purchase" (SH), specifically SH1, SH3, SH4, and SH2, exhibit high factor loading coefficients of 0.862, 0.850, 0.848, and 0.836, respectively These substantial factor loadings suggest a strong correlation among the SH-related factors, which significantly influence the dependent variable.

In summary, the result ensures that the dependent variable is capable of effectively reflecting students' online shopping behavior based on the Sale Hunting Students’ Purchase factors

Extraction Method: Principal Component Analysis a 1 components extracted

Note: SH: Sale Hunting Students’ Purchase

Preliminary Analysis

Non-response bias is a critical consideration in survey research, as it arises when significant differences exist between participants and non-participants, potentially skewing results and failing to represent the target population accurately (Thompson et al., 2014) In this study on factors influencing students' online shopping behavior, it is essential to recognize that non-participating students may possess differing perspectives and experiences, which could impact the study's sample representativeness Furthermore, the method of survey distribution, whether online or in-person, can influence participation rates, particularly since students may be less motivated to engage with online surveys.

The normality test, utilizing the Kolmogorov-Smirnov and Shapiro-Wilk tests, assesses whether data adhere to a normal distribution These tests are crucial, as numerous statistical analysis techniques, including regression analysis, necessitate normally distributed data.

In Table 4.11, the significance level (Sig.) for all variables is 0.000 in both the Kolmogorov-Smirnov and Shapiro-Wilk tests, indicating that the hypothesis of normal distribution is rejected for all variables This rejection of the null hypothesis demonstrates that none of the variables adhere to a normal distribution, which is crucial in data analysis as many statistical tests rely on this assumption.

Statistic df Sig Statistic df Sig

4.3.3 Harman’s Single Factor / Common Method Variance (CMV)

The Harman’s Single Factor test results indicate that the first component accounts for 32.164% of the total variance, which is below the 50% threshold However, the Principal Components Analysis (PCA) suggests that Common Method Variance (CMV) is not a significant issue in this data, indicating that responses are not heavily influenced by a single latent factor This finding supports the validity of the research results, although it remains essential to explore additional methods for testing CMV in future research.

The results from Table 4.12 indicate that the first principal component of PCA explains a significant 79.996% of the total variance, while subsequent components contribute less This suggests that utilizing the principal component can effectively simplify complex multidimensional data Such findings highlight the effectiveness of PCA in reducing data dimensionality and offer valuable insights into the data's underlying structure.

The PCA results reveal that a principal component accounts for most of the variance in the data, thereby simplifying research findings and facilitating further exploration of students' online shopping behavior This technique effectively reduces data dimensionality, enabling researchers to uncover and analyze hidden relationships within the data.

Component Initial Eigenvalues Extraction Sums of Squared Loadings

Extraction Method: Principal Component Analysis

Measurement Analysis

Figure 4.1: The result of research model

To test for the possibility of common method bias (CMB), the study used the Variance Inflation Factor (VIF) index for all measured variables in the model According to Kock

In 2015, it was established that a Variance Inflation Factor (VIF) below 3.3 indicates that a model is not significantly impacted by common method bias (CMB) The analysis revealed that most observed variables in the model had VIFs ranging from 1.6 to less than 2.7, all below the critical threshold of 3.3 This finding suggests that there are no significant indications of multicollinearity or common method bias present in the model.

The VIF values in this research model are all below 3.3, indicating the absence of significant common method bias Consequently, the analytical results are deemed reliable and unaffected by spurious correlations arising from common measurement.

Table 4.13: Common Method Bias (CMB)

The study employs the Heterotrait-Monotrait Ratio (HTMT) index to assess the discriminability of concepts within the model, utilizing the correlation matrix of latent variables As noted by Henseler et al (2015), an HTMT value under 0.85 signifies that the concepts are distinctly discriminable.

The analysis indicates that all variable pairs exhibit HTMT values below 0.85 Notably, the most significant relationship is observed between Attitude (AT) and Perceived Risk (PR), with a coefficient of 0.727, suggesting that students who perceive higher risks are likely to adopt a more cautious attitude.

The relationship between Attitude (AT) and Perceived Usefulness (PU) is significant, with a HTMT value of 0.617, suggesting that perceived shopping benefits enhance positive attitudes Conversely, Perceived Ease of Use (PEOU) shows weak correlations with other variables, particularly with AT (0.079) and Purchase Intention (PR) (0.055), aligning with earlier Structural Equation Modeling (SEM) findings.

The analysis revealed a strong correlation between Sale Hunting Students' Purchase (SH) and Price (P) (HTMT = 0.612), Attitude (AT) (0.702), and Subjective Norm (SN) (0.601), highlighting the significant influence of price and social factors on this behavior Additionally, the model demonstrated adequate discriminability among the measured concepts.

Table 4.14: Hererotrait-monotrait ratio (HTMT)

AT P PU PEOU PR SH SN

Structural Analysis

The SmartPLS analysis reveals that the R-squared index for the Attitude (AT) variable is 0.491, indicating that approximately 49.1% of the variance in students' attitudes towards sale hunting behavior is explained by three independent factors: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Perceived Risk (PR) This average level of explanation highlights the significant impact of cognitive factors related to e-commerce platforms on students' attitudes towards sale hunting behavior.

The R-squared value for Sale Hunting Students’ Behavior (SH) is 0.496, indicating that 49.6% of the variance in students' sale hunting behavior is explained by three key independent variables: Attitude (AT), Price (P), and Subjective Norm (SN) This suggests that the model effectively accounts for nearly half of the factors influencing students' online shopping behavior, particularly in their pursuit of promotions.

The R-squared Adjusted index is 0.488 for AT and 0.492 for SH, indicating a minimal difference of only 0.003 - 0.004 compared to the initial R² This slight variation suggests that the model remains largely unaffected by the number of independent variables and sample size, demonstrating that the chosen variables significantly contribute to the model's effectiveness.

In summary, both the R-squared and R-squared Adjusted indexes show that the research model is capable of explaining relatively well the formation of students' attitudes and sales hunting behavior

Note: AT: Attitude, SH: Sale Hunting Students’ Purchase

4.5.2 Model Fit / Standardized Root Mean Square Residual (SRMR)

The study evaluated the overall fit of both the measurement and structural models using various fit indexes from PLS-SEM, including SRMR, d_ULS, d_G, Chi-square, and NFI These indexes were analyzed by comparing the saturated model, which assumes all relationships exist, with the estimated model, which represents the actual model tested in the research.

The results indicate that the SRMR index of the estimated model is 0.066, which is below the 0.08 threshold suggested by Hu & Bentler (1999) This low standardized deviation between the actual correlation matrix and the predicted matrix demonstrates that the model exhibits a strong overall fit.

The discrepancy indices d_ULS (Unweighted Least Squares discrepancy) and d_G (Geodesic discrepancy) are 1.745 and 0.399, respectively While d_ULS is slightly higher than the saturated model's value of 0.893, both indices remain within an acceptable range This indicates a minimal discrepancy between the theoretical model and the actual data, reinforcing the model's suitability.

The estimated model has a Normed Fit Index (NFI) of 0.844, which is near the standard threshold of 0.90 While it does not meet the "very good" criteria, this value indicates that the model demonstrates an acceptable level of fit and aligns reasonably well with the actual data.

The evaluation of the model indicates a strong fit with the data, effectively representing the proposed theoretical relationships This enhances the reliability of both the measurement and structural models in elucidating students' sales hunting behavior.

Table 4.16: Standardized Root Mean Square Residual (SRMR)

Hypothesis H1: There is a significant relationship between Perceived Usefulness (PU) and Attitude (AT)

Hypothesis H0: There is no significant relationship between PU and AT

The p-value of 0.000 and a T-statistic of 8.920 indicate that perceived usefulness significantly influences students' attitudes, leading to the acceptance of H1 and rejection of H0 When students view online shopping as time-saving, easy for price comparison, and convenient, they are likely to develop a more positive attitude towards participating in sales hunting programs Research by Huang (2023), Ha et al (2019), and Nguyen & Do (2020) further supports the notion that perceived usefulness is a crucial factor affecting consumers' attitudes towards online shopping.

Hypothesis H2: There is a significant relationship between Perceived Ease of Use (PEOU) and Attitude (AT)

Hypothesis H1: There is no significant relationship between PEOU and AT

The analysis reveals a p-value of 0.979, which exceeds the significance level of 0.05, alongside a very low T-statistic of 0.026, indicating a lack of statistical evidence that ease of use influences students' attitudes Consequently, the null hypothesis (H0) is accepted while the alternative hypothesis (H2) is rejected This outcome suggests that today's students, particularly those from Generation Z, are already adept at using digital platforms, rendering "ease of use" a less significant factor in shaping their shopping attitudes.

Sajid et al (2022) highlighted that, particularly among young and tech-savvy users, ease of use is no longer a key factor influencing online shopping attitudes.

Hypothesis H3: There is a significant relationship between Perceived Risk (PR) and Attitude (AT)

Hypothesis H1: There is no significant relationship between PR and AT

The test results indicate a highly significant relationship between perceived risk and attitude, with a p-value of 0.000 and a T-statistic of 11.670, leading to the acceptance of H3 and rejection of H0 This demonstrates that students are likely to develop positive attitudes towards online shopping when they feel secure and perceive minimal risk during transactions or while receiving goods These findings align with the research conducted by Jadhav & Khanna (2016), which also highlighted the critical role of perceived risk in shaping consumer attitudes towards online shopping.

Hypothesis H4: There is a significant relationship between Price (P) and Sale Hunting Students’ Purchase (SH)

Hypothesis H0: There is no significant relationship between P and SH

The p-value of 0.000 and a T-statistic of 6.121 indicate a statistically significant relationship between price and students' purchase behavior, leading to the acceptance of H4 and rejection of H0 This suggests that students, who typically have limited income, are particularly sensitive to price changes Consequently, offering attractive discounts or promotions can significantly influence their purchasing decisions during online sales Supporting this, research by Suharyati et al (2021) and Sah, G K (2021) highlights that price is a crucial factor affecting consumer shopping behavior in the e-commerce landscape.

Hypothesis H5: There is a significant relationship between Attitude (AT) and Sale Hunting Students' Purchase (SH)

Hypothesis H0: There is no significant relationship between AT and SH

The test results indicate a p-value of 0.000, which is below the significance threshold of 0.05, confirming a statistically significant relationship between attitude and sale hunting purchase behavior Consequently, H5 is accepted while H0 is rejected, demonstrating that a positive attitude towards online shopping, particularly during promotional events, enhances students' purchasing behavior This finding aligns with the research conducted by Nguyen &.

Do (2020), which shows that consumer attitude plays an important mediating role in shopping behavior, especially in the context of e-commerce and promotional marketing

Hypothesis H6: There is a significant relationship between Subjective Norm (SN) and Sale Hunting Students’ Purchase (SH)

Hypothesis H0: There is no significant relationship between SN and SH

The findings indicate a significant impact of subjective norms on sale hunting purchase behavior, with a p-value of 0.000 and a T-statistic of 5.115, leading to the acceptance of H6 and rejection of H0 Influences from friends, relatives, and online communities, such as reviews and shared deals, are crucial in shaping students' purchasing decisions This aligns with the research by Ibrahim et al (2023), which also highlighted the strong effect of subjective norms on consumers' online shopping behavior in contemporary society.

Table 4.17: Result for research model

CONCLUSION

Discussion and Conclusion

As e-commerce continues to expand in major cities like Hanoi, students are increasingly shifting their shopping behavior towards digital platforms and becoming more responsive to promotional campaigns Research indicates that the factors driving students to seek out sales on e-commerce sites extend beyond mere cost-saving motives While price remains a crucial element, particularly for students with limited budgets, it is not the sole determinant of purchasing decisions Positive attitudes towards online shopping, characterized by trust, enthusiasm, and a willingness to engage in promotional offers, significantly influence the transition from awareness to action in students' shopping behaviors.

In addition, the perception that online shopping is useful, time-saving, accessible and convenient also contributes to reinforcing this attitude

The perceived risk factor serves as a psychological barrier for students; if they believe a transaction is unsafe or fear receiving low-quality goods or being scammed, their willingness to engage in sales hunting diminishes, regardless of appealing prices This often-overlooked factor significantly influences the behavior of young users.

Social factors, including friends' opinions, online community influences, and viral trends, significantly impact shopping behavior among students Today's students are not only guided by their personal preferences but are also heavily swayed by the experiences, reviews, and actions of their peers.

The interplay of personal, psychological, and social factors has led to a deeper understanding of consumer behavior, particularly in the realm of sale hunting, which is becoming increasingly prevalent in today's e-commerce landscape.

This study offers a fresh theoretical perspective on the role of Perceived Ease of Use (PEOU) among young consumers, specifically Generation Z The findings reveal that PEOU has a minimal impact on students' attitudes towards bargain hunting behavior.

Tam (2024) and Wang & Tseng (2011) on online shopping behavior, which emphasize the important role of ease of use in shaping consumers’ attitudes towards online shopping

In the digital age, students are well-versed in e-commerce, leading to a shift in their shopping motivations The perceived ease of use, such as product searching and bargain hunting, is no longer a primary factor influencing their engagement on e-commerce platforms This change offers valuable insights for businesses and marketers aiming to develop effective sales strategies that resonate with today's student audience.

The study offers valuable insights for e-commerce businesses, brand managers, and marketers aiming to engage with student customers, a vibrant and increasingly influential demographic in the consumer landscape.

Price plays a crucial role in influencing consumer purchasing behavior To boost sales, businesses should implement discount campaigns and personalized promotions tailored to individual shopping histories, while also targeting sensitive periods like the start of the month, holidays, and weekends.

To enhance trust and minimize cart abandonment rates, it is crucial to create a secure shopping experience by implementing clear return policies, providing transparent product information, and ensuring timely customer support.

Moreover, the strong impact from social factors suggests that businesses need to promote community marketing, exploit influencers, micro-influencers and encourage user- generated content to promote natural spread

Businesses should prioritize communicating practical values that resonate with students, such as saving time, effectively finding deals, and ensuring transaction security, rather than merely emphasizing ease of use when promoting online shopping.

Limitation and Future Research

This study presents valuable insights but also has limitations that warrant discussion for future research The sample is limited to students in Hanoi, which restricts the generalizability of the findings to broader populations, particularly working individuals and other demographic groups Variations in income, lifestyle, and shopping habits could influence the results, and the small sample size further limits coverage Future research should aim for a more representative sample with a larger diversity of income levels and consumer groups across different regions Additionally, factors such as consumer personality traits and familiarity with online shopping were not addressed, yet they may significantly impact shopping behavior For instance, extroverted individuals might be more inclined to shop online, while introverts may face social constraints Understanding these factors as potential moderators could enhance insights into consumer behavior in online shopping contexts.

The limitations of previous studies are significant, as highlighted by Suharyati et al (2021), which focused solely on Indonesia, limiting the generalizability of its findings to other cultures Similarly, Dam & Cuong (2021) restricted their research to students from a single university, rendering their results inapplicable to diverse consumer groups Both studies exhibited a lack of sample diversity and failed to account for other influencing factors, such as demographics and consumer experience To enhance the understanding of consumer behavior, future research should broaden the sample size and incorporate additional moderating factors.

Recommendation

Research indicates that, alongside price, technological and social factors significantly influence students' online shopping behavior E-commerce platforms should enhance the usability and perceived value of their products and services to attract young consumers Additionally, fostering community engagement and connecting consumers with influencers on social media can boost interaction and purchasing decisions Future studies should explore psychological and emotional factors affecting student shopping behavior to gain a more comprehensive understanding of the influences on online shopping decisions, enabling the development of targeted marketing strategies for this demographic.

1 Ajzen, I (1991) "The theory of planned behavior." Organizational Behavior and Human Decision Processes 50(2): 179-211

2 Ajzen, I and M Fishbein (1980) Understanding attitudes and predicting social behavior Englewood Cliffs, N.J, Prentice-Hall

3 Bhatnagar, A., Misra, S., & Rao, H R (2000) On risk, convenience, and internet shopping behavior Communications of the ACM, 43(11), 98-105

4 Box, G E P., et al (1978) Statistics for experimenters : an introduction to design, data analysis, and model building New York ; Chichester, Wiley

5 Bryman, A (2012) Social research methods Oxford, Oxford University Press

6 Burke, R R (1997) Do you see what I see? The future of virtual shopping Journal of the Academy of Marketing Science, 25(4), 352-360

7 Chevalier, S (2022) Share of social buyers among internet users in selected countries in Southeast Asia as of 3rd quarter 2022 Statista

8 Cho, Y C., & Sagynov, E (2015) Exploring Factors That Affect Usefulness, Ease of Use, Trust, and Purchase Intention in the Online Environment International Journal of Management & Information Systems (IJMIS), 19, 21

9 Chow, W S., & Chan, L S (2008) Social network, social trust, and shared goals in organizational knowledge sharing Information & Management, 45(7), 458-465

10 Collins English Dictionary (n.d.) Bargain hunting Collins

11 Cox, D F., & Rich, S U (1964) Perceived risk and consumer decision-making: The case of telephone shopping Journal of Marketing Research, 1(4), 32-39

12 Creswell, J W (2014) Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.) SAGE Publications.

13 Dam, T C., & Cuong, D T (2021) Nghiên cứu những yếu tố ảnh hưởng đến hành vi mua sắm trực tuyến của sinh viên tại Trường Đại học Công nghiệp Thành phố

I don't know!

14 Darian, J C (1987) In-home shopping: Are there consumer segments? Journal of Retailing, 63(2), 163–186

15 Davis, F D (1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology MIS Quarterly, 13(3), 319–340 Doi: https://doi.org/10.2307/249008

16 Dillman, D A., Smyth, J D., & Christian, L M (2014) Internet, phone, mail, and mixed-mode surveys: The tailored design method (4th ed.) Wiley

17 Faith, D O., & Agwu, M E (2014) A review of the effect of pricing strategies on the purchase of consumer goods International Journal of Research in Management, Science & Technology, 2(2), 88-102

18 Fishbein, M., & Ajzen, I (1975) Belief, attitude, intention, and behavior: An introduction to theory and research Addison-Wesley

19 Fornell, C., & Larcker, D F (1981) Evaluating structural equation models with unobservable variables and measurement error Journal of Marketing Research, 18(1), 39-50 https://doi.org/10.1177/002224378101800104

20 Gan, C., & Wang, W (2017) The influence of perceived value on purchase intention in social commerce context Internet Research, 27(3), 772–785 doi: https://doi.org/10.1108/IntR-06-2016-0164

21 Gefen, D., & Straub, D W (2000) The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption Journal of the Association for Information Systems, 1(1), 1–30 Doi: https://doi.org/10.17705/1jais.00008

22 Gefen, D., & Straub, D W (2000) The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption Journal of the Association for Information Systems, 1(1), Article 8.doi: https://doi.org/10.17705/1jais.00008

23 Gentry, M., & Pesendorfer, M (2021) Pricing with bargain hunting consumers Games and Economic Behavior, 129, 549–569

24 George, D., & Mallery, P (2003) SPSS for Windows step by step: A simple guide and reference (4th ed.) Allyn & Bacon

25 Google (2024) Báo cáo e-Conomy SEA 2024: Nền kinh tế số Google Blog

Retrieved December 18, 2024 from https://vietnamese.googleblog.com/2024/11/bao-cao-e-conomy-sea-2024- nen-kinh-te.html?sc32786532729

26 Ha, N T., Nguyen, T L H., Nguyen, T P L., & Nguyen, T D (2019) The effect of trust on consumers’ online purchase intention: An integration of TAM and TPB

Management Science Letters, 9(9), 1451–1460 doi: https://doi.org/10.5267/j.msl.2019.5.006

27 Hansen, T., Jensen, J M., & Stubbe Solgaard, H (2004) Predicting online grocery buying intention: A comparison of the theory of reasoned action and the theory of planned behavior International Journal of Information Management, 24(6), 539–

550 doi: https://doi.org/10.1016/j.ijinfomgt.2004.08.004

28 Hasslinger, A., Hodzic, S., & Opazo, C (2007) Consumer behaviour in online shopping Kristianstad University

29 Hoa, N T., et al (2021) Factors affecting online purchase behaviour in Vietnam Academy of Accounting and Financial Studies Journal, 25(4) Retrieved January

25, 2025 from https://www.abacademies.org/articles/factors-affecting-online- purchase-behaviour-in-vietnam-10876.html

30 Hoàng, N T H., Lê, N S., & Nguyễn, C (2023) Các yếu tố ảnh hưởng đến hành vi mua sắm sản phẩm thời trang trên ứng dụng di động của Gen Z tại Thành phố

Hồ Chí Minh Journal of Science and Technology – IUH, 66(6), 56–72 doi: https://doi.org/10.46242/jstiuh.v66i06.4989

31 Hong, W., Thong, J Y L., Wong, W M., & Tam, K Y (2002) Determinants of user acceptance of digital libraries: An empirical examination of individual differences and system characteristics Journal of Management Information Systems, 18(3), 97–124 doi: https://doi.org/10.1080/07421222.2002.11045692

32 Huang, T (2023) Using SOR framework to explore the driving factors of older adults' smartphone use behavior Humanities and Social Sciences Communications, 10(1), Article 690 doi: https://doi.org/10.1057/s41599-023- 02221-9

33 Ibrahim, N A N., Husseini, F., Aisyah, N., Camelia, N., Khazlyn, N., Faiz, W., & Kahar, N (2023) Online shopping behaviour in youth: A systematic review of the factors influencing online shopping in young adults International Journal of

Academic Research in Business and Social Sciences, 13(2), 157–166 doi: https://doi.org/10.6007/IJARBSS/v13-i2/16257

34 Jadhav, V., & Khanna, M (2016) Factors influencing online buying behavior of college students: A qualitative analysis The Qualitative Report, 21(1), 1–15.doi: https://doi.org/10.46743/2160-3715/2016.2445

35 Komalasari, F., Christianto, A., & Ganiarto, E (2021) Factors influencing purchase intention in affecting purchase decision: A study of e-commerce customer in Greater Jakarta Bisnis & Birokrasi: Jurnal Ilmu Administrasi dan Organisasi, 28(1), Article 1.doi: https://doi.org/10.20476/jbb.v28i1.1290

36 Kotler, P., & Keller, K L (2016) Marketing management (15th ed.) Pearson Education

37 Koufaris, M (2002) Applying the technology acceptance model and flow theory to online consumer behavior Information Systems Research, 13(2), 205–223.doi: https://doi.org/10.1287/isre.13.2.205.83

38 Long, T H., Nhu, N T Q., & Tuấn, T (2023) Nghiên cứu các yếu tố ảnh hưởng đến quyết định mua hàng trực tuyến trên Shopee của sinh viên Trường Đại học Đồng Tháp TNU Journal of Science and Technology, 228(11), 123–130.doi: https://doi.org/10.34238/tnu-jst.7974

39 Makhitha, K M., & Ngobeni, K (2021) The influence of demographic factors on perceived risks affecting attitude towards online shopping SA Journal of Information Management, 23(1), Article 1283 doi: https://doi.org/10.4102/sajim.v23i1.1283

40 Merriam-Webster (n.d.) Bargain hunter In Merriam-Webster.com dictionary Retrieved Retrieved January 15, 2025 from https://www.merriam- webster.com/dictionary/bargain%20hunter

41 Moon, J.-W., & Kim, Y.-G (2001) Extending the TAM for a world-wide-web context Information & Management, 38(4), 217–230

42 Moshref Javadi, M H., Dolatabadi, H R., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A R (2012) An analysis of factors affecting on online shopping behavior of consumers International Journal of Marketing Studies, 4(5), 81–98 doi: https://doi.org/10.5539/ijms.v4n5p81

43 Mosunmola, A., et al (2023) Perceived value dimensions on online shopping intention: The role of trust and culture Academy of Marketing Studies Journal,

18(1), 312 Retrieved January 16, 2025 from https://www.abacademies.org/articles/Percieved-value-dimensions-on-online- shopping-intention-the-role-of-trust-and-culture-1939-6104-18-1-312.pdf

44 Nga, L P., & Tam, P T (2024) Key factors affecting online shopping attitude and intention: A case study of consumers in Vietnam Journal of Eastern European and

Central Asian Research, 11(1), 66–78 doi: https://doi.org/10.15549/jeecar.v11i1.1547

45 Nga, P L., & Tam, P T (2024) Key factors affecting online shopping attitude and intention: A case study of consumers in Vietnam Journal of Eastern European and

Central Asian Research, 11(1), 66–78 doi: https://doi.org/10.15549/jeecar.v11i1.1547

46 Nguyen Thi Phuong, G., et al (2024) The influence of promotional programs and key factors on e-commerce purchasing behaviour among students in Southern Vietnam Journal of Applied Economic Sciences, 19(2(84)), 209–227 https://doi.org/10.57017/jaes.v19.2(84).08

47 Nguyen, C., & Do, T (2019) Factors affecting the decision to shop online via e- commerce platforms in Vietnam Journal of Science and Technology, 37 Retrieved January 15, 2025 from https://ssrn.com/abstract906450

48 Nguyễn, N T., Đặng, T H Ý., & Huỳnh, Q T (2025) Nghiên cứu các yếu tố ảnh hưởng đến quyết định mua sản phẩm điện tử trực tuyến của người tiêu dùng tại thành phố Cao Lãnh Dong Thap University Journal of Science, 14(1), 53–63 doi: https://doi.org/10.52714/dthu.14.1.2025.1476

49 Nguyễn, T Á H (2022) Các nhân tố ảnh hưởng đến quyết định mua hàng của khách hàng trên sàn thương mại điện tử tại TP Hồ Chí Minh Đại học Đà Lạt

Retrieved January 16, 2025 from https://scholar.dlu.edu.vn/thuvienso/bitstream/DLU123456789/185701/1/CVv39 S152022035.pdf

50 Nguyễn, T P G., & Thái, Đ T (2023)(2023) Factors affecting the satisfaction of students at Industrial University of Ho Chi Minh City when purchasing online on e-commerce platforms ResearchGate Retrieved January 16, 2025 from https://www.researchgate.net/publication/376406541

51 Nguyen, T T H., Nguyen, N D., & Nguyen, T B L (2021) Factors influencing online shopping behavior of University students in Hanoi, Vietnam: A model and empirical study International Journal of Intelligent Enterprise, 8(2/3), 197-215

52 Pappas, N (2016) Marketing strategies, perceived risks, and consumer trust in online buying behaviour Journal of Retailing and Consumer Services, 29, 92–103 https://doi.org/10.1016/j.jretconser.2015.11.007

53 Pauzi, S F F., et al (2017) Factors influencing consumers' intention for online grocery shopping: A proposed framework IOP Conference Series: Materials Science and Engineering, 215(1), 012013 doi: https://doi.org/10.1088/1757- 899X/215/1/012013

54 Perea y Monsuwé, T., Dellaert, B G C., & de Ruyter, K (2004) What drives consumers to shop online? A literature review International Journal of Service

Industry Management, 15(1), 102–121 doi: https://doi.org/10.1108/09564230410523358

55 Sah, G K (2021) Factors Affecting Online Shopping Behaviour of Boutique Products in Kathmandu Valley Dristikon: A Multidisciplinary Journal, 11(1), 61–

73 doi: https://doi.org/10.3126/dristikon.v11i1.39134

56 Tan, T D., & Giang, N T P (2023) The perceived usefulness affects online purchasing behavior as a mediator of online purchasing intention of IUH students

- Laptop products ResearchGate Retrieved January 16, 2025 from https://www.researchgate.net/publication/376396003

57 Tang, H., et al (2021) Factors affecting e-shopping behaviour: Application of the theory of planned behaviour Behavioural Neurology, 2021, Article ID 1664377

58 Tatista (n.d.) E-commerce in Vietnam Statista.Retrieved December 18, 2024 from https://www.statista.com/topics/5321/e-commerce-in- vietnam/#topicOverview

59 Thư Viện Pháp Luật (n.d.) Danh sách các trường đại học công lập ở TP Hồ Chí Minh, TP Hà Nội đầy đủ chi tiết Thư Viện Pháp Luật Retrieved January 26, 2025 from https://thuvienphapluat.vn/hoi-dap-phap-luat/83A3143-hd-danh-sach-cac- truong-dai-hoc-cong-lap-o-tp-ho-chi-minh-tp-ha-noi-day-du-chi- tiet.html#:~:text=H%C3%A0%20N%E1%BB%99i%20c%C3%B3%2097%20tr

%C6%B0%E1%BB%9Dng,H%C3%A0%20N%E1%BB%99i%20c%C3%B3%2 026%20tr%C6%B0%E1%BB%9Dng

60 Truong, T.-T., Nguyen, T T., Pham, T T N., & Tran, T T T (2024) Green values for sustainable development: Exploring factors influencing e-commerce purchase trends in the era of digital transformation—Insights from Ho Chi Minh City

ResearchGate Retrieved January 16, 2025 from https://www.researchgate.net/publication/386471475_Green_Values_for_Sustain able_Development_EXPLORING_FACTORS_INFLUENCING_E-

COMMERCE_PURCHASE_TRENDS_IN_THE_ERA_OF_DIGITAL_TRANS FORMATION_INSIGHTS_FROM_HO_CHI_MINH_CITY

61 Võ, T T., Đỗ, T A., & Đàm, T C (2021) Các yếu tố ảnh hưởng đến ý định mua sắm trực tuyến trên trang thương mại điện tử Tiki của khách hàng tại thành phố

Hồ Chí Minh is a significant city in Vietnam, where various factors influence online shopping behavior on the Tiki e-commerce platform Understanding these factors is crucial for enhancing customer engagement and optimizing marketing strategies The research highlights the importance of consumer preferences and the impact of digital marketing on purchasing decisions in this urban area.

62 Wang, T L., & Tseng, Y F (2011) A study of the effect on trust and attitude with online shopping International Journal for Digital Society, 2(2), 433–440 doi: https://doi.org/10.20533/ijds.2040.2570.2011.0052

[SURVEY] FACTORS AFFECTING THE DECISION TO HUNT FOR SALES ON E- COMMERCE PLATFORMS OF STUDENTS IN HANOI

My name is Le Minh Chau, a double bachelor of Marketing - Business Administration, Hanoi National University International School & HELP University

- Malaysia I am currently conducting a survey on the factors affecting the decision to hunt for Sales on e-commerce platforms of students in Hanoi for my graduation thesis

Please take a moment to complete the survey; your participation is greatly appreciated Rest assured, all personal information will be kept strictly confidential and will only be used for research purposes.

Your feedback is invaluable to my research, and I appreciate your participation in the survey As a token of gratitude, I will be sending a small gift to all participants at the end Thank you for your contributions!

Instructor: Dr Ho Nguyen Nhu Y, Faculty of Economics and Management, International School, Vietnam National University, Hanoi

For any questions, please contact: 21070755@vnu.edu.vn

Question 2: Do you like to buy products with discount vouchers on e-commerce platforms?

PU1:Online shopping helps me find information quickly

PU2: Online shopping saves time

PU3:Shopping online will help avoid unpleasant hassles

(e.g traveling to buy things, waiting in line to pay, etc.)

PU4: I can shop online anywhere

PEOU1: When shopping online I can easily find information about products

PEOU2: Online shopping allows for easy payment when ordering

PEOU3: Online shopping makes it easy to compare features between products

PEOU4: Online shopping makes it easy to find products that match your needs

PR1: I am worried about the product quality not being the same as described on the website

PR2: I am worried that there are many risks to the goods during delivery

PR3: I am concerned about delays in receiving the product

PR4: I am concerned about the security of my personal information

P1: Price is the most important factor when I decide to hunt for sales on e- commerce platforms

P2: I am willing to buy products on e-commerce platforms when the price is low

P3: Shopping on e-commerce platforms makes it easy for me to compare prices of products

P4: Shopping on e-commerce platforms helps me receive many incentives and price promotions

AT1: I trust that the websites

I decide to purchase from provide honest information about the products

AT2: I am willing to make payment in advance immediately after confirming the transaction

AT3: I trust that the websites

I decide to buy from will honor their promises (returns, warranties)

AT4: I believe that the websites I decide to buy from have the best interests of the customer in mind

SN1: Family and friends influence my decision to use online shopping services

SN2: My colleagues influence my decision to use online shopping services

SN3: Media influences my decision to use online shopping services

SN4: I see most of the people around me are using online shopping services

SH1: When the conditions are suitable, I prioritize using discount vouchers when shopping online

SH2: I will prioritize using discount vouchers on e- commerce platforms more in the future

SH3: I am willing to recommend friends and relatives to prioritize using discount vouchers on e- commerce platforms

SH4: I will prioritize using discount vouchers on e- commerce platforms in the near future

[KHẢO SÁT] NHÂN TỐ ẢNH HƯỞNG ĐẾN QUYẾT ĐỊNH SĂN BÁN TRÊN NỀN TẢNG THƯƠNG MẠI ĐIỆN TỬ CỦA SINH VIÊN TẠI HÀ NỘI

Xin chào tất cả mọi người,

Tôi là Lê Minh Châu, cử nhân kép ngành Marketing và Quản trị kinh doanh từ Trường Quốc tế Đại học Quốc gia Hà Nội và Đại học HELP - Malaysia Hiện tại, tôi đang tiến hành khảo sát các yếu tố ảnh hưởng đến quyết định mua sắm trên nền tảng thương mại điện tử của sinh viên tại Hà Nội cho đồ án tốt nghiệp của mình.

Xin vui lòng dành ít thời gian quý báu của bạn để hoàn thành khảo sát giúp tôi Tất cả thông tin cá nhân sẽ được bảo mật hoàn toàn và chỉ được sử dụng cho mục đích nghiên cứu.

Mọi ý kiến đóng góp của các bạn đều rất quý giá và hữu ích cho nghiên cứu của tôi Sau khi kết thúc cuộc khảo sát, tôi xin gửi tặng một món quà nhỏ đến tất cả mọi người Xin chân thành cảm ơn sự hỗ trợ và đóng góp của các bạn!

Người hướng dẫn: TS Hồ Nguyễn Như Ý, Khoa Kinh tế và Quản lý, Trường Quốc tế, Đại học Quốc gia Hà Nội

Mọi thắc mắc vui lòng liên hệ: 21070755@vnu.edu.vn

Phần 1: Thông tin cá nhân

Câu hỏi 1: Trình độ học vấn

Học sinh Đại học/Cao đẳng

Câu 2: Bạn thích mua hàng có voucher giảm giá trên sàn thương mại điện tử? Đúng

Phần 2: Câu hỏi về nhân khẩu học

Câu hỏi 1: Giới tính của bạn

Câu hỏi 3: Thu nhập của bạn

Phần 3: Câu hỏi nghiên cứu

Khẳng định 1 (Hoàn toàn không đồng ý)

PU1:Mua sắm trực tuyến giúp tôi tìm kiếm thông tin nhanh chóng

PU2: Mua sắm trực tuyến tiết kiệm thời gian

PU3:Mua sắm trực tuyến sẽ giúp tránh được những khó chịu rắc rối (ví dụ: đi du lịch để mua đồ, xếp hàng chờ thanh toán, v.v.)

PU4: Tôi có thể mua sắm trực tuyến ở bất cứ đâu

PEOU1: Khi mua sắm trực tuyến tôi có thể dễ dàng tìm thấy thông tin về sản phẩm

PEOU2: Mua sắm trực tuyến cho phép thanh toán dễ dàng khi đặt hàng

PEOU3: Mua sắm trực tuyến giúp dễ dàng so sánh tính năng giữa các sản phẩm

PEOU4: Mua sắm trực tuyến giúp bạn dễ dàng tìm thấy sản phẩm phù hợp với nhu cầu của bạn

PR1: Tôi lo lắng về chất lượng sản phẩm không giống như mô tả trên trang web

PR2: Tôi lo ngại rằng có nhiều rủi ro đối với hàng hóa trong quá trình giao hàng

PR3: Tôi lo ngại về sự chậm trễ trong việc nhận được sản phẩm

PR4: Tôi lo ngại về sự an toàn của thông tin cá nhân

P1: Giá cả là yếu tố quan trọng nhất khi tôi quyết định săn lùng doanh số trên các sàn thương mại điện tử

P2: Tôi sẵn sàng mua sản phẩm trên thương mại điện tử nền tảng khi giá thấp

P3: Mua sắm trên sàn thương mại điện tử khiến tôi dễ dàng so sánh giá cả các sản phẩm

P4: Mua sắm trên sàn thương mại điện tử giúp ích cho tôi nhận được nhiều ưu đãi và khuyến mãi về giá

AT1: Tôi tin tưởng rằng các trang web tôi quyết định truy cập mua hàng từ cung cấp thông tin trung thực về các sản phẩm

AT2: Tô Tôi sẵn sàng thanh toán trước ngay sau khi xác nhận giao dịch

AT3: Tôi tin tưởng rằng các trang web tôi quyết định mua sẽ tôn trọng lời hứa của họ (trả lại, bảo hành)

AT4: Tôi tin rằng những trang web tôi quyết định mua từ đó luôn quan tâm đến lợi ích tốt nhất của khách hàng

SN1: Gia đình và bạn bè ảnh hưởng đến quyết định của tôi sử dụng dịch vụ mua sắm trực tuyến

SN2: Đồng nghiệp ảnh hưởng đến quyết định sử dụng của tôi dịch vụ mua sắm trực tuyến

SN3: Phương tiện truyền thông ảnh hưởng đến quyết định sử dụng trực tuyến của tôi dịch vụ mua sắm

SN4: Mình thấy hầu hết mọi người xung quanh đều đang sử dụng dịch vụ mua sắm trực tuyến

SH1: Khi có điều kiện thích hợp, tôi ưu tiên sử dụng voucher giảm giá khi mua sắm trực tuyến

SH2: Tôi sẽ ưu tiên sử dụng voucher giảm giá vào nền tảng thương mại điện tử nhiều hơn trong tương lai

SH3: Tôi sẵn sàng giới thiệu bạn bè và người thân ưu tiên sử dụng voucher giảm giá trên các sàn thương mại điện tử

SH4: Tôi sẽ ưu tiên sử dụng voucher giảm giá vào sàn thương mại điện tử trong thời gian tới

SOCIALIST REPUBLIC OF VIETNAM Independence – Freedom - Happiness

EXPLANATORY REPORT ON CHANGES/ADDITIONS BASED ON THE DECISION OF GRADUATION THESIS COMMITTEE FOR UNDERGRADUATE PROGRAMS WITH DEGREE AWARDED BY

Student’s full name: Le Minh Chau Graduation thesis topic: Factors Affecting Sales Hunting Of Students On E- Commerce Platforms In Hanoi.

Tiêu đề	Factors affecting the decision to hunt for sales on e-commerce platforms of students in Hanoi
Tác giả	Le Minh Chau
Người hướng dẫn	Dr. Ho Nguyen Nhu Y
Trường học	Vietnam National University, Hanoi
Chuyên ngành	Bachelor in Marketing
Thể loại	Graduation project
Năm xuất bản	2025
Thành phố	Hanoi

Định dạng
Số trang	84
Dung lượng	2,19 MB