Introduction
Background of the Study
The rise of the Internet and advancements in technology have profoundly transformed global lifestyles, leading to a remarkable surge in online shopping This evolution has created new business opportunities, enabling companies to operate in cyberspace and connect with customers worldwide, free from geographical constraints.
(Singh, Jayashankar, & Singh, 2001) Meanwhile, consumers can purchase goods and services virtually anywhere, 24 hours a day, 7 days a week without worrying about store hours, time zones, or traffic jams (Li & Gery, 2000)
The Internet has rapidly transformed into a popular shopping platform, with online purchases surging significantly over the past decade ACNielsen reported that the number of global online shoppers rose from 627 million in 2006 to 875 million in 2008, highlighting the growing trend of e-commerce.
Among internet users, the highest percentage shopping online is found in South Korea, where 99 percent of those with internet access have used it to shop, followed by the United
Kingdom (97%), Germany (97%), Japan (97%) with the United States eight, at 94 percent
According to a study by Indvik (2013), the e-commerce market is projected to grow significantly from $857 billion in 2011 to $1,860 billion by 2016 Additionally, online retail sales in the U.S were $308 billion in 2011, with expectations to rise to $546 billion by 2016.
Online shopping in Vietnam is still a new technology breakthrough since it has just begun to assault the Vietnamese retailing sector with online shopping services The Vietnam
E-commerce Report 2013 published by Vietnam E-Commerce and Information Technology
Agency (VECITA) showed that in term of the business sectors, the rate of enterprises in wholesale, retail sector joining e-marketplaces stayed in the moderate rate of 15%
According to VECITA (2014), the finance and estate sectors led participation in e-marketplaces, with rates of 28% and 20%, respectively Additionally, 85% of surveyed enterprises reported that the efficiencies gained from participating in e-marketplaces were rated as moderate to good.
In a 2013 VECITA survey, 15% of respondents reported low levels of efficiency in e-commerce, while 41% of businesses experienced revenue growth through this channel Conversely, 13% reported a decline in revenue, and 46% noted little to no change Notably, the effectiveness of e-commerce has remained relatively stable over the past few years (VECITA, 2014).
Source: Survey of Vecita in 2013
Figure 1.1: Trends of revenue from electronic means
In 2013, Vietnam's internet user base reached 36% of the population, with 57% engaging in online transactions, according to the Vietnam E-commerce and Information Technology Agency Each online buyer spent an average of $120, primarily on clothing, shoes, and cosmetics, followed by technology products, air tickets, food, and books (Quantrimang, 2013) The e-commerce market in Vietnam was valued at $2.2 billion in 2013, with projections indicating further growth by 2015.
Vietnam will have 40-45% of Internet users Goes along with the increase of Internet user in
By 2015, the e-commerce legal framework is expected to be finalized, alongside significant advancements in logistics and payment infrastructure Research indicates a rising trend in Internet users making online purchases, with an anticipated increase in consumer spending of $30 per online shopper compared to 2013 This positions the e-commerce market in 2015 to be competitive with major global players, such as the United States, which boasts a market size of $343 billion.
Japan ($127 billion), UK ($124 billion) and China ($110 billion), the e-commerce market is still quite small (PwC, 2013)
Appropriately, although sales of products from the Internet account for only a small percentage of total retail sales, millions of consumers shop and buy on the Internet
Many companies in Vietnam are rapidly establishing an online presence, despite uncertainty regarding the impact of the Internet on their businesses To boost online shopping in Vietnam, it is crucial to prioritize understanding consumer online shopping behavior and the factors that influence it Research shows that a significant number of Vietnamese, particularly the youth, primarily use the Internet for non-shopping activities, such as seeking information.
(87%), using social network or forum (73%) or accessing e-mail (71%); and only 20% for shopping online (VECITA, 2014)
Source: Survey of E-commerce and Information Technology Agency 2013
Figure 1.2: Purposes of accessing Internet
According to Levin, Levin, and Weller (2005), consumers utilize various information sources to verify product quality and increase satisfaction, influenced by the significance of different product attributes Consequently, researchers suggest that online products can be classified based on whether their primary attributes are digital or non-digital.
Digital products, defined as items whose attributes can be communicated through the Internet, present lower inherent product risk in online channels compared to non-digital products that require physical inspection Research indicates that consumers value the tactile experience of inspecting apparel, leading them to favor traditional stores for such purchases Conversely, when it comes to digital products like books, consumers prioritize immediate access to product information, making online shopping their preferred choice.
Numerous studies have explored the factors influencing consumers' online purchase intentions, utilizing traditional behavioral intention models and theories, including the Theory of Reasoned Action (TRA) developed by Fishbein and Ajzen in 1975.
Acceptance Model (TAM) (Davis, 1989) and Theory of Planned Behavior (TPB) (Ajzen,
1991) In this research, TAM is used as the foundation of the research It is believed to be the most appropriate model, which can explain Information Technology (IT) or Information
System (IS) behavioral intention well and operate in valid and reliable instruments
Problem Statement
The influence of a shopper's personal attitudes plays a significant role in consumer decision-making and behavioral intentions Specifically, service attitude serves as a crucial link between consumer characteristics and their ability to fulfill needs effectively (Ajzen, 1991).
Consumer behavior in online shopping is significantly influenced by individual characteristics such as personality, demographics, and perceptions of the benefits of online shopping (Goldsmith & Flynn, 2004) Understanding the relative importance of these determinants is crucial for comprehending consumer actions Additionally, behavioral intention is shaped by an individual's attitude towards the act, alongside their awareness of the benefits, self-efficacy, and control over both internal and external resources These factors contribute to the development of the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior.
The Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) offer valuable insights into e-business by helping to explain and predict consumers' intentions regarding online shopping Understanding these theories is essential for analyzing consumer behavior in the digital marketplace.
Choi and Geistfeld (2004) highlight that online shopping is often viewed as riskier compared to traditional brick-and-mortar retail transactions Furthermore, they emphasize that consumer behavior varies by culture, raising questions about the applicability of findings from Western consumers intending to shop online to Eastern countries.
Dann and Dann (2001) highlight that the growth of online shopping is driven by the easy access to information, making consumers more informed and efficient While numerous studies have explored online shopping globally, there remains a need for a more detailed analysis of consumers' online shopping intentions in specific countries (Salisbury).
In Vietnam, the internet remains a relatively new platform for connecting retailers and consumers, making customer retention a significant challenge for e-retail stores (Dai, Forsythe, & Kwon, 2014) To enhance comprehension in this field, it is essential to seek precise answers to relevant questions.
In 2013, Vietnam experienced significant advancements in online transactions, particularly in business-to-business (B2B) and business-to-consumer (B2C) sectors The choice of e-commerce business models is crucial for enhancing customer awareness and boosting revenue The emergence of group buying websites like muachung.vn, hotdeal.vn, and nhommua.vn at the end of 2010 transformed the e-commerce landscape in Vietnam, driving strong customer engagement and generating substantial economic benefits Despite this growth, the e-commerce market in Vietnam remains highly fragmented, particularly within the consumer-to-consumer (C2C) and B2C segments.
Notable e-commerce platforms in Vietnam, such as vatgia.com, enbac.vn, and 5giay.vn, alongside major retailers like thegioididong.com and nguyenkim.com, have become popular online sales channels among Vietnamese consumers (Ha, 2014; Quantrimang, 2013) This study aims to deliver insights that can help e-retailers enhance consumer engagement and boost online shopping in Vietnam By implementing effective strategies, e-retailers can become more appealing to encourage Internet shopping among Vietnamese consumers.
In both traditional and online shopping contexts, consumers exhibit varying degrees of reliance on different information sources based on the product category Research indicates that shoppers in physical stores often depend on diverse sources of information, such as personal experience and search results, to guide their purchasing decisions This behavior is similarly observed in the online shopping environment, where the type of product significantly influences the sources of information that consumers trust.
(Nelson, 1970) However, many extant researches on online shopping have ignored the effect of different product category on shoppers’ intention in Vietnam (Dai et al., 2014)
More importantly, eligible product categorization scheme (e.g., search vs experience) in traditional retail setting may not readily apply to the online setting Although some products
When purchasing apparel online, consumers face inherent risks, yet existing research has largely overlooked the nuances of how risk perceptions vary by product category Notably, Biswas and Biswas (2004) stand out as an exception, providing insights into the relationship between specific risk perceptions and online purchase intentions across different product types.
Despite the promising potential of Vietnamese consumers, there remains a significant gap in understanding online shopping habits in Vietnam For online retailers to thrive, they must deliver real value to their customers Therefore, it is essential for internet marketers to grasp consumer expectations and intentions related to online shopping Conducting research can empower these retailers to better understand their customers, meet their needs and desires, and ultimately create meaningful value.
Research Objective
The purpose of this research is to identify the determinants affecting consumer’s online purchase intention, using product category as moderating factor in Ho Chi Minh
City, Vietnam More specifically, the research mainly seeks to achieve the following objectives:
- To determine factors influencing consumers’ online purchase intention in Ho Chi Minh City
- To examine the moderating effect of product category on the relationship between the factors and consumers’ online purchase intention.
Research Question
Based on the discussion above, and to accomplish the objective of this study, the proposed questions to be answered in this research are as follow:
- Does perceived usefulness of online shopping contribute towards consumer’s online purchase intention?
- Does perceived ease of use contribute towards consumer’s online purchase intention?
- Does perceived risk contribute towards consumer’s online purchase intention?
- Does brand orientation contribute towards consumer’s online purchase intention?
- Does prior online purchase experience contribute towards consumer’s online purchase intention?
- Are there differences above impacts among various product categories?
Above factors (perceived usefulness, perceived ease of use, perceived risk, brand orientation, and prior online purchase experience) are further discussed in the second chapter under conceptual framework.
Research contributions
The explanatory model has been examined through the existing literature to find a suitable model to explain consumers' behavioral intentions in purchasing products online
In this study, we utilize product category as a crucial control variable to examine the relationship between various factors and online purchasing intentions The Technology Acceptance Model (TAM) is employed due to its proven effectiveness in elucidating the disparity between behavioral intentions and actual purchasing behavior, particularly in the context of technology-related products (Adam, Nelson, & Todd, 1992; Mathieson, 1991; Davis, Bagozzi, &).
The model proposed by Warshaw (1989) effectively explains consumers' purchase intentions in online shopping, highlighting the moderating influence of product types, an area with limited existing literature This research enhances the understanding of consumer behavior in the digital marketplace.
This study's findings extend beyond theoretical contributions, significantly impacting various aspects of business management The key practical contributions can be summarized as follows:
- This study provides useful information for the business management to prioritize their resources for time, budget allocation, human resources, and investment
This study aims to enhance marketers' understanding of customer intentions in online shopping by examining consumer behavioral intentions and identifying key variables influencing these behaviors within specific product categories By gaining insights into these factors, marketing managers can effectively develop targeted marketing strategies and tactics that address the needs of online consumers, ultimately increasing consumer satisfaction and optimizing the marketing mix for better engagement.
- It provides useful input so that Web developers can design sites and pages of which content and layout is more compelling and effective to attract more business from consumers
Policymakers can enhance online business participation by improving infrastructure and regulations related to the Internet, fraud prevention, security, and privacy issues This will encourage more consumers to engage in buying and selling products online.
Scope of the study
Due to the nature of this research, a number of delimitation of scope had to be set for the study as below:
This research focuses exclusively on residents of Ho Chi Minh City who are over 15 years old, chosen for its leading Internet penetration rate.
According to Cimigo's 2011 report, over 50% of the population in urban Vietnam has accessed the internet, with Ho Chi Minh City surpassing this average at 62%.
- Survey is conducted in this research to gather information among people who has browsed online shopping pages (visitors) as well as those who has experience in online shopping (purchasers)
According to ACNielsen (2008), the most commonly purchased items online worldwide include books (41%), clothing, accessories, and shoes (36%), as well as video/DVD/games and airline ticket reservations (24%) In Vietnam, clothing, shoes, and cosmetics dominate online purchases, making up 62% of sales, followed by technology products at 35%, household items at 32%, air tickets at 25%, and books and stationery at 20%.
Source: Survey of E-commerce and Information Technology Agency 2013
Figure 1.3: Popular online products on e-commerce website purchased
Clothing, footwear, electronics, books, and stationery dominate e-commerce sales, comprising 79% of the surveyed sites This alignment reflects consumer demand, with 62% of individuals opting to buy these products online.
Based on above discussion, the study selected clothing, electronics and books as objects of product category
Source: Data gathered by E-commerce and Information Technology Agency 2013
Figure 1.4: The types of products or services introducing on e-commerce websites
Organization of the Study
This research is organized as five chapters
Chapter 1 offers an introduction to the study, including background to the study, statement of the problem, research objectives, research questions, the significance of the study, and scope of the study
Chapter 2 makes a brief reference to the theories that have been used These literatures summarize briefly the knowledge of recent studies, describes the conceptual model, and hypotheses
Chapter 3 presents research methodology, including the research design, population and sampling plan, the instruments, procedures and methods of data analysis
Chapter 4 presents the description and analysis of the data collected
Finally, Chapter 5 reports in detail research findings and implications Further, this chapter concludes with discussion on the results derived, limitations and future research.
Literature Review
Introduction
This chapter aims to review existing literature to provide a comprehensive overview of the field, enabling the researcher to develop a conceptual model that tests and identifies the factors influencing consumers' online purchase intentions.
Behavioral Intention Models
The most popular theories which have been used to study behavioral intention in technological products are the Theory of Reasoned Action (TRA), the Theory of Planned
Behavior (TPB), the Technology Acceptance Model (TAM) and the Diffusion of
Innovation Most of these theories have been developed from the Theory of Reasoned
Action originally proposed by Fishbein and Ajzen (1975) The Diffusion of Innovation
The research primarily concentrated on Internet adoption, assessing the likelihood of innovation acceptance rather than emphasizing online shopping behaviors Consequently, the study explores three widely recognized theories, including the Technology Acceptance Model (TAM).
TPB, and TRA in order to gain a better understanding on the relationships between belief, attitude, and behavioral intention of consumers when buying products online
2.2.1 Theory of Reasoned Action (TRA)
The theory of reasoned action (TRA), developed by Fishbein and Ajzen (1975,
The Theory of Reasoned Action (TRA), proposed by Ajzen in 1980, suggests that individual behavior is primarily influenced by behavioral intentions This model serves to predict both attitudes and behaviors, highlighting the distinction between behavioral intention and actual behavior This separation enables a deeper understanding of the factors that may restrict the influence of attitudes on behavior.
According to Fishbein (1980), behavioral intentions play a crucial role in determining an individual's actual use of an innovation, influenced by their attitude towards the behavior and the subjective norms surrounding it An individual's attitude reflects their positive or negative feelings about engaging in a behavior, while the subjective norm pertains to their perception of whether significant others believe the behavior should be performed.
A person's voluntary behavior is influenced by their attitude towards that behavior and their perception of how others would view them if they engaged in it This interplay between individual attitude and social norms shapes their behavioral intention, as outlined by Fishbein and Ajzen (1975).
Figure 2.1: Theory of reasoned action model
The Theory of Reasoned Action (TRA) has limitations in fully explaining the mechanisms behind innovation use and individual behavioral intent, and it has been criticized for overlooking social factors that influence behavior (Grandon & Peter, 2004) Despite these criticisms, TRA's inclusion of subjective norms is a significant advantage, as they can impact behavior in specific contexts Additionally, TRA has demonstrated strong predictive power in forming consumer behavioral intentions across a range of products, including fashion, beer, toothpaste, dog food, mineral water, and facial tissue (Grandon & Peter, 2004).
2.2.2 Theory of Planned Behavior (TPB)
The theory of planned behavior, proposed by Ajzen in 1991, enhances the predictive capacity of the theory of reasoned action by incorporating perceived behavioral control This theory explores the connections between beliefs, attitudes, behavioral intentions, and actual behaviors across various fields, including advertising, public relations, and healthcare Perceived behavioral control is crucial, as it reflects individuals' perceptions regarding the ease or difficulty of executing a specific behavior.
The Theory of Planned Behavior (TPB) posits that behavior is influenced by both intention and perceived behavioral control Ajzen (1991) emphasized that a person's intention to engage in a behavior is the key element of TPB, as it reflects the motivational factors that drive behavior This intention serves as an indicator of an individual's willingness to perform the behavior.
Figure 2.2: Theory of planned behavior
The Theory of Planned Behavior has been widely applied in various studies, including those focused on weight loss, sexual behavior, waste recycling, student class attendance, spreadsheet software usage, and information technology (Richard & Joop de Vries, 2000; Taylor & Todd).
1995) Nevertheless, the Theory of Planned Behavior lacks sufficient scale development for studying online shopping behavior
The Technology Acceptance Model (TAM) extends the theoretical framework of the Theory of Reasoned Action (TRA) as proposed by Ajzen and Fishbein (1980) Developed by Davis in 1989, along with Bagozzi and Warshaw, TAM aims to elucidate the reasons behind users' acceptance or rejection of information technology by modifying TRA This model highlights how external variables affect users' beliefs, attitudes, and intentions regarding technology use, focusing on two key cognitive beliefs.
TAM: perceived usefulness and perceived ease of use Perceived usefulness was defined as
Perceived usefulness refers to an individual's belief that utilizing a specific system can improve their job performance, as defined by Davis (1989) Additionally, perceived ease-of-use is characterized by the belief that employing the system will require minimal effort.
1989) TAM also suggests that the external factors affecting intention and actual use through mediated effects on perceived usefulness and perceived ease of use (see Figure 2.3)
The Technology Acceptance Model (TAM) effectively explains and predicts user behavior, emphasizing two key metrics: perceived ease of use and perceived usefulness Research demonstrates that these metrics consistently exhibit high reliability and validity, underscoring their importance in understanding user acceptance of technology.
The Technology Acceptance Model (TAM) is widely recognized as the leading theory for examining users' behavioral intentions toward technological products Its effectiveness has been validated through numerous studies, experiments, and organizational surveys across various domains, including microcomputers, software, spreadsheets, email, and the World Wide Web.
1995; Taylor & Todd, 1995) In addition, TAM has been tested and proven in various countries such as US, Canada, Taiwan, China, India, Thailand, Malaysia, Iran and
Singapore (Jayawardhena, Wright, & Dennis, 2007) Hence, TAM seems to be a suitable model to develop a conceptual model to examine the factors affecting online purchase intention in Vietnam
In summary, the preliminary theoretical model to test factors affecting online purchase in this study is based on TAM Previous empirical studies repeatedly confirm that
The Technology Acceptance Model (TAM) effectively accounts for the gap between behavioral intentions and actual behaviors in various information technologies Recognized for its robustness and simplicity, this model lays the groundwork for a unique preliminary study model presented in this research Furthermore, specific constructs and scale measurements have been established to evaluate the theoretical model that identifies the factors influencing online purchasing behavior.
Overview of the Preliminary Model
The Technology Acceptance Model (TAM) has been widely applied across various contexts, supported by numerous empirical studies While the original framework included specific attitudes, many researchers have since removed these constructs from their models, as noted by Venkatesh.
The removal of attitudes from the Technology Acceptance Model (TAM) is supported by three key reasons First, previous experimental studies indicated that attitudes have a non-significant effect on behavioral intention, while perceived usefulness emerged as a crucial determinant (Davis et al., 1989) Although perceived usefulness influences attitudes, the latter may not significantly predict behavioral intention after prolonged exposure to technology Second, researchers argue that excluding attitudes simplifies the model, resulting in fewer indicators without significantly diminishing its predictive capability (Mathieson, 1991; Davis, 1989) Lastly, the streamlined model enhances clarity and focus on the most impactful factors influencing technology acceptance.
The Technology Acceptance Model (TAM) suggests that perceived usefulness is a key factor influencing technology adoption, regardless of users' attitudes towards it Individuals may choose to use a technology primarily for its productivity benefits, even if they do not feel positively about its impact Consequently, this study excludes attitudes from its structural model, focusing solely on the role of perceived usefulness (Davis et al., 1989).
In the Technology Acceptance Model (TAM), perceived usefulness (PU) is the primary factor influencing behavioral intention, while perceived ease of use (PEOU) indirectly affects this intention through PU (Davis et al., 1989) Both PU and PEOU have demonstrated high reliability and validity in various studies (Adam et al., 1992), and they significantly explain the variance between behavioral intentions and actual behaviors (Kamarulzaman, 2007) Therefore, this study will retain the constructs of perceived usefulness and perceived ease of use from the original TAM framework.
Despite the widespread application of the Technology Acceptance Model (TAM) among researchers, Lee, Kozar, and Larsen (2003) suggest that the original model requires integration and extension for a deeper comprehension of IT adoption According to Amin (2007), there are three approaches to enhance TAM, including the incorporation of factors from related models.
Forsythe, & Kwon, 2014), “by introducing additional or alternative belief factors” (Moon &
Research has explored the antecedents and moderators of perceived usefulness and perceived ease of use, as highlighted by Kim (2001) and Kamarulzaman (2007) Various studies, including those by Amin (2007) and Chang (2004), have utilized one or more of these approaches to enhance the original Technology Acceptance Model (TAM).
The Technology Acceptance Model (TAM) has evolved, with TAM2 expanding the original framework to encompass perceived usefulness and usage intentions This extension incorporates factors such as social influence, cognitive instrumental processes, and user experience Tested in both voluntary and mandatory contexts, TAM2 demonstrated a strong correlation with user adoption, accounting for 60 percent of the variance in usage intentions (Venkatesh & Davis, 2000).
The Technology Acceptance Model (TAM) has been widely utilized in various studies to analyze online purchase intentions across different countries Researchers have expanded TAM by incorporating factors such as gender, product category, brand orientation, and perceived risk, enhancing its applicability and relevance in understanding consumer behavior in the digital marketplace.
2014; Broekhuizen & Huizingh, 2009), online trust (Thamizhvanan & Xavier, 2013; Wen et al., 2011; Ling et al., 2010), prior online purchase experience (Dai et al., 2014;
Thamizhvanan & Xavier, 2013; Brown et al., 2003), social influence (Zamri & Idris, 2013;
There are many studies supporting that behavioral intention has a significant impact on usage and this variable can predict actual behavior in real world (Igbaria et al., 1995;
Taylor & Todd, 1995; Mathieson, 1991) As a result, this study measures purchase intention as a predictor of actual purchase A detailed explanation of the preliminary model, its constructs and hypotheses are next.
Conceptual Framework and Proposed Hypotheses
This study utilizes the Technology Acceptance Model (TAM) as a foundational framework to explore the factors influencing consumers' online purchase intentions By leveraging the established reliability and validity of perceived usefulness (PU) and perceived ease of use (PEOU) within TAM, the research aims to incorporate additional constructs to enhance the understanding and predictive accuracy of online consumer behavior in Ho Chi Minh City (Igbaria et al., 1995; Taylor & Todd, 1995).
City Additional constructs include in brand orientation, perceived risk, and prior online purchase experience
Perceived usefulness refers to an individual's belief that utilizing a specific system will improve their job performance (Davis, 1989) Behavioral intention is influenced by cognitive choice, meaning that potential online shoppers may react positively or negatively to the idea of making purchases online This study posits that the ability to attract online shoppers is largely dependent on the technology's usability and perceived usefulness.
Davis (1989) defines perceived usefulness (PU) as the belief that using an application enhances performance, particularly in the context of online shopping where benefits outweigh the drawbacks of physical retailing While research on Internet retailing from the Technology Acceptance Model (TAM) perspective is limited, the PU construct has received substantial support in various technological applications For example, Dai et al (2014) demonstrated that PU positively influences user intention in Intranet media.
(2007); Moon and Kim (2001); Venkatesh and Davis (2000); Igbaria et al (1995);
Mathieson (1991); Davis et al (1989) reported that perceived usefulness is significant and positively influences the behavioral intention Hence, it is expected that:
H1: Perceived usefulness has a significant positive impact on the consumer’s online purchase intention
2.4.2 Perceived ease of use (PEOU)
Perceived ease of use, as defined by Davis (1989), is the extent to which individuals feel that using a system requires minimal effort This concept highlights the convenience of online shopping for consumers, especially when compared to traditional shopping, which often involves challenges such as time constraints, anxiety, limited parking, crowded spaces, and traffic congestion (Yulihasri).
Amin (2007) conducted a study using the Technology Acceptance Model (TAM) to assess e-commerce acceptance, revealing that the ease of use significantly impacts consumers' intentions to shop online Similarly, Peng, Wang, and Cai (2008) identified perceived ease of use as a crucial factor influencing consumers' willingness to make online purchases The findings suggest that consumers view online shopping as beneficial when the process is user-friendly.
Further, the dimension of perceived ease of use included characteristics such as controllable, flexible, easy to learn, clear and understandable, easy to become skillful, and easy to use
According to Yulihasri et al (2011), ease of use will influence the consumers’ intention to purchase online As such, it could be hypothesized that:
H2: Perceived ease of use has a significant positive impact on the consumer’s online purchase intention
The concept of perceived risk, introduced by Bauer in 1960, highlights the uncertainties consumers face when considering new products or services This perceived risk, as discussed by Taylor in 1974, encompasses various concerns that can be heightened in the realm of online shopping, where physical access to products and sales personnel is limited.
In the realm of online retailing, customers encounter several prevalent risks, including security, privacy, and product risks (Chen & Barnes, 2007) Among these, security plays a crucial role in shaping customer trust, as it pertains to the protection of information technology systems and the safeguarding of financial data, including credit card information (Bart).
Shankar, Sultan, & Urban, 2005) Perceived privacy is defined as the ability of consumers to control the informational dissemination provided in the online transactions (Dai et al.,
Research by Goldsmith and Goldsmith (2002) indicates that online apparel shoppers experience a greater sense of product risk compared to those shopping in traditional stores This heightened perception of risk, particularly related to product uncertainty, can adversely influence consumers' intentions to shop online, as noted by Bhatnagar, Misra, and Rao (2000) Subsequent studies, including those by Dai et al (2014), further emphasize these findings.
Lee et al., 2003; Miyazaki & Fernandez, 2000; Warrington, Abgrab, & Caldwell, 2000 found that the perceived risk exhibits a significant negative influence on intention to purchase online So, it could be hypothesized that:
H3: Perceived risk has a significant negative impact on the consumer’s online purchase intention
A brand is a distinctive name, term, design, or symbol that uniquely identifies a seller's products or services, setting them apart from competitors (Aaker, 1991) In the online marketplace, brand identity serves as a cognitive anchor and recognition point, helping customers navigate the uncertainty they often face.
Brand orientation refers to the alignment of a product with its brand, which evolves based on market intelligence (Aaker, 1991) Customers often rely on trusted corporate and brand names as substitutes for detailed product information when making online purchases (Julie et al., 2006).
Research by Kamins and Marks (1991) highlights that a strong brand image and high familiarity with a brand enhance consumer attitudes and increase purchase willingness Subodn and Srinivas (1998) further emphasize that brand image shapes consumer perceptions, encompassing all information and expectations related to a product or service, which directly influences their purchasing decisions and quality inferences Additionally, numerous studies demonstrate that brand loyalty significantly boosts purchase intentions in traditional online retail environments.
(Thamizhvanan & Xavier, 2013; Kwek et al., 2010) Studies carried out by Ling et al
(2010); Jayawardhena et al (2007) conclude that brand orientation is positively related to the customer online purchase intention Thus, we propose:
H4: Brand orientation has a significant positive impact on the consumer’s online purchase intention
2.4.5 Prior online purchase experience (POPE)
Helson (1964) posited that an individual's response to judgment tasks is influenced by their past experiences, contextual background, and the stimuli involved Despite its growth, online shopping remains a relatively new experience for many consumers, who often view it as riskier compared to traditional shopping methods (Laroche, Yang).
McDougall, & Bergeron, 2005) Therefore, web-shopping consumers depend heavily on experience quality in which can be obtained only through prior purchase experience
Repeated actions in online shopping lead to quicker decision-making in future purchases, as past experiences significantly influence customer behavior Shoppers assess their online buying experiences based on various factors, including service quality, perceived risk, payment options, product information, visual appeal, privacy, personalization, security, navigation, delivery terms, and overall enjoyment (Burke, 2002).
Customer experience significantly influences the growth of Internet shopping, as highlighted by Elliot and Fowell (2000) Consumers prioritize the overall buying process, and Kwek et al (2010) suggest that prior online experiences enhance the likelihood of future purchases Seckler (2000) notes that individuals who start with small online purchases gradually gain confidence and skills, leading to more substantial online buying Consequently, consumers become more responsive to marketing messages, advertisements, sales strategies, website interactions, and brand engagement.
Customers with prior online shopping experiences are generally more confident and less risk-averse, as these experiences help alleviate uncertainties (Solomon et al., 2007) In contrast, first-time online shoppers tend to exhibit greater caution compared to those who have previously purchased online (Lee et al., 2003) Positive outcomes from past online purchases encourage customers to continue shopping online in the future (Shim et al.).
Warrington, 2001) Unfortunately, if these past experiences are evaluated negatively, customers will be reluctant to engage in online shopping in the future
Chapter summary
This chapter contextualizes the examination of factors influencing consumers' online purchase intentions, emphasizing the moderating role of product category through a review of existing literature The findings suggest that the Technology Acceptance Model (TAM) is an appropriate foundational model for this study, given its robust theoretical framework, high reliability, and broad acceptance Key constructs of TAM, namely perceived usefulness and perceived ease of use, significantly account for variations in behavioral intentions Consequently, a preliminary research model is proposed, integrating these TAM constructs with three additional factors: prior online purchase experience, perceived risk, and brand orientation, to create a tailored model for consumers in Ho Chi Minh City, Vietnam Notably, the study posits that product category serves as a moderating variable in the relationship between these factors and online purchase intentions, leading to the formulation of six testable hypotheses.
Research Method
Introduction
This chapter outlines the research design of the study, detailing the measurement and questionnaire design, the instruments utilized for data collection, the methods employed for data collection, and the approaches taken for data analysis.
Research design
This study employed a quantitative research design, utilizing the survey method due to its effectiveness in social, business, and information sciences Surveys are valuable for gathering data on human attitudes, behaviors, and characteristics (Gray, 2009) They offer a rapid, cost-effective means of collecting precise information about populations, enabling researchers to generate substantial data quickly (Zikmund et al., 2010).
To conduct this survey research, a comprehensive literature review was performed to establish a solid conceptual foundation A self-administered questionnaire was then meticulously designed and pre-tested to improve clarity and readability, ultimately aiming to minimize non-response rates among participants (Gray, 2009).
The finalized questionnaire was administered to the target population through both face-to-face interactions and online platforms following pilot testing Subsequently, the collected data was imported for analysis.
SPSS software is utilized for various testing and analysis methods, including exploratory factor analysis, reliability analysis, correlation analysis, and multiple regression The findings and their implications are presented and discussed in relation to the results obtained.
Measurement and Questionnaire Design
The instrument was developed using measures from validated questionnaires utilized in previous research Multi-scaled items for the constructs were drawn from various studies Table 3.1 presents the list of measurements and their sources, which will be clarified and adapted for this study.
I intend to purchase online in the future
Chou & Kimsuwan (2013); Wen, Prybutok, & Xu (2011); Kwek, Tan, &
I will keep using online shopping in the future
I think it would be very good to purchase products through Internet in addition to traditional methods
I would strongly recommend others to use online shopping
I would frequently use “online shopping”
2 Perceived Ease of Use (PEOU)
It is easier to use the Internet to find products that I want to buy
Wen, Prybutok, & Xu (2011); Moon & Kim
It is easy for me to learn how to use online shopping, even as the first time
It will be impossible to make an online transaction without expert help
Using online shopping do not requires a lot of mental effort
It is easy for me to become skillful at using online shopping
Using online shopping enables me to search and buy something more quickly
Wen, Prybutok, & Xu (2011); Moon & Kim
Use online shopping enables me to save my money Using online shopping helps me to make better purchase decisions (efficiency and effectiveness)
Use online shopping makes it easier for me to obtain goods
Overall, I believe “online shopping” is useful and advantageous
If I buy products/services from a web-retailer that I am familiar with, I would prefer to buy well—known brand name
It is important for me to buy products/services from the web retailer with well-known brand names
Once I find a brand I like through web-shopping, I stick with it
I think paying with a credit card to shop online is risky
I think online shopping would put my privacy at risk
I am worried whether I can get a product on time
I am worried that product quality may not meet my expectations
I am concerned that I may NOT receive the item purchased
I may buy the same product at a lower price from somewhere else
It is DIFFICULT for me to compare the quality of similar products
Prior online purchase experience (POPE)
After doing an online shopping, I know reliable retailer's web sites Thamizhvanan &
After doing an online shopping, I become more skillful
I am experienced with the use of the retailer's web site
The questionnaire was structured into three key sections: the first section collected demographic information about the respondents, the second section explored the general online purchasing behaviors of potential participants, and the final section outlined the independent and dependent variables to be assessed in the survey.
The questionnaire was developed in English and Vietnamese All items in the questionnaires adopted five-point Likert scales, where 1 = “strongly disagree” and 5 “strongly agree”
Before the main survey, a pretest was conducted to evaluate the clarity and effectiveness of the questionnaire and survey procedures This pilot test involved a convenience sample of 25 university students from the International School of Business (ISB) Participants were asked to complete the initial survey questions on-site, with the understanding that their responses would not be included in the final data analysis Feedback regarding the clarity and comprehensibility of the items, along with suggestions for revisions, was utilized to refine the questionnaire.
SPSS to measure the scale reliability and it is displayed in Appendix B with the Cronbach’s alpha of 0.854 The Cronbach’s alpha values of each variable were also greater than 0.7
Sweet and Grace-Martin (2008) suggest that an alpha value exceeding 0.7 indicates high reliability, leading to the decision not to alter the questionnaire The complete questionnaires can be found in Appendix A1 (English).
Data Collection Method
The target population of this study was focused on the Internet users aged between
16 and 45 including online shopping visitors (non-shoppers) and purchasers (shoppers) in
Ho Chi Minh City, Vietnam In this study, purchasers are those consumers who had been involved in the online transaction in the last six months
This research utilized a self-administered questionnaire survey, employing both web-based and face-to-face methods The web-based survey was chosen for its ability to gather large, geographically dispersed samples efficiently It offers faster response rates, simplifies data processing by allowing direct importation into spreadsheets or analysis programs, and is cost-effective (Gunn, 2002) Furthermore, it combines the advantages of mail surveys, enabling respondents to thoughtfully consider their answers at their convenience (Sekaran).
Web-based surveys offer the advantage of allowing respondents to reference websites they have previously visited or made purchases from, enhancing the relevance of their answers In contrast, face-to-face surveys provide significant benefits, including a clear structure, adaptability, and personal interaction, which enhances control over the survey process Additionally, face-to-face surveys improve data quality through the use of physical stimuli and the ability to observe respondents directly, making them a valuable method for gathering insights (Alreck & Settle, 2004).
In this research, the sampling method was convenience sampling This sampling method was chosen because it can gather information quickly and efficiently at low costs
In a study aimed at understanding consumer Internet shopping behaviors, an invitation email containing a survey hyperlink was sent to 600 convenient participants, including the author's colleagues and friends on platforms like Facebook and Google Circle The questionnaire employed a forced-answer format, ensuring that participants could not submit their responses without complying with the specified rules All collected data was securely stored in Google Drive, a free cloud-based system, accessible only to the researcher.
All data were collected anonymously And participants were not asked to provide any identification such as ID, actual name, detailed demographic information
242 sets of questionnaire were collected from a total of 600 distributed through web- based survey 174 over 242 questionnaire collected was usable with a response rate of 42%
The researcher conducted an on-the-spot survey with 160 respondents, comprising university students and building officers in Ho Chi Minh City, chosen for their high engagement in online shopping (VECITA, 2014) Out of 185 distributed questionnaires, 173 were collected, but 28 were deemed unusable due to incompleteness or invalid responses Consequently, 145 valid questionnaires were included in the analysis, resulting in a total sample size of 319 for testing and analysis.
Data Analysis Method
The analysis of data was conducted using SPSS version 20, beginning with descriptive statistics to summarize the variables Subsequently, factor analysis was performed to group the variables, employing principal components extraction with Varimax rotation To ensure internal consistency, the reliability of each measure was assessed using Cronbach’s alpha Finally, correlations among the variables were calculated, and multiple regression analysis was utilized to explore the relationships represented in each hypothesis.
Once the data had been collected and prepared for analysis, basic statistical and descriptive analysis was developed for the study
To ensure the validity of the scale items utilized in this study, which were either adopted from existing research or created by the researchers, an Exploratory Factor Analysis (EFA) was performed EFA is typically employed when researchers lack specific hypotheses regarding the underlying factor structure of their measurement tools.
For accurate Exploratory Factor Analysis (EFA), it is essential to represent each factor with multiple measured variables Research indicates that having at least 3 to 5 measured variables per factor enhances the reliability of the analysis (Hogarty et al., 2005).
Correlation matrix analysis is essential in determining the significance of loadings, as outlined by Hair, Anderson, Tatham, and Black (1995), who established a guideline where loadings of ±0.30 are minimal, ±0.40 are important, and ±0.50 are practically significant If the correlations do not exceed 0.30, researchers should reevaluate the appropriateness of using factor analysis as their statistical method (Tabachnick & Fidell, 2007; Hair et al., 1995).
To determine the appropriateness of respondent data for factor analysis, it is essential to utilize several tests, including the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy, introduced by Kaiser in 1970, and Bartlett's Test of Sphericity, developed by Bartlett in 1950 These tests help evaluate the suitability of the data for effective factor analysis.
The KMO index ranges from 0 to 1, with 0.50 and above considered suitable for factor analysis (Hair et al., 1995) The Bartlett's Test of Sphericity should be significant (p <
0.05) for factor analysis to be suitable (Hair et al., 1995)
The researcher employed Principal Component Analysis (PCA) for factor extraction, as it is widely recognized as the default method in many statistical programs and is frequently utilized in Exploratory Factor Analysis (EFA) (Thompson, 2004) Henson and Roberts (2006) recommended PCA for developing preliminary solutions in EFA This study implemented various extraction rules and approaches, including Kaiser’s criteria, which considers eigenvalues greater than one.
1 rule) (Kaiser, 1960), the Scree tests (Cattell, 1966), the cumulative percent of variance extracted, and parallel analysis (Horn, 1965)
Rotation analysis enhances interpretability by maximizing high item loadings and minimizing low item loadings, resulting in a clearer solution (Hogarty et al., 2005) In this study, varimax rotation was employed, a widely-used method for orthogonal rotation that ensures all factors remain uncorrelated (Thompson, 2004).
After conducting factor analysis, the reliability of the data was assessed using internal consistency measures The reliability of the scale was evaluated through Cronbach’s alpha coefficient, with a value exceeding 0.7 indicating high reliability and strong internal consistency.
The questionnaire utilized a multiple-item method, resulting in an average score for each construct This average score was subsequently applied in further analyses, including correlation and multiple regression analysis (Park & Kim, 2003).
In order to investigate the relationship between two variables, Pearson correlation coefficient (r) was used to measure the strength of the relationship (Sweet & Grace-Martin,
2008) The correlation coefficient ranges from -1 to +1 The correlation of 0.30 is considered a “good” correlation, and a correlation above 0.40 is considered “strong”
(Garson, 2011; Sweet & Grace-Martin, 2008) However, Garson (2011) recommends that the correlation coefficient should not exceed 0.80 in order to avoid multicollinearity
Multiple regression analysis is a powerful statistical tool widely utilized in social and natural science research to explore the relationship between independent and dependent variables This study employed multiple regression for several reasons, notably its ability to identify a set of independent variables that significantly explains the variance in a dependent variable, as assessed through the R-square significance test Furthermore, this analytical method offers valuable insights into the dynamics of the variables involved.
The "net strength" of each independent variable's relationship with the dependent variable can be assessed by analyzing the weight of beta coefficients This comparison allows researchers to evaluate the impact of each independent variable on the dependent variable effectively (Garson, 2011).
Before performing multiple regression analysis, the following assumptions had been tested and qualified to provide reliable results (Garson, 2011):
(1) Normality The normality of the residuals can be diagnosed by applying a histogram of residuals, as well as using normal probability plot
(2) Linearity If the relationship is linear, the plot of residuals does not form any curve (“n” shape or “u” shape) (Hair, Black, Babin, Anderson, & Tatham, 2006)
(3) Independence of error term The Durbin-Watson statistic was used to test if autocorrelation is present (Garson, 2011) Norusis (2005) recommends that the Durbin-
Watson statistic should be close to 2 if there is no correlation between error terms Also,
Garson (2011) provides the rule of thumb which is the Durbin-Watson coefficient should be fall between 1.5 and 2.5 to indicate the independence of error terms
(4) Absence of multicollinearity The simplest way of assessing multicollinearity is through the examination of the correlation matrix for the independent variables If the
A Pearson correlation coefficient exceeding 0.80 indicates significant multicollinearity To assess multicollinearity, the tolerance statistic and variance inflation factor (VIF) are also utilized Hair et al (2006) suggest a tolerance value threshold of 0.10, while Garson (2011) recommends that VIF should be greater than 1.0 to mitigate multicollinearity issues.
(5) Absence of heteroscedasticity In this study, the author uses scatterplot diagram, which shows the variation in error, variance of the residuals
(6) Absence of outlier and influential observations The author use Normal P-P Plot of Regression Standardized Residual to identify this assumption
A moderator effect occurs when a third variable, M, influences the relationship between two variables, X and Y To establish this effect, it is essential to demonstrate that the nature of the relationship between X and Y varies as the values of the moderating variable M change (Sharma, Durand, &).
Multiple regression is favored for its flexibility in coding categorical variables, as noted by Gur-Arie (1981) and supported by Cohen et al (2003) To effectively implement this method, specific steps must be followed.
1 Standardize all variables to make interpretations easier afterwards and to avoid multicolliearity, dummy code categorical variables and manually create product terms for the predictor and moderator variables
2 Fit a regression model (block 1) predicting the criterion from the predictors and the moderator Both effects and the model in general (R 2 ) should be significant
3 Add the interaction effect to the previous model (block 2) and check for a significant R 2 change as well as a significant effect by the new interaction term
If both are significant, then moderation is occurring
When analyzing regression results, it's crucial to focus on unstandardized coefficients (B) instead of standardized coefficients (β) This is particularly important in equations with interaction terms, as the β coefficients for these terms are not accurately standardized and therefore lack interpretability (Cohen et al., 2003; Aiken & West, 1991).
Chapter summary
This chapter outlines the primary research methodology employed in the study, detailing the research design and the selected data collection and analysis techniques A pretest survey was administered to academic personnel, research experts, colleagues, and friends The questionnaire utilized in the model was based on previous studies, and data collection was conducted through both online and face-to-face questionnaires.
The data analysis for this study will involve exploratory factor analysis, reliability testing using Cronbach’s alpha coefficient, and multiple regression analysis conducted with SPSS The findings from the field research and the subsequent data analysis will be detailed in the following chapter.
Research Results
Introduction
In this chapter, the survey data were analyzed using previously outlined methodologies, starting with a description of respondents' demographic profiles Subsequently, factor analysis was conducted to assess the validity of the constructs, followed by reliability analysis to ensure consistency Finally, multiple linear regression analysis was employed to test the developed hypotheses.
Profile of Respondents
The survey participants were Internet users aged between 16 and 45 including online shopping visitors (non-shoppers) and purchasers (shoppers) in Ho Chi Minh City A total of
A total of 242 questionnaires were collected through online surveys, while 173 were gathered via face-to-face interactions However, 96 of these questionnaires—68 from the online survey and 28 from the in-person survey—were deemed unusable due to incompleteness or invalidity, indicating a lack of cooperation or seriousness among some respondents.
Alternatively, the responses from participants who were not online shopping visitors or purchasers relating to one of product categories in this survey were considered invalid
Therefore, subsequently only 319 usable questionnaires (76.87 percent) were used for data analysis using SPSS software version 20
The demographic profile of the surveyed respondents was presented in Table 4.1
Details could be obtained in appendix C This included gender, age group, marital status, education, monthly income, occupation and internet experience
The survey respondents were predominantly male (50.8%) and female (49.2%), with a significant majority (77.8%) aged between 21 and 30 years Additionally, most participants were single (75.2%) and held a bachelor's degree or professional qualification.
The survey revealed that a significant portion of respondents, 66.8%, had an income below VND 5,000,000, with 30.4% earning in this bracket, while 27.0% earned between VND 5,000,000 and 10,000,000 This income distribution is largely influenced by the presence of university students still engaged in their studies Furthermore, more than half of the participants (53.6%) were employed in IT-related fields Notably, 63.9% of respondents reported having over six years of internet usage experience, and 27.3% had between four to six years of experience.
Source: Analysis of field data
According to Table 4.2, over half of the respondents (62.7%) spend more than four hours daily on the Internet, indicating significant online engagement Additionally, only 17.9% of respondents reported no experience with online purchases, suggesting a high adoption rate of 82.1% for online shopping Among those with purchasing experience, the majority (53.0%) shopped online 1 to 2 times, while 28.2% made purchases more than five times, and 18.8% shopped 3 to 5 times The most popular categories for online purchases included clothing (38.9%) and electronics.
(33.2%) were the two most common items purchased or visited by the respondents
Hours spent on the Internet (daily)
Frequency of purchase in last 06 months
Kind of product visiting or purchasing
Source: Analysis of field data
Descriptive statistics
The collected data was organized for easy interpretation by the researcher, with means and standard deviations for each model variable detailed in Appendix D Utilizing a five-point Likert scale, where a score of 5 reflects strong agreement and 1 reflects strong disagreement, the means for nearly all 23 items exceeded the neutral position of 2.5 This indicates a robust level of agreement among respondents regarding the survey statements The descriptive statistics for the constructs measured will be discussed in detail in the following section.
Perceived usefulness (PU) There were 5 measurement items for this construct
Respondents rated factors related to the speed and convenience of online shopping highly, with means exceeding 3.6 for the ability to search and purchase quickly (PU1), the perceived usefulness of online shopping (PU5), and the ease of obtaining goods (PU4) In contrast, the ability to save money (PU2) and make informed purchase decisions (PU3) received lower mean ratings Nevertheless, all items scored above the neutral mean, indicating general agreement among respondents Summary statistics, including means and standard deviations for perceived usefulness, are detailed in Table 4.3, arranged from highest to lowest mean.
Table 4.3: Means and standard deviations of items measuring perceived usefulness
No Perceived usefulness (PU) Mean Std Deviation
PU1 Use online shopping enables me to search and buy something more quickly 4.03 0.774
PU5 Overall, I believe “online shopping” is useful and advantageous 3.61 0.804
PU4 Use online shopping makes it easier for me to obtain goods 3.61 0.853
PU2 Use online shopping enables me to save my money 3.18 0.959
PU3 Use online shopping helps me to make better purchase decisions (efficiency and effectiveness) 3.14 1.007
Source: Analysis of field data
Perceived ease of use (PEOU) There were 4 measurement items for this construct
Respondents expressed a strong preference for the ability to make online transactions without expert assistance (PEOU2), with a mean rating exceeding 4.2 Additionally, the ease of learning to use online shopping for the first time (PEOU1) and the capacity to become proficient in it (PEOU4) received favorable ratings of 3.93 and 3.87, respectively However, the perception that online shopping does not require significant mental effort (PEOU3) received the lowest mean score at 3.62 Despite this, all items scored above the neutral mean, indicating general agreement among respondents Detailed summary statistics, including means and standard deviations, are presented in Table 4.4.
Table 4.4: Means and standard deviations of items measuring perceived ease of use
No Perceived ease of use (PEOU) Mean Std Deviation
PEOU2 It will be POSSIBLE to make an online transaction without expert help 4.23 0.743
PEOU1 It is easy for me to learn how to use online shopping, even at the first time 3.93 0.861
PEOU4 It is easy for me to become skillful at using online shopping 3.87 0.860
PEOU3 Use online shopping do not requires a lot of mental effort 3.62 0.979
Source: Analysis of field data
Perceived risk (PR) was assessed using three measurement items, with concerns about privacy risk (PR2) receiving high ratings from respondents, averaging over 3.6 Additionally, participants expressed agreement that the quality of products received during online shopping often falls short of expectations (PR3) and that using credit cards for payment poses risks (PR1), both reflecting means above 3.4 Importantly, all measurement items maintained means above the neutral threshold Summary statistics for the means and standard deviations of the perceived risk items are presented in Table 4.5.
Table 4.5: Means and standard deviations of items measuring perceived risk
No Perceived risk (PR) Mean Std Deviation
PR2 I think online shopping would put my privacy at risk 3.69 0.911
PR3 I am worried that product quality may not meet my expectations 3.55 0.943
PR1 I think paying with a credit card to shop online is risky 3.48 0.967
Source: Analysis of field data
Brand orientation (BO) is assessed through three key variables, with respondents generally rating the items related to BO positively A notable factor within this construct is BO3, which highlights the tendency of consumers to remain loyal to a preferred brand discovered through online shopping.
When purchasing products or services online, I prioritize well-known brand names to ensure quality and reliability Familiarity with a web retailer significantly influences my buying decisions, as indicated by a mean score of over 3.6 in brand orientation Summary statistics, including means and standard deviations, are detailed in Table 4.6, highlighting the importance of brand recognition in consumer behavior.
Table 4.6: Means and standard deviations of items measuring brand orientation
No Brand orientation (BO) Mean Std Deviation
BO3 Once I find a brand I like through web-shopping, I stick with it 3.95 0.840
BO2 It is important for me to buy products/services from the web retailer with well-known brand names 3.73 1.004
BO1 If I buy products/services from a web-retailer that I am familiar with, I would prefer to buy well—known brand name
Source: Analysis of field data
Prior online purchase experience (POPE) significantly influences consumer decisions in online shopping Respondents indicated familiarity with reliable retailers' websites (POPE3) and reported increased skillfulness in online shopping (POPE2) as key factors in their purchasing choices Although experience using the retailer's website (POPE1) received the lowest average rating of 3.73, all measurement items maintained scores above the neutral mean Detailed summary statistics, including means and standard deviations for these variables, can be found in Table 4.7.
Table 4.7: Means and standard deviations of items measuring prior online purchase experience
No Prior online purchase experience (POPE) Mean Std Deviation
POPE3 After doing an online shopping, I know reliable retailer's web sites
POPE2 After doing an online shopping, I become more skillful 3.90 0.761
POPE1 I am experienced with the use of the retailer's web site 3.73 0.754
Source: Analysis of field data
The study on online purchase intention (OPI) revealed that respondents rated the five measurement items positively, indicating a strong preference for online shopping alongside traditional methods Specifically, participants expressed a commitment to continue using online shopping (OPI2) and showed a future intention to use it (OPI1), with ratings exceeding 3.8 Additionally, the willingness to recommend online shopping to others (OPI4) and the frequency of use (OPI5) also received favorable ratings, with means of 3.63 and 3.47, respectively.
Obviously, all of the measurement items had means higher than the neutral mean Table 4.8 reported the summary statistics of means and standard deviations of these variables as below
Table 4.8: Means and standard deviations of items measuring online purchase intention
No Online purchase intention (OPI) Mean Std Deviation
OPI3 I think it would be very good to purchase products through Internet in addition to traditional methods
OPI2 I will keep using online shopping in the future 3.85 808
OPI1 I intend to shop online in the future 3.84 795
OPI4 I would strongly recommend others to use online shopping 3.63 848
OPI5 I would frequently use “online shopping” 3.47 903
Source: Analysis of field data
Factor analysis
The factor analysis aimed to uncover the underlying structure among the variables in the study (Hair et al., 2006) To evaluate the validity of the constructs in the questionnaires, 23 items were analyzed using Principal Components.
Extraction with Varimax rotation (see detail in appendix E) Table 4.9 showed the Kaiser-
Meyer-Olkin (KMO) measure of sampling adequacy and Barletts’s test of Sphericity of independent and dependent variables Based on the output displayed, KMO values were
The independent variables had a value of 0.809, while the dependent variable scored 0.819, both exceeding the threshold of 0.5 Additionally, Bartlett’s test yielded a p-value of less than 0.001, indicating high statistical significance These findings confirm that the relationships among the variables are statistically significant and suitable for exploratory factor analysis, allowing for a concise set of factors (Tabachnick & Fidell, 2007).
Table 4.9: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.809 0.819
Bartlett's Test of Sphericity Approx Chi-Square 1680.179 1037.999 df 153 10
Source: Analysis of field data
The Rotated Component Matrix presented in Table 4.10 illustrates the factor loadings for each item, which were organized by size The analysis confirmed that all 18 items corresponded effectively with the construct, as they were categorized into five distinct components, ranging from Factor 1 to Factor 5 Notably, each item exhibited loadings exceeding 0.5 and was associated with a single factor following the rotation process.
The analysis revealed three key factors influencing online purchasing behavior: the first factor, labeled Perceived Usefulness (PU), included five items related to the perceived benefits of the product The second factor, identified as Perceived Ease of Use (PEOU), encompassed items reflecting the simplicity of using the platform Finally, the third factor, Prior Online Purchase Experience, consisted of three items related to previous purchasing experiences.
The influence of brand names is encapsulated in Factor 4, termed Brand Orientation (BO), while the items related to risk are categorized under Factor 5, known as Perceived Risk (PR) Each factor comprises a set of closely related items that collectively represent a broader evaluative dimension, as highlighted by Hair et al (2006).
In general, each item was closely correlated with their specific factors respectively
For instance, the items of the “usefulness” construct were loaded under Factor 1 (Perceived
The analysis demonstrated that the scale items were valid, as they showed no correlation with other factors (Factor 2 to Factor 5) Additionally, as indicated in Table 4.11, all factors exhibited eigenvalues greater than 1, with the highest being 4.633.
1.939, 1.731, 1.589, and 1.119) were considered significantly (see details in Appendix E, section E.1)
PU5 Overall, I believe “online shopping” is useful and advantageous .730
Use online shopping helps me to make better purchase decisions (efficiency and effectiveness)
PU2 Use online shopping enables me to save my money .705
PU4 Use online shopping makes it easier for me to obtain goods .655
PU1 Use online shopping enables me to search and buy something more quickly .605
PEOU3 Use online shopping do not requires a lot of mental effort .811
PEOU1 It is easy for me to learn how to use online shopping, even as the first time .687
PEOU4 It is easy for me to become skillful at using online shopping .685
PEOU2 It will be IMPOSSIBLE to make an online transaction without expert help .561
POPE1 I am experienced with the use of the retailer's web site .830
POPE3 After doing an online shopping, I know reliable retailer's web sites .312 752
POPE2 After doing an online shopping, I become more skillful .396 706
It is important for me to buy products/services from the web retailer with well-known brand names
If I buy products/services from a web-retailer that I am familiar with, I would prefer to buy well—known brand name
BO3 Once I find a brand I like through web- shopping, I stick with it .719
PR2 I think online shopping would put my privacy at risk .845
PR1 I think paying with a credit card to shop online is risky .813
PR3 I am worried that product quality may not meet my expectations .692
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 7 iterations
Source: Analysis of field data
The analysis revealed that each factor explained a greater amount of variance than individual items, with the first factor contributing 14.648%, the second 12.989%, and the third 11.865% Collectively, the five components accounted for 61.171% of the total variance, indicating that 61.171% of the variance could be attributed to the 18 items represented by these five factors.
Component Initial Eigenvalues Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
Source: Analysis of field data
The analysis indicates that each group of items is unidimensional, as they are strongly correlated and represent a single concept within one factor Furthermore, the high loading of each scale item on this single factor confirms the construct validity (Hair et al., 2006).
Reliability Test
The scale reliability was test by Cronbach’s alpha coefficient The reliability analysis of the scale items for each construct (PU, PEOU, PR, BO, POPE, and OPI) was presented in
Table 4.12 while the detailed result could be obtained in appendix F
As observed, the Cronbach’s alpha coefficient for almost constructs, except Perceived Risk ranged from 0.715 (Perceived Ease of Use) to 0.890 (Online Purchase
The intention values exceeded 0.7, indicating strong measurement reliability, while the Perceived Risk value of 0.692 was deemed acceptable These findings suggest that all scale items effectively measured the same construct, demonstrating high reliability and good internal consistency (Field, 2005; Sweet & Grace-Martin, 2008).
No Items Cronbach's Alpha Cronbach's Alpha Based on Standardized Items
Source: Analysis of field data
The values under the column “corrected item – total correlation” pointed out the correlations between each item and the rest of the values from every construct (Field, 2005)
An item to be correlated well with the rest of the items as the score should have greater than
0.3 As displayed in Appendix F, each of the items for each construct obtained corrected item – total correlation score of over 0.3 and less than 0.8, excepted item OPI3 (“I will keep using online shopping in the future”) was at 0.838 So, those were concluded that the construct reliability was confirmed for this data set
Based on factor and reliability analyses, key determinants influencing the intention to purchase online include perceived risk, perceived ease of use, perceived usefulness, brand orientation, and prior online purchase experience.
Therefore, the research model and hypotheses which were mentioned in Literature Review chapter were kept to do further analyses
To perform further analyses such as: correlation and multiple regression, proxy variables were computed by getting average of related items Accordingly, Perceived
The Usefulness (PU) variable is calculated as the average of PU1 to PU4, while the Perceived Ease of Use (PEOU) variable is derived from the mean of PEOU1 to PEOU4 Additionally, the Perceived Risk (PR) variable is determined by averaging PR1 to PR3.
Band Orientation (BO) variable was mean of BO1 to BO3; Prior Online Purchase
Experience (POPE) variable was mean of POPE1 to POPE3; and Online Purchase Intention
(OPI) variable was mean of OPI1 to OPI5.
Correlation Analysis
Table 4.13 reveals that all variable pairs are significant at the 0.01 level, with the exception of the relationship between Perceived Risk and the other independent variables Nevertheless, nearly all proposed hypotheses show significant relationships.
Chapter 2 were found to be statistically significance at level 0.01, except perceived risk correlated significantly with online purchase intention at level 0.05 It meant that the PU (r
The study found significant positive correlations between several factors and Online Purchase Intention (OPI), with Perceived Usefulness (PU) showing the strongest correlation at r = 0.638 Other notable correlations included Perceived Ease of Use (PEOU) at r = 0.421, Perceived Online Privacy Experience (POPE) at r = 0.596, and Brand Orientation (BO) at r = 0.270 Conversely, Perceived Risk (PR) was negatively correlated with OPI at r = -0.127.
PU PEOU PR BO POPE OPI
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
Source: Analysis of field data
Multiple Regression
Before conducting regression analysis, it is essential to address six key assumptions: normality, linearity, independence of the error term, absence of multicollinearity, absence of heteroscedasticity, and the exclusion of outliers and influential observations (Field, 2005) Detailed results for these assumptions in the context of multiple regression can be found in Appendix G.
The histogram generated from SPSS displayed a unimodal and symmetric distribution, indicating normality Additionally, the scatterplot confirmed that the assumptions of linearity and absence of heteroscedasticity were satisfied, as the residuals were randomly scattered without any discernible patterns when plotted against the predicted values.
The independence of the error term was confirmed with a Durbin-Watson value of 2.043, indicating strong independence as it is close to the ideal value of 2 (Field, 2005, p.189) Additionally, the multicollinearity statistics provided further insights into the data's reliability.
The VIF values ranged between 1.0 and below 10, indicating no multicollinearity issues, while the tolerance statistics exceeded 0.2 Additionally, the correlation analysis revealed that the independent variables had low correlations, with correlation coefficients remaining below 0.80.
The analysis confirmed that the data was free from multicollinearity Additionally, the normal probability plot demonstrated a straight line, indicating no outliers or influential observations, while the residuals displayed a uniform spread when plotted against the predicted values.
In Chapter 2, the proposed hypotheses were evaluated through multiple linear regression analysis, which allowed for an examination of the influence and significance of independent variables on their relationship with dependent variables.
The R-Square value for the current study's model was approximately 0.544, indicating that 54.4% of the variability in the dependent variable can be explained by the combined predictors This suggests a relatively strong linear relationship between the independent and dependent variables.
(Perceived Usefulness - PU, Perceived Ease Of Use - PEOU, Perceived Risk - PR, Brand
Orientation - BO, and Prior Online Purchase Experience - POPE) and dependent (Online
R R Square Adjusted R Square Std Error of the Estimate Durbin-Watson
537 a 544 536 47077 2.043 a Dependent Variable: Online Purchase Intention
Source: Analysis of field data
Field (2005) asserts that hypotheses are validated when beta coefficients are significant, indicated by a p-value under 0.05 and a t-value exceeding 2.0 Furthermore, a smaller p-value coupled with a higher t-value enhances the predictive power of the independent variable The standardized beta coefficient (β) reveals the nature of the relationship, whether positive or negative, between the dependent variable and each independent variable.
The ANOVA table from the multiple regression analysis (Table 4.15) indicates a p-value of less than 0.001, significantly lower than the 0.05 threshold This suggests that at least one of the five independent variables is relevant for modeling the dependent variable, Online Purchase Intention.
Squares df Mean Square F Sig
Total 152.026 318 a Dependent Variable: OPI Online Purchase Intention b Predictors: (Constant), POPE Prior Online Purchase Experience, PR Perceived Risk,
BO Brand Orientation, PEOU Perceived Ease of Use, PU Perceived Usefulness
Source: Analysis of field data
Table 4.16 displays the results of the multiple regression analysis for the research model outlined in Chapter 2 The findings support Hypothesis H1, indicating a positive relationship between Perceived Usefulness and customers' intention to make online purchases in Ho Chi Minh City.
Perceived Usefulness and Online Purchase Intention (β = 0.427 > 0, t = 9.665> 2, p < 0.001) was statistically significant This hypothesis had the most significant relationship among the others Therefore, the hypothesis H1 was supported
Hypothesis H2 suggested a positive relationship between Perceived Ease of Use and customers' online purchase intention As indicated in Table 4.16, a significant positive relationship was confirmed between Perceived Ease of Use and online purchase intention.
Purchase Intention (β = 0.105 > 0, t = 2.392 > 2, p = 0.017 < 0.05) Thus, the hypothesis H2 was supported in this study
Table 4.16: Result of Multiple Regression Analysis a
Standardized Coefficients t Sig Collinearity Statistics
B Std Error Beta Tolerance VIF
Source: Analysis of field data
Hypothesis H3 explored consumer risk perception regarding online shopping intentions in Ho Chi Minh City While the bivariate analysis indicated a weak statistical association between risk perception (PR) and online purchase intention (OPI), the relationship became significant when other independent variables were included in a multivariate analysis This suggests that the variations in the dependent variable were influenced by multiple independent variables, leading to an overlap in explained variation As shown in Table 4.16, the results revealed a significant p-value, highlighting the importance of considering additional factors in understanding consumer behavior.
A statistically significant relationship was found between Perceived Risk and customers' intention to shop online, with a confidence level of 98.6% The β value of -0.094 (t = -2.464) indicates a negative relationship, supporting the acceptance of hypothesis H3.
Hypothesis H4 predicted that the greater the brand orientation, the higher the consumers’ online purchase intention The result manifested that there was a positive relationship and it was statistically significant (β = 0.106 > 0, t = 2.700 > 2, p = 0.007 <
0.05) In the other word, brand orientation was significant independent variable to predict intention to online shopping and hence hypothesis H4 was supported
Hypothesis H5 explored the connection between consumers' online purchase experiences and their intention to engage in online shopping The findings, as shown in Table 4.16, indicate that this hypothesis is statistically significant, with a p-value of less than 0.05 (p < 0.001) and a notable t-value.
6.560 which was higher the acceptable threshold Alternatively, the β value was equal to -
0.342, which stated this was a positive relationship Those indicated that hypothesis H5 was supported in this study
To evaluate the moderating effect of the product category variable, we conducted a hierarchical moderator regression analysis (HMRA) as outlined by Sharma et al (1981) For a comprehensive overview of the moderating test, please refer to Appendix H The analysis followed specific procedural steps to ensure accurate results.
Step 1: Center the continuous predictor variables (Centered Perceived Usefulness (CPU), Centered Perceived Ease of Use (CPEOU), Centered Perceived Risk (CPR),
Centered Brand Orientation (CBO), and Centered Prior Online Purchase Experience
(CPOPE)) and dummy code moderator variable (DCLO, DELECS) to eliminate multicollinearity effects between the predictor and moderator, and the interaction terms
Step 2: Enter the predictors and moderators main effects (hierarchical)
Step 3: Enter the interactions between predictors and moderators
Chapter summary
This chapter outlines the data analysis conducted for the research, which achieved a response rate of 76.87% through web-based and on-the-spot surveys Initially, descriptive statistics of the dataset were presented, followed by factor analysis and reliability analysis to assess the validity and reliability of the data Subsequently, correlation analysis was performed to examine the relationships between the proposed hypotheses.
Finally, the multiple regression analysis was deployed after six assumptions were tested for obtaining the reliable results
A multiple regression analysis was performed to evaluate six hypotheses derived from the literature review The findings confirmed that perceived usefulness, perceived ease of use, perceived risk, brand orientation, and prior online purchase experience significantly influence consumers' online purchase intentions Additionally, the product category was found to play a moderating role in these relationships The subsequent chapter will discuss the implications and insights derived from these findings.
Besides, the limitations of this research would be presented and the recommendations for further research would be suggested at the end of the research.
Discussion and Conclusion
Discussion
This study investigates the connections between consumers' online shopping experiences and their intention to purchase online, focusing on brand orientation, perceived risk, and the usefulness and ease of use of online shopping, with product category serving as a moderating factor Six hypotheses formulated in Chapter 2 are evaluated using multiple regression analysis, with results detailed in Table 5.1 As outlined in Chapter 4, five key factors—Usefulness, Ease of Use, Prior Online Purchase Experience—significantly influence online shopping intentions.
Brand Orientation, Perceived Risk) in this research received good support from both the literature review and the survey This result implies that Usefulness, Ease of Use, Prior
The online purchase experience, perceived risk, and brand orientation are key predictors of consumers' intention to engage in online shopping, aligning with findings from prior research However, the influence of these factors varies significantly across different product categories, highlighting the moderating role of product type A summary of the six hypotheses and their outcomes is presented in Table 5.1.
Table 5.1: Summary of Hypotheses Results
Hypothesis 1: Perceived usefulness has a significant positive impact on the consumer’s online purchase intention Supported
Hypothesis 2: Perceived ease of use has a significant positive impact on the consumer’s online purchase intention Supported
Hypothesis 3: Perceived risk has a significant negative impact on the consumer’s online purchase intention Supported
Hypothesis 4: Brand orientation has a significant positive impact on the consumer’s online purchase intention Supported
Hypothesis 5: Prior online purchase experience has a significant negative impact on the consumer’s online purchase intention Supported
Hypothesis 6: Product category moderates the effect of the factors on consumers’ online purchase intention Supported
Source: Result of this study analysis
The discussion in details of factors influencing to the consumer’s online purchase intention are showed below
5.2.1 Relationship between perceived usefulness and consumer’s online purchase intention
This study reveals a significant positive correlation between perceived usefulness and consumers' intention to purchase online It suggests that when retailers enhance the perceived applicability, flexibility, and interactivity of their systems, customers are more likely to increase their intention to shop These findings align with previous research conducted by Wen et al (2011), Peng et al (2008), and Moon.
Kim (2001) who extended TAM in the context of online shopping to predict that Perceived
Usefulness is an important factor in determining users’ acceptance of online shopping
Through findings, they have provided evidences of the significant effect of Perceived usefulness on intention to purchase online
According to Appendix D, consumers find online shopping beneficial due to its speed and convenience, with a mean perception score of 4.03 for quick searches and purchases, and 3.61 for ease of obtaining goods Unlike traditional retail, virtual shopping enhances the information environment by offering extensive product details, comparisons, and various options Online shopping allows consumers to avoid the hassle of dressing up and traveling to stores; instead, they can effortlessly browse websites from their computers at their convenience This accessibility provides immediate information for product selection, enabling shoppers to compare features and prices without the need to visit multiple physical locations.
In addition, the users expect that they can get products easily and conveniently
Online shopping allows consumers to order products globally, breaking the limitations of local retailers With most online retailers offering shipping to various locations, shoppers can save time and access a wider range of products This convenience and efficiency enhance the shopping experience, leading consumers to view online shopping as a valuable tool, ultimately increasing their intention to adopt it.
5.2.2 Relationship between perceived ease of use and consumer’s online purchase intention
Research has shown that perceived ease of use is a crucial factor influencing online shopping acceptance This study confirms a significant positive correlation between perceived ease of use and online purchase intention, suggesting that user-friendly websites can enhance the adoption of online shopping among consumers in Ho Chi Minh City These findings align with earlier studies (Yulihasri et al., 2011; Peng et al., 2008; Moon and Kim, 2001), which also highlight the positive relationship between perceived ease of use and consumers' online purchasing intentions.
According to Appendix D, the ease of making online transactions without expert assistance (mean = 4.23) and the ability to learn online shopping quickly (mean = 3.93) significantly influence consumers' intentions to shop online As highlighted in Chapter 2, customers are drawn to online shopping not only for its convenience but also for its wide selection, ease of learning, user-friendliness, flexibility, and access to information A well-designed user interface can enhance consumers' perception of online shopping as easy, even for first-time users.
The most important factors determining whether customers return to a website are ease of use and the presence of user-friendly features User-centered design is critical
Understanding customer wants and needs is crucial for fostering loyalty and encouraging repeat business By consistently delivering on promises, companies can meet customer expectations and enhance their satisfaction A website should clearly convey the value placed on customers, utilizing well-organized product images and information to improve the online shopping experience This approach not only boosts consumer enjoyment but also elevates their expectations regarding product quality, ultimately driving increased engagement in online shopping.
5.2.3 Relationship between perceived risk and consumer’s online purchase intention
The findings related to Hypothesis H3 reveal a significant negative impact of Perceived Risk on users' intention to shop online This aligns with previous studies indicating that perceived risk is a crucial factor affecting consumers' online shopping intentions (Lee et al., 2003; Miyazaki & Fernandez, 2000) Consequently, perceived risk acts as a barrier to online shopping intentions, especially in Ho Chi.
According to Appendix D, Vietnamese consumers express significant concern regarding privacy risks, with a mean score of 3.69, suggesting a reluctance to share personal information Despite these privacy concerns, cash on delivery remains the predominant payment method among retailers, while the use of credit cards is still limited, particularly among student users.
In Vietnam, credit card-related crimes remain relatively uncommon, resulting in lower financial risk perceptions among Vietnamese consumers compared to those in developed countries such as Korea, Taiwan, and the United States.
Today, risk is no longer a significant barrier to online transactions, as e-commerce has gained immense popularity among businesses (Bart et al., 2005) Many consumers have experienced the convenience of online shopping, which saves both time and money while helping them meet their shopping goals Furthermore, a substantial portion of survey participants identified as experimental purchasers, highlighting the growing acceptance and engagement in online shopping.
In this study, 47% of participants reported shopping online at least three times, while over 82% engaged in online shopping more than once This indicates that perceived risks did not significantly hinder the adoption of online shopping among respondents.
Shoppers new to online purchasing tend to be more risk-averse compared to experienced buyers, who trust well-known and reliable retailers, believing the chances of not receiving their products are low However, online shopping has its limitations, such as the inability to physically touch or feel items and the challenge of accurately representing aesthetic products like apparel online Additionally, products may sometimes arrive damaged or differ from their online images In these situations, strong return, refund, and warranty policies from retailers can alleviate consumer concerns about product risk, encouraging them to make online purchases.
Consumers feel more secure when they deciding to buy product through the particular retailer rather than buying from those retailers who does not have good reputation
The consumers also can easily search for company’s information through Internet and it helps them to distinguish the trustworthiness of the retailers
5.2.4 Relationship between brand orientation and consumer’s online purchase intention
This research highlights a strong positive correlation between brand orientation and consumers' intention to make online purchases Specifically, it suggests that a well-known or popular brand enhances consumers' likelihood of shopping online These findings align with previous studies conducted by Jayawardhena et al (2007), Julie et al (2006), and Ward & Lee.
2000) which suggested brand orientation is positively related to the customer online purchase intention
The significant positive relationship between brand orientation and intention to purchase online reveals that the importance of the brand name’s influences According to
Appendix D, sticking to a brand that they like through web-shopping (mean = 3.95) and importance of buying products/services from the web retailer with well-known brand names
(mean = 3.73) are the relevant influences to their intention to buy online These indicate that the well-known brands will affect the intention of consumers to adopt the online shopping
Implications
This research reinforces the Technology Acceptance Model (TAM), highlighting that perceived usefulness (PU) is the key factor influencing the adoption of technology-related products, particularly in online purchasing intentions The TAM model, effective in Western contexts, is also relevant for consumers in Vietnam This study contributes significantly to the TAM theory by enhancing the understanding of Internet marketing Furthermore, it identifies perceived risk (PR), brand orientation (BO), and prior online purchase experience (POPE) as additional factors that impact consumer behavioral intentions.
This research employed a mixed-methods approach, integrating qualitative and quantitative data from literature and empirical studies on technological products to analyze online purchasing behavior in Ho Chi Minh City (HCMC) To ensure accurate responses, the study conducted one-on-one interviews, preventing multiple submissions of the questionnaire Building on previous studies, it highlights the significance of product categories in influencing managerial outcomes Furthermore, the research demonstrates how product categories impact the relationship between consumer experience, perceived risk, ease of use, perceived usefulness, brand orientation, and online purchase intentions.
In summary, this study offers a significant contribution to the body of knowledge regarding behavioral intention prediction of the Technology Acceptance Model in the
In the context of Ho Chi Minh City (HCMC), the Technology Acceptance Model (TAM) not only elucidates the behavioral intentions toward technology-related products but also offers valuable insights into high-involvement items such as books, clothing, and electronics This research demonstrates the application of advanced techniques in model development, data collection, and data analysis using SPSS.
The research findings offer valuable insights for e-retailers to develop and implement effective business strategies aimed at enhancing customers' online purchase intentions Notably, the study reveals that the factors influencing consumers' online purchase intentions are applicable in both low and high uncertainty avoidance countries, providing a comprehensive framework for e-retailers to optimize their approaches across diverse markets.
Vietnam), particularly among young consumers in HCMC
To enhance consumer satisfaction during online transactions and foster stronger relationships, businesses should implement personalized online services E-retailers can attract brand-oriented customers by offering loyalty programs or club memberships Additionally, marketing tools, including advertisements in media and press, significantly influence consumers' intentions to shop online and encourage frequent usage.
Marketers can enhance their brand visibility and attract more potential customers by leveraging diverse channels, including social media platforms, word-of-mouth marketing, and informal seminars These strategies help build customer popularity and contribute to establishing a well-known brand.
Usability testing is important for finding problems and improvements in a web site
Evaluating usability can be achieved through methods such as heuristic evaluation, cognitive walkthrough, and user testing, each focusing on different aspects of user experience E-retailers should ensure that their websites offer comprehensive product quality and search information, as well-presented product details can significantly enhance consumers' perceptions of both the website and the quality of its information This insight serves as valuable guidance for web developers to create user-friendly and engaging content, ultimately improving online shopping experiences and fostering consumers' intentions to shop online To mitigate consumers' risk perceptions, e-retailers must consistently provide honest and trustworthy information to potential shoppers.
E-commerce in Vietnam is far behind other developed countries such as Singapore, Taiwan, Korea, and Malaysia (Quangtrimang, 2013) The main reasons for the limited growth in Vietnam are language, telecommunication infrastructure and low Internet penetration in the country (VECITA, 2014) Moreover, companies often face many problems from selling products online, such as high transportation costs, high bank charges, high competition, high access charges, poor security, poor network reliability and negative attitudes of online consumers (PwC, 2013) So far, no companies have a successful online business in Vietnam If government wants to promote e-commerce in Vietnam, they have to improve the infrastructure of telecommunication to improve speed and accessibility
To enhance Internet accessibility for Vietnamese consumers, it is essential to lower hardware and software prices Furthermore, implementing robust regulations and laws is crucial to combat Internet fraud, thereby reducing the perceived risks for the general public.
The government must prioritize the advancement of new technology and recognize the development of the Internet as a key component of national policy to align with global trends.
This study provides valuable insights into the key factors influencing online purchasing intentions among consumers in Ho Chi Minh City The findings are advantageous for marketers and web developers, as they can inform the design and execution of effective marketing strategies that enhance consumer satisfaction and boost online sales Additionally, the research offers essential knowledge for telecommunication and Internet service providers, aiding in the improvement of Internet accessibility to promote e-commerce growth in the region.
Chi Minh City Finally, the findings also provide good inputs for the Vietnamese government to improve the penetration of the Internet and e-commerce in general.
Limitations
This research has several limitations that should be noted in future research as below
The use of convenience sampling in this research may hinder the representation of the intended population, as highlighted by Zikmund et al (2010) With 85.9% of respondents under 20 years old, approximately 77.1% earning 15,000,000 VND or less, and 75.2% being single, the sample may reflect a demographic primarily interested in the Internet However, to enhance the study's validity, it is essential to broaden the sample to include diverse occupations, age groups, and income levels Furthermore, the sample size of 319 is insufficient to represent the entirety of HCMC and Vietnamese consumers.
Secondly, the literature regarding online shopping in Vietnam is generally limited
This research utilizes cross-cultural references due to a scarcity of relevant literature, which may limit its applicability to the Vietnamese context and potentially fail to accurately represent the actual situation in the country.
Thirdly, there is a possible problem of self-selection and self-reporting in this study
A web-based survey garnered responses from 55% of participants, providing a quick and efficient response rate However, this method may lead to average answers rather than genuine opinions Additionally, self-reported demographic data and Internet usage statistics could introduce potential biases in the results.
Favorable results from a model in any study are relative rather than absolute, as good model fit does not necessarily reflect reality but rather indicates a strong representation of the relationships among the factors involved There is a possibility that significant factors may have been omitted or inadequately measured within the model To ensure an optimal goodness of fit, this study employed multiple criteria of goodness of fit indices, grounded in both theoretical and practical considerations.
Recommendations
To enhance the study of customer online purchase intention, four recommendations are proposed for future research Firstly, employing a probability sampling technique is advised to better assess customer online purchase intentions Additionally, increasing the sample size in future studies is essential to improve explanatory power and minimize the generalization of results.
The gathered quantitative and qualitative data can be utilized to identify new factors affecting online shopping behavior and to create an innovative model of consumer behavior in the online shopping landscape (Thamizhvanan & Xavier, 2013).
The primary objective for future research should focus on developing behavioral intention through the Technology Acceptance Model (TAM), specifically by integrating the interactive aspects of the Internet to accurately measure online shopping behavior, rather than relying on self-reported data.
The TAM model primarily views attitude as a cognitive aspect, potentially overlooking valuable subjective information (Davis et al., 1985) Future research should incorporate an affective component to better assess users' subjective evaluations of the Internet Expanding the range of scales and their scope could mitigate method bias, which is often an issue with limited scales Additionally, exploring consumer satisfaction levels would be beneficial.
Internet purchases and the factors influencing satisfaction and dissatisfaction with their experience to shopping online.
Conclusion
A modified Technology Acceptance Model (TAM) effectively explains and predicts the factors influencing online purchase intentions among consumers in Ho Chi Minh City, Vietnam The study identified five constructs and eighteen measurement items within the modified TAM, demonstrating an explanatory power of 54.4 percent, with five out of six hypotheses being statistically significant Additionally, the research confirms the moderating role of product category on the relationship between various factors and online purchase intention This study significantly contributes to the understanding of behavioral intention in online purchasing, highlighting that Perceived Usefulness (PU) is the most crucial factor affecting consumer purchase intentions.
Internet, followed by prior online purchase experience (POPE), perceived ease of use
(PEOU), brand orientation (BO), and perceived risk (PR) ranked in order from high to low
Marketers should take into account the key factors discussed in the previous section to effectively enhance customers' intentions for online shopping and boost sales through digital channels.
This study reveals that consumers are more inclined to shop online when they perceive the process as useful, although they often browse for information and entertainment due to the perceived risks associated with online purchases For companies aiming to enhance online sales, it is crucial for marketing managers to strike the right balance between functionality and design, employing effective search engines and a strategic marketing mix Implementing risk-reducing strategies can help minimize perceived risks, thereby boosting consumers' intent to buy online Marketers should also develop innovative strategies to enhance the perceived usefulness of online shopping, aligning with consumer needs to optimize resources and increase purchase intentions Additionally, web developers can leverage insights from this study to improve website design, ensuring ease of use and efficient product searches, ultimately helping e-businesses stay competitive in a rapidly evolving market.
Vietnamese government must improve and develop infrastructure and draw up regulations to facilitate and support this new emerging technology, which will ultimately be beneficial to the country as a whole
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We invite you to participate in a survey focused on online shopping, which seeks to identify the factors influencing consumers' online purchase intentions, particularly the moderating role of product categories This survey is open to individuals aged 15 and older and consists of three sections Your insights, even if you have never made an online purchase, are crucial for shaping the future of e-commerce in Ho Chi Minh City.
City and Vietnam as well
This survey requires only basic information and takes under 12 minutes to complete It is open to anyone who browses the Internet and visits online shopping websites, including those who have made online purchases in the past.
As a postgraduate student at the Internet School of Business, I am researching individuals' perceptions, beliefs, and intentions regarding online shopping Your responses will be combined with those of other participants, ensuring that your personal information remains strictly confidential.
Results will be reported in general terms, with no specific individuals identified in the report
Thanks for sharing your time and experiences
The demographic profile includes gender options of male and female, and age brackets ranging from 16 to over 35 years Marital status is categorized as single or married, while education levels span from high school to postgraduate qualifications Monthly income ranges are defined, starting from less than 5,000,000 VND to over 25,000,000 VND Occupations are divided into IT-related and non-IT-related fields Lastly, internet experience is classified into segments from less than 6 months to 7 years or more.
How many hours a day do you spend on the Internet? o Less than 1 hour o 1 – 2 hours o 2 – 4 hours o More than 4 hours
In the past six months, how frequently have you made online purchases? Options include none, 1-2 times, 3-5 times, or more than 5 times Additionally, please specify one type of product you have either visited or purchased, choosing from books, electronics, or clothing.
Respondents are requested to indicate the extent to which they agreed or disagreed with each question using 5 scales (1 = strongly disagree; 2 = disagree; 3 = neutral; 4 = agree and 5 = strongly agree)
1 Use online shopping enables me to search and buy something more quickly o o o o o
2 Use online shopping enables me to save my money o o o o o
3 Use online shopping helps me to make better purchase decisions
4 Use online shopping makes it easier for me to obtain goods o o o o o
5 Overall, I believe “online shopping” is useful and advantageous o o o o o
1 It is easy for me to learn how to use online shopping, even as the first time o o o o o
2 It will be IMPOSSIBLE to make an online transaction without expert help o o o o o
3 Use online shopping do not requires a lot of mental effort o o o o o
4 It is easy for me to become skillful at using online shopping o o o o o
1 I think paying with a credit card to shop online is risky o o o o o
2 I think online shopping would put my privacy at risk o o o o o
3 I am worried that product quality may not meet my expectations o o o o o
Brand Orientation 1 2 3 4 5 o o o o o would prefer to buy well—known brand names
2 It is important for me to buy products/services from the web retailer with well-known brand names o o o o o
3 Once I find a brand I like through web-shopping, I stick with it o o o o o
1 I am experienced with the use of the retailer's web site o o o o o
2 After doing an online shopping, I become more skillful o o o o o
3 After doing an online shopping, I know reliable retailer's web sites o o o o o
1 I intend to shop online in the future o o o o o
2 I will keep using online shopping in the future o o o o o
3 I think it would be very good to purchase products through Internet in addition to traditional methods o o o o o
4 I would strongly recommend others to use online shopping o o o o o
5 I would frequently use “online shopping” o o o o o
- End of Questions - Thank you so much for your assistance and cooperation
APPENDIX A2 BẢNG KHẢO SÁT HÀNH VI MUA HÀNG QUA MẠNG Ở TPHCM
Tôi tên Trịnh Minh Long, học viên cao học của Viện đào tạo quốc tế ISB- trường Đại học Kinh tế Thành Phố Hồ Chí Minh
Tôi đang nghiên cứu đề tài "Các yếu tố tác động đến quyết định mua hàng trực tuyến của người dùng", với trọng tâm là vai trò điều tiết của loại sản phẩm trong quá trình ra quyết định này Nghiên cứu này nhằm khám phá những yếu tố nào ảnh hưởng đến hành vi mua sắm trực tuyến và cách mà loại sản phẩm có thể thay đổi sự tác động của những yếu tố đó.
Chúng tôi rất mong Anh/Chị dành thời gian trả lời phiếu khảo sát này, vì mọi ý kiến của Anh/Chị đều có giá trị cho nghiên cứu Chúng tôi cam kết bảo mật thông tin của Anh/Chị và chỉ sử dụng cho mục đích nghiên cứu.
Trong quá trình thực hiện khảo sát nếu Anh/Chị có thắc mắc, vui lòng liên hệ địa chỉ email long.minh.trinh@gmail.com
Xin chân thành cám ơn sự hỗ trợ nhiệt tình của Anh/Chị
Phần A: Thông tin sơ bộ cá nhân
Giới tính o Nam o Nữ
Đối tượng nghiên cứu được phân loại theo độ tuổi từ 16 đến trên 35, tình trạng hôn nhân bao gồm độc thân và đã lập gia đình Về trình độ học vấn, các cấp độ từ tốt nghiệp cấp 3 đến sau đại học được xem xét Thu nhập hàng tháng được chia thành các mức từ dưới 5 triệu VND đến trên 25 triệu VND Nghề nghiệp được phân loại thành hai nhóm: liên quan đến công nghệ thông tin và không liên quan Cuối cùng, kinh nghiệm sử dụng Internet được phân chia theo thời gian từ dưới 6 tháng đến 7 năm hoặc nhiều hơn.
Phần B: Câu hỏi tổng hợp
