INTRODUCTION
Research background
In the past, consumers had to visit physical stores to purchase goods, which was time-consuming and inconvenient, especially for those living far away However, with the advancement of the Internet, online shopping has become increasingly popular, providing a convenient alternative for buyers Today, consumers can easily purchase products and services online, enjoying a wider range of options The benefits of online shopping include convenience, speed, 24/7 availability, and the ability to shop from anywhere, making it an attractive choice for modern consumers.
In recent years, Vietnam's e-commerce sector has experienced rapid growth, driven by the surge in Internet usage This trend has integrated e-commerce into various business sectors and everyday life, establishing it as a vital resource for both enterprises and consumers The significant increase in Internet users prompted the Vietnam E-Commerce and Information Technology Agency (VECITA) to revise its 2015 revenue forecast for the e-commerce sector from $1.3 billion to over $4 billion, highlighting the industry's dynamic expansion.
With over 30 million Internet users in Vietnam, economic growth is expected to increase Internet access significantly in the next two years By 2015, it is estimated that 40-45 percent of the population will be online, with each Vietnamese individual projected to spend at least $150 annually on e-commerce A 2014 survey by tuoitrenews.vn revealed that 61 percent of 781 online shoppers made purchases through e-commerce websites, while 51 percent used group-buying platforms, 45 percent relied on social forums, 19 percent utilized e-marketplaces, and 6 percent shopped via mobile apps The leading online product categories included fashion, shoes, and cosmetics at 62 percent, followed by technology products at 35 percent, household items at 32 percent, and flight tickets at 25 percent.
The rapid growth of electronic commerce has prompted research into how traditional offline consumer behaviors, particularly impulse buying—which accounts for 30 to 50 percent of retail sales—translate to the online environment Studies indicate that impulse buying is prevalent in online shopping, as the convenience of e-commerce removes traditional constraints of time and space Recent findings suggest that Internet shoppers exhibit higher impulsivity than those shopping in physical stores However, the understanding of online consumer behavior remains limited, with existing research failing to adequately address the phenomenon of online impulse purchases Despite the burgeoning e-commerce landscape in Vietnam, there is a significant lack of studies focused on online shopping and impulse buying in this market Thus, further research is essential to expand the understanding of impulse purchasing behaviors in Vietnam's online retail sector.
This research aims to expand the understanding of impulse buying behavior beyond traditional in-store retailing by examining the factors that influence online impulse purchases With the rise of online shopping, it is crucial to adapt the concept of impulse buying to reflect the unique dynamics of the digital marketplace The study will identify key factors affecting impulse buying online and provide actionable insights to enhance customers' impulse buying behavior.
Research objectives
Unlike traditional shopping, where sales associates and cashiers play a crucial role, online customers complete transactions independently While impulse buying in physical stores is often influenced by persuasive sales tactics, store layout, and enticing product displays, the factors affecting impulse buying in online environments remain less understood Research has yet to conclusively identify which elements significantly impact impulse buying during online transactions This study aims to explore these factors in depth to better understand their effects on online consumer behavior.
(1) To understand clearly about impulsive buying and online impulsive buying
This study aims to explore and evaluate key factors influencing online impulse buying of fashion products in Vietnam, including impulsiveness, electronic word of mouth, hedonic shopping motivation, convenience orientation, sales promotions, and website quality.
(3) How do these factors affect to the impulsive buying behavior when people go shopping online, especially fashion product market.
Research methodology and scope
The research focuses on online consumers in Ho Chi Minh City, Vietnam's largest city, collecting data through in-depth interviews with two groups: young company officers and university students To validate the factors influencing online impulsive buying, the researcher analyzed responses from a web-based questionnaire survey conducted with 400 online customers The questionnaire specifically targeted fashion products, which are the most sought-after items in the Vietnamese e-commerce market.
The researcher will utilize Microsoft Excel and SPSS for data management and analysis SPSS will be employed to assess scale reliability through Cronbach’s Alpha, conduct factor analysis to identify items with similar responses from participants, and apply multiple regression to explore the relationships between independent and dependent variables.
Significance of the study
This study aims to enhance the understanding of online impulse buying, particularly within the fashion product category Additionally, it will equip online retailers with a valuable tool to analyze both current and potential customers, offering insights that can inform store design and marketing communication strategies.
Structure of the study
This study is organized into five chapters:
This chapter presents the research background of the study, research objectives, research scopes and methodology, the significance of the study, and the research structure
Chapter II: Literature review and hypotheses
This chapter outlines the essential theories and definitions of key concepts, along with theoretical modeling and proposed hypotheses Additionally, it introduces the conceptual model that underpins the study.
This chapter mentions about the research design, research methodology and illustrate the process of conducting the research
In this chapter, the researcher summarizes the characteristics of the collected samples and presents the findings from the data analysis These results serve as the foundation for drawing conclusions regarding the research hypotheses outlined in Chapter 2.
This chapter presents the key findings of the research, highlights the contributions to management theory and practice, and discusses the study's limitations, offering insights for future research directions.
LITERATURE REVIEW
Impulse Buying
In traditional decision-making, consumers meticulously gather information and evaluate options before making informed choices; however, this model does not accurately reflect many purchasing behaviors, particularly those made under low involvement conditions Impulse buying, characterized by immediate purchases driven more by emotions than rational thought, often results from learned responses to environmental cues Research indicates that impulse buying significantly contributes to product turnover, with estimates suggesting that impulsive buyers make up 1% to 8% of the population, while other studies claim that up to 90% of consumers engage in this behavior Fashion items, including accessories and shoes, are among the most frequently purchased on impulse.
Impulse buying has been recognized as a significant aspect of marketing for over seventy years, beginning with Clover's research in 1950 Since then, numerous researchers have contributed to our understanding of impulse purchasing, highlighting its relevance in consumer behavior.
Unplanned buying, including impulse purchases, was first defined by Stern (1962), who categorized impulse buying into four types: pure, reminder, suggestion, and planned This framework remains foundational in contemporary research Rook (1987) later described impulse buying as a sudden urge to purchase, often leading to emotional conflict and diminished consideration of consequences However, Piron (1991) critiqued this definition for being too narrow, suggesting that impulse buying is simply an unplanned purchase triggered by a stimulus, with decisions made on the spot Beatty and Ferrell (1998) expanded on this by defining impulse buying as a spontaneous purchase without prior intentions, emphasizing the role of urges rather than pre-shopping plans Additionally, Wells, Parboteeah, and Valacich (2011) highlighted that both individual and environmental characteristics significantly influence impulse buying behavior Muruganantham & Bhakat (2013) further identified four key factors that drive impulsive buying: external stimuli, individual traits, and environmental influences.
Impulse buying is influenced by a combination of internal and external stimuli, as well as situational and demographic factors External stimuli, controlled by marketers, aim to attract consumers through strategic marketing cues within the shopping environment In contrast, internal stimuli relate to individual personality traits that drive impulse purchases Situational factors such as retail location, shopping time, and seasonal trends also play a significant role in shaping impulse buying behavior Research indicates that consumer demographics and local cultural influences further impact impulsive purchasing decisions Unlike typical shoppers, impulse buyers often disregard the consequences of their purchases, focusing instead on immediate gratification and being drawn to the allure of products.
Recent studies have significantly enhanced our understanding of impulse buying, despite some ongoing controversies regarding its causes The majority of research highlights several key factors that influence this behavior.
First, impulse buying is an unplanned decision to buy a product or service, made just before a purchase
Consumers are influenced by a combination of internal and external factors when it comes to impulse buying This type of purchasing behavior stems from a buyer's spontaneous response to external triggers, which can vary significantly based on individual consumer traits.
Stern (1962) identified four types of impulse buying: pure, reminder, suggestive, and planned A pure impulse purchase occurs when a consumer buys something unplanned after encountering a stimulus, such as buying a book on tiki.vn without any prior shopping goal In contrast, a reminder impulse purchase happens when a consumer recalls a need for a product after seeing it, like purchasing a perfume upon realizing they are running low after viewing it online Suggestive impulse buying involves an individual recognizing a need for a previously unknown product after seeing it, such as an unplanned purchase made on Amazon based on product recommendations Lastly, a planned impulse purchase occurs when a shopper enters a store with a list but decides to buy based on promotions or discounts, like shopping on Nike.com during Black Friday Despite their differences, all these types of impulse purchases share the commonality of being unplanned, driven by exposure to stimuli This overview aims to deepen the understanding of impulse buying based on Stern's research.
The researcher will review some famous model involves impulse buying to help us have a clear picture of factors affecting impulse buying
Impulse buying behavior is influenced by both internal and external factors, as outlined by Churchill and Peter (1998) Their model simplifies the impulse buying process, starting with product awareness, where consumers browse without a specific intention to purchase During this browsing, stimuli trigger an urge to buy impulsively, leading to a purchase decision made without prior information search or alternative evaluation Following the purchase, consumers may reflect on their decision, experiencing either positive or negative consequences Internal factors such as mood, desire, and hedonic pleasure, along with external elements like visual merchandising and promotional signage, play crucial roles in prompting impulse purchases by providing insights into new products and fashion trends.
Figure 2.1: Churchill and Peter’s model of Impuse buying process
2.1.3.2 Impulse buying model of urban customers in Vietnam
Nguyen Thi Tuyet Mai et al (2003) conducted a study using both qualitative and quantitative methods to explore impulse buying behaviors among urban Vietnamese consumers The findings indicate that factors such as individualism, age, and income significantly influence these behaviors Notably, personal-use products emerged as the most frequently purchased items on impulse, highlighting a contrast with Vietnam's collectivist culture The study suggests that, despite cultural differences, consumers in transitional economies like Vietnam may exhibit similar impulse buying tendencies as those in advanced economies.
Online Impulse Buying
Research on online impulse buying primarily emerged in the 2000s, coinciding with the rise of the internet and e-commerce Despite its significance, studies in this area remain limited due to the distinct differences between online and traditional purchasing behaviors Notably, Donthu and Garcia (1999) discovered that online shoppers exhibit higher levels of impulsivity compared to their non-online counterparts, a finding supported by subsequent studies from LaRose (2001) and Koski (2004).
LaRose (2001) identified key website features such as product recommendations, suggested items, price categories, and sales notifications that encourage unregulated and impulse buying Costa and Laran (2003) quantitatively modeled how the online environment influences impulsiveness, in-store browsing, and the frequency of impulse purchases, linking these behaviors to positive emotions Koski (2004) highlighted five aspects of online shopping that promote impulse buying: anonymity, 24/7 access, a wider variety of goods, targeted marketing, and the convenience of credit card use Rhee (2006) found that online impulse buyers exhibit greater engagement with apparel and have a favorable view of website attributes like design and product presentation Additionally, Zhang et al (2006) noted that both consumer traits, such as gender, and environmental factors, like subjective norms, positively influence impulse buying.
Research from 2009 identified two main categories of external cues on apparel websites that trigger impulse purchases: promotional offers and idea cues, such as sales promotions, free shipping, and featured items These findings indicate that online shoppers prioritize certain external cues, with promotional offers being the most sought after Additionally, Wells et al (2011) highlighted that impulse buying is influenced by both individual traits—such as age, shopping motivation, and impulsiveness—and environmental factors, suggesting that specific situational stimuli can enhance the likelihood of impulsive purchases.
In addition, Chaudhuri (2015) proved the impact of hedonic shopping motivation and demographic characteristics (age, income and gender) on the customers in the impulse buying behavior
Previous reviews identified two main sets of factors influencing impulse buying: consumer characteristics (such as age, income, gender, hedonic shopping motivation, and impulsiveness) and environmental factors (including website design, promotions, and features related to online shopping) However, most existing research has focused on only one or two of these factors, lacking a comprehensive analysis Additionally, there has been no study conducted specifically in the Vietnamese market Therefore, it is essential to assess and identify the relevant factors that impact online impulse buying in Vietnam The following section will present the study's hypotheses and develop a conceptual model based on existing literature and research.
Factors affecting online impulse buying
Impulsiveness, often characterized as the tendency to make spontaneous and unplanned purchases, has been extensively studied in both traditional and online shopping environments Defined by Beatty and Ferrell (1998) as the urge to buy with little consideration of consequences, and by Rook and Fisher (1995) as a spontaneous and unreflective buying behavior, impulsiveness significantly influences consumer behavior Research by Wells et al (2001) indicates that impulsiveness serves as a precursor to online impulse buying, while Zhang et al (2006) found a positive correlation between impulsiveness and the intention to shop online Wood (1998) describes impulsiveness as a weakness of will, leading consumers to make decisions driven by emotions rather than optimal judgment Individuals scoring higher on impulsiveness scales are more likely to act on buying urges (Dholakia, 2000), suggesting that high impulsivity consumers are more prone to impulse buying than their low impulsivity counterparts Therefore, this study posits that high impulsivity consumers will exhibit greater online impulse buying behavior.
H1: Impulsiveness has positive impact on online impulse buying
Since Arndt (
In the traditional shopping environment, impulsive buying behavior is influenced by various factors such as environmental stimuli, personal traits, and budget constraints However, the dynamics change significantly in online retail, where consumers cannot physically interact with products Instead, they rely on product displays, user experiences, and recommendation systems to compensate for this limitation As auxiliary decision-making resources increase, consumers feel more empowered to make purchases Chatterjee (2001) highlights the impact of online reviews on consumer behavior, emphasizing that the high transparency of online information enables consumers to research products extensively, thus enhancing their decision-making process compared to traditional word-of-mouth He argues that online comments can significantly influence consumers' cognition, beliefs, attitudes, and actual purchasing decisions.
Early research on online impulse buying highlighted the influence of product comments, suggestions, and promotions on non-binding network purchases (2001) QinWu (2009) emphasized that online reviews significantly affect impulse buying behavior, noting that the format, timing, and content of these comments play a crucial role Ya-ping Chang (2012) further indicated that a higher volume of positive online reviews can trigger impulsive buying among consumers More recently, Y Huang et al (2013) discovered that Electronic Word-of-Mouth enhances merchandise awareness and significantly impacts consumer behavior Based on these findings, the following hypothesis is proposed.
H2: Electronics word of mouth has positive impact on online impulse buying
Hedonic shopping motivation is driven by an individual's desire for fun and pleasure during the shopping experience Defined by Arnold and Reynolds (2003), it encompasses six categories: adventure shopping, social shopping, gratification shopping, idea shopping, role shopping, and value shopping Research by Cinjarević and Petric (2011) highlights that adventure, gratification, idea, and value shopping significantly influence impulse buying behavior, as consumers seek excitement, self-fulfillment, and the latest trends While hedonic motivation focuses on emotional satisfaction and enjoyment, utilitarian motivation pertains to practical and rational purchasing decisions Utilitarian shoppers typically buy necessities like food and clothing regularly, whereas hedonic shoppers indulge in luxury and non-essential items less frequently.
This research concentrates exclusively on hedonic motivation in customer decision-making, as previous studies have largely overlooked utilitarian motivation in the context of impulse buying (Cinjarevic, Tatic, and Petric, 2011; Kim and Eastin, 2011) Hedonic motivation is particularly pertinent here, as it delves into the emotions and feelings that customers experience while shopping Consequently, we can formulate a conceptual hypothesis based on these insights.
H3: Hedonic shopping motivation has positive impact on online impulse buying
In the marketing literature, the concept of convenience was introduced by Copeland
Convenience goods are frequently purchased items that consumers seek at easily accessible stores Research in traditional retail highlights two key factors in providing convenient service: time-saving and effort minimization According to Berry et al (2002), higher time costs linked to a service diminish consumers' perceived convenience Additionally, consumers' perceptions of convenience are adversely affected by the cognitive, physical, and emotional efforts required during the shopping process.
Convenience orientation refers to consumers' attitudes towards saving time and effort in planning, purchasing, or using products and services Fitch (2004) highlights its growing significance in consumer buying behavior and store patronage, with the Internet emerging as a convenient shopping alternative that may foster impulse buying While numerous studies have explored the effects of convenience orientation on buying behavior (Fitch, 2004; Jones et al., 2003), few have specifically addressed its impact on impulse buying, particularly in online settings Hansen and Olsen (2015) demonstrated a significant direct influence of convenience orientation on online impulse buying, underscoring the need to further investigate this relationship.
H4: Convenience orientation has positive impact on online impulse buying
Sales promotions play a crucial role in stimulating impulse buying in e-commerce, as highlighted by various researchers (Dholakia, 2000; Sandy Dawson and Minjeong Kim, 2009; Peter Hulten and Vladimir Vanyushyn, 2014) Online retailers utilize diverse promotions, such as free gifts, discounts, and free shipping, to attract shoppers Often, consumers enter online shopping without prior knowledge or intention to purchase specific products, yet enticing promotions can motivate immediate purchases Many shoppers perceive online prices to be lower than those in physical stores, making promotions essential for informing consumers about product availability, raising awareness of marketing activities, encouraging repeat visits, and ultimately boosting buying behavior.
The internet has transformed retail by enabling targeted direct marketing and personalized promotions, such as tailored email suggestions based on customers' purchase histories Additionally, consumers can encounter banner ads that lead them directly to product sales sites, potentially boosting impulse buying However, consumers now have greater control over the marketing messages they receive, making the impact of these promotional strategies on impulse buying a subject of debate This study aims to investigate the relationship between sales promotions and impulse buying, guided by the following hypothesis.
H5: Sales promotion has positive impact on online impulse buying
Environmental cues play a significant role in influencing impulse buying, with individuals often experiencing the urge to make impulsive purchases when stimulated by certain circumstantial factors during a shopping interaction Marketers in traditional retail settings manipulate atmospheric cues to trigger impulse purchases, and similarly, online retailers utilize website characteristics to influence consumer behavior Research has identified various online environmental cues that lead to impulse purchases, which can be categorized into Task-relevant cues and Mood-relevant cues, as proposed by Eroglu and colleagues These cues can significantly impact consumer behavior, making them a crucial consideration for online retailers seeking to encourage impulse buying.
Task-relevant cues are essential elements on websites that help consumers achieve their shopping goals, including merchandise descriptions and navigation aids These cues play a significant role in online shopping, with security being the top priority for users during transactions Additionally, download speed is crucial, as users prefer quick responses and are unlikely to wait more than a few seconds Ease of navigation is also vital in influencing users' decisions to commit to or abandon a website during the pre-purchase stage While this review does not aim to provide an exhaustive list of Task-relevant cues, it emphasizes their importance and acknowledges that their presentation and perception can vary among online users.
Mood-relevant cues play a significant role in enhancing the shopping experience by creating a pleasurable atmosphere, even though they do not directly impact the completion of shopping tasks (Eroglu et al., 2001) These cues can effectively influence an online user's emotions and shopping behavior, thereby increasing the hedonic value of the online experience Due to their importance in mood creation on websites, we will refer to them as Mood-relevant cues.
The quality of a website is determined by the presence of various characteristics, including task-relevant and mood-relevant cues A high-quality website effectively incorporates both high and low task-relevant cues, creating a superior online interface, while a lower quality website lacks these essential environmental cues Research indicates that high-quality environmental cues significantly impact online impulse buying behavior (Parboteeah et al., 2009).
Based on preceding literature review, we can build the following conceptual hypothesis:
H6: Task-relevant cues (website quality) has positive impact on online impulse buying
H7: Mood-relevant cues (website quality) has positive impact on online impulse buying.
Hypothesis & Research Model
The research identifies five key factors that influence customers' online impulse buying: impulsiveness, electronic word of mouth, hedonic shopping motivation, convenience orientation, and sales promotion, along with website quality, which encompasses task-relevant and mood-relevant cues Based on these findings, several hypotheses are proposed.
H1: Impulsiveness has positive impact on online impulse buying
H2: Electronics word of mouth has positive impact on online impulse buying
H3: Hedonic shopping motivation has positive impact on online impulse buying H4: Convenience orientation has positive impact on online impulse buying
H5: Sales promotion has positive impact on online impulse buying
H6: Task-relevant-cues (website quality) has positive impact on online impulse buying
H7: Mood-relevant-cues (website quality) has positive impact on online impulse buying
This study will explore how various factors, including age, gender, and income, impact online impulse buying behavior The literature review highlights the connections between these factors and their influence on impulsive purchasing decisions, which is illustrated in the accompanying figure.
Consumer’s characteristics
This study will explore the impact of various factors, including age, gender, and income, on online impulse buying behavior While these factors will not be tested as specific hypotheses, the research will assess their overall influence on impulsive purchasing tendencies in the online environment.
Gender plays a significant role in impulse buying, with research showing mixed results regarding its impact Some studies indicate that women are more prone to impulsive purchases compared to men, suggesting a higher level of impulsivity among female shoppers Conversely, other findings propose that men may exhibit greater impulse buying behavior, as women tend to plan their purchases more carefully This inconsistency highlights the complexity of gender influences on consumer behavior, warranting further investigation into the nuances of impulse buying across genders.
While some studies have indicated a significant link between gender and impulse buying, others have found no notable relationship (Bellenger et al., 1978; Gutierrez, 2004; Ghani et al., 2011) This research specifically targets fashion products, predominantly purchased by women, prompting the researcher to explore gender differences in online impulse buying to enhance understanding in this area.
Age significantly impacts impulse buying behavior, with younger individuals exhibiting more impulsive tendencies than their older counterparts (Bellenger et al., 1978) Today's youth frequently engage in online shopping, driven by their reliance on the Internet for schoolwork and social interactions This growing trend is facilitated by numerous online access points and often supported by parental encouragement, enabling adolescents to become adept online shoppers Additionally, young people are generally quick to adopt new technologies, including smartphones and social networks, which further enhances their skills as consumers Research by Hill and Beatty (2011) highlights that younger shoppers are more likely to engage in impulse purchases online compared to older individuals Consequently, this study will explore age-related differences to deepen the understanding of online impulse buying behavior.
Research indicates that higher income levels significantly contribute to impulsive purchasing behaviors, particularly in the United States compared to other countries (Abratt & Goodey, 1990) Impulse buying tends to be more prevalent among consumers who can afford it, as income positively influences their purchasing behavior (Wells, Farley & Armstrong, 2007) Consumers with higher incomes are generally less sensitive to price changes and are more likely to engage in impulsive purchases than those with lower incomes (Butkeviciene, Stravinskiene & Rutelione, 2008) Therefore, this study aims to explore the impact of income on online impulse buying.
This chapter reviews literature on impulse buying, specifically focusing on online impulse buying and the factors influencing it Existing research has utilized various methods to measure impulsive buying behavior among consumers, yet online impulse buying has not been extensively tested in a tailored context This study evaluates six key components affecting online impulse buying in Vietnam's fashion market: impulsiveness, electronic word of mouth, hedonic shopping motivation, convenience orientation, sales promotion, and website quality The chapter provides a comprehensive overview of impulse buying through established theoretical frameworks and discusses the evolution of online impulse buying within the fashion sector Additionally, a research model is proposed, consisting of seven hypotheses that explore the interrelationships among these concepts.
H1: Impulsiveness has positive impact on online impulse buying
H2: Electronics word of mouth has positive impact on online impulse buying
H3: Hedonic shopping motivation has positive impact on online impulse buying H4: Convenience orientation has positive impact on online impulse buying
H5: Sales promotion has positive impact on online impulse buying
H6: Task-relevant-cues (website quality) has positive impact on online impulse buying
H7: Mood-relevant-cues (website quality) has positive impact on online impulse buying
Besides, the researcher also consider characteristic of customer affect to online impulsive buying behavior With different gender, age, income and education, how these characteristic impact to their intention.
RESEARCH METHODOLOGY
Research Process
The research process of this study is described in the following figure:
The research process began with the identification of the research problem, followed by the establishment of research objectives and questions aimed at addressing this issue A literature review was then conducted to explore relevant theories related to factors influencing impulse buying behavior, which informed the development of a model and hypotheses for the study Subsequently, a preliminary questionnaire was created based on measurements utilized in previous research The study will proceed with two phases: qualitative research and quantitative research.
Qualitative research
Based on prior research on factors influencing impulse buying of fashion products in Vietnam, the researcher proposed seven hypotheses in Chapter 2 Following this, a preliminary questionnaire was developed and refined through in-depth interviews with 10 individuals in Ho Chi Minh City This process aimed to ensure the relevance of the items for Vietnamese consumers and to enhance the clarity of the measurement scales Although many constructs were derived from existing literature, this step was crucial for adapting them to the local context Valuable feedback and suggestions from interviewees were incorporated to improve the official questionnaire.
Qualitative research was conducted by a researcher, assisted by two colleagues, at a coffee shop in District 1, Ho Chi Minh City Participants were selected from the researcher's company staff and last-year students, following a pre-prepared interview script Each meeting lasted two hours and included two groups of interviewees: the first group consisted of three officers from a mobile game company and two MBA students from ISB, while the second group included five students from the university.
The study focused on the economic landscape of Ho Chi Minh City, interviewing participants aged 18 to 35 who are financially independent Out of the respondents, seven had experience with online purchases while three had not A predetermined qualitative interview guideline was utilized, as detailed in Appendix 1, with researchers actively guiding participants for clarity Interviewees shared their personal opinions, often critiquing previous ideas and engaging in discussions to ensure comprehensive understanding of the questions The researchers summarized the feedback, ensuring consensus among the interviewees on the content discussed.
The in-depth interviews conducted revealed that participants acknowledged the significance of several factors influencing online impulsive buying, including electronic word of mouth, convenience orientation, hedonic shopping motivation, sales promotion, task-relevant cues, mood-relevant cues, and impulsive buying behavior However, it was noted that the measurement scale used did not fully align with the specific conditions of the Vietnamese market, indicating a need for adjustments to better reflect local circumstances.
The qualitative research findings led to modifications in measurement scales prior to their application in the main quantitative survey The constructs measured included Impulsiveness (4 items), Electronic Word of Mouth (6 items), Convenience Orientation (7 items), Hedonic Shopping Motivation (11 items), and Sales Promotion (5 items) Task-relevant and Mood-relevant cues were each assessed with 3 items Impulse Buying Behavior was evaluated using 3 items The survey instrument was developed based on validated scales from prior studies, with Impulsiveness adapted from Rook and Fisher (1995), Electronic Word of Mouth from Eugenia Y Huang et al (2013), Convenience Orientation from Mathieson (1991), Hedonic Shopping Motivation from Babin, Darden, and Griffin (1994), Sales Promotion from Alkharabsheh et al (2011) and Lai and Vinh (2012), and both Task-relevant and Mood-relevant cues from Loiacono et al (2007) Finally, Impulse Buying Behavior was adapted from Parboteeah et al (2009).
Impulsiveness - four items according to Rook and Fisher (1995)
- “Just do it” describes the way I buy things
- I often buy things without thinking
- “I see it, I buy it” describes me
- “Buy now, think about it later” describes me
Electronic word of mouth - six items according to Eugenia Y Huang et al (2013) eWOM1 eWOM2 eWOM3 eWOM4 eWOM5 eWOM6
- I often read the eWOM to ensure that I can buy the right goods or brand
- When I do not know or do not know much on a product, in order to increase the understanding of it, I will read some of the product eWOM
- I often use the eWOM information to help me make the best choice of goods
- I frequently read the experience of others share on the network before I buy
- I think that most of the eWOM information is credible
- When the eWOM content of product more consistent, the more I believe these eWOM information
Convenience orientation - seven items according to Mathieson (1991)
- Online shopping would provide me on time delivery
- Online shopping provides me with product information & other customs feedback
- Online shopping allows me ordering product easily
- Online shopping allows me to obtain information on product easily
- Online shopping would provide me with information 24-hours a day
- Online shopping provides me with more value than money that spends
- Online shopping provides me in-depth information
Hedonic shopping motivation - eleven items according to Babin, Darden and Griffin (1994)
- Shopping to me is truly a joy
- I shop not because I have to, but because I want to
- Shopping is like an escape from my daily routine life
- The time spent in shopping is truly enjoyable to me
- I enjoy being immersed in exciting new products while shopping
- I enjoy shopping for its own sake and not because of that I need to purchase something
- While shopping, I am able to act on the spur of the moment
- While shopping I can feel the excitement of the hunt
- While shopping, I am able to forget my other problems
- While shopping I feel a sense of adventure
- Any shopping is a very nice time out to me
Sales promotion - five items according to Alkharabsheh et al, 2011; Lai and Vinh, 2012
- Information about sales promotion on my most favorite online retailer’s website is accurate
- Information about sales promotion on my most favorite online retailer’s website is very fast
- Information about sales promotion on my most favorite online retailer’s website is clarifies and details
- Information about sales promotion on my most favorite online retailer’s website is attractive
- PR5 Information about sales promotion on my most favorite online retailer’s website is useful
Task-relevant cues - three items according to Loiacono et al (2007)
- Information on my most favorite online retailer’s website is effective
- My most favorite online retailer’s website adequately meets my information needs
- The information on my most favorite online retailer’s website is pretty much what I need to carry out my tasks
Mood-relevant cues - three items according to Loiacono et al (2007)
- My most favorite online retailer’s website is visually pleasing
- My most favorite online retailer’s website displays visually pleasing design
- My most favorite online retailer’s website is visually appealing
Impuse Buying Behavior - three items according to Parboteeah et al., (2009)
- As I browsed my most favorite online retailer’s website, I had the urge to purchase items other than or in addition to my specific shopping goal
- Browsing my most favorite online retailer’s website, I had a desire to buy items that did not pertain to my specific shopping goal
- While browsing my most favorite online retailer’s website, I had the inclination to purchase items outside my specific shopping goal.
Quantitative research
Based on qualitative research findings, the researcher refined the questionnaire to better align with the Vietnamese market and enhance clarity The revised questionnaire utilized five-point Likert scales, ranging from 1 (strongly disagree) to 5 (strongly agree), and included additional questions to gather demographic information such as age and gender.
The questionnaire was adapted and developed in English, then was translated into Vietnamese to distribute to respondents
The well-designed questionnaire facilitated a comprehensive survey conducted through various methods, including online distribution via Google Docs, email, and social networks, as well as physical copies delivered to customers at a university and three Coopmart supermarkets located in Districts 1, 3, and 7 Data collection was anticipated to be completed within two weeks.
The collected data underwent a cleaning process to ensure accuracy, followed by testing the reliability of the scale and the validity of the questionnaire using Cronbach’s alpha coefficient and Exploratory Factor Analysis (EFA) To assess the hypotheses, a multiple regression method was employed, with the implications and findings clearly stated and reported.
Research indicates that the required sample size is influenced by the estimation method used Hair et al (2010) recommend a general guideline of a minimum sample size of 100, with at least 5 observations per scale In this study, which includes 8 factors and 42 scales, the minimum sample size should be calculated as 42 multiplied by 5.
According to Tabachnick and Fidell (1991), the minimum sample size for standard multiple regression analysis should be calculated using the formula n > 50 + 8m, where m represents the number of independent variables In this study, with 7 independent variables, the minimum required sample size is n > 50 + 8*7, resulting in a total of at least 106 observations necessary to conduct the multiple regression analysis.
In this study, the researcher aimed to gather data from 400 customers, a sample size deemed suitable for exploratory factor analysis (EFA) and multiple regression analysis The sampling method employed was convenience sampling, and all participants were required to have prior experience with purchasing fashion products online before completing the questionnaire.
The analysis of the collected data was conducted using SPSS Version 20 (Statistical Package for the Social Sciences), employing Cronbach’s alpha for reliability assessment, exploratory factor analysis (EFA), and multiple regression analysis to substantiate the research findings.
A descriptive analysis was conducted to evaluate the sample characteristics, calculating mean scores on various measurement scales to assess factors influencing online impulse buying Utilizing a 5-point Likert scale, scores equal to 3 indicated a neutral assessment, while scores above 3 reflected positive agreement, and scores below 3 signified areas needing improvement or disagreement.
The reliability of the measurement scales was assessed using Cronbach’s alpha, with a coefficient of 0.7 or higher indicating acceptable reliability (Pallant, 2005) Additionally, items with a Corrected Item-to-Total Correlation below 0.3 should be removed to enhance the reliability of the scale.
The main goal of Exploratory Factor Analysis (EFA) was to identify the factors influencing the observed measures and assess the strength of their relationships (DeCoster, J., 2004) This study utilized Principal Component Analysis (PCA) to extract the relevant factors, employing Varimax rotation for optimal clarity.
The result was considered to be acceptable when following conditions were met (Pallant, 2005):
For effective exploratory factor analysis (EFA), a sample size of at least 150 is essential, with a minimum of five cases for each of the 47 variables utilized in this study, resulting in a required sample size of 235 With 270 valid responses collected, the data meets the necessary criteria for conducting EFA.
Factor analysis was appropriate to data if:
The Kaiser-Meyer-Olkin value (KMO) is 0.6 or greater
The Bartlett’s test of sphericity is statistically significant: p < 0.05
The number of factors was determined when:
The components have an eigenvalue of 1 or more
The total variance explained by these components should be above 50%
Factor loading criteria should be 0.5 or more to ensure a practical significance
In this research, Multiple Linear Regression method was used to test the research model and hypotheses Pallant (2005) explained the conditions to accept the result were:
The sample size is: n > 50 + 8m (where m is the number of independent variables)
Normality and linearity should exist
Researcher also used R-square value to express how much of the variance in the dependent variable was explained by the model
This chapter outlines the research design and methodology employed to investigate the factors influencing online impulse buying of fashion products in Vietnam, focusing on impulsiveness, electronic word of mouth, hedonic shopping motivation, convenience orientation, sales promotion, and website quality The study utilized a mixed-method approach, combining qualitative and quantitative research Initially, qualitative research aimed to clarify concepts and adapt measurement scales related to online impulse buying Interviews were conducted with ten participants, including company staff and final-year students, using a pre-prepared script Based on qualitative findings, measurement scales were refined for the quantitative survey, which included 4 items for impulsiveness, 6 for electronic word of mouth, 7 for convenience orientation, 11 for hedonic shopping motivation, 5 for sales promotion, and 3 items each for task-relevant and mood-relevant cues Impulse buying behavior was assessed with 3 items, all utilizing a five-point Likert scale The quantitative survey targeted approximately 300 respondents, and the data analysis involved testing reliability with Cronbach’s alpha, exploratory factor analysis (EFA), and regression analysis using SPSS software.
ANALYSIS RESULTS
Sample description
A total of 400 questionnaires were distributed to respondents, with 352 valid responses (n = 352) received for analysis The data were processed using SPSS Version 20.0 software, and the demographic characteristics and customer ranges of the sample are detailed in Table 4.1.
Gender: the gender demographics of the respondents were also considered Based on table 4.1 about gender division, the percentage of female (60.2 percent) was higher than male (39.8 percent)
Age: based on table 4.1 about age division, the percentage of age from 26 - 35 years old was highest (34.4%), next was 18-25 years old
Education: based on table 4.1 about aducation division, the percentage of person who had graduated from bachelor level was highest (45.7%), next was college level
Income: Of the 352 respondents, income level from 5 to less than 10 million VND per month were 119 persons (33.8%), next from 10 to less than 15 million VND per month were 89 persons (25.3%)
Table 4.1: Demographic Characteristics of the Study
This study utilized Cronbach’s Alpha statistics to assess the internal consistency and reliability of group items Typically, researchers consider an alpha value of 0.7 as the minimum acceptable threshold, although lower values may be permissible based on specific research goals (Hair et al., 2007) According to Fermalennally & Bernstein (1994), a measurement is deemed valid and reliable if its alpha exceeds 0.6 Additionally, a cut-off value of 0.3 for item-total correlation was implemented to eliminate items that did not meet this standard.
The measurement scales for factors influencing online impulse buying behavior, as shown in Table 4.2, demonstrated high reliability, with Cronbach’s alpha values ranging from 0.706 for "Impulse Buying Behavior" to 0.945 for "Electronic Word of Mouth." Specifically, the Cronbach’s alpha values for various dimensions were 0.873 for Impulsiveness, 0.945 for Electronic Word of Mouth, 0.861 for Convenience Orientation, 0.919 for Hedonic Shopping Motivation, 0.836 for Sales Promotion, 0.825 for Task-Relevant Cues, and 0.866 for Mood-Relevant Cues Most item-total correlations were above 0.3, with the exception of HSM8, which had a correlation of 0.244 Notably, eliminating HSM8 increased the Cronbach’s alpha for the Contents scale from 0.919 to 0.939, indicating that all other items were reliable and suitable for exploratory factor analysis Overall, the Cronbach’s alpha for Impulse Buying Behavior at 0.706 exceeded the acceptable threshold of 0.60, confirming its reliability.
Table 4.2: Cronbach's Alpha measures of variables in the study
Variables Scale Mean if Item
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Electronic word of mouth: Alpha = 0.945 eWOM1 18.6903 8.915 868 930 eWOM2 18.7216 8.931 842 933 eWOM3 18.6023 9.950 661 953 eWOM4 18.6818 8.668 893 927 eWOM5 18.6619 9.073 844 933 eWOM6 18.7017 8.848 882 928
In this phase, principal components analysis using the varimax rotation method enhances variance along new axes, effectively reducing the number of variables with high factor loadings within the same factor, which simplifies interpretation (Trong & Ngoc, 2008).
The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy assesses whether the partial correlations among variables are sufficiently small for exploratory factor analysis (EFA) to be appropriate A KMO value ranging from 0.50 to 1 indicates that EFA can be applied Additionally, Bartlett's test of sphericity examines whether the correlation matrix is an identity matrix; a result of p < 0.05 suggests that the variables are correlated, confirming the suitability of EFA (Hair et al., 1998)
According to Hair et al (1998), factor loadings are crucial for determining practical significance, with a threshold of >0.3 as the minimum requirement, >0.4 as important, and >0.5 indicating real significance They recommend a minimum sample size of 350 when using a factor loading of >0.3, while a sample size of around 100 should use a factor loading of >0.55, and for a sample size of about 50, the threshold should be >0.75 In this study, with a sample size of 207, a cut-off value of 0.5 for factor loading was applied to eliminate items that did not meet this criterion.
4.3.1 Exploratory Factor Analysis for independent variables
After testing measurement scale by Cronbach’s alpha, HSM8 was eliminated, 38 variables retained of factors affect impulse buying behavior were put into EFA
The initial exploratory factor analysis (EFA) indicated that the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.886, suggesting that the sample was suitable for factor analysis Additionally, the significant p-value of less than 0.05 further confirmed the appropriateness of the sample for this analysis.
Table 4.3: KMO and Bartlett's Test (EFA for independent variables the first time)
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .886 Bartlett's Test of Sphericity Approx Chi-Square 11681.767 df 703
There were seven factors extracted at Eigenvalues of 1.251 and total explained variance of 71.052 (table 4.4)
Table 4.4: Total Variance Explained (EFA for independent variables the first time)
Initial Eigenvalues Extraction Sums of Squared Loadings
Extraction Method: Principal Component Analysis
Variance met requirement but variables HSM6 had loading factor 0.05 Therefore it would be used oneway Anova in the next step
Table 4.15c: Test of Homogeneity of Variances
IPB Levene Statistic df1 df2 Sig
The result of Levene test (table 4.15a) shows that Sig (p-value) = 0.682 > 0.05 Therefore it would be used oneway Anova in the next step
Table 4.16a: Test of Homogeneity of Variances
IPB Levene Statistic df1 df2 Sig
Based on the Results table 4.16b, it can be interpreted that there was a partial significant difference between education groups to online impulse buying behavior by one-way ANOVA (F = 2.521, p = 058)
Sum of Squares df Mean Square F Sig
The result of Levene test (table 4.17a) shows that Sig (p-value) = 0.332 > 0.05 Therefore it would be used oneway Anova in the next step
Table 4.17a: Test of Homogeneity of Variances
IPB Levene Statistic df1 df2 Sig
Based on the Results table 4.17b, it can be interpreted that there was a statistically significant difference between age groups to online impulse buying behavior by one-way ANOVA (F 3.978, p = 004)
Sum of Squares df Mean Square F Sig
Table 4.17c indicates that individuals with a monthly income exceeding 20 million exhibit the highest online impulse buying behavior, with a mean score of 4.0988 Following this group, those earning between 15 and 20 million per month also show significant impulse buying tendencies, while individuals with incomes below 5 million demonstrate the lowest levels of such behavior This data suggests a clear correlation between higher income levels and increased online impulse buying behavior.
Discussions
A literature review and preliminary study identified seven key factors influencing online impulse buying behavior: Hedonic shopping motivation, Electronic word of mouth, Convenience orientation, Impulsiveness, Sales promotion, Mood-relevant cues, and Task-relevant cues Quantitative research and Exploratory Factor Analysis (EFA) confirmed these dimensions' significant positive relationships with online impulse buying behavior Notably, Sales promotion emerged as the most influential factor (β = 0.340, sig = 0.000), followed by Impulsiveness (β = 0.203, sig = 0.000), Electronic word of mouth (β = 0.144, sig = 0.001), Mood-relevant cues (β = 0.137, sig = 0.001), Hedonic shopping motivation (β = 0.097, sig = 0.027), Task-relevant cues (β = 0.090, sig = 0.028), and Convenience orientation (β = 0.084, sig = 0.033) Consequently, all seven hypotheses (H1 to H7) were accepted.
This chapter presents the analysis results and key findings of the study, along with a discussion of the research outcomes The subsequent chapter will summarize the overall conclusions of the study and explore its implications for management, theory, and future research directions.
