INTRODUCTION
Problem discussion
As the country develops, building a home has evolved into more than just providing shelter; it now serves as a personal sanctuary that reflects the homeowner's identity This shift emphasizes the need for aesthetically pleasing and diverse living space designs and interior decorations With an average GDP growth rate of 6% annually and advancements in the construction and real estate sectors, rising household incomes are driving an increased demand for home furniture.
Vietnam is a leading player in the global furniture market, ranking first in Southeast Asia, second in Asia, and fourth worldwide in furniture exports, according to an EVBN report In 2015 alone, Vietnam's furniture exports to Europe reached $7.2 billion, with an additional $1.7 billion for home decoration items The furniture manufacturing industry in Vietnam is projected to grow steadily at an annual rate of 9.4% However, the current market is primarily export-oriented, leaving the domestic sector largely reliant on imported goods from countries like China, Malaysia, and Thailand.
The domestic furniture market holds significant potential for businesses, with various segments catering to different income groups Understanding consumer preferences for home furnishings is challenging due to the diverse customer base, necessitating research into consumer behavior Currently, there is a lack of studies in Vietnam that analyze consumer behavior regarding interior products Thus, my thesis focuses on "Factors Affecting Consumer Behavior in the Home Furniture Market: A Case Study of Hanoi."
What are the factors which affects consumers purchasing intention when buying home furniture?
Research objective
This thesis aims to assist furniture manufacturers and retailers in Vietnam by analyzing consumer behavior and purchase intentions within the industry It will explore various factors that influence customers' purchasing decisions, both positively and negatively By understanding these behaviors, manufacturers and retailers can tailor their strategies to better align with market demands, ultimately enhancing their competitiveness in the Vietnamese furniture market.
Research scope
This research explores consumer behavior theory and builds on existing studies, employing a two-phase approach that includes qualitative and quantitative research Initially, secondary data is synthesized to identify factors influencing consumer buying behavior Subsequently, a consumer opinion survey is conducted in Hanoi to gather primary data Based on the analysis of this data, the author proposes several solutions aimed at helping businesses enhance their market presence in the home furniture sector in Hanoi.
Object and scope of the study
- Object: Home furniture consumers in Hanoi
- Scope of the study: Research is carried out to find out the purchase intention of home furniture in the period of 2017 - 2020 and vision to 2025.
Thesis outline
This thesis is structured into six key chapters: introduction, literature review, hypotheses and framework development, research methodology, hypotheses testing results, and conclusion and recommendations Following the main body, the document includes references and two appendices, which contain questionnaires in both English and Vietnamese.
LITERATURE REVIEW
Overview of consumer behavior
Consumer behavior, as defined by M Khan (2007), encompasses the study of the purchasing process, including what, how, where, when, and in what quantity consumers decide to buy It also involves evaluating the sources from which to make purchases Additionally, consumer behavior examines various internal and external factors influencing these decisions, such as self-concept, social and cultural background, age, family dynamics, attitudes, personality traits, and social class.
Consumer behavior encompasses the actions taken by individuals during the stages of researching, purchasing, utilizing, and assessing products and services to meet their needs It reflects how consumers decide to allocate their resources—such as money, time, and effort—when engaging with goods and services to fulfill personal requirements (Kotler, 2007).
Marketers analyze consumer behavior to understand their needs, preferences, and purchasing habits This involves examining what consumers want to buy, the reasons behind their choices, the brands they select, and the factors influencing their buying decisions, such as where, when, and how much they purchase By gathering this information, marketers can develop effective strategies to encourage consumer shopping.
Consumer behavior encompasses the thoughts, feelings, and actions involved in the decision-making process when purchasing goods or services This behavior is influenced by the interaction between external environmental stimuli and internal psychological processes.
Consumer intentions serve as a crucial indicator of the likelihood of engaging in specific behaviors, particularly in the context of purchase decisions (Ajzen, 1991) Purchase intention is recognized as the primary predictor of actual buying behavior (Montaủo and Kasprzyk, 2015), and this research emphasizes it as the key variable for investigation (Armitage and Conner, 2001) This construct operates at the pre-purchase stage, highlighting the motivational aspects that influence customer behavior Understanding consumer behavior necessitates an exploration of attitudes, evaluations, and internal factors that contribute to purchase intent (Fishbein and Ajzen, 1977) Therefore, this study posits that purchase intention directly influences purchase decisions, with a specific focus on identifying the factors that affect the intention to purchase home furniture.
Factors affecting consumer behavior
Consumer behavior is shaped by various external factors and individual characteristics, which influence how consumers perceive and respond to stimuli The environment plays a significant role in shaping consumer traits, while consumer behaviors also impact the surrounding environment (Blackwell et al., 2006) Additionally, purchasing decisions are heavily influenced by cultural, social, personal, and psychological factors (Kotler & Armstrong, 2004), as illustrated in Figure 2.2.
Figure 2.1: Characteristics Influencing Consumer Behavior
Cultural factors that exert intensely influence on consumer behavior consist of culture, subculture, and social class factor
Social factors such as reference groups, family, and social roles and status also impact consumer behavior
A purchaser’s decisions are also influenced by personal characteristics such as the purchaser’s age and life-cycle stage, occupation, economic situation, lifestyle, and personality
An individual’s purchasing choices are further influenced by four major psychological factors Four factors consist of motivation, perception, learning, and beliefs and attitudes
Economic scientists were pioneers in examining consumer behavior, offering insights into solutions for consumption challenges They viewed individuals as social and rational beings, with key economic factors influencing decisions including personal income, family income, income expectations, liquid assets, and government policy.
Previous research about purchasing behavior of consumers
In this study, the author mentioned the consumer behavior model of Kotler
(2007) and the theory of reasoned action (TRA) and several researches related to consumer behavior in the furniture market
According to Kotler's model, consumer behavior is influenced by four key elements of the marketing mix, which interact with the consumer's subconscious to drive decision-making In price-sensitive markets, selecting the appropriate pricing strategies is crucial for gaining a competitive edge and effectively guiding product selection.
Consumers react to various external stimuli, including the marketing mix and environmental factors in the market The marketing mix, often referred to as the four Ps, consists of a series of strategically planned stimuli developed by the company to influence consumer behavior.
The environmental stimuli are supplied by the economic, political, and cultural circumstances of a society Together these factors represent external circumstances that help shape consumer choices
The internal factors influencing consumer decisions are often referred to as the "black box," which encompasses various elements within an individual's mind This includes personal characteristics such as beliefs, values, motivations, and lifestyle The decision-making process is integral to this "black box," as consumers identify problems that require solutions and evaluate how purchasing decisions can address these issues As consumers react to external stimuli, their internal "black box" processes these factors to determine their response, ultimately influencing whether they choose to make a purchase or not.
Similar to the economic man model, this approach posits that consumer responses stem from a deliberate and rational decision-making process, regardless of internal mental processes However, many marketers question this notion, believing that consumers frequently make impulsive or emotionally driven purchases In reality, marketers recognize that these emotional and irrational tendencies often render consumers more receptive to marketing stimuli.
Consumer purchasing behavior is often viewed as a mystery or "black box," as individuals may not fully comprehend the factors influencing their choices This lack of understanding makes the exchange process unpredictable, posing challenges for marketers trying to decipher consumer motivations.
The Theory of Reasoned Action (TRA), developed by Fishbein and Ajzen in 1975, is a prominent concept in social psychology This theory explores the relationship between attitudes, intentions, and behaviors, providing a framework for understanding how individuals make decisions An overview of the TRA is illustrated in Figure 2.3.
The Theory of Reasoned Action (TRA) explores how beliefs, norms, attitudes, intentions, and behaviors are interconnected It posits that an individual's intentions to behave in a certain way directly influence their actual behavior These intentions are shaped by the person's subjective norms and attitudes towards the behavior Furthermore, the TRA indicates that modifying an individual's belief structure can alter their attitudes in response to external stimuli Additionally, it highlights the role of external variables, which indirectly affect behavior by impacting subjective norms.
The TRA model focuses on the construction of a system of observation of two groups of variables, which are:
+ attitudes defined as a positive or negative feeling in relation to the achievement of an objective;
+subjective norms, which are the very representations of the individuals’ perception in relation to the ability of reaching those goals with the product
The author emphasizes the importance of purchase intention to actual purchase decision
When purchasing furniture, several key factors come into play that significantly influence the decision-making process This research highlights the most important aspects considered during furniture purchases and examines their effects on consumer choices.
Research “Factors Affecting Furniture Purchase in Pakistan”, M F Qureshi &
A Kamaran et al give 5 factors to consider including: (1) Price, (2) Customer service,
The study conducted by M F Qureshi & A Kamaran et al (2020) highlights the significant impact of product quality and brand loyalty on customer attitudes towards furniture purchases It reveals that while price influences buying decisions, product quality plays a crucial role in shaping customer perceptions Additionally, brand loyalty emerges as a key factor in influencing customer attitudes The research also confirms a positive relationship between customer reviews and customer attitudes, supporting the hypothesis that reviews significantly affect consumer behavior.
The study by Parmana et al (2019) on the impact of marketing mix factors on purchasing decisions for wooden furniture at Furnimart Bogor reveals that consumer perceptions significantly influence their buying behavior Key factors include product design, completeness, quality, discounts of up to 70%, convenient payment options, accessible store locations, prompt delivery, store convenience, appealing sales promotions, excellent service assistance, and social media marketing Notably, the SEM analysis identifies price and promotion as the most influential factors in the purchasing decision.
Research “Consumer behavior in purchasing home furnishing products in Thailand”, Thanyamon consider 7 factors that have influences on furniture purchasing decision process including: (1) Product quality, (2) Product design, (3) Brand loyalty,
When selecting furniture, key factors influencing consumer choices include product quality, design, and price Additionally, aspects such as after-sales services, store location, and delivery speed also play a significant role in the decision-making process (Thanyamon Sakpichaisakul, 2012).
In their 2009 study, "Understanding Furniture Decision Making Process and Design Preference using Web-Based VR Technology," So-Yeon Yoon and Ji Young Cho highlight the critical role of product design in influencing consumer purchasing decisions Their research underscores how web-based virtual reality technology can enhance the understanding of design preferences, ultimately impacting the furniture selection process.
The "Consumer Behavior Model on the Furniture Market" identifies five key factors influencing the furniture purchasing decision: product quality, price, customer service, social media, and customer reviews Among these, product quality is deemed the most critical criterion when consumers evaluate furniture for purchase.
Table 2.1 shows some of the research works on this topic and the factors investigated in those prior studies
Table 2.1 Factors influence purchasing intention home furniture
1 Attitude 1 M F Qureshi & A Kamaran et al (2020)
2 Product quality 4 M F Qureshi & A Kamaran et al (2020)
3 Product design 1 Parmana et al (2019)
3 So-Yeon Yoon & Yi Young Cho (2009)
4 Price 1 M F Qureshi & A Kamaran et al (2020)
5 Store location 1 Parmana et al (2019)
6 Social media 1 Parmana et al (2019)
7 After-sales service 1 Parmana et al (2019)
Literature gap
In the last five years, Vietnam has witnessed the emergence of approximately 400,000 to 500,000 townhouses and luxury apartments, as noted by Mr Phan Dang Chuong, Deputy General Director of Ernst & Young Vietnam Limited Each apartment typically invests around 100 to 200 million VND in interior design, resulting in a substantial market demand exceeding 100,000 billion VND Consequently, understanding the factors that influence consumer buying behavior is crucial, as these factors can vary significantly across individuals, cultures, and regions This research will primarily focus on the Vietnamese market.
This study also aims to examine how the impact of factors changes when furniture consumption trends and furniture demand are changing.
Hypotheses development
2.5.1 Relationship between customer attitude and purchase intention
Consumer purchase intentions are influenced by various factors, including personal preferences, social and monetary standings, and perceived quality, value, and price Internal and external motivations also play a crucial role in shaping these intentions Research identifies six key stages leading to the decision-making process: knowledge, awareness, interest, preference, persuasion, and purchase This concept has been extensively studied globally to better understand the elements that impact consumer purchasing behavior Key brand elements such as product quality, involvement, image, loyalty, knowledge, and attributes significantly affect a buyer's intention to purchase.
Attitude plays a crucial role in shaping an individual's predisposition and is positively correlated with behavior (Allport, 1935) It reflects the degree to which a person evaluates behavior positively or negatively (Fishbein and Ajzen, 1977) In this study, we define attitude as the consumer's assessment regarding the purchase of home furniture, building on the insights of Andrews and Bianchi (2013).
According to the Theory of Reasoned Action (TRA), a positive attitude toward a specific behavior leads to a stronger intention to engage in that behavior (Amaro and Duarte, 2015) Therefore, if consumers have a favorable perception of purchasing home furniture, their intention to buy such items will likely increase This leads to the first research hypothesis: a positive consumer assessment of home furniture will enhance the intention to purchase.
H1: Attitude has a significant positive effect on purchase intention
2.5.2 Relationship between product quality and customer attitude
Product quality is defined as the set of characteristics that determine a product's ability to meet specified requirements Brands influence perceived quality by positioning their products as superior to competitors Higher product quality significantly impacts customer attitudes, as demonstrated by research showing a strong correlation between the two To meet consumer expectations, manufacturers must ensure their products are durable and reliable.
2013) Hence the second hypothesis is:
H2: Product Quality has a significant positive effect on customer attitude
2.5.3 Relationship between design product and customer attitude
Psychologist Carl Jung (1967) observed that the self-archetype is manifested through self-expression in physical forms, with the home serving as a reflection of one's self-image (Cooper, 1976) Additionally, So-Yeon Yoon and Ji Young Cho (2009) highlighted the crucial role of product design in influencing purchasing decisions Consequently, this leads to the formulation of the third hypothesis.
H3: Product Design has a significant positive effect on customer attitude
2.5.4 Relationship between price and customer attitude
Price is a crucial factor influencing consumer purchasing decisions, prompting businesses to adopt various pricing strategies to meet the diverse needs of customers (Hansen H, 2013) The relationship between price and buying behavior is complex; high-priced items are not always dismissed by consumers In fact, elevated prices can enhance perceived value, as customers often associate higher costs with superior quality (Alhadda A).
Price is a crucial determinant in consumer purchasing decisions across various products and services, except in cases where the product is essential for survival, such as lifesaving medications.
H4: Price has a significant positive effect on customer attitude
2.5.5 Relationship between store location and customer attitude
Location is crucial in retailing, significantly influencing consumer store choice An ideal location is perceived as convenient for shopping, with good access to public transport, ample parking, and a friendly environment Store characteristics, such as layout, sales staff presence, atmospherics, and merchandise presentation, also play a vital role in attracting consumers Retailers design stores and implement marketing strategies to encourage impulsive purchases, which are influenced by in-store stimuli According to Bellenger (1978), consumer shopping behavior can be categorized into planned and impulse purchases; the latter occurs due to in-store factors like layout, signage, visual merchandising, and atmosphere Understanding these in-store characteristics is essential for retailers, as impulse purchases represent a significant portion of supermarket sales.
Thus, the fifth hypothesis is:
H5: Store location has a significant positive effect on customer attitude
2.5.6 Relationship between social media and customer attitude
Social media serves as a platform for individuals to express and share their ideas, thoughts, and opinions while fostering connections similar to those established over centuries Alongside these advancements, significant shifts in consumer behavior have emerged, driven by changes in values, lifestyles, and a quest for value for money This has given rise to a new generation of tourists who are more informed, independent, and individualistic (Poon, 1993) However, travel-related consumer behavior has also become increasingly paradoxical (Marabella, 2004) For instance, consumers may exhibit conflicting holiday preferences, such as desiring luxury travel experiences while simultaneously seeking the best hotel rates online (Gretzel et al., 2006).
Hence the sixth hypothesis is:
H6: Social media has a significant positive effect on customer attitude
2.5.7 Relationship between after-sales service and customer attitude
After-sales service is essential for ensuring that customer needs are fully satisfied, aligning with their expectations Effective customer service addresses the specific requirements of each client, fostering a positive relationship Enhanced customer service leads to a more favorable customer perception of their purchases, ultimately improving overall satisfaction and loyalty.
K, Khaksar SMS, 2011) Some qualities of good customer service include Promptness i.e., ensuring that things are always on time and no customer has to be kept waiting (Hsu
Politeness and professionalism are crucial elements in enhancing customer experience Being sociable and sweet in conduct helps to please customers, making their interactions more enjoyable (Jahanshahi et al., 2011) Additionally, maintaining professionalism through concise and relevant conversations ensures that customers feel valued and understood (Bashir et al., 2012).
Personalization in customer experience is crucial, as it involves addressing customers by their names, making them feel valued and honored (Jasmand et al., 2012) The nature of customer service depends on the type of product or service offered, the specific needs of the customers, and whether the service is problem-oriented or focused on enhancing the overall customer experience (Agnihotri et al., 2015).
Hence the seventh hypothesis is:
Research model
RESEARCH METHODOLOGY
Research process
The research was conducted by following steps which shown in the figure below:
Data collection
3.2.1 Data collection method: survey method
Therefore, in this study, the survey is used to be the main method due to the following reasons:
The researcher has developed a user-friendly online survey that allows for quick administration (Cherry, 2018) Participants can easily complete the designed questionnaire by filling out the forms and submitting their responses via provided links The collected data is then instantly compiled into charts or Excel format, ensuring real-time updates on the results.
Surveys are a cost-effective data collection method, as they are generally less expensive than focus groups or interviews, which require direct meetings and time constraints for respondents (Cherry, 2018) In contrast, online surveys can be created at no cost, allowing participants to complete them at their convenience.
Finally, the survey method can be applied to use for collecting information on a broad range of sample which is includes personal fact, the attitudes, respondents’ behaviors, and opinions (Cherry, 2018)
In general, the survey is the most suitable method to gather the necessary data required for this analysis
According to Hair et al (2014), the minimum sample size for a questionnaire analysis should be at least five times the number of items, meaning each variable requires a minimum of five respondents This study, adhering to a quantitative approach with a total of 32 variables, calculates the necessary sample size as 32 multiplied by 50 However, the research successfully collected 270 samples from participants, exceeding the minimum requirement.
Convenience sampling involves selecting members of a target population based on practical criteria such as geographical proximity, accessibility, and willingness to participate Often referred to as "accidental samples," these samples are formed by choosing individuals who are readily available in the researcher's vicinity In the context of online surveys, respondents typically include individuals aged 18 and older, who have experience in purchasing furniture, and come from diverse backgrounds in terms of occupation, education, and housing types.
Questionnaire design
This research article utilizes a Likert scale to analyze the factors influencing home furniture purchasing decisions By employing a series of evaluations with four or more items, participants can express their level of engagement and opinions effectively.
The study employs Likert scales due to their proven reliability and validity, including test-retest reliability, concurrent validity, and predictive validity (Jacoby & Mattel, 1971) This approach is particularly effective as it captures the directional component of user actions, with only a minor emphasis on intensity (Peabody, Cronbach, 1962) Thus, the Likert scale is well-suited for this research's focus on understanding user behavior.
In addition, the Likert scale offers a perfect way to assess attitudes, awareness, beliefs, values and behaviors
The questionnaire of this research was designed to be simple for the respondents easily to understand and answer This approach is in harmony with Easterby-Mith et al
In 2008, it was suggested that shorter questionnaires with simpler questions increase response rates The questionnaire was originally designed in English and later translated into Vietnamese for local respondents The questions included were adapted and modified from similar ones used in previous studies.
The questionnaire is used in this study composed of two sections:
• The first section is introduction and it contained 10 questions for demographic information
• The second section consisted 32 measuring items of 8 constructs including Purchase intention, Customer attitude, Product quality, Product design, Price, Store location, Social media, After-sales service
Table 3.1 Measuring items for questionnaire
1 Buying home furniture is something I would do
2 I feel comfortable in sharing my information about furniture product
3 I usually buy a few new products when I move to a new residence
4 Even when there is no need I still like to see furniture products
5 I often think about buying new products Thanyamon
6 I am interested in information of new furniture models
7 I believe it is necessary to buy new furniture regularly
8 My family influences my furniture purchasing a lot
9 A salesperson can influence my furniture selection process
10 I like products with a lifelong durability
11 I like products that integrate many functions
12 I usually read the product specifications
13 I like solid products no matter how heavy or thick they appear
14 I like new model rather than current model furniture
15 I usually choose products that match my home décor Éva & Judit
16 If there are two similar products, I would choose the nicer one even though the price might be higher
17 When I buy furniture, I choose the cheapest one
18 I usually buy on sale furniture Parmana et al
19 I like cheaper furniture only if it meets quality requirements
20 For furniture, higher price equals higher quality
21 Discounted furniture means it is left- over or obsolete item
22 I usually buy new furniture at the store near my house
23 I like furniture stores that are conveniently located
24 I prefer independent stores to stores located in shopping malls
25 I tend to buy a furniture that a celebrity I like endorsing it Éva & Judit
26 I tend to buy a furniture that I've seen advertised before
27 I often watch furniture ads on social media
28 I will look at the product review before buying
29 I will buy furniture if store provides delivery service
30 I feel more satisfied if speed delivery Ying Li et al
31 I like products with return policy Parmana et al
32 I will buy furniture if store provides installation service
(2012) Each item will be measured by 5 - Likert scale:
Analyzing data plan
This research utilized SPSS 22 software for data analysis, chosen for its flexibility in managing sample sizes and measurement model construction Following the collection of online questionnaire responses, each questionnaire item was systematically coded, as illustrated in Table 4.2.
Table 3.2 Encoded terms for data testing
INT1 Buying home furniture is something I would do
INT2 I feel comfortable in sharing my information about furniture product
When relocating to a new home, I typically purchase several new items to enhance my living space Additionally, I enjoy browsing furniture products even when I don't have an immediate need for them This habit often leads me to contemplate potential new purchases for my home.
ATD1 I am interested in information of new furniture models.
ATD2 I believe it is necessary to buy new furniture regularly
ATD3 My family influences my furniture purchasing a lot.
ATD4 A salesperson can influence my furniture selection process.
PQ1 I like products with a lifelong durability
PQ2 I usually read the product specifications
PQ3 I like products that integrate many functions
PQ4 I like solid products no matter how heavy or thick they appear
PD1 I like new model rather than current model furniture
PD2 I usually choose products that match my home décor
PD3 If there are two similar products I would choose the nicer one even though the price might be higher
P1 When I buy furniture, I choose the cheapest one
P2 I usually buy on sale furniture
P3 I like cheaper furniture only if it meets quality requirements P4 For furniture, higher price equals higher quality
P5 Discounted furniture means it is left-over or obsolete item
SL1 I usually buy new furniture at the store near my house
SL2 I like furniture stores that are conveniently located
SL3 I prefer independent stores to stores located in shopping malls
SM1 I tend to buy a furniture that a celebrity I like endorsing it
SM2 I tend to buy a furniture that I've seen advertised before
SM3 I often watch furniture ads on social media
SM4 I will look at the product review before buying
ASS1 I will buy furniture if store provides delivery service
ASS2 I feel more satisfied if speed delivery
ASS3 I like products with return policy
ASS4 I will buy furniture if store provides installation service
With SPSS software, perform data analysis through tools such as descriptive statistics, frequency tables, reliability testing of scales, exploratory analysis, regression
To assess the reliability of a direct scale, the Cronbach Alpha coefficient is commonly utilized as an internal consistency index, indicating whether the scale's questions share a similar structure A higher Cronbach's Alpha coefficient signifies greater internal consistency Employing the Cronbach's Alpha reliability method prior to conducting exploratory factor analysis (EFA) helps in identifying and eliminating unsuitable variables, as these can lead to the emergence of spurious factors.
Cronbach's Alpha reliability coefficient assesses the relationship between measured variables but does not specify which variables to retain or exclude To enhance the reliability of the scale, it is essential to combine this coefficient with the variable-total correlation coefficient, allowing for the removal of variables that contribute minimally to the intended concept (Hoang Trong and Chu Nguyen Mong Ngoc, 2008) Key criteria for evaluating scale reliability should be carefully considered during this process.
The Cronbach Alpha reliability coefficient is a crucial metric for assessing scale reliability, with values greater than 0.8 considered excellent Scores ranging from 0.7 to 0.8 are deemed acceptable, while coefficients of 0.6 and above may be utilized, particularly when the research concept is novel or underexplored in the given context (Nunnally, 1998; Peterson, 1994; Slater, 1995).
Hoang Trong and Chu Nguyen Mong Ngoc, 2008) ) In this study, the author chose a scale with a reliability of 0.6 or more
The variable-to-sum correlation coefficient indicates that observed variables with a correlation of less than 0.3 are deemed ineffective and will be discarded A scale is only accepted when the reliability coefficient, measured by Cronbach's Alpha, meets the necessary standards.
Factor analysis is a statistical method employed to condense data by identifying the primary factors from a larger set of observed variables This technique not only simplifies the data but also retains the essential information, ensuring that the reduced factors are meaningful for further analysis Exploratory factor analysis specifically assesses the conceptual validity of measurement scales.
Implementation method and evaluation criteria in EFA exploratory factor analysis:
For multi-directional scales, the Principal Axis Factoring method with Promax rotation is employed, focusing on extracting EigenValues factors of 1 or greater, as this approach provides a more accurate representation of the data compared to Principal Components with Varimax rotation (Nguyen Dinh Tho and Nguyen Thi Mai Trang, 2007) In contrast, unidirectional scales utilize the Principal Components factor extraction method, with an acceptable scale defined by a total variance extracted of at least 50% (Nguyen Dinh Tho and Nguyen Thi Mai Trang).
In exploratory factor analysis (EFA), it is crucial for the factor loading to be at least 0.5 to ensure practical significance The acceptable thresholds for factor loading values are as follows: a minimum of 0.3 is acceptable, 0.4 is considered important, and 0.5 indicates practical significance When selecting factor loading values, a sample size of 270 allows for values greater than 0.3, while a smaller sample size of around 100 necessitates higher factor loading values for meaningful results.
0.55; if the sample size is about 50 then the factor loading factor must be greater than 0.75
From the above theoretical basis, the model uses 35 observed variables for EFA factor analysis and the implementation follows the following steps:
To measure the component concepts using unidirectional scales, it is essential to apply the Principal Components factor extraction method with Varimax rotation, focusing on factors with EigenValues greater than 1 Subsequently, verify the associated requirements to ensure the robustness of the analysis.
+ Bartlett test: observed variables are correlated with each other in the population
+ Consider the KMO value: if the KMO is in the range from 0.5 to 1, then factor analysis is appropriate for the data (Hoang Trong and Chu Nguyen Mong Ngoc, 2008)
+ To analyze EFA with practical value, remove observed variables with factor loading coefficient less than 0.5
+ Review the parameter Eigen Values (representing the variation explained by each factor) with a value greater than 1
+ Consider the total variance extracted (requires greater than or equal to 50%): indicate the extracted factors explaining the % variation of the observed variables
Scales that meet satisfactory evaluation criteria are included in the Pearson correlation analysis, which examines the linear relationship between dependent and independent variables This analysis supports the appropriateness of linear regression The Pearson correlation coefficient (r) ranges from -1 to +1, with values closer to 1 indicating a strong correlation between the two variables A value of r = 0 signifies no linear relationship exists between them (Hoang Trong and Chu Nguyen Mong Ngoc).
After concluding that two variables have a linear relationship with each other, this causal relationship can be modeled by linear regression (Hoang Trong and Chu Nguyen Mong Ngoc, 2008)
The study performed multivariate regression by Enter method: all variables were included once and related statistical results were considered
The process of hypothesis testing is carried out according to the following steps:
+ Evaluate the fit of the multivariable regression model through R2 and R2 adjusted
Test hypothesis about the fit of the model
+ Test the hypothesis about the significance of the regression coefficients for each component
+ Hypothesis testing of the normal distribution of residuals: according to the histogram of the normalized residuals; see mean as 0 and standard deviation as 1
+ Check the assumption of multicollinearity through the value of tolerance (Tolerance) or the variance magnification factor VIF (Variance Inflation Factor) If VIF
> 10, there is multicollinearity (Hoang Trong and Chu Nguyen Mong Ngoc, 2008)
The influence of various factors in a research model can be assessed by examining their beta coefficients; a larger beta coefficient indicates a greater level of influence for that particular factor compared to others.
DATA ANALYSIS & HYPOTHESIS TESTING
Descriptive data
Samples were collected using a convenient method through survey questionnaires After excluding invalid responses due to missing critical information or age criteria, a total of 270 valid responses were retained for quantitative analysis The summarized results are presented in the following table.
Table 4.1 Description of respondents’ profile
Eligible answers were chosen from 312 surveys that collected from 270 people
+ People aged 21 – 40 accounted for the most with 33.7% and 31.5% Next is the group 41-50 years old, accounting for 18.9% The last group over 50 years old, accounting for 15.9%
+ According to sex, the relative sample is not too big difference between male and female, in which female accounted for 59.3% and male 40.7%
+ In terms of education level, 15.1% of the surveyed sample have a degree High school; University accounted for 55.6% and Postgraduate accounted 29.3%
+ By occupation, the group of Employee accounted for the most with 53.7%; Self-employed accounted for 12.9%; Housewife accounted for 11.5%; Other specific occupations: 51.4%
+ By personal income, the group Under 6 million accounts for 13.7%; 5 – less than 10 million accounts for 14.1%; 10 - 15 million, accounting for 28.1%; 15 – 25 million accounted for 21.5%; 25 – 40 million accounted for 11.1%; group Over 40 million accounted for 11.5%
+ By Total income, the group Under 15 million accounted for 11.5%; 15-25 million counted for 15.2%; 26-40 million accounted for 26.3%; 41-60 million accounted for 14.4%; group over 100 million accounted for 12.2%
+ According to residential type, the group of Tube house accounted for 20%; Highrise building accounted for 30.4%; Condominium accounted for 37.4%; group of Villa accounted 12.2%
The survey sample effectively represents the broader population, as each group within the sample has a sufficient number of participants—exceeding 30 individuals—allowing for robust statistical analysis.
Analysis and result
4.2.1 The reliability of the scale
The reliability of the scale was tested with the following results:
Table 4.2 Cronbach's Alpha analysis results table
Cronbach’s Alpha if Item deleted
Cronbach's alpha coefficient of the factor: 0.797
Cronbach's alpha coefficient of the factor: 0.841
Cronbach's alpha coefficient of the factor: 0.689
Cronbach's alpha coefficient of the factor: 0.611
Cronbach's alpha coefficient of the factor: 0.754
Cronbach's alpha coefficient of the factor: 0.896
Cronbach's alpha coefficient of the factor: 0.711
Cronbach's alpha coefficient of the factor: 0.796
The results show that all factors are statistically significant because Cronbach's Alpha coefficient is greater than 0.6, in which:
+ Product Quality with Cronbach's Alpha 0.797 and the total variable correlation coefficient from 0.588 - 0.629 so the variables will be kept
+ Product Design with Cronbach's Alpha 0.841 and the total variable correlation coefficient from 0.663 - 0.735 so the variables will be kept
+ Price with Cronbach's Alpha 0.689 and the total variable correlation coefficient from 0.355 - 0.531 so the variables will be kept
+ Store Location with Cronbach's Alpha 0.611 and the total variable correlation coefficient from 0.320 - 0.453 so the variables will be kept
+ Social Media with Cronbach's Alpha 0.754 and the total variable correlation coefficient from 0.420 - 0.662 so the variables will be kept
+ After-sales Service has the highest Cronbach's Alpha coefficient of 0.896 and the total correlation coefficient at the allowable level of 0.711 - 0.841, showing that the component variables have a close relationship
+ Attitude with Cronbach's Alpha 0.711 and the total variable correlation coefficient from 0.441 - 0.542
+ Finally, Intention with Cronbach's Alpha 0.796 and the total variable correlation coefficient from 0.531 - 0.669
The evaluation of the scale's reliability has identified eight key factors: Product Quality, Product Design, Price, Store Location, Social Media, After-sales Service, Attitude, and Intention These factors will be incorporated into the exploratory factor analysis (EFA) to further assess their impact.
4.2.2.1 Exploratory factor analysis (EFA) for 6 independent variables
The model identifies six independent variables supported by 23 statistically significant observed variables These independent variables will be incorporated into the scale test using exploratory factor analysis (EFA) to ensure reliability.
EFA analysis for 6 independent variables was performed with the hypothesis H0: The observed variables have no correlation in the population The results obtained from the analysis are summarized as follows:
▪ Barlett test: Sig = 0.000 < 5%: Rejecting hypothesis H0, observed variables in EFA analysis are correlated with each other in the population
▪ KMO coefficient = 0.922 > 0.5: factor analysis is required for the data
▪ There are 6 factors extracted from EFA analysis with:
EigenValues of all factors are > 1: satisfactory
The exploratory factor analysis demonstrated compliance with the requirements, as the total value of variance extracted reached 65.903%, exceeding the 50% threshold This indicates that the six extracted factors collectively account for 65.903% of the data's variation Furthermore, the observed variables exhibited a difference in factor loading coefficients greater than 0.3, highlighting the high discriminatory value of the factors.
Table 4.3 Table of results of EFA analysis of independent variables
4.2.2.2 Exploratory factor analysis (EFA) for 2 dependent variables
Attitude scale used to measure customer’s attitude includes 4 observed variables The results of the EFA analysis showed:
▪ 5 observed variables are grouped into 1 factor Factor loading coefficients are all > 0.5, so they have practical significance
▪ Each observed variable has a difference in factor loading coefficient ≥ 0.3, so it ensures the distinction between factors
▪ KMO coefficient = 0.838 > 0.5 factor analysis is required for the data
▪ The Chi-square statistic of Bartlett Test reached the significance level value of 0.000 Therefore, the observed variables are correlated with each other on the overall scale
The extracted variance is 61.411%, showing that one factor can be explained 61.411% variation of the data, so the scale drawn is accepted Factor extraction with Eigenvalue = 3,071 met the requirements
Intention scale used to measure customer’s purchase intention includes 5 observed variables The results of the EFA analysis showed:
▪ 5 observed variables are grouped into 1 factor Factor loading coefficients are all > 0.5, so they have practical significance
▪ Each observed variable has a difference in factor loading coefficient ≥ 0.3, so it ensures the distinction between factors
▪ KMO coefficient = 0.812 > 0.5 factor analysis is required for the data
▪ The Chi-square statistic of Bartlett Test reached the significance level value of 0.000 Therefore, the observed variables are correlated with each other on the overall scale
The extracted variance is 60.017%, showing that one factor can be explained 61.411% variation of the data, so the scale drawn is accepted Factor extraction with Eigenvalue = 2,611 met the requirements
Based on the results of EFA analysis, the extracted factors of the main research hypotheses are satisfactory.
Hypotheses testing
A validity test was conducted to ensure that the results accurately measured the intended variables This was achieved through Pearson correlation analysis, which assesses the relationship between these variables A correlation value between -1 and +1 indicates the strength of their relationship, with values closer to +1 signifying a stronger correlation.
Table 4.4 Results of Pearson correlation analysis
INT ATD PQ PD P SL SM ASS
According to the results, the independent variables all have a strong linear correlation with the dependent variable, the correlation coefficients are statistically significant (p