(LUẬN văn THẠC sĩ) factors influencing customer intention to purchase automobile in hanoi

INTRODUCTION

Rationale of the research

Vietnam's economic transformation from state-owned management to a market-oriented system has been a remarkable success story, leading to significant improvements in economic development and living standards for its citizens The country has achieved impressive GDP growth rates, showcasing its effective economic renovation efforts.

(2018), GDP growth rate of the country during 2015, 2016, and 2017 are 6.7%, 6.2%, and 6.8% respectively and it is forecasted up to 7.1% and 6.8% in 2018 and

2019 Asian Development Bank (2018) also estimates that GDP growth rate of Vietnam is highest compared to GDP growth rate of other countries in Southeast Asia in 2018

Figure 1.1: GDP growth rate of countries in Southeast Asia

According to the Asian Development Bank (2018), the living standards of the Vietnamese population are improving, as evidenced by the notable increase in GDP per capita growth rate The report highlights a rise from 5.1% in 2016 to 5.8% in 2017, indicating a positive economic trend Future forecasts suggest continued growth in GDP per capita for Vietnam.

In 2018, the growth rate of Vietnam, along with Cambodia, Laos, Myanmar, the Philippines, Indonesia, Malaysia, Thailand, Singapore, and Brunei, was recorded at 6.1% (Asian Development Bank, 2018) Vietnam achieved the second highest GDP per capita growth rate at 6.6%, following Cambodia (Asian Development Bank, 2018) The country has shown political stability, with an improving political stability index over the years (The Global Economy, 2018) A significant trend is the increasing urbanization, with the urbanization rate rising from 28.5% a decade ago to 34.2% in 2016 (World Bank, 2018) Additionally, rising disposable income has influenced consumer behavior in Vietnam (Nielsen, 2018), contributing to a high consumer confidence index that ranks the nation among the top 10 globally (The Conference Board, 2018).

As social development accelerates, the demand for travel and relaxation is rising, driven by the need to alleviate daily stress and pressure (Wang 2001, Terry 2010) Tourism has become an essential aspect of human life, designed and shaped by people for their own enjoyment and well-being It serves as a vital outlet for individuals seeking respite from their busy lives.

Tourism offers significant benefits by allowing individuals to explore and appreciate diverse cultures, mysteries, histories, and customs of new destinations, while also promoting relaxation and enjoyment of life through natural support (Nakajima 2003, Cheia 2013) Additionally, it plays a vital role in enhancing health and boosting human labor productivity (Roxana 2012, Chen 2013).

Vietnamese consumers show a strong preference for non-food products, allocating their disposable income towards holidays and vacations, new clothing, technology, home improvements, out-of-home entertainment, and medical services Notably, spending on holidays and vacations accounts for approximately 44% of their consumption, ranking just below new clothing at 49% (Nielsen, 2018).

Figure 1.2: How do Vietnamese consumers spend their spare cash?

The Vietnamese automobile industry is still in its early stages of development, having predominantly featured government-owned vehicles imported from the Soviet Union and Eastern European socialist states before 1992 The economic reforms initiated in 1986 aimed at liberalizing the economy created significant opportunities for the automotive sector, allowing over 30 regional assemblers to obtain auto assembly licenses (PricewaterhouseCoopers – PwC, 2007) This led to the entry of global automotive manufacturers like Mitsubishi, Toyota, and Isuzu into Vietnam in 1995 Recent trends indicate a rise in automobile production and sales across ASEAN nations, with statistics from Pugliese (2017) highlighting vehicle sales growth in the region from 2013 to 2016.

Table 1.1: Vehicle sales in the ASEAN region by market, 2013-16

Table 1 shows that Vietnam gains high percentage of change during 2013-

In 2016, Vietnam's vehicle sales reached 271,833 units, significantly lower than neighboring countries like Indonesia, Thailand, and Malaysia, which sold more than double that amount Despite this, Vietnam exhibited a strong annual growth rate in its automobile market, indicating high potential for development, particularly as other markets like Indonesia and Thailand experienced declines in vehicle sales This competitive landscape necessitates that automobile manufacturers in Vietnam adopt clear business strategies to navigate market threats, capitalize on opportunities, and leverage their strengths to address weaknesses effectively.

Recent studies in Vietnam have aimed to understand customer purchasing intentions in the automobile market For instance, Mai & Ngo (2017) explored how event sponsorship affects brand awareness and purchase intentions, finding that while both event sponsorship and involvement have some influence, their overall impact is minimal Conversely, Bui (2015) used qualitative methods to assess innovative vehicles but relied on secondary data, lacking empirical evidence on the factors influencing purchasing intentions Addressing these gaps, this study focuses on identifying key factors that significantly affect customer purchasing intentions in Vietnam's automobile market and measuring the extent of their impact.

Research objectives

This study is developed with following research objectives:

 To get in-depth understand about customer purchasing intention and customer purchasing process

 To examine factors influencing customer intention to purchase automobile in previous studies

 To measure the influences of factors on customer intention to purchase automobile in Hanoi, Vietnam

 To provide recommendations for automobile dealers to strengthen their sales.

Research object

Research object refers to customers who want to purchase automobile in Hanoi, Vietnam.

Scope of the study

This study examines the factors influencing customer intention to purchase automobiles in Hanoi, Vietnam's capital By identifying and measuring these factors, the research aims to assist both scholars and industry professionals in developing targeted marketing strategies that can enhance sales revenue and volume in Vietnam's automotive sector.

This research was conducted in Hanoi, Vietnam, where a questionnaire was developed and distributed to customers intending to purchase automobiles from three dealers: Toyota, Honda, and others.

Significance of the study

This study aims to assess customer purchasing intentions for automobile products in Vietnam, providing valuable insights for auto dealers to understand consumer preferences and tailor effective marketing strategies As the Vietnamese auto market experiences growing demand from local citizens for personal and business vehicles, this research will also serve as a valuable resource for other researchers, summarizing previous studies on customer purchasing intentions and highlighting key findings relevant to the Vietnamese market.

Structure of the research

This study is conducted with five chapters

Chapter 1 Introduction The main part of this chapter is to provide rationale of choosing researched topic as well as proposing research objectives This chapter provides research object, scope of the study, and what the benefits of the readers could get.

LITERATURE REVIEW

Definitions of Terms

Consumer behavior has become a focal point for researchers over the years (Nguyen, 2015), evolving through various interpretations without a unified definition (Bray, 2008) Walter (1974) initially defined consumer behavior as the process through which individuals determine what, when, how, and from whom to purchase products and services Subsequently, Mowen (1993) expanded this definition to encompass consumers' actions related to acquiring, consuming, and disposing of goods and services, along with the feedback received during their usage.

Consumer behavior, as defined by Schiffman and Kanuk (1997), encompasses the actions of individuals seeking products and services that meet their needs, making purchases, utilizing those products, and ultimately disposing of them They categorize consumers into two groups: personal consumers, who buy for personal use, and organizational consumers, who acquire products and services for business purposes This distinction highlights the varied motivations behind consumer purchasing decisions.

Consumer behavior refers to the process through which individuals select, buy, use, and dispose of products based on their needs and demands According to researchers like Hoyer et al (2013), it encompasses the entirety of customers' decisions regarding product acquisition, consumption, and disposal This study defines consumer behavior as the combination of consumers' actions and psychological processes related to information processing, purchasing, utilizing, and disposing of products.

Customer purchasing intention encompasses a comprehensive process that includes searching for, purchasing, utilizing, and providing feedback on products and services (Enis, 1974) According to Kotler (2000), consumer behavior, from a marketing perspective, involves a series of actions and evaluations related to products and services More recently, Kumar (2010) defines consumer behavior as the attitudes and actions of consumers towards specific offerings from companies, in relation to competing products and services in the market.

2.1.2 Definition of customer purchasing intention process

In this study, the definition of customer purchasing intention process is proposed by Riley (2012) This process is illustrated in the figure below:

Figure 2.1: Customer purchasing intention process

The customer purchasing intention process begins with recognizing their needs and problems (Riley, 2012) Kotler & Armstrong (2010) emphasize that customers are motivated to buy products or services primarily when they face urgent needs or issues that drive them to seek solutions The next step involves customers actively searching for relevant information about the products or services that can address their specific needs.

Customers typically research specific features of products or services to determine how well they meet their needs or solve their problems (Kotler & Armstrong, 2010) The third stage in the customer purchasing intention process involves evaluating alternative options, where customers compare the characteristics and functions of various products or services (Riley, 2012) The goal at this stage is for customers to identify the product or service that stands out as the most superior option (Wright).

Purchasing desire products and services

The purchasing process involves several key steps, beginning with the customer's decision to buy products or services that align with their specific needs (Kotler & Armstrong, 2010) After the purchase, customers engage in an evaluation phase where they assess their satisfaction with the products or services over time According to Khan (2006), this self-assessment can yield either positive or negative feedback, depending on how well the offerings meet their expectations and address their issues (Kardes et al., 2011).

The concept of a brand has evolved significantly over the years, traditionally viewed as a trademark for specific products and services since the late 19th century (Fullerton, 1988; McCrum, 2000; Moore and Reid, 2008) Feldwick (1991) describes a brand as a guarantee of authenticity, while the American Marketing Association (1960) defines it as a term, design, name, or symbol that distinguishes a company's offerings from its competitors This highlights the intangible aspects associated with products and services (Gardner and Levy, 1955; Wood, 2000) In a more recent update, the American Marketing Association (1995) broadened the definition to encompass any features of products and services that enable firms to differentiate themselves in the marketplace.

Products and brands are distinct concepts, as highlighted by Gunelius (2018) A product is created by firms and sold to customers for monetary exchange, while a brand aims to enhance customer perception and experience related to that product Unlike products, which can be easily replicated by competitors and replaced with new offerings, a brand is unique and irreplaceable According to Kotler et al (2003), a product encompasses everything available in the market that fulfills customer needs In contrast, Keller (1998) defines a brand as a collection of mental associations that shape customer perceptions of a product's or service's value.

As brands have become a crucial element in marketing theories and practices, it is essential to explore their functions Numerous studies, including those by Hoeffler and Keller (2001) and Yoo et al (2000), highlight the importance of establishing a strong brand as a competitive advantage in the marketplace (Keller, 2002) This section will discuss the various functions of brands.

The primary function of a brand is its influence on product-related perceptions, significantly affecting customer evaluations of products and services Research indicates that brand associations play a crucial role in shaping perceived quality, purchasing intentions, and actual purchasing rates (Day and Deutscher, 1982; Rao and Monroe, 1989; Brown and Dacin, 1997) Additionally, familiarity with a brand increases the likelihood of customers choosing to buy its products (Feinberg et al.).

Brand function significantly influences both attitudinal and purchase loyalty Attitudinal loyalty leads to customers being willing to pay higher prices for products and services, while purchase loyalty provides firms with the potential to capture a larger market share.

The price-related effects of a brand play a crucial role in its overall function, as research indicates that strong brands can lead to significant price differentials (Simon, 1979; Agrawal, 1996; Sethuraman, 1996) Brands also provide firms with resilience against price increases (Sivakumar and Raj, 1997), allowing companies with effective brand management to experience less price sensitivity Consequently, these firms can avoid engaging in price competition with smaller, less established brands (Bemmaor and Mouchoux, 1991; Russel and Kamakura, 1994; Bucklin et al., 1995).

(1994) indicates that when the firms conduct unique advertising messages to their customers, they avoid future price competition

The third function of brand is identified as communication-related effects

Research indicates that well-known or liked brands enhance communication effectiveness (Sawyer, 1981) The halo effect occurs when positive emotions are generated through brand advertising (Brown and Stayman, 1992) Additionally, humor in advertising fosters favorable attitudes towards a brand (Stewart and Furse, 1986; Chattopadhyay and Basu, 1990), while comparative advertising tends to elicit negative reactions from consumers (Belch, 1981; Kamins and Marks, 1991) Empirical evidence shows that loyal customers exhibit higher purchasing rates when exposed to more advertising (Raj, 1982), and they are also more forgiving of negative information regarding strong brands (Ahluwalia et al., 2000).

The final role of a brand involves its impact on distribution channels Research indicates that companies with strong brands typically experience higher acceptance rates for new products and services (Montgomery, 1975) Furthermore, products associated with well-established brands are more likely to be stocked and sold in retail outlets, as they convey a strong image of quality to consumers (Lal and Narasimhan, 1996).

Customer intention to purchase auto

Customer intention to purchase a car is defined as the behavior influenced by the vehicle's utility value, which encompasses both transportation needs and the enhancement of personal status (Chang & Hsiao, 2011) Additionally, Shende (2014) emphasizes that this intention is shaped by customers' preferences and specific needs, whether for business purposes, family requirements, or simply upgrading from older models.

RESEARCH METHODOLOGY

Research philosophy

Before the discussion of research philosophy, the researcher proposes using research onion framework which is provided by Saunders et al (2009) It is illustrated as below:

The research onion framework, as outlined by Saunders et al (2009), serves as a comprehensive guide for researchers to select the most appropriate methodologies for their academic studies At its core, the framework emphasizes data collection and analysis, while considering the research timeline, which can be cross-sectional or longitudinal The next layer focuses on research methods, categorized into mono, mixed, and multi-method approaches Mono methods are further divided into quantitative and qualitative techniques, while mixed methods combine both, and multi-methods apply them across various studies Research strategies, including experiments, surveys, case studies, action research, grounded theory, ethnography, and archival research, are tailored to specific research situations and objectives These strategies are framed by research approaches, which can be deductive or inductive Ultimately, the overarching research philosophy encompasses all elements of the research onion, incorporating positivism, interpretivism, pragmatism, and realism.

Research philosophy is a crucial aspect of the analytical process, guiding how data is collected and analyzed (Saunders et al., 2009) Various research philosophies exist, including functionalism, realism, objectivism, positivism, subjectivism, and interpretivism (Collins, 2010) Among these, positivism and interpretivism are the most favored approaches in research (Neville, 2005).

Positivism research philosophy is the application of neutral language into observation of social phenomenon and of nature science to explore the facts (Levin,

Positivism research philosophy is utilized when social phenomena can be analyzed through natural sciences or quantified using measurable data (Fisher et al., 2004) This approach focuses on objective facts and specific correlations, allowing for a deeper exploration of social phenomena (Denscombe, 2002).

Interpretivism research philosophy is utilized when social phenomena are dynamic or cannot be adequately explained by natural sciences (Saunders et al., 2009) This approach addresses abstract social phenomena that depend on the observed context, allowing researchers to gain a deeper understanding of these complexities (Gephart, 1999) Additionally, interpretivism is particularly relevant when researchers aim to comprehend social phenomena rationally or are working with unstructured data during the data collection phase (Bryman & Bell, 2007).

This research philosophy is valuable for researchers aiming to uncover significant insights from human behavior or social perspectives, with the key findings articulated through verbal descriptions (Nudzor, 2009).

Based on the general understandings about positivism and interpretivism research philosophy, this study will follow positivism due to various explanatory factors:

This study aims to elucidate the relationship between customer purchasing intentions for auto products and four key factors: brand awareness, perceived value, brand personality, and product quality Additionally, it explores how these factors causally influence Vietnamese customers' purchasing intentions, highlighting the rationale for adopting a positivist research philosophy, which is well-suited for examining causal correlations in this context.

Second, one of the main goal of positivism research philosophy is to gain better understanding about a reality and social phenomenon is an objective reality (Schutt,

2006) It is noted that Vietnamese customer purchasing intention exists independently with the observers, therefore, it is better to apply positivism research philosophy

Managing data in interpretivism research philosophy is typically more challenging than in positivism due to the unstructured nature of the data involved (Altinay & Paraskevas, 2008) To streamline the process and minimize preparation efforts, researchers often prefer to utilize positivism research philosophy.

Research approach

Once positivism is established as the research philosophy, the next step in the research onion process is selecting the appropriate research approach Typically, researchers utilize either a deductive or inductive approach, as outlined by Remeny et al (1998) These two approaches differ significantly in their methodologies and applications.

The deductive research approach is highly valuable in academic sciences as it follows a process that begins with general theories and culminates in specific findings (Bryman & Bell, 2007) This approach relies on established theories previously discussed by researchers, which are then applied to particular situations to achieve empirical verification (Anderson, 2004) Ultimately, the goal of the deductive research approach is to determine the applicability of these theories in real-world contexts (Saunders et al., 2009).

The inductive research approach is utilized by researchers aiming to formulate new theories through the observation of social phenomena across various cases (Anderson, 2004) This method transitions from specific observations to general conclusions (Saunders et al., 2009) Additionally, it posits that a social phenomenon validated in multiple instances will also hold true in individual cases (Hackley, 2003).

Based on the general understandings about deductive and inductive research approach, this study will follow deductive due to various explanatory factors:

Research approach and philosophy are interconnected, with two primary pairs: positivism paired with deductive reasoning and interpretivism with inductive reasoning (Saunders et al., 2009) It is commonly noted that positivists tend to adopt a deductive research approach, whereas interpretivists typically favor an inductive approach (Schiffman & Kanuk, 1997) Given the previous discussion leading to the selection of positivism as the research philosophy, a deductive research approach will be utilized.

The nature of the research topic plays a crucial role in determining whether to use a deductive or inductive approach (Creswell, 1994) Topics that have been extensively studied by researchers typically lend themselves to a deductive research methodology Chapter 2 highlights that customer purchasing intention is a widely explored subject, particularly in relation to auto products, with numerous empirical studies supporting it Consequently, adopting a positivist research philosophy is a fitting choice for this investigation.

The deductive research approach benefits from the positivism research philosophy, making it less time-consuming and requiring less effort in data preparation (Saunders et al., 2009) Since positivism focuses on structured data, the application of the deductive research approach becomes simpler and more efficient.

Based on the reasons of choosing deductive research approach, this study is developed with following steps:

The research process begins with identifying problems related to the topic, which aids in formulating research objectives and questions, as detailed in Chapter 1 Next, the researcher reviews relevant literature to explore customer purchasing intentions and identify factors affecting these intentions towards auto brands in Vietnam Following this, a questionnaire is developed to quantitatively measure the relationships between variables Finally, a pre-test of the questionnaire is conducted with volunteers who evaluate its content and overall suitability.

Problem Statement Literature Review Quantified questionnaire

The research process begins with a pre-test and data analysis, leading to key findings and discussions After developing and testing the questionnaire, the researcher distributes it to selected respondents to gather their responses This collected data is then analyzed, enabling the generation of insightful research findings Each segment of the data analysis results is thoroughly discussed, culminating in a conclusion that summarizes the research topic Finally, the researcher offers recommendations aimed at enhancing customer purchasing intentions for automotive products.

Research strategy

Research strategy is emphasized as how the data to be collected and it provides direct supports to both research philosophy and research approach (Saunders et al.,

In this study, the case study method will be the primary research strategy, as it is one of the most widely utilized approaches in research, alongside surveys This choice is supported by various explanatory factors that highlight its effectiveness in providing in-depth insights (Collins, 2010; Gable, 1994).

First, the selection of research strategy is not only based on research philosophy and research approach but also research objectives (Saunders et al.,

In 2009, Saunders et al highlighted that case studies are essential for research objectives aimed at emphasizing the specific correlations among variables, as these correlations are often best observed within particular case studies.

Case studies are frequently utilized in deductive research, which progresses from general principles to specific conclusions, allowing researchers to derive key findings from particular instances (Schiffman & Kanuk, 1997) Additionally, case studies align well with positivist research philosophy and the deductive approach (Saunders et al.).

2009) Moreover, case study is deployed with a questionnaire in order to collect required data for specific findings

Third, case study is more relevant compared to survey strategy due to case study overcomes the issue of only a snapshot from case study (Gable, 1994)

Moreover, other researchers identify that case study can provide in-depth understanding about social phenomenon which is complex and it can extend experiences and knowledge of the fact (Soy, 1997).

Research method

There are two research methods that are often used in academic sciences, including quantitative and qualitative (Saunders et al., 2009)

Quantitative research is utilized to clarify causal relationships between dependent and independent variables, relying on well-structured data typically gathered through surveys or structured interviews This method aligns with positivism research philosophy and a deductive approach, offering concrete insights based on data collected from a broader population The key advantage of this approach is its ability to reflect overall opinions, making it more reliable than interviews conducted with a limited number of respondents or experts.

Qualitative research methods rely on data gathered from expert interviews and direct observations, emphasizing verbal descriptions (Boxill et al., 2009) This approach is particularly effective for understanding the opinions and attitudes of groups related to social phenomena (Mack et al., 2005) Unlike quantitative research, which focuses on objective assessments, qualitative research is characterized by its subjective evaluation (Nykiel, 2007).

Based on the general understandings about quantitative and qualitative, this study will follow quantitative research method.

Data collection

Primary data will be gathered through a well-structured survey targeting customers interested in purchasing automobiles in Hanoi, Vietnam The questionnaire consists of two sections: the first part collects demographic information such as gender, age, education, and monthly income, while the second part assesses respondents' opinions on automobiles and related factors.

Secondary data will be collected with application of desk data collection method The source of secondary data includes:

 Books and journals in term of empirical evidences of causal relationship between factors and customer intention to purchase automobile

 Published reports about Vietnam auto market as general and Hanoi automobile market as particular.

Survey of questionnaire

This study utilizes a structured questionnaire designed with multiple items for each factor, allowing respondents to easily select options that reflect their opinions The questionnaire's layout is outlined in the table below Additionally, a 5-point Likert scale will be employed, ranging from 1 (strongly disagree) to 5 (strongly agree), to gauge respondents' levels of agreement.

Table 3.1: Survey of Questionnaire’s Items Factors Items

 In term of ownership, I intent to purchase automobile

 I would like to have automobile

 If I have an opportunity, I would like to suggest my relatives and my friends to purchase automobile

Brand  I can recognise some characteristics of this brand awareness  I can differentiate this brand from others

 This brand comes up first in my mind when I want to purchase a car

 When I see an advertising about car, I always think about this brand first

 When I think about a car, I can recognise this brand name immediately

Perceived value  This brand provides good value for the money

 The quality of this brand is reliable

 I am proud to own/use a car of this brand

 What I get from this brand is worth the cost

 This brand matches my personality

 I can quickly recall the symbol or the logo of this brand

 I have a clear image of the type of person who would use this brand

Product quality  I can expect superior performance from this brand

 The quality of this brand is very high

 In term of overall quality, I would rate this brand high value

In addition to the questionnaire survey, gathering demographic information from respondents is essential for this research Relevant demographic data includes gender, age, annual income, and occupation Gender is categorized as male and female, while age is segmented into groups such as 25-35 and 36 and above.

The demographic data collected includes age groups of 46, 46-55, and over 55 years, along with annual income brackets of less than 120 million VND, 120-240 million VND, 240-360 million VND, and over 360 million VND Additionally, respondents' occupations were recorded, categorizing them as private service employees, government workers, freelancers, and others.

The chosen sample size for this study is approximately 200 customers, which aligns with empirical evidence from previous research on optimal sample sizes According to Hair et al (1998), a minimum sample size should range from 100 to 150 In contrast, Norusis (2005) argues for a larger minimum sample size of at least 300, highlighting the ongoing debate in the field regarding appropriate sample sizes for research.

According to Hoang & Chu (2008), the minimum sample size for a questionnaire should be at least five times the number of variables included Given that the survey comprises 18 variables, this indicates that the minimum sample size required is 90 respondents.

In 2008, the study involved 90 respondents, but after evaluating three different sample sizes, the final decision was made to use a sample size of 200 respondents This size meets the minimum requirements established by Hair et al (1998), Norusis (2005), and Hoang, ensuring the validity of the research findings.

Before distributing the survey, the researcher conducts a pilot test with five voluntary respondents to evaluate the questionnaire's effectiveness Feedback from these participants focuses on the clarity of content, ease of understanding, and appropriateness of wording, ensuring no misunderstandings arise The pilot test results indicate unanimous agreement among respondents that the questionnaire is well-constructed and easy to follow Additionally, they confirm that the items measuring brand awareness, perceived value, brand personality, product quality, and customer purchasing intention are adequate, with no requests for additional items.

Data analysis techniques

This study employs various data analysis techniques, including descriptive statistics, reliability tests, exploratory factor analysis (EFA), ANOVA, and multiple linear regression Each technique has specific requirements, which will be detailed in this section and verified in the subsequent chapter.

Table 3.2: Summary of data analysis techniques

Descriptive statistics  Standard deviation should be higher than

 Mean value = 3.5: Agreement Reliability test  Overall Cronbach‘s alpha > 0.6

 Cronbach‘s alpha when deleting one item

< overall Cronbach‘s alpha Explanatory factor analysis

 Factor loading values > 0.50 Independent Sample T-

 T-Test returns p-value < 0.05 indicates for different evaluation

One-Way ANOVA  F-Test returns p-value < 0.05 indicates for different evaluation

Multiple linear regression  Higher adjusted R-Square, higher explanation from independent variables

Descriptive statistics is a crucial data analysis technique that allows researchers to identify patterns within collected data According to Nicholas (2006), it encompasses various subsets, including frequency, mean, standard deviation, and interquartile range In this study, frequency analysis is utilized to determine the number of respondents in each demographic category, while mean value analysis assesses the level of agreement or disagreement among respondents regarding survey statements Sekaran (2003) notes that a mean value between 2.5 and 3.5 indicates a neutral attitude, while values below 2.5 or above 3.5 reflect disagreement or agreement, respectively.

A reliability test is a crucial data analysis technique that enables researchers to assess the interrelatedness and consistency of variables within a single factor (Tavakol & Dennick, 2011) This test has three specific requirements, with the first focusing on the overall reliability level of a factor, measured by Cronbach's alpha, where a minimum value of 0.6 is considered acceptable (Nunnally).

In order to meet the necessary criteria for reliability, the inter-relatedness among variables must be established, with an expected value exceeding 0.3, as demonstrated by the Corrected Item-Total Correlation (Gerrard et al., 2006) Additionally, the analysis of Cronbach's alpha indicates that the value for "Cronbach's alpha if Item Deleted" should be lower than the overall Cronbach's alpha (Malhotra et al.).

Exploratory Factor Analysis (EFA) is a statistical method that assists researchers in identifying optimal combinations among variables (Hair et al., 1998) To ensure the validity of EFA, three key requirements must be met: first, the KMO value should exceed 0.5, and Bartlett’s Test must be statistically significant at the 5% confidence level (Gerbing & Anderson, 1988) Second, once the component matrix is generated, each variable associated with a specific component should have a factor loading value greater than 0.5 (DeCoster, 1998) Lastly, the cumulative eigenvalues of the newly extracted components must reach a minimum threshold of 50% (Dewberry, 2004).

ANOVA analysis is essential for examining how demographic characteristics impact various variables (Park, 2003) There are two main types of ANOVA: the Independent Sample T-Test, used for gender comparisons, and One-Way ANOVA, which applies to age, education, and monthly income If the p-value from the T-Test or F-Test is below 0.05, it indicates that demographic differences significantly affect respondents' assessments.

Multiple linear regression analyzes the relationship between dependent and independent variables (Cohen et al., 2003) The strength of this relationship is represented by the R-Square value, where a higher R-Square indicates a greater explanatory power of independent variables on the dependent variable Additionally, a T-Test is utilized to assess the statistical significance of the independent variable's influence on the dependent variable, with a p-value anticipated to be less than 0.05.

EVALUATION OF CUSTOMER INTENTION TO PURCHASE

Demographic description

Demographic information will be analyzed using frequency analysis in SPSS software, which will yield counts and percentages for each demographic item The results will be visually presented in various figures for clearer illustration.

The survey results indicate that male respondents significantly outnumber female respondents, with males comprising 52.5% of the total participants compared to 47.5% for females This trend suggests that men are generally more attracted to automobile products than women.

The data indicates that the majority of respondents fall within the 25-46 age range, with 67 participants aged 25-35 and 59 aged 46-55, while only 7 respondents are over 55 years old This suggests that automobile products are primarily appealing to younger consumers, particularly those between 25 and 46 years old.

Figure 4.3: Income per annum summary

Figure above shows that the groups 120-240VNDm and more than 360VNDm as income per annum have more respondents than the groups less than

The survey reveals distinct income brackets among respondents: 47 individuals earn less than 120 million VND annually, equating to approximately 10 million VND per month Meanwhile, 60 respondents fall within the 120 to 240 million VND range, earning between 10 and 20 million VND monthly Additionally, 45 respondents have an annual income between 240 and 360 million VND, corresponding to a monthly income of 20 to 30 million VND Finally, 48 respondents report an annual income exceeding 360 million VND, which translates to more than 30 million VND per month.

Figure above provides the last demographic information as occupation of

A survey conducted with 200 respondents revealed a diverse range of occupations among participants Specifically, 45 individuals are employed in private services, while 46 are engaged in government services.

53 respondents who are considered themselves as freelancers and 56 respondents who are working in other occupations.

Descriptive statistics analysis

The upcoming section focuses on conducting descriptive statistics to analyze mean values derived from the survey questionnaire Each survey item will have its mean value calculated and compared against benchmarks of 2.5 and 3.5 A mean value below 2.5 indicates disagreement, while a value above 3.5 signifies agreement with the survey statement Conversely, a mean value falling between 2.5 and 3.5 reflects a neutral stance, indicating neither agreement nor disagreement among respondents.

The Freelancer and 56 Others rule is significant due to the survey conducted using a 5-point Likert scale, where responses range from 1 (strongly disagree) to 5 (strongly agree).

Variable Contents Mean Standard deviation

CPI1 In term of ownership, I intent to purchase automobile 3.31 0.464

CPI2 I would like to have automobile 3.24 0.431

CPI3 If I have an opportunity, I would like to suggest my relatives and my friends to purchase automobile

BAW1 I can recognise some characteristics of this brand 3.20 0.953

BAW2 I can differentiate this brand from others 3.34 1.015 BAW3 This brand comes up first in my mind when I want to purchase a car 3.33 1.012

BAW4 When I see an advertising about car, I always think about this brand first 2.85 1.102

BAW5 When I think about a car, I can recognise this brand name immediately 2.33 0.814

PVA1 This brand provides good value for the money 3.50 0.808

PVA2 The quality of this brand is reliable 3.88 0.767 PVA3 I am proud to own/use a car of this brand 3.66 0.915 PVA4 What I get from this brand is worth the cost 3.71 0.842

BPE1 This brand matches my personality 3.28 1.032

BPE2 I can quickly recall the symbol or the logo of this brand 3.33 1.038

BPE3 I have a clear image of the type of person who would use this brand 3.27 1.073

PQA1 I can expect superior performance from this brand 3.47 1.017

PQA2 The quality of this brand is very high 3.38 0.985 PQA3 In term of overall quality, I would rate this brand high value 2.12 0.894

The table presents the mean and standard deviation values for consumer purchasing intention (CPI), which includes CPI1, CPI2, and CPI3, with mean scores of 3.31, 3.24, and 3.39, and standard deviations of 0.464, 0.431, and 0.488, respectively As the mean values fall between 2.5 and 3.5 and the standard deviations are below 1.0, it indicates that respondents maintain a neutral attitude toward their intention to purchase an automobile, express a desire to own one, and recommend it to friends and family Additionally, consumer purchasing intention for automobile products in Hanoi is considered normal, supported by the standard deviation values being less than 1.0.

Brand awareness (BAW) is represented by five components: BAW1, BAW2, BAW3, BAW4, and BAW5, with mean values of 3.20, 3.34, 3.33, 2.85, and 2.33, respectively Respondents displayed a neutral attitude towards BAW1 through BAW4, indicating they can recognize automobile brands, differentiate between them, recall a brand when considering purchases, and think of a brand when exposed to related advertisements Conversely, BAW5 received a mean value of 2.33, suggesting disagreement with the notion of recognizing a brand name when thinking about automobiles While the mean value analysis indicates overall neutrality, standard deviation analysis shows that only the neutral attitude towards BAW1 is statistically valid, as its standard deviation is below 1.0 In contrast, BAW2, BAW3, BAW4, and BAW5 have higher standard deviations, implying that the neutral stance may not accurately reflect respondents' attitudes towards these items.

Perceived value, denoted as PVA, encompasses four components: PVA1, PVA2, PVA3, and PVA4, with mean values of 3.50, 3.88, 3.66, and 3.71, respectively These scores indicate that respondents generally agree that the brand offers good value for money, is reliable, instills pride in ownership, and is worth the cost Additionally, the standard deviations for these items—0.808, 0.767, 0.915, and 0.842—are all below 1.0, confirming a strong consensus among respondents regarding their perceived value of the brand.

Brand personality, represented as BPE, encompasses three components: BPE1, BPE2, and BPE3, with mean values of 3.28, 3.33, and 3.27, respectively These scores indicate a neutral attitude among respondents regarding the alignment of the brand with their personalities, the recognizability of its symbols and logo, and the clarity of the brand image associated with its owners However, the standard deviations for these items exceed 1.0, suggesting that the neutral attitudes may not accurately reflect the true sentiments of the respondents towards the brand personality.

Product quality, denoted as PQA, includes three components: PQA1, PQA2, and PQA3, with mean values of 3.47, 3.38, and 2.12, respectively The mean values for PQA1 and PQA2 indicate that respondents have a neutral attitude towards the expectation of superior brand performance and recognizable high quality However, the higher standard deviation of PQA1, exceeding 1.0, suggests that this perception may not be consistent In contrast, PQA3's mean value of 2.12 shows that respondents disagree with the notion of high overall brand quality, and its standard deviation of less than 1.0 supports the reliability of this finding.

Reliability test

The reliability test is essential for assessing the reliability of each variable in the survey scale, providing outputs such as Cronbach's alpha, Corrected Item-Total Correlation, and Cronbach's alpha if Item Deleted These outputs help researchers evaluate the reliability of each factor, including brand awareness, perceived value, brand personality, product quality, and customer purchasing intention The results of these tests are detailed in the accompanying tables.

Table 4.2: Reliability level of consumer purchasing intention

Cronbach‘s alpha if Item Deleted

In term of ownership, I intent to purchase automobile

I would like to have automobile

If I have an opportunity, I would like to suggest my relatives and my friends to purchase automobile

The analysis indicates that the Cronbach's alpha for consumer purchasing intention stands at 0.689, demonstrating an acceptable level of reliability for the survey scale The correlation ratios for CPI1, CPI2, and CPI3 are 0.500, 0.471, and 0.546, all exceeding the threshold of 0.3 Notably, removing any of these items results in lower Cronbach's alpha values of 0.601, 0.638, and 0.542, confirming that the original scale maintains its reliability Overall, the consumer purchasing intention factor meets all criteria for acceptable reliability.

Table 4.3: Reliability level of brand awareness

I can recognise some characteristics of this brand

I can differentiate this brand from others

This brand comes up first in my mind when I want to purchase a car

When I see an advertising about car, I always think about this brand first

The table indicates that the Cronbach's alpha for brand awareness is 0.869, demonstrating a strong reliability level for the survey scale Additionally, the correlation ratios for BAW1, BAW2, BAW3, BAW4, and BAW5 are 0.823 and 0.752, further supporting the reliability of the brand awareness measurement.

The analysis revealed Cronbach's alpha values of 0.616, 0.601, and 0.715, all exceeding the acceptable threshold of 0.3 Upon removing certain items, the new Cronbach's alpha values increased to 0.810, 0.826, 0.861, 0.868, and 0.841, though they remained below 0.869 Overall, the factor demonstrates a good level of reliability, meeting all necessary conditions for the reliability test.

Table 4.4: Reliability level of perceived value

I can differentiate this brand from others

This brand comes up first in my mind when I want to purchase a car

When I see an advertising about car, I always think about this brand first

The table indicates that the Cronbach's alpha for perceived value is 0.794, demonstrating a strong reliability level for the survey scale Additionally, the correlation ratios for items PVA1, PVA2, PVA3, and PVA4 are 0.632, 0.656, and 0.639, respectively.

The Cronbach's alpha values for the items are 0.791 and exceed 0.3 After removing certain items, the new alpha values are 0.730, 0.721, 0.727, and 0.791, which are lower than the original 0.794 Overall, this factor demonstrates an acceptable level of reliability, as all conditions of the reliability test have been satisfied.

Table 4.5: Reliability level of brand personality

This brand matches my personality

I can quickly recall the symbol or the logo of this brand

I have a clear image of the type of person who would use this brand

The analysis indicates that the Cronbach's alpha for brand personality is 0.824, demonstrating an acceptable reliability level for the survey scale The correlation coefficients for BPE1, BPE2, and BPE3 are 0.654, 0.729, and 0.656, all exceeding the threshold of 0.3 When any of these items are removed, the new Cronbach's alpha values drop to 0.782, 0.707, and 0.781, respectively, indicating a decrease from the original 0.824 Overall, the brand personality factor exhibits good reliability, with all criteria for reliability testing satisfied.

Table 4.6: Reliability level of product quality

I can expect superior performance from this brand

The quality of this brand is very high

In term of overall quality,

I would rate this brand high value

The analysis reveals that the Cronbach's alpha for product quality is 0.663, indicating an acceptable level of reliability for the survey scale The correlation ratios for items PQA1, PQA2, and PQA3 are 0.508, 0.497, and 0.422, respectively, all exceeding the threshold of 0.3 Furthermore, removing these items results in new Cronbach's alpha values of 0.520, 0.535, and 0.633, which are lower than the original 0.663 Overall, the product quality factor demonstrates an acceptable reliability level, with all reliability test conditions satisfied.

Explanatory factor analysis for independent variables

Exploratory Factor Analysis (EFA) aims to refine the research model by grouping highly correlated items, thereby reducing the dataset's item count and assessing the validity of the initial research model To ensure the suitability of EFA, the KMO and Bartlett's Test results are provided below.

Table 4.7: KMO and Bartlett’s Test for independent variables

The KMO value is 0.737, exceeding the threshold of 0.5, indicating suitability for factor analysis Additionally, Bartlett's Test, a Chi-Square Test, produced a Chi-Square value of 1,242.971 and a p-value of 0.000 with 105 degrees of freedom, confirming statistical significance at a 5% confidence interval Therefore, exploratory factor analysis (EFA) is appropriate for this data.

Table 4.8: Total Variance Explained Table for independent variables

The EFA results indicate that four components possess initial eigenvalues exceeding 1.0, specifically 3.959, 2.255, 1.989, and 1.748 Components with eigenvalues below 1.0 are excluded from selection The percentage variances for these four components are 26.396% for Component 1, 15.034% for Component 2, 13.262% for Component 3, and 11.654% for Component 4 Together, these components account for a cumulative variance of 66.346%, surpassing the 50% threshold.

Table 4.9: Rotated Component Matrix for independent variables Variable Component 1 Component 2 Component 3 Component 4

Component 1 includes all items of brand awareness while Component 2, Component 3, and Component 4 includes all variables of perceived value, brand personality, and product quality Factor loading values of each item are higher than 0.50 Therefore, it is concluded that original research model is good and it can be used for further analysis.

Explanatory factor analysis for dependent variables

After performing Exploratory Factor Analysis (EFA) on the independent variables, the researcher aims to analyze the dependent variables The initial step involves assessing the suitability of EFA for the dependent variable data, which is determined by calculating the KMO value and conducting Bartlett’s Test The results of these calculations are presented below.

Table 4.10: KMO and Bartlett’s Test for dependent variables

The KMO value is 0.661, exceeding the threshold of 0.5, indicating suitability for factor analysis Additionally, Bartlett's Test, a Chi-Square Test, yields a Chi-Square value of 99.167 and a p-value of 0.000 with 3 degrees of freedom This result demonstrates statistical significance at a 5% confidence interval, confirming that exploratory factor analysis (EFA) is appropriate for this data.

Table 4.11: Total Variance Explained Table for dependent variables

The EFA results indicate that there is one component with an initial eigenvalue greater than 1.0, specifically Component 1, which has an eigenvalue of 1.851 In contrast, Component 2 and Component 3 have eigenvalues of 0.631 and 0.517, respectively, both of which are below 1.0 Consequently, Component 1 is selected, effectively grouping the data of dependent variables into a single component Additionally, Component 1 accounts for 61.708% of the variance explained, surpassing the 50% threshold.

Table 4.12: Rotated Component Matrix for dependent variables

Component 1 includes all items of customer purchasing intention Factor loading values of CPI1, CPI2, and CPI3 are 0.757, 0.782, and 0.817 which are all higher than 0.50 The researcher combines all variables of customer purchasing intention through average function in order to generate a new variable.

ANOVA analysis

ANOVA analysis includes two main components: the Independent Sample T-Test and One-Way ANOVA The Independent Sample T-Test is used for analyzing gender variables, while One-Way ANOVA is applicable for demographic information with more than two categories Essentially, One-Way ANOVA is ideal for demographic variables that have multiple dimensions, whereas the Independent Sample T-Test is limited to those with only two dimensions.

Table 4.13: Independent Sample T-Test for Gender

Levene‘s test T-Test for Equality of Means

The analysis reveals that brand awareness is the only factor showing a significant difference in assessment between male and female respondents, evidenced by a Levene's Test F-Test of 8.100 and a p-value of 0.005, which is below the 0.05 threshold The T-Test results further indicate significant differences, with values of -2.142 and -2.167 for equal and unequal variances, respectively, both yielding p-values of 0.033 and 0.031 Conversely, factors such as customer purchasing intention, perceived value, brand personality, and product quality do not exhibit significant differences, as demonstrated by their respective F-Tests and T-Test results, all of which have p-values exceeding 0.05 Specifically, customer purchasing intention shows an F-Test of 1.298 (p-value 0.256) and T-Test values of 0.040 (p-value 0.968) and 0.040 (p-value 0.068) Perceived value has an F-Test of 1.113 (p-value 0.293) with T-Test values of 0.226 (p-value 0.833) and 0.228 (p-value 0.820) Brand personality presents an F-Test of 1.434 (p-value 0.233) and T-Test values of 0.741 (p-value 0.460) and 0.736 (p-value 0.462) Lastly, product quality shows an F-Test of 0.119 (p-value 0.731) and T-Test values of 1.400 (p-value 0.163) for both tests Thus, male and female respondents do not differ in their assessments of these additional factors.

Table 4.14: On-Way ANOVA for Age

The F-Test values for customer purchasing intention, brand awareness, perceived value, brand personality, and product quality are 0.263, 3.423, 2.552, 1.394, and 3.405, respectively The corresponding P-values are 0.852, 0.018, 0.057, 0.246, and 0.019 This indicates that only brand awareness and product quality show statistical significance at a 5% confidence interval, suggesting that younger and older respondents assess these factors differently, likely due to their maturity levels Conversely, there are no significant differences in younger and older respondents' assessments of purchasing intention, perceived value, and brand personality.

Table 4.15: On-Way ANOVA for Age with Tukey B test

The data indicates that respondents over 55 years old exhibit the highest scores in customer purchasing intention, brand personality, and product quality, while showing a lower score in brand awareness, likely due to their limited engagement with new brands Conversely, those aged 25-35 demonstrate the highest brand awareness but score the lowest in brand personality and product quality, reflecting their need for more time to develop consumption experiences and evaluate product quality Meanwhile, respondents aged 36-45 and 46-55 display medium scores across these factors.

Table 4.16: On-Way ANOVA for Income per annum

The F-Test results indicate that customer purchasing intention, brand awareness, perceived value, brand personality, and product quality have values of 0.901, 1.943, 1.384, 2.547, and 0.442, respectively Corresponding P-values are 0.442, 0.124, 0.249, 0.057, and 0.723, suggesting that annual income does not significantly influence the respondents' assessments.

Table 4.17: On-Way ANOVA for Income per annum with Tukey B test

Subset for alpha = 0.05 Customer purchasing intention

The data indicates that government employees exhibit the lowest customer purchasing intention but rate brand personality the highest In contrast, those in private services show average scores across all factors Freelancers lead in customer purchasing intention and perceived value, while individuals in other occupations rate product quality the highest.

Table 4.18: On-Way ANOVA for Occupation

The F-Test results indicate that customer purchasing intention, brand awareness, perceived value, brand personality, and product quality have values of 1.242, 0.117, 1.564, 0.583, and 0.456, respectively Corresponding P-values are 0.296, 0.950, 0.199, 0.627, and 0.713, demonstrating that occupation does not significantly influence the respondents' assessments.

Table 4.19: On-Way ANOVA for Occupation with Tukey B test

Subset for alpha = 0.05 Customer purchasing intention

The data indicates that respondents with higher incomes, particularly those earning between 240-360 million VND, assign greater scores to customer purchasing intention, perceived value, and product quality Conversely, individuals with annual incomes below 120 million VND tend to give the lowest scores in these areas.

RECOMMENDATIONS TO VIETNAMESE AUTOMOBILE

Conclusion

This study aims to achieve four research objectives, with the first two focusing on gaining a comprehensive understanding of customer purchasing intentions and processes, as well as examining factors that influence these intentions in the context of automobile purchases These objectives are addressed in Chapter 2 – Literature Review Customer purchasing intention reflects the attitudes and actions of consumers towards specific products and services compared to alternatives in the market It is recognized as a multi-step process that includes identifying needs and problems, seeking information, evaluating alternative options, desiring to purchase products and services, and conducting post-purchase evaluations.

The third objective of this study is to assess the factors influencing customer purchasing intentions for automobiles in Hanoi, Vietnam Utilizing multiple linear regression as the primary data analysis technique, the research develops a linear regression model to evaluate how customer purchasing intentions are affected by four key variables: brand awareness, perceived value, brand personality, and product quality.

The explanatory variables account for 52.8% of the variations in customer purchasing intention, with all research hypotheses being accepted Notably, brand awareness exerts the strongest influence on purchasing intention, as indicated by its highest Beta value among the factors analyzed.

The final research objective is to offer recommendations aimed at enhancing sales for automobile dealers, which will be detailed in the subsequent section These recommendations are based on findings derived from mean value analysis and multiple linear regression techniques.

Recommendation

Mean value analysis indicates that respondents do not readily associate a specific car brand with its name, suggesting a lack of strong brand integration in consumers' minds Furthermore, respondents also rated the overall quality of this brand as low, reflecting a negative perception In Hanoi, consumer purchasing intentions for automobile products are significantly influenced by four key factors: brand awareness, perceived value, brand personality, and product quality Notably, brand awareness plays the most critical role in shaping these purchasing intentions Consequently, automobile dealers in Hanoi should focus on enhancing brand awareness, which can be effectively achieved through targeted marketing campaigns.

Auto dealers can effectively promote new and existing automobile products through various advertising channels Traditional marketing methods, such as radio and television, allow them to showcase special offers and introduce new models along with their features Additionally, leveraging social media platforms like Facebook is crucial, especially considering its popularity among Vietnamese users This enables auto dealers to reach potential customers with advertisements for new products and promotional offers, maximizing their marketing impact.

To enhance customer experience at auto dealerships, it is crucial to provide comprehensive training for employees Well-trained staff can offer valuable guidance to customers who visit to explore automobile products Employees should possess in-depth knowledge about key aspects such as fuel consumption, special features, value-added services, and warranties, ensuring they can effectively assist potential buyers in making informed decisions.

To effectively leverage Facebook for advertising, companies should prioritize understanding the cost structure, particularly the Cost Per Click (CPC), which averages US$0.35 globally With additional options like Cost Per Like (averaging US$0.23) and Cost Per App (averaging US$2.74), businesses must carefully select the most suitable advertising type to optimize their budget and enhance profitability It's crucial to calculate the advertising costs beforehand to avoid overspending, which can lead to increased operating costs and reduced profitability over time Utilizing diverse ad types, such as Cost Per Click and Cost Per Like, can also amplify visibility, as increased likes can influence other users on the platform.

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As a university student in Vietnam, I am conducting research for my thesis on the "Factors Influencing Customer Intention to Purchase Automobiles in Hanoi." To gather essential data, I have created a questionnaire to understand your purchasing intentions regarding automotive products in the city This survey also seeks your insights on related factors impacting your decisions Your responses are crucial to my research, and I assure you that all personal information will be kept confidential and will not be used for commercial purposes.

If you have any questions, please contact me through:

Part I: Please choose the item which is closest to your situation Gender

Part II: Please provide your assessment towards following statements using following scales:

1 – Strongly Disagree; 2 – Disagree; 3 – Neutral; 4 – Agree; 5 – Strongly Agree

I intend to purchase an automobile, as I have a strong desire to own one If given the chance, I would also recommend that my relatives and friends consider buying a car.

This brand stands out to me due to its distinct characteristics, making it my top choice when considering a car purchase Whenever I encounter car advertisements, this brand is the first that comes to mind, and I can easily recognize its name and logo I take pride in owning a vehicle from this brand, as it offers excellent value for money and reliable quality The brand aligns with my personality, and I have a clear image of the type of person who uses it I consistently expect superior performance and would rate its overall quality as high, confirming that what I receive from this brand is worth the investment.

FREQUENCIES VARIABLES=GENDER AGE OCCUP INA /ORDER=ANALYSIS

Gender Age Occupation Income per annum

Frequency Percent Valid Percent Cumulative Percent

Frequency Percent Valid Percent Cumulative

DESCRIPTIVES VARIABLES=CPI1 CPI2 CPI3 BAW1 BAW2 BAW3 BAW4 BAW5 PVA1 PVA2 PVA3 PVA4 BPE1 BPE2 BPE3 PQA1 PQA2 PQA3

/STATISTICS=MEAN STDDEV MIN MAX

N Minimum Maximum Mean Std Deviation

RELIABILITY /VARIABLES=CPI1 CPI2 CPI3 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA

Total 200 100.0 a Listwise deletion based on all variables in the procedure

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

RELIABILITY /VARIABLESW1 BAW2 BAW3 BAW4 BAW5 /SCALE('ALL VARIABLES') ALL

RELIABILITY /VARIABLES=PVA1 PVA2 PVA3 PVA4 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA

RELIABILITY /VARIABLES=BPE1 BPE2 BPE3 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA

RELIABILITY /VARIABLES=PQA1 PQA2 PQA3 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA

FACTOR /VARIABLES BAW1 BAW2 BAW3 BAW4 BAW5 PVA1 PVA2 PVA3 PVA4 BPE1 BPE2 BPE3 PQA1 PQA2 PQA3

/MISSING LISTWISE /ANALYSIS BAW1 BAW2 BAW3 BAW4 BAW5 PVA1 PVA2 PVA3 PVA4 BPE1 BPE2 BPE3 PQA1 PQA2 PQA3

/PRINT INITIAL KMO EXTRACTION ROTATION /FORMAT SORT BLANK(.5)

/CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC

/CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .737 Bartlett's Test of Sphericity Approx Chi-Square 1242.971 df 105

Extraction Method: Principal Component Analysis

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %

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Extraction Method: Principal Component Analysis a 4 components extracted

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

Rotation Method: Varimax with Kaiser Normalization

FACTOR /VARIABLES CPI1 CPI2 CPI3 /MISSING LISTWISE

/ANALYSIS CPI1 CPI2 CPI3 /PRINT INITIAL KMO EXTRACTION ROTATION /FORMAT SORT BLANK(.50)

/CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC

/CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .661 Bartlett's Test of Sphericity Approx Chi-Square 99.167 df 3

Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative %

Principal Component Analysis a 1 components extracted

Rotated Component Matrix a a Only one component was extracted The solution cannot be rotated

T-TEST GROUPS=GENDER(1 2) /MISSING=ANALYSIS

/VARIABLES=CPI BAW PVA BPE PQA /CRITERIA=CI(.95)

Gender N Mean Std Deviation Std Error Mean

Equality of Variances t-test for Equality of Means

F Sig t df Sig (2-tailed) Mean Difference

95% Confidence Interval of the Difference

Customer purchasing intention Equal variances assumed 1.298 256 040 198 968 00207 05154 -.09957 10370

Brand awareness Equal variances assumed 8.100 005 -2.142 198 033 -.23950 11182 -.46001 -.01898

Perceived value Equal variances assumed 1.113 293 226 198 822 02105 09319 -.16272 20482

Brand personality Equal variances assumed 1.434 233 741 198 460 09460 12774 -.15731 34651

Product quality Equal variances assumed 119 731 1.400 198 163 14775 10555 -.06039 35589

ONEWAY CPI BAW PVA BPE PQA BY AGE

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Squares df Mean Square F Sig

ONEWAY CPI BAW PVA BPE PQA BY OCCUP /MISSING ANALYSIS

ONEWAY CPI BAW PVA BPE PQA BY INA /MISSING ANALYSIS

REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN

/DEPENDENT CPI /METHOD=ENTER BAW PVA BPE PQA

Perceived value, Brand personality, Brand awareness b

Enter a Dependent Variable: Customer purchasing intention b All requested variables entered

Std Error of the Estimate

1 726 a 528 518 25209 a Predictors: (Constant), Product quality, Perceived value, Brand personality, Brand awareness

Model Sum of Squares df Mean Square F Sig

Total 26.232 199 a Dependent Variable: Customer purchasing intention b Predictors: (Constant), Product quality, Perceived value, Brand personality, Brand awareness

Product quality 050 024 104 2.102 037 a Dependent Variable: Customer purchasing intention

ONEWAY CPI BAW PVA BPE PQA BY AGE /MISSING ANALYSIS

Upper Bound Customer purchasing intention

* The mean difference is significant at the 0.05 level

The article presents the means for groups within homogeneous subsets, utilizing a harmonic mean sample size of 21.091 It notes that the group sizes are unequal, and the harmonic mean of these sizes is applied However, it is important to highlight that Type I error levels cannot be guaranteed.

The article discusses the use of harmonic means for analyzing groups within homogeneous subsets, highlighting that the sample size is 21.091 It notes that the group sizes are unequal, necessitating the use of the harmonic mean to accurately represent these sizes However, it is important to mention that the Type I error levels are not guaranteed in this analysis.

The article discusses the display of means for groups divided into homogeneous subsets, utilizing a harmonic mean with a sample size of 21.091 It highlights that the group sizes are unequal, and therefore, the harmonic mean of these sizes is applied However, it is important to note that the Type I error levels cannot be guaranteed.

The article presents the means for groups organized into homogeneous subsets, utilizing a Harmonic Mean Sample Size of 21.091 It highlights that the group sizes are unequal and emphasizes the importance of using the harmonic mean of these sizes However, it is noted that the Type I error levels are not guaranteed in this context.

The article presents means for groups categorized into homogeneous subsets, utilizing a harmonic mean sample size of 21.091 It highlights that the group sizes are unequal, necessitating the use of the harmonic mean of these sizes However, it is important to note that Type I error levels are not guaranteed in this context.

ONEWAY CPI BAW PVA BPE PQA BY OCCUP /MISSING ANALYSIS

Upper Bound Customer purchasing intention

Private services Government services 04914 07618 917 -.1483 2465 Freelancer -.06710 07365 799 -.2580 1237 Others 01259 07274 998 -.1759 2011 Government services

Private services -.04914 07618 917 -.2465 1483 Freelancer -.11624 07322 388 -.3060 0735 Others -.03655 07230 958 -.2239 1508 Freelancer Private services 06710 07365 799 -.1237 2580 Government services 11624 07322 388 -.0735 3060 Others 07969 06963 662 -.1007 2601 Others Private services -.01259 07274 998 -.2011 1759 Government services 03655 07230 958 -.1508 2239 Freelancer -.07969 06963 662 -.2601 1007 Brand awareness Private services Government services 32261 16589 213 -.1072 7525 Freelancer 21736 16038 529 -.1982 6329 Others 35429 15840 117 -.0562 7647 Government services

Private services -.32261 16589 213 -.7525 1072 Freelancer -.10525 15944 912 -.5184 3079 Others 03168 15744 997 -.3763 4396 Freelancer Private services -.21736 16038 529 -.6329 1982

Government services 10525 15944 912 -.3079 5184 Others 13693 15162 803 -.2560 5298 Others Private services -.35429 15840 117 -.7647 0562 Government services -.03168 15744 997 -.4396 3763 Freelancer -.13693 15162 803 -.5298 2560 Perceived value Private services Government services -.02548 13726 998 -.3812 3302 Freelancer -.22883 13270 314 -.5727 1150 Others -.16329 13106 598 -.5029 1763 Government services

Private services 02548 13726 998 -.3302 3812 Freelancer -.20334 13192 415 -.5452 1385 Others -.13781 13027 715 -.4754 1997 Freelancer Private services 22883 13270 314 -.1150 5727 Government services 20334 13192 415 -.1385 5452 Others 06553 12546 954 -.2596 3906 Others Private services 16329 13106 598 -.1763 5029 Government services 13781 13027 715 -.1997 4754 Freelancer -.06553 12546 954 -.3906 2596 Brand personality Private services Government services -.24829 18677 545 -.7322 2357 Freelancer -.13130 18057 886 -.5992 3366 Others 21228 17833 634 -.2498 6744 Government services

Private services 24829 18677 545 -.2357 7322 Freelancer 11699 17950 915 -.3481 5821 Others 46057 * 17726 049 0013 9199 Freelancer Private services 13130 18057 886 -.3366 5992 Government services -.11699 17950 915 -.5821 3481 Others 34358 17071 187 -.0988 7859 Others Private services -.21228 17833 634 -.6744 2498

Government services -.46057 * 17726 049 -.9199 -.0013 Freelancer -.34358 17071 187 -.7859 0988 Product quality Private services Government services -.05926 15732 982 -.4669 3484 Freelancer -.09657 15210 921 -.4907 2976 Others -.16779 15022 680 -.5570 2215 Government services

Private services 05926 15732 982 -.3484 4669 Freelancer -.03731 15120 995 -.4291 3545 Others -.10852 14931 886 -.4954 2784 Freelancer Private services 09657 15210 921 -.2976 4907 Government services 03731 15120 995 -.3545 4291 Others -.07122 14379 960 -.4438 3014 Others Private services 16779 15022 680 -.2215 5570 Government services 10852 14931 886 -.2784 4954 Freelancer 07122 14379 960 -.3014 4438

* The mean difference is significant at the 0.05 level

Tiêu đề	Factors Influencing Customer Intention To Purchase Automobile In Hanoi
Tác giả	Đinh Hoàng An
Người hướng dẫn	PGS.TS. Lê Anh Tuấn
Trường học	Đại học quốc gia Hà Nội
Chuyên ngành	Quản trị kinh doanh
Thể loại	thesis
Năm xuất bản	2019
Thành phố	Hà Nội

Định dạng
Số trang	122
Dung lượng	1,08 MB