INTRODUCTION
Background to the research and research problem
Since the late 1990s, Vietnam's economy has experienced significant growth, averaging a rate of 6.4% over the last decade, driven by major economic reforms and the Open Door Policy (The World Bank, 2015) This economic expansion has led to rapid development in the residential housing industry With a population of nearly 91 million in 2014, growing at an annual rate of 1.06%, and an urbanization rate of 3.3%, a substantial portion of the population—33.1%—resides in urban areas (Thanh Nien News, 2014) In major cities like Ho Chi Minh City, which had around 7.9 million residents in 2014, urbanization is advancing quickly, exacerbating the housing shortage in developing countries like Vietnam (Morel, Mesbah, Oggero & Walker, 2001) The demand for housing often outpaces supply, highlighting the urgent need for homes and apartments for individuals and families (Zang, as cited in Phan, 2012).
Since 1990, Vietnam's real estate market has experienced significant fluctuations, characterized by periods of rapid price increases followed by declines (Phan, 2012) Following an economic downturn from 2012 to 2013, the market began to recover, with Hanoi recording 11,450 successful transactions in 2014—more than double the amount in 2013—and Ho Chi Minh City seeing a 30% increase with 10,350 transactions (Manh Tung, 2014) Driven by population growth and rapid urbanization, the demand for housing and apartments has surged, yet the market still grapples with challenges, including substantial outstanding loans and high inventory levels Current apartment inventories are estimated at around 26 trillion VND, while land inventories stand at approximately 28.5 trillion VND, indicating a serious crisis in the sector (Xuan Than, 2014; Manh Tung, 2014).
The Vietnamese government has introduced amended land laws and a VND 30 trillion credit package for home buyers to revitalize the real estate market Consequently, Vietnam's residential housing sector is witnessing a recovery phase, aligning with the government's initiatives to renovate the industry Nevertheless, challenges persist within the economy (CBRE Vietnam, 2014).
As of 2014, Vietnam faced significant real estate challenges, with inventories reaching 92.690 trillion VND and 19,210 unsold apartments, including 7,520 in Ho Chi Minh City (CBRE Vietnam, 2014) Trinh Dinh Dung (as cited in Nguoi Dua Tin News, 2014) emphasized that government efforts alone are insufficient to revive the real estate market A primary factor contributing to this crisis is the mismatch between market supply and consumer demand, stemming from builders’ lack of accurate customer information and market insights (Trinh Dinh Dung, as cited in Nguoi Dua Tin News, 2014) To thrive in this challenging environment, industry marketers and analysts must gain a deep understanding of homebuyers' decision-making criteria (Ratchatakulpat, Miller & Marchant, 2009) and their satisfaction levels concerning housing features and service quality (Torbica & Stroh, 2001).
Real estate companies face challenges due to insufficient customer insights and fluctuating market conditions, prompting a need for a deeper understanding of their clients Understanding customer decision-making behavior, particularly in apartment purchases, is crucial Bettman, Luce, and Payne (1998) highlight that consumer choices involve a complex process of selection, consumption, and disposal of products and services, which is significant for consumers, marketers, and policymakers alike They emphasize that effective customer decision-making requires extensive information gathering, making it a vital area of focus for real estate professionals.
Understanding customer decision-making is crucial for real estate companies, as it enables them to develop effective marketing strategies that significantly influence customer perceptions This insight is essential for the success of any business.
In recent decades, the understanding of real estate purchase decisions has evolved significantly, prompting developers to tailor their strategies to enhance customer satisfaction and meet sales targets (Piron, 1993; Spetic, Kozak & Cohen, 2005) Kupke (as cited in Abdullah et al., 2012) highlights that purchasing real estate is one of life's most significant decisions, involving a long-term commitment that can profoundly impact an individual's life Given the unique characteristics of real estate, the decision-making process for buying an apartment is distinct from typical business purchases, making it essential for suppliers to comprehend purchaser behavior for more effective responses (Kinnard, as cited in Phan, 2012).
National and cultural characteristics significantly influence house purchasing decisions, indicating that findings from one context may not apply to another (Opoku & Abdul-Muhmin, 2010) In Vietnam, a transitional market with distinct cultural traits, collectivism is prevalent (Hofstede, as cited in Swierczek & Thai, 2003) Relationships take precedence in decision-making, as individuals prioritize the opinions and support of family and acquaintances Vietnamese buyers typically consult colleagues, seniors, and family members before making a purchase decision (McKinney, 2000) Consequently, apartment developers in Vietnam should closely examine consumer purchasing behavior from the consumers’ perspective to gain a deeper understanding of their target market.
Numerous studies have explored apartment purchasers' perceptions regarding attributes, financial status, service quality, purchasing decisions, and customer satisfaction in various countries However, these investigations often treat these concepts in isolation Limited research has been conducted in Vietnam, a collectivist culture where purchasing decisions are significant Notably, Phan (2012) examined factors influencing house purchase decisions in Vietnam but focused solely on house attributes rather than customer behavior and satisfaction in apartment purchasing This research aims to analyze how apartment purchasers in Ho Chi Minh City make decisions based on their satisfaction with apartment attributes and the service quality provided by developers in the real estate industry.
Research objectives
This study aims to analyze how various factors, including apartment attributes, financial status, and service quality, influence the purchasing decisions and satisfaction levels of customers involved in apartment transactions within the real estate sector of Ho Chi Minh City, Vietnam.
- The relationship between apartment attributes and apartment purchasing decision;
- The relationship between financial status and apartment purchasing decision;
- The relationship between apartment developers’ service quality and apartment purchasing decision;
- The relationship between apartment attributes and satisfaction level
- The relationship between apartment developers’ service quality and satisfaction level.
Research methodology and research scope
This research involved two phases: a qualitative study and a quantitative study The questionnaire was translated from English to Vietnamese, and in-depth interviews with six participants were conducted during the qualitative phase to align the items with Vietnamese cultural characteristics and enhance the official questionnaire In the quantitative phase, data was gathered through a convenience sampling method using a self-administered survey The collected data was analyzed using SPSS 16 and Amos 22, with Confirmatory Factor Analysis (CFA) employed to assess reliability and validity Subsequently, Structural Equation Modeling (SEM) was utilized to evaluate the proposed model.
This research focuses on Vietnamese customers involved in apartment purchases within the real estate sector in Ho Chi Minh City, which is the largest city in Vietnam and a hub for numerous real estate companies.
Research significance
Research findings indicate that real estate companies can enhance their marketing and sales strategies to significantly influence customer purchasing decisions Additionally, these insights can assist policymakers in implementing effective measures to foster the growth of the Vietnam real estate sector.
Research structure
This thesis comprises five chapters, beginning with an introduction that outlines the research background, problem, objectives, significance to management practice, research scope, and data analysis methodology The second chapter reviews relevant literature, synthesizing theories related to apartment purchase decisions and their connections to apartment attributes, financial status, service quality, and customer satisfaction, while also presenting the conceptual model and hypotheses The third chapter details the research methodology employed to empirically test the model, followed by the fourth chapter, which analyzes the data results in relation to the research questions and hypotheses Finally, the concluding chapter summarizes the research hypotheses and problems, discusses theoretical and practical implications based on the findings, and identifies limitations for future research.
LITERATURE REVIEW
Apartment attributes and apartment purchasing decision
The wide range of apartment developers offers numerous options for potential buyers, but without clear criteria for evaluation, making a purchase decision becomes challenging According to Hawkins, Mothersbaugh, and Best (2011), key apartment features that align with customer expectations serve as essential criteria for assessing choices Typically, apartment customers focus on the primary attributes they consider most valuable, evaluate these different features, and determine their willingness to pay for the desirable ones (Kotler & Keller).
Numerous studies have identified key real estate attributes that significantly influence apartment buyers' decision-making processes (Ratchatakulpat et al., 2009; Haddad et al., 2011; Opoku & Abdul-Muhmin, 2010; Alonso, 2002; Pope, 2008; Spetic et al., 2005; Wang & Li, 2006) Building on these findings, Zeng (2013) and Ratchatakulpat et al further explore these factors to enhance understanding of consumer behavior in the real estate market.
In 2009, key housing attributes were categorized into four main groups: intrinsic, extrinsic, environmental, and location attributes Intrinsic housing attributes encompass factors such as housing size, type, and internal design (Cupchik, Ritterfeld & Levin, as cited in Zeng, 2013; Ratchatakulpat et al., 2009) Extrinsic attributes focus on the exterior design and outdoor spaces of the property (Bhatti & Church, as cited in Zeng, 2013; Ratchatakulpat et al., 2009) Environmental attributes are characterized by aspects like the neighborhood and its overall surroundings (Cheshire & Sheppard; Fierro, Fullerton & Donjuan-Callejo; Pasha & Butt, as cited in Zeng, 2013).
In recent studies, researchers have examined the impact of location attributes, such as proximity to central business districts, schools, and transportation, on housing preferences (Zeng, 2013; Ratchatakulpat et al., 2009) Utilizing Rosen’s hedonic model (1974), these studies focus on how potential home buyers assess the utility-bearing characteristics of residential apartments, considering them as unique products valued for specific amenities and features (Bitter, Mulligan, & Dall'erba, 2007; Taylor, 2008; Fierro et al., 2009) Home buyers first identify key attributes and benefits that align with their expectations and then prioritize these factors based on perceived value, ultimately choosing to invest in the attributes they deem most valuable (Bao & Wan, 2007; Farmer & Lipscomb, 2010; Sunding & Swoboda, 2010).
In summary, various attributes of residential houses can significantly influence consumers' housing purchase decisions, either positively or negatively (Alonso, 2002; Opoku & Abdul-Muhmin, 2010; Spetic et al., 2005; Wang & Li, 2006) Additionally, the importance of these housing attributes may differ across different national contexts Therefore, based on existing literature, a hypothesis is proposed regarding these variations.
H1 Customer’s residential apartment purchasing decision in Vietnam is positively influenced by apartment attributes.
Financial status and apartment purchasing decision
Buying an apartment is often regarded as the most significant financial decision in a person's life, impacting household budgets due to its long-term commitment, from the initial down payment to ongoing monthly payments (Abdullah et al., 2012) Therefore, understanding one’s financial status is crucial for potential apartment buyers According to Xiao and Tan (2007), being aware of their financial situation enables customers to select an apartment that fits their budget comfortably They emphasize that financial status encompasses the necessary capital and borrowing costs associated with real estate transactions.
Financial status encompasses more than just monetary assets; it includes the expenses associated with purchasing an apartment Key components of financial status are house prices, mortgage loans, income, and payment terms, which collectively influence an individual's ability to buy property (Opoku & Abdul-Muhmin, 2010; Hou, 2007).
According to Adair, Berry, and McGreal (1996) and Daly et al (2003), the financial considerations for purchasing an apartment extend beyond merely the "house price." It is essential to evaluate the unique characteristics of the apartment and the specific payment transactions involved in the purchasing process.
In today's real estate market, apartment buyers are increasingly discerning, equipped with strong bargaining power and a variety of choices, which makes them sensitive to factors such as interest rates, maximum mortgage limits, monthly payment capacities, and payment duration This heightened scrutiny influences their purchasing decisions, particularly regarding apartment pricing and payment methods (Opoku & Abdul-Muhmin, 2010).
Numerous studies have utilized the hedonic price model to understand customer preferences in apartment selection, revealing that buyers assess the marginal utility of apartment attributes against their marginal prices This perspective positions apartments as products valued for their utility-bearing features However, the decision to purchase an apartment is challenging, particularly for young families in developing countries, due to significant financial implications To align with customer needs, it is essential to focus on the equilibrium price of apartments concerning payment terms Comparisons between purchasing and renting costs help buyers make informed decisions, with many shifting from renting to buying if capital costs are perceived as affordable Intelligent consumers aim to maximize utility within constraints of search costs and limited information, estimating which offers provide the greatest perceived value This study aims to analyze how apartment attribute costs and payment methods can enhance perceived value, thereby increasing customers' willingness to pay.
H2 The customer’s apartment purchasing decision is positively influenced by customer’s financial status.
Apartment service quality and apartment purchasing decision
Service quality is essential for organizations aiming to enhance their competitive edge in the market Initially introduced by Parasuraman et al (1985), the service quality model comprises five key dimensions: reliability, responsiveness, assurance, empathy, and tangibles This framework has been widely adopted by various researchers and has significantly influenced the development of marketing strategies (Gannage, as cited in Nahmens & Ikuma, 2009; Kotler & Keller, 2009).
Apartment developers play a crucial role in providing housing services, with customers perceiving these services through interactions and dynamic events within residential housing systems During their decision-making process, customers actively search for information, evaluate and compare offerings from various suppliers, and ultimately choose the option that best meets their needs The purchasing decision can be influenced by personal circumstances and the quality attributes of the apartment developers Consequently, the level of service quality provided by these developers significantly impacts home buyers' purchase decisions and their satisfaction after the purchase.
In the evolving residential housing service market, it is crucial to comprehend customer expectations and their evaluation of services Housing customers prioritize service quality from providers, and identifying the quality factors of an apartment enhances the perceived customer value of housing services, serving as a competitive advantage Customers' perceptions significantly influence their valuation of specific services, their choices among various providers, and their assessment of service delivery.
In their studies, Nahmens & Ikuma (2009) and Forsythe (2008) utilize the five key dimensions of service quality to evaluate the perceived service quality in the U.S construction housing sector Their research aims to determine whether the service quality experienced by home buyers meets or surpasses their expectations Building on their findings, the SERQUAL model's dimensions are further broken down into 21 specific housing service quality attributes These attributes serve as metrics to assess the quality of services offered by apartment developers and to gauge consumer perceptions of service value throughout the design and construction phases.
Research by Nahmens & Ikuma (2009) and others highlights that home buyers in the US are likely to make a purchase decision when they are satisfied with service quality Key dimensions influencing overall satisfaction include reliability, responsiveness, assurance, empathy, and tangibles Atterhog (2005) further notes that residential service providers can enhance customer satisfaction and influence purchasing decisions by bridging the gap between customer expectations and perceptions This study identifies housing service quality as a crucial factor in housing purchase decisions, emphasizing its impact on consumer value perception during the apartment buying process Based on these findings, it is hypothesized that service quality significantly affects home buyers' decisions.
H3 Consumer’s apartment purchasing decision is positively influenced by the service quality provided by apartment developers.
Apartment attributes and customer satisfaction level
Customer satisfaction is defined as the relationship between customer expectations and perceived performance, reflecting the quality of product attributes It serves as a subjective assessment of how well a service encounter meets customer expectations In studies of customer satisfaction and dissatisfaction, attention is given to the disparity between pre-purchase expectations and post-purchase perceptions.
Customer satisfaction is influenced by the performance of product attributes relative to expectations; when performance falls short, dissatisfaction arises, while exceeding expectations leads to satisfaction (Peter & Olson, as cited in Nahmens & Ikuma, 2009) Therefore, customers gauge their satisfaction based on a comparison between their expectations for an apartment and the attributes offered by real estate developers.
Previous studies have highlighted the crucial role of housing attributes in determining customer satisfaction within the real estate sector (Nahmens & Ikuma, 2009) According to Longenecker et al (as cited in Nahmens & Ikuma, 2009), customer satisfaction is integral to post-purchase evaluations, which encompass various factors such as housing attributes, performance, and services The value derived from an apartment and the services offered by real estate developers significantly shape customer expectations Research indicates that buyers' overall satisfaction is closely linked to the comprehensive offering of housing attributes that align with consumer needs (Torbica & Stroh, 2001; Forsythe, 2008) A positive perception of these attributes correlates with higher satisfaction levels among home purchasers Furthermore, Opoku & Abdul-Muhmin (2010) identified key housing aspects—such as structure, sales dealer, workmanship, construction quality, materials, pricing, and appreciation value—that notably affect buyers' expectations Thus, exploring the positive relationship between apartment attributes and post-purchase satisfaction can guide marketers in delivering suitable housing products and services to prospective and existing homeowners.
H4 The overall satisfaction level of consumers after the purchasing decision is positively influenced by the evaluations of housing attributes.
Apartment service quality and customer’s satisfaction level
A study by Nahmens & Ikuma (2009) highlights key service quality determinants that influence customer satisfaction in the housing industry The primary factors that enhance customer satisfaction include attentiveness, responsiveness, care, and friendliness Conversely, the main aspects that lead to customer dissatisfaction are integrity, reliability, responsiveness, availability, and functionality, as noted in research by Power and Associates.
According to Ikuma (2009), the services offered by sales staff and daily housing maintenance are key factors in fulfilling homeowners' selection criteria and enhancing overall satisfaction in Florida The study emphasizes that streamlining the transaction process significantly enhances customer satisfaction, while addressing issues such as inefficiency, chaos, incompetence, and isolation in housing services can reduce dissatisfaction Therefore, it is essential for real estate developers to grasp the criteria that customers use to assess housing services, as these elements directly influence their perceptions of service quality and overall satisfaction with the offerings provided by apartment developers.
The relationship between apartment service quality and customer satisfaction is crucial, as highlighted by Torbica and Stroh (as cited in Zeng, 2013), who assert that customer satisfaction arises from comparing pre-purchase expectations with actual experiences Nahmens & Ikuma (2009) further emphasize the significant positive link between housing service quality and customer satisfaction, indicating that real estate companies must actively monitor satisfaction levels and adapt their operations to exceed consumer expectations in order to foster loyalty and maintain a competitive edge Ultimately, enhanced perceptions of housing service quality lead to greater overall satisfaction with apartment developers, underscoring the importance of customer evaluations of service attributes.
H5 The overall satisfaction level of customers after the purchasing decision is positively influenced by their evaluations of the service quality provided by apartment developers.
Conceptual model
The conceptual model illustrated in Figure 1 outlines the key factors influencing apartment purchasing decisions in Ho Chi Minh City, Vietnam It highlights that apartment attributes, financial status, and service quality significantly affect both the purchasing decisions and overall customer satisfaction of real estate buyers This model emphasizes the positive relationships between these antecedents and the outcomes of apartment transactions in the region.
These are all hypotheses that were proposed in the study:
H1 Customers’ residential apartment purchasing decision in Vietnam is positively influenced by apartment attributes
H2 The customer’s apartment purchasing decision is positively influenced by customer’s financial status
H3 Consumer’s apartment purchasing decision is positively influenced by the service quality provided by apartment developers
H4 The overall satisfaction level of consumers after the purchasing decision is positively influenced by the evaluations of apartment attributes
H5 The overall satisfaction level of customers after the purchasing decisions is positively influenced by their evaluations of the service quality provided by apartment developers
This chapter provides a theoretical framework for the concepts within the model, highlighting how purchasing decisions are influenced by apartment attributes, financial status, and service quality It also examines the impact of these factors on customer satisfaction, supported by existing literature Based on these insights, five hypotheses are proposed for the research The following chapter will outline the methodology employed to analyze the data and test these hypotheses.
METHODOLOGY
Research design
The real estate industry in Ho Chi Minh City, Vietnam, presents unique challenges due to its inherent long-term characteristics, necessitating strong marketing competencies for companies to achieve a sustainable competitive advantage (Kinnard, as cited in Phan, 2012) To explore these dynamics, the study involved two phases: a qualitative study and a main survey conducted in this major urban center, where many real estate firms are based The survey questionnaire was initially crafted in English and later translated into Vietnamese with assistance from language experts The qualitative phase consisted of in-depth interviews with six customers who had experience purchasing apartments, conducted in various comfortable settings such as real estate offices and cafés During these interviews, the researcher engaged each participant by reading items from the measurement scale, ensuring clarity and understanding, and soliciting feedback until no further suggestions were provided.
Based on respondent feedback, the survey questionnaire was revised for clarity and comprehension (refer to Appendices A, B, and C) Following these modifications, a self-administered quantitative survey utilizing convenience sampling was conducted to gather data for testing the research hypotheses.
Research Model & Hypotheses Literature Review
Structural Equation Modeling Confirmatory Factor
Participants completed a survey using a five-point Likert scale, ranging from "strongly disagree" (1) to "strongly agree" (5) The questionnaire was distributed to respondents through electronic mail, Google surveys, and hard copies (refer to Table 3.1) Data analysis was conducted using SPSS and AMOS to evaluate the measurement and structural models.
As mentioned above, the final questionnaires consisted of five main measurement scales: apartment attributes, financial status, apartment developers’ service quality, apartment purchasing decision, and customer satisfaction level
Apartment purchasing decision (Coded as PurchaseDecision in CFA & SEM model)
Apartment purchasing decision was measured by four items, accessing customer purchasing behavioral process (Piron, 1993)
Apartment purchasing decision (adapted from Piron, 1993) Coding
1 I experienced a desire to purchase an apartment PurchasD1
2 I felt like I had to purchase it from the first time I saw the apartment PurchasD2
3 The urge to purchase an apartment overcome me PurchasD3
4 I have purchased an apartment PurchasD4
The satisfaction level measurement scale was modified for the Vietnamese context based on Nahmens & Ikuma (2009) by removing irrelevant items and simplifying the language for better comprehension As a result, the final scale consists of five items that effectively assess customer post-purchasing behavior.
Satisfaction level (Adapted from Nahmens & Ikuma, 2009) Coding
1 I personally feel good that I have bought an apartment SatisfLev1
2 I am pleased that I have bought an apartment SatisfLev2
3 The attributes of the apartment that I have bought meets my expectations SatisfLev3
4 The services provide by suppliers of the apartment that I have bought meets my expectations
5 My purchase decision is much better than other buying decisions in the past SatisfLev5
Nahmens & Ikuma (2009) developed measurement scale for apartment developers’ service quality from the scales of Parasuraman et al.(1993), Forsythe (2008), Kotler & Keller,
In 2009, a scale was adapted for the Vietnamese real estate market, focusing on the component services of apartments that significantly influence end customers Developers must consider these factors when creating specifications for apartment services to meet customer needs effectively.
Apartment service quality (adapted from Nahmens & Ikuma, 2009) Coding
1 Providing service as promised SerQua1
2 Readiness to respond to home buyers’ requests SerQua2
4 Employees who are consistently courteous SerQua4
5 Availability of after sales service SerQua5
6 Convenience of service office hours SerQua6
The scale of financial status was developed by Ratchatakulpat et al., (2009) from the measurement scales of Adair et al., (1996), Daly et al., (2003) (as cited in Ratchatakulpat et al.,
In 2009, the measurement scale was tailored to the Vietnamese context by removing unsuitable items to enhance respondent comprehension The final scale consists of four key components: access to a substantial amount of capital, borrowing costs, and payment terms.
Financial status (Adapted from Ratchatakulpat et al., (2009) Coding
The measurement scales for apartment attributes are based on the frameworks developed by Adair et al (1996), Daly et al (2003), and Abelson & Chung (2005), as referenced in Ratchatakulpat et al (2009) These scales encompass intrinsic apartment attributes, extrinsic apartment attributes, environmental attributes, and location attributes, all tailored to align with Vietnamese cultural characteristics Intrinsic apartment attributes are assessed through five criteria, including the apartment's structure, size and number of rooms, layout and decorative style, and architectural materials Extrinsic apartment attributes are evaluated based on three factors: the overall appearance of the building, garden size, and exterior spaces Additionally, environmental and location attributes are measured by a total of eleven items—five for environmental attributes, reflecting the quality of the living environment, and six for location attributes, indicating the advantages of the apartment's location.
Apartment attributes (adapted from Adair et al., 1996; Daly et al., 2003;
Abelson & Chung, 2005,as cited in Ratchatakulpat et al., 2009)
1 Area of structure of the apartment IntrinsA1
2 Size of rooms in the apartment (living room, bed room, kitchen…) IntrinsA2
3 Number of rooms in the apartment (living room, bed room, kitchen…) IntrinsA3
4 Layout & decorate style of the apartment IntrinsA4
1 The appearance of the whole building ExtrinA1
2 Presence of garden and size of garden ExtrinA2
3 Exterior spaces refer to public area, such as the public aisle, elevator, recreation room
1 Air quality of the living area EnvirA1
3 Width of road and passages EnvirA3
4 Rain water drainage system EnvirA4
1 Location close to schools and nurseries LocA1
2 Location close to health centre and hospital LocA2
3 Location close to market, shopping centre LocA3
4 Location close to recreation places LocA4
5 Location close to workplaces LocA5
6 Location that you feel easy to get transportation vehicles LocA6
Finally, the completed questionnaire in English version and Vietnamese version were presented in Appendix D and E.
Quantitative study
The study tested the model and hypotheses using a dataset gathered from customers involved in apartment purchases within the real estate sector of Ho Chi Minh City Due to time constraints, a convenience sampling method was employed, utilizing self-administered questionnaires distributed throughout the city.
To ensure statistical significance, the sample size must be sufficiently large According to Hair, Black, Babin, Anderson, and Tatham (as cited in Prajogo, 2007), the minimum sample size for effective statistical analysis should be at least five times the number of variables, with a minimum of 100 participants In this study, which includes thirty-nine variables, the required sample size is calculated to be 195 observations To achieve this, the author distributed 337 questionnaires to participants.
A total of 299 responses were gathered from individuals interested in purchasing apartments and who have engaged in apartment transactions in Ho Chi Minh City, resulting in a response rate of approximately 88.72 percent.
Table 3.1 Source of data collection Source Distributed Collected Response rate Eliminated Valid
A total of 85 questionnaires were discarded due to invalid responses, including 45 participants without a demand for apartment purchases and 14 respondents who selected only one option The remaining valid data, consisting of 45 completed questionnaires, met the minimum sample size requirements for this research, proving to be satisfactory for analysis.
The author utilized SPSS 16 to calculate Cronbach’s alpha and Amos 20 for confirmatory factor analysis (CFA) to assess the reliability and validity of each measurement component in the research model The measurement scale's reliability was evaluated through composite reliability (CR), while convergent validity was determined using average variance extracted (AVE) and discriminant validity was assessed through item correlations For robust measurement, Cronbach’s alpha should be at least 0.6, factor loading should exceed 0.5, AVE must be a minimum of 0.5, and composite reliability should surpass 0.7 If necessary, inappropriate items would be removed based on convergent and discriminant validity The CFA indicated model fit if CMIN/DF was less than 2 with a p-value greater than 5%, and the comparative fit index (CFI) assessed the fit by comparing data with the hypothesized model, with values of 0.90 or higher deemed acceptable The non-normed fit index (NNFI) addressed negative bias, with both NFI and NNFI values recommended to be between 0 and 1, ideally exceeding 0.95 for good model fit Additionally, the root mean square error of approximation was considered in the evaluation of model fit.
The Root Mean Square Error of Approximation (RMSEA) addresses sample size issues by evaluating the difference between the proposed model and the population covariance matrix, with a value of 0.06 or lower indicating an acceptable model fit, as noted by Nguyen Dinh Tho & Nguyen Thi Mai Trang (2008) Subsequently, structural equation modeling (SEM) was employed to test the hypothesized model and estimate path coefficients for each proposed relationship within the structural model Finally, bootstrapping was utilized to reassess the model's suitability and reliability.
The study utilized five measurement scales to establish convergent and discriminant validity, analyzed through Confirmatory Factor Analysis (CFA) prior to testing the hypothesized model with Structural Equation Modeling (SEM) The first-order constructs included financial status, service quality of apartment developers, the behavioral process of apartment purchasing decisions, and satisfaction levels Additionally, the second-order construct, apartment attributes, was divided into four sub-components: intrinsic attributes, extrinsic attributes, environmental attributes, and location attributes.
This chapter outlines the research process, including the construction of measurement scales, sample size calculation, and the methods used to analyze the data The study was conducted in two phases: a qualitative phase involving in-depth interviews, followed by a quantitative phase utilizing a main survey The insights gained from the interviews informed adjustments to the measurement scale and questionnaire prior to the quantitative survey The main survey yielded 214 valid questionnaires, which were analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) The subsequent chapter will focus on the data analysis from the main survey.
DATA ANALYSIS
Respondents’ demographics
The data collected for this study on apartment purchasing demand in Ho Chi Minh City were analyzed using SPSS software, revealing key demographic insights about respondents The analysis showed a slight majority of female respondents at 55.6%, with males representing 44.4% A significant portion of participants, 62.1%, were young adults aged 25 to 35, indicating a trend towards younger buyers in the apartment market The age distribution included 5.6% under 25, 29.9% between 36 and 50, and only 2.3% over 50 Marital status indicated a predominance of married individuals at 75.2%, while singles accounted for 15.9%, and divorced or separated respondents made up 8.9% Family size revealed that the largest groups consisted of two or three members, at 35.5% and 41.1%, respectively Education levels were fairly balanced, with 42.1% holding college degrees and 43.9% holding university degrees, while 7.0% had completed high school or obtained a postgraduate degree The career distribution included 11.2% workers, 51.4% officers, 26.6% managers or business owners, and 10.7% freelancers Monthly income was notably high, with 48.1% earning between 9 and 14 million VND, and 41.6% earning over 14 million VND, while only 10.3% earned less than 9 million VND This demographic profile suggests that respondents have moved beyond basic needs and are now focusing on housing, which may influence their purchasing decisions and satisfaction levels However, the data may not fully represent the broader demographic landscape, serving primarily to enhance the researchers' understanding of the respondents.
Table 4.1 Respondents’ characteristics Demographic profile Category Frequency Percentage (%)
Demographic profile Category Frequency Percentage (%)
4.2.1 CFA for the first-order constructs
The financial status was assessed using four items, and the initial confirmatory factor analysis (CFA) demonstrated a good fit for the data However, the factor loading for FinanSta2 was insignificant (-.011 < 0.5), contributing to an average variance extracted (AVE) of less than 0.5 Consequently, the author removed FinanSta2 and re-evaluated the construct, resulting in a revised model fit with the following statistics: Chi-square/df=2.106; P=0.122; CFI=0.993; TLI=0.978; NFI=0.986; RMSEA=0.070; PCLOSE=0.267, as illustrated in Figure 4.1.
Figure 4.1 CFA model of financial status
In the initial confirmatory factor analysis (CFA) for the service quality construct, the standardized regression weights for items SerQua1, SerQua2, SerQua3, SerQua4, and SerQua5 were found to be above 0.5, with values of 0.58, 0.55, 0.73, 0.66, and 0.79, respectively While these estimates demonstrated a good fit for the data, the factor loading for SerQua6 was not significant, recording a value of 0.047, which is below the acceptable threshold of 0.5.
Table 4.2 The first run of CFA (of financial status, service quality, purchasing decision and satisfaction level)
The analysis reveals significant correlations between various factors influencing customer behavior Financial status is strongly linked to FinanSta4 (0.81) and FinanSta3 (0.85), while service quality is positively correlated with SerQua5 (0.79), SerQua4 (0.66), and SerQua3 (0.73) Purchase decisions are heavily influenced by PurchasD4 (0.86) and PurchasD3 (0.90), indicating a strong connection between these factors Furthermore, satisfaction levels are closely tied to SatisfLev2 (0.85) and SatisfLev1 (0.87), suggesting a significant impact on customer satisfaction.
After removing insignificant item SerQua6, the model of service quality was measured by five items: SerQua1, SerQua2, SerQua3, SerQua4 and SerQua5 Figure 4.2 displayed the result of CFA analysis
Figure 4.2 CFA model of service quality
In the initial run of the CFA model for purchase decision, the factor loading for PurchasD2 was deemed insignificant (0.15 < 0.5) After excluding this item, the job performance construct was effectively assessed using three significant items: PurchasD1 (0.74), PurchasD3 (0.90), and PurchasD4 (0.86) The revised CFA model demonstrated a strong fit to the data, evidenced by the following metrics: Chi-square/df = 0.909, P = 403, CFI = 1.000, TLI = 1.002, NFI = 0.995, RMSEA = 0.000, and PCLOSE = 0.582.
Figure 4.3 CFA model of purchasing decision
In the initial run of the Confirmatory Factor Analysis (CFA) for the satisfaction level construct, the standardized regression weights were significant, with values of 0.87 for SatisfLev1, 0.85 for SatisfLev2, 0.75 for SatisfLev3, and 0.78 for SatisfLev4 However, SatisfLev5 had a factor loading below 0.5, prompting the author to remove this item and conduct a second CFA The results indicated a good model fit, as illustrated in Figure 4.4, with the following metrics: Chi-square/df=2.893, P=.000, CFI=.962, TLI=.885, NFI=.958, and RMSEA=.020.
Figure 4.4 CFA model of satisfaction level
The author assessed the reliability and convergent validity of each construct by calculating Cronbach’s α, composite reliability (CR), and average variance extracted (AVE) based on standardized loadings of items The results indicated that the Cronbach’s α and composite reliability for financial status, service quality, purchase decision behavioral process, and satisfaction level exceeded the acceptable threshold of 0.7, with values of 0.84, 0.80, 0.87, and 0.88 for Cronbach’s α, and 0.84, 0.79, 0.87, and 0.89 for composite reliability, respectively Therefore, the reliability of the first-order constructs was deemed acceptable.
Table 4.3 Summarized of CR, AVE and Cronbach’α (first order constructs)
Notes: CR: composite reliability; AVE: averaged variance extracted
The second run of the Confirmatory Factor Analysis (CFA) revealed a low averaged variance extracted (AVE) value for service quality at 0.48, which is below the acceptable threshold of 0.5 Despite this, the Cronbach's α and composite reliability values were significant, exceeding 0.7, indicating that the AVE value for service quality remains acceptable In contrast, the AVE values for financial status, purchase decision, and satisfaction level were higher than 0.5, measuring 0.64, 0.69, and 0.66, respectively Overall, the CFA model for the first-order constructs—financial status, service quality, purchase decision, and satisfaction level—demonstrated a good fit for the data, as detailed in Table 4.3.
4.2.2 CFA for second-order constructs
The second-order construct of apartment attributes encompasses four key sub-components: intrinsic, extrinsic, environment, and location attributes Initial analysis using the Confirmatory Factor Analysis (CFA) model revealed a poor fit, indicated by a Chi-square/df ratio of 2.350 and fit indices including CFI at 0.860, TLI at 0.837, NFI at 0.787, and RMSEA at 0.080 Additionally, the factor loadings for IntrinsA1, IntrinsA5, EnvirA3, LocA4, LocA5, and LocA6 were found to be below the acceptable threshold of 0.5, as detailed in Table 4.4.
Table 4.4 The first run of CFA of Apartment Attributes
After eliminating insignificant items, the study measured intrinsic apartment attributes, extrinsic apartment attributes, environmental attributes, and location attributes, each using three items In the second run of the Confirmatory Factor Analysis (CFA) model, the data demonstrated a good fit with the model, indicated by a Chi-square of 113, degrees of freedom (df) of H, and a p-value of 0.000 The model fit indices included a Chi-square/df ratio of 2.065, Comparative Fit Index (CFI) of 0.955, Tucker-Lewis Index (TLI) of 0.939, Normed Fit Index (NFI) of 0.918, and Root Mean Square Error of Approximation (RMSEA) of 0.071 Additionally, all items exhibited significant factor loading greater than 0.5.
Figure 4.5 CFA model of Apartment Attributes
The reliability of apartment attributes was confirmed by high Cronbach’s α and composite reliability (CR) values, both exceeding 0.70 Additionally, the averaged variance extracted (AVE) values for the sub-components were significant, surpassing 0.5, indicating strong convergent validity Furthermore, the correlations between each pair of sub-components were below 0.8 and significant at the 0.001 level, supporting both within-construct discriminant validity and overall reliability.
Table 4.5 Summarized of CR, AVE and Cronbach’α (Apartment Attributes)
Standardized loadings Reliability (CR; AVE)
Notes: CR: composite reliability; AVE: averaged variance extracted
Table 4.6 Correlation (of Apartment Attributes)
Note: r(SE): correlations with standard errors
4.2.3 CFA for the final measurement model
In the final measurement model of the CFA, the author eliminated insignificant items (FinanSta2, SerQua6, PurchasD2, SatisfLev5, IntrinsA1, IntrinsA5, EnvirA3, LocA4, LocA5, and LocA6) due to their low factor loadings, which were all below 0.5 Despite these removals, all constructs and sub-constructs were still represented by over three observed items, ensuring the content validity of the constructs was maintained The final measurement model demonstrated a good fit to the data, with significant and substantial factor loadings for the remaining items of the first and second order constructs, exceeding 0.5 and achieving p