Backgroundto theresearchandresearchproblem
Vietnam’se c o n o m y hasbeengrowingrapidlysincethelate1990swhensignificanteconomi creformedandthecountry’sOpenDoorPolicy wereimplemented.Vietnam’sgrowthrateisona v e r a g e 6.4%i n thelastdecade (TheWo rl d Bank,2015) Followingthesechanges,Vietnam’sresid entialhousing industryalsoexperiencedfastdevelopmentandexpansion.However,Vietnampopula tionwasnearly91 millionpeoplein
2014andthepopulationincreasedby1.06percentannually(TheWorldBank,2015).Additionally,the urbanizationrateinVietnamisalsoincreasingat3.3percentannuallyand33.1percentofthepopulationi slivinginurbanareas(ThanhNienNews,2014).InmostofthebigcitiesinVietnam,particularlyinHo
C h i MinhCity,thebusinesscenterofVietnam,hasaround7.9millionpeoplein2014andtheurba nizationtoitssuburbsisquitefast.Thus,theincreasinginpopulationandrapidurbanizationi n thedevel opingcountrieslikeVietnamcreatethehouseorapartmentshortageatcriticallevels(Morel,Mesbah,O ggero&Walker,2001).Moreover,anincreasinghouseorapartmentdemandoftensurplusthepaceofits supply(Zang,ascitedinPhan,2012).Asaresult,thedemandforah o u s e oranapartmentistheurgentd emandforeachindividualandhouseholds.
TherealestatemarketinVietnamhasbeensignificantlyfluctuatingsince1990.Itmightb e seen asthreetimesfeverandd e c l i n i n g p r i c e s i n t h e twodecades(Phan,2012).Aftert h e economicdepr essionintheperiodof2012-
2013,therealestatemarkethasbeenshownsignsofrecovery.ThesuccessfulrealestatetransactionsinHa noiin2014are11,450transactions(upbymorethan2 timescomparedt o 2 0 1 3 ) andi n HCMC i t y a re1 0 , 3 5 0 transactions(up3 0 % comparedwith2 0 1 3 )
( M a n h Tung,2014).Ingeneral,Vietnamreale s t a t e markethastakenadvantagesofp o p u l a t i o n increasingandrapidurbanizationwhichcreatedahugeh o u s e o r apartmentdemand,butneverthelessitstillfacesmanydifficulties.Thelargerealestateoutstandingl oansanda bi g numbero f inventoriescreateda serious crisis (XuanThan, 2014),p a r t i c u l a r l y thea pa rt me n t inventoriesandlandinventories.Theapartmentcurrentinventoriesarer o u g h l y e quivalentt o 2 6 0 0 0 b i l l i o n VNDandlandinventoriesarer o u g h l y equivalentt o 2 8 , 5 0 0 b i l l i o n VND (ManhTung,2014).
The Vietnamese government has introduced amended land laws and a VND 30 trillion credit package to support homebuyers, aiming to revitalize the real estate market As a result, Vietnam's residential housing sector is currently undergoing a recovery phase, aligning with government efforts to innovate the industry However, challenges persist within the economy, as evidenced by the CBRE Vietnam report from 2014, which indicated that Vietnam had VND 92.690 trillion in real estate inventories, including 19,210 unsold apartments (7,520 in Ho Chi Minh City) and 13,516 unsold low-rise buildings (755 in Ho Chi Minh City).
According to Trinh Dinh Dung (as cited in Nguoi Dua Tin News, 2014), government efforts alone are insufficient to rescue Vietnam's real estate market, primarily due to a mismatch between supply and customer demand Builders of houses and apartments often lack accurate information about their customers' preferences and the current market conditions To thrive in this challenging environment, marketers and analysts in the real estate sector must possess a deep understanding of home or apartment buyers' decision-making criteria (Ratchatakulpat, Miller & Marchant, 2009) as well as their satisfaction levels concerning housing attributes and service quality (Torbica & Stroh, 2001).
The lack of accurate information about customer preferences and real estate market conditions compels real estate companies to prioritize understanding their clients A significant challenge lies in gaining deep insights into customer behavior and decision-making when purchasing an apartment According to Bettman, Luce, and Payne (1998), customer choices involve the processes of selection, consumption, and disposal of products and services, which can be complex and crucial for consumers, marketers, and policymakers alike They emphasize that customer decision-making is a vital aspect of customer behavior that necessitates extensive information gathering Therefore, understanding customer decision-making can significantly enhance real estate marketing strategies, ultimately contributing to the success of businesses in the industry.
In recent decades, the concept of real estate purchasing decisions has evolved significantly, influencing how developers respond to customer buying behaviors to meet sales targets and enhance satisfaction (Piron, 1993; Spetic, Kozak & Cohen, 2005) Kupke (as cited in Abdullah, Nor, Bazlin, Jumadi & Arshad, 2012) emphasizes that real estate purchasing 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 to purchase an apartment is a distinct behavioral process, differing from typical business purchases, and understanding buyer behavior is crucial for suppliers to optimize their responses (Kinnard, as cited in Phan, 2012).
National and cultural characteristics significantly influence house purchasing decisions, indicating that context-specific factors may not apply universally (Opoku & Abdul-Muhmin, 2010) The Vietnamese market, characterized as transitional, exhibits unique cultural traits, with Vietnam being a collectivist society (Hofstede, as cited in Swierczek & Thai, 2003) In this culture, personal relationships take precedence over important decisions, as individuals prioritize care, support, and responsibility for their family and acquaintances Vietnamese buyers typically consult colleagues, seniors, and family members before finalizing agreements or decisions (McKinney, 2000) Consequently, apartment developers in Vietnam should conduct a thorough examination of consumer purchasing behavior from the consumers' perspective to gain a deeper understanding of their customers.
Uptopresenttime,thenumerousstudieshavebeenundertakenabouttheperceptionsofapartm entpurchasersintermsoftheapartmentattributes,financialstatus,servicequality,purchasingdecisiona ndcustomersatisfactionlevelinothercountries(Haddad,Judeh,Haddad,2 0 1 1 ; Zeng,2 0 1 3 ; Opoku& Abdul-
Previous studies have primarily focused on identifying concepts separately, with limited research conducted on the influence of collectivist culture on purchasing decisions in Vietnam Notably, Phan's 2012 study examined key factors affecting house purchase decisions in Vietnam, but it only addressed the impact of house attributes on purchase intention, neglecting customer behavior and satisfaction regarding apartment purchases Therefore, this research aims to explore how apartment purchasers in Ho Chi Minh City make decisions based on their satisfaction with apartment attributes and the service quality provided by real estate developers.
Researchobjectives
Researchmethodologyandresearchscope
Inthisresearch,twophasesofstudywereundertaken:aqualitativestudyandaquantitative study.Thequestionnairewast r a n s l a t e d fromEnglishi n t o Vietnamese.Throughqualitativestudy, in- depthinterviewswithsixpeoplewereconductedinordertoadjusttheitemsclosingtofeaturesofVietnam eseculturesandtomaketheimprovementfortheofficialquestionnaire.Inthequantitativestudy,theau thorcollecteddatabyusingaconveniencesamplingapproachandemployedself- administeredsurvey.Foranalyzingt h e collectedd a t a , SPSS16andAmos22wereusedtotestthemode l.Forthereliabilityandvalidity,theresearcherusedCFA.Then,SEMwasusedtotestthehypothesizedmo del.
Duetothelimitationoftime,thisresearchisthereforelimitedtoVietnamesecustomerswhohav eapartmentpurchasingtransactionsi n realestatei n d u s t r y i n t h e HoC h i M i n h City;sinceit is oneofthebiggestcitiesinVietnamandmostof realestatecompaniescentralize here.
Researchsignificance
Basedo n t h e researchr e s u l t s , s o m e usefulmanagerialimplicationsw e r e suggestedt o he lpt h e realestatecompaniest o havet he marketingandsalesstrategiesimpactinggreatly oncustomerp urchased e c i s i o n andhelpp o l i c y makerst o u s e suitablep o l i c i e s t o developt h e Vietna mrealestate industry.
Researchstructure
This thesis is structured into five chapters The introduction outlines the research background, problem, objectives, significance to management practice, and methodology for data analysis The second chapter reviews existing literature on concepts related to apartment purchase decisions, examining their connections with apartment attributes, financial status, service quality, and customer satisfaction It also presents the conceptual model and hypotheses The third chapter details the research methodology employed to empirically test the model Chapter four showcases the results of the data analysis and their relevance to the research questions and hypotheses The final chapter concludes by addressing the research hypotheses and problems, offering implications for theories, policies, and practices based on the findings, while also identifying limitations for future research.
Thischapterm a i n l y introducest h e theories,w h i c h a r e p r o p o s e d bym a n y scholarsi n academicfieldandarerelatedtoalltheconceptsandresearchmodel.Theauthorfirstlyclarifiest h e def initionso f purchasedecisionandthent h e relationshipsamongpurchased e c i s i o n withapartmentattri butes,financialstatus,servicequalityandcustomers’satisfactionlevelconductedbypreviousstudiesare alsodiscussedforproposingaconceptualmodelandhypotheses.
The purchase decision is a critical stage in customer purchasing behavior that unfolds over time (Piron, 1993; Kotler & Keller, 2009) Recently, this behavior has attracted significant attention from marketers and researchers due to its vital role in predicting operational success and achieving a sustainable competitive advantage (Parasuraman, Zeithaml & Berry, 1985) Insights from research and the experiences of successful marketers help to clarify how customers make purchasing decisions and where they choose to buy products The purchase decision-making process involves several comparisons to align customer characteristics with product attributes, as illustrated in Figure 2.1 (Zeng, 2013) Ultimately, customers may apply different criteria to evaluate which product best meets their needs, depending on the type of purchase and their expectations of performance (Blackwell et al., as cited in Zeng, 2013).
The purchasing decision process typically involves five stages: need recognition, information search, evaluation of alternatives, purchase, and post-purchase evaluation However, not all customers follow these steps in a linear fashion; their past experiences and preferred brands can lead them to make immediate choices Additionally, consumers from different market segments may have varying purchasing decisions based on their perceptions of the attributes they find important.
Purchasinganapartmenti s o n e o f t h e m o s t significanteconomicd e c i s i o n s thatpeoplemake,anditrequiresthepurchasertogatheralotofinformationregardingitsfeatures(Haddad,
Real estate functions as both a financial asset and a physical entity, encompassing intrinsic and extrinsic attributes that influence consumer behavior (Judeh & Haddad, 2011; Anastasia & Suwitro, 2015) The decision-making process for purchasing an apartment differs significantly from other business decisions due to the unique, durable, and long-term nature of real estate (Kinnard, as cited in Phan, 2012) Each property is a highly differentiated product, with its specific location being fixed and unique (Kinnard, as cited in Phan, 2012) Therefore, a deeper understanding of the factors that influence buyer behavior is essential for apartment developers to enhance their understanding and predict purchasing decisions in the real estate market (Daly, Gronow, Jenkins & Plimmer, as cited in Anastasia & Suwitro, 2015).
Therearevariouscustomerdecision- makingmodelshavebeenproposedintheliteraturei n recentdecades.However,m a n y researchersbe lievethata specific,situationandproduct- orientedmodelis neededin studyingpurchasing(Erasmusm,Boshoff&Rousseau,ascitedin Koklic
&Vida,2009).Theyalsoadmitthattherearelacksstudiesofpurchasingdecisionsthatconsumersarem ostconcernedabout“behavioralpurchasingdecisionmaking”.Additionally,thestudiesaboutrealestate purchasingbehavioraldecisionsinVietnamarenotyettobefoundinp u b l i c literatures.Thus,th isstudywillprovideabetterunderstandingofpurchasingbehavioraldecisionmakingcontext andin fluencesto allapartmentparticipantswhicharethedevelopers,purchasers,andrealestateagents.
The wide range of apartment developers offers numerous options for potential buyers, but without clear criteria for evaluation, making a purchasing decision becomes challenging According to Hawkins, Mothersbaugh, and Best (2011), apartment features that align with customer expectations serve as crucial criteria for evaluating choices Typically, apartment customers prioritize the attributes they perceive as most valuable, assess these different features, and determine their willingness to pay for them (Kotler & Keller, 2009) Previous studies have identified several real estate attributes that significantly influence apartment customers' purchasing decisions (Ratchatakulpat et al., 2009; Haddad et al., 2011; Opoku & Abdul).
Muhmin,2010;Alonso,2002;Pope,2008;Speticetal.,2005;Wang&Li,2006).Basingontheresearch es,Zeng(2013)andRatchatakulpatetal.
In 2009, housing attributes were categorized into four main types: 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 elements like exterior design and outdoor space (Bhatti & Church, as cited in Zeng, 2013; Ratchatakulpat et al., 2009) Environmental attributes pertain to aspects related to the neighborhood (Cheshire & Sheppard; Fierro, Fullerton & Donjuan).
Callejo;Pasha& B u t t , ascitedi n Zeng,2 0 1 3 , & Ratchatakulpatetal.,2 0 0 9 ) andp o l l u t i o n (Y usuf& Resosudarmo;Zabel& Kiel,ascitedinZeng,2013).Locationattributescompriseofthedistancet othenearestcentralbusinessd i s t r i c t , schools,andt r a n s p o r t ( C h a y & Greenstone;Pasha
Many researchers, as cited in studies by Pope and Jaren (2013) and Ratchatakulpat et al (2009), utilize Rosen’s hedonic model (1974) to analyze customer preferences for housing structural amenities and attributes The residential apartment is viewed as a unique product valued for its utility-bearing characteristics and other factors influencing housing purchases (Bitter, Mulligan, & Dall'erba, 2007; Taylor, 2008; Fierro et al., 2009) Potential homebuyers first identify key attributes and benefits that align with their expectations, subsequently assessing the importance of each Ultimately, they are inclined to invest in the attributes that offer the highest perceived value (Bao & Wan).
Insummary,itisindicatedthatmanyattributesofresidentialhousespresentedcanhavedirecto rindirect,positiveornegativeimpactsonconsumers’housingpurchasechoicedecisions(Alonso,20 02;Opoku&Abdul-
Muhmin,2010;Speticetal.,2005;Wang&Li,2006).However,theresearchersalsorealize thattherel ativeimportanceofvarioushousing attributesmightvaryacrossnational contexts.Hence,basedon theliteratures,it ishypothesized:
H1.Customer’sresidentialapartmentpurchasingdecisioni n Vietnami s p o s i t i v e l y influencedbyap artmentattributes.
Purchasing an apartment is often regarded as the most significant financial decision in a person's life, impacting household budgets due to the long-term commitment involved, 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), awareness of their current financial situation enables customers to select apartments they can comfortably afford Financial status encompasses not only available capital but also borrowing costs associated with real estate transactions (Xiao & Tan, 2007) Moreover, it includes factors such as house prices, mortgage loans, income, and payment terms (Opoku & Abdul).
In 2003, it was emphasized that the financial factors influencing apartment purchases extend beyond just the house price, taking into account the unique characteristics of the apartment and its payment transactions Key elements such as interest rates, maximum mortgage limits, maximum monthly payments, and the duration of payments are crucial components of financial status Today, apartment buyers exhibit diverse attitudes and possess strong bargaining power, with a wide range of available options and lower switching costs Consequently, they are increasingly critical and sensitive when making purchasing decisions, especially regarding apartment prices and payment methods (Opoku & Abdul-Muhmin, 2010).
Several studies have utilized the hedonic price model developed by Rosen (as cited in Taylor, 2008) to analyze customer apartment selection criteria, focusing on the demand for housing amenities and attributes (Taylor, 2008; Fierro et al., 2009) These studies reveal that customers tend to purchase apartments by comparing the marginal utility of each attribute to its marginal price (Rosen, as cited in Zeng, 2013) As a result, an apartment should be viewed as a product valued for its utility-bearing characteristics and other purchasing factors However, acquiring an apartment can be a challenging decision, significantly impacting a household's budget, especially for young families in developing countries, who may find it difficult to pay a substantial amount upfront (Xiao & Tan, 2007) To effectively meet customer needs, it is crucial to align willingness to pay, ability to pay, and the price expectations of apartment developers, focusing on payment terms (Sunding & Swoboda, 2010) Comparisons among apartment purchasers will help evaluate the costs of buying versus renting, aiding in making informed choices.
(2009)realizeifapurchaser evaluatesthecoststouse capitaltopurchaseischeapandeasytoachieve,heorshewillshiftfromrentingtopurchasinganapart ment.Intelligentcustomersareassumedtobeutilitymaximizing,withintheboundsofsearchcostsand limitedknowledge,mobility,andincome(Kotler&Keller,2009).Theyestimatewhichofferwilldelive rthemostperceivedvalueandmakepurchasedecisionsbasedo n theirestimate(Hawkinsetal.;Sol omon,ascitedinZeng,2013).So,thisstudyisgoingtoe x a m i n e bothapartmentattributes’ costsandpayingmethodscancreatemoreperceivedvaluetostimulatecustomers’willingnesstopayt o p urchaseanapartment.Giventhisdiversifyingresults,thecurrentstudyproposesandteststhesecondhypo thesis:
Service quality is a crucial factor for organizations aiming to enhance their competitive advantage The concept was first introduced by Parasuraman et al (1985), who identified five dimensions of service quality: reliability, responsiveness, assurance, empathy, and tangibles This model has been widely adopted in various market research studies (Gannage, as cited in Nahmens & Ikuma, 2009; Kotler & Keller, 2009) According to Nahmens & Ikuma (2009), apartment developers offer housing services that customers perceive through interactions and dynamic events within residential housing systems Customers actively seek information, evaluate services from different suppliers, and make decisions based on perceived quality attributes Consequently, the quality of services provided by apartment developers significantly impacts homebuyers' purchasing decisions and their post-purchase satisfaction (Torbica & Stroh, 2005; Forsythe, 2008; Nahmens & Ikuma, 2009).
In the developing residential housing service market, it is crucial to understand what customers seek and how they evaluate services Housing customers prioritize service quality from providers, as defining quality factors of an apartment enhances the perceived customer value of housing services and serves as a competitive advantage Customers’ perceptions significantly influence their valuation of services, their choices among different providers, and their evaluation of service delivery Research by Nahmens & Ikuma and Forsythe highlights the five main dimensions of service quality, which are essential for assessing perceived service quality among homebuyers These dimensions are further divided into 21 housing service quality attributes that measure the effectiveness of apartment developers in meeting consumer expectations during the design and construction process Studies indicate that homebuyers' purchasing decisions are strongly influenced by their satisfaction with service quality across all five dimensions: reliability, responsiveness, assurance, empathy, and tangibles Additionally, effective management of gaps between customers' expectations and perceptions can enhance satisfaction and influence purchase decisions Overall, housing service quality is identified as a critical factor in the purchasing decision process, significantly impacting consumers' value perception and decision-making in the apartment purchasing context.
H3.Consumer’sapartmentpurchasingdecision i s p o s i t i v e l y influencedbytheservicequ al it y p rovidedbyapartmentdevelopers.
Customer satisfaction is defined as the evaluation of whether customer expectations are met based on perceived performance and product quality (Oliver, as cited in Zeng, 2013) It is an objective assessment of the degree to which a customer's expectations for a specific service encounter are fulfilled (Mossel & Valk, as cited in Zeng, 2013) In studies of customer satisfaction and dissatisfaction, the focus is on the difference between pre-purchase expectations and post-purchase perceptions (Peter & Olson, as cited in Nahmens & Ikuma, 2009) Dissatisfaction arises when the performance of product attributes falls short of expectations, while higher satisfaction levels are achieved when performance exceeds those expectations (Peter & Olson, as cited in Nahmens & Ikuma, 2009).
2 0 0 9 ) Intuitively,i t i s expectedthatt h e customers’satisfactionleveli s t h e q u a l i t y o f c u s t o m e r comparisonbetweentheirexpectationsa b o u t anapartmentandapartmentattributesp r o v i d e d by reale s t a t e developers.
Research has highlighted the importance of housing attributes in influencing overall customer satisfaction within the real estate industry (Nahmens & Ikuma, 2009) Longenecker et al (as cited in Nahmens & Ikuma, 2009) emphasize that customer satisfaction should be evaluated post-purchase, taking into account various factors such as housing attributes, performance, and services Customer expectations are significantly shaped by the value derived from apartments and the services offered by real estate developers Studies show that a buyer's overall satisfaction is linked to the comprehensive offering of housing attributes that align with consumer needs and desires (Torbica & Stroh, 2001; Forsythe, 2008) The findings suggest that more favorable perceptions of housing attributes in the real estate sector correlate with higher overall satisfaction among home purchasers Similarly, Opoku & Abdul's research supports these conclusions.
M u h m i n (2010)realizethatthefollowingaspectsofhousingattributescansignificantly influ encehousebuyers’expectationsoftheirnewhouse:housingstructure,housingsalesdealer,w o r k m a n s h i p , h o us i n g construction,materials,priceandappreciationvalue.Asa result,investigatingt hepositiverelationshipbetweenapartmentattributesandapartmentowners’post- purchasesatisfactionmayhelpthemarketerstoprovidetherighthousingproductsandservicest o bot hpotentialhomebuyersandhouseownerswhointendtobuyasecondhouse.Hence,thefourthhypothe sisis:
Basingo n t h e literatureo f h o u s i n g i n d u s t r y andservicequality,s t u d y byN a h m e n s & I kuma( 2 0 0 9 ) revealsthattherea r e s o m e serviceq u a l i t y determinantsthatcans a t i s f y andd i s s a t i s f y housingcustomers.Thepredominantsatisfiersareattentiveness,responsiveness,carean dfriendlinessandt h e predominantdissatisfiesareintegrity,reliability,responsiveness,availability,andf unctionality.O t h e r workbyP o w e r andAssociates(ascitedi n N a h m e n s &
According to Ikuma (2009), the services offered by sales staff and daily housing maintenance are crucial for meeting householders' selection criteria and overall satisfaction in Florida The study highlights that streamlining the transaction process significantly enhances customer satisfaction, while addressing issues such as inefficiency, chaos, incompetence, and isolation in housing services can reduce customer dissatisfaction Therefore, it is essential for real estate developers to comprehend the criteria customers use to evaluate housing services, as this understanding directly influences their perceptions of overall service quality and satisfaction with the offerings provided by apartment developers.
Apartmentattributesandapartmentpurchasingdecision
The plethora of apartment developers offers numerous options for potential buyers, but without clear criteria for evaluation, making a purchasing decision becomes challenging According to Hawkins, Mothersbaugh, and Best (2011), apartment features that align with customer expectations serve as essential criteria for assessing choices Generally, customers tend to focus on the primary attributes they find most valuable, evaluate these features, and determine their willingness to pay for them (Kotler & Keller, 2009) Numerous studies have identified various real estate attributes that significantly influence apartment customers' purchasing decisions (Ratchatakulpat et al., 2009; Haddad et al., 2011; Opoku & Abdul).
Muhmin,2010;Alonso,2002;Pope,2008;Speticetal.,2005;Wang&Li,2006).Basingontheresearch es,Zeng(2013)andRatchatakulpatetal.
In 2009, housing attributes were categorized into four main types: 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 available outdoor space (Bhatti & Church, as cited in Zeng, 2013; Ratchatakulpat et al., 2009) Environmental attributes relate to the characteristics of the neighborhood (Cheshire & Sheppard; Fierro, Fullerton & Donjuan).
Callejo;Pasha& B u t t , ascitedi n Zeng,2 0 1 3 , & Ratchatakulpatetal.,2 0 0 9 ) andp o l l u t i o n (Y usuf& Resosudarmo;Zabel& Kiel,ascitedinZeng,2013).Locationattributescompriseofthedistancet othenearestcentralbusinessd i s t r i c t , schools,andt r a n s p o r t ( C h a y & Greenstone;Pasha
Many researchers, including Pope and Jaren (2013) and Ratchatakulpat et al (2009), utilize Rosen's hedonic model (1974) to explore customer preferences for housing structural amenities and attributes Residential apartments are examined as unique products valued for their utility-bearing characteristics and other factors influencing housing purchases (Bitter, Mulligan, & Dall'erba, 2007; Taylor, 2008; Fierro et al., 2009) Potential homebuyers first identify key attributes and benefits that align with their expectations, subsequently assessing the importance of each attribute Ultimately, they are inclined to invest in the attributes that provide the highest perceived value (Bao & Wan).
Insummary,itisindicatedthatmanyattributesofresidentialhousespresentedcanhavedirecto rindirect,positiveornegativeimpactsonconsumers’housingpurchasechoicedecisions(Alonso,20 02;Opoku&Abdul-
Muhmin,2010;Speticetal.,2005;Wang&Li,2006).However,theresearchersalsorealize thattherel ativeimportanceofvarioushousing attributesmightvaryacrossnational contexts.Hence,basedon theliteratures,it ishypothesized:
H1.Customer’sresidentialapartmentpurchasingdecisioni n Vietnami s p o s i t i v e l y influencedbyap artmentattributes.
Financialstatusandapartmentpurchasingdecision
Purchasing an apartment is often regarded as the most significant financial decision in one's life, profoundly impacting a household's budget due to the long-term commitment involved, 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 current financial situation enables customers to select an apartment that fits comfortably within their budget They further categorize financial status into two main components: the financial element of real estate, which necessitates access to substantial capital and borrowing costs (Xiao & Tan, 2007) This indicates that financial status encompasses not only the available funds but also the associated costs of purchasing an apartment Additionally, it includes factors such as house prices, mortgage loans, income, and payment terms (Opoku & Abdul).
In 2003, it was emphasized that financial factors influencing apartment purchases extend beyond just the house price, taking into account the unique characteristics of the apartment and the payment transactions involved Key financial elements include interest rates, maximum mortgage limits, maximum monthly payments, and the duration of payment plans Today’s apartment buyers possess diverse perspectives, strong bargaining power, and a wide array of choices, along with lower switching costs Consequently, they exhibit heightened scrutiny and sensitivity when making purchasing decisions related to apartment prices and payment methods (Opoku & Abdul-Muhmin, 2010).
Numerous studies investigating customers' apartment selection criteria have utilized the hedonic price model developed by Rosen, which is widely adopted to analyze the demand for housing amenities and attributes (Taylor, 2008; Fierro et al., 2009) These studies indicate that customers tend to purchase homes by comparing the marginal utility of each apartment with its marginal price Consequently, an apartment is viewed as a product valued for its utility-bearing characteristics and other purchasing factors However, buying an apartment poses a significant challenge, particularly for young families in developing countries, as paying a large sum upfront is often unfeasible (Xiao & Tan, 2007) To effectively meet customer needs, it is essential to align the willingness to pay, ability to pay, and developers' price expectations, focusing on payment terms to determine equilibrium prices (Sunding & Swoboda, 2010) Comparisons among apartment purchasers will help evaluate the costs of buying versus renting, facilitating better decision-making.
(2009)realizeifapurchaser evaluatesthecoststouse capitaltopurchaseischeapandeasytoachieve,heorshewillshiftfromrentingtopurchasinganapart ment.Intelligentcustomersareassumedtobeutilitymaximizing,withintheboundsofsearchcostsand limitedknowledge,mobility,andincome(Kotler&Keller,2009).Theyestimatewhichofferwilldelive rthemostperceivedvalueandmakepurchasedecisionsbasedo n theirestimate(Hawkinsetal.;Sol omon,ascitedinZeng,2013).So,thisstudyisgoingtoe x a m i n e bothapartmentattributes’ costsandpayingmethodscancreatemoreperceivedvaluetostimulatecustomers’willingnesstopayt o p urchaseanapartment.Giventhisdiversifyingresults,thecurrentstudyproposesandteststhesecondhypo thesis:
Apartmentservicequalityandapartmentpurchasingdecision
Service quality is a crucial factor that enhances organizational competitiveness, as established by Parasuraman et al (1985) through their five dimensions of service quality: reliability, responsiveness, assurance, empathy, and tangibles This model has been widely adopted in various market disciplines (Gannage, as cited in Nahmens & Ikuma, 2009; Kotler & Keller, 2009) According to Nahmens & Ikuma (2009), apartment developers deliver housing services that customers perceive through interactions and activities within residential housing systems Customers actively seek information, evaluate and compare services from different suppliers, and make purchasing decisions based on perceived quality attributes Consequently, the quality of services provided by apartment developers significantly influences homebuyers' purchasing decisions and their overall satisfaction after the purchase (Torbica & Stroh, 2005; Forsythe, 2008; Nahmens & Ikuma, 2009).
In the developing residential housing service market, understanding customer needs and their evaluation of service quality is crucial Housing customers prioritize service quality from providers, and defining quality factors can enhance perceived customer value, offering a competitive advantage Customers' perceptions significantly influence how they value services, choose between providers, and assess service delivery Research by Nahmens & Ikuma and Forsythe highlights five main dimensions of service quality, which are essential for analyzing homebuyers' perceptions and whether the service quality meets or exceeds their expectations These dimensions are divided into 21 housing service quality attributes, which help measure the quality of services provided by apartment developers Studies indicate that homebuyers' satisfaction with service quality strongly impacts their purchasing decisions, with all five dimensions—reliability, responsiveness, assurance, empathy, and tangibles—playing a significant role Additionally, reducing gaps between customers' service quality expectations and perceptions can enhance satisfaction and influence purchasing decisions Overall, housing service quality is identified as a key factor in the purchasing process, significantly affecting consumers' value perception and decisions in the apartment buying process.
H3.Consumer’sapartmentpurchasingdecision i s p o s i t i v e l y influencedbytheservicequ al it y p rovidedbyapartmentdevelopers.
Apartmentattributesandcustomer satisfactionlevel
Customer satisfaction is defined as the relationship between customer expectations and perceived performance, reflecting the quality of product attributes (Oliver, as cited in Zeng, 2013) It represents an objective evaluation of how well a service encounter meets customer expectations (Mossel & Valk, as cited in Zeng, 2013) In studies of customer satisfaction and dissatisfaction, the focus is often on the difference between pre-purchase expectations and post-purchase perceptions (Peter & Olson, as cited in Nahmens & Ikuma, 2009) Dissatisfaction arises when product performance falls short of expectations, while satisfaction is more likely when performance exceeds those expectations (Peter & Olson, as cited in Nahmens & Ikuma, 2009).
2 0 0 9 ) Intuitively,i t i s expectedthatt h e customers’satisfactionleveli s t h e q u a l i t y o f c u s t o m e r comparisonbetweentheirexpectationsa b o u t anapartmentandapartmentattributesp r o v i d e d by reale s t a t e developers.
Research has shown that key housing attributes significantly impact overall customer satisfaction in the real estate industry (Nahmens & Ikuma, 2009) According to Longenecker et al (as cited in Nahmens & Ikuma, 2009), customer satisfaction should be evaluated post-purchase, considering factors such as housing attributes, performance, and services The value received from an apartment and the services provided by real estate developers heavily influence customer expectations Studies indicate that a home buyer’s overall satisfaction is derived from the comprehensive offering of housing attributes that align with consumer needs and desires (Torbica & Stroh, 2001; Forsythe, 2008) Positive perceptions of housing attributes correlate with higher levels of satisfaction among home purchasers (Opoku & Abdul).
M u h m i n (2010)realizethatthefollowingaspectsofhousingattributescansignificantly influ encehousebuyers’expectationsoftheirnewhouse:housingstructure,housingsalesdealer,w o r k m a n s h i p , h o us i n g construction,materials,priceandappreciationvalue.Asa result,investigatingt hepositiverelationshipbetweenapartmentattributesandapartmentowners’post- purchasesatisfactionmayhelpthemarketerstoprovidetherighthousingproductsandservicest o bot hpotentialhomebuyersandhouseownerswhointendtobuyasecondhouse.Hence,thefourthhypothe sisis:
Apartmentservicequalityandcustomer’ssatisfactionlevel
Basingo n t h e literatureo f h o u s i n g i n d u s t r y andservicequality,s t u d y byN a h m e n s & I kuma( 2 0 0 9 ) revealsthattherea r e s o m e serviceq u a l i t y determinantsthatcans a t i s f y andd i s s a t i s f y housingcustomers.Thepredominantsatisfiersareattentiveness,responsiveness,carean dfriendlinessandt h e predominantdissatisfiesareintegrity,reliability,responsiveness,availability,andf unctionality.O t h e r workbyP o w e r andAssociates(ascitedi n N a h m e n s &
According to Ikuma (2009), the services offered by sales staff and daily housing maintenance are crucial for meeting homeowners' selection criteria and overall satisfaction in Florida They emphasize that expediting transaction processes significantly enhances customer satisfaction, while minimizing inefficiencies, chaos, incompetence, and isolation in housing services can reduce dissatisfaction Therefore, it is essential for real estate developers to comprehend the criteria customers use to evaluate housing services, as this understanding directly influences their perceptions of overall apartment service quality and satisfaction.
The theory suggests a strong relationship between apartment service quality and customer satisfaction levels According to Torbica and Stroh (as cited in Zeng, 2013), customer satisfaction arises from a comparison between pre-purchase expectations and actual performance during and after the apartment experience Similarly, Nahmens and Ikuma (2009) highlight a significant positive correlation between housing service quality and satisfaction To foster customer loyalty and maintain a competitive edge, real estate companies must actively monitor satisfaction levels, adapt their operations, and exceed consumer expectations Consequently, a more favorable perception of housing service quality directly enhances overall customer satisfaction with the apartment developer The strength of this relationship is contingent upon customers' evaluations of the overall attributes of apartment service quality.
Apartment service quality influencedbytheir evaluationsoftheservicequalityprovidedbyapartmentdevelopers.
Conceptualmodel
Figure1 d e p i c t s a conceptualmodele x p l a i n i n g t h e r o l e o f antecedentso f apartme ntpurchasingdecision such asapartmentattributes,financialstatus,apartment servicequality on purchasingdecisiona n d customersatisfactionlevelo f customerswhohaveapartmenttransactionsint herealestateindustryinHoChiMinhCity,Vietnam.Specifically,themodelproposesthatapartme ntattributes,financialstatus,apartmentservicequalityhavepositiveimpactsonpurchasingdecisionand customer satisfactionlevel.
H3.Consumer’sapartmentpurchasingdecisioni s p o s i t i v e l y influencedbythe serviceq u a l i t y providedbyapartmentdevelopers.
Insummary,thischapterpresentstheoreticalbackgroundofeachconceptinthemodel.Basedon discussionofliteraturereview,purchasingdecisionisaffectedbyapartmentattributes,financialstatus,a ndapartmentservicequality.Then,t h e influenceo f t h e s e antecedents(e.g.apartmentattributesanda partmentservicequality)oncustomersatisfactionlevelisalsoconsidered.Therelationshipofthesefacto rsalreadytestedbymanypreviousscholarsispresentedfortheconceptualmodel.Hence,therearefiveh ypothesesproposedforthisresearch.Thenextchapterwilldiscussaboutmethodologythatusedtoan alyzethedataandtesthypothesesoftheresearchmodel.
Thischapterpresentsdetailinformationofaresearchmethodologyofthisstudy.First,itstarts withresearchprocessandsampledescription.Then,measurementscalesarepresentedtodevelopquest ionnaires,followedbyd a t a collectionmethod.Afterthat,in- depthinterviewi s conductedtohelpmeasurementscalesclearerandunderstandable.Throughthefinalq uestionnaire,t h e datao f quantitatives u r v e y i s usedt o testt h e measurementandstructuralmodels.
Researchdesign
Researchprocess
Due to the unique characteristics of the real estate industry, which necessitate strong marketing competencies for companies to maintain a sustainable competitive advantage, a study was conducted in Ho Chi Minh City, Vietnam's largest urban center for real estate The research comprised two phases: a qualitative study and a main survey The survey questionnaire was initially crafted in English and subsequently translated into Vietnamese with the assistance of English experts In-depth interviews were conducted over a week with six customers who had purchased apartments, taking place in various suitable locations such as real estate offices and cafés The researcher ensured clarity by reading each item of the measurement scale aloud and addressing any misunderstandings, gathering valuable insights until no further suggestions were provided.
Basedonthefeedbackofrespondents,thesurveyquestionnairewasslightlymodifiedtomakei tclearerandmoreunderstandable(seeAppendixA,B,&C).Afterthequestionnairewasmodified,the se lf-administratedquantitativesurvey withconveniencesampling wasconductedt o collectdata fortesting theresearch’shypotheses.
Participantsself-completeda s u r v e y withalli t e m s weremeasuredbyfive- pointLikertscale,anchorpoints including “stronglydisagree”(=1),“disagree”(=2),“neitherdisagre enoragree”(=3),“agree”(=4),and“stronglyagree”(=5).Thequestionnairewasmainlydeliveredtor espondentsv i a electronicmail,Googles u r v e y andhardcopies( s e e Table3.1).S P S S andAMOS wereused totestthemeasurementandstructuralmodels.
Measurementscales
Asmentionedabove,thefinalquestionnairesconsistedoffivemainmeasurementscales:apartm entattributes,financialstatus,apartmentdevelopers’servicequality,apartmentpurchasingdecision, andcustomer satisfactionlevel.
Apartmentp u r c h a s i n g decisionwasmeasuredbyf o u r items,a c c e s s i n g customerpurch asingbehavioralprocess(Piron,1993).
2 IfeltlikeIhadto purchaseitfromthefirsttimeIsaw the apartment PurchasD2
(2009)toVietnamc o n t e x t byeliminatingu n s u i t a b l e itemsandsplittingitemst o helpresponde ntst o understand.Thus,t h e finalmeasurementscalefort h i s constructhasfiveitems,accessingcustome rp o s t - purchasingbehavior.
3.The attributesoftheapartmentthatIhaveboughtmeetsmyexpectations SatisfLev3 4.Theservicesp r o v i d e bysupplierso f t h e apartmentthatI haveboughtmeets myexpectations
5.Mypurchasedecision ismuchbetter thanotherbuyingdecisions in thepast SatisfLev5
Nahmens&Ikuma(2009)developedmeasurementscaleforapartmentdevelopers’servicequal ityfromthescalesofParasuramanetal.(1993),Forsythe(2008),Kotler&Keller,(2009)
(ascitedinNahmens&Ikuma,2009).ThisscalewasadaptedtobesuitableinVietnamrealestatemarket. These itemsarereferredto ascomponentservicesofapartmentandhavealargeimpacto n endcustomer s,andt h i s shouldb e takeni n t o accountwhendevelopingt h e specificationsfortheapartmentservice sprovidedbydevelopers.
Apartment service quality(adaptedfromNahmens &Ikuma,2009) Coding
(ascitedinRatchatakulpatetal.,2009).Theseitemswereadaptedt o Vietnamc o n t e x t byeliminatingu n s u i t a b l e itemst o help respondentstounderstand.Thus,thefinalmeasurementscalehasfouritems,accessingarelativelarge amountofcapital,borrowingcostsandpaymentterms.
Measurementscalesforthefourcomponentsofapartmentattributeswouldbeusedfromt h o s e developedbyAdairetal.,(1996),Dalyetal.,(2003),Abelson&Chung(2005)
The study by Ratchatakulpat et al (2009) categorizes apartment attributes into intrinsic, extrinsic, environmental, and location attributes, with measurement scales tailored to Vietnamese cultural features Intrinsic apartment attributes are assessed through five criteria, including the apartment's structure, size, number of rooms, layout and decorative style, and architectural materials Extrinsic attributes are evaluated based on three factors: the overall appearance of the building, the garden and its size, and external spaces Additionally, environmental and location attributes are measured by a total of eleven items, with five focusing on the quality of the living environment and six on the apartment's location.
2 Sizeofroomsin theapartment(livingroom,bedroom,kitchen…) IntrinsA2 3.Numberofroomsin theapartment(livingroom,bedroom,kitchen…) IntrinsA3
3.Exteriorspacesrefertop u b l i c area,suchast h e pu bl ic aisle,elevator, recreationroom
Finally,t h e completedquestionnairei n EnglishversionandV i e t n a m e s e versionwerepresen tedin AppendixDandE.
Quantitativestudy
Sample
Themodelandhypothesesweretestedusingdatasetcollectedfromcustomerswhohaveapartme ntpurchasingtransactionsinrealestateindustryintheHoChiMinhCity.Duetolimitedtime,theconvenie ncesamplingapproachwithself-administratedquestionnaireswasconductedi n HoChi MinhCity.
Aboutsamples i z e , t h e s i z e o f t h e samplewasn e c e s s a r i l y b i g e n o u g h t o gu aranteestatisticalsignificance.Hair,Black,Babin,AndersonandTatham(ascitedin Prajogo,2007) statedthattheminimumsampleforappropriateuseforstatisticalanalysisisequaltoorgreaterthanfivet imesofnumberofvariables,butnotlessthan100.Themodelinthisstudyconsistedo f thirty- ninevariablessothatthenecessarysamplesizeshouldbe:n7*55observations.Theauthor delivered337questionnairestoparticipantsinordertoobtainasamplesizeofabout
185.Afterdatacollection,total299responsesfromrespondentswhohaveapartmentpurchasingdemand andhavehadapartmentbusinesstransactionsinHCMCitywerecollected;theresponserate wasapproximately88.72percent(Table3.1).
Table3.1Sourceofdatacollection Source Distributed Collected Response rate Eliminated Valid
Then,total85questionnaireswereeliminatedbecausetheywereinvalid(45respondentswhodo nothaveapartmentpurchasingdemandandhavenothadapartmentpurchasingtransactionsinreal estateindustryintheHoChiMinhCity;14 respondents justchoseoneoptionforallq u e s t i o n s ; and2 6 surveyswerereturnedb a c k withoutanswers).Final ly,2 1 4 questionnaireswereusedasvaliddataforthisresearch.Incomparisonwithminimumsamples i z e , t h i s numberofdata wassatisfactory.
Data analysisprocedures
The author utilized SPSS 16 to calculate Cronbach’s alpha and Amos 20 for conducting Confirmatory Factor Analysis (CFA) to assess the reliability of each measurement component and the validity of all scales Additionally, the composite reliability (CR) was evaluated to determine the reliability of the measurement scales among constructs in the research model CFA results indicated that average variance extracted (AVE) was employed to establish convergent validity, while correlation between items (r) was used to assess discriminant validity To ensure adequate measurement quality, Cronbach’s alpha for each construct should be at least 0.6, factor loadings should reach a minimum of 0.5, and the minimum value of AVE should be 0.5, as supported by relevant literature.
According to Moreno et al (as cited in Chong, Ooi, & Tan, 2010), the composite reliability should exceed 0.7, as recommended by Nunnally To ensure convergent and discriminant validity, any inappropriate items will be removed if necessary The Confirmatory Factor Analysis (CFA) will assess model fit, with acceptable criteria being a CMIN/DF value of less than 2 and a p-value greater than 0.05 Additionally, the Comparative Fit Index (CFI) will evaluate discrepancies between the data and the hypothesized model while addressing sample size issues inherent in the chi-squared test of model fit A CFI value of 0.90 or higher is generally considered indicative of an acceptable model fit, along with the Non-Normed Fit Index (NNFI), also known as the Tucker-Lewis Index.
TLI)resolveds o m e o f t h e issueso f negativebias,thoughNNFIv a l u e s m a y sometimesfallbeyon dthe0to1range.ValuesforboththeNFI andNNFIshouldrangebetween0and1,withacutoffof.95 orgreater,indicating agoodmodel fit.The rootmeansquareerrorofapproximation
(RMSEA)avoidedissuesofsamplesizebyanalyzingthediscrepancybetweenthehypothesizedmodel, witho p t i m a l l y c h o s e n parameterestimates,andt h e populationcovariancem a t r i x A valueo f 0 0 6 o r lessw a s indicatedanacceptablemodelfit.RMSEAwassmallerthan8 %
(NguyenDinhTho&NguyenThiMaiTrang,2008).Then,structuralequationmodeling(SEM)testedthe hypothesizedmodelandestimatedpathcoefficientsforeachproposedrelationshipinthestructuralmodel Finally,bootstrapwasu s e d t o r e - t e s t t h e suitableandreliabilityofthemodel.
Asbeingmentionedabove, fivemeasurementscalesweresufficientforconvergentanddiscri minantvalidity,wereanalyzedbytheConfirmatoryfactoranalysisbeforethehypothesizedmodelwastes tedbySEM.The first-orderconstructswere financialstatus,apartmentdevelopers’servicequality,a p a r t m e n tpurchasingd e c i s i o n behavio ralprocess,andsatisfactionlevel.Thesecond- orderconstructwereapartmentattributes,whichconsistedoffoursub- components:intrinsicapartmentattributes,extrinsicapartmentattributes,environmentattributes,andl ocationattributes.
Insummary,thischapterdescribedtheresearchprocess,measurementscaleconstruction,calcul ationo f samples i z e , andresearchmethodconductedt o analyzet h e collecteddata.Thiss t u d y was designedintotwostages:firstwasqualitativestudy(in- depthinterview),secondwasquantitatives t u d y ( m a i n survey).Thei n - d e p t h interviewwasconductedt o m o d i f y t h e measurementscaleandthequestionnairewasadjuste dslightlyandbeforethequantitativesurvey.Mains u r v e y h a d samples i z e whichi n c l u d e d total2 1
Chapter4presentstheanalysisresultsofthesamplesizen!4.ThisprocessusedSPSSt o revie wt h e sample’scharactersandthedescriptivestatistictestedthenormaldistributionofvariablesbasing onrespondents’demographics.Then,AMOSwasusedforconfirmatoryfactoranalysist o e x a m i n e t h e reliabilityandv a l i d i t y o f t h e firstorderconstructs,t h e secondorderconstructandthefinalmeas urementmodel.Inaddition,structuralequationmodelingwasusedt o testtheconceptualmodelandb ootstrapmethodwasusedt o measuret h e parameterestimates.Basedontheanalysis’sresults,theexpla nationforfindingresearchwasfinallydiscussed.
Respondents’ demographics
CFA forthefirst-order constructs
Financialstatuswasmeasuredby4items.ForthefirstrunofCFA,theCFAmodeloffinancials tatusfittedthedatawell.However,thefactorloadingofFinanSta2wasnotsignificant(-.0110.70).Abouttheconvergentvalidity,t h e averagedvarianceextracted(AV E)valueo f apartmentattributes’sub- componentsweresignificant(>0.5).Inaddition,correlationsofeachpairofsub- componentswerelessthan0.8insignificantat0 0 0 1 Therefore,thesefindingssupportedreliability,c onvergentvalidity,andwithin-constructdiscriminantvalidity.(SeeTable4.5,Table4.6)
Table4.5SummarizedofCR,AVEandCronbach’α(Apartment Attributes)
CFA forthefinalmeasurementmodel
RunningtheCFAforthefinalmeasurementmodel,theauthorremovedtheinsignificantitems( FinanSta2,SerQua6,PurchasD2,SatisfLev5,IntrinsA1,IntrinsA5,EnvirA3,LocA4,LocA5,andLocA 6)becausetheirfactorloadingswerelowerthan0.5(-0.01,0.04,0.15, 0.15,
0.15,0.12,0.06,-0.01,-0.07and-0.07respectively).Alltheconstructsandsub- constructsweres t i l l measuredbyover3observeditems.Therefore,themeasurementscalesarestill retainedthecontentvalidityofconstructs.Thefinalmeasurementmodelalsoachievedagoodfittotheda ta(seefigure4.6).Thefactorloadingsoftherestitemsoffirstandsecondorderconstructsweresignific antandsubstantial(>0.5,p