831 Factors Affecting Customer Intention To Use Online Food Delivery (Ofd) Services In Ho Chi Minh City 2023.Docx

STATEBANKOFVIETNAM THE MINISTRY OF EDUCATION AND TRAININGHOCHIMINHUNIVERSITYOFBANKING  VOTHIKIMNGANSTUDENT CODE 030805170155 FACTORS AFFECTING CUSTOMER INTENTION TO USEONLINEFOODDELIVERY(OFD)SERVIC[.]

Thenecessityofthe topic

Nowadays,theterms"orderingfoodonline"or"OFDapps"arenol o n g e r unfamiliar to customers, especially to the young generation With just a few simplestepsonthephone,customerscanhavetheirfavoritemealsin15-

20m i n u t e s without travelling or waiting in line OFD services refer to internet-based foodordering and delivery systems that connect customers with partner restaurants viatheir websites or mobile applications In retail e-commerce, Online Food

Deliveryownsanenormousmarketsegment.Inthepastfewyears,OFDmarkethasremark ablyevolvedworldwide.InVietnam,theOFDsectormightreach4 4 9 million USD by 2024– up from around 207 million USD today (EuromonitorInternational,2020).

Compared to the previous years, eating-out activity at restaurants has plummeted by58% globally due to government lockdown orders The outbreak of COVID– 1 9 has caused adverse aftermath on the restaurant industry, as a result of the severepandemic consequences, more customers have adjusted their lifestyles and shoppinghabits from bricks to clicks Therefore, the pandemic appears to have a situationaleffectpositivelyinenhancingcustomerintentiontowardOFDservices.

Likewise, HCMC also issued social distancing directives with stay-in-place andstay-at-home orders forcing food service operations to be closed or limited As aresult, the shopping process in HCMC differs from the previous regular condition,converted almost every activities to online, including food purchasing. Meanwhile,Online Food Delivery (OFD) services have grown tremendously by delivering foodto customer homes Restaurants have started digital transformation by promotingthrough social networks or primarily cooperating with OFD Brands in order tomaintaincustomers,stabilizerevenueandpromptlyadapttotheepidemicsituation.

Despite the fact that the OFD market has already gained considerable achievements,morecustomershaveusedandareintendingtouseOFDservicesduringthepa ndemic, according to a recent survey by Sach Trang, the proportion of peopleusing OFD services has increased from 68% in 2020 to 88% in 2021 BecauseCovid-19 is a highly contagious human– to – human disease,withmaximumcontact restriction and the government’s distance directives, people have tended topurchasefoodanddrinksthroughOFDbrandsinsteadofleavingtheirhouse.Customers prefer OFD over offline shopping because of convenience, competitiveprice,diverseoptions,fastdelivery andmoreaccesstoinformation.

To meet the rising demand of customers, OFD brands have significantly developedandimprovedqualitybyexpandingsupplies,improvingappfeatures,etc.Neve rtheless, there are many other factors that affect the purchase intention ofcustomers.

To compete and develop in the current context, OFD brands need to beaware of the external influences on their business strategy as well as other factorsfromthecustomersin ordertocomeupwithaneffectivecompetitivestrategy.

The findings of this study may be valuable for both restaurants and OFD brands interms of determining which factors are most significant in generating customerintenttoutilizeOFDservices.Understandingsuchfactorsmaysuppor trestaurantsin choosing distribution channels for their operations Furthermore, OFD enterprisesmay gain a better knowledge of the components of their websites and services thatcustomers value the most Additionally, whereas OFD usage intention has beenextensively researched in developed countries, it has not been thoroughly analyzedindevelopingcountriessuchasVietnam.

Withtheaboveconsiderablechangesandtheincreasinglypopularstatusofpurchases through apps, the study"Factors Affecting Customer Intention To UseOnline Food

Delivery (OFD) Services In Ho Chi Minh City"is to analyze thefactorsaffectingthepurchaseintentionofHCMCcustomersandtoproposesolutionsin ordertoimprovestrategiesofOFDbrands.

Researchobjectives

GeneralObjective

The overall objective of this study is to evaluate the factors affecting the customerspurchasei n t e n t i o n t h r o u g h t h e o n l i n e f o o d d e l i v e r y a p p l i c a t i o n i n H o C h i M i n h City The research result would provide proposed strategies in order to optimize thesalesefficiencyofOFDbusinesses.

SpecificObjectives

 Objective 1: Investigating and analyzing the factors affecting customers purchaseintentionthroughtheOFDapplicationsinHoChiMinhCity.

 Objective 2: Evaluating the effect of factors from which to examine and appraisethestrengthsandweaknessesinsalesactivitiesonOFDapplicationsto customers inHoChiMinhCity.

 Objective 3: Proposing practical implications and strategies to develop sales activitiesofOFD businessestocustomersinHoChiMinhCity.

Researchquestions

(1) Which are the factors that affect customer intention to use Online FoodDeliveryservicesinHoChiMinhCity?

(2) To what extent do these factors affect on customer intention to useOnlineFoodDeliveryservicesinHoChiMinh City?

Subjectandscopeoftheresearch

 SurveySubject:Cus to mers inHCMC whoare intendingtouseOFDserv ices.

Researchmethodanddata

 Primary data: Based on the research questionnaire, survey forms were sentdirectly to respondents or via social media / emails to collect data fromresponses ofcustomersin HCMC.

Gatheringtheoreticalbasisandresearchworksrelatedtopurchase intention through books and scientific journals in the archives ofBankingUniversity.

 Researchmethod:Thisthesisusesacombinationofquantitativea n d qualitativem ethods,thedataisprocessedandanalyzedusingSPSS20.0software Firstly,domestic and international researches are reviewed to developandadjustthescale,therebybuildinganappropriateresearchmodel.Subsequently, scale reliability are tested through Cronbach's Alpha and EFAexploratory factor analysis Regression analysis is conducted to evaluate theimpact of the components Finally, the author summarizes the research resultsandproposes implications.

Practicalsignificanceoftheresearch

This study provides research material on the factors affecting customer intention topurchase through OFD apps in Vietnam Aside from providing the latest updateddata,theseresultscouldhelpbusinessmanagerstoconsultandbuild effectivestrategies It is also a premise for researchers to conduct future studies specialize inanalyzingcustomerintentandbehavior.

Thesisstructure

Thischapterillustratesthenecessityofthestudybyprovidingag e n e r a l introductionan ditscontributiontoOFDindustry.Furthermore,thisc h a p t e r provides an overview sight of the customer intention towards ordering food online.TheResearchObjectives,Questions,SubjectandScopearealsobeingrestated.

This chapter indicates the related concepts, theoretical basis as well as referencemodels that this study will base on After that, a summary of international anddomestic researches about OFD usage intention would be presented in order tospecifytheresearchgapsandfinallyproposetheresearchmodel.

In this chapter, the author will introduce the research process and the measurementscales, which will be developed with additional adjustments, in order to improve themodel's stability and suitability for the collected data Chapter 3 presents researchprocess,scaledevelopmentandstatisticaldataanalysistechniquesindetail.

Descriptivestatistics,reliabilityanalysisandappropriatescale,researchmodelcalibrated will be presented in this chapter According to the results, the author willevaluatewhichfactors have most significantimpactoncustomerintention.

This chapter will base on the research results in Chapter 4 to conclude the problemand proposesome solutions or strategies toimprove and enhancet h e q u a l i t y o f OFD brands For upcoming improvements, the research limitations and directionsforfurtherstudiesarealsomentioned.

Concepts

According to Dao (2018) “Customer buying behavior is all actions that customersreveal in the process of exchanging products, including: investigate, purchase, use,evaluate and spending on goods which satisfy their needs” It is also possible toconsidered as the way in which customers making decisions to use their assets(money,time, )to purchaseandutilizegoods/servicesto achievetheirsatisfaction.

Kotler & Keller (2011) assumed buying behavior is the way of how individuals orgroups purchase and dispose products to satisfy their needs and desires. Similarly,Solomon et al (1995) defined customer buying behavior as the process of selecting,acquiring, utilizing, and disposing of products with the intention to fulfill personalsatisfaction It has been also stated as “a process, which through inputs and their usethoughprocessandactionsleadstosatisfactionof needsandwants”(Enis,1974).

Even though the above-mentioned definitions are numerous, they ultimately lead tothe same conclusion: customer buying behavior is a process of selecting, acquiring,and disposing of products and services based on the requirements and desires of thecustomers Nevertheless, experts and academics share a mutual agreement that thisprocess is susceptible to continuous modification throughout time as the buyingcharacteristicsofcustomersvaryowingtotheirphysicalandpsychologicaldeman ds.

According to Ajzen (1991), “Intention is a motivating factor that motivates anindividual to be willing to perform a behavior” Morinez et al (2007) defined it as acircumstance in which a buyer is inclined to acquire a specific product in specificcondition The process of purchase intention is complicated due to the fact that it isconnected to customer behavior, perceptions and attitudes Ghosh (1990) impliedthat purchase intention could be used efficiently to predict thebuying process.Additionally,duringthepurchasingprocess,customersmightbeinfluencedbyint ernalor externalfactors.

Purchase intentions can evaluate the effectiveness of new distribution channel andhelp to decide which geographic markets and customer segments that should betargeted (Morwitz et al., 2007) To analyze consumer behavior, it is crucial tounderstand the attitudes, evaluations, and internal elements that lead to the purchaseintention(FishbeinandAjzen,1977).

Astechnologyevolves,customerpurchasesbecomeAppPurchaseIntent,andpurchase intent from there also becomes App Purchase Intent (Close & Kukar-Kinney,

2010) In general, these actions are similar to the traditional form, the onlydifference is that they are performed through the application network. Delafrooz etal (2011), stated that “App purchase intention is the certainty that a customer willconduct the purchase through the application” Online shopping intention is thestrength of customer intents to make purchases through the Internet. Meskaran et al.(2013)describedonlinepurchaseintention as willingnesstobuyviatheinternet.

Likewise, online purchase intention has been defined as the degree to which a buyeris willing to acquire a product from an online application (Pavlou, 2013). George(2004)statedthatonlineshoppingisconsideredasthefrequencywithwhichcustome rs buygoods orserviceson socialnetwork.Comparedwith theWebsite and traditionalsaleschannels,onlinepurchasesonsmartphoneapplicationshaveadvancedan d unique forms (ChristianFuentes et al., 2017).

Fromthere,itcanbeunderstoodthattheintentiontopurchasethroughtheapplication in this research is the willingness to perform the behavior of purchasingproductson OFDapplications.

Online fooddelivery (OFD) is definedas“the processwhereby food thatwasordered online is prepared and delivered to the consumer” (Li et al., 2020) Theexpansion of OFD services has been enhanced by various OFD brands in Vietnamsuch as Grab Food, Gofood, Baemin, etc “OFD services are the process of orderingfooddirectlyfromlocalrestaurantsthroughmobileapplicationstogetfooddeliver ed to a specific location” (Bagla & Khan, 2017) When a customer makes anorder from a restaurant via OFD app on mobile phone and choose payment method,the meal will be prepared and then delivered to the customer by a delivery driver.Customers willusetheapptofollowtheorder statusandcontactthedrivers.

Pigatto et al.(2017) admitted that OFD services allow customers to select their ownrestaurants, favorite food and wait for the meal to be delivered Furthermore, due toCOVID-19, the OFD market has witnessed the increase in customer rate as a resultofsocialdistancingdirectivesandsafeoperatingmethod(Maida,2020).OFDservices provide numerous advantages for the customers such as no travelling, notime-consumingandno mistakenorders(TheOtherStream,n.d.).

Chai & Yat (2019) also mentioned that OFD services offer meals from a diversechoice of restaurants at various times and locations conveniently. Furthermore, itdisplays the latest and most accurate information about the restaurants, promotion,food selection, online reviews and feedbacks of previous customer’s experience, oreventrackstheirorderstatusandlooksupforthedeliverydriversp o s i t i o n (Alalwan,2020).DuetothedevelopedtechnologiesofOFDcompanies,theF&B industry can be able to enlarge their market, strengthen client relationships,boostproductivityandminimizemistakenorders(See-Kwongetal.,2017).

Theoreticalperspectives

 Need recognition :customers recognize their own needs and want to satisfythem Those needs arise in daily life, probably from personal desire orcommercials,friendsoffriends,etc.

 Information search : when customers have interests in a product, they willseektheproductinformationthroughpersonalsources,advertisements,frien ds,theInternetandsoon.

 Evaluation of alternatives : customers will use the available information,based on the wanted product characteristics to evaluate between brands andmakethefinaldecision.

 Purchase decision : Purchase intention could not turn into Actual purchaseif one of these two factors occurs: Attitudes of others; and Unexpectedsituations.

 Post-purchase behavior : after using the product, customers will evaluatethroughmanya s p e c t s (price,quality, )toconsidertheirintentiont o continueusingor notusingtheproductin the future.

Previous studies have applied several theories to investigate customer intention,especially Theory of Planned Behavior (Ajzen, 1991) which is an updated andexpandedversionofTheoryOf Reasoned Action–TRA (Fishbein& Ajzen,1975).

The TRA model has only two factorsa f f e c t i n g i n d i v i d u a l i n t e n t i o n a n d b e h a v i o r that are Attitude (ATT) and Subjective Norms (SN), whereas, the TPB model hasadded the third factor, Perceived Behavioral Control (PBC).

TPBtheory,consumershavefactualbasisandmotivationintheirdecision- makingprocess and give reasonable choice among various options The best method topredict individual behavior is when the intention and behavior are determined by aperson'sBehavioralIntention(BI).The3keyfactors including:

 Attitudes :referstoanindividual'sextentoffavorableorunfavorableassessment of a behavior, a person's evaluation of the outcomes obtainedfrommakingaspecificbehavior.

 Subjective Norms :A person's perception is influenced by the opinions andjudgmentsofimportantpeople(parents,spouses,friends,etc.)thatthe behaviorshouldor shouldnotbe performed.

 Perceived behavioral control :relates to an individual's perception of theease or difficulty of carrying out a behavior; it depends on the resourcesavailabilityand thesituation to performthebehavior.

Perceived Risk (opposite of Perceived Safety) is identified as the level of risk thatconsumers believe in terms of purchasing on e-commerce platform and it negativelyaffectcustomerintention.

Additionally, it also the main factor prevents the desire stage convert to actualpurchase during the buying process The use of technology frequently entails risks,whichincludetwoaspects:

 Product Quality Risk :the possibility that products received by customeraredifferentfromtheimageontheweb/app,theproductqualityislow,etc

 Online TransactionRisk :possibleriskswhencustomersmakee- commercetransactionsonelectronicdevices(passworddisclosure,datatheft,finan cialfraud,insecurityinthepaymentsystem,etc.)

TheT P R m o d e l i s u s e d a s t h e t h e o r e t i c a l b a s i s i n m a n y r e s e a r c h e s o n p u r c h a s e intent and behavior; recently applied to OFD applications, and it is proved thatPerceivedRiskhasanegativeimpactonOnlinePurchaseIntention.

Davis (2000) developed the Technology Acceptance Model to explain and predicttechnology adoption and actual use The TAM model based on the TRA theory,examinestherelationshipandinfluenceofrelevantfactors.

It can be seen that Perceived Usefulness and Perceived Ease of Use impact onBehavioral Intention and finally leads to Actual System Use In addition, these twofactorsareaffectedbyExternalVariables:

 Perceived Usefulness :is defined as “the degree to which a person believesthat using a particular system would enhance his or her job performance”(Davis,1989). o Communication:link all thesubjectstogetherinaninformation system. o Systemq u a l i t y : a h i g h q u a l i t y s ys t e m w i l l h e l p t o c o n d u c t o n l i n e t a s k performanceeasilyandextractinformationeffectively. o Informationquality:the outputthatcomesfromthesystemneedstobe trustworthy,accurate,andtimely. o Servicequality:the systemhastoprovidearesponsiveandsecureexperienceto maketheusersfeelconvenientduringtheusingprocess.

 Perceived E a s e o f U s e :r e f e r st o “ t h e d e g r e e t o w h i c h a p e r s o n b e l i e v e s that using a particular system would be free of effort” (Davis, 1989, p.320).Besides, this also depends on various external variables such as: ability touseelectronicdevices, usingexperience,knowledge,traininglevel,etc.

Li and Zhang (2002) proposed a model of ten factors in the context of onlineshopping in order to define the connections between these variables.It is clear thatAttitudetowardsOnlineShoppingaredirectly impactedby

5factors(ExternalEnvironment,Demographics,PersonalCharacteristics,Service/ ProductCharacteristics, and Web/App Quality) In which, Service/Product Characteristicsand Web/App Quality have direct influence on Consumer

Satisfaction The diagramclearlydisplaysth ep roc essst ages co mp ri sed of :A nt ece den ts, A t t i t u de , In te nt io n,

Decision Making, and Online Purchasing Consumer Satisfaction stands separatelyandexistsinallstagesrelyingonthecustomer'sparticipationintheonlinepurcha singprocess,andthistwo-wayrelationshipcould affectbackandforth.

Empiricalstudies

Chanmi et al (2021) analyzed the influence of six factors (Perceived Usefulness,PerceivedEaseofUse,PriceSavingBenefit,TimeSavingBenefit,SafetyPerc eption, and Trust) on Customer Intention to use OFD (CIU) during COVID-19.The research collected data from 1045 consumers over 18 years old in U.S.A Theresult showed that, inthe context ofpandemic, all ofthe factors, substantiallyinfluenceonCIUinpositive way andPerceivedUsefulnesswasthestrongest.Customers are more willing to use OFD if they believe it would be beneficial tothem.Trust wasthe second s t r o n g e s t fac to r, customersare m o r e concern edab ou t the food quality and the order accuracy whether it is as good as the food served atthe restaurant or not.

Furthermore, young customers (Y/Z Generation) are moreinclinedtoutilizeOFDthanelderlypeople(BabyBoomersGeneration).Additionall y, both Price Saving Benefits and Time Saving Benefit considerablyenhancedCIU,customersrealizedthatOFD bringsmorebenefitsthanrisks.

Saqib et al (2021) evaluated the impact of 4 factors on OFD usage intention in thecontext of COVID-19 The results showed that Innovativeness and Optimism havepositive effects on customer intentions significantly In developing countries,peopleare more willing to experience new innovative technology and they believe that itprovides a great deal of efficiency and flexibility (time and cost saving, better price,etc.).Incontrast,DiscomfortandInsecurityhavenegativeeffects,d u e t o individu als who are anxious and hesitant to utilize OFD apps, especially the elderlygroup(lack of experience and control over smartphones) Customers are reluctant tobuy food online since the internet is still a risky platform due to the absence ofphysicalinteraction.

Sangeetaetal.(2020)identifiedthecharacteristicsofIndianconsumersw h o ordered food through OFD apps and those who did not during the epidemic Thestudy collected data from 462 respondentsand six factors were investigated toanalyze the key distinctions between the two types of OFD customers The positiveresults of Purchase Frequency, Perceived Benefits and Product Involvement revealthat customers who have those characteristics are tend to use OFD services morefrequent In contrast, there is a reduction in the intention of customers who ownPerceived Threat Moreover, the OFD purchasing rate of young generation is 56%higher,comparetotheoldgroup.

(2017)investigatedtherelationshipbetween7variablestowards OFD services usingtheTAMmodel.Theoutcomei m p l y t h a t with a higher perception of Post-usage Usefulness and Convenience Motivation,customers’ attitude and OFD usage intention will increase substantially Due to thefact that customers can be able to order hot meals at anytime and anywhere withouttravelling,thefactorTimeSavingisalsoincluded.Subsequently,Perceiv edSafetyis considered to have noticeable effect on customer intention They tend to have apositive attitude when OFD apps can offer them with entertainment and enjoyment.Therefore, companies can run sales promotions to attract more customers with goodpricesand discounts.

The study of Chetan et al (2019) examined the factors that affect on ConsumerBehavior toward OFD services through an intimate relationship with ConsumerAttitudes The research data was gathered from 170 customers in food retailingindustry across India The result showed that elements like Convenience, Controland Ease of Information have positive relationship and the most significant impactonCustomerSatisfaction.ThisillustratesthefactthatwhenusingOFDapps,customer s can easily order food with just a few clicks regardless of time and place.They are being able control by having their own decisions in terms of restaurants,foods e l e c t i o n , p a y m e n t m e t h o d , e t c M o r e o v e r , O F D a p p s p r o v i d e v i s u a l f o o d information and real-time order tracking quickly However, Technology Anxietyfactorhasanegativeresult,itmeansthatagroupofelderlycustomershavedifficult iesinperformingtasksonsmartphoneswhileorderingfood.

Annaraud & Berezina (2020) inspected Customer Intention to utilize OFD servicesthrough the assessment of Food Quality, Fulfillment, Customer Service, Control andConvenience.Theauthorcollecteddatafrom303respondentswhofrequentlyexperience OFD platforms (Uber Eats, Seamless, GrubHub, etc.) across America.Theresearchcameupwithseveralpracticalimplicationstoenhancecustomersatis factiontowardsOFDservices.Restaurantsshouldequipheatpreservationdeviceandfirmpa ckaginginordertomaintainthefoodqualityintheb e s t condition Besides, staffs need to prepare food precisely to ensure order accuracyand avoid wrong food items being delivered OFD brands have to simplify theorderingprocessaswellasdisplayinformationsufficientlytoincreasethecredibility.

A chatbot or a call button should also be integrated into the applicationsothatcustomerscanreportproblemspromptly.

Yen (2015) has published the topic "Research On The Factors Affecting The OnlineShopping Intention of Consumers" With the aim of discovering and evaluating thefactors that have significant impact on the online shopping intention of consumersthrough internet platforms Consequently, it then allows the later researchers to referandutilizethesefindings.ThefollowingfactorsthathasmajorimpactontheConsumerOF DPurchaseIntentwerepointedoutinthestudy:(1)PerceivedConsumer Benefits; (2) Usability; (3) Perceived Safety; (4) Subjective norm Theauthor conducted the research through a series of analysis and regression equationswiththedataof244observedsamplesinVietnamOFDmarket.

Khoa (2019) has discovered several factors affecting Online Purchase Decisions onsmartphonea p p l i c a t i o n s i n H C M C T h e r e s e a r c h u s e s t h e T A M t e c h n o l o g y acceptance model (Davis, Bagozzi & Warshaw, 1989) Along with the EFA andCronbach'sAlphaanalysison300samplesfromcustomerswhoshoponlinefrequently Theobtainedresultsindicates:UsefulProperties;EaseofUse;Infrastructure

Conditions; Return Policy have positive impact (+) on the Decision toPurchaseO n l i n e o n s m a r t p h o n e a p p l i c a t i o n s I n c o n t r a s t , P e r c e i v e d R i s k h a s a negativeimpact(-)onCustomerDecisiontopurchaseonline.

In the context of Covid-19, Lien & Trang (2021) conducted research on the topic"Factors Influencing The Online Shopping Intention of Consumers In Ho Chi MinhCity During Covid-19 Period” The study is directly related to the modification thatthe pandemic has affected on Customer Buying Intention A total of 200 observedsamples were processed and tested by analytical methods The outcome reveal 5factors that influence substantially on Customer Purchase Intention including: (1)Perceived Usefulness; (2) Reference Group; (3) Safety & Security; (4) Reputation.On the contrary,(5)PerceivedRisk is considered asthe elementh a s n e g a t i v e impactoncustomerintention.

Trang(2021)conductedastudytoidentifythefactorsthataffectConsumersIntention to use Beamin application to order food online A total of 178 responses inHCMC were gathered for the research data Subsequently, thed a t a w a s p r o c e s s e d bySPSS,exploratoryfactoranalysistechniques,reliabilitytesting,d i f f e r e n c e testing and building regression equations Together with the use of inheritancemodel and TAM model, the author has made the following conclusions:

(1) ThePerceived Usefulness factor has the strongest impact on the intention to use Baeminapplication,followedbythefactors(2)SocialInfluence,

Factors affectingcustomeri ntention to useonline fooddelivery servicesbefore andduring theCOVID-19 pandemic.

PerceivedUsefuln ess;Perceived Ease ofUse; Price SavingBenefit;

TimeSaving Benefit;SafetyPer ception;Trust& OF D

All of the factorspositivelyinfl uence onOFD UsageIntention.

FoodDeliveryOr dering(OFDO)se rvicesin Pakistan: theimpactofCovid

Innovativeness; Optimism havepositive effects,meanwhile,Dis comfort;Insecu rity havenegative effectsonOFDUsag e

Customersrespo nse toonline fooddeliveryserv ices duringCOVID-

Age;PurchaseF requency;Affecti ve andInstrument al

PurchaseFr equency;Pe rceivedBenef its;

Benefits;Perceive dThreat;ProductInvo lvement &OFD UsageIntention.

Involvementimp act positivelybut PerceivedThreat impactnegatively onOFDUsage Intention.

Consumerexper iences,attitude andbehavioralin tentiontowardOnli ne FoodDelivery

ConvenienceMoti vation; Post- UsageUsefulness;Pe rceived Safety;Price and TimeSaving;

Withahigherp erception ofPost- usageUsefulne ss;Convenien ceMotivation

;PerceivedSa fety,Custome rsAttitude willincrease significantly.

Understandingco nsumerbehaviort owardsutilization ofonline fooddeliveryplat forms.

Convenience;Co ntrol; Ease ofInformationha vepositive effectson CustomerSatisfa ction andOFD UsageIntention.

Predictingsatisf action andintentionsto useonline fooddelivery:

Food Quality;Fulfillme nt;CustomerServic e;Control;Convenien ce &Behavioral Intention.

All of thevariablesimp actstrongly on bothCustomerSatisfa ction andBehavioral Intentions.

Research on thefactors affectingthe onlineshoppingint ention ofVietnameseco nsumers.

PerceivedConsum erBenefits;Usabili ty;Perceived Safety;Subjective norm&Consumer OFD

Research onfactors affectingonline purchasedecisions onsmartphoneap plications inHCMC.

EaseofUse;Percei vedRisk;Infrastruct ureConditions;Re turn Policy

All of the factors(exceptPerc eived Riskhas negativeresult) havepositive impact(+) on OnlinePurchase Decisionon Smartphone.

HCMC consumers in theperiod of Covid-19.

(2021) Model(TAM) Reputation;Percei ved Risk;Reference Group;Safety andSecurity&Onli neShopping

Intention. positively,where as,Perceived Riskimpactnegat ively onOnlineShoppin g

PerceivedUsefuln ess;SocialInfluence;Pe rceived Ease ofUse;

All of the factors(exceptPerc eived Risk)have significanteffect s onBaemin UsageIntention.

Researchgap

After reviewing the studies with related topics to this thesis, discussed below areseveralresearchgapsthatarenoted:

 Internationalstudies:althoughOFDserviceintentionandbehaviorh a s been thoroughly researched in developed countries (US, UK, India, etc.),thereisa lackofstudyonOFDservicesinVietnam.

 Studies in Vietnam:the majority of studies mainly focused on the overallonline shopping intention, yet, OFD usage intention has not been specificallyinvestigated.Ont o p o f t h a t , there a r e n os i m i l a r r es e a r c he s t h a t h a v e bee n conducted during the COVID-19 pandemic in HCMC, therefore, differencesmightbeavailablewhen comparing thisstudytoothers.

The author conducted this thesis "Factors Affecting Customer Intention ToUseOnline Food Delivery (OFD) Services In HCMC" with the aim of filling the aboveresearchgapsandcontributingtothedevelopmentof theOFDindustryinVietnam.

Researchmodelandhypothesesdevelopment

Amongallofthereferencemodels,theTAMModelisusedinmostoftheresearches regarding to technology, e-commerce and online shopping Therefore,this study utilizes the TAM Model of Davis, Bagozzi & Warshaw (1989) as thefoundation to build and develop the research model with the first two main factors:Perceived Usefulness and Perceived Ease of Use In addition, through the review ofprior studies related to OFD Usage Intention, other variables such as SubjectiveNorm,PerceivedSafetyandPerceived

Based on the inheritance of previous studies, the author decided to choose 5 factorsaffectingCustomerIntentiontoUseOFDinHCMC,including:

Perceived Usefulness (PU) is “the prospective user’s subjective probability thatusing a specific application system will increase his or her job performance withinan organizational context” (Davis, Bagozzi & Warshaw, 1989) Punj (2012) foundthat young customers tend to prefer online shopping because it prevents them fromtime- consumptiona n d t h e l o s s o f o p p o r t u n i t y c o s t s P r e v i o u s r e l a t e d s t u d i e s h a s alsoindicatedthatPUhasasignificantpositiveimpactoncustomerintentiontowards OFD usage If the consumers realize that OFD applications help them tosave time, reduce efforts and provide flexibility, they will form the intention to useOFD services In this research, PU is understood as the extent to which customersbelieve that using OFD service is useful to order food online As a result, hypothesisH1isproposed:

H1:Perceived Usefulness has a positive effect (+) on Customer Intention toUseOFD. b) PerceivedEaseofUse–PEU

Perceived Ease of Use (PEU) refers to “the extent to which customers believe that aparticularsystemcanbeusedwithoutefforts”(Davis,1986).Accordingt o Ramayah and Ignatius (2005), if the web/app interface is straightforward to accessandeasytooperate,customerintentiontopurchaseonlinewillincreasesubstantiall y Prior studies have concluded that PEU has a significant positive effecton willingness to use OFD services The higher the PEU, the stronger the OFDUsage Intention (Roh & Park, 2019) Aneasy- to-useapplication will stimulatecustomers to shop and attract potential customers to utilize Therefore, hypothesisH2isproposed:

H2:Perceived Ease of Use has a positive effect (+) on Customer Intention to UseOFD. c) SubjectiveNorm–SN

I n f l u e n c e , a s thep e r c e i v e d s o c i a l p r e s s u r e t o e m b a r k o r n o t t o e m b a r k a b e h a v i o r S N c o m e s from opinions and judgments of reference groups (parents, spouses, friends, etc.)whether that the behavior should or should not be performed, it may increase orreduce the customer intention There is a intimate relationship between SN andbuying intention (Chang,

1998) For instance, in the studies ofY e n ( 2 0 1 5 ) a n d Trang (2021), the results showed that SN has a considerable impact on the intentiontouseOFDservicesof customers.Thus,hypothesisH3isproposed:

H3:SubjectiveNormhasapositiveeffect(+)onCustomerIntention to UseOFD. d) PerceivedSafety–PS

PerceivedSafety (PS)referstoan individual'sperceptionoftrusta n d c e r t a i n t y when engaging in a particular behavior (Dowling, 1994) It is considered to be theoppositedefinitionofPerceivedRisk.Pavlou(2003)classifiesrisksinto:fin ancial risk, product risk (low quality), privacy risk (personal info illegally disclosed) andsecurity risk (credit card info stolen) Bhatnagar et al (2000) implied that if theabove risks can be minimized, PS will increase, so will the intention to shop online.In terms of big and familiar OFD brands, customers often feel safe and secure whileusing the services It can be concluded that PS has a positive influence on customerintention.Subsequently,hypothesisH4isproposed:

H4:PerceivedSafetyhasapositiveeffect(+) onCustomerIntention to UseOFD. e) PerceivedPrice–PP

Perceived Price (PP) is not a real price of a product, it is the price assessed by thecustomer Depends on their personal evaluation, the product will be “cheap” or“expensive”in their perceptions (Kashyap & Bojanic, 2000) According to Jiang

&Rosenbloom(2005),becauseconsumerscannotviewtheactualproductwhenpurchase online,theyoftenmeasureproductquality byprice.RelatedtoOFDservices, customers believe that ordering meals through applications will be money-saving and able to compare prices because there are a lot ofpromotions and diverseselectionofrestaurants(Hasslingeretal., 2007).HypothesisH5 isproposed:

Researchprocess

This thesis is conducted through 6 phases and each step ensures the objectivity andgeneralityofthetopic Theresearchprocessis presentedinfigure3.1asfollows:

Firstly, the author defines objective, questions, scope and subjects of the research.The next step is literature review comprises of: concepts, theoretical perspectivesandempiricalstudiesreview.Afterthat,theresearchmodelandthehypothe seswill be presented based on the previous foundation Qualitative research identifies thecomponents and adjusts the scales, whereas, quantitative researchmeasures theinfluence of factors on OFD Usage Intention Data are collected and then analyzedusingSPSS20.0.Thefinalstepis tocompleteandrevisethewholestudy.

Scaledevelopment

In order to conduct the scale development, the author has inherited based on theTAMModelandreferredtotheavailablescalesfrompreviousstudies.

The study is measured using 5-Point Likert Scale in which respondents express theirdegreeofagreement(1-Stronglydisagree;2-Disagree;3-Neutral;4-Agree;5

I order food on mobileappstosavemoreti me andefforts.

I think that using OFDservicessavesmoreti me andefforts.

Icaneasilycompareamongdiffe rent options(restaurants, meals,etc.)on OFDapps.

I can order food on mobileapps anytime andanywhere.

I think that using OFDservices helps me to orderfoodatanytimeand anywhere.

Ireceiveplentyof information(promotions, new menus,etc.)through OFDapps.

OFDservices because myfamily, friends andcolleaguesareusingOF

SN3 The current society thinksOFDisagoodoption.

IintendtouseOFDservicesbe causethecurrentsociety (people, media,internet,etc.)thinksOFD is agoodoption.

SN4 Orderingfoodonlineis Ithinkthatusing OFD suitableforthecurrent situation. servicesissuitableforthe currenttrendandsituation.

PS1 OFDservicesaresafeto use   Tu Thi

I feel secure when makingpaymentsthroughfamil iar

The more detailed andclear the restaurant/dishesinformation, the safer IfeelwhenusingOFD services.

PP3 Reasonableprices PricesofOFDservicesare reasonableto me.

I will recommend othersto order food on themobileapp(s)thatIuse.

Qualitative research

The purpose of qualitative research is to explore, supplement and modify the scalesofobservedvariablestoconductquantitativeresearch.Thereare2stagesinqualitati ve research, including:(1) Proposing research model and preliminary scalebasedontheoreticalbasisandreviewofrelatedstudies;(2)Theauthorw i l l perform one-to-one discussions with 5 OFD customers to adjust the preliminaryscale.The 5 participants are customers who have more than 2 years of experience inorderingfoodonlinethroughOFDapplications.

Inordertomakethescalemoreadequateandstraightforward,one-to-onediscussions will be performed to specify needed modifications for the preliminaryscale:

 Participants will be given a qualitative research questionnaire consisting oftwoparts(Appendix1:DiscussionOutline).Inthefirstpart,theywill be askedtoindicatethefactorsaffectingcustomersintentiontouseOFDservices in HCMC (base on individual experience and viewpoint) In thesecond part, participants will provide suggested adjustments and personalopinions about the scales statements whether they are clear and easy tounderstand.

 After completing the interview, the author will modify the questionnairebasedonthecollecteddata.

 Discussagainwiththeparticipantsusingmodifiedscales.Whenthediscussionqu estionsgivethesameresultsrepeatedlywithoutanynewchanges,thequalitativerese archprocess will end.

Participantsevaluatethescalesstatementstoseeifthey areclearandeasy tounderstand, and whether there is any need to add, remove or modify any variables.Themajority of opinions agreed with thedeveloped scales of factors affectingcustomers intention to use OFD services in HCMC Some comments indicated thatthe statements need to add and refine a few words to make the sentences morecoherent,detailedandeasiertounderstand,thus,therespondentsdonotg e t confuse dormisinterpretthequestions.

In order to finish the qualitative research process, after synthesizing the collecteddata,theauthormodifiedthescaleandcompletedthequantitativesurveyquestio nnairewith24observedvariablesintotal(Appendix2:SurveyQuestionnaire). a) PerceivedUsefulness(PU)scale

PU refers to the extent to which customers believe that OFD services gain manybenefits for them The preliminary scale consists of 4 observed variables, apart fromwordadjustments,therearenochangesafterthequalitativeresearch pro cess.The

PU2 Icaneasilycompareamongdifferentoptions(restaurants,meals, etc.)onOFDapps.

PU3 I think that using OFD services helps me to order food atanytimeandanywhere.

PU4 Ireceiveplentyofinformation(promotions,new menus, etc.)throughOFDapps. b) PerceivedEaseof Use (PEU)scale

PEU is defined as the degree to which customers believe that there are no effortsneeded to use a particular system There are a few corrections were made to get thestatements more accurate The preliminary scale includes 4 observed variables andthere are no changes after the qualitative research process The PEU modified scaleispresentedasfollows:

PEU4 Icaneasilyorderandreceivefoodthrough OFDapps. c) Subjective Norm(SN) scale

SNi s u n d e r s t o o d a s t h e p e r c e i v e d s o c i a l p r e s s u r e t o e m b a r k o r n o t t o e m b a r k a behavior The preliminary scale consists of 4 observed variables, apart from wordadjustments, there are no changes after the qualitative research The SN modifiedscaleispresentedasfollows:

SN1 Myfamily,friendsandcolleagues supportme usingOFDservices.

SN3 I intend to use OFD services because the current society(people,media,internet,etc.)thinksOFD isa goodoption.

PSreferstoanindividual'sperceptionofuncertainty andtheconsequencesofengaging in a particular behavior After the qualitative research process, 4 observedvariables remain unchanged Only one statement needed word addition to be moredetailed.ThePSmodifiedscaleispresentedasfollows:

PS4 The moredetailedand cleartherestaurant/dishesinformation,thesaferIfeelwhenusingOF

PP is the price that assessed by the customer, the product is “cheap” or

“expensive”dependsontheirpersonalevaluation.Thereisnostatementadditio nsordeletions,sothenumberofobservedvariablesremainsthesameat4.ThePP modifiedscaleispresentedasfollows:

PP4 PricesareclearlypresentedonOFD apps. f) Customer IntentiontoUseOFD (CIU)scale

CIU is defined as the customer intention to continue using or will use the OFDservices The scale stays unchanged with 4 observed variables after the qualitativeresearch.Rewritethevariable“IamconsideringusingOFDappstoorder food”to“I will use OFD apps to order food” The CIU modified scale is presented asfollows:

Inconclusion,themodel“FactorsaffectingcustomerintentiontouseOFDservicesinHCM”uses7componentsand24observedvariablesintotal.

Quantitativeresearch

Quantitative research aims to evaluate the influence of factors through a processconsists of data collection, data analysis and descriptive statistics The data, afterbeing collected from the survey questionnaire, will be analyzed to assess the scalereliabilityandvalidityas wellas totest themodel fit.

The sampleofthis study is selected by theQuota sampling (anon- probabilitysampling method) According to Hair et al (2014), the minimum sample size to usetheEFAmethodis50,preferably

100ormore.Theratioofobservationsp e r analytic variable is 5:1 or 10:1, some researchers suggest that this ratio should be20:1.“Numberofobservations”simplymeanstherequirednumberofvalidquestionnai resand“Measurementvariable”isameasurementquestionint h e survey.

By selecting targeted survey respondents who have known and are intending topurchase through OFD applications, the author will conduct the survey and collectresearch data In this study, a survey with 24 questions using 5-Point LikertScale(correspondingto24observedvariables belongingtodifferentfactors),t heauthor applied the formula of Hair et al (2014) with a ratio of 5:1 Therefore, the samplesize being used in this study is 200 in order to ensure the minimum reliability toperformthedataanalysis.

The data collection method of this study is using a questionnaire designed by theauthor from the proposed research model to interview the survey respondents. ThesurveysubjectsarecustomerswhohavehadOFDserviceuseexperienceandcustomersw hoareintendingtouseOFDserviceinHCMC.Thesurveywasconducted during the Covid-19 outbreak, thus, in order to ensure the safety, surveyforms were sent online or delivered directly to the respondents After finalizing thequestionnaire, Google forms were sent via social networks, emails and then theauthorgathered completed responses.

 Step 1 :Collect the responses, remove invalid ones (same point for allvariables or blank answers); then data is encrypted, entered, cleaned andanalyzedusingSPSS20.0software.

 Step 5 :Multivariable regression analysis and test the hypothesesSeveralanalyticalmethodswillbeusedinthisstudy,including:

Cronbach Alpha coefficient The larger the Cronbach Alpha coefficient, the higherthe internal consistency reliability As stated by Tho & Trang (2007), CronbachAlpha analysis needs to be done to eliminate inappropriate variables because theycangeneratedummyvariables.

The Cronbach Alpha reliability coefficient does no indicate which variables shouldberemovedandwhichshouldbekept,itonlyshowswhetherthem e a s u r e d vari ablesarerelatedornot.Therefore,combiningwithCorrectedItem-TotalCorrelation is necessary to exclude variables that do not contribute much to theconceptto bemeasured(Trong&Ngoc, 2005).

 CronbachAlphaReliabilityCoefficient:higher than0.8isagoodscale;from

0.7 to 0.8 is usable; lower than 0.6 can be used in case the research concept isnew or new in the research context (Nunnally, 1978; Peterson, 1994; Slater,1995;citedbyTrong&Ngoc,2005).Theauthorselectsscalesw i t h Cronbac h'sAlphacoefficientofatleast≥0.6inthisresearch.

 Corrected Item-Total Correlation: The higher this index, the stronger thecorrelation between the observed variable and the remaining variables. Hence,theobservedvariableswhichhavethisindexlowerthen0.3mustberemoved.

EFA analysis is used to reduce a set of many interdependent measurable variablesinto a smaller set of variables (called factors) so that they are meaningful but stillcontainstheoriginal information content(Hair et al.,2009).

 Method :For multidimensional scale, use Principal components analysis withVarimaxrotationandbreakpoint whenextractingfactorswithEige nvalues≥

1.Forunidimensionalscale,thePrincipalComponentsfactorextractionmethod is used When the total variance extracted is ≥ 50%, then the scale isacceptable(Tho & Trang, 2007).

 Standard :Factor loadings must be ≥ 0.5 to ensure the practical meanings ofEFA The value levels of factor loadings: > 0.3 is the minimum acceptablelevel; > 0.4 is important; > 0.5 is practical significance Criteria for choosingthe value of factor loadings: if the sample size is at least 350, it is possible tochoose factor loading > 0.3; if the sample size is about 100, the factor loadingshould be>0.55;and ifthe samplesizeisabout 50,thefactorloadingmustbe

Fromtheabovetheoreticalbasis,themodelof“FactorsAffectingCustomerIntention To Use OFD Services In HCMC” with 24 observed variables using EFAanalysis as follows In this study, the author will use Principal components analysiswith Varimax rotation and breakpoint when extracting factors with Eigenvalues > 1.Consequently,relatedrequirementswillbe tested,compriseof:

In order to test the hypotheses, satisfactory scales are put into Pearson correlationanalysis and regression analysis Pearson correlation analysis is used to examine thelinear relationship between the dependent variable and the independent variables Ifthe absolute value ofPearson gets closer to 1, the stronger the linear correlationbetween these two variables Correlations between the independent variables mustalsobeinvestigatedduetothefactthatsuchcorrelationsmayimpactsignif icantlyon the results of regression analysis, leads to multicollinearity (Trong & Ngoc,2005). b) Multivariateregressionanalysis

Enter method of multiple regression will be applied to measure the factors affectingcustomerintentiontowardsOFDservices.

 R 2 and adjustedR 2 areusedto assessthemultivariable regressionmodelfit.

Finally,toensurethemodelreliability,theauthorwilldetectthefollowingassumption violations The assumption of the independence of the residuals throughthe Durbin-Watson statistic has a value from 0 to 4 After the regression analysis,Independent samples T-test is used for theGender variable; One-Way ANOVA testfor Occupation and Income variables to examine the difference in satisfaction ofcustomers intending to purchase throughOFD application by Gender, OccupationandIncome.

Chapter 3 presents preliminary scale development and detailed research methods. Inthequalitativeresearch,usingtheone-on- onediscussiontechnique,severaladjustmentsweremadeforthescalerefinement.Inthequ antitativer e s e a r c h , throughasurveybythequestionnaire,theauthorcollectedd atawithasamplesizeof2 0 0 O F D c u s t o m e r s T h e s c a l e o f f a c t o r s a f f e c t i n g c u s t o m e r i n t e n t i o n t o u s e OFD services includes 5 components based on 24 observed variables By usingSPSS20.0,dataisencrypted,entered,cleanedandanalyzed.

Inaddition,thischapteralsoindicatesrelatedsectionssuchassamplesizecalculation and data analysis methods (reliability assessment, EFA & regressionanalysis).

In this chapter, the research results are presented with the following contents:descriptionofsurveysamplecharacteristics,scalereliabilitytesting,resultsofc orrelation analysis and regression analysis; and hypotheses testing In addition,chapter 4 also inspects the difference between demographic categories towardsOFDUsageIntentionofcustomers.

Characteristicsofthesurveysample

The sample was collected through the survey questionnaire and was conductedduring the period from April 19th to May 9th, 2022 A total of 300 questionnaireswere distributed, of which 100 were delivered directly to the respondents and 200Google forms were sent via social networks and emails The survey results obtained236 responses (offline: 83 & online: 153) Discard 36 unsatisfactory responses dueto issues such as: answers are duplicated; blank or not fully - filled sheets Inconclusion, there are 200 valid answer sheets to be used for the research analysis.Table4.1presentsthestatisticalanalysisofcollectedsample:

Table4.1.Characteristicsofthesurvey sample Characteristics(n 0) Frequency Percent(%)

AccordingtothesurveysampleanalysisresultsinTable4.1,totalof200participants took part in the survey Base on the Gender Statistics, the percentage ofmen using OFD services is 51% and women is 49% It can be seen that there is notmuch of a difference (only 2%) between the two gender groups in terms of orderingfoodonline.

Age Statistics reveals the two age groups that are 18 – 25 and 25 – 30 accounted forthemajoritywiththerateof23%and69.5%respectively.Mostoftheparticipa ntsin these two age groups have Internet knowledge and experience in using OFDservices Meanwhile, the above

30 age group accounted for the lowest rate with7.5%.Themainreasonmightbethelackoftechnologyadoption intheelderly.

Occupation Statistics points out the two groups with highest proportion are officestaff with 68.5% and civil servant with 15.5% There are some records indicate thatemployees working at companies tend to order food online through OFD appstogether to utilize promotions and preferential shipping price The remaining 3groups, including freelancer, housewife and others, account for low rates of 9.5%,2.5%and4%,respectively.

Through the Income Statistics, it is clear that the 10 – 15 million group has the mostOFD service users, which constituted the highest proportion with 78% and the 5– 10 million group ranked second with 13.5% People in these two income groups aremostly officestaffs- theprimary OFDcustomers.Thelowestistheunder5million groupw it ht he n u m b e r of s u r v e y participantsbei ng 0 Th e r e m a i n i n g tw og r o u p s arethe15–20million group(6.5%)andtheabove20milliongroup (2%).

Code N Minimum Maximum Mean Standard

ThepointofagreementwithobservedvariablesinthePUgroupmostly variesfrom 3.7to4.0,inthe“4”intermediaterangeoftheLikertscale.Therefore,OFDcustomersinHC MCagreewiththeviewpointsinPerceivedUsefulnessscale.

The point of agreement with observed variables in the PEU group mostly variesfrom 3.9 to 4.0, in the “4” intermediate range of the Likert scale Therefore, OFDcustomersinHCMCagreewiththeviewpointsinPerceivedEaseofUsescale.

ThepointofagreementwithobservedvariablesintheSNgroupmostly variesfrom 3.4to3.8,inthe“4”intermediaterangeoftheLikertscale.Therefore,OFDcustomersinHC MCagreewiththeviewpointsinSubjectiveNormscale.

The point of agreement with observed variables in the PS group mostly at 3.0, in the“4” intermediate range of the Likert scale Therefore, OFD customers in HCMCagreewiththeviewpointsinPerceivedSafetyscale.

ThepointofagreementwithobservedvariablesinthePPgroupmostlyvariesfrom 3.3to3.6,inthe“4”intermediaterangeoftheLikertscale.Therefore,OFDcustomersinHC MCagreewiththeviewpointsinPerceivedPricescale.

The point ofagreement withobservedvariables intheCIUgroupm o s t l y v a r i e s from 3.4 to 3.7, in the “4” intermediate range of the Likert scale.Therefore, OFDcustomers in HCMC agree with the viewpoints in CustomerIntention to Use OFDscale.

Testingthescalereliability

Before conducting EFA exploratory factor analysis, the first step is to test the scalereliability.Cronbach'sAlphaanalysisteststhedegreeofcorrelationbetweenobserve dv a r i a b l e s i n o n e f a c t o r a n d e x a m i n e s w h e t h e r t h e y a r e r e l i a b l e o r n o t

Moreover, it indicates which of the observed variables has participated in measuringthe factor's concept, and which has not If the Cronbach's Alpha value is ≥ 0.6, thescale will be acceptable. Besides, observed variables which have Corrected Item- TotalC o r r e l a t i o n v a l u e ≤ 0 3 m u s t b e r e m o v e d ( N u n n a l l y & B u r n s t e i n , 1 9 9 4 ) Using the statistical data processing SPSS 20.0 software, each observed variable isputintothetestandgivesthefollowingresults:

Cronbach'sAl pha if ItemDeleted

Customer IntentiontoUseOFD(CIU):Cronbach’sAlpha=0.908

BasedontheanalysisresultsofTable4.3,allofthecomponentscaleshaveCronbach's Alpha coefficient higher than 0.6, it means that the observed variableshave strong correlation with each other in one same factor Additionally, all of theCorrected Item-Total Correlation values are greater than 0.3 (ranges from 0.333 to0.859),thus,allvariablesareaccepted.

Duringt h e a n a l y s i s , i t w a s f o u n d t h a t t h e v a r i a b l e “ P P 3 ” i n t h e P e r c e i v e d P r i c e scale will increase Cronbach's alpha coefficient if it is removed (from 0.822 up to0.922).Therefore,theauthorkeptothervariablesandremovedtheo b s e r v e d variabl e“PP3”forfurtheranalysis:

Cronbach'sAl pha if ItemDeleted

ExploratoryFactorAnalysis(EFA)

In the EFA analysis, Principal components analysis will be used with VarimaxrotationandbreakpointwhenextractingfactorswithEigenvalues>1.Severalrelev antrequirementswillbeassessed,asfollows:

Through the above results of testing the scale reliability, the author performed EFAanalysis to with the following observed variables: PU1; PU2; PU3; PU4; PEU1;PEU2;PEU3;PEU4;SN1;SN2;SN3;SN4;PR1;PR2;PR3;PR4;PP1;PP2;P P4.

Theoutput results aredisplayedinTable4.5,Table4.6andTable4.7:

Table4.5.KMO &Bartlett'stestresultof independentvariables

In Table 4.5, KMO value = 0.753 > 0.5 and the Bartlett test with Sig = 0.000 50%, itmeans that5 e x p l o r a t o r y factors explained 74.701% of the dataset variance As a result,these 5 factors can beusedinfurther dataanalysis.

 Factor1(includesPS1;PS2;PS4;PS3)measuresthecomponentsof“Perceived Safety”,thus,keepthenameunchanged“PerceivedSafety”.

“SubjectiveNorm”, thus,keepthenameunchanged“Subjective Norm”.

 Factor 3 (includes PEU1; PEU4; PEU2; PEU3)measurest h e c o m p o n e n t s of“ P e r c e i v e d E a s e o f U s e ” , t h u s , k e e p t h e n a m e u n c h a n g e d “ P e r c e i v e d EaseofUse”.

 Factor 4 (includes PP4; PP2; PP1) measures the components of

 Factor 5(includes PU3; PU1; PU2; PU4)measuresthecomponents of“PerceivedUsefulness”,thus,keepthenameunchanged“PerceivedUsefulnes s”.

From the above results, there is no need to adjust the original theoretical model. Inaddition,theauthorwilltesttheresearch model's hypothesesas thenextstep.

Through the above results of testing the scale reliability, the author performed EFAanalysis to with the following dependent variables: CIU1; CIU2; CIU3; CIU4. TheoutputresultsaredisplayedinTable4.8,Table4.9andTable4.10:

Table4.8 KMO &Bartlett'stestresultofdependent variables

In Table 4.8, KMO value = 0.831 > 0.5 and the Bartlett test with Sig = 0.000 50%, it means that exploratory factorexplained 78.649% of the dataset variance As a result, this factor can be used infurtherdataanalysis.

Afterthecomponentsbeingrotated,resultsinTable4.10indicatecomponentsinclude: CIU4;CIU1; CIU3; CIU2 measures the factors of “Customer Intention toUseOFD”,thus,keepthenameunchanged“CustomerIntentiontoUseOFD”.

Pearson'scorrelationanalysisandMultivariateregressionanalysis

Thea u t h o r u s e s P e a r s o n ’ s c o r r e l a t i o n c o e f f i c i e n t t o q u a n t i f y th e c l o s e n e s s o f t h e linearr e l a t i o n s h i p b e t w e e n t w o q u a n t i t a t i v e v a r i a b l e s B e f o r e c o n d u c t i n g l i n e a r regression analysis, it is necessary to consider the correlation relationship betweenthedependentvariableandeachindependentvariable,aswellasbetweentheindependen t variables.

According to Trong & Ngoc (2008), the Pearson correlation coefficient is used toquantify the closeness of the linear relationship between quantitative variables. Inaddition,i t c h e c k s t h e c o r r e l a t i o n b e t w e e n d e p e n d e n t v a r i a b l e a n d i n d e p e n d e n t variables;the absolutevalueoft h e c o r r e l a t i o n c o e f f i c i e n t ( r ) i n d i c a t e s h o w c l o s e the linear relationship is The higher the correlation coefficient between dependentvariable and independent variables (near 1), the closer the relationship between thevariables is If the correlation coefficient is positive, it is a reciprocal relationship.Onthecontrary,ifthecorrelationcoefficientisnegative,thenitisareverserela tionship.

The value of r indicating that there is no linear relationship between two variablesdoes not mean that there is no relationship between them Therefore, the linearcorrelation coefficient should only be used to express the closeness of the linearcorrelation(Trong,2008).Iftwovariablesarecorrelated,thenthePearson’scorrelat ion coefficient │r│ > 0.1 Testing between two independent variables forcorrelation does not help to notice the problem of multicollinearity in regressionanalysis.

Multicollinearity is a phenomenon in which the independent variables are closelycorrelated The problem with multicollinearity is that they provide the model withvery similar information and it is difficult to separate the effect of each variable onthe dependent variable Another consequence of the rather tight correlation betweenthe independent variables is that it increases the standard deviation of the regressioncoefficients and reduces the statistical value in testing their significance, thus, thecoefficients tend to be less meaningful with multicollinearity, while the coefficientofdeterminationRsquaredisstillquitehigh.Duringmultipleregre ssionanalysis, multicollinearitywasdiagnosedbySPSSusingtheCollinearityDiagnosticoption.

In order to determine, measure and evaluate the influence of 5 groups of factorsobtained from EFA analysis, the author conducted multivariable linear regressionmodelanalysisbyusingSPSS20.0softwarewithencryptedvariablesasfollows:

PerceivedUsefulness PU1;PU2;PU3;PU4 F_PU

PerceivedEaseofUse PEU1;PEU2;PEU3;PEU4 F_PEU

SubjectiveNorm SN1;SN2;SN3;SN4 F_SN

Perceived Safety PS1;PS2;PS3;PS4 F_PS

PerceivedPrice PP1;PP2;PP4 F_PP

UseOFD CIU1;CIU2;CIU3;CIU4 F_CIU

F_PU F_PEU F_SN F_PS F_PP F_CIU

The correlation between the factors F_PU, F_PEU, F_SN, F_PS, F_PP and thedependent variable F_CIU all have Sig < 0.01, it means that there is a strongcorrelationbetweentheindependentvariables and thedependentvariable.

The correlation between the factors F_PU, F_PEU, F_SN, F_PS, F_PP has Sig.

From the above analysis results, it can be seen that Customer Intention to Use OFDand the factors (Perceived Usefulness; Perceived Ease of Use; Subjective Norm;Perceived Safety; Perceived Price) are closely related to each other At the sametime,itshowsthatmultipleregressionmodelcanbeusedtomeasuretheinflu enceofthesefactorsonOFDUsageIntentionofcustomersinHCMC.

F_CIU=C+β1*F_PU+β2*β1*F_PU+β2*F_PU+β1*F_PU+β2*β2*F_PU+β2*F_PEU+β1*F_PU+β2*β3*F_PU+β2*F_SN+β1*F_PU+β2*β4*F_PU+β2*F_PS+β1*F_PU+β2*β5*F_PU+β2*F_PP+β1*F_PU+β2* ε

Regressionresultsarepresentedinthefollowingtablesafterperformingtheregression onSPSS20.0software, usingthe“Enter”method.

Model R R 2 Adjusted R 2 Std.Errorof theEstimate Durbin-Watson

Withtheprimaryconsiderationofthistopicistofindtherelationshipandexplanationlevel ofthefactorsaffecting“Customer IntentiontoUseOFD”,thenR 2 = 0.832 and adjusted R 2 = 0.827 The multiple regression model is suitable inmeasuringthedegreeanddirectionoftheindependentvariablesimpacti n t h e modelont hedependentvariable.

ThroughtheregressionanalysisresultsinTable4.13,inordertotesttheautocorrelation, the Durbin - Watson (d) quantity is used to perform the test Thisquantity d has a value that varies from 0 to 4 If the residuals do not have a first- orders e r i a l c o r r e l a t i o n , t h e v a l u e o f d w i l l b e c l o s e t o 2 ( T r o n g & N g o c , 2

Durbin - Watson test gave the result d = 2.164 (not too far from the value 2),therefore, it can be concluded that the residuals are independent of each other orthere is no correlationbetween the residuals In conclusion, the autocorrelationassumptionisnotviolated.

In the next step, the author will check whether the built regression model is suitablebyANOVAanalysis.

Model SumofSquares df MeanSquare F Sig.

Based on the ANOVA analysis result in Table 4.14, the coefficient F = 191.494 andthecoefficientSig=0.00010(Trong&Ngoc,2008).

The regression equation according to the standardized Beta coefficient of this studyisrewrittenasfollows:

F_CIU=0.223*F_PU+β2*F_PU+β1*F_PU+β2*0.686*F_PU+β2*F_PEU+β1*F_PU+β2* 0.249*F_PU+β2*F_SN+β1*F_PU+β2*0.172*F_PU+β2*F_PS+β1*F_PU+β2*0.468*F_PU+β2*F_PP

 In the condition that other factors remain constant, when the factor

"PerceivedUsefulness" increases by 1 standard deviation unit, then the factor

 Likewise, in the condition that other factors remain constant, when the factor"Perceived Ease of Use" increases by 1 standard deviation unit, then the factor"CustomerIntentionto UseOFD"willincreaseto0.686 standarddeviationunits.

 Next,intheconditionthatotherfactorsremainconstant,whenthefactor"SubjectiveNo rm"increasesby1standarddeviationunit,thenthefactor"CustomerIntentionto

"PerceivedSafety"increasesby1standarddeviationunit,thenthefactor"CustomerInte ntionto UseOFD"willincreaseto0.172 standard deviationunits.

Hypothesistesting

H 2 :Perceived Ease of Use has apositiveeffect(+)onCustomerInte ntionto UseOFD.

H 5 :Perceived Price has a positiveeffect( + ) o n C u s t o m e r I n t e n t i o n toUseOFD.

Comment:this factor has a positive influence (+) on “Customer Intention to

UseOFD” People can conveniently order food through OFD apps at any time and anyplace, thus, they can save time and reduce efforts The more customers perceive theusefulness,thehighertheintentiontouseOFDservices. b) PerceivedEaseof Use

Comment:this factor has a positive influence (+) on “Customer Intention to

UseOFD” Customer intention to order food online will rise significantly if the OFDinterface is easy to access and straightforward to operate If the app is utilizedsimply,consumerswill feelcomfortableandtendtouseOFDapps morelikely. c) Subjective Norm

Hypothesis H3:Subjective Norm has a positive effect (+) on Customer Intention toUseOFD.

Comment:this factor has a positive influence (+) on “Customer Intention to

If they are suggested or supported by parents, friends, etc., their willingnesstoorderfoodonlinethroughOFDappswillincreasesubstantially. d) PerceivedSafety

Hypothesis H4:Perceived Safety has a positive effect (+) on Customer Intention toUseOFD.

Comment:this factor has a positive influence (+) on “Customer Intention to

UseOFD” The more customers perceive the safety from purchasing online, the moretheirwillingnesstouseOFDservices.BigandfamiliarOFDbrandsmaintaincredibility and brand loyalty well because they frequently implement safety policiesand programs to gain customer trust, especially at the payment stage so that userscouldovercome the fear offinancialrisks. e) PerceivedPrice

Hypothesis H5:Perceived Price has a positive effect (+) on Customer Intention toUseOFD.

Comment:this factor has a positive influence (+) on “Customer Intention to

UseOFD” Customers often believe that purchase meals through apps will be money-saving and able to compare in prices Because there are a lot of promotions anddiverse price selection, thus,customerswillbe stimulated to useO F D s e r v i c e s ratherthan thetraditionalmethod.

NormalDistribution

The residuals may not follow the normal distribution for the following reasons:incorrect use of the model, non-constant variance, insufficient number of residualsfor analysis Therefore, it is necessary to perform many different assessments todetect violations The study investigates the distribution of residuals by buildingHistogramandP-PPlothistogram.

The frequency distribution histogram in Figure 4.1 reveals that the residuals arenormally distributed with the mean value close to 0 (Mean = 6.25E-17) and thestandard deviation(Std Dev =0.987) close to 1.It isclear that the residualsdistribution is approximately normal, for that reason, it can be concluded that thenormaldistributionhypothesisisnotviolated.

Summaryandconclusion

The main objective of this thesis is to measure the factors affecting the purchaseintention through OFD applications of HCMC customers, from which the authorrecommendsappropriatepracticalimplicationsforOFDenterprisestodevelopbus inessstrategiesrelatedto customerintention.

Based on theoretical basis with domestic and international studies related to onlinepurchase intention of customers, the research model and hypotheses were proposed.In thequalitative research, theauthor performed one-to- onediscussionswith5customers who have more than 2 years of experience in using OFD applications;afterbeingadjusted,thefinalscalesinclude5componentsand24observedva riables The questionnaire was then used in the survey and obtained 200 validresponses.

In the quantitative research, the data analysis was carried out using SPSS 20.0software, including: descriptive statistics, scale reliability testing (Cronbach's Alphaanalysis), EFA analysis, regression analysis and hypotheses testing The analysisresults are as follows: Perceived Usefulness (β = 0.223); Perceived Ease of Use (β =0.686); Subjective Norm (β 0.249); Perceived Safety (β = 0.172) and PerceivedPrice (β = 0.468) In which, Perceived Ease of Use and Perceived Price have thestrongestimpactonCustomerIntentiontoUseOFD.

The above result is consistent with the context in HCMC (high rate of elderlypeople), therefore, if the OFD interface is easy to access and simple to operate, theywill feel comfortable and prefer to use OFD apps In addition, Vietnamese peoplearei m m e n s e l y p r i c e c o n s c i o u s , t h u s , i f o n l i n e o r d e r i n g f o o d h a s b e t t e r p r i c e a n d equal quality with food bought directly at restaurants, the numberofc u s t o m e r s usingOFDserviceswillincreasesignificantly.

Resultcomparisonwithpreviousstudies

The TAM model (Davis, Bagozzi & Warshaw, 1989) is used in this thesis as thetheoreticalbasistobuildanddeveloptheresearchmodeloffactorsaffectingcustomer intention to use OFD services According to the results, both PerceivedUsefulnessandPerceivedEaseofUsehavepositiveimpactonOFDUsageInten tion This is close to the TAM model as well as the research results of

“Factorsaffecting customer intention to use online food delivery services before and duringtheCOVID- 19pandemic”(ChanmiHongetal.,2021)and“Factorsaffectingcustomeri n t e n t t o u s e B a e m i n a p p l i c a t i o n t o p u r c h a s e o n l i n e f o o d i n H C M C ” (Trang,2 021).

SubjectiveNormandPerceivedSafetyarecomponentsthatbothp o s i t i v e l y influenc e on OFD Usage Intention This is similar when comparing with the resultsof

“Consumer experiences, attitude and behavioral intention towards OFD services”(Vincentetal.,2017)and“FactorsaffecttheonlineshoppingintentionofVietnames econsumers(Yen,2015).

Finally,thefactorPerceivedPricealsohasapositiveeffectonOFDU s a g e Intention. This is one of the factors that has the most significant impact based onresearch results This coincides with the study results of Chanmi Hong et al.

(2021),Vincente t a l ( 2 0 1 7 ) a n d r e s e a r c h o n F a c t o r s a f f e c t i n g c u s t o m e r i n t e n t t o u s e Baeminappl icationto purchaseonlinefoodinHCMC(Trang,2021).

Through the above comparison, it can be seen that 5 factors in this thesis (PerceivedUsefulness, Perceived Ease of Use, Subjective Norm, Perceived Safety & PerceivedPrice) all have considerable effect on customer intention to use OFD services, notonly in Vietnam but also in many countries around the world This is completely inlinewiththestatementsaboutthestrengthsof OFDapps:convenient, straightforward and money-saving Besides, opinion from the reference groups andsafety issues are the core aspects that customers frequently concern about whenusing OFD services Nevertheless, the influence level of factors compared withprevious studies are slightly dissimilar This is due to the fact that the researchresultsvarydifferentlydependonthesubject,period,andthescopestudy.

Researchcontributions

OFD industry is a potential field and contributes a lot to economic development.Nevertheless, this industry has only developed significantly within the last 3 yearsand remained a lack of in-depth research in Vietnam This study contributed anadditional scientific paper related to OFD services, by building a theoretical modelindicatingthefactorsaffectingcustomerintentiontouse OFDinHCMC.

Thisstudy hasprovidedadministratorswithamorespecificviewofcustomerinsights as well as the importance of building factors that increase the intention toorder food online From the research results,the author will also present severalpractical recommendations to help develop strategies, accordingly, satisfy customerneedsandimproveOFDservicesinVietnam.

Implicationsand recommendations

Research results show that Perceived Usefulness (β = 0.223) has a rather low impacton CIU This is due to the fact that people in Vietnam are still used to buying streetfood or eating at restaurants so they have not realized the benefits of OFD services.Businesses need to optimize the app convenience by extending the operating time orexpandingtheproductportfolio(rawmaterials,ingredients, )toprovidetimeflexibility and change customer shopping habits Besides, there should be marketingcampaignsfocused on OFDadvantages in ordertoraisecustomerawareness.

This factor has the strongest impact on CIU with β = 0.686, so administrators needto focus on building easy-to-use interfaces on OFD apps High speed access isnecessary to minimize the waiting time, likewise, the purchase process needs to besimplified Function buttons have to be displayed on convenient positions and easyto operate Eliminate redundant details, present full product information and pricesandcooperatewithe- walletfirmstomakethepaymentprocessquickly.Additionally,therehavetobeclearinstruction sfornewusers(especiallyt h e elderly)soasnottocreateanyobstaclesduring thepurchasingprocess.

According to the results, Subjective Norm (β = 0.249) influences positively onCIU In Vietnam, with a culture of respecting community spirit and esteemingfamily opinions, reference groups (parents, spouses, friends, etc.) still affect onOFDusageintentionofcustomers.Therefore,businessesneedtostrengthenmarketing and social media programs to increase the impact of social groups oncustomers.ConsentfromfamilyandreferralsfromfriendswillboostCIUsignificantl y Moreover, it is necessary to implement referral programs(withpromotions and incentives) from existing customers to gain new ones When theyrealize the benefits from using OFD services, they will recommend and promotetotheirrelativesorfriends.

PerceivedSafety hasa positive effect on CIU withβ =0.172.Customerso f t e n suffer from the great fear of financial risk (bank account numbers and passwords arestolen)whichreduceswillingnesstouseOFDservices.InordertoincreasePerceivedSafety, companies need to apply security commitments and compensationpolicy when customers' personal information is exposed due to errors from appsystems In order to ensure every electronic transactions are completely safe andaccurate,businessesneed to develop hacker- proofpayment systemsaswellasto set uphotlinesforcustomerstocontactwhenthereisanunexpectedincident.Inaddition,OFDapp smustprovideactualproductimagesclearlyandestablishappropriatereturnpolicytogain customertrustsaswellasbrandloyalty.

Perceived Price (β = 0.468) is the second most influential factor on CIU Price playsan important role in not only online shopping but also in traditional shopping toVietnamese people Therefore, in order to attract more customers, OFD managersneedtocomeupwithstrategiesrelatedtothisfactor.Implementprime- timepromotions and enhance discount strategiesto compete with traditionalm e t h o d , then stimulate the buying intention Purchasing on OFD apps also helps customerssave travel costs and waiting-in-line time In addition, “price comparing” functionshould be added on the app and create a “price filter” so that users can be able tocomparepricesamongrestaurantsconveniently.

Limitationsanddirectionsforfurtherresearches

During the research process, the author encountered a number of shortcomings intermsoftime,financeandinsufficientresearchknowledge.Consequently,t h i s thesis inevitably has certain limitations The study uses non-probability samplingmethod,sothesamplerepresentativenessisnothigh.Moreover,duetot h e influen ce of COVID-19, the survey and discussions could only be conducted online.Therefore, the responses are only relative and not close to the reality of the surveysubjects It is recommended that further studies should use a larger sample size withbetter representativeness Additionally,Perceived Ease of Use and Perceived Pricearefactorsthatneedtobeinvestigatedmoreprecisely.

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 Havem o r e t h a n 2 y e a r s o f ex p e r i e n ce i n using O FD a p p s too rd er f o o d online.

My name is Vo Thi Kim Ngan, currently a finaly e a r s t u d e n t a t H C M C

U n i v e r s i t y of Banking It is my pleasure to have your participation in discussing the researchscales of my graduation thesis “Factors Affecting Customer

Your answers will have a great contribution to the research and all information willbekeptconfidential(onlyusedforresearchpurposes).

1 In your opinion, what factors affect the customer intention to use OFD services?Pleaseexplainindetail?

2 Inyouropinion,arethefollowingstatementsunderstandable andappro priate?If you have any suggestions to add, adjust or remove any statements, please fillinthebelowtable.

Be able to compare among differentoptions.

I can easily order and receive foodthroughOFDapps.

I intend to use OFD servicesbecause my family, friends andcolleaguesareusingOFDservices

The more detailed and clear therestaurant/dishesinformation,th esafer I feel when using OFDservices.

I will recommend others to orderfoodonthe mobile app(s)thatIuse.

Thank you for your assistance,Mysincereappr eciation!

MynameisVoThiKimNgan,afinalyearstudentmajoringinBusinessAdministration at University of Banking Currently, I am conducting my graduationthesis“FactorsAffectingCustomerIntention ToUseOFDServicesInHCMC”.

Please take a moment to answer the survey below Your responses will contribute agreat significance to the research I guarantee that all information will be keptconfidentialandonlyusedforresearchpurposes,notforanyotherpurposes.

❑No,Ihavenot ❑Yes,Ihave ❑IamintendingtouseOFDapps

❑Usefulness ❑Easeofuse ❑Familyopinions ❑Safety ❑Price

❑ShopeeFood ❑Grabfood ❑Gofood ❑Baemin ❑Loship

❑Officestaff ❑Civilservant ❑Freelancer ❑Housewife ❑Others

❑Under5 million ❑From5–10 million ❑From10–15 million

(1-Strongly disagree;2 -D i s a g r e e ; 3 -Neutral;4-Agree;5-Stronglyagree).

2.I can easily compare among differentoptions(restaurants,meals,etc.)on

4.I receive plenty of information(promotions,newmenus,etc

3.I intendtouseOFDservicesbecausethe currentsociety(people, media,internet,      etc.)thinksOFDisagoodoption.

4.T h e m o r e detailedand clearthe restaurant/ dishesinformation,thesaferIfeelwhenusing

N Minimum Maximum Mean Std.Deviation

Scale Mean ifItemDeleted Scale Variance ifItemDeleted Corrected Item-

Scale Mean ifItemDeleted Scale Variance ifItemDeleted Corrected Item-

Scale Mean ifItemDeleted Scale Variance ifItemDeleted Corrected Item-

Scale Mean ifItemDeleted Scale Variance ifItemDeleted Corrected Item-

InitialEigenvalues ExtractionSumsofSquaredLoadings Total %ofVariance Cumulative% Total %ofVariance Cumulative%

Kaiser-Meyer-OlkinMeasureofSamplingAdequacy .831 Bartlett'sTestofSphericity Approx.Chi-Square 547.945 df 6

InitialEigenvalues ExtractionSumsofSquaredLoadings Total %ofVariance Cumulative% Total %ofVariance Cumulative%

F_PU F_PEU F_SN F_PS F_PP F_CIU

Std Error of theEstimate Durbin-Watson

Model Sum ofSquares df MeanSquare F Sig.

Gender N Mean Std.Deviation Std.ErrorMean

Levene's Test for Equality ofVariances t-test for Equality ofMeans

95%ConfidenceI nterval of theDifference Lower

IndependentSamplesTest t-testforEqualityofMeans 95%ConfidenceIntervaloftheDiffe rence Upper

Sum ofSquares df MeanSquare F Sig.