STATEBANKOFVIETNAM MINISTRYOFEDUCATIONANDTRAINING BANKINGUNIVERSITYOFHOCHIMINHCITY NGUYENDONGOCTRAN (Studentcode 030633171432) FACTORS AFFECTING ONLINE SHOPPING BEHAVIOR OFBANKINGUNIVERSITYSTUDENTSDUR[.]
Background
Nowadays,onlineshoppingbecomemorepopularbecauseoftheconvenienceandspeeditoffers.Justo neclicktogetwhatyouneedwithoutgoingtothestore.Especiallysincethe occurrence of Covid-19, it has changed the consumer trend and is very reasonablereason for thebooming online shoppingenvironment. Winningwill belongto thebusiness that changes quickly and seizes the fastest opportunity Over the past fewdecades, the Internet has evolved into a vast global marketplace for the exchange ofgoods and services In many developed countries, the Internet has been used as animportantmeans,providingawiderangeofproductswith24- houravailabilityandwidecoverage.AccordingtoChen(2009),researchonconsumeronlineshoppin gbehaviorisone of the most important research programs in the past decade Online shopping (alsoknownasonlineshoppingandInternetbuying/buying)referstotheprocessofpurchasing a product or service through the Internet Online shopping has distinctadvantagess u c h a s :
C o n s u m e r s c a n e a s i l y a c c e s s p r o d u c t i n f o r m a t i o n f r o m m a n y different sources: websites, social networks (facebook, instagram, ) In particular, thewebsites providing product price comparison services from other websites have providedverygoodsupportforconsumersinmakingonlinepurchases.Theonlineformprovides a wide variety of products and services, especially clothing, electronics, toys, andcosmetics Most e-commerce exchanges allow customers to review a product afterpurchasing it Therefore, consumers can learn about products carefully before decidingto buy Especially saving time for consumers, especially students while studying atschool.However,theproportionofVietnameseconsumers,especiallystudents,participati ng in online shopping was still lower than in other countries in the region andaround the world during the Covid translation According to Sylke et al (2002) in anumberofothercountries,e- commercebusiness-to-consumer ismuchlowerthanthe expected share of total retail enterprise due to its certain limitations Therefore, in orderto attract consumers, especially students who shop more online, it is very necessary toknow the factors that affect the shopping behavior of customers online That is why Ichose the topic “Factors affecting online shopping behavior of Banking
UniversitystudentsduringtheCovid19pandemic”astheresearchtopic.Fromtheresearchresults,the author will give detailed explanations about the factors affecting online shoppingbehavior of Banking students during the Covid 19 translation, and at the same time findsolutionstohelpsellers.Onlineretailisclosertoshoppers.
Theurgencyofthetopic
A recent Nielsen survey released at the Online Marketing Forum 2020 showed that thenumber of consumers shopping online increased by 25%, while in traditional channelssuchassupermarkets,marketsandgroceriesonlyincreased7%,3%and6%respecti vely.Ms.LeMinhTrang,representativeofNielsenVietnamCompany,assessedthattheonlineshopp ingtrendinCovid-19hasopenedupmoreonlinebusinessopportunities when people choose to shop at home increased Online shopping has a 2-digit annual growth rate and this number is likely to grow in Vietnam Due to theCOVID- 19pandemic,worldtradeandtrade,educationandotheractivitiesweredisrupted.Theglobalsu pplychainhasbeenbrokenbecausethecountriesarelocked.Asa developing country, Vietnam has suffered parallels by this outbreak Therefore, thisresearchisdesignedtohelponlineretailershavemoreoverviewoftheircustomers,fromwhich more solutions to attract customers through online shopping sites, especiallyduringCovidtranslation.Thisstudyisalsoessentialtoevaluateoverallfactorsaffectingonline shopping behavior of students, to help them increase awareness of their ownshopping behavior Although there are many theories and research models in the worldthat explain the factors affecting the behavior of online shoppers, there are not manyspecific studies in the country about the factors that affect the behavior of onlineshoppers.F a c t o r s a f f e c t i n g o n l i n e s h o p p i n g b e h a v i o r o f c o n s u m e r s , e s p e c i a l l y t h e student segment during Covid translation time 19 Models around the world whenappliedtoVietnamwillnotbeappropriateduetotheculture,economy,society.Therefore,based on the models in the world and the studies in the country over the pasttime,buildingamodelsuitablewiththesituationofVietnamisessential.
Researchobjectives
Findoutthefactorsthatinfluencestudents'onlineshoppingbehaviorduringtheoutbreakand the impact of each of these factors, and contribute to the research team's opinion onresearchers'solutions,providingonlineshoppingservicestoattractcustomersduringtheCovid19pandem ic.
- Systematize the theoretical basis of students' online shopping behavior during theCovidpandemic19
- FindoutthefactorsaffectingonlineshoppingbehaviorofthestudentsoftheUniversity of Banking in Ho Chi Minh City during Covid 19, at the same timeresearchtheinfluencelevelofeachofthesefactors
- Contributesolutionsforserviceproviderstopurchaseonlineandattractmorecustomers during the Covid 19pandemic.
Researchquestion
- Whatfactorsaf fec ti ng t h e online sho pp in gb eha vi or of HCM Ban ki ng Uni versitystudentsduringCovid19?
Researchobjectand Researchscope
Study subjects are factors affecting online shopping behaviors of Banking Universitystudents in Ho Chi Minh City during Covid 19 Subjects selected for the survey areBanking University students This is a young audience, knowledgeable about onlineshopping,sotheirbuyingbehaviortoacertainextentcanrepresentalltypesofconsumer s in society The study period isfromMarch2020toMarch2021.
ResearchMethods
- Firstly, for students, time and cost are two issues that need to be considered whenconductingtheinvestigation,sothetopicchoosesthissubjectsothatitdoesnottakemucht imeandcostsforsamplingresearch.assist.
- Second, "students of Banking University of Ho Chi Minh City" are those who havebeenusingonlineshoppingregularly.
Qualitativeresearch:toexplorethefactorsaffectingonlineshoppingbehaviorofstudents during Covid time and adjust the scale of the factors in the proposed researchmodel.Qualitativeresearchresultsserveasabasisforquestionnairestocollectinfor mationtoperformquantitativeresearch.
Quantitative research: used to measure the influence of these factors on students' onlineshoppingbehaviorduringCovidtime.
Researchcontent
ResearchonconsumerbehaviorofthestudentsegmentoftheBankingUniversityofHoChiMinhC itywithonlineshoppingincludingcultural,social,personal,psychological,
and customer purchasing decision-making process From there, evaluate the onlineshopping form during the epidemic, with the shopping demand for this segment, see ifthere is any change compared to before the Covid outbreak 19 The study will use thetheory and empirical research by domestic and foreign authors who have done researchononlineshoppingbehavioringeneral,tohaveanalysisandunderstandingofthisissuef orthestudentsegment.Fromtheresultsobtainedduringtheresearchprocess,theauthorwill give an explanation of the factors affecting online shopping behavior of Ho ChiMinh City Banking University students during Covid
19 and give their opinion Theauthor's solutionstoattractuserstoshoponlineduringthepandemic.
Researchcontribution
BuyingonlineisnolongeranewfieldinVietnam,butwhenbuyinggoodsduringCovid19thereisadif ference.Thetopiccontributesadifferentperspectiveandmoreoverviewinthefieldofonlineshoppin gduringthepandemic,throughbuildingatheoreticalmodelexplainingthefactorsaffectingtheonlinebuyi ngbehaviorofstudents.Thescalesintheproposed research model were implemented in India, Iran, Bangladesh , this studyadjusted and tested the scales in the Vietnamese environment through survey data.experiment at Banking University, should contribute to research and application in theVietnamesemarket.Therefore,inadditiontoinheritingthepreviousstudies,thisresearch will provide a clear solution for online retailers to compete most effectivelyduringCovid19.
After the study is successful, the topic will help the reference to identify the factorsaffectingonlineshoppingbehaviorduringthetimeofCovid19ofthecurrentstudentofHo
Chi Minh City University of Banking and the degree of influence of the abovefactors.ThetopicwillnottargetgeneralcustomersbutwillfocusonstudentsofHCMCBank.
Structure ofresearch
Introducing an overview of the topic, this chapter includes main contents such asresearch reasons, research questions and objectives, research scope and object, andresearchmethods.
Thischapterincludesthemaincontentssuchasdetailingresearchmethods,describingresearchsam ples,makinghypothesesandproposingresearch models.
BasicConcepts
Onlineshoppingconcept
Online shopping is a form of commerce and electronics that allows customers to buyproductsorservicesdirectlyfromsellersovertheinternet.Onlineshoppingisoneoftheformsofe- commerce,becomingincreasinglyimportantindevelopingB2Ce- commercesincethelatetwentiethcentury.Onlineshoppingisdefinedasaservicewhereconsumersuse electronic devices with an Internet connection to make purchases (Turban et al.,2006) Online shopping encompasses a wide range of social changes, markets that arecharacterized by globalization, and the shift of the current economy based on knowledge,information, and technology in life.
Every day, some businesses have taken advantageofonlinesalesinsteadoftraditionalsales,whichwillhelpthemreducetransactioncosts,searc h,developabroadermarket,andreduce thegapbetweenbuyers andsellers.
Monsuwe,Dallaert,andRuyter(2004)havecomparedbothofflineandonlineandfoundthatshoppingo nlineismoreaccessiblethanshoppingoffline,whichtakeslesstimeandeffort In addition, consumers can access more information related to products andservices, helping them compare prices and product quality with other manufacturers.
Ingeneral,customerscaneasilyreceiveanydataandinformationfromtheinternet(Wang,Ye, Zhang &Nguyen, 2005) When shopping online, consumers cannot touch and feela product before buying, but online services provide more information about theirproducts and services so that the customer can rate the products and services when theyneedit(Lim&Dubinsky,2004).
Conceptof consumerbehavior
Consumer behavior refers to consumers' behavior in purchasing, using, evaluating,andrejectingproductsandservicesthatconsumersexpectwillsatisfytheirneeds Consumerbehaviorfocu sesonwhetherwithexistingresources(time,money,effort),theindividualwilldecidehowtousethemont heconsumptionofrelevantitems.Itincludeswhatthey buy,why,whentobuy,where,howoftentheybuythem,howoftentheyusethem,howthey are assessed after purchase, how these ratings affect those next purchase, and howtheydisposeofthem.
Consumer behavior involves both aspects: the mental decisions (thoughts) and thephysicalactionsofthebodycreatedfromthosedecisions/thoughts Consumersarethosewho buy and/or use products or services provided in the market Consumers are generallydividedintotwobasicgroups:personalconsumersandorganizationalconsumers.Personal consumers are those who buy goods or services for their purposes (e.g., brushes,combs ), for families (e.g., television,table ), for relatives (lipstick , clothes…),friends (gift) These consumers are also referred to as end-users/ultimate consumers.Institutional consumers include business organizations (enterprises), administrative andnon-business units, They are the buyers of products and services for the operation ofagencies and organizations In consumer behavior research, the focus is usually onindividual consumers since end consumption is the factor that covers all the differenttypesofconsumerbehaviorandisrelevanttoallpersonasabuyer,consumer,orboth.
Consumerdecision-makingprocess
Researchers have found that consumers seem motivated and influenced by family,friends, and advertising when buying products/services and areaffected by mood,circumstances, and feelings These factors combine to form a general, comprehensivemodel of consumer behavior, which both reflects the cognitive side and the emotionalsideoftheconsumerdecision-makingprocess.
The consumer decision-making process consists of three phases: the input phase, theprocessings t a g e , a n d t h e o u t p u t s t a g e T h e i n p u t s t a g e i n f l u e n c e s c o n s u m e r s ' p e r c e p t i o n s ofproductneeds,includingtwoprimarysourcesofinformation:thecompa ny'smarketingefforts(products,pricing,promotions,anddistributionchannels)andfactors.Exter nalsocialfactorsimpactconsumers(family,friends,neighbors,culture,andsubculture).The processingphasefocusesonconsumerdecisionslike.Howthe individualpsychologicalfactors(motivation,perception,education,personality,opinion)af fecttheoutputfactorsinfluenceaffectconsumers'perceptionofneed.information,seekinfor mationbeforepurchasingandreevaluatehowtheselectionprocessis.Experiencegainedthroughev aluatingchoiceswill,inturn,affecttheinherentpsychological attributes of consumers The output stage consists of two activities thatare closely related: purchasing behavior and post-purchase evaluation Buying behaviorfor low-cost, non-permanent products can be influenced by manufacturer promotionsand possibly a trial purchase if the consumer feels satisfied, maybe they will come backto buy again continued The trial is the stage of surveying how consumers are judgingbyusingaproductdirectly.Theactofreturningtobuyacustomer's productprovesthatthe consumer has accepted it For products that are relatively durable such as laptops(whicharerelatively durableduetoquicklybecomingoutofdate)signalacceptance.
Researchershavefoundthatwhenbuyingproducts/services,consumersseemmotivatedand not only influenced by family, friends, advertising, but also influenced by mood,circumstances, feelings All of these factors combine to form a general, comprehensivemodel of consumer behavior, which both reflects the cognitive side and the emotionalsideoftheconsumerdecision- makingprocess.
The consumer decision-making process consists of three phases: the input phase, theprocessings t a g e , a n d t h e o u t p u t s t a g e T h e i n p u t s t a g e i n f l u e n c e s c o n s u m e r s ' p e r c e p t i o n s of product needs, including two main sources of information: the company's marketingefforts (products, pricing, promotions and distribution channels) and factors externalsocial factors impact on consumers
(family, friends, neighbors, culture and subculture).Theprocessingphasefocusesonconsumerdecisionslike.Howtheindividualpsy chologicalfactors(motivation,perception,education,personality,opinion)affecttheoutput factorsaffectconsumers' perceptionofneed.information,seekinformationbefore purchasing, and reevaluate how the selection process is Experience gainedthroughevaluating choices willinturn affectthe inherentpsychological attributes of consumers.Theoutputstageconsistsoftwoactivitiesthatarecloselyrelated:purchasingbehavior and post- purchase evaluation Buying behavior for low-cost, non-permanentproducts can be influenced by manufacturer promotions and possibly a trial purchase ifthe consumer feels satisfied, maybe they will come back to buy again. The trial is thestage of surveying how consumers are judging by using a product directly. The act ofreturning to buy a customer's product proves that the consumer has accepted it Forproducts that are relatively durable such as laptops (which are relatively durable due toquicklybecomingoutofdate)signalacceptance.
Generaltheoriesandresearchmodelsofconsumerbehavior
Theoryof ReasonedAction–TRA
The theory of reasoned action (TRA), developed by Fishbein and Ajzen, is one of themost influential theories used to explain human behavior According to this theory,behavioral intent can be explained by attitudes towards behavior and subjective norms,designed to explain behavior in general Attitudes towards behavior are defined as anindividual's"positiveornegativefeelings(performanceevaluation)"abouttheperforman ceofthetargetbehavior"(FishbeinandAjzen,1975).
Studies over the decades show that attitudes do not predict much of behavior. Vicker(1969) concludes, "in general, attitudes do not appear to be related or weakly related tobehavior" From the results of these studies, Fishbein and Ajzen (1980) discovered themethod of predicting behavior from their reasoned action theory (TRA) attitudes andconcluded that it is not attitude but rather planned behavior is a predictor of behavior.Behavioral Intention (I) is the most crucial factor determining a person's behavior Thisintent is defined by Attitude (A) for the performance of the behavior and theSubjectiveNorms(SN)relatedtothebehavior.TheIntentisseenastheclosestandmostimportantpredictor ofbehavior,anditisinfluencedbyattitudesandsubjectivenorms.
Theoryof PlannedBehavior–TPB
TheoryofPlannedBehavior(TPB)ofAjzen1991isthedevelopmentandimprovementfrom the rational action theory model (Fishein & Ajzen, 1975) According to plannedbehavioral theory, attitudes, subjective norms, and perceptions of behavioral controlinfluence a consumer's intent to act. Compared with TRA, the TPB model adds acognitive factor that controls behavior affecting behavioral intention In addition, theelement of belief about facilitation involves the perception factor of behavioral control.According to Bunchan (2005), behavioral theory with TPB was born to overcome thisweakness.
AccordingtoTPB,"behavioralintent"ofcustomersisaffectedby"attitudes","subjectivenor ms"and"perceptionofbehavioralcontrol".TPBhasbeenwidelyaccepted and used in studies to predict specific user intent and behavior of individuals.The empirical studies have shown the suitability of this model in studying consumerbehavior in the context of online shopping Hansen et al (2004) tested both TRA andTPB models, and the results showed that the TPB model explains customer behaviorbetterthantheTRAmodel.Moreover,giventhe relevanceoftheresearc hcontextin
Factorsaffectingonlineshoppingbehavior
Attitude
Attitude is an emotional appreciation, a tendency to act in a good or bad way about anobject or idea Attitude puts people in the thought of liking or disliking, feeling familiarwith, or alienating a particular thing or idea When buyers have a positive or positiveattitude,theyturntothebrandtheyarelookingfor.Intoday'scompetitiveenvironment,determ ining the buyer's attitude towards the product is very important because it is thefactor that strongly influencestheirbehavior Since then, companies haveset upmarketing strategies to adapt to the market situation, affect and change customers'attitudesi n t h e m o s t p r o f i t a b l e d i r e c t i o n
Online shopping will directly affect users' attitudes towards online shopping, and theirattitudeswillhaveasignificantimpactononlinebuyingbehavior.Onlinebuyerattitudes significantly and positively influence their online buying behavior (Ariff et al.,2014).The consumer's attitude towards engaging inbehavior has been shown to be a strongpredictor of behavior (Fishbein & Ajzen 1975) Attitude refers to online consumeracceptanceasashoppingchannel(Olsonetal.,2001).Previousstudieshaveshownthat online shopping attitudes are an important predictor of online purchases (Yang et al.,2007).
Price
Priceis anessentialfactoraffectingbuyingbehaviorofcustomers.Pricescanbeexpressedinamountschar gedforproductsorservicesortheamountthatconsumerspaytoreceive,buyoruseproductsorservices(Kotler&Armstrong,2012).Satit,Tat,Rasli,ChinandSukati(2012)arguethat,betweenproduct,price,locationandpromotion,priceis the only factor that influences consumers more in purchasing decision in most cases.To attract consumers, companies often offer competitive prices, launching multiplepromotionsfortheirproducts.AccordingtoAndreti,Zhafira,Akmal,andKumar(2013),most consumers go to convenience stores to buy goods because the price is reasonableand has a significant impact on customers Munusamy and Hoo (2008) find that thepricing strategy significantly influences the customer's motivation and the consumer'sbuying decision Consumers care about the price and consider the price when decidingtobuyaproduct.
Subjectivenorms
Ajzen (1991) defines subjective norms as the perception of influencers who should orshould not perform the behavior According to the theory of reasoned action - TRA(Azjen&Fishbein1980),humanbehaviorisformedfromintention,basedonconsumerattitud es towards behavior and perception Consumer behavior is influenced by thosearound them, such as family, friends, co-workers, the media, and the influencers ofbuying behavior If they say good / bad about the product, it will affect the buyingbehaviorofconsumers.Subjectivestandardscanbedescribedastheindividual's perception of social pressures for performing or not performing a behavior.Previousstudieshavesuggestedthatthereisapositiverelationshipbetweensubjectivenormsandintenti ons.Inthecontextofonlineshopping,Lin(2007)arguedthatsubjectivebenchmarks reflect consumer perception of the group's influence on the ability to shoponline.
Perceivedbehavioralcontrol
Perceived behavioral control is defined as an individual's perception of how easy ordifficult it is to perform a certain behavior It denotes the degree of control over thebehavior'sperformance,nottheresultsofthebehavior.Inthecontextofonlineshopping,behavioral control perceptions describe consumer perceptions of the availability ofnecessaryresources,opportunitiestomakeonlineshopping.Perceivedbehavioralcontrol has been shown to have a positive impact on consumers' online shoppingintentions.Perceptionsofbehavioralcontrolreflecttheperceptionofinternallimitations(self -efficacy) as well as external limitations in behavior such as resources available.Perceptions of behavioral control directly influence online shopping behavior
(George2004)andhaveastrongrelationshipwith onlineshopping(Khalifa&Limayem2003).
Perceivedrisk
According to Bauer (1960), risk-aware consumer behavior of IT products includes twofactors: perception of risk related to products/services and perception of risk related toonlinetransactions.online.Theproduct/service- relatedriskperceptioncomponentincludes risk perceptions such as loss of functionality, financial loss, time consuming,andtotalriskperceptionwiththeproduct/service.AccordingtoPavlou(2003),theri sksinonlineshoppingincludeeconomicrisk,sellerrisk,privacyrisk,andsecurityrisk.Fourcriteria measure perception of risk: not receiving the product, difficulty to test actualproducts, unable to come into contact with the product, not pushing the product beforepurchasing Forsythe et al., 2006 Meanwhile,
Corbitt et al (2003) argued that two criteriameasureconsumerriskperceptioninonlineshopping:financialriskandproductris k-
Online shopping intentions product may not meet customer expectations The risk perception component related toonline transactions is the risks that can occur when consumers perform e- commercetransactionsonelectronicdevicesrelatedtosecurityandsafety- completeauthenticationandriskawareness about online transactions.
Overviewofresearch
Domesticresearch
- Factors affecting online shopping intentions of Vietnamese consumers: Expandedresearchplanningbehaviortheory(HaNgocThang,2016)
ThestudydiscussesthefactorsinfluencingVietnameseconsumers'onlinebuyingintentionsbase donthetheoryofplannedbehavior.Questionnairesweresentdirectlyviathe Internet to the respondents in 5 months, with
423 valid responses to be analyzed.Dataareanalyzedaccordingtotheprocessfromfactoranalysistoreliabilitytestingandregr essionanalysis.Theresultsshowedthatattitudesandperceptionsthatcontrolconsumerbehaviorpos itivelyaffectbuyingintentionsonline.Meanwhile,perceivedriskhasanegativeeffectonconsumers'in tentiontobuyonline.
- Analysis of factors affecting online shopping behavior of Can Tho city consumers (NguyenThiBaoChauandLeXuanDao,2014)
ThepurposeofthisstudyistoidentifyfactorsaffectingonlineshoppingbehaviorofCanTho city consumers. Data collected from 130 consumers (100 who shop online and 30whodonotshoponline),usingfactoranalysis,multivariateregressionanddifferentiation to identify the factors affect online shopping behavior of Can Tho citypeople Research results have shown financial and product risk factors, variety of productselection,trust,websiteresponsiveness,timerisk,comfort,convenience,Priceaffectsaconsu mer's decision to continue (or start) shopping online In particular, the comfortfactorhasthe greatest impact ononlineshoppingbehavior.
ThisstudyusesthemodifiedTAMmodelasthetheoreticalbasistobuildanddeveloparesearch model of factors affecting online shopping intentions Research is done byqualitative and quantitative methods The first is done through direct interviews with
5subjectswhohaveexperienceinonlineshoppingformorethan2years.Respondentsareyoungpeople,f rom22-25yearsold,mostofthemhaveexperiencein usingtheInternetandhaveknowledgeofonlineshoppingservices.Usingquantitativesurveyq uestionnaires according to the convenient method, the results obtained 171 matchingquestionnaires The author used: test the scale (assess the reliability of Cronbach alphaand analyze the discovery factor EFA) Test hypotheses of the multivariate regressionmethodperformedonSPSS20.Throughresearch,thetwocomponentsofperceivingtheu sefulness, the perception of ease of use, the components of the expected price and thereliabilityareallhavethesameeffectononlineshoppingintentions.EspeciallythePriceExpectation component has a strong impact on online shopping intent Meanwhile, theperception of risk related to online transactions and the perception of risk related to theproduct/servicehasanegativeimpactonintenttouse.
Foreignresearch
- AnAnalysisofFactorsAffectingonOnlineShoppingBehaviorofConsumers(Javadi,Dolata badi,Nourbakhsh,Poursaeedi,vàAsadollahi, 2012)
The purpose of this study is to analyze the factors that influence consumers' onlineshopping behavior The study uses a model to examine the impact of cognitive risk,infrastructure variables and return policy on subjective attitudes and norms, perceivedbehavioral control, innovation in particular areas and attitudes about online shoppingbehavior.RespondentswereonlineconsumerstoresinIranwhowererandomlyselectedt hrough200questionnairesscatteredamongonlinestores.Theauthorusesregressiontoanalyze data, test the hypotheses of learning Research has determined that financial risksandnon- deliveryrisksnegativelyaffectonlineshoppingattitudes.Theresultsalsoshowthatd o m a i n - s p e c i f i c i n n o v a t i o n a n d s u b j e c t i v e i n d i c a t o r s p o s i t i v e l y a f f e c t s h o p p i n g
Online shopping behavior Attitude behavior Moreover, online shopping attitudes positively influence consumers' onlineshoppingbehavior.
- Factors Affecting Consumers’ Internet Shopping Behavior During the COVID- 19Pandemic: EvidenceFromBangladesh(NegerandUddin,2020)
The research investigates product factors, price factors, time-savingfactors, paymentfactors, security factors, administrative factors and psychological factors on shoppingbehavior.C o n s u m e r i n t e r n e t d u r i n g t h e c o r o n a v i r u s ( C O V I
Internet shopping behavior Time saving factor
Bangladesh Data collected from 10 May 2020 to 10 June 2020 from 230 Bangladeshionline consumers Data were analyzed using descriptive statistical analysis, reliabilityanalysis,andmultivariateregressionanalysis.Theresultsshowedthatallfactorsexce ptprice and security were positively and significantly associated with consumers' internetshoppingduringtheCovid19epidemicinBangladesh.
Emergency: The Case of Kuwait during the COVID-19 Pandemic (Alhaimer,2021)
ThisstudyinvestigatesvariousriskfactorsthatalteronlineshoppingbehaviorinKuwaitduringtheCO VID-19pandemic.Useonlinequestionnairesdistributedthroughmultiplesocial networking platforms Overall, 385 responses were collected via onlinequestionnaires and data analyzed using AMOS 21 for structural equation modelingpurposes The results show that risk tolerance, the severity of the risk, and the risk oflegalpenaltiespositivelyaffectconsumers'onlinebuyingattitudesinKuwait.Incontrast,pr oductrisk,financialrisk,andnon- deliveryriskarenotsignificantlyaffected.Theriskofconvenienceistheonlyfactorthat negativelyaffectsattitudes.Furthermore,the author found that the official penalties imposed on those who violated the door lockrule could have a direct and positive effect on consumer behavior towards onlineshopping in this era Translate.Factors affecting users' attitudes and behavior towardonline shopping during normal non-urgent times are different from factors during timesofemergency.
Proposedresearchmodel
The research is based on Ajzen and Fishbein's Theory of Reasoned Action (1980),TheoryofPlannedBehavior(TPB)byAjzen(1991)alongwithresearcharticlesreference d at home and abroad on online shopping behavior such as: Ha Ngoc Thang(2016),NguyenLePhuongThanh(2013),Javadietal.
(2012),NegerandUddin(2020),Alhaimer (2021) This will serve as a basis for selecting and proposing an appropriateresearchmodel.Fromthetheoreticalbasisandsummaryofpreviousresearchresults,thepropose d research model includes five factors affecting online shopping behavior ofBanking University students during theCovid-19 period including: These include: (1)Attitude,(2)Price,(3)SubjectiveNorms,(4)Perceivedbehavioralcontrol,(5)Perceivedrisk.
Online shopping behavior Subjective Norms
H1:AttitudehasapositiveimpactononlineshoppingbehaviorofstudentsduringCovidH2 : Price has a negative impact on online shopping behavior of students during
Research design
Methodofbuildingascale
Thestudyhas2researchobjectives relatedtotheestablishmentof the scale,including:
Nominal scale is built to distinguish and identify research objects The topic has builtnominalscalesincluding:Gender,Schoolyear,Schoolofstudy,Income,Howlonghaveyoubeenu singtheInternet,HowmanyhoursdoyouusetheInternetinaday,Shoppingsites often buy during the Covid pandemic, products often purchased by participants.The advantage of this scale is that it is easy to set up as well as highly specific andprovidesusefulinformation.
The hierarchical scale is built to quantify and arrange problems in order, to measureattitudes,consciousness,opinions,interestsandperceptions.Thescalesandobservationsintheto picusetheLikertscale(5levels)andaredescribedindetailinatabletoidentifythe main factors affecting the decision to choose a foreign language center of thestudents.student.
Themodelhas5scalesofindependentfactors(with25observedvariables)andascaleofdepe ndentfactors(with5observedvariables)builtonatheoreticalbasis.
HypothesisH0has5factorsincluding:Attitudefactor,Pricefactor,Subjectivenor msfactor,Perceivedbehavioral controlfactor,Perceived riskfactor.
The dependent factoristhe online shopping behavior ofstudents at Banking
Researchscale
Primaryd a t a w a s c o l l e c t e d t h r o u g h s u r v e y s a n d i n t e r v i e w s T h e e n t i r e s t u d y w a s conductedusingsurveyquestionnairesbeca useitwaseasierforrespondentstocompletethequestionnaire.Thequestionnairewaspostedtostudentgr oupsofBankingUniversityof Ho Chi Minh City The responses from students obtained through the questionnairewillberecordedthroughtheresearcher's emailaddress.
Secondary data is collected from external sources such as books, journals, researcharticlesandinternetdatabasestoprovideinformationontheoreticalfoundations,res earchmodels,research methods,the scale…
The scale for the Attitude variable in this study is referenced from the scale in the studyofSamar,S.,M.Ghani,andF.Alnaser(2017);Rahi,S.,M.Ghani,andA.Ngah (2018)
A1 Ifindonlineshoppingtobeworth using, especiallyduringCovid'stime Samar,S., M.Ghani,and
Shipping price is my main considerationwhendecidingtouseornottous e online shoppingservices
Hypothesis H2 : Price has a negative impact ononline shopping behavior ofstudentsduringCovid
The scale for the Subjective Norms variable in this study is referenced from the scale inthestudyofNguyenNgocTram(2015);RehmanandAyoup(2019)
Hypothesis H3 : Subjective Normshave a positive impact ononline shoppingbehaviorofstudentsduringCovid
NguyenNgocTram(2 PBC2 Idon't shoponlineifIdon'thaveacredit 015) card
PBC4 WhetherIuseonlineshoppingor notis entirelyup tome
Whentransferringmoneyonline,Iamafraidthat I will lose money due to careless errorssuchaswrongaccountnumberor wrong amount
The scale for the Online shopping behavior variable in this study is referenced from thescaleinthestudyofNguyenNgocTram(2015);Huangetal.(2013)
015) OSB2 Ihavemore choices whenIshop online
OSB4 Icanavoidcrowdswhenshoppingonlin e duringCovidtime OSB5 Iwillcontinuetoshop onlineinthefuture Huangetal (2013)
8 Shippingpriceis mymainconsiderationwhen decidingtouseor not touseonlineshoppingservices
25 Whenatransactionerroroccurs,Iam worried thatIcannotgeta refundfrom theseller
Chooseasample
Overall
Methodofsampling
Thisstudyusesnon- probabilitysamplingtechniquewithconvenientsamplingformtocollectsurvey data for the followingreasons:
- Firstly, this study isexploratory,sothe non-probability samplingmethodwithconvenient sampling form provedto be themostsuitable.
- Second, for students, time and cost are two issues to consider when conducting thesurvey, so the topic chooses this sampling method so that it doesn't take much timeandmoneyto spend on the survey.studysample.
- Thirdly, this sampling method helps the researcher to easily approach the surveysubjectscomparedtoothersamplingmethods.
Samplesize
For the exploratory factor analysis model, according to Hair, Anderson, Tatham andBlack(1998),thesamplesizeisdeterminedbasedon:
Ifthemodel hasm scales,nisthe number ofsamples n=5*m
Informationgathering
- Thebasicfeatureoftheself-answeredquestionnaireis thatthe subjectwill nothavetospecifyhisidentity,thusensuringtheconfidentialityof personalinformation.
- Theresponserateto thisformofsurveyis usuallyveryhigh.Thebasicstepsinthequestionnairedesign process:
Informationgatheringprocess
Because the research object of this topic is "students of Banking University of Ho ChiMinh City", the collection of primary information by self-answering questionnaires intheformofForm-
GoogleDocssoftwareisdone.usedtodesignbyonlinequestionnaire thensentthroughstudentgroupsoftheschool.Itisdifficultforstudentstohand- deliversurveyquestionnairesbecausethey areonalongschoolbreak.
When the subject to be investigated completes the answer and presses the
"Submit"button, the questionnaire will be recorded through the researcher's email only aftercollecting the required number of samples, the survey questionnaire is closed and theresearchdatacollectionends.
DatacollectedfromsurveyquestionsiscodedandenteredusingSPSS20.0dataanalysissoftwaretofacilit atedata analysis later.
Statistical dataanalysistechnique
Testingthereliabilityofascale
The research topic uses Cronbach's Alpha coefficient and factor analysis to test thereliability of each scale of factors affecting online shopping behavior of students atBankingUniversityofHoChiMinhCityduringtheCovidpandemic.
According to Hoang Trong and Mong Ngoc (2010), many researchers agree that whenCronbach'salphaisfrom0.8orhighertocloseto1,thescaleisgood,from0.7tonearly
0.8isusable.TherearealsoresearcherswhosuggestthatCronbachalphaof0.6orhighercanbeusedinsom eothercases.Forthisresearchtopic,inordertoensurethereliabilityof the scale, only the analytical factors withCronbach's coefficient greater than 0.7 willbe considered as the scale with accepted and retained reliability In addition, the totalvariablecorrelationrelationshipisalsoconsidered,onlythosevariableswithcoefficientsgreaterthan0.3willbekeptinthemodel.
Testingthereliabilityofthemodel
Since the original hypothesis comes from different sources, the probability of error ishigh Therefore, the EFA factor analysis is used in the study to discover whether thevariablesinthemodelarecorrectastheoriginalhypothesisandtoreducethevariables that are more or less related to each other to combine them into new ones group offactorsless.
Correlationcoefficientandregressionanalysis
Before conducting regression, it is necessary to determine the correlation coefficientbetween the variables in the model through the correlation coefficient matrix calculatedbySPSS
20.0softwaretodeterminethedegreeoflinearassociationbetweentheindependent variables and the independent variables set up and dependent variable inthe regression model In addition, the correlation coefficient matrix also helps to detectthephenomenonofmulticollinearitybetweentheindependentvariables,therebyover comingthedefectsofthemodel.
Then, regression analysis was used with qualitative dependent variable as
“Onlineshopping behavior”, expected independent variable as “Attitude”, “Price”,
The t-test is used to test whether there is a difference between the mean of a singlevariable and a particular value, with the initial hypothesis that the mean of that variableisequaltowithaspecificnumber.
Anova analysis is used to test the hypothesis of equal mean of the sample groups with aprobabilityoferrorof5%.IfSig 0.05, it is not enough to confirm that there is a difference betweengroupsforthedependentvariable
Conclusionofchapter3
The main objective of this chapter is to introduce the research process of the topic aswell as the methods carried out in the implementation process, with two main parts:research design and statistical data analysis techniques millet The topic has built theofficial survey scale and questionnaire for the research paper With the objective reasonthatthetopicisonlyexploratory,thestudents'limitedbudgetandwanttohave convenienceinapproachingtheresearchobject,thetopiccametothedecisiontousethesampling technique. non-probability sample, in the form of convenient sampling byonline questionnaire - sent directly through groups with a link to the questionnairedesigned online Next, process and encrypt the collected data to be ready for data analysisusingSPSS20.0software.
Inanalyticaltechniques,thetopicusesCronbach'sAlphacoefficienttotestthereliabilityofthescale,factor analysistoreducemoreorlessrelatedvariablesintogroupswithlessfactors Moreover, Anova test shows the relationship between qualitative variables andbuying behavior Besides, the topic also determines the correlation coefficient betweenthe variables in the model to determine the degree of linear association between theindependent variable and the dependent sea in the model Regression analysis to studythe relationship between two variables, model and quantify the relationship so that theimpactlevelofthefactorscanbedetermined.Fromthere,showtheproposedmodel.
Dataanalysis
Unsuitableanswersheets
The timetostartsending outquestionnaires and receivingresponses from surveysubjects was conducted in about three weeks After closing the online questionnaire, intheprocessofdataentry,thesubjectselectedtheappropriateobservations.
On June 7, 2021, when the survey ended, there were 165 observed samples recorded bySPSS20.0software.Intheprocessofre- filteringthedata,thestudyexcluded5observationalsamplesbecausethesesampleshavenevershopped onlineone- commercesites.Thus,afterfilteringtheobservedsamples,theremaining160samplesarevalidandputint oprocessingandanalysis.
Dataencryption
The survey participants' gender was coded and received two values (1: male; 2: female)forprocessingconvenience.
Thesurveyparticipant'sschoolyearwascodedandreceivedfivevaluesas(1:year1;2:year2;3:year 3;4:year4;5:other).
The survey participants' majors were coded and received seven values: (1: BusinessAdministration;2 : A c c o u n t i n g – A u d i t i n g ; 3 : F i n a n c e –
B a n k i n g ; 4 : I n t e r n a t i o n a l Economy;5:InformationSystemsManagement;6:Economi claw;7:Englishlanguage).The surveyparticipant's income is coded and receivesfive values(1: no income,dependentonfamily;2:from1-3millionVND;3:3-5millionVND;4:from5-7million dong;5:from7-10milliondong).
Internetusagetimeisencryptedandgets4values(1:5years).
How many hours of Internet use in 1 day are encrypted and get four values (1: 3hours)
Shopping sites that often buy during the Covid epidemic are encrypted and receive fivevalues(1:Shopee,2:Lazada,3:Tiki,4:Sendo,5:Other)
Productsc o m m o n l y p u r c h a s e d w h e n s h o p p i n g o n l i n e a r e e n c r y p t e d a n d r e c e i v e f o u r values (1: Clothing, shoes; 2: Cosmetics; 3: Electronics (phone, televisions); 4:
Other)Attitudes,p r i c e s , s u b j e c t i v e n o r m s , p e r c e i v e d b e h a v i o r a l c o n t r o l, p e r c e i v e d r i s k a r e classifiedinto5groupstofacilitatedataprocessing.In whicht hereare thefollowinggroups:1isStronglydisagree,2isDisagree,3isNeutral,4isAgree,5isStrong lyagree.
Samplediscription
The topic examines how the sample is distributed according to the following criteria:Gender, School year, Major, Income, Time to use the Internet, How many hours to usethe Internet in a day , Shopping sites often purchased during the Covid epidemic,Productsoftenpurchased,Attitude,Price,Subjectivenorms,Perceivedbehavioral control,Perceived riskand Onlineshoppingbehavior.
(Source: Author investigated and analyzed)TheresultsofTable
4.1showthat,in160observedsamples,thesex"Female"appeared93 times (accounting for 58.1%), the sex
"Male" appeared 67 times (accounting for41.9%) Thus, this study is biased towards the "Female" gender because the number offemale students at Banking University of Ho Chi Minh City is much larger than thenumberofmalestudents.
(Source: Author investigated and analyzed)TheresultsofTable4.2showthatintermsofsamplestructurebyacademicyear,outof16
0 survey samples, there are 24 first-year students (accounting for 15%), 31 second- yearstudents(accountingfor16.9%),and31third-yearstudents(accountingfor16.9%).accounting for 16.9%), 69 fourth-year students (accounting for 43.1%) The remaining5peoplechoseOther(accountingfor3.1%).
(Source: Author investigated and analyzed)The results of Table 4.3 show that about the sample structure by discipline, out of 160survey participants, there are 48 students of Business Administration (accounting for30%), 24 students of Accounting - Auditing (accounting for 15%) %), 31 studentsmajoringinFinanceandBanking(19.4%),22studentsinInternationalEconomy(13.8
%), 10 students majoring in Information Systems Management (accounting for6.3%), 12 students majoring in
Economic Law (accounting for 7.5%), 13 studentsmajoringinEnglishLanguage(accountingfor8.1%).
(Source: Author investigated and analyzed)The results of Table 4.4 show that about the sample structure by income, out of 160observedsamples,thereare36studentswhohavenoincomeyet,dependontheirfamily(accounting for 22.5%), 47 students have an income of 1-3 million (accounting for29.4%),46studentswithincomeof3- 5million(accountingfor28.7%),14studentswithincome of 5-7 million (accounting for 8.8%), 17 students with income of 7-
(Source: Author investigated and analyzed)TheresultsofTable4.5showthataboutthesamplestructurebytimeofInternetuse,outof16
0observedsamples,2studentsusetheInternetforlessthan12months(accountingfor 1.3%), 12 students use the Internet for 1-2 years accounting for 7.5%), 39 studentsusing it for 3-5 years (24.4%), 107 students using it for 5 years or more (66.9%) Thus,this study leaning towards students using more than
5 years is reasonable because moststudentstodayhaveaccesstothe Internetquiteearly.
(Source: Author investigated and analyzed)TheresultsofTable4.6showthatthesamplestructurebytimeusestheInternetforhowman y hours in a day, out of 160 observed samples, 2 students use less than 1 hour(accounting for 1.3%), 23 students used 1-2 hours (14.4%), 41 students used 2-3 hours(25.6%), 94 students used more than 3 hours (58.8%) Thus, this study leans towardsstudents using more than 3 hours because most students spend a lot of time on socialnetworks.
(Source: Author investigated and analyzed)TheresultsofTable4.7showthatintermsofsamplestructureaccordingtoe-commercesites often purchased during the Covid epidemic, out of 160 observed samples, 78students chose Shopee (accounting for 48.8%), 67 students chose Lazada (accountingfor 41.9) %), 14 students chose Sendo
(accounting for 8.8%), 1 student chose another(accountingfor0.6%).Thus,thisstudyisbiasedtowardsstudentsusingShopeebecauseSho peeisapopulare-commercesitetodaybecauseofitslowprice,whichissuitableforstudents.
(Source: Author investigated and analyzed)The results of Table 4.8 show that in terms of sample structure according to commonlypurchased products, out of 160 observed samples, 55 students chose clothes and shoes(accountingfor34.4%),49studentschosecosmetics(accountingfor30.6%),47studentschose electronic (accounting for 29.4%), 9 students chose other (accounting for 5.6%).Thus, this study is inclined towards students who choose to buy clothes and shoesbecausethisitemiseasytobuy.
N Minimum Maximum Mean Std.Deviation
(Source: Author investigated and analyzed)TheresultsofTable
4.9showthatin160surveyparticipants,thereisagreatfluctuationin opinion about the importance of the
ATTITUDE factor with the smallest point being1( s t r o n g l y d i s a g r e e ) a n d h i g h e s t p o i n t b e i n g 5 ( s t r o n g l y a g r e e ) , w h i l e t h e t e a m ' s expectation was "positive", so this fluctuation produced a standard deviation. However,the average value of the factors A1, A2, A3, A4, A5 shows that they all revolve aroundtheaveragevalueat3- 5(orfromneutraltostronglyagree).Thus,theoriginalhypothesisof positive sign in the model is still supported In other words, the "standard deviation"iswithintheallowablelimit.Therefore,theobserveddataareconsideredappropriate.
N Minimum Maximum Mean Std.Deviation
(Source: Author investigated and analyzed)The results of Table 4.10 show that out of 160 survey participants, there is a hugefluctuation in opinion about the importance of the PRICE factor with the smallest pointbeing 1 (strongly disagree) and the highest point being 5 (strongly agree), while theteam'sexpectationis"positive",sothisfluctuationproducesstandarddeviation.However, the average value of the factors “P3, P4, P5” shows that they all revolve aroundthe average value at the level of 3-5 (or from neutral to strongly agree) “P1, P2” has anaverage value near 3. Thus, the initial hypothesis of positive sign in the model is stillsupported.Inotherwords,the"standarddeviation"iswithintheallowablelimit.Therefore,theo bserveddataareconsideredappropriate.
N Minimum Maximum Mean Std.Deviation
(Source: Author investigated and analyzed)The results of Table 4.11 show that out of 160 survey participants, there is a hugefluctuation in opinion about the importance of the SUBJECTIVE NORMS factor withthe smallest point being 1 (strongly disagree) and the highest point being 5 (stronglyagree), while the team's expectation is "positive", so this fluctuation produces standarddeviation However, the average value of the factors SN1, SN2, SN3, SN4, SN5 showsthat they all revolve around the average value at 3-5 (or from neutral to strongly agree).Thus, the original hypothesis of positive sign in the model is still supported In otherwords, the "standard deviation" is within the allowable limit Therefore, the observeddataareconsideredappropriate.
N Minimum Maximum Mean Std.Deviation
(Source: Author investigated and analyzed)The results of Table 4.12 show that out of 160 survey participants, there is a hugefluctuationinopinionabouttheimportanceofthePERCEIVEDBEHAVIORALCONT
ROL factor with the smallest point being 1 (strongly disagree) and the highestpoint being 5 (strongly agree), while the team's expectation was "positive", so thisfluctuation produced standard deviation However, the average value of the factors“PBC4, PBC5” shows that they all revolve around the average value at the level of 3-5(or from neutral to strongly agree), only the variable “PBC1 , PBC2, PBC3” haveaverage values near 3 Thus, the initial hypothesis of positive sign in the model is stillsupported.Inotherwords,the"standarddeviation"iswithintheallowablelimit.Therefore,the observeddataareconsideredappropriate.
N Minimum Maximum Mean Std.Deviation
(Source: Author investigated and analyzed)The results of Table 4.13 show that out of 160 survey participants, there is a hugevariation in opinion about the importance of the PERCEIVED RISK factor with thesmallestpointbeing1(stronglydisagree).andthehighestpointbeing5(stronglyagree),whilet h e t e a m ' s e x p e c t a t i o n w a s " p o s i t i v e " , s o t h i s f l u c t u a t i o n p r o d u c e d s t a n d a r d deviation.However,theaveragevalueofthefactorsPR1,PR2,PR3,PR4,PR5shows that they all revolve around the average value at 3-5 (or from neutral to strongly agree).Thus,theoriginalhypothesisofpositivesigninthemodelisstillsupported.
N Minimum Maximum Mean Std.Deviation
(Source: Author investigated and analyzed)The results of Table 4.14 show that out of 160 survey participants, there is a greatfluctuation in opinion about the importance of the ONLINE SHOPPING BEHAVIORfactor with the smallest point being 1 (strongly disagree) and the highest point being5(stronglyagree),whiletheteam'sexpectationwas"positive",sothisfluctuationproduced standard deviation However, the average value of the factors OSB1,OSB2,OSB3,OSB4,OSB5showsthattheyallrevolvearoundtheaveragevalueat3-5(orfromneutral to strongly agree Thus, the original hypothesis of positive sign in the model isstillsupported.
Reliabilityanalysisandappropriatescale
TestingofscalereliabilitybyCronbach'salphacoefficient
The resultsof testingthe scaleof the factors in theresearchmodel according toCronbach'sAlphacoefficientarepresentedin detailasfollows:
(Source: Author investigated and analyzed)TheresultsofTable4.15showthatthetotalCronbach'sAlphacoefficientis0.858intherange
0.89.Inaddition,thecorrelationcoefficientofallcomponentscalesisgreaterthantheminimumsta ndardof0.3.Therefore,thescaleofATTITUDEvariablemadeupof 5 component scales including A1,2,3,4,5 has high reliability and is used for in-depthanalysisinthe nextsection.
(Source: Author investigated and analyzed)TheresultsofTable4.16showthatthetotalCronbach'sAlphacoefficientis0.6
0.79.Noticethatthecorrelationcoefficient between the variables is not high If you delete any scale in this variable, theCronbach'sAlphacoefficientisnotgreaterthan0.7.Thereby,wefindthatthereliabilityof the PRICE variable is not high, the variable PRICE is a subjective variable, notsuitable for inclusion in the research model. Therefore, it is necessary to remove thisvariablefromthemodel.
(Source: Author investigated and analyzed)TheresultsofTable4.17showthatthetotalCronbach'sAlphacoefficientis0.818intherange
0.8-0.89.Inaddition,thecorrelationcoefficientofallcomponentscalesisgreaterthan the minimum standard of 0.3 Therefore, the scale of the SUBJECTIVE NORMSvariable made up of 5 component scales including SN1,2,3,4,5 has high reliability andisusedforin- depthanalysisinthenextsection.
(Source: Author investigated and analyzed)TheresultsofTable4.18showthatthetotalCronbach'sAlphacoefficientis0.705intherange
= 0.185 is less than the minimum standard of 0.3 In addition, looking at the column ofCronbach'sAlphacoefficientifthetypeofcomponentscaleshowsthatwhenPBC4,5isremoved from the structure of the PERCEIVED BEHAVIORAL CONTROL variable,the total Cronbach's Alpha coefficient will increase from 0.705 to 0.721 and 0.705 to0.748 Therefore, it is necessary to remove PBC4,5 and perform a reliability test of thesecondPERCEIVEDBEHAVIORALCONTROLscale.
(Source: Author investigated and analyzed)The results of Table 4.19 show that the total Cronbach's Alpha coefficient is 0.872,rangingfrom0.8to0.89.Inaddition,thecorrelationcoefficientofallcomponentscalesis greaterthantheminimum standardof0.3.Therefore,thescaleofthevariablePERCEIVED BEHAVIORAL CONTROL created by 4 component scales includingPBC1,2,3 hashighreliabilityand isused forin-depthanalysisin thenextsection.
(Source: Author investigated and analyzed)The results of Table 4.20 show that the total Cronbach's Alpha coefficient is 0.928,ranging from 0.9-1 In addition, the correlation coefficient of all component scales isgreaterthantheminimumstandardof0.3.Therefore,thescaleofthevariablePERCEIVED RISK is made up of 5 component scales including PR1,2,3,4,5 with veryhighreliabilityandisusedforin-depthanalysisin thenextsection.
(Source: Author investigated and analyzed)TheresultsofTable4.21showthatthetotalCronbach'sAlphacoefficientis0.804intherange
0.89.Inaddition,thecorrelationcoefficientofallcomponentscalesisgreaterthantheminimumst andardof0.3.Therefore,thescaleofthevariableONLINESHOPPING BEHAVIOR made up of 5 component scales including OSB1,2,3,4,5 hashighreliabilityandisusedforin- depthanalysisinthenextsection.
ExploratoryfactoranalysisEFA
(Source: Author investigated and analyzed)TheresultsofTable4.22showthattheKMOcoefficientofthemodelis0.776,whichishig her than the standard KMO coefficient of 0.5, which passes the Bartlett test at thesignificance level of 0.000 (error 0%).
Therefore, factor analysis for the research modelisappropriate(selectedvariablesinthemodel areworthstudying).
(Source: Author investigated and analyzed)The results of Table 4.23 show that the factor analysis model gives 4 factors withEigenvaluesgreaterthan1,thisfactortogetherexplains70.819%>50%.Therefore,thismodelisc orrectwiththeoriginalhypothesis,theresearchmodelconsistsof4independent variables.
(Source: Author investigated and analyzed)The results of Table 4.24 show that after performing factor rotation by the Varimaxmethod, 18 observed variables (scales) have formed converging groups with all valuesgreater than the minimum standard of 0.5 All observed variables converge in the orderofeachvariable.
(Source: Author investigated and analyzed)The results of Table 4.25 show that most of the discriminant values of the observedvariables exceed the minimum allowable limit of 0.3 However, the variable SN5 has adiscriminantcoefficientoflessthan0.3,soitisexcluded.Continuethesecondexploratoryfac toranalysiswiththeremaining 17variables.
Table4 26:KMOcoefficientand2 nd Bartlett'stest
(Source: Author investigated and analyzed)TheresultsofTable4.26showthattheKMOcoefficientofthemodelis0.776,whichislarg er than the standard KMO coefficient of 0.5, which passes the Bartlett test at thesignificance level of 0.000 (error 0%).
Therefore, factor analysis for the research modelisappropriate(selectedvariablesinthemodel areworthstudying).
The results of Table 4.27 show that the factor analysis model gives 4 factors withEigenvalues greater than 1, which together explain 71.836% > 50% Therefore, thismodeliscorrectwiththeoriginalhypothesis,theresearchmodelconsistsof4independent variables.
The results of Table 4.28 show that after performing factor rotation by the Varimaxmethod, 17 observed variables (scales) have formed converging groups with all valuesgreaterthantheminimumstandardof0.5.Theobservedvariablesconvergedintheorderofeach variable.
The results of Table 4.29 show that most of the discriminant values of the observedvariablesexceedtheminimumallowablelimitof0.3,sotheyareaccepted.Atotalof17re maining observed variables will be kept in the model and reduced in the next section.Factor analysis forthedependentvariable
(Source: Author investigated and analyzed)TheresultsofTable4.30showthattheKMOcoefficientofthemodelis0.799,whichislarg er than the standard KMO coefficient of 0.5, which passes the Bartlett test at thesignificance level of 0.000 (error 0%).
Therefore, factor analysis for the research modelisappropriate(selectedvariablesinthemodel areworthstudying).
(Source: Author investigated and analyzed)TheresultsofTable4.31showthatthefactoranalysismodelgivesexactly1factorwithEi genvaluesgreaterthan1,thisfactortogetherexplains56.288%>50%.Therefore,this modeliscorrectwiththeoriginalhypothesis,theresearchmodelincludes1dependentvari able.
(Source: Author investigated and analyzed)The results of Table 4.32 show that after performing factor rotation by the Varimaxmethod, 5 observed variables (scales) have formed convergent groups with all valuesgreaterthantheminimumstandardof0.5.Therefore,thisisthefinalresulttoincludeinthev ariablecollapsein thenextsection.
Therefore,all17observedvariablesareusedasthescale.Andtheobservedvariablesof4factorsaf fectingonlineshoppingbehaviorare:
- Factor 2: includes variables about subjective norms, name this factor as subjectivenorms.
- Factor 4: includes variables about perceived risk, name this factor as perceived risk.Finally, 4 factors corresponding to the indicators will be tested in relation to onlineshoppingbehaviorinthenextsection,asfollows:
- Subjective normsfactorsinclude4variables: SN1,SN2,SN3, SN4
- Perceivedbehavioralcontrolfactorscontrollingbehaviorinclude3variables:PBC1,PBC2,PB C3
- Perceivedriskfactorsincludes5variables:PR1,PR2,PR3,PR4,PR5
Testingofmodelsandhypotheses
The given regression model is quite suitable, with the adjusted R2 coefficient 0.259,which means that 3 independent variables explain 25.9% of the decision to choose andF = 19,518 and the significance level sig = 0.000 < 0.05, so it can be confirmed theexistence of a relationship between attitude, subjective norms, perceived behavioralcontrolandonlineshoppingbehavior.Atthesametime,wehavearatherhightoleranc ecoefficient (from 0.871to 0.977) and a low VIFv a r i a n c e e x a g g e r a t i o n f a c t o r ( f r o m
1 0 2 4 to 1,148 less than 10) Therefore, there is no multicollinearity among the independentvariablesinthemodel.
(Source: Author investigated and analyzed)The results of Table 4.33 show that the correlation coefficients of the independent anddependentvariablesarebothsignificant,representedbytwo**signs(the"*"signifies
Subjective norms Online shopping behavior
Attitude thelevelofsignificancewithintheallowederror).Allvariableshaveerrorlessthan0.05.However,thevaria ble"Perceivedrisk"isnotsignificant,shownby2**andhasanerrorgreaterthan0.05,whichisnotappro priate,soitisexcluded,theremainingvariablesareconsideredsuitableforrunningthe regressionmodel linearityinthenextsection.
(Source: Author investigated and analyzed)TheresultsofTable4.35showthatthegivenregressionmodelisquitesuitable,withtheadjust ed R2 coefficient = 0.259, which means that 3 independent variables explain25.9% of the decision to choose and F 19,518 and the significance level sig = 0.000