CHU'ONG I. GUI THIEU.........................................................................................................1

I. TONG QUAN VE THUJONG MAI DIEN TO........................................................1

II. DAT VAN DE...........................................................................................................6

III. MUC TIEU VA PHAM VI NGHIEN CIIU...........................................................7

1. Muc tie‘ u nghién ctiu.......................................................................................................72. Pham vi nghie‘ n ciiu.........................................................................................................8

PHAN A. CAC MO PIINH NGHIEN CIIU TRIICIC DAY..................................................1 0

I. MO HINH CHAP THUAN CONG NGHE (TAM)..............................................10

I. I . C:i c kie‘n tr u c chinh..........................................................................................101.1 . I Nhan th tic st h tt u ich................................................................................10I.1.2 Nhfin thitc tinh dé st dung..........................................................................10I.1.3 Thai dfi hitdng de‘n viéc sit dung................................................................11

1.2. Mo hinh TAM..................................................................................................11II. MO HINH CHAP NHAN SP DUNG THU'ONG MAI DIEN TU' («-CAM)... 12

II.I. C:i c kié» truc chinh........................................................................................12II. 1. 1 Nhfin thiic ’i ro lié n quan dén san ph am/dich vu (Perceived Risk with Product/Service - PRP)................................................................................................12II. I .2 Nh:i n th tic rui ro lié n quan den giao dich trdc tuyen (Perceived Risk in the Context of Online Transaction).........................................................................1 4

11.2. Mo hinh e-CAM..................................................................................................15

III. MO HINH KET HOP VE CHAP THUAN VA SH DUNG CONG NGHE(UTAUT)...............................................................................................................................17

III. 1. C:i c rie n trfi c chinh........................................................................................17III. 1.1 Ky vong ké I qua thu’c hién (Performance Expectancy)..........................17III.1.2 Ky vpng no lirc (Effort Expectancy).......................................................17III. 1.3 Anh hircrng xa hfii (Social Influence)........................................................17III. 1.4 C:ie diéu kié n thufin tié n (Facili tating Conditions).................................17III.2. Mo hinh UTAUT.............................................................................................18

PHA’NN BB.. LAP MO’ HINH PHUONG TRINH CAU TRUC (SEM — STRUCTURALEQUATION MODELLING)...................................................................................................19

I. GIDI THIEU.............................................................................................................19II. CAC THANH PHAN CUA SEM..........................................................................20

III. KIEN TRUC SEM...................................................................................................21

III. 1 . Chi dinh mo hinh (Model Specification)....................................................21Ill.2. Nhfi n da rig trio hinh (Model Identification).................................................22

III.3. lf cic luring mo hinh (Model Estimation)....................................................23III.4. Danh g iii dfi thich h0p cua ‘ hinh (Assesin g Fit of the Model)............24

III.5. Hieu chinh mo hinh (Model Modification).................................................25Ill.6. Trinh bay mo hinh cufii cii ng (Final Presentation of Model)....................25

THIE‘TT LA P MMOO HINH NGHIEN HU VA CAC GIA THUYET26

I. LAP MO HINH TAM-ECAM................................................................................27

I. I .Lit a chon c:ic kién true ngoai sinh...............................................................271.2. C:ic kién truc chinh ................................................................. .................... 3 I

1.2.I Thu:1 t ng ti (Terminology) ........................................................................ 3 II.2.2 Thiet ke giao dién (Screen Design)...........................................................32I.2.3 C:ic dieu kién thuan tien (Facilitating Conditions)..................................32I.2.4 Nh:in thiic ’i ro lié n quan d é n san pham/d ich vu (PRP) va Nha n thiic ruro l ié n q ua n dé n giao dich tr iic iuyé n (PRT)......................................................32I.2.5 C:ie bien nh:i n kh:i u hoc (Demographic)................................................32

1.3. Mo hinh TAM-ECAM dit dinh.....................................................................33II. CAC GIA THUYET NEN TANG..........................................................................34

III. PHIIONG PHAP NGHIEN CIIU...........................................................................35

III. 1 . Dit liéu nghié n ciiu........................................................................................35III.2. Thu tuc thu thap diI 1i(u...........................................................................35III.3. C:ic do litd ng.............................................................................................35

CHUDNG IV. PH AN TICH DC LIEU...........................................................................37

I. CAC THONG KE CHUNG...............................................................................38I. I .Thong ké e:ic dac tinh nhan khau hoc..........................................................38

1.2. Thong ké kinh nghi(m sft dung Internet.......................................................38I. 3. Thong ké dit dinh mua ha rig tritc tu yé n.........................................................39

II. KIEM DINH MO HINH TAM-ECAM..................................................................39

II. 1. X:ic dinh dfi tin cay va do gi:i tri..............................................................................39II.2. 1 Do tie cfiy....................................................................................................................39

II.2.2 Do gia iri.....................................................................................................................40

1. Phfin tich nhan to khang dinh cap nhan to (bitcic 1)..................................412. Ph:in tich nhan to khang dinh cho to:in bfi mo hinh (bitfic 2)....................43

II.2. Trinh bay mo hinh TAM-ECAM cuoi cung.................................................45III. KIEM DINH CAC GIA THUYET.........................................................................45

CHU'DNG V. SO SANH KET QUA PHAN TICH.........................................................47

I. SO SANH V€iI KET QUA CUA MO HINH e-CAM..........................................48

II. so sANH VII KET QUA CUA MO HINH TAM va UTAUT...................49

CHUDNG VI. KET LUAN VA DE XUA T..........................................................................50

I. TOM TAT KET QUA ..................................................................................... 5 I

II. DE XUAT.............................................................................................................................51

II. I . H:i in y quan ly ............................................................................................ 5 III.2. C:i c gici i ha n cii a lu:i n v:in...........................................................................5211.3. Dé xuat nghien ctiu titong lai........................................................................52

TAI LIEU THAM KHA O.......................................................................................................54

PHU LUC 2. KET QUA PHAN TICH NHAN TO CAP 1.....................................................62

PHU LUC 3. KET QUA PHAN TICH NHAN TO TOAN B O MO HINH......................7 1

B ang II. 1. Cac loai rii i ro.................................................................................................13Hinh II. 3. Ké t qua kié in tra e-CAM tai My....................................................................16Hinh II. 4. Ké t qua kié in tra e-CAM tai Ha n Quoe........................................................16B ang III. I . Tom ta I lit a chon bié n.....................................................................................27B ang III. 2. Giai thich ly do cac bien Khfi rig dit0c cho n (KQ hieu Yes ci co 1 7).........28B ring III. 3. Giai thich ly do cac bié n Du’c1c cho n (Ky hi(u Yes fi cfi t 7)...................30B ring III. 4. Tom ta I cac bié n ngoai sinh ditct c chon ch o mo h i nh ................................ 3 IBang IV. 1. Thong ké dac tinh nhfi n khfi u hoc...................................................................38

B ang IV. 2. Thong ké kinh nghié in sit dung Internet......................................................38B ring IV. 3. Thong ké dit dinh mua ha rig tritc tuyé n..........................................................39B ring TV. 4H0Bang IV. 5. C:ie ch1 so thich help CFA bitdc........................................................................41

Bang IV. 6. Hé so c:i c chi b:t o tit phfin tich CFA bitilc 1..................................................42

B ang IV. 7. C:i c ch1 so thieh hci p trong CFA bir6c I....................................................42B ring IV. 8. Do gi:i tri phan bi(I..........................................................................................43Bang IV. 9. Hé so c:i c chi b:t o tit phfi n tich CFA bitilc 2..................................................43

Bang IV. Ill. Do gi:i tri pha n bie I....................................................................................44B ang IV. 1 1. C:ic chi so thich h0p trong CFA bitdc 2....................................................44Hinh IV. I . Ke I qu :i chay mo hinh TAM-ECAM...........................................................45Bang IV. 12. Gi:i tri c:ic h( so dit0ng dan va I-value......................................................46Bang V. 1. Ké I qua so s:i nh theo mo hinh e-CAM.............................................................48

Bang V. 2. So s:inh I:ie d‹i ng em a PRP va PRT le n B I................................................48

Chil vié t th tBI CFAECAM EFA FAX I PEU PRP PRT PUSCREEN SEM TAM TERM I TMDTUTAUT

qua chia’ thontin. Trong mot vat tru0ng h0p, cfing nghé thong tin mci‘ c:i c lié n ké t

I. TONG QUAN VE THHtJNG MAI DIEN TH

Co thé hinh dung mo t c:i ch d0n gi:i n, TMDT la mot mo hinh kinh doanh du0c kich hoat thong qua cong nghé thong tin. Mfi t mo hinh kinh doanhtrinh bé y 1 “ké hoach dit6c to chiic " ran cho viéc I:ing théin gi:i tri kinhté bang ca ch ap dung bi quyé t cho 1 tap h0p tai nguyén nhani muc dich tao ra san pham hay dich vu co thé tié u thu du’ci c” (Miles et al., 2000). Mo hinh ph:i i dinh vi sit I:ing tritfi ng tif0ng lai cii a to chiic thong qua viéc ph:i t trié n kié n thitc (nghia la bi quyé t) va thu tha p tai nguyén. Ca c mo hinh kinh doanh truyé n thong tap trung vao thu thap tai nguyé n va t chfit va viéc thay thé s:I n phfiin v:i dich vu . Cac thii tuc, c0 ché kié m so:it, ba o c:to, c:1u trfic quan Iy, quan he gifra cac to ch(ic, va mug dung cong nghe thong tin cu a chii ng tfip trung vao viéc bao dam sfin ph:i nn va dich vu dif0c di chuyé n h ttu

iiu, phu0ng pha p va ky thufi I da dang co d:i c tinh khia canh qu:I n ly hoat dong cfia chfi rig. Nhttng chiic n:i ng to chiic co khuynh hufing ph:i n vung v:i thircrng xuyén hoat dong doc la p de tot u(r ké t qu:i thuc hién cua chiing tii ph:in con la i cii a to chiic (nghia la cac phan ngani). Nhtrng mo hinh nay ‘rig dd m dif0ng

va t chat clia to chiic va viéc thay thé san phfim v:i dich vu, nhitng diéu yé u‘u no duy tri nhting diéu kién thua n 10i cua

h0p dong vfii cac doanh nghiep kha c dé va n chuyén sa n pha in, hay de cung ‘p truc tié p c:ie dich vu ciia no. Viéc chia sé riii ro giffa to chiic va nha cung cfip khi xfiy ra. Vai tro cua cong nghé thong tin bién doi tit so ni dong hoa computer d0n gia n den kich hoa t tich h0p be» trong thong

TMDT da b:t o trufic nhiéu c0 hfii mcii cho c:ic to chiic kinh doanh thong qua viéc mci rong v:i n:i ng cao thi knifing ciia chting cir ng vfii vi(c kéo da i vaba nh tritfi ng cac ké nh cung cfip. TMDT

‘in co viéc trao doi (vi du , tuna va

b:i n) s:i n pha in, dich vu va thong tin thong qua ma rig ma y tinh, bao gfi m Internet (Kalakota & Whinston, 1996) [5]. Cfing nghé thong tin dii lam cho c:i c to chiic cfi kha n:i rig ph:i t trié n c:ic chién lif0c tfi p tru rig va o mfi hinh TMDT va thifc hié n nhifng thay doi t:i n goc doi vfii c:ich thiic ho thitc hié n kinh doanh.

chiic gop chung tai ‘n cii a ho cho thu 10i kinh té v:i vit0n Hi thi tritongmd rong. Nhfrng pha t trié n gan dfiy trong cfing nghiép hang khfi ng minh hoa cho khai niém nay, d:ic bié I 1:i c:i c lién minh Star and One World. Thay vi mo t Cong ty da u tir ta 1 nguyén cii a ho va chlu rim ro khi gla nh:i p th1 tru’éng mdi, cac Cfi ng ty hang khfi ng da h0p nha t vfi i nhau trong mot lién minh h0p tae dé cung

n:i y I:i in l0i cho ho v:i v:i cho cong

chiing lir ha nh. Cong nghé thfi rig tin da trfi thanh ngufin kich hoat c0 b:In cho phép tich h0p theo chiéu ngang mo t each tha nh cfing ciia c:ie thanh vié n thi trttfing dé dat hirng thinh thong quavié c trao doi thong tin dién tit’. Viéc lien minh ‘n éu c:in c:ic thé nh vié nph:i1 phoi h0p, va thiet tap tiéu chuan th irc hién chung cho ca c qua trinh kinh doanh cua ho dé bao dam thanh cfi ng cii a no. Thanh cfi ng tit0ng tit cu rig dat dif0c trong viéc tich h0p theo chié u doc cfia ho. Vi du , vé ma y bay co thé mua dir0c thong qua nhfing nha ban lé, nhu’ Orbitz and Trave1ocity . com. Do do, Internet va ta t cfi cac cfi ng nghé thong tin lié n dfii cii a no da thay doi phir0ng tién to chiic kinh doanh trong hinh th itc TMDT.

Cfing nghé thong tin co thé trfi thanh ‘n cu manhdat dif0c 10i the canh tranh chié n lit0c chifa titng co trong thi tritfi ng cfia ho. Dién hinh la trong tru0ng h0p cua American Airlines va Sabre trong nhitng na ni 1980 (Copeland & McKenny, 1998), cfing nghé da chiing to la mo t nhfin tfi c:in b:I n cho viéc canh tranh hiéu qu:i cua Cfi ng ty va dinh hinh lai ca ch thtic kinh doanh da dii0c qua n 1y trong cong nghiép va n chuyen. Khfi ng lam theo niiic cfing nghé tu0ng du0ng ciia cac Cfi ng ty dan dau nganh, cac Cong ty khac kho co thé duy tri canh tranh. U.S Justice Department da mo I:I r:ing 10i thé canh tranh 1 chié u nhit vié c trfi né n dfic quyé n khi dé lai viéc kié m soat cho mot so it. Do dfi , cfi ng nghé thong tin

‘c uan canh kinh doanh.

Ngay nay, nhiéu to chiic dang tint thfiy chinh bfin than ho trong nhtrng tinhhuong tit6ng tit. Khfi ng lain theo 1 mo hinh TMDT da d:i I ho vao vi tri

‘’nn dinh va thié u tinh canh tranh, chuyé n den vat tro thfip h0n trongthi timeing cii a ho. H0n nCa, viéc song con co thé doi hfii d:i u tir fern v:to cfi ng nghé thong tin dé cho phé p ho tré thanh nhfing doanh nghiép dié n tii s½n sun e -hu.sines.s read 5 ).. ChIi d6n gii::IInn mang dén coinng nghhéé thong tin v:i xa y du’ng c0 st ha tang cfing nghé thong tin dé ho tr0 TMDT cfi thé tham chi khfi ng dd in bd o nifi t cach y nghia viéc ton tai lau dai cua doanh nghiép. Difa vao nhiéu cfing nghé h0n dé gifii quyé t v:in de co thé khfi ng ph:i i 1:i ca u tra UI can thiét. Viéc diéu chinh hanh vi ca nha n cling nhit to chtic pha l

diinc xem xét. Dé da t dii‹ic nhieu fen ich cu a cong r ghé thong tin va TMDT, mot to chiic ph:I i d:i nh gi:i mot c:ich nghiém tuc cfiu trilc v:i qui trinh ciia no (nghia 1:i, kinh doanh, san xu5t, phan phoi, ...), va xac dinh b:i rig c:i ch nao no co thé tich h0p hiéu qua cong nghé thong tin vao chinh ba n tha n de co thé da t dii0c ca c muc ké t qua th iic hién ca o h0n. Bfii vi ca c mo hinh kinh doanh thi rat khac nhau, chuyé n tit mot mo hinh kinh doanh bricks cmd motci r (truyén thong) thanh 1 mo hinh TMDT thu’fi ng yéu c5u viéc ta i thié t ké hay tai la p ta n goc cac gut trinh va ca u truc. Ca c framework ciia Leavitt (1965) va Scott Morton (1995) chi ra ’rig viéc thay doi chién luci c to chirc hay vié c sir dung cong nghé thong tin cu a no sé a nh hitfing s:1u s:ic dén c:ic linh vifc kh:i c cfi a to chiic. Do d fi , chI d0n gi:i n inang dén cac cong nghé Internet thi khfi ng thé tit bd o ba o da in cho viéc tha nh cfi ng TMDT (v.d. , Toys R Us).

H0n n iia doi vfii viéc thay doi to chuc, viéc thay doi ha nh vi ca nhau cling phai xay ra. Mfi t vai nghié n ciiti chu yéu vé quan 1y cong nghé thong tin de right manh niC rang viéc thuc hién cfi ng nghé thong tin thanh cfing yé u cau thay doi hanh v1 mo t cach tich cite (Davis, 1998; Davis & Bagozzi, 1989; Venkatesh & Davis, 1996, 2000). Thié u thu’c hié n nhffng thay doi nhif v ii y cé thé ‘n trct chuyé n doi; ch I d0n gi:I n mang dén cong nghé thong tin khfi ng thé tit dong dan dén viéc chap nhan cir a no. Do dfi , qufin ly viéc thirc hié n cfing nghé thong tin trong to ch iic co thé chiing niinh la mo t birfic chit yé u dé thitc hién th:i nh cfing mo I mo hinh TMDT.

Viéc chuyé n doi tir mot mo hinh hricks cmd molar (truyén thong) sang mot trio hinh TMDT dita ra mot vat tha ch thiic ton. Doi vfi i to chitc, TMDT phfin anh mo t loai hinh kinh doanh mcii, va trinh bay nifi t mo hinh kinh doanh theo cong nghé thong tin moi theo do no ph:i i d léu chlnh tha nh nhitng phif0ng ph:i p hién h:i nh v:i viéc thitc hanh qu:i n IQ kinh doanh. Mang dé n ca c cf“inn nnghhhéee mmooii sinh ra mo t so thach thitc khfi n thé vif0t ua thon qua dfiu tu thém vao cfi ng nghé. Tuy nhié n, viéc dau tu tang thém co thé co inot so hiéu quit han ché trong viéc giai quyé I vfin de toan cuc. Trong mo I v:i i tritfi ng h0p, no co thé khuéch dat, 1:inn tr:i nn trong hay t:i ng citing vfin dé (nghia la hiéu qu d trai ngii0c) khi cac qui trl nh trfi nén khfi kiém ché h0n vfii toe dfi nhanh h0n hay la bi co lfip. Nhfrng tha ch thiic 1dn h0n dé n tit vié c yé u cfiu c:i c gia i ph:i p ve TMDT, va thitc hien (nghia la, diéu chlnh thu’c tié n va dat du’0c sit chap nh5n) va tich h0p cfi ng nghé thong tin vao cau trii c hién tai vfii ta in nhin la cfi to chuc cung hoat dong thong nha t vfii nhau. Dieu nay yéu c:1u viec mang la i v:i cam ké t cho c:ic thay doi t:i n goc

da u loa i hinh kinh

doanh moi. Ca n cii v:i o dong c0 chien lu0c nén t:ing cii a TMDT (nghia la , dé canh tra nh hié u qua ; dé ra v:i cung c:1 p mot dich vu khfing -trii rig-I:i p cho thi tru’fing rong hci n; duy tri, uang cao hay gi:i nh thi pha n) va thuc hié n IT.

Vfii TMDT, c:1c mo hinh kinh doanh co khuynh h itfi ng dinh hiffi ng lai to ch itc theo hiid ng canh tranh tré n thi trttfing toan cau thong qua ca c

mang dién tiI, da c biét la Internet. Tir0ng phan vfii ca c mo hinh kl nh doanh truyén thong, thong tin trfi thanh nguon Hi nguyén chinh. Do dfi ,

viéc chit j tern h0n dir‹ic ta p trung vao trao doi thong tin, can bang kien thirc dat du0c thong qua tit0ng I:ic cfi a to chiic vfii c:i c thifc thé cfi a no,

va sau dfi la tich h0p va hop ta c ha r h dong, ca be r trong to ctic va vol ca c to chuc rhac. Cac qui trinh hoa I dong tfip trung vao Hi u\i hfi a

nhiéin vu c:i nhfin phfii du0c tai lap dé cho phép to ch iic nhii la 1 tong thé nham can bang thong tin cii a no de da p ring tot h0n yéu cau cii a th1

tracing, va ta n dung 10i the cua cac lié n niinh hay doi tac co thé hoac la giii p gifii quyé t viéc giao hang hoa hay dich vu, hoac n5ng cao/md rong

thi trttfing ciia no. Do dfi , viéc thuc hién 1 mo hinh TMDT bao nh iing thay doi chua hé co ‘ v:i kich hoat

IT, ngiféi ta yéu cfiu I:ii lap to chiic de thich h0p hon vcli I loai hinh kinh doanh kh:ic. Hammer va Champy (1993) dinh nghia t:i i 1:ip la “tit duy lai mot ciich c0 ban va thié t ké lai tfin goc ciic qui trinh hoat dong dé da t dir0c nhirng c:ii thién :in tii0ng trong nhirng do lu0ng ké t qu:i thiic hién du0ng théi va quan tro rig, nhif chi phi, ch:1 I lit0ng, dich vu v:i toc dfi ” (p. 32). Mo t

nghé‘c va thiic hié n cac qui trinh kinh doanh.

Nhu cau tai lap trfi thanh hié n nhién h0n vfii TMDT. Do cfing nghé thong tin cho phép va duy tri nhft ng mo hinh nay, cong nghé thong tinlfin trong c:i c phif0ng tién s:i n xufi t. Viéc thay doi vat tro tii cfing nghé thong tl n hfi tr0 hoa t dong sang cong rig he thong tin la ha t nha n cii a 1 doanh nghiép va trfi né n hiid ng di quan trong cir a su pha t trié n doanh nghiép d:i n den thay doi loai hinh (Earl & Kahn, 2001).

Su song con cii a I doanh nghiép hién nay xoay quanh vi(c lam ca ch n:t o de c:in b:i ng tot giira cfing nghé thong tin v:i kién thiic cii a no. Diéu nay d:i c

s:i n pham va dlch vu khuyé n khich thong tin thu thfip dir0c thong qua cac giao dich va tu0ng tac kinh doanh, va kién th iic tich thy dif0c cu a to chiic (Cohen & Levinthal, 1989, 1990; Hurley & Hult, 1998; Prahalad & Hamel, 1990). Kié n thitc cii a to chtic cang lfi n, hoac la st hitu not bfi hay thong qua cac lién minh, thi c0 hoi doi mdi va song con

biét‘ can bfin.

ca rig nhieu (Cockburn & Henderson, 1998; Cohen & Levinthal, 1990; Lane& Lubatkin, 1998; Lane et at., 200 1). Cong nghé thong tin giii p pha I trié n doi mdi (Dewell & Jones, 2000). Vi du, nhié u to chiic hién dang chuyén qua c:i c he thong qua n 1y quan hé kh:i ch h:i ng (CRM) kich hoat cong nghé thong tin de quan Iy tu0ng ta c ciia ho vfii khach ha rig, thu du0c hieu bié t s5u sae h0n ve nhu cfiu cua khach hang ciia ho, va duy tri lOi thé canh tranh (hay dat 10i thé canh tranh). CRM giii p ha p dfin, pha I trié n va duy tri quan hé kha ch ha rig tha nh cong theo their gran (Berry & Para surama n, 199J ; Day, 2000), va xay dung long trung thanh khach hang (Kohli et at., 2001) thong qua doi thoai 2 chiéu hiéu qua va hfru hié u (Peppers et at., 1999) b:i rig viéc hié u bié I hanh vi kh:ich hang, lien lac j titong vfii kh:i ch h:i ng dé:inh hiici ng ha nh vi cii a ho v:i khfii dong ca c chien dich do1 melt vfi1 muc dich duy tri kha ch ha rig hién tai va thu hut kha ch hang mii (Kannan & Rao, 2001). Cac to chiic phil i cfin b:i ng kié n thiic cfi a ho (giffa nhitng yen cfiu khac) cho CRM dé thu l0i tif quan hé kha ch hang (Massey et at., 2001). Dédot mcii lié n tuc va thanh cfi ng, to ch iic phai co khd uang chia

’ thon tin

va kién thitc ca bén trong va gifta khach hang va nha cung c5p thong qua viéc thie t ké ca c gut tnnh va cau triic ci'i a no (Dewett & Jones, 2000). Do dfi , thitc hi(n CRM (Customer Relationship Management) nghia la cfin ba rig kien thiic to chiic qua cong nghé thong tin. H0n n tia, dé thu I01 tit co rig nghé thong tin, to chiic khfi ng ph:I i ch I thitc hié n IT, no cu rig phfi i thirc hién nhting thay doi to ch iic chu yéu phd n anh chié u hitting mdi cua no. CRM song song veil nhttng phif0ng pha p va ky thu a t thu thap klén th iic khac, nhu viéc khai thac data va quan IQ kién th iic, la nhitng vi du minh hoa cho tié u diem Hi nguyé n thong tin cii a mo hinh TMDT.

Chuyén doi mot to ch iic hrick.s and mcta r s ang TMDT bao

‘in viéc dinh

huét ng lai nhirng ciich thiic thiic h:i nh va gut trinh quanh Internet. Vd i hfiu hé t c:ie to chiic, vié c thay doi bié u thi c:ie thiic quan ly kinh doanh kha c

c‹ich thtic iu r› gift mr mii ” (trang 7). H0n n tia, nhfing tha y doi tai la p cho phép va xii c tién dong thong tin cfi bén trong to chiic va vfii doi tac ké nh cung ca p cfia no . TMDT cling la phu0ng tié n cau triic to ch iic dé tir0ng tae hft u hiéu va hieu qua vfi1 nh trng to chiic khac thong qua thi trifct ng die n tir, la Internet.

Loai hinh TMDT dif0c ph a n chia tha nh c:i c linh viic nhif trong nen kin h ID khfing cci Internet.

trirfi ng chain h0n nhiéu so vfii nh iing nam bung nhifng nhitng

II.

B2B (business-to-business): doanh nghlép thifc hién kinh doanh vfi1 c:i c doanh nghiép kh:ic thong qua Internet.

B2C (business-to-consumer): cac doa nh nghiép ban san pham va dich vu cho khach hang thong qua Internet.C2B (consumer-to-business): khach hang khfii d5u c:ie giao dich vamua san pham thong qua Internet (vd. Pr1ce1ine .com).C2C (consumer-to-consumer): nhffng cufi c d:i u gi:i tritc tuyé n thong du rig nha I, n01 kh:ich ha rig triic tiép mua va ba n vfii nha u (vd. eBay.com).G2C (government-to-consumer): chinh phu thuc hién cac hoat dong triic tuyé n vfii khach hang (vd. U.S. Portal Service).Mobile commerce (in-commerce): sit dung c:ic thié I bi khfi ng day dna trén song vfi tuyén nhif dren thoai di dong (cell phone) va ma y tr6 1y c:i nhfin kj thu at so (PDAs) dé to chiic c:i c giao dich TMDT thong qua hé thong lién lac khfi ng day.Online banking: truy ca p vao cac dl ch vu nga n hang vé ca nhan hay doanh nghiép tif dich vu thit0ng mai trifc tuyén hay qua rna rig cfing cong nhu Internet.

TMDT da gay dit0c sit chit j da i chfing cii rig nhit céi ng dong kinh doanh vé nghién ciiu. Sau dfi , h:iu nhii ciing nhanh nhii khi xu:i t hién, TMDT tu tha t ba i nhanh chong v:i trfi né n mcl nha I tron g n én kinh Ie.

‘ cii n chain chap trong nhitng

TMDT van

1:i mot linh vifc mcl rong vfing v:ing va ma nh mC cua nen kinh Ie. Ca c thong ké cfi a phong thit6ng mai My cho thfiy tong doanh so ban lé n:i nn 200 1 cii a TMDT ID 32.6 it vsD v:i chi¾m 1% doanh so ban lé toan bfi (nam 2000 la 0.9% chia tong doanh so ban lé). Mac dii so lié u nay cho th5y toc dfi tang

Ngoai viéc quan ta in chinh vé c0 st ha tfing ky thua t va dié u kié n kinh téxa hfii, nhfin thitc cfia ngit‹ii siI dung vé c:ic hé thong thit0ng mai dié n tfi‘rig la yé u ‘n cfia vié c trié n khai,

duy tri va pha I tri én he thong thu0ng ma 1 dién ti't. Nha n thiic cu a ngufii sir

dung da dir0c nghién cuu fi nhieu niific trén the gio l dira can ba n theo mo hinh TAM. Nhiéu nha nghié n ciiu d:i sit dung Mo hinh chfip thu:in cfi ng nghé (Technology Acce ptance Model - TAM) (Davis 1989) dé du’ doan sir ch:i p thu:in cua ngitct i sit dung vé cac ring dung thif6ng mat dié n tit (Devaraj 2002; Hsu va Lu; Olson va Boyer 2003). Olson va Boyer (2003) da cho rang anh hu’fi ng cu a kh:ie bié I ca nhan vé sit ch5p thu5n cong nghé mdi nhu cac trang web thif0ng mai dié n tit thiffi ng khfi ng du0c chit trong trong khi Lightner (2003) ba o ve y kien lien quan den ca c dac diem nhat dinh vé kinh nghiém mua hang truc tuyén co quan hé vfii cac dac tinh nhan kh5u hoc. Joongho Ahn, Jinsoo Park, va Dongwon Lee (2001) da tich h0p TAM vfii thuyé I nhan thiic rii i ro (Theories of perceived risk — TPR) trongmot nghién ctiu thitc nghiém de cha p thuan thu0ng ma i dién th

III.

vfii mo hinh e-CAM (E-commerce Adoption Model).

Nhir vay, cac yéu to nao co thé tac dong vao nhan thiic chia ca nhan trong vié c sir dung cac hé thong thir0ng mai dién tit la mot van dé quan trong c5n quan tain trong cac nghién ciiu vé nhan thiic va hanh vi.

Vfii hié n thuc ngay cang ph:i t trié n cii a thu0ng niai dié n tit, tfii chon thuc hié n dé tai “Khfio s:i t mi) t s0 yéu IO I:i c d(ng van sij san sang ciia thu’dng in:j i di(n tit’” vci i dinh hirci ng nghién ciiu kh:i ni ph:i trong linh viic nhan thiic vé hoat dong thu0ng mai dién tif B2C.

MUC TIEU VA PHAM VI NGHIEN CNU1. Mijc tiéu nghién emu

(j Vié I Nam, vfii c0 set ha I:1 rig ha n ché v:i c:i c diéu kié n kinh té xa hfi i

chiia dir0c thu:in l0i cho viéc ph:i t trién thir0ng niai dién tit, nghién ciiu n:iy di tt h hll’fift$ ta ttllft$ kha 0 Sa I HUI So yél1 to da dtl’0C kie III Ud thll’C

ft$hi(III trong c:ic nghié n ciiu triffic dfi y tré n thé gicii. Song song vfii vié c

kié in tra lai ca c yéu to ta c dong va o nhan thiic ciia ngufii sit dung, nghién ciiu na ykhfio sa t tinh

ngii0i si'i dung vfii cac hé thong thir0ng mai dién

tit la c:i c website thit0ng ma i. Nfi i c:i ch kh:i c, nghién citu t:i p trung v:to kh:t o s:i I hé thong thit6ng mai dié n tit B2C vfii yéu to nhfi n thiic la muc tiéu khao sat.

Nghién emu cung dlnh hif fing xa c dinh gl a tr1 chia mot v:to sir chfip thu:in hé thong thong tin riot chung v:i thu0ng niai dién tit nfi i rié rig, c:i c kié n trii c n:i y dif0c ch:i t lo c tif nhiing nghié n ciiu thitc nghié in tru’‹ic day tré n thé gid i.

so de xua t cho ca c ngh lén cuu tir0ng la 1 thong qua mot so k é t qua tiin dif0c tif nghién ciiu nay. Ciic h:i nn y ro rang cfia ké I qua tit t5p dft tiéu thu’c nghiém cling sé du0c ph5n tich va dié n dich cho phu h0p moi trifct ng kinh doanh thitc té nh:inn cung c:ip mot so y titct ng ban d:i u cho nhitng doanh nghiep na o dii dinh thiet la p v:i pha I trien he thong thu’0ng ma i dié n td' trong hié n ta i va tu0ng lai.

2. Phgm vi nghién emuPha in vi nghié n ciiu cfi a de I:i i dif6c gicii ha n trong vfi n de nh:i n thiic ciia c:inh5n ve c:ic he thong thu’Ong mai dié n tit da sit dung. Vfii c6 ma u han chéva so lit0ng c:i c trang web dif0c kh:t o s:i I co tinh ph:i n I:in

‘n ddéé tai chI

gioi han tron g viéc d:inh gi:i mo t so kiln triic chinh in:i khon g ké I lu:I n nh:in quit cho mo hinh nghié n ciiu.

Y NGHIA THU'C TIENKé I qua nghié n ciiu sé dong gop vao hiéu bié t chung doi vfii vié c ch:i p thufin cii a ngiféi sir dung TMDT, la mot hoat dong kinh té mdi dang rfi t can c:i c nghién ciiu cu thé cho viéc :ip du rig thanh cong trong thifc té . Cac yé u to tim thay trong qua trinh nghién emu co thé du0c sir dung de hoach dinh vié c thié I 1:ip hé thong TMDT trong titng doanh nghiép cu thé. Vié c n:i y cling giii p cac doanh nghiép nha n thiic tain quan trong ciia viéc uang cao hé thong phuc vu kha ch ha rig trong TMDT (doi ta c, ngiicr l tieu thu,...).

“i›sxeax›

PHAN A. CAC MO HINH NGHIEN CNU TRUC DAY

I. MO HINH CHAP THUAN CONG NGHE (TAM)

Trong ni'fa ‘i the k ’ 20, nhié u 1y thuyé t da d it0c hinh thanh va dit0c kié mnghiém nh:i nn ngh len elm su cha p thu a n cong nghé

dung.

Fishbein va Ajzen ( 1975) da dé xuat Thuyét Hanh Dong H0p Ly (Theory of Reasoned Action - TRA), Ajzen (1985) dé xu5t Thuyé t Hanh Vi Dif Dinh (theory of planned behavior - TPB), va Da vis (I 986) da de xua I Mo Hinh Chap Nha n Cfing Nghé (Technology Acceptance Model - TAM). Cac 1y thuyé t nay da dir0c cong nhan la ca c cong cu httu ich trong viéc dii doa n th:ii dfi cfi a ngitét i sit du rig. D:ic biét, TAM da dit0c cfing nhfi n rong r:i i lamot mo“ hinh tin

ca y va manh trong viéc mo hinh hfi a vi(c cha p nhann IT

dung. ”Muc tiéu ciici TAM let cung cdp mét st gif i thich c‹ic

kh‹i

1989, trang 985). Do dfi , muc dich chinh cii a TAM la cung cfip mo t c0 set cho viéc khao sa t tac dong chia cac yé u to bén ngoai vao cac yéu to bén trong la tl n tuci ng (beliefs), tha i dfi (a tti tudes), va y dinh (intentions).

TAM dit0c he thong dé dat muc dich tré n b:i rig c:i ch nhan dang inot so it c:i c bié n 'n tan (fundamental variables) da du0c cac nghién emu triific dfi de

xua t, c:ie bié n nay cci lié n quan dé n thanh phfin c:inn tinh (affective) va nhan thiic

(cognitive) cii a viéc ch:i p thuan computer [ 16].

TMDT la san pham cua phat trié n cfing nghé thong tin (Information Technology - IT), do dfi , trio hinh kh:I o s:i t c:i c yé u to I:i c dong v:to viéc chap thuan IT cling dir0c ap dung thich h0p cho viéc nghlen c iiu va n de tu0ng tir trong TMDT. TAM dir0c trinh bay trong Hinh 1.

1.1. Cite kién trim chinh

I.1.1 Nhan thii’c sij hif u ich“L:i c:ip dfi in:i mo t ngitct i tin r:i rig sit du rig mo t he thong d:i c thu se n:i rigcao ké t qua thirc h1(n ci'ia ho ” (Da vis 1989, trang 320).I.1.2 Nhan thiic tf nh dé sit dqng“L:i c:i p dfi ma mo t ngitfii tin r:i rig sit du rig mo t hé thong d:ic thu sé khfi rig

(Davis 1989, trang 320).

I.1.3 Th:ii diy hu’dng dén vi(c sit’ dqng“La ca in gia c tich cuc hay tiéu cue (co tinh itoc lu0ng) ha nh vi muc tiéu ” (Fishbein v:i Ajzen 1975, trang 2 16). Dinh nghia nay lay tir Thuyé t ha nh dong h0p IQ (Theory of Reasoned Action - TRA)

1.2. M6 hinh TAM

TAM dif0c trinh ba y trong Hinh 1 1:i mo hinh dit0c gici i thlé u lan dau cii a Davis ( 1986). Sau n:i y, cac nghién citu bo sung cii a Thompson ct at. ( 1991) va Davis (1993) dé xua t né n bfi thanh ph:i n Du Dirh sit Dung va noi triic tié p Thanh Phan Th:ii Do sang Thanh Phan Hanh Vi. Thompson et at. (1991) da chung to Du Dinh Sir Dung nén du0c loai trii bfii vi chung ta quan tain va o ha nh vi thifc su (sir dung h( thong). Ha nh vi nhu va y da xa y ra trong qua khit, trong khi Du Dinh Hanh Vi la “xtc sud I c hit qucin mci ngu‹i .s é d u ng .sé t/idc hié n hé nh vi n‹i 5 trcng chit d é ” (Fishbein va Ajzen 1975, tra rig 1 2) va do dfi no lien quan t‹l i hanh vi tif0ng 1a1. Do dfi , néu nghién cuu co dir dinh khao sat hanh vi chap thuan cfing nghé trong qua khti thi nén bfi thanh ph5n Dif Dinh Hanh Vi [16].

Difa tré n ca c nghié n ciiu thifc nghiém sau khi

“ hinh TAM

cong bo, kié n trilc th:ii dfi (Attitude construct - A) da dif6c bfi ra khfi i mo hinh TAM nguyén th ii y (Davis, 1989; Davis et al., 1989) vi no khong tain trung gran day dii cho sit t:ic dong cu a PU lé n hanh vi d it dinh (behavioral Intention - B I) (Venkatesh, 1999). H0n n iia, mot va i nghlén cuu sau dfi (Adams et at., 1992; Fenech, 1998; Gefen and Straub, 1997; Gefen va Keil, 1998; Igbaria et aI., 1997; Karahan »« i siraub, 1999; Lederer et aI. , 2000; Mathieson, 1991 ; Straub et at., 1995; Teo et al., 1999; Venkatesh va Morris, 2000) da khfi ng xem xé t tac dong chia PEU/PU lén Thai Do (attitude - A) va/hoa c BI. Thay vao dfi , ho t5p trung vao tae dong trim tié p chr a PEU va/hoac PU lén 'c Su Dung Hé Thong Thirc Sir [6].

Trong dé t:i i nay, tfii co y dinh kh:I o sii t c:1 h:i nh vi trong qu:i khit va quan trong la du’ dinh hanh vi trong tir0ng lai né n sé sit dung kié n trim BI (ha nh vi dir dinh) va bfi di kié n trtic A (thii i dfi ) theo nhii ké t qu:i trong c:i c nghién ciiu tritfic dfi y.

lfinh 11. 1. Mti hinh Lh:i i niém

II. MO HINH CHAP NHAN SSI DUNG THlltJNG MA I D IE N TU' (e-CAM)'

II.I. Cite kién trfic chfnh11.1.1 Nhan thifc ’i ro lién quan dén sdn ph:i in/djch vq (Perceived

Risk with Product/Service • PRP)Bauer (1960) dé c:ip vé nhan thiic rfi i ro nhit 1:i yé u to chityéu doi véii hanh vi ngucii tiéu dung co thé la yéu to chinh :inh hircrng viéc ho:i n chuyé n ciia ngufii duyé t web dén ngiféi mua hang thu’c sir. Cox and Rich (1964) dé c:ip dén nh:in thiic riii ro nhii tong ciia nhan thiic ba t dinh bfii ngucri tiéu diing trong 1 tinh huong mua ha rig d:ie thii. Cunningham(1967) nhan thiic

két qua thiic hié n tot, nguy hiém,

v:i chi phi. Roselius ( 197 1) nhfi n dang 4 loai mfi I in:it tiéu quan dén c:1c loairui ro: théi gian, sir may rfii, b:i n

‘, va tié n bac. Jacoby va Kaplan (1972)

phfin loai nhfin thitc rii i ro cfi a ngitfii tiéu dung thanh 5 loai ’i ro sau:ly, t5m 1y hoc, xa hfi i, ta i chinh, va ké t qua thirc hién (chtic na rig) (physical, psychological, social, financial, and performance (functional)) dit0c lié I ké trong Bang II. 1. Taylor (1974) dé nghi r:i ng sit brit dinh va nhan th itc rui roco the sinh ra ba n khoa n

h iffi ng tlen trinh ra quyet dinh tléu

dii ng. Murphy va Err is (1986) dinh nghia nhan th iic

’i ro nhit su

chu quan dung ve ké t qua ta o ra I sai la in mua ha rig. [6]

kh0n¿‹hwo hennhwkyvonp

TimeRisk

Bfing H. 1. C:i c to:ji rfii re

(NOT- ) é chfi dnt gtao sae pham

Khi chiing ta khfing thé thfiy hay cha in triic tiép sfin phfim/dich vu trong thi trufing dién tir (nghia la , ca c dac tinh vfi hinh), ngiffii tiéu diing cci the cfim thay b:i n khoan hay khfi ng ch:ic chan khi ho cci giao dich v‹Ii nhCng nguci i ba n h:ing trirc tuyén. Vi du, sa n phfim/dich vu du0c giao cho ngirfii tiéu diing co the khfing thuc hién nhu du’0c mong d0i. H0n nii’a, ngu’c1i tiéu diing cfi thé du’fic ye u cfi u chiu chi phi nhif vfin chuyén v:i boc d6, khi tr:I lai hay trao doi san phfi in/dich vu. C:ic t:ic gift nh:in dinh m‹ir c/tii'c nfing v:i in‹i/ iii c hinh ifune tionci l loss and finunc-ia / loss) nhii c:ic loai riii ro tién quan dén san phfi in/dich vu han ché ngitéri tiéu diing thuc hién c:ie giao dich true myén. [6]

H6n niia, khi viéc mua s:I n phfim/dich vu thfit ba i, chiing ta co thé mfi t thdi gian, su thu:in tién v:i no luc I:i y san ph:i nn/dich vu diéu chinh hay thay the. M:ic da u thfii gran I:i no lirc phi tiéu ba c v:i bié n dong giira c:ic ca nhfin, ca c t:i c gift nhfin dinh thfii gian nhu’ mot chi phi ma ngu’ct i tie u diing ph:I i trfi cho sfin ph:i in/dich vu. Do dé* , ca c t:ic gia nha n dinh its théti give (time less ) nhu I rii i ro t:i rig them vcli s:I n phfi in/dich vu. [6]

Sau khi mua san phfi in/dich vu qua Internet, ngitéii tiéu thu co thé tim thfiy I sfi n ph:inn/dich vu chat lif0ng b:i rig hoac cao h0n v‹ii mite gia tha p h0n. Do dci, c:i c t:ic gift nh:i n dinh 1 loa i riii ro kh:ic, mfi t c0 hfii (oypor/uiii/y loss) , I:i riii ro thirc hi(n I ha nh dong in:i ngufii tiéu dfi n g sé bfi 16 thifc hién

diéu gi kh:i c in:i ho thifc sit ‘n la m. [6]

Do dfi , c:i c t:ic gi:i dinh nghia nh:i n thiic

sfin phii in/dich vu (PRP)

nhit tong chung ciia ba t dinh hay ba n kho:i n du0c nhan thi?c bfii I ngufii tiéu diing trong 1 sfin pha in/dich vu d:ic trung khi mua hang truc myén. C:ic t:ic gia nhfin dinh 5 loai PRP nhu’ sau: mé/ chit:c n‹ing, mét tai chinh, tén théi

gran, mii t cc hé i v‘a nh‹i n thiic¶unctionci l loss, financial loss, time loss, opponent ty loss, va overall perc’eived risk w i th produc’ t/service ). [6]

11.1.2 Nha n thiic riii re tién quan dén giao d[ch trijc tuyén (PerceivedRisk in the Context of Online Transaction)

Va i nghién ciiu trong pham v1 giao dlch truc tuyén (Hoftma n et at., 1999; Jarvenpa a and Tractinsky, 1999; Jarvenpaa et at., 2000; Ratnasingha ni, 1998; Swaminathan et at., 1999) cho r:i rig sit tin cfiy hay tin nhié m cfi a khach hang sé du0c cai thié n bring cach gia t:i ng tinh trong suot cua tién trinh giao dich (vi du, ph0i bay to:in bfi d:ic tinh, ngufi n ‘ c, v:i nghia

cii a nha cung ca p), gift lai dfi lié u c:i nhan Hi thiéu yéu cii u tit ngifét i tiéudu rig, va bfii vié c ta o ra tra rig th:i inao dir0c cung c:ip. [6]

" ra rig va h0p ph:i p ciia bfi I ky thong tin

Bhimani ( 1996) ch1 ra sir de doa doi v‹ii viéc cha p nha n TMDT co thé biéu lo tu nhirng ha nh dong khfi ng h0p ph:i p nhir viéc nghe trom, lo password, chinh sira d ti’ tiéu, d:i nh lita, va quit n0. Do dfi , Bhimani (I 996) va Ratna singham ( 1998) dé right c:ie yéu ca u ca n ba n cho TMDT I:i la in thfia man nhirng v:i n dé sau: su chiing thirc (authentication), su c:ip phép (authorization ), su 'n sa rig (availability), s r tin can (confiden tia lity), to an

ve n dii lie u (data integrity), khfing khitfic tit (nonrepu diction), va c:i c dich

- n chon (selective a pplica tion services). [6]

Swamina than et at. ( 1999) kha rig dinh rang ngufii tiéu diing da nh gla nhft ng ngirct i ban hang triic tuyén trufic khi ho thirc hién giao dich triic tuyén va do dfi cac da c tinh cua ngifét i ban hang dong vai tro quan trong trong vié c x tic tién giao dich. [6]

Rose et at. ( 1999) nhan dang cac trfi ngai ky thufi t va chi phi tién quan cua chung va nhitng gioi ha n dac thii doi vfi1 TMDT B2C, bao gom tri hoa n download, gifii han chr a giao dién (limitations of the interface), cac van dédo tim (search problems), do lufing thanh ‘rig ting dung Web khfi ng thichh0p, an toan yé u, va thiéu c:i c tiéu chu:i n Internet. Do dfi , ho ph:i t biéu

thifc hién nhfing giao dich kinh doanh voi c:ie thif0ng gia khfing thanh tha t hoac néu nhiing thong tin nhay du’Oc fun trong nhiing c0 set dii tiéu khfing an toa n, std de doa an toa n to n ta i ngay c:i khi dii lie u dit0c b:i o ve hoa n h:i o trong giao dich. [6]

Do dfi , c:i c t:ic gia dinh nghia nhan thiic riii ro trong pham vi giao dich tructu yé n (PRT) nhif I

’ i ro giao dich khfi di in:i ngifiti tié u du rig co thé doi

Kliio

Nhzallio riiro vdi s5 npb4m/dkkvu{RRR)

da u khi bfi c lfi nhiing phit0ng tie n dién tit cfi a viéc thitc hié n thif6ng ma i. Bon loa i PRT du0c nh5n dinh nhu’ sau: su bi ma t @rivncy), su an toan— chiing thiic (sec uri ty - uu/hen/icu/ion), khfi rig khirdc tit (nonrepudiciti on) , v:i nhan thiic rfi i ro toa n bfi vé giao dich tru’c tuyé n (overall perceived risk on online transaction). [6]

B.2. Mtr hinh e-CAMJoongho Ahn, Jinsoo Park, va Dongwon Lee (fiisk-foc used E- Commerc e Adoption Model - A Cross Country Stud5, Jun 2001) da tich hci p TAM va thuyé t nhan thiic rui ro (theories of perceived risk - TPR) trong mo t nghiénemu thirc nghié m trong c:i hai nu‹ic My va Han Quoc dé ’ i tthhiicchh sir

chit pnh:i n su dung TMDT (Xem Hinh 2). Nghién c iiu nay da cung cap kien thirc vé c:i c yéu to ta c dong dé n viéc chuyé n ngifct i sit dung Internet thanh khach hang tie in nang. Nhfin th itc tinh dé sir dung (perce ived ease of use - PEU) va nhfin thiic sit hffu ich (perceived usefulness - PU) ph:I i dif0c nfing cao, trong kh1 nhan thuc ru1 ro tién quan den sa n pham/dich vu (perceived risk relating to product/serv ice - PRP) va nhan thitc rui ro tién quan dé n giao dich truc tuyén (perceived risk relating to online transaction - PRT)ph:i i dif0c gi:i in di. Tuy ké t

“ hhiinnhh e—CAM

Quoc cho két qua khac nhau (thfim chi trai ngu0c nhau — xeni Hinh 3 va Hinh 4), nhitng khfi ng vi the in:i trio hinh gifim gi:i tri, ngit6c lai, no cho th5y ca c yé u to tae dong lén viéc ch5p thua n sir dung TMDT cii a tirng vii ng va n hfia khac nhau la khac nhau dang ké [6]. Do dfi , tfi i du dinh sir dung mo hinh e-CAM tich h0p vfii TAM trong nghié n ciiu nay nham kiém tra

Hinh II. 2. Mii hinh e-CAM

thu’c nghiém cu thé tai Vié t Nam.

16

Hinh O. 3. Eét quit kiem tra e-CAM t9i M)

Hinh Tf. 4. Et t qué kitnr tra e-CAM t9i Him Qu6e

III. MO HINH SET HI)P VE CHAP THUAN VA SP DUNG CONG NGH E) (UTAUT)

III.I. C:ie kién triic chf nhIII.1.1 Ky vpng két qud thijc hi(n (Performance Expectancy)Ky vong ké t qua thuc hién du0c dinh nghia la ca p dfi ma mo t ca nhan tin ‘rig sfi dung

hién cfi ng viéc. Kién trfic nay du0c tong h0p tit 5 kié n triic kha c co tién quan trong ca c mo hinh riot ba t da dircic thiic ngh iém tnrfic dfi , ca c kien truc khac da du0c tich h0p trong kié n truc nay la : Nhan thiic sir h iiu ich (tu mo hinh TAM), Dong CO Bé n Ngoai (tii trio hinh MM), thich h0p cfing viéc (tit

“ hinh MPCU), 101 the co lien quan (tir mo hinh IDT), v:i KQ Vong K¼ tQua (tir mo hinh SCT). Cac kié n trii c nay dir0c danh gia la tir0ng tit nhau va c:ie tae gift da chon loc cac thang do tit dfi cho kien true Ky Vong Ké t Qua Thifc Hié n nay. [19]

III.1.2 Ky vpng ' lijc (Effort Expectancy)Ky vong no lifc dif0c dinh nghia la miic dfi de ké t h0p vfii ‘c sit dung hé thong (thong tin). Kié n truc nay cling dir0c tich h0p tir 3 kié n true du’ci c xem la tit0ng tit trong c:i c mo hinh riot b:1 t hién tai dé lifa chon thang do phu h0p. C:ie kié n true kha c d:i du0c tich h0p trong kién trtic nay la: Nhan Th itcTinh D8‘ Sit

Dung(tit trio hinh TAM/TAM2), sit phiic tap (tif mo hinh

MPCU), va De Sir Dung (tir mo hinh IDT). [ 19]

III.1.3 Anh hifdng xa hfyi (Social Influence)Anh hiffi ng xa h‹i i dif0c dinh nghia la miic dfi ma mot ca nhan nhan th itc r:i ng nhiing ngitfii quan trong kh:ic tin r:ing anh/co ta nén sit dung hé thong moi. Anh hitfi ng xa hfi1 du0c tich h0p tir cac k lén truc kh:ie tit0ng tir nhau la : Tiéu Chu:i n Chfi Quan (Subjective Norm, 1:iy tit mo hinh TRA, TAM2, TPB/DTPB va C-TAM-TPB), Cac Nhan To Xa Hoi social Factors, lay tit mo hinh MPCU), v:i Hinh Anh (Image, lay tif “ hinh IDT).

III.1.4 C:ie diéu ki(n thuan ti(n (Facilitating Conditions)Ciic diéu kié n thu:i n tie n dif0c dinh nghia I:i miic dfi in:i mo t c:i nhfi n tinrang c0 sci ha ta rig to chiic va ky thua t ton tai de hfi tr0 viéc sit dung héthong. Dinh nghia cac kh:1i nié m noi bfi I vfi i 3 kien trfi c kh:i cnhau: Nhan Th ti« K ns so:i t Hinh Vi (thy tir mi5 hinh TPB, DTPB, C- TAM -TPB), Ca c Dlé u Klé n Thuan Tién (la y tif mo hinh MPCU), va Su Tii0ng Thich (lay tit mo hinh IDT). [19]

III.2. Mtr hinh UTAUTViswa nath Venkatesh, Micha eI G. Moris, Gordon B. Davis, va Fred D. Davis da thié I 1:ip mo hinh UTAUT (Unified Theory of Acceptance and Use of Technology). Mo hinh nay la su ké t h0p mot so thanh phan chr a 8 1y thuyé t/mo hinh trufic dfi vfii muc tiéu thié t 15p mot quan diém chung nhfi tphuc vu cho viéc nghién cuu sit ch:i p thu a n

dung ve he thong

thong tl n moi [ 19]. T:i nn mo hinh/IQ thuye I tha nh pha n da du0c xem xé t la :

TRA (Theory of Reasoned Action) TAM (Technology Acceptance Model) MM (Motivation Model)TPB (Theory of Planned Behavior)C-TAM-TPB (a model combining TAM and TPB)MPCU (Model of PC Utilization) IDT (Innovation Diffusion Theory)

scT (Social Cognitive Theory)Mo hinh UTAUT la mo I mo hinh ké t h0p tif c:i c 1y thuyé I da dit6c bié I dé n va cung c5p né n tang hiiét ng da n cho ca c nghié n emu trong tu0ng lai fi tinh vuc cfi ng nghé thong tin. Ba rig cach chiia dung cac s iic manh kham pha du0c ké t h0p cii a tirng mo hinh rié rig bié t va cac anh hiring chit yé u, UTAUT dita ra c:ic IQ thuyé t tich liiy trong khi vfin duy tri c:i u trii c chi tié t. s‹i do efiu trtie mo hinh UTAUT du’0e trinh bay nhu’ sau:

hinh II. 5. Mti hinh UTAUT

19

PHAN B. LAP MO HINH PHI.I€JNG TRINH CAU TRUC (SEM —STRUCTURAL EQUATION MODELLING)

Ph:in nay term ui t rig:in gon v:i khfi ng thién vé d:ic tinh ky thufit ciia c:ie v:i n dé ca n ba n co lie n quan tron g SEM, ba o goin c:i c va n de udc lu0ng, thich h0p mo hinh, va c:ic gi:i thiét thong ké .

SEM (Structural Equation Modelling) la mo t ky thua t mo hinh thongké ra t tong qua t, dir0c sir dung rong khoa hoc nghién c iiuhanh vi. No co thé dii0c xeni la su ké t h0p chia phan tich nha n to va hfi i guy hay ph5n tich d«ét»g di». sir quan t5m tron sEM ih«ét n ii vao cac kién trii c ly thuyé t, dir0c trl nh bay bfii cac nhan to nga ni. Cac quan he giffa c:i c kien triic ly thuyé t dif0c trinh bay bfii c:i c hé so hfi i guy hay hé so duéng dan gibra cac nhan to. SEM am chi 1 c5u triic cfi a c:i c hiép tit0ng quan (covariances) giiia cac bié n dif0c quan sat, cac quan he nay cho ra mot ten khac la mo hinh hfia cau trite hiép tit0ng quan (covariance st ructu re modelin g). Tuy nhié n, triohinh co thé du0c met rong thém bao

“ ni trun binh

quan sa t dir0c hoac cac nha n to trong mo hinh, lam cho ten mo hinh ‘u trii c hié p tif0ng quan it chinh x:i c. Nhié u nha nghié n ciiu chI

d0n gia n right mo hinh loa i n:i y l:i “ca c

“ hinh Lisrel,” diéu na y

cling it chinh xfi c. LisREL ID chft viet t:i t chia Linea structuralRELations (c:ic quan hé ‘u trii c

tuyéntinh), va ten n:i y dif6c

Joreskog sir dung cho mo t trong nhiing chif0ng trinh SEM dau tién thong dung nha t. C:i c mo hinh phii0ng trinh c:i u trii c rig:i y nay

‘n cua SEM xah0n phif0ng trinh Lisrel ban dau. Vi du, Browne ( 1993) thao lua n kha n:i ng 1:an thich h0p c:1c ditong cong phi tuyén.

SEM cung ca p mo t khung thu5n tién va r5t tong qua t cho ca c ph5n tich thong ké bao gfi m cac thii tuc da bié n truyé n thong, vi du ca c triréi ng h0p da c bié t la phfin tich nhan to, pha n tich hfii guy, phfin tich pha n blé t, va tii0ng quan canonical. SEM thiffi ng du0c minh hoa bang biéu dfi du0ng dan. Phu0ng trinh thong ké nay thuci ng du0c trinh bay trong mot hé phu’0ng trinh ma tra n. Trong dau th5p nién 70, khi ky thufi t nd y dif0c gifii thié u lfin d:in trong nghién ciiu xa hfi i va nghién elm ha nh v1 , phan mem thiffi ng yéu cau cat da t chi ro mo hinh theo diéu kié n cii a nhfrng ma tran nay. Do dfi , cac nha nghién c iiu da phai loc viéc trinh bay ma tran tu bié u dfi ducrng dan, va

cung c:ip phfin mé m vfii 1 chuoi ma tr:i n cho c:i c tfip h0p tham so khac nhau, nhu’ la hé so nha n to va cac hé so hfii quy. Cac phfin mé‘min ddiirr00cc phhaatt trié n gaann day cho phép cac nha nghhiiéénn ciiu chi dinh trttc tié p mo hinh nhu’ la 1 bié u do dirfing dan. Viéc nay hié u qua vfi i ca c va n dé d0n gla n, nhung co the ga y m(thinh co tinh phuc tap h0n. Vi ly do nay, phan men sEM hi(n tai cling van ho tr0 cac da c tinh ky thua t ciia mo hinh loai c5u lé nh-hay ma trfin.

Path analysis (ph5n tich duéng xu hu’cIng) la ky thu5t thong ké diing de kiem tra quan he nh:i n qua giita ha i ha y nhieu bien . Dida trén he thong phit0ng trinh tuyé n tinh.

Path analysis la thanh pha n phu cu« sEM, eat thu tuc da bién ma theo dinh nghia cua Ullman ( 1996), “cho phé p kiém trci mot tft p qucin hé gi iia mo I hej ’ nhié u bié n doc liip, home’ let tién tuc’ home’ Ie row rci c’, re mfi/ hay nh iéu l›ién phu thud c, hri‹ic /‹i //én tuc home let réi roe. ’ SEM tién quan dén cac bién do lu0ng du0c (measured variable) va cac bién ng5m (latent variable). Mfi t measured variable la mot bié n co thé du0c quan sa t trirc tié p va dii0c do timeing. Bién do luci ng du0c cling dii0c bié t den nhif bién qua n sa t dir0c (observed variable), bién chi bao hay bié n biéu thi (indicator or manifest variables). Mo t latent va ria hle la mot bién khfi ng the dit0c quan s:i t trifc tiép v:i phfii dii0c suy ra ivi measured ver ricible. Latent variables du0c am chi bfii hiép tu0ng quan (covariances) gitia hat hay nhiéu measured variable s. Chung cling du’0c bié t den nhu’ la ca c nhan to (nghia la, ph:i n tich nhfin to), c:i c bié n kié n trfic hay c:i c bién khfing quan sa I dii0c (constructs or unobserved variables). SEM la su ké t h0p gifra hfi i guy da bién v:i phan tich nha n to. Path analysis chi tién quan de n c:i c bié n do 1ikctng (measure d variables).

II. CAC THANH PHAN CUA SEMCo hai thanh ph5n: mo hinh do luéng (measurement model) va mohinh ‘ u trfi c (structural model).

4 Measurement model: tién quan dé n quan he giila measured variables valatent variables.

4 Structural model: chi tién quan dén c:i c quan hé giiia cii c latent variables ma thfi i.

KV hiéu tronc s EM:

C:ic bié n do tiling dif0c: hinh chit nha t hay vufi rig

C:ic bié n rig:i in: clip ha y hinh tron

C:ic khofin sa i so: (“nhieu ” ciia c:ic bié n rig:1 in) dit0c dna va o bién dfi SEM, dai dién bfii “E’ s” cho c:ic bién do lu0ng va “D’s ” cho c:ic bién ngani. C:ic kho:i n sai so dai dién phir0ng sai ph:i n dir trong c:ic bié n khfi rig dit0c tinh cho c:ic diténg da n (pathways) dit0c gi:I thié I trong mo

Tham

La c:ie bién, he so hfi i guy va hié p tir0ng quan giila cac bién.

Phu0ng sai co thé du0c chi ra b:i rig miii tén hai d:i u két thiic tai cung motbié n, hoa c d0n gia n h0n, ky hi(u b:i rig so trong ho p vC bié n hay cung

C:i c hé so hfi i guy du0c trinh bay doc theo miii té n mo t chié u chI ra duéng da n dir0c gi:i thié t giira hai bién (co trong so du’Oc :i p dung cho ciic bién trong ciic phu0ng trinh hfi i guy tuyén tinh)

Hiép phii0ng sai dii0c ké t h0p v‹ii c:ic miii tén vong cung hai d:i u giira hat bién hoac ca c sai so va biéu thi vfi huérng (no directionali ty). Data cho SEM la cac phii0ng sai 'u va hiép phu0ng sai niau lfiy tir tong thé (ky hiéu S, phif0ng sai niau quan s:i I dit0c va ma trfin hiép phit6ng Lai).

III. EIEN TRUC SEMMuc tiéu trong viéc xay difng 1 bié u dfi xu hitting (path diagram) hay mohinh phit0ng trinh cau titi c, la tim mot mo hinh du thich h0p vfii dtt tiéu (S)dé phuc vu nhif la 1 dai dién co ich lié u.

Co 5 bufic tron u kié n tr6c S EM:

’a dfi tin c:1y va gi:i i thich chi tié t dii

1. ChI dinh mo hinh (Model Specification)2. Nhan dang mo hinh (Model Identification)3. Uclc lif6ng mo hinh (Model Estimation)4. Danh gi:i dfi thich h0p cua mo hinh (Asse sing Fit of the Model)5. Hlé u chinh mo hinh (Model Modification)

BI.I. Chi djnh mfi hinh (Model Specification)L:i viéc chinh thiic b:i t dan mot mo hinh. Trong bitfic nay, c:i c tham so dif0c xac dinh la co dinh hay tir do. Them sé cé dinh (fixed pa ramete re) khfi ng dit0c itfic lif0ng tit dii tiéu va dit0c g:i n mot c:i ch tiéu bién b:i rig 0 (chI ra

khfi ng co quan he giiia c:ic bién). Cac ditfi ng dan cfi a ca c tham so co dinhdu0c gan nha n

‘ trif khi

co dirélng dan nao du0c vC) trong biéu aa sEM. Th« n ‘ tit do (Freeper rcimctcrs) dif0c iffic lif0ng tit dft lié u quan sa t va du’Oc ngiffii diéu tra tin

0. Viec xac dinh thamla ra t quan trong vi no xac dinh tham

tir do trong SEM sir dung dé so sanh

bié u dfi ’ thu ét v‹ii ma trii n hiép phu’0ng sai va phu0ng sai tong the niautrong viéc kié in tra tinh thich h0p cu a mo hinh (bitfic 4). Viéc chon tham so nao la co dlnh v:i tham tir do tuy thuoc va o ngufii nghién cuu. Sri lua chon nay trinh bay mot gia thuyét tién dé vé duéng xu hufing trong hé thong I:i quan trong trong the hé cii a c:i u trim tién quan cfia hé thong du’0c quan sa t (vi du, phif0ng sa i niau dii0c quan s:i t v:i ma tran hiép phii0ng sai).

BI.2. Nhan d:jng mii hinh (Model Identification)Viéc nh:i n dang quan tfiin dé n ‘c co hay khfi ng gi:i tri duy nh:i t cho moi v:i moi tha in so tu do cci the thu th:ip du0c tir dii tiéu quan sa t. No phu thufic vao viéc lua chon mo hinh va dac tinh ky thufit ciia c:ic tham so co dinh, r:i ng bufic va tit do. Mfit tham so bi rang bufic khi no trong mot t:ip h0p vfii c:ie tha in so kh:i c. Ca c mo hinh ca n pha i dif0c nha n da n g hoa n chinh dé co thé irfic lii0ng dir0c (budc 3) va dé kiém dinh gi:i thuyé t vé qu an he g iita c:i c bién.

Co cii c dang mo hinh co cfiu trfic la just-identified, overidentified, hay underidentified.

just-identified model: trong dfi tir0ng ung 1- 1 gitra data va cac tham so ’u truc. Nghia so tham so dif0c itfic lit0ng. Tuy nhie n, in:ic dfi u khil n:i rig cfi a mo hinh la dat dif6c mo t gi:Ii ph:i p duy nh:1 I cho tfi t cfi c:ic tham so, just-identified model khfi ng co sit quan t:i nn cfia khoa hoc gia vi bfii no khfi ngdo va do dfi khfi ng the bi loai bfi .

Overidentified model: la mo hinh trong dfi so tham so co the ifclc lit0ng du0c thi nhc› han st 6ié in dft lié u (data points) (nghia la, phu’0ng sai, hiép tit0ng quan cu a c:1c bién quan s:i t dit0c). Trnh trang n:i y tao ké t qu:i ra do nr do dii0ng cho phép loai bfi mo hinh, do dfi du0c sfi dung mo t ca ch khoa hoc h0n. Muc dich cua sEM I:i chi ra mé› t m‹5 hinh nhir vay dap ring cac tiéu chua n cua overidentification.

Underidentified model: la mo hinh trong dfi so tham so du0c iific lir0ngvit0t qu:i so phit0ng sai v:i hiép tif0ng quan. Nhit ‘ mo hinh bao gfi m

’a

thong tin khfi ng j nghia (tit dff tiéu dfiu vao) cho viéc dat dit0c giil i phap x:ie dinh vé if clc 1u’0ng tham so; nghia la, vfi so c:ie giai phap la kha di cho underidentified model.

III.3. H‹ic luetng m8 hinh (Model Estimation)Trong birfic n a y, ca c gia tri khfii da u cu a tham so tit do dii0c chon de sinh ra 1 ma tr5n hiép tuci ng quan tong thé du’0c irfic lu0ng (estimated population covariance matrix), Z(8), tit mo hinh. Cac gia tri khd i dfiu co thé du0c chon bfii ngu’éi nghién c iiu tit thong tin ban dau, bfii cac chu’0ng trinh may tinh du0c sir dung dé xay dung SEM, hay tir phan tich hfii guy da bién. Muc tiéu cua ufic 1u’cing la dé sinh ra mot X(8) hoi tu trén ma tr:i n hié p tir0ng quan tong thé quan s:i I dit6c, S, vfii ma tr:i n phfin did (residual matrix) (kh:ic bié I gifta X(8) va S) trfi né n toi thiéu. Nhié u phu’0ng phap co thé dir0c sir dung dé sinh ra Z(8). Viéc chon cac phu0ng phap du0c hiid ng da n ba rig dac tinh cfi a data bao gfim kich thiffic v:i ph:i n phoi man. Hfiu hé t c:ic tién trinh dit6c

h:i in Hi

Trong dfi :s = vector bao gfim phif0ng sai va hiép phit0ng sai cua c:i c bié n quan

n(8) - vector bao ‘in c:ic phif0ngsai nhif du’0c du’ do:in bfii mo hinh.W = ma trfi n trong so

(mo I va i t:ie gi:i xem Q nhu la F)

sai corresponding vé hiép phit0ng

Ma tr:i n trong so, W, trong ham trén, phii h0p vdi phir0ng ph:i p ircic lu0ng du0c chon. W duOc chon dé toi thiéu Q, va Q(N-I) cho viéc thich h0p ham, trong hfiu hé t c:ic tritong h0p mo t thong ké ph:in phoi X'. Ké t qu:i thifc hié n

- bbii a nh hiffi ng

belt kich thu0c ma u, sai so pha n pho l , nh:i n to pha n

phoi, va gift thié t rang cac nhfin to va sai lfip (Ullman 1996). Mfi tva i ph it0ng ph:i p itfic lu0ng dit0c sit dung thong du rig nha I la:

Gencrammed Least squares (GLS)

Furs - /i tr[([S - Z(8)]W ')']Trong dfi :

tr = to:in tit theo diii (trace operator), chéo chinh ciia ma trii n

‘rig c:i c yéu

W"= ma tran trong ‘ Hi uti, phai

dir0c chon bfii nha nghién ctiu

(chon lima thong thii0ng nha t la S ')

Ma:ximum Likelihood (ML)

Fvm = loglZ l - loglsl + tr(S£") - pTrong tritong h0p nay, W = S" va p

“ lif0n bién dit0c do likct ng

Asymptotically Distribution Free (ADF) Estimator (Ham if‹ic lifting ti(do phtl n phfii ti§m c$n)

FUS [ • n(B)] ’W" [S • n(8)]

W, trong ham nay, bao gfi ni cac yéu to xeni xét trong kurtosis.

Ullman (1996) va Hoyle (1995) thao 1ua n vé ca c thu5n 10i va gidi han chiac ham if‹1c litctng tré n diy.

ML va GLS hfiu ich cho dC tiéu phfin phoi chufin khi c:ie nhan to va sai so I:i dfic I:ip, ADF hiru ich cho c:ic dit tiéu khfi ng ph:i n phoi chu:i n, nhiing chI co gia tri khi kich thl1’fic ma u lfin h0n 2.500. Ullma n i ha in l1’fi c

l11'0I1$ tot nhat cho dif lié u khfi ng phii n phoi chuiin va/hoac phu thufic gifia c:ie nhfin to v:i sai ‘ la Scaled ML. Bfi I ké ham n:t o dif0c chon, két qufi inong d0i cu a tién trinh iffic lit0ng 1:i dat dif0c mo I h:i nn thich h0p “ n den 0. Mfi tha ni thich h0p vfii so dié ni la 0 ch I ra

hié p phu0ng sai dir0c

u’fic lu0ng ciia mo hinh va ma tr5n hiép phu0ng sai niau nguyén thuy la

BI.4. D:inh gi:i diy thich hi)p cfia mfi hinh (Assesing Fit of the Model)

Nhit d:i ph:1 n tich, gi:i tri h:inn thich h0p gfin den 0 dit0c mong d0i cho dfi

thich h0p ’ hinh. Tuy nhfi h6n 3, mo hinh la thich h6p tot (Ullman 1996).

Dé co dfi tin c:i y trong kié m dinh dfi thich h0p mo hinh, kich thitfic niau tif1 00 den 200 dii0c yéu cau (Hoyle 1995).

Ullman (1996) th:to lufin sit da dang cfi a c:ic ham thich h6p ph:i n phoi khfi ng-X’, ma ong ta got la “cac chi so thich h0p so sanh (comparative fit indices.) ” Hoyle ( 1995) dé cap dén diéu nay nhii “cac chi so thich h0p phu thufic (adjunct fit indices).” Mfi t cach c:in bfin, nhifng phif6ng ph:i p n:i y so sa nh dfi thich h0p ci'ia mot mo hinh doe 1:ip (mo t mo hinh khang dlnh khfi ng co quan hé gifta cite bién) dé thich h0p mo hinh du’ci c irfic lu’0ng. Ké t qua

“c so sanh va 1, vfii 0.90 hoac tonh0n du0c chap nh:in nhii la c:ie gia tri chi ra dfi thich h0p. C:i Hoyle va

Ullman dé right sit dung nhieu chi so khi x:i c dinh c:i c dfi thich h0p mohinh.

III.5. Hi(u chinh m6 hinh (Model Modification)Né u ma tran phii0ng sai/hié p phu0ng sai du0c iific lu0ng bang mo hinh

ca ch thich h0p ma tra n phif0ng sai/hiep phu0ng sat m5u, cac gia thuyé t cfi the dir0c hié u chinh va mo hinh du’Oc kiém dinh lai. Dé diéu chinh 1 mo hinh, cac dirci ng dan nici i dir0c - thém hay

‘ du’ctc thaytu do ho:ic tu tit do dén co dinh. Diéu quan trong dé nhét la khi trong c:ic thii tuc thong ké khac , la viéc hié u chinh mo hinh sau viéc kiém dinh tan d:i u 1:i in gia ta rig c0 hfii cii a vap pha i sai lam loai I.

C:ic thii tuc thong thu0ng dir0c sit dung cho viéc hiéu chinh mo hinh la Lagrange Multiplier Index (LM) va Kiém dinh Wald. C:i hai loai kiéni dinh nay b:t o c:to c:ie thay doi trong gia tri X' khi c:ic dirérng dan dif0c diéu chinh.

LM yéu cau du co hay khfi ng viéc gia tang cac tham ‘ tu do gia tang sitthich h0p ci'i a

" hinh. Kic:in dinh Wa Id yeu cau co hay khfi ng

cac tham so tit do gia tang sit thich h0p mo hinh.

De die u chinh ty Ie sai lfim loa i I gia t:Ing, Ullman (1996) yéu c:i u sir dung in fi t g i:i tri x:i c sua t thap (p<0.01 ) khi I:in g them hay bfi ca c tha in so. Ullman cling yéu c:i u so sii nh giii tri chéo (cross-validation) vfii c:ic niau khac. Vi trii t tu ciia c:ie tham so tir do co thé :inh hufing dé n vié c lu’a chon cua c:ic tham so kh:ic, LM nén du0c :ip dung trudc kiém dinh Wald (nghia 1:i, c‹i rig the in v:i o ta t ca ca c tham so trucic khi ba t da u xfi a chting) (MacCullum 1986, dii trich dan ciia Ullman 1996).

III.6. Trinh bay md hinh ’i‘ cciiiinng (Final Presentation of Model)Khi mo hinh da dat du0c dfi thich h0p chap nhan dir0c, cac irfic lir0ng rié rig bié I vé cac tham so tit do du’0c danh gi:i . Cac tham so tu’ do ducic so sanh vfii gia tri rong (null value), ski dung thong ké phan phoi z. Thong ké z dat diinc ba rig ca ch chia tham so ufic lu0ng cho sai so chua n ciia ufic lu0ng dfi . Ty Ie cua kié m dinh nay phai vu’0t +/-1.96 dé quan hé trfi né n co y nghia. San khi c:i c quan hé rié ng bié I trong mo hinh dif0c danh gi:I , c:ic iffic lit0ng tham so du0c chua n hfi a cho vlé c trinh bay mo hinh cuo cir ng. Kh1 cac irfic lit0ng tham so dif0c chu:i n hfia, chfi rig co thé dif0c gi:i i thich tham chié u vfii ca c tham so khac trong mo hinh va cu’é ng dfi cfia du’fi ng xu hirérng co tién quan trong mo hinh co tr é du0c so sanh.

PU PEU RU PEU PU PEU RU PEURU

Spxzdskee Spxzdskee Spesdsteet Spxzdskee Sp o»dtso Spesdsteet Spesdsteet Sp u»d×tsuiSpesdsteet3 EUCEXPERIENCE

I. LAP MO HINH TAM•ECAMPh:in n:i y trinh bay mo hinh nghién ciru ciia dé tar, du0c da t ten la TAM- ECAM do c:i c kié n trilc chinh ké I h0p tit hai trio hinhTAM va e-CAM da du0c thinh bay trufic day.

C:ic kien trtic noi sinh l:i PU (Nh:in thifc su hiru ich), PEU (Nha n thirc tinhdé sir dung), BI (Hanh vi dir dinh), PB (Hanh vi mua thirc su’).

Ciic kien triic ngoai sinh la TERMI (Thu:1 I ngii), SCREEN (Thiét ké giao dién), FACI (Cac diéu kien thuan tién), PRT (Nh:in thiic rii i ro tién quan dén giao dich truc tuyén), PRP (Nhan th iic riii ro tién quan dé n san phfi in/dich vu).

I.I. Lija chpn c:ie kién trite ngo:yr sinhViéc lima chon ca c bién ngoai sinh dira vao nhié u nghién emu thu’c nghié in

Hat kiln trim PRT (Nha n thirc rim ro lien quan dé n gia o dich trirc tuyén) va PRP (Nhan thiic rtii ro lié n quan dé n san pha in/dich vu) dircic 15y tit mo hinh e-CAM [6], hai kié n tiii c nay da dir0c thié t ké cho viéc khfio sat nhfin thtic trong mai dién tft va du’0c kiém tra thuc nghié m

Van de con lai la lua chon ca c kien trim ngoai sinh ta c dong ten PU va PEU. De d0n gia n, tfii l5p bang trinh bay tit t ca c:ie kié n trim tham kh:t o tir c:i c nghié n ciiu triffic co lié n quan dén vié c chon bié n trong dé t:ii n:iy.

Bring III. 1. Trim tiit lifa chp n bién

l J [141 RELEVANCE PEU Eli br×/

6 7

I3 [14] RELEVANCE PU E-libr»I 4 [14] TERIMINOLOGY PU E-librzyID [?4] SCREENDESIGN PU E-lbra16 [?0] CO1MPUTERSELF-EFFICACY PEU IS17 [10] COIMPUTERANXIETY PEU lSI 8 [?0] COIMPUTERPLAYFULNESS PEU lS19 [?0] PERCEIVEDENIOYIMENT PEU lS20 [?0 OBIBCTIVEUSABILITY PEU IS3! [2!] SUB}ECTIVENORV RU IS12 [?1] IIMAGE PU lS²³ £²U *OBRELEVANCE PU IS

Bfing HI. 2. Gifii thich lj do c:i c bién Khting difpc chpn (Ky hi(:u Yes n cfyt 7)

3 [16] EUCEXPERIENCE4d

[ltl[6

**CEXPERIENCETRAINING

6 [16] TRAINING

ll d "8#lén ciu ota lesi4. Honnta,viéo h%lr0 oé‹h6 do ih8npqua bién Fz‹i1iianp Condi‹ions(Cizd 6ukMz ‹huiz fltn] ronp [?0] phu hopvdi m8i

2

!3 [?4]!4 [?4]!D [24]!6 [?0]

RELEVANCE

COMPUTERANXIETY

OBRCMVEUSABILITY SUB!ECITVENORM

OUFRUTQUALITYRESULTDEMONSTRABILNY

Theo [24]: Ctic d4c tinh hé th6ng c6 lché n4ng énh hu’dng tnrc tiép cé PEU vii PU ve" hé thfing thong tin. C:ie nghién erm bao gom cac bié n ngoai sinh cua TAM da 6m thay quan h co y nglfia giifa c:ie bie n h théing va cac kién trim niém tin cua TAM. Tuy nhié n , cac nghié n emu nay ho aj c la sit dung nhitng bie“n gi:i (dummy variable) de trinh bily IS lchd c nhau hay thita nhé n 1 1ci“en triic toiin bp dcin, nhir 1:I nhé n thiic chat lu’cing h thfing (perceived system quality) hay chat lifting két 9u:i (output quality), de thay thé cho c:ie d(ic finh he: thong. Hoat dong dctn gi:in th:ii qu:i n:iy Ichfing 1:inn not b(at I:ie dong cii a c:ie d aj c tinh h( thong rie‘rig bi I lén via:c chap nh3an cua ngu’iti sit dung. Do d‹i , co st( can thi¼ t de nha n dang nhitng daj c tinh h( thong dac thii va kiem tra c:ie ta c dong rié ng bic:I cua chiing Ie‘n cé PEU va PU vé c:ie thu vi n sfi hoa. Quan he: girla c:ie be tinh h( thfing da dang v:i c:ie Icien ture mém tin trong TAM co thé du’ctc Iciem tra thong qua Icién trim sit dung. Thay vi kiem tra finh de st? dung hay su’ hif u ich, c:ie nhil nghie‘n ciiu tchoa hpc thit vie‘n da tép trung véo vi(c sir dung c:ic thit vi(n sfi h6a.

• • tnew when

3

[4]g.

htathSCRRRNDESIGN

Viéc sit dung thl ditctc ‹huh nghia nhu’ la c:ich thiic dé n/iiz ifié’ndn va fiiéu qud nhu: thé nd n mot hé thfing computer co thé co the du’ctc sit dung bdi mot tap help ngu’fii sit dung d(ic thii. Hai thanh phan sit dung nay c‹i m(ot tdctng dong gan giii vdi kie“n trii c PEU va PU cfia TAM. Do d6, nhifng dinh nghia wén déy cung cfip cho ch6ng tot nen t3ng dé lciem tra anh hitfing cfi a c:ie yen to da dang ve viec sit dung hé thong trén c:ie lcié n trim niém tin ciia TAM. Theo c:ie tinh vie yé u to sit dung ciia I.indgaard, chung Hi dé xuat 3 dii. c tinh h( thong (Relevance, Terminology, Screen design) nhd ca c tha nh phan x:ie itinh ngoai sinh quan trong cii a TAM. Chirng dir dif‹(c chpn vi chirng thififng dit9c d$ c$p trong ly thuyCt lclioa h9c thit' vi(n ciia si( thich d:ing ciia chfing d0i vdi ph9m vi thu vi(n sfi hoa (nhit lii relevance, terminology', vii screen design lii v6n c6 trong céc h( th6ng hfii phijc th6ng tin) va miic do lciem so:it cap cho nhitng ngutti thiét ke thd vi(n so hfia. Thiti gran hoi d:ip (response time), la mot d:ie tinh hé thong Ich:ie, da khfi ng dit‹Jc dia van trong nghién erm na y vi nhitng ngitili thi¼ t ké thh vi(n s6 h6a c6 It tciem so4 t trén n6 v6i ngu’6i siI dung riot ke“t dén hé thong th6ng qua the Internet.

Theo [3]: Thu’cing mai tnrc tuye n (nhu’ e-bay) nh6 rlt nhiéu v:to cfing right: d5 ho trci tie“n trlnh Icinh doanh. Khi TMDT du’pc t6 ch(rc chtnh thong qua nrcing ttic gitfa tchéch ha rig va c:ie h( thfing computer; mot mite mctng t:ie can ddpc b:in d:inn de dat ddctc tirong téc mong dni. Trong ph9m vi nity, fitting téc phfii firm nhi$u hdn vdi vi(c he‹ c:ich d( tin nbi(m c0ng ngh( ciia kh:ich hitng d8 hitnh d(ng the« aii« ‹; • ii••• phpc nhffng th8ng tin cti y nglfia nhlft cung clip cho cite quyé’t di.nh. Hfiubl va Murray (2002) ph:i t hic:n thay Ich:ich hang tin tu’‹l ng nhieu hct n vao nhittig gidi thiéu du’ctc hinh thanh b‹li c:ie I:ie nhan gi‹1i thiéu dién tii theo c:ich ho se‘ sit dung nhifng thuoc finh ciia s:in pham du’nc gi6i thi(u de tham Ich:to cho nhiing quyé I di. nh mua hang hi(en tai hay tu’ctng lai.TO c6c IQ luén tre‘ n, cd thé xem E-commerce lit 1 he‘ thong hfii phuc th6ng tin, do d6 nhan c:ie bién theo [24] va bii c:ie bién theo [21]

B g I . .

. sion

4 26 [!6]?7 [16]

28 [4]

29 [4]

g t g u .

1) Theo [24]. Kién thiic ve tinh vu’c do tim la m3ot yéu tc› 1‹iém so:i I bé n trong 1‹1i:i c cc› thé anh hitfi ng tich cite len nha n tliitc tinh de sit dung ve thit vi(n so hfia. Nghié n emu vé c:ie he th‹› ng phuc hot thong tin chi ra rang lcié n tliirc pham vi co the her trct viéc do tim

dung I:i c1i nh?ng thci ng tin lié n quan ID

Bfing III. 4. Ti) m ta I c:ie bién ngo:j i sinh dif9c ch9n cho m8 hinh

PEU PEU

PEU

@ diy, kié n trii c KNOWLEDGE OF SEARCH DOMAIN dit0c tich h0p vao k én tru « FACILITATING CONDiTioNs » ‹i« i« » aa» de d«w ‹h a trong mo hinh UTAUT ké I h6p c:i c kié n trti c da nghién ciiu. Difct ng dan va ca c muc do cua kié n trim nay cling dif0c hié u chinh theo UTAUT [19].

1.2. Cite kién trfic chfnh1.2.1 Thua t ngft (Terminology)Thufi t ngfr dé cap dén tit, cau, va chit vie t tat du’0c sir dung bfii he thong.Ngitfii sit du rig cfin de trinh c:i c truy

‘n cfi a ho den he thong thong qua c:i c

cum tir co cau trim n0i ma kien thiic cii a thu ii t ngC duOc sir dung bfii he thong la khfi ng thé thié u dii0c. Nfi i c:i ch kh:i c, diéu cii ng quan trong la

’ do tim so hfi a mot ca ch ro ra n g va dim g da n . Mo I v a n dé chinh v‹ii thu a t ngfi he thong thong tin la nhiing bié I rig it dif0c sit du rig khfing thich h0p. Nhit da du0c ghi chti bci i Talja et at., tir vung ma ngiicri si’r dung diing de dién ta nhu c:1 u thfing tin cua ho thuct rig kh:ic vcli thu:1 t ngif cua nhilng ngitct i cung c:ip thon g tin. Diéu kh:ic bié t n:i y khfing nhfing la in kho kh:i n cho ngiicr i sit du rig tit0ng I:ic v‹1i he thong ma con gi:Inn l0i ich tie in ta rig ma hé thong so hfi a co thé cung c:ip cho ngucii sit dung [24].

Kién triic na y d:I dif0c kié in tra thifc nghié in trong moi trifct ng thit vié n so her a va cho thii y két qu:i t:ie dong tich cu’c dé n nhan thiic tinh dé sir dung [24].

Tfii dita kié n trtic n:i y vao trong mo hinh theo két

’ nghhiiéénn ciiu

tit [24],

kié n trti nay I:ie dong trill tiép lé n kié n true Nhan Th itc Tinh Dé Sir Dung.

1.2.2 Thi8t ké giao di§n (Screen Design)

Screen design la cach thiic thong tin dii0c trinh bay tré n man hinh. No da du0c nhan bié t triific da y bfii ca c nha nghién c iiu he thong thong tl n la viéc thié t ké giao dién v:i in:i trinh bay thong tin cfia mo t hé thong thong tin co thé anh hiicing Iéin dén chién Iu0c do tint thong tin cua ngirci i sir dung va ké t qua thuc hién cua ho [24]. Trong c:i c nghié n emu vé ca c hé thong hfii phuc thong tin, giao dién ngir0i sir dung da dii0c bao cao la quan trong tu0ng dif0ng vfii phu0ng tién hfii phuc (retrieval engine) trong viéc anh hu’fi ng thitc hlé n he thong [24]. Mot thi¼ t k@ in:i n hinh tot co thé tao ra 1 mol tritct ng ao tién right ma ngitfii sit dung co thé dé dang nhfi n bié I ca c nhfi m phir0ng tié n giii p diéu hiidng, di chuyé n tit do khap nOi va qué I c:i c ké I qua do finn, va 1:inn cho viéc do tiin him hiéu h0n.

Kié n trtic nay da dif0c kié m tra thuc nghiém trong moi triféng thu vié n so hfi a va cho thay ké t qua tac dong tich circ dé n nhan thiic tinh de sir dung [24].

Tfii difa kié n trtic na y vao trong mo hinh theo ké t ’ nnghhiiéénn emu tit

[24],giong nhif thu:1 I right, kién trfi c n:i yNhfin Th iic Tinh De Si'i Dung.

‘rig t:ic dong

tritc tié p Ie n kié n trilc

1.2.3 C:ie diéu ki(n thuti. n ti(n (Facilitating Conditions)C:ic diéu kié n thuii n tié n dif0c dinh nghia la miic dfi ma mot ca nh:i n tinr:ing c0 st ha I:Ing to chiic va ky thufi I ton tai dé ho tr0 ‘c sfi dung héthong. Kié n trtic nay ducic tain theo mo hinh UTAUT cho phii h0p vfii tinhthong nh:i t cfi a ca c ly thuyé t va mo hinhnghiém tru6c day.

‘n t:I n da dif6c nghié n citu thitc

1.2.4 Nh9 n thu’c rfii ro Inn quan dén sfin pht1m/d[ch vu (PRP) vaNh9 n thu”c ’i re tién quan dén giao d[ch trifc tuyén (PRT)

Ha i kien true n ay lay tfi

’ hinh e-CAM,

kécycéké

va thifc nghié in trong moi tritct ng thif0ng mat dién tif tai hatva Han Quoc. Khfi ng co sfra doi gi vfii hai kié n trim nay.

1.2.5 C:ie bién nhd n khfi u hpc (Demographic)

‘c gia MQ

GRNDRR AGE

SCREEN

Cac kié n trii c nhfi n khfiu hoc co :inh hitfing tru rig gian lé n c:i c kié n triic chinh va da ducic nghién ctiu thuc nghiém theo cac nghién emu trufic day. Cac bién nhan khau hoc chu yéu la gifii tinh, giao duc, thu nhap, sac toc, nghe nghiép, va th5m chi la vii ng dia 1y (Bellman, Lohse, & Johnson, 1999) [1 6]. Trong dé ta i na y, to i chon cac da c tinh nha n kha u hoc la Tuoi, Gifii Tinh, Giao Duc, Nghé Nghiép. Dac tinh Thu Nhap khfi ng du0c chon vi 1y do lay niau khfi ng chinh xa c, do dfi cfi thé bi chéch. H0n nfia, theo mo hinh UTAUT [19], da chI nghién ciiu cac d:ic tinh nhfin khfiu hoc chinh 1:i Tuoi va Gifil Tinh tii vlec tong h0p ca c ly thuyé t/mo hinh nghién ciru trufic. Dirfing dan tit bié n nha n khau hoc du’0c ké th ita tit mo hinh UTAUT. Tuy nhié n, lu5n van chi du’ dinh ph5n tich tae dong trung gian cfia ca c bié n nhan kh:i u hoc trong timeing h0p c6 niau thu th:ip dif0c phu h0p vfii ca c pha n tich chéo gitra cac nhfi ni. Trii0ng h0p cfi niau khfi ng dfi 1ctn dé phan tich chéo giita c:i c nhfi m [1] thi c:ic d:i c tinh nhfi n khfiu hoc nay dit0c sit dung dé kié m tra cac thong ké mo ta tain nén tang cho viéc danh gi:i chung va du’a ra cac kién nghi cho cac nghién ciiu tu0ng lai.

1.3. M6 hinh TAM-ECAM dif d(nhMo hinh na y co mo t va i thay doi so vfii du dinh luc dau, kié n truc PB (h:i nh vi niua thirc su) dir0c lam theo mo hinh e-CAM vfii 2 muc do la Tan So Mua Hang Trifc Tuyén va Tong Gi:i Tri H:i ng Mua Trifc Tuyén trong vong 6 thang qua ké tit nga y khao sat. Trong diéu kien cii a lufin van hié n tai, chung tfi i (tfi i va ngu0i to chuc khao sa t lay niau tai mot Cfi ng ty dich vu Marketing) da khfi ng tim ra tong thé nay né n da chuyén qua khao sat tong

lfinh III. 1. Mii hinh TANt-ECA Yt dh dinh

thé co dit dinh niua. Vfii viéc chuyé n doi tong thé nghién citu nhit trén, sit thay doi 1:tin anh hiring den mu c do cua PB (xem questionnaire trong Phu luc 1) chI con 1 niuc (C. 10). Sau khi khao sa t thirc té, co nhiéu hfi i dap da dé trom muc do fi kién trtic nay do trong kié n true BI (c5u C.8 va C.9) ho da danh dau vao muc I hoa c 2 (nghia l:i , Cha c Chan Khfi ng Sir Dung hoa c Khfi ng Si'i Dung tu0ng ting theo thii tit). Ma hfi a bang 0 du0c tinh thé m cho nhfrng muc hfi i d:i p nay.

Nhit ‘y, vfii thang do khfi ng phu h6p v‹1i cfiu trfic cfi a trio hinh dif dinh nénkié n true PB chi de tham khao va du0c loai bfi khfii ph5n tich.

CAC GIA THUYET NEN TANGTheo ké t qua cfia nghién ctiu trufic day [24], Thu5t Ngf t va Thiét Ké Giao Dién co tac dong du0ng Ién nhan thuc tinh dé sir dung. Gid thuyé t HI va H2 du0c phat bié u nhu sau:

HI: Thuti t Ngff (TERMI) cii I:i c d(ng ditdng tr i(c tiép lén Nh(i n Thu”c Tinh Dé Su’ Dijng (PEU).

H2: Thi8t K é Giao Di(n (SCREEN) c‹i t:ic d9ng difdng trijc tiép lénNh8n Thii’c Tinh Dé Sir Dijng (PEU).

Theo ké t qua nghién ciiu cua mo hinh UTAUT, cac diéu kién thuan tién co anh huci ng true tiep fen viéc sit dung he thong thifc su. Trong diéu k lén thit0ng mai dién ti'f va theo muc tiéu nghién ciiu vé hanh vi dif dinh, tfii hiéu chinh k len truc FACI tae dong triic tlep ten kien truc B I la kien triic nfi i sinh cuoi cii ng cua mo hinh. Gia thuyét H3 du0c pha I bié u nhu sau:

H3: C:ie Diéu Ki(n Thuan Ti(n (FACI) co I:ie dfyng difdng trijc tiép lénHanh Vi Dr( Djnh (BI)

C:ic gi:I thuyé t ké tiép du0c ph:i t biéu thong nha t vfii mo hinh TAM.

H4: Nh(i n Thif’c Tinh Dé Su’ Dung (PEU) I:i c d§ng diffing lén Nhfjn Thu”c St( Hifu Ich (PU)

H5: Nh$n Thu”c St( Hffu Ich (PU) I:i c d(ng dif ong lén Du' D(nh Hanh Vi (BI)

H6: Nh(i n Thu’c Tinh Dé Sri Dung (PEU) I:ic d9ng du’iing lé n Du’ D(nhHanh Vi (BI)

Cac gid thuyé t sau dii0c pha t biéu thong nhat v6i mo hinh e-CAM.

H7: Nhan Thfic Rfii Ro Lién Quan Dén Giao D(ch Tri(c Tuyén (PRT) tire dpng am lén Dr( Djnh thanh Vi (BI)

H8: Nhfi n Thii'c Riii Ro Lién Quan Dé n Sdn Ph5 in/D(ch Vq (PRP) tae dfyng :im lén Dij D(nh thanh Vi (BI)

H9: Nhfi n Thiic Riii Ro Lién Quan Dén Giao D(ch Trijc Tuyén (PRT) va Nhan Thifc Riii Ro Lién Quan Dé n Sé n Phfi m/D jch Vij (PRP) tifdng quan difdng viii nhau

III. PH£I€JNG PHAP NGHIEN CNUIII.I. Dfi’ li(u nghién emu

Tong thé sit dung TMDT la khfi ng th bi¼t duci c. so »giféi cfi thé tin dung cling khfi ng thé biét do cac kho khan vé bao ma t danh sach khach hang cua ca c to chirc st hitu danh sa ch nay. Do dfi , hu’fi ng khao sat la chon danh sach ngitct i biét vé TMDT qua danh s:i ch theo 1 danh ba dién thoai vu rig da co. Viéc chon nguci i “bié t” vé TMDT du0c thuc hié n qua cac cau hfi i sang loc trufic bang dién thoai, sau dfi sang loc lai ba rig mot so cau hfi i chinh th iic khi phfi ng v:in trifc tié p.

‘n tru’c tiép la 111 niau vfii nhieu trinh dfi hoc van, nghe nghiep, dfi tuoi khac nhau. Doi tif0ng slnh vien dir0c

‘ inau thu thfip dit0c.

HI.2. Thii tijc thu thap dif li(u

Dfi tiéu du0c thu tha p bang c:ich phfing ‘n trttc tié p

thong qua

questionnaire. Ngiffii hoi d:ip dit0c s:i ng loc b:ing mot ‘ c:1‘uu

hhfoiti

trifc tié p

ghi tron g bd rig que stionna ire, nhiing ngiffii hoi da p khfin g da p ting muc tiéusC khfing du0c tlép tuc phfing van.

Barig questionnaire da dif0c pilot triffic vcii 8 ngiréi dé dam bfi o tinh gi:i tri nfi i dung. Ba rig questionnaire hoan chinh sau khi da pilot du’0c sit dung cho vié c thu th:1 p dit lié u .

Vié c phfing va n d:i dif6c to chiic trong kho:Ing niia thé rig. Dé da in b:i o gi:i tri ciia nhirng muc du0c phfi ng va n, d:i thifc hién kiém tra qua dién thoai ho:i c kié in tra ta i nha ngifct i ho i d:ip ho:i c kié in tra gi:i n tiép ta i

v:in pho rig sau khi phfi ng ’n vlén thifc

ngitct i hfii d:i p d:i dit0c thitc hié n vfii gi:i in s:i I vié n. Tru rig binh ton kho:I rig20 phii t dé phfi ng questionnaire.

BI.3. Cite do lifdng

‘n vién thuc hié n phfi ng van xong mot bfing

• TERMI : sit dung thang do Likert 5 dié m va 3 inu c do, c:ic muc do lfiy tif nghién c iiu thirc nghiém vé kha c biét ca c nhfin va da c tinh hé th6ng do WEIYIN HONG, JAMEs v.L. THONG, WAI-MAN WONG, va KAR-YAN TAM thu’c hié n [24].

• scREEN : sir dung thang do Likert 5 dié in va 2 muc do, cac muc do coTERMI.

• FACI : sir dung thang do Likert 5 diém va 3 muc do, cac muc do lay ni nghién ciru tong h0p cac mo hinh tha nh 1 mo hinh duy nhfi I UTAUT [19]. Cac muc do n:i y d:i dif6c hié u chinh lai

“i trifct ng thit0ng

• PEU : ski dung thang do Likert 5 dié m va 4 muc do, cac muc do nay"n goc tit nghién ciiu mo hinh e-CAM [6].

• PU

• BItit0ng tit PEU.

sir dung thang do Likert 5 dié m va 2 niuc do, muc do thu nhfi I lfiy tit trio hinh UTAUT [19] v:i mu c do thii hai 1:1y tif nghién c iiu thuc nghiém cua Paul J. Hu, Patrick Y.K. Chau, Olivia R. Liu Sheng, Kar Yan Tann vé viéc kiém tra mo hinh TAM trong viéc diéu tri qua mang [12].

• PRT : ski dung thang do Likert 5 dié m va 4 muc do, cac muc do naylfiy tit mo hinh e-CAM [6].

• PRP sit dung tha rig do Likert 5 diem v:i 5 muc do, c:ie muc do nay lay tit mo hinh e—CAM [6].

• AGE : ghi nhan tuoi thuc sit, sau dfi sap xép la i dii tiéu theo thangdo khoang.

• EXPE : kinh nghiém sit dung Internet du0c do b:i ng thang do kho:Ing, c:ic kho:i ng chia dii0c tinh theo thang do trong mo hinh e- CAM [6]

• CAREER : thang do chi danh, ghi nhan nghé nghiép, sau dfitheo tirng nhfi m nghé . Tong cong co 10 nhfi m nghé dir0c bao

Tutii

I. CAC THONG KE CHUNGI.I. Thfing ké c:ie dac tinh nh$n kh$u h pc

Cac da c tinh nhfin khau hoc dir0c trinh bay trong Bang IV. 1 sau:

Bé rig IV. 1. Thfing ké dac tinh nh:i n kh:i n hp c

I 09012 1 0.8I10 9,01

7 1 63.96

Nehi: nghiép

65 ?8.568 7,21I 0,90I 0,90

I 0,90

1.2. Thfing ké kinh nghi(m sit dpng InternetThong ké kinh nghié ni sir dung Internet du0c trinh bay trong Bfing IV.2

Bfing IV. 2. Thong ké kinh nghi(m sit' dijng Internet

T4ns% Phantr4m1 0,905 4,5021 4892

1.3. Thfing ké dij djnh niua hang trijc tuyén

Thong ké du dinh niua hang trirc tuyén dir0c trinh ba y trong Bd ng IV. 3 sau:

Ba rig IV. 3. Th(i’ng k é dij djnh niua h:i ng trijc tuyén

II. KIEM D{NH MO HINH TAM-ECAM

B.1. X:ie d(nh dfy tin cay va do gi:i tr(11.2.1 D( tin c$yInternal consistency reliability (con got la dfi tin cay cua ca c tha nh phan), xem xé t pham vi ca c muc du0c sir dung dé danh gia mot kié n ti6c phan anh so dié m chung cho kié n trilc dfi (Barki and Hartwick, 1994), trong bai na y du’ci c do luéng bang hé so Cronba ch’ s alpha (Cronbach and Meehl, 1955) kié m dinh c:i c thang do rié rig bié I va thang do to:in bfi . Xem ké t qua trong Bang IV.4.

Bé rig IV. 4. H( sfi do tin cay

Nhé n tit TERMI

SCREEN

FACl

SD Cronba ch Alpha 0,5II7tl

0,6706

tI,5726

Mean SD Cronhach Alpha

PU 3 38408 0,9097 PU 4 38649 07069 PEU

PEU 1PEU 2PEU 3

37117 0.77913756R 0,R9663,477? 0,8618

I1,67I18

PEU 4 3 7M 7 07484 I1,8043

B!1 33063 07 09 B!2 36U36 0 .R973 PRT

PRT 1PRT 2PRT 3

, , 43, 68 ,99

, 0. 9

0,7B6S

PRT 4 3,6 , 6 PRP

He so Cronbach alpha theo yé u

I1,7654

‘u Hi thiéu la 0.7 doi vcli nghién emu thu’cnghié in, trong trifct ng h0p nghié n ciiu kh:i in ph:i thi co thé xem xé t mot ph:in tich nhan to kh:inn ph:i theo phu0ng ph:i p truyé n thong va phan tich nhfin to khang dinh theo phir0ng ph:ip hién dai (Bollen 1989) [ 14]. Theo hitfing dfi n n:i y, phfi n tich nhfi n to kh:inn ph:i b:i rig ph:in me in SPSS se dit0c thitc hi(n dé I a in tie n dé cho ca c pha n tich ké tiép.

11.2.2 Do gi:i tr[

Do giii tri phfin bié t (discriniinant validity) cii a nifi t do luéi ng du0c xii c dinh la phii h0p né u do lu’éi ng nay co the ph:i n bié t mot c:i c thfi a d:ing tir c:ie kién trii c lié n quan.Do gi:i tri hfi i tu cii a mo t do lii0ng dii0c x:ic dinh la phu h0p néu c:ic chI b:t o kh:ie nhau cho do lu’é ng nay lién quan dé n kien trim kh:i i nié m tif0ng

4

Dé da nh gi:i dfi gi:i tri hfi i tu

phfi n bié t (convergent validity va

discriniinant validity), phii n tich nhan to kh:I ng dinh (CFA — Confirmatory Factor Analysis) du0c thiic hié n. CFA dir0c thirc hién theo hai biiéic nhu sau [I8] :

Bu’dc 1 : Kiém tra dfi gia tri hfi i tu (convergent validity) va dfi gia tri ph5n bié t (discriniinant validity) cho nhiing nha n to ‘ I (first-order factors) fi ca p dp nhan to. Buoc n:i y kié in tra toa n bfi mo hinh do lucing ba o gfim c:ic nhfin to cfip 1 co trong nghié n ciiu lié n quan dén c:i c chi b:i o (indicators) cua tirng nh:i n to.

Birfic 2 : Kiem tra dfi gia tri hfi1 tu (convergent validity) va dfi gi:i tri pha n bié t (discriniinant validity) cho to:i n bfi kié n triic cfi a trio hinh. Bitfic n:i y

(factor scores) I:i in gi:i tri d:1 u v:i o. Bitcic n:i y bao goin c:ic nh:i n to cfip 1 vac:ic nh:i n to c:ip 2 (second-order factors).

Ly do thi¾ t k¼ ha i buoc nhii trén dna vao

c6 niau trong nghién

cuu khfi ng du0c ton tank ( 1 11 niau) dé lam thich h0p mot mo hinh toan diénco qu:i nhieu tham so [ 18].1. Ph$n tich nhfi n tfr kha rig d(nh cfi p nh5n tit (bit‹ic 1)Ké t qufi kiém tra birfic du’cic trinh bay trong Bang IV.5 va Bang VI.6. Bang IV.6 cho thfiy hé so dfi tin cfiy (Cronbach’o ciia hat kien trilc PRT va PRP dat miic yéu c5u cho nghién emu kham pha (>0. 6). Mfi t so chi bao bi loai khfi i mo hinh vi khfi ng phii h0p trong tién trinh lam thich h0p trio hinh. Cac chi so thich h0p trong ké t qua CFA biiétc du’0c trinh bay trong Ba rig IV.5.

B:ing IV. 5. C:i c chi ski thich hdp CFA bifdc 1

C:ie chi si“t thich hip (FFiitt Indices)

ii:i tri yééuu ecdanu

hét qu:i’ thhc hién

D(bo)’ df ?30RMA ±05 00478CN ? 0.90 0978

? 0.80 0993? 090 09?8? 090 0949

RMR 0.0431cT1 ? 090 0980

4

C:ie chi biro Cronbach'n

“ thich hip (Fit Indices) ii:i tri yéu can hét qu:i thhc hién

Ciic hé so diffing dfi n tif c:i c chi b:t o Hi c:i c kié n tiiic tif6ng ring den co j nghia vcli t-value lérn h0n :i1 .96 (Xem B:ing IV.6). C:ic hé so du’éng dan nay ké t h0p vcii c:ic chi so thich h0p mo hinh trong B:lng IV.5 cho th:i y mo hinh cfip nh:i n to dat dfi gi:i tri hfii tu (convergent validity).

Do gi:i tri ph:i n bié t (discriniinant validity) du0c danh gi:i ba rig c:ich so s:inh phit0ng sai dit0c chia giifa c:i c nhfin to v:i phit0ng sai trung binh dif0c trich. Tritct ng h0p phit0ng sai dii0c chia giira ca c kiln trtic nhfi h0n phif0ng Lai trung binh dit0c trich I:i dat dfi gia tri ph:in bié t. BErig IV.7 cho th:1 y mo hinh dat dfi gi:i tri ph:i n bié t.

Bfing IV. 6. H( “ cac chi bfio tif phfi n tich CFA bir0c 1

TERMI

SCREEN

FACI

PRT

PRP

SCREEN 1"'SCREEN 2"'

FACI 4 FACI2"' FACI3"

PRT I"'

PRT 3"PRT 4

PRP 1"'

0,776"

0,7 16**

0,849

0,64 J

PRP 4'“PRP 5 0,467**

B:ing IV. 7. C:i c chi sfi thich hdp trong CFA bifdc 1

Df(b.‹u‹wdo)/’/df

4

GFI AGF! NFI

RIURCTl

“ thich hip (Fit Indices) ii:i tri yéu can hét qu:i thhc hiénS05 0047R10.90 097R

Bfing IV. 8. D( gift trj phfi n bi(t‘

2. Ph$n tich nhfi n tfi kha rig d(nh cho toan bo mii hinh (bir‹ic 2)Sau khi bud c I da thuc hié n vfii két qufi mo hinh dat dfi thich h0p tot, I:i y

ciia c:ic nha n to vira du0c pha n tich 1:inn gia tri dau va o cho

bufic 2. Cac kién tr6c FACI, PRT, PRP trong bufic 2 du0c xeni la cac bién dfi c 1:ip (ngoai sinh) [15, 18]. Ciic nhfin to chifa dit0c phfi n tich trong bitfic 1 la c:ie nhan to bfic hai (second order factor) bao ‘in PU, PEU, va BI. C:ic nhfin to bfic hai nay dif6c d ifa vao mo hinh vfi i to:in bfi c:i c chi b:t o cii a no (ca c bién quan sa t du’0c). Ké t qua ph5n tich cac hé so duét ng da n du0c trinh ba y trong Bang IV.9.

Bfing IV. 9. H(

C:ie chi b:to PM

PU_l ’PU 2PU 3 PU4

“ c:ie chi b:to tif ph:i n tich CFA bu’dc 2

Ne ©chu4nhéa Cronbach 'o04721

028"0,?8*³

PEU 4BI

BI 1

Bfing IV. 10. D by gi:i tr j ph:i n bi(tién trim B I f'U f'EUBI 9.549PU 0. 194 0.637PEU 0.108 0.745 0.891

4

t

p trong CFA bu’dc 2

C:ie chi s(i thich h9p (Fit Indices) Gi:i tri yr:u can K¼ t qué thu'c hiénJ' 44.369Df J b4c tir doJ 22

RMSEAGFI

± 0 J?

0.90

009094 R

AGF! 0R32NFI ? 090 0R34

RIURCTl

Ciic gi:i tri trinh ba y trong c:i c b:I rig tré n cho thfi y mo hinh cfiu triic toan bfi da t du0c do gia tri hfi i tu va dfi gia tri pha n bié t. Vfii muc tiéu chit tro rig v:i o kh:i in pha c:i c yéu to co lié n qu an dén sit s:i n s:i rig cfi a thif0ng ma

i dien tit trong dé tar nay cii ng vfii da y trong tinh vuc lién quan, Hi tain ditng lain thich h0p mo hinh fi day vfii m iic dfi thich h0p chap nhan du0c (xem [4], trang 112-118). Trong cac nghién cuu sir dung SEM, ngifcli nghien c iiu ca n phai biét khi na o dung tain thich h0p mo hinh, nguéri ta ca n phai xem xét tiéu viéc chI dinh lai mo hinh co d:in dén vfin dé mo hinh qu:i thich h0p hay khfi ng, nhit Wheaton da chI

‘in u!i thich ” (Wheaton, B (1987). As.sessement oyyit /n overidentified models with latent va ricihlcs. sociological Methods & Re a search, 16, p. 118-154). Nhu’ vay, trong nghién ciiu kha in ph:i n a y, “ hinh TAM-ECAM

tai nay du0c xem la phu h0p cho ca c phan tich tié p theo.

11.2. Trinh bay mfi hinh TAM•ECAM cuoi ciing

Hinh IV. 1. Két qu:i ch:jy mfi hinh TAM-ECAM

III.

Mo hinh TAM-ECAM cuoi ciing dif0c trinh ba y trong Hinh IV. 1. Cac hé so ditfing dfin 1:i c:i c he so chufin hfia. Kié n trii c FACI dif0c ph:i n tich trong CFA bitfic 1 da loa i khfii mo hinh cuoi cung vi cho he so khong phii h0p. Mo hinh TAM-ECAM cuoi cii ng cung khfi ng tinh I:i c dong cii a c:i c bién nhan kh5u hoc (AGE, GENDER, EXPE) ten ca c duérng dan tit PU -9' BI va PEU D BI nhif mo hinh gia thuyét da dé xua t (xem lai chu0ng 3) do mo hinh TAM-ECAM cuoi cung trong dé tar nay chua du0c thich h0p ra t tot vfii dit tiéu nghién c iiu nén mo hinh cuoi ciing chi du0c su dung tain tlén dé cho cac nghién ciiu tir0ng lai.KIEM D[NH CA C GIA THUYET

“, mo hinh TAM -ECAM CAM, va UTAUT da ducic rut gon lai thanh mo hinh TAM-ECAM cuoi cung vfii cac thanh phan chinh co tu mo hinh e-CAM. Cac kién truc khac khfi ng phii h0p da dii0c 1oal bo khfii mo hinh cuoi cung. Chung ta sC kiém dinh cac gia thuyét du0c ‘u trong Chir0ng thuyé I nao va b:i c bfi gi:I thuyé I nao.

46

Ké I qu:i torn t:i I viéc kié m dinh c:ic gi:I thuyét dit0c trinh bay trong BangIV. 12.

B:ing IV. 12. Gifi trj c:i c h( str dififng

H5H6

HR RRP -->(-) BIH9 RRT (+)<-->(+) RRP

‘n va t -value

Hé sit t-value Xéc nhfi n

0.99 4,4906,906 4,100-6,74 - I,0?00,113 2,250

Nhit vay, vfii trio hinh TAM-ECAM d:i dit0c iffic lif6ng, chi co gi:i thuyé I H4, H7, va H9 la dif0c xac nha n . Ca c gia thuyé t con la i bi ba c bfi theo ke t qufi phan tich dir tiéu thu thap dii0c. Dac biét la hai nhan to PRT (nhan th iic rui ro trong giao dich truc tuyé n) va PRP (nhfin thitc rfii ro lién quan dén s:in phfi in/dich vu) dé u co I:i c dong km dang ké lén BI (dif dinh hanh vi).

Ké tié p, dé x:i c nhfi n ké I qufi nghié n ciiu, chit rig ta sé so s:i nh ké I qua thu du0c trong nghién ctiu n:iy v‹ii c:ic ké t qua M c:ic nghién ciiu kha c la in tién dé cho nghién ciiu nay trong chir0ng tiép theo.

H7 P B —)H PRP PB —)

SO SANH Vtil KET QUA CUA MO HINH e-CAMMuc tléu nghien c iiu ta p trung chinh vao khao sa t cac yéu to tae dong va osit s:in s:i ng trong thif6ng mai dién tit. M:it khac, ké I

’ kkhh::IIoo s:i I cho th:i y

c:i c yéu to trong mo hinh e—CAM du0c x:i c nh:i n theo dii tiéu nghién ciiu trong dé ta i n:i y, nén chung ta xem xét tru’cIc c:ie yé u to nhan thiic riii ro trong giao dich trite tuyén (PRT) va nhan thtic rii i ro lién quan den s:i n pham/dich vu. Ket qua so sa nh c:ie gi:i thuyé t theo hat ta p dir tiéu da du0c kh:t o s:i I tai My va Ha n Quoc du0c trinh bay trong ba rig sau:

Bé rig V. 1. Ké t qu:I so s:i nh theo mti hinh e-CAM

Dié thuyét Quan hé

H4 PEU PU (+J

9PRTPRP+

USA EOREA \/I T NAM

Nhit v:1 y, gift thu yé t H4, H7, va H9 khfip vcii ké t qu:I ciia dii lie u kh:t o s:i t ta i My, trong dfi gia thuyé I HI va H7 hoa n toa n kh‹ip v‹ii ké t qu:i kha o s:i ttai c:’i hhaaii nnuu’’cfIicc ttrréé nn.. KKiieenn

ttrruime PPBB ttrroonng

mmoo hhiinnhh ee-—CCAAMM ddaa dduu’’00cc tthhiiéétt kkéé

dé do likct ng ha nh vi in ua ha rig thifc sit, trong khi kié n triic tit0ng iing la BItrong mo hinh nay dif0c thié t ké dé do tiring ha nh vi dif dinh sir dung héthong thu0ng niai dién tit. Tréntae dong truc tié p den kié n trtic sir'

mo hinh TAM, kién truc BI codung hé thong thuc su’. Cac nghién emu

lién quan den mo hinh TAM da cho tha y kien trim BI co tac dong truc tiep lé n kié n trilc Sir Dung Thitc Sir (Usage) [ 19, 2 1], do dfi , kié n trii c BI 1:i chI bao chinh cfia kien true PB. Vfii két qua tré n, chung ta xem kié n true BI va PB la co lién quan ch:it ché vfi i nhau v:i co thé so s:i nh dif6c.

Tiép theo, chfing ta so sanh a nh hufi ng truc tiép va tong anh hufi ng cuaPRT va PRP fen BI theo kit ’ n hhiiéénn cuudong dit0c trinh bay trong bang sau:

Ba rig V. 2. So sfi nh tfi c dfyng ciia PRP vfi PRT lén BI

BIUSA KOREA (:t Nani

flirecl Total Direct Total Ilirect Total

49

Ta thfiy ké I qufi t:i c dong cfi a PRT vé PRP lén BI tai Vié I Nam gfin tit0ng

II. SO SANH V €II KET QUA CUA MO HINH TAM × UTAUT

Kién triic FACI (c:ic diéu kié n thu:1 n tié n) lfi y tit mo hinh UTAUT d:i khfing d:ip ring du0c dfi tiéu ciia mo hinh TAM—ECAM trong nghién ciiu n:i y. Xem la i mo hinh UTAUT [19], chfing ta th:i y kién trfic FACI chI co a nh hitting rfi t th:1 p lé n BI vd i hé so diféng dan la 0. 8. Kién trii c n:i y da du0c thié t ké trong nghié n ciiu longi tudinal vfii moi triffin g hé thon g thfi n g tin. Khi co t:ic dong dong thfii cfi a gicii tinh (GENDER) va tuoi (AGE) thi kién triic n:i y finh hiicing lén BI manh h0n mot chii t so véii khi khfing co t:ic dong ttf011 g tac. Cu thé la khi co tae dong tl1’0ft$ ta c thi he so ductng da n la 0. 22 (p>0.05).

Kiln trim TERMI (thu a t ngii) va kiln trfic SCREEN (thiét ké giao dién) dit0c 1:inn theo mo hinh TAM trong nghién ciiu sfi du rig th if vién so hfia [24] cling khfi ng dii0c dti lieu nghién ctiu na y hfi tr0 va da bi loai khfii mo hinh trong ph5n tich CFA bufic 1.Cac kié n truc PEU (nhfin thiic tinh dé ski dung), PU (nhan th iic su hint ich)trong mo hinh TAM va cac mo hinh md rong tit mo hinh TAM dé u co tacdong trirc tié p va gian tiép Ié n BI. Hé

‘ du0n dan toi thiéu tit PEU D PU,

‘u to

I. TOM TAT KET QUA

Dé tai tap trung vao nghién c iiu nhan thiic cfi a ngir0i ski dung tiém nang véthit0ng mai dién tit nh:inn khfio s:i I mot t:ic dong vao sit s:in sangtrong tinh vuc nay. Dé Hi da thu tha p va lam theo mot so kié n trim chinh co tac dong vao Dir Dinh Hanh Vi dé thié t lap mo hinh TAM-ECAM cho nghién c itu thuc nghiém.

Ké t qufi phan tich cho thfiy cac yéu to nhan thiic v:to d it dinh ha nh vi la Nhan Thiic Rii i Ro Trong Giao Dich Tritc Tuyén (PRT) va Nh:in Th iic Rii i Ro Llén Quan Din Sa n Ph:i nn/D1ch Vu (PRP). Cac kién tr6c nay xac nhan ké t qud da dir0c nghién ciiu tit My va Han Quoc. Hcin nfra, PRT va PRP lai lié n quan manh vfii nhau da ch itng to nhan thirc su rim ro trong moi trufi ng th ii0ng ma i di(n tfr la cao. Ngoa l ra , tir0ng ting v6i mo hinh TAM da dir0c xac nhan rong rai tré n thé gici i, ké t qua tu nghién ciiu nay cung cho tha y su ta c dong chia Nha n Thifc Tinh Desii Dung(PEU) lé n Nhan Thiic Sit Hifu Ich (PU) la dang ké trong mai dién tit mac dau dir tiéu co the khfi ng da i dién.

‘i trifct ng thif0ng

Dfi tiéu n ghién ciru kh‹in g x:i c n ha n du0c t:i c do rig cii a PEU v:i PU Ie n Dif Dinh Hanh Vi (BI), trong tru0ng h0p PEU D BI lai co t:i c dong ngii0c d:iu nhitng ta c dpng na y lai khfi ng tra i v‹I i két qu:i cii a c:ic ké t qua cii a ca c nghién ciiu trirfic do vi khfi ng co y nghia. T:i c dong ngii0c dfiu n:i y co thé do I:ip dii tiéu chua dii0c da i dién cho nghién ciiu.

II. DE XUAT

1.1. H:t in y qufin ly

Nghién emu nay cung ca p thé m ca c ba rig ch iing vé su thich h0p cii a viéc sit dung mo hinh TAM dé do luci ng cii c chiéu hufi ng kh:i c nhau ciia viéc sit dung thirc su thu0ng ma i dién tit’.Cho thfi y nhilng nhfi n to kh:i c ve vié c nhfi n thiic rii i ro (PRT va PRP) giiiphiéu tot h0n vé su chfip nh:i n sir dung thir0ng mai dién tir. Do dfi , trong thiétlap thir0ng ma 1 dlén tir, siic manh ci'ta

“ hinh TAM se dii0c n:i ng cao ba rig

c:i ch dem vao sir tii c dong cfia nha n thiic rii i ro vd i chinh bfin thfin s:in ph:i nn/dich vu cii rig nhit pha in vi giao dich truc tuyén.Dé ra c:i c h:i in j thifc h:i nh cho c:i c doanh nghié p hié n dang cung c:ip s:i n phfi in/dich vu qua Internet ciing nhu dang hoach dinh dé I:i ni diéu nay. MOt diéu hié n nhié n la khi chuyén ngiffii sir dung Internet thanh ngiffii sir dung hé thong thir0ng mai dién tit thirc sir, PEU v:i PU ph:1 i du0c nang cao, PRP va PRT gia in di.

Tit ké t qu:I nghié n ciiu cho thfi y ngiffii tié u dii rig xein xé t rfi i ro lién quandé n giao dich trttc tuyén nhir la mot trong nhirng nha n to quan trong khi honiua hang tré n Internet. Do dfi , giam béi t riii ro nay la trong cua ngiféi b:i n hang trirc tuyé n.

“c uan tain quan

De gia in rui ro giao dich, ngifét i b:i n hang truc tuyén né n thiét l5p 1 c0 ché manh dé I:i n dung cong nghé Internet. Tuy nhié n, khi in:i Hi pham van lufi n ton tai trong xa hoi chia chii ng ta, néu credi t card bi init hay b1 danh c:ip, b:i I ky ngifct i n:t o co thé lam dung thong tin cfi a card nay (Ratnasingham, 1998). Do dfi , ngifél i b:i n hang trim tuyén nén tain cho ngirfii tl éu dii ng da t tin cay toa n bfi va o su bi ma t, tinh an toa n, tinh chinh truc va su san sang ciia cac thong tin ban hang. H0n ntia, dé gia ni riii ro chia ngiifii tiéu diing vfii san pha in/dich vu, nhfrng ngir0i ba n ha rig truc tuyén nén xay dung su tin nhié ni v6i ngir0i tiéu dung bang cach tao cho ho so tin ca y hoan toan va o san pha in/dich vu desc cung ca p.Tinh dé sd' dung va sir hfiu ich cling la cac yé u to quan trong can quan tain trong viéc thié t ké , su dung hé thong thii0ng mai dién tir.Ké t qua cho th5y nhan thitc chia ngu’fi i sfi dung Vié t Nam (doi vfii tap dir tiéu nay) chira san sang cho ‘c sir dung thir0ng ma i dién tfr vi nhan thiic rim ro ca o nhung chira nhan thfic su hfiu ich cii a thu0ng ma i aten th.

1.2. C:ic giiii h:jn ciia luti n van

Nghién c iiu trong Lua n va n na y da xac nhan mo t yéu chinh tain nén

tang cho tinh 'n san cii a hé thong thir0ng mai dién tit trong tap dii tiéunhfi’ng ngifél i sit dung tiém n:i rig va co mot so nhan thiic chinh vé thif0ng mai dién tit. Tuy nhié n, ciing con mot so han ché chinh sau:

Mau khfio sa t tu0ng doi phan tan va c6 niau con gifii han cho viéc nghién emu theo mo hinh phu0ng trinh co cfiu triic sEM .

khau hoc) do 'u con han ché va mo hinh chira du0c thich h0p tot.

C:ic thang do my tain theo ca c nghién ctiu trufic dfi nhung du‹ic dich to tiéng Anh qua tiéng Vié t v:i chua dii0c thirc nghiéni tai Vié t Nam trong tinh vuc na y, do dci co thé chua d:ip ring du0c yéu cau nghién ciiu.

H.3. Dé xufi I nghién cii’u tifdng lai

C:ic n ghién ciiu M0n g la i tron g tinh vuc n a y nén l a niau l‹in h0n (trén 200 in f‘luu) v‹1i doi tif0ng tfip trung v:to nhilng ngitfii sit du rig thitc sit tiém n:ing h0n la nhfing ngufii chi biet ve thif0ng mai dién tit.

53

Khdo s:i t c:ic yéu t6 na«g t4c nhir tuoi, g'icii tinh, kinh nghi(m, gi:t o di;c.. cci :inh hufing the nio lén du dinh sit dung thuc su.

Kiem tra lai c:ic thang do cho phii h0p vdi diéu kien Viet Nam v:i mtiimrang thira»g mai di(n tiI.

TAI LIEU THAM KHAO

[3]

[4]

[6]

[7]

[9]

[ 14]

Barbara M. Byrne. 1998. St in‹ tura I Equation Model ing wi th LISREL, PRELIS, and SIMPLIS . Basic Concepts, App lications, and Programming. Lawre rice Erlbaum Associates.

Fiona Nah, Keng Siau, Yuhong Tran, M in Ling. 2002. Knowled 8 • Man‹•8 ³º²••² Mechanism ‹n £-commerce. A study of online reiai lin ancl auction s/ies . Journal of Compute r In formation Systems.

George Kuk, Fannie T. Yeung. 2001. liner rc/iv//j lit E-commerce. QuarterlyJournal of electronic commerce.

James J. Jiang, Maxwell K. Hsu, Gary Klein, B irish an Lin. 2000. :-cnninicrr c user/›e/inv/ur mcdel. An Emp ii ical Stud y. lOS Press.

la-She n Chen, Russell K.H. Ching. 2lltl2. A Pi’cpc.sed Frame w'ci k fciTransitmumg to an E-Business Model. Quarterly Journal of electronic commerce.

Joongho Ahn, Jinsoo Park, Dongwon Lee . 6/2001. Risk—J‘ocused E—commerce Adoption Model - A Cress Conrite Studj . Un'iversity of Minnesota.

Louis M. Rea, Richard A. Parker. 1997. DC *’8ººin And Conclucting Sun ey Research. Jossey—Bass Publishers.

Lou is Raymond, Samir Blili. 2000. Organnation Lear n ing as n Foundation nJ' Elec tronic Commerce in The Network Org annation. I ntemational Journal of Ele ctron ic Commerce .

Marios Koufaris. 2002. Appl yrs the Technolo y Accepiancc M cclel and Flew Them /o Online Consumer Beha vioi . Information sinews Research.

Mauricio S. Featherman, Paul A. Pavlou. 2002. Predicting E-services Adoption: A Pe rcci ved fi‹s1 Fa ce/s Perspeciive. Eight Americas Confere rice on Information Systems.

Naresk K. Malhotra. 1999. Ma rketing Reseai ch An App lied Orientation. PrenticeHall.

Paul J. Hu, Patric k Y.K. Chau, Olivia R. Liu Sheng, Kar Yan Tann. 1 999.>³º'º*²’••‘º 8 The Technolcg› Acceptance Model Usin Phisicicn A ccepiance ofTelemedicine Technclcg \ . Journal of Management Information Systems.

R itu Agarwal, Jayesh Prasah. 1 998. A Conceptua I end Operc iion‹i I Defi' ni tion of Persona I lnnovat iveness in The Domain oJ‘ Iii/firmn /‹on Technolog y. Institu te for operations research and the Management sciences.

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[ 17]

[24]

Robert A. Ping, JR. 1995. A Pat simonious :x/‹mn/iiig Technique Jet Interaction cmd Quadratic Lci tent Var tables. Jounal of Marketing Research, Vol. XXXII, p 336-347.

Said S. Al-Gahtani v:i Malcolm King. 1999. Attitude.s, Sati.sfa ci ten And U.sage. Factors Contributing to Each in The Acceptance of' Inf'oi motion Technologv. Behaviour & Information Technology.

Stephanie Jakle Movahedi - Lankarani. 2002. E-‹ omnierce. Resou rces jor DoinBusiness or The Internet. EB SCO Publishing.

Tao Ga o. 1 998. Effects of Rela tionsh ip Quality on Customer Perceived Value in Organizational Purchasing. Virginia Polytechnnic Institute and State University.

Venkatesh et at. 9/2003. User Acceptan‹’e oJ lnJo variation Tec’hnolo y. Towa rd a Unified V‹cw. MIS Qu aterly.

Viswan ath Venkatesh. 2000. Deieririinan is of Percei› ed Eas e Of Use. Inter g n8 Control, lntrinst c’ Mott vci tion, and Emotion into The Te‹’hnolog j Ac’ceptan‹’e Model. Information sh wn Research.

Viswan ath Ve nka tesh, Fred D. Dav is. 2000. A Theeretreat Extension Of The Technolcgy Accep tance Model. Fcui’ Len giiudinal Field Stud y. Management Scre rice.

N —W . M itchell. 1998. A Re Ie For Consume r Risk Per c’eptions In Grocery Reta ili" 6British Food Journal.

Wanda J. Orlikowski va Debra C. GASH. 4/1994. Technetcgreat Frames.’ Making Senxe o( /n(oi mn/inn Tel hnolog x in 0 ‘6³'² •tion . ACM Transactions on Information Systems, Vol. 1 2, No. 2.

Weiy in Hong, la mcs Y.L. Thong, War -Man Wong, an d Kar-Yan Tann. 2002. Deter minan ts oj User A‹’ceptanc’e of Di gitci I Libi a rich. An Enip i iical Exci urination of Indi v iduci l Diffei ence.s and 5y.stem Charscieri.site.s. Journal of Manage ment Information Systems.

Yogesh Malhotra, Dennis F. Galletta. 1999. E›tend ing the TAM /o Account Jor Secret I Infiuen c’e.’ Theoretic’a I Bcises and Enipiricci I Validation. 3 2nd Hawaii Intern ational Conference on System Scie rices.

Internet trong th‹›i Code Route

Code P.oute

PHU LUC 1. BANG CAU HOIDE TAI: NG H IE N CLJU THUONG MAI DI E N TO

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Giait sa r vi én Kié iri tra ta i nha Kiém tra qua let. Kié m tra ra i van phong.I 2.1 3.I 4.

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Code Route

B. THOI QUE N SL/ DU NG I NTE RNET

C. DA N H GIA VE THUQN G lv\Al DIE N TL/

TEKMI

FACI

C4

R. J

R.4

PEU

R .4

C.

R.‹R.

web:|nonq rai:

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Code Route

Code Route

’ I ién trong Code Route

PHA N D: THON G TI N CA N HA N

XlN DUOC PHE P H© l Ant»/Chi“ T S© TH©NG TIN CA NHAN

Code Route

D.2 EDUphncnhAddnnA

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6

Code Route

D.2 EDUCé p dc› hoc vén cao nMt ma Anh/chi dat di/oc hié n nay ID gi / \

SAJ Khéc T x in qhi r‹›)..

Code Route

XIN CAM ON SU I—IOP TAC CUA A NH/C!—II PA T “U. X I N CI-IAO TAIvI BIET!

6

PHU LUC 2. KET QUA PHAN TICH NHAN TO CAP 1

L I S R E L 8.30

Karl 0. J“reskog & 0ag S”rbom

This program is published exclusively by Scientific Software International, Inc.

7383 U. Lincoln Avenue, Suite 100 Lincolnwood, iL 6071?, U.S.A.

Rhone. (800)Z47-6iiS, {84?)67t-07?0, Fax. (84?)6?5-?i40Copyright by Scientific Software international, Inc., 1981-?000

Use of this program is subject to the terms specified in the Universal Copyright convention.

Website. www. ssicentra1. com

The following lines were read from file

Testing convergent & Discriminant of First Order Factors Include all First Order FactorsObserved Variables 3A07_1 6A01_2 9RT_19R1_4 9RP_1 9R9_5covariance Matrix From File E:\MBA\THESI$\E_LI$REL\FIRSTOR0\FIRST.O0V&ample &ize - 111Latent Variables &CREEN TERM? FACI BRT BRP 6QUATIOUS:NA07 2 -0.878*6A01 54C1’1 -ECCNPRT 1 -0.808*PRT

9RL 4 -9R1PRP’1 -0. 6Z1’RRP 9R9_5 -9R9Relationships Path DiagramNumber of Decimals 3 Admissibility Check - Off Iterations - Zt0Method of Estimation: Maximum Likelihoodiisusi ouievi: ×i si-s sc as sr PcRnd of Problem

Testing convergent & Discriminant of First Order Factors

covariance Matri× to be Analyzed

0.119

FACI_?

0.Z65 0.44Z0.?040.041

6

Testing convergent & Discriminant of First Order Factors

Parameter specifications

PRT

RRT

PRR

PRP

9

TH6TA-DELTA

PRT I RRT 4 RRP 1

Testing convergent & Discriminant of First Order Factors

Number of Iterations - 1?

LISREL Estimates (Maximum Likelihood)

PRT PRR

PRP t

0.6?I

0.405(0.166)

PRT PRR

0.S9Z (0.159) Z.456

PRT 0.ZZ6 0(0.100) {0.165)

Z.Z56 Z.1Z3

PRR 0.349 0.531 I.3?8(0.14Z) {0.146) (0.608) Z.456 Z.?77 Z.184

BRT 4

0.398 0.698 0.771 0.148 0.488 0.78Z(0.161) {0.I?I) (0.I?3) (0.?44) {0.£13) (0.136)Z.470 5.767 6.?83 0.607 Z.?90 5.737

Squared Multiple Correlations for x - Variables

PRT I

0.??9

RRT 4 RRR 1

Goodness of Fit Statistics

Degrees of Freedom - 6Minimum Fit Function chi-Square - 7. 9ZZ (P - 0.?44)

Normal Theory Weighted Least Squares chi-Square - ?.505 (P - 0.?7?) Estimated Non-centrality Parameter {NCP) - i.50590 Percent Confidence Interval for NCR - (0.0, i?.770)

Minimum Fit Function Value - 0.07?0 Population Discrepancy Function Value (F0) - 0.013790 Percent Confidence Interval for F0 - (0.0,

0.1i6) Root Mean Square Error of Approximation (RMSEA) 0.047890 Percent Confidence Interval for RMSEA - (0.0

0.159) P-Value for Test of Olose Fit (RMsEA < 0.05) - 0.440

6×pected Cross-Validation lnde× {6CVl) - 0.3419Q Percent confidence Interval for EUVI - (Q.3?? ;

0.44j) SCVI for Saturated Model 0.38?RVI for Independence Model - 1.105

chi-Square for independence Model with 15 Degrees of Freedom - 109.553 independence Alc - I?I.553

Model AIO - 37.505Saturated AIC - 4£.000 Independence cAlc - 143.810 Model CAIO -

93.148Saturated CAic - 1i9. 900

Normed Fit index (UFI) - 0. 9?8 Non-Normed Fit index (NNFI} - 0.949

Parsimony Normed Fit index {PNFI} - 0.37i comparative Fit Index (cFI} -

0.980 Incremental Fit Index (IFI} - 0. 98i Relative Fit index (RFl} - 0.819

65

Root Mean Square Residual {RMR) - 0.0431 Standardized RVR - 0.0451

Goodness of Fit Inde× (GFi) - 0.978 Adjusted Goodness of Fit Index {AGFI) - 0.9ZZ Parsimony Goodness of Fit Inde× {BGFI) - 0.Z79

Testing convergent & Discriminant of First Order Factors

Fitted Covariance Matrix

BRT 4

1.0000.4Z60.?Z60.4360.?690.175

Fitted Residuals

0.1600.3090.1900.1?4

1.0000.44£0.1660.109

PRT I

RRT 4

RRP 1

Summary Statistics for Fitted Residuals

Smallest Fitted Residual -0.068Median Fitted Residual 0.000Largest

Stemleaf

Fitted Residual

Plot

Standardized Residuals

PRT I

0.596-0.890

RRT 4

RRP 1

Summary Statistics for Standardized Residuals

Smallest Standardized ResidualMedian Standardized Residual Largest Standardized Residual

RRT 1 -0.013 0.017 0.000PRT’4 0.018 -0.044 0.000RR R 1 -0.0?7 0.06? 0.038PRP’5 -0.056 0.150 -0.068

Stemleaf Riot

0|993?0000000000|1S6

Testing convergent & Discriminant of First Order Factors

Qplot of Standardized Residuals

a

a

e

Standardized Residuals

Testing convergent & Discriminant of First Order Factors

67

Modification indices and Expected change

Modification Indices for LAMBDA-X

PRT PRR

Expected Change for LAMBDA-X

RRT PRP

RRT 1 -0.086 0.0?4RRT’4 0.165 -0.046RR R’1 -0.050 0.054RR R’5 0.033 -0.035

Standardized Expected change for LAMB0A-X

PRT 4PRP 1PRP t

-0.5070.3590.0?7-0.053

completely Standardized Expected cnange for LAMBDA-X

PRT PRR

No Non-Zero Modification indices for PHI

Modification indices for TH6TA-DELTA

0.Z6?3.885

PRT I

0.6791.049

RRT 4

RRP 1

Expected change for TKETA-DELTA

PRT 4 PRP 5

PRT 1 —0.0509R0 4 0.225PRR 1 —0.048999 —0.117

0.043-0.IB80.043 0.0690.&61 —0.083

—0.0640.049

completely Standardized expected change for THETA —DELTA

Maximum Modification Inde× is 3.88 for Element ( 6, ?) of IKETA-

DE£TA covariance Matri× of Parameter Estimtes

LX A ,Z 0.000 0 ?63LX 6,5 0.000 0 OOO 0.0?7PJ 1,1 —0.045 0 OOO 0.000 0.0?5PK ?,I -0.017 -0 056 0.000 0.009 0.010PK ?,Z 0.000 —0 075 0.000 0.001 0.01? 0.0Z7PR 3,1 —0. 0?6 0 OOO —0.005 0.0&4 0.006 0.001PK S,Z 0.000 —0 05S —0.005 0.001 0.009 0.018PR 3,3 0.000 0 OOO —0.07? 0.00? 0.00? 0.00?T0 &,1 —0.03? 0 OOO 0.000 0.008 0.005 0.000T0 ?,? 0.019 0 OOO 0.000 —0.008 —0.003 0.000T0 3,3 0.000 0 0?7 0.000 0.000 —0.004 —0.00?T0 4,4 0.000 —0 &01 0.000 0.000 0.014 0.0?1T0 S,S 0.000 0.000 0.0?6 0.000 0.000 0.000T0 6,6 0.000 0.000 —0.011 0.000 0.000 0.000

covariance Matrix of Parameter estimates

0.026-0.004 -0.0070.000 0.000 0.0150.000 0.000 —0.014

TD 5,? 0.000 0.000TD 6,6 0.000 0.000

T0 4,4 T0 S,5 T0 6,6

T0 S,S T0 6,6

Testing convergent Oiscriminant of First Order

Factors correlation Matrix of Parameter

Estimates

0. 0A3-0.1880. 0A3 0.069 —0. 06A0.161 -0.083 0.0A9

0.609 1.0000.484 0. A

86

LX 1,1 1.000L× 4,£ 0.000 1.000LX 6,5 0.000 0.000ex i,i —0. 8 is 0.000 1.000PM 2,1 -0.486 -0.705 0.5A8 1.000PR ?,? 0.000 —0.886 0.035 0.7£ 6 1.000PJ 3, 1 -0.5?9 0.000 0.596 0.433 0.058PR 3,? 0.000 —0.71? —0.195 0.06? 0.638 0.733

-0.765 0.023 0.035 0.0Z0TD &,1 —0.671 0.000 0.000 0.3Z9 0.3£ 6 0.000TD ?,? 0.447 0.000 -0.389 -0.?I7 0.000TD 3,3 0.000 0. A 30 0.000 —0.30A —0. 3?1TD A, 4 0.000 -0.807TD S,S 0.000 0.000TD 6,6 0.000 0.000

correlation Natri× of Parameter Estimates

0.000 1.0000.000 -0.334 1.000

—0.306 0.000 0.000 1.0000.575 0.000 0.000 —0.460

TD 5,? —0.144 0.000 0.000 0.000TD 6,6 0.096 0.000 0.000 0.000

correlation Matrix of Parameter

Estimates T0 4,4 T0 S,5 T0

6,6

TD 4,4ID 5,5TD 6,6 0.000 —0.36A

Testing convergent Oiscriminant of First Order

Factors Standardized Solution

£AMBDA-X

PR9

0.7760.550

0. 9Z3

0.A 67

Testing convergent & Discriminant of First Order Factors

completely standardized solution

PRT

0.7760.550

PRR

0.7160.467

TH6TA-DELTA

RRT PRP

0.598 0.698

PRT I

RRT 4 RRP 1

0.488

The Rroblem used 7776 Bytes (- 0.0% of Available

workspace) Time used: 0.059 Seconds

PHU LUC 3. KET QUA PHAN TICH NHAN TO TOAN B(1 MO HINH

L I S R 6 L 8.30

Karl C. J”reskog & Dag S”rbom

This program is published exclusively by Scientific Software International, Inc.

7383 U. Lincoln Avenue, Suite 100 Lincolnwood, IL 6071Z, U.s.A.

Rhone. (800)Z47-6iiS, {84?)67t-07?0, Fax. (84?)6?5-?i40Copyright by Scientific software International, Inc., 1981-?Q00 Use of this program is subject to the terms specified in the

Universal Copyright convention. Website: www.ssicentra1.com

The following lines were read from file E:\MBA\THE&IS\R_LISREL\FINAL\ME_DA.$PJ:

izsiiuu axz rirw cousiRvciINOLUOINO FACTOR SCORE AND RAW DATAObserved VariablesrAci PRi rRr zu_i rv_z rv_s zv_‹ rzv_i Pzv_z rzv_s zzv_4 si_i si_z Covariance Matrix From File E.\MBA\T8ESIS\E LISREL\FIN AL\ME DA.COV Sample Size - 111Latent Variables BI PU PEU EQUATIONS:BI Z -0. 938’B1

! FACI -BI9RL -B19R9 -B1PU 4 -0.83’PU!9U 1 -93

96U 4 -0.833*93U P6U’1 -R6U! PEU £ -PEU! RSU’3 -PEU 9ACHS:PER -> BI PM PU -> BIRelationships&et Error Variance of BI -0.01&et covariance of PRT BRR Free&et Error covariance of RU_j PU_4 Free Set Error Covariance of B? Z BRT Free&et Error covariance of B?_?

PRR Free Path DiagramNumber of Decimals - S Admissibility Check - Off Iterations > Zt0Method of Estimation: Maximum Likelihoodiisusi ouievi: ×i sc as si-s Pc srEnd of Problem

7

Covariance Matri× to be Analyzed

RRT0.?61

BI I

PRP 0.16? 0.8919U 2 -0.052 0.028PU’3 0.005 —0.&199U 4 0.089 0.078 0.Z65

0.193 1.000BI ? -0.155 -0.??7 0.796PEU’I 0.060 0.&18 0.078PEU 4 0.193 0.044

covariance Matri× to be Analyzed

BI Z

1.0000.A58

Parameter Speuifiuationz

LAMB0A —Y

BI

£AMBDA-X

BI

6

7

Uote: This matrix is diagonal.

PRT PRP

PRT 9R9

THETA-EPS

THETA-DELTA

Number of Iterations - 67

LISmL Estimates (Maximum Likelihood)

RRT

LAMB0A-Y

7

LAMBDA—X

BI 7.976{7.258}1.099

covariance Matri× of ETA and KSI

BI 0.8A9 0.1940.108 0.891

Note: This matrix is diagonal.

7

0.546

Squared Multiple Correlation» for Structural Equations

0.988 0.980

Squared Muitipie correlations for Reduced Form

0.980

Reduced Form

BI(0.121)1.006

(0.186)4.493

0.&S3 0.874(0.0A 7){0.118)

0.9£1

0.Z?6

Z.8¼

60.561

4.9910.149

(0.064)

7

B? B

Squared Multiple Correlations for Y - Variables

BRT

0.049

9R9

0.0190.439

Squared Multiple Correlations for Y - Variables

B? Z

6.056 £.89£

Squared Multiple Correlations for X - Variables

Goodness of Fit Statistics

Degrees of Freedom - ??Minimum Fit Function Chi-Square - 46.630 (P - 0.00164)

Uormal Tneory weighted Least Squares Chi-Square - 44.369 (R - 0.003i9} Estimated Non-centrality Parameter (NCP) - ?Z.36990 Percent confidence interval for NCP - {7.i31 ; 45.37?)

Minimum Fit Function Value - 0.4Z4 Population discrepancy Function Value (F0) - 0.?03

90 Percent confidence Interval for F0 - (0.0648 ; 0.41?) Root Mean Square Srror of Approximation (RHSEA) - 0.0961

90 Percent Confidence Interval for RM$EA - (0.0543 ; 0.1j7) R-Value for Test of Close Fit (RNSEA < 0.05) - 0.037?

S×pected cross-Validation lnde× (6CVI) - 0.8??90 Percent confidence Interval for 6CVI - (0.683 ; 1.031)

ECVI for Saturated Model - 0.8186cVI for independence Model - ?.718

cni-Square for Independence Model witn 36 Degrees of Freedom - ?80. 969 Independence AIC - 298.969

Model AIC - 90.369Saturated AIC - 90.000

Independence CAIC - 33Z.S55Model CAIC - 171.688

Saturated CAic ? t6. 9?9

Normed Fit index {UFI) - 0.854

7

Non-Normed Fit Index (NNFI) - 0.835 Parzimony Normud Fit Index {PNFI} - 0.JI0 Comparative Fit Index (CFI) - 0.899 Inoromental bit lndoz (IF1} -

0.905 Relative Fit index (RFi) 0.7?8

critical N (Cfl) 96.0A?

Root Mean Square Residual {RNR) - 0.06IS Standardized RMR - 0.0875

Goodness of Fit Indez (UFI) - 0.918 Adjusted Goodness of Fit index (AGFI) - 0.83? Parsimony Goodness of Fit Inde× I POFI) - 0.449

Fitted covariance Matriz

RRT RRP

0.?610.168 0.891

RU RU

Z S

—0.008—0.008

—0.010—0.010 1.000

RU 4 —0. 0?0 —0.0?? 0.442 1.000BI 1 —0.104 —0.118 0.068 0.068 0.161BI Z -0. 15S -0.??8 0.064 0.064 0.15I

PEU 1 —0.008 —0.009rr ‹—0.0ii —0.0ia

Fitted covariance Matri×

Fitted Residuals

0.0000.000-0.0A4

0.109—0.0050.0000.0680.Z04

0.1570.£19

0.0000.0980.07B0.1240.0A90.09¼

—0.109

0.1560.7I8

—0. 0?6

0.3690.515

—0.0510.016

Summary Statistics for Fitted Residuals

Smallest Fitted eesiduai

7

Median Fitted Residual Largest Fitted Residual

Stemleaf Riot

0|55678

Standardized Residuals

PRT

RRT -0.??£RRP -0.??Z -0.ZZ?RU ? -0.907 0.415RU’3 0.?7£ -I.?£?

Standardized Residuals

1.1591.4151.484 -0.414 -0. 9?4 0.??£0.574 -0.086 -0.83? -0.0951.4?£ 0.734 -?.£98 0.186

Summary Statistics for Standardized Residuals

Smallest Standardized Residual -2.93Z Median Standardized Residual 0.000largest Standardized Residual - 4.318

Stemleaf Plot

0|99886544???£II0000000|?????34667

Largest Negative Standardized Residuals Residual for RSU_4 and RU_? -£.93? largest Positive Standardized Residuals Residual for PEU 4 and PRT 4.318

Qplot of Standardized Residuals

7

a

a

e

-S.5.-3.5 3.5

Standardized Residuals

Modification indices and Expected change

Modification indices for LAMBDA-Y

RRTPR9 0.006

P9 29U 3

Expected change for LAMBDA-Y

RRT PRR9U 2 0.134

0.191

9U 4 -0.1140.077 -0.010-0.07Z 0.048

RRT PR9

6U 2 0.146

0.060

RRT

Standardized Expected Change for LAMB0A-Y

completely Standardized S×pected change for LAMBDA-Y

0.134

0.077

No Non-Zero Modification Indices for LAMBDA-

X No Non-Zero Modification Indices for BETA

No Non-Zero Modification Indices for GAMMA

No Non-Zero Modification Indices for RMI

Modification indices for RSI Note: This matrix is diagonal.

6×pected change for RSlNote: This matri× is diagonal.

Standardized Expected change for PSI Note: This matrix is diagonal.

Modification indices for THETA-ERS

0.039

Z.940 0.339

0.601

5.648

Modification indices for THETA-ERS

Expected Change for TKETA-EPS

RRT RRR

RRT

RU ? -0.084 0.055RU’3 -0.008 -0. 1£9RU’4 0.005 0.076BI’I -0.384 -0.?44

0.0650.0740.086 -0.019 0.000

-0.063 0.001 -0.004 -3.?33

expected Change for Th6TA-6PS

completely Standardized Expected cnange for THETA-ERS

PRT

RRTPRPRU ? -0.165 0.059PU’3 -0.015 -0.137 0.065PU 4 0.010 0.081 0.074BI’I -0.753 -0.Zt9 0.086 -0.019 0.000Bl £ -0.063 0.001 -0.004 -3.?3I

completely Standardized S×pected change for TH6TA-SPS

Modification indices for TH6TA-DELTA-6PS

8.076 6.507 0.6491.076

Modification indices for THETA-DELTA-EPS

0.6792.67?

Expuotud Chango for IKFTA—DELTA—ER&

Expected change for iRETA—0ELTA—EPS

completely Standardized Expected Change for THETA-DELTA-ER&

0.041—0.Z64 —0.050

Completely Standardized E×pootod Changu for THETA— DELTA—ER&

BI Z

Modification indices for THETA— DELTA

Expected Ohange for IKETA-DELTA

0.£97

completely Standardized Expected Change for THETA-DELTA

0.Z 97

Maximum Modification Inde× is 18. JZ for Elomont ( ?, 1) of THETA DELIA —

EPsILON covariance Matrix of Parameter Estimates

rY i,i 0.00srY z,i 0.00 0.0iiLY 3,7 0.000 0.000 0.0Z0LY A,7 0.000 0.000 0.000 0.016L× &,1 0.000 0.000 —0.001 0.000 0.019BS &,? —0.00? —0.006 0.03? —0. 00A 0.059 5Z.678

BI I

-0.037 0.086 0.133 0.088 -0.301 0.0410.1S4 —0.093 —0.?6A —0.1?7 0.37 A —0.050

40.793—0.411 0. 0S50.377 —0.05? 0.0650.147 —0.001 0.0000.000 0.0000.000 0.0000.000 0.0000.013 —0.0010.0?S —0.00?0.059 —0.0050.139 —0.0110.01Z 0.0000.000 0.0000.000 0.000

—0.005 0.0000.0?9 —0.003-0.?I6 0.016

0.003 0.000 —0. 00A —0.0610.000 0.000 0.000 -0.0140.000 0.000 0.000 —0.001

-0.006

covariance Matri× of Parameter Estimtes

0.0160.000 0.0170.000 0.006 0.0080.000 0.0060.000 0.000 0.0040.000 0.000 0.0000.000 0.000 0.0000.000 0.000 —0.0050.000 0.000 0.000

-0.001 -0.003 -0.006 0.000

covariance Matri× of Parameter Estimtes

0.0£6 -0.154 -45.134—0.007 0.013 0.1 ¼¼0.00Z -0. 0?1 -0.151

0.000 —0. 17Z0.000 0.0000.000 0.0000.000 0.000

Correlation Matrix of Parameter Estimatee

BE 1,2

Correlation Matrix of Parameter Estimatee

GA 1,1

GA 2,1 -0.345 1.000PM 1,1 0.Z35 -0.685 1.000PS 2,2 0.970 -0.123 0.083 1.000TE 1,1 0.000 0.000 0.000 0.000 1.000TE 2,1 0.000 0.000 0.000 0.000 0.445 1.000TE 2,Z 0.000 0.000 0.000 0.000 0.110 0.445TE 3,3 0.016 -0.022 0.027 0.012 0.000 0.000E „4 0.0&0 -0.08 0.0 0 0.0:: 0.000 0.000+E &,4 0. 03 -0. 8 0. 0.039 0.000 0.000TE 5,5 0.193 -0.532 0.323 0.074 0.000 0.000TE 6,6 0.0?8 0.000 0.000 0.015 -0.01? -0.011TE 7,1 0.002 0.000 0.000 0.001 -0.318 -0.£85TE 7,£ 0.000 0.000 0.000 0.001 -0.106 -0.£86TE 7,7 -0.011 0.000 0.000 -0.006 0.052 0.063TD 1,1 0.041 -0.135 0.175 0.011 0.000 0.000TD ?,£ -0.256 0.666 -0.643 -0.110 0.000 0.000

Correlation Matrix of Parameter Estimates

TE 4,4 TE 6,6

0.300 1.0000.000 0.000 1.0000.000 0.000 -0.009 1.0000.000 0.000 -0.034 0.000 1.000-0.004 -0.007 0.031 -0.004 0.058 1.0000.004 0.007 0.029 0.000 -0.174 -0.9740.000 0.000 -0.255 0.016 0.518 0.1?30.000 0.000 0.049 0.000 -0.600 -0.0830.001 0.00? -0.031 0.004 -0.059 -0.9960.049 0.012 0.000 0.000 0.000 0.0000.030 0.038 0.000 0.000 0.000 0.0000.009 0.030 0.000 0.000 0.000 0.0000.000 0.000 -0.110 0.000 -0.019 -0.0120.000 0.000 0.026 -0.135 -0.035 -0.0110.000 0.000 0.087 -0.£33 -0.121 -0.0390.000 0.000 0.160 -0.008 -0.227 -0.0740.068 0.131 0.000 0.000 0.000 -0.030-0.200 -0.041 0.000 0.000 0.000 -0.002-0.057-0.042 -0.081 0.000 0.000 0.000 0.0120.000 0.000 0.006 0.000 -0.360 -0.0110.000 0.000 -0.064 0.000 0.565 0.110

TE 2,2TE 3,3 1.000TE 4,4 0.001TE 5,4 0.004TE 5,5 0.007

TE 6,6 -Q .TE 7,1 -0.106TE 7,£ -Q .TE 7,7 0.045TD 1,1 0.000TD ?,? 0.000

Q000 -0.Q0 ->000.000 0.000 0.000Q. 000 0.Q00 0.0000.000 0.000 0.0010.006 0.0?Z 0.041

-0.066 -0.??7 -0. 4?5

Correlation Matrix of Parameter Estimates

1.0000.369

-0.336

Standardized Solution

RRT PR9

BI 1BI £

BI

0.9ZZ 0.864

0.563

0.66?

6.906

Bl -6.711PU 0.990

correlation Matrix of ETA and KSi

0.Z 64

86

Note: This matrix is diagonal.

Regression Matrix ETA on KSI {Standardized)

BI

completely Standardized Solution

LAMB0A-Y

RRT -0.??£RRP -0.136

0.863

0.565

6.906

correlation Matri× of 6TA and KSI

0.Z 64 0.I?4

0.990

PSIUote. This matrix is diagonal.

TH6TA-EPS

RRT

0.9810.9?1

0.?56 0.561

B? Z

Regression Natri× 6TA on KSl {Standardized)

Total and Indirect Effects

Total Effects of KSI on ETA

BI

1.006

4.493

indirect effects of KSl on 6TA

Total Effects of ETA on ETA

BI 7.976(7.258)1.099

largest Eigenvalue of B*B' (Stability index) is 63.6i6

Totai Effects of STA on Y

(0.055) (0.995)—7.£ A6 —0.986

(1.308)—0.850

7.976 (?.?S8) 1.099

(6.808)

Indirect Effects of STA on Y

-0.981(0.995}-0.986

7.976 (?.?S8) 1.099

89

(6.808)

Total Effects of KSI on Y

(0.016)

-0.806

1.006

1.006

Standardized Total and Indirect Effects

Standardized Total Effects of KSi on ETA

BI 0.I?4

Standardized Indirect effects of KSl on STA

Bl 6.835

Standardized Total Effects of ETA on ETA

6.906

Standardized Total Effects of ETA on Y

PRT -0.113PRR -0.1Z8

PU 4

0.9ZZ 0.864

0.66?6.5655.970

completely Standardized Total Effects of ETA on Y

-0.9400.281

PU 4 0.66?

0.863

Standardized Indirect effects of 6TA on Y

completely Standardized Indirect Effects of ETA on Y

6.3705.96Z

PRT

standardized Total Effects of KSI on Y

completely Standardized Total Effects of KSI on Y

PRT -0.0Z8

91

-0.017 0.Z780.£7B0.65¼0.115

The Problem used 14704 Bytes (- 0.0› of Available workspace)

Time used: 0.070 Seconds