bab v penutup 5.1. kesimpulan - e-journal.uajy.ac.ide-journal.uajy.ac.id/6604/6/mm501950.pdf ·...

BAB V

PENUTUP

Pada bab lima ini, penulis mengambil kesimpulan dari hasil penelitian

yang telah dilakukan. Selanjutnya, penulis merumuskan saran bagi pihak-pihak

yang berkepentingan. Kesimpulan dan saran tersebut adalah sebagai berikut :

5.1. Kesimpulan

Penelitian ini bertujuan untuk mengetahui bagaimana pengaruh pengetahuan

e-commerce terhadap jual beli online, reputasi perusahaan, resiko pembelian,

teknologi yang dirasakan terhadap kepercayaan online, dan juga melihat pengaruh

teknologi yang dirasakan dan kepercayaan online terhadap niat beli ulang.

Berdasarkan hasil analisis dan pembahasan yang telah dilakukan oleh penulis,

maka dapat diambil kesimpulan sebagai berikut :

a. Pengetahuan e-commerce mengenai jual beli online secara positif tidak

berpengaruh terhadap kepercayaan konsumen pada situs lazada.co.id.

b. Reputasi situs lazada.co.id secara positif mempengaruhi kepercayaan

konsumen terhadap situs jual beli online.

c. Resiko dalam melakukan pembelian secara online melalui lazada.co.id

akan berpengaruh negatif terhadap kepercayaan konsumen terhadap situ

jual beli online.

d. Kemudahan dan manfaat dari teknologi yang dirasakan akan berpengaruh

signifikan yang positif terhadap kepercayaan pada situs lazada.co.id.

e. Kemudahan dan manfaat dari teknologi yang dirasakan akan berpengaruh

positif terhadap niat pembelian produk.

p g p p

yang telah dilakukan. SSSelelelanjutnya, penulis merumumumuskan saran bagi pihak-pihak

yang berkepentiiinngngan. Kesimpuulalalan dan saran tersebut adalah sh sh sebagai berikut :

5.1. Kesisisimpulan

Penelitiaaan in in ini beeerrtujuan uuntntntukukuk mememengngngetetetahahahuiu bagaimimana pppenenngagg ruh penenengetahuan

eee-commmererercecece tetterhadddaapap jual beli onlinene, reputasi perususahahahaan, rerer sisisikkko pembmbmbelian,

teknololologogogi yii annngg dg irasakanan terhadap kepepercayaan onlilinen , dan jugagaga meliiihahahat pt pt pengarruruh

teteeknknknoloo ogii yang dirasakann dad n keperercayaan ononlline terhadap nininiat bebebelilili uluu ang.g.g.

BeBeBerdrr asaararkan hhasasilil analisis dadan pn pembabahah sann yyang telah ddililakakukan olo ehhh pepepennnulis,ss

maka ddad pat diambil kesisimpmpululan ses babaggai bi berrikikut ::

a. Penggetahuauann ee-cocommmmerercce mengenai jujualal bebelili ononliline secara pppositititifff tidaadakkk

berpenengagagarururuh th th terererhahadadadap kp kp kepepepererercac yaannn kokokonsnsnsumumumenen papapadadada sisisitututus ls lazada.co.id.

b. Reputasi situs lazada.co.id ssece ara positif mempengaruhi kepeeerrcrcayayayaaaaaan

kokok nsumen ttte hrhrh dadadapapap sisisitututus js js juauu l beli ooonlnlnlininine.e.e.

c.cc ReReResikokoko dadadalalalam mm mm lelel kkakukukukanaa peeembmbmbeliann sn secararraaa ono lililinenene memem laluluui li li laazazadadada.co.id

akan berpengaruh negatif tf tf terhadapp kp epercayaan konsumen terhadap situ

jual beli online.

d. Kemudahan dan manfaat dddara i teknnnologi yang dirasakan akan berpengaruh

signifikan yang positif terhadadadap kp kp kepercayaan pada situs lazada.co.id.

75

f. Kepercayaan terhadap situs berpengaruh secara positif terhadap niat

pembelian produk secara online melalui lazada.co.id.

g. Teknologi yang dirasakan berpengaruh positif terhadap niat beli yang

dimediasi oleh kepercayaan online.

5.2. Keterbatasan Penelitian

Penulis menyadari bahwa dalam melakukan penelitian ini terdapat

banyak keterbatasan. Keterbatasan dalam penelitian ini antara lain :

a. Penelitian ini hanya membahas bagaimana niat pembelian dan

tidak membahas variabel yang berpengaruh terhadap kepuasan

pelanggan setelah melakukan pembelian melalui situs lazada.co.id.

b. Penelitian ini hanya terbatas pada pembelian online berdasarkan

berbagai variabel yang mempengaruhi situs jual beli online, dan

tidak membahas pengaruh pembelian online berdasarkan

produknya secara langsung, item pertanyaan pada kuesioner

penelitian pada variabel resiko pembelian dan niat beli ulang.

c. Penelitian ini hanya menggambarkan perilaku konsumen dalam

melakukan pembelian secara online melalui situs lazada.co.id saja,

sedangkan situs jual beli online di Indonesia yang lain tidak

diikutsertakan dalam penelitian ini.

d. Kepuasan konsumen penting untuk dibahas, mengingat hal tersebut

merupakan tujuan yang ingin dicapai oleh para pemilik situs jual

beli online agar mampu menunjukkan kemampuan bersaing dan

keberhasilan situs tersebut.

dimediasi oleh kepercaaayayayaan online.

5.2. Keteteerrbrbatasan Penelitianaa

Penulis mmmennnyayayadadd ri bab hwa dalam mememelalalakukukukakakan penelitiannn ini terdapat

banyakkk kkketeteterererbbbatasannn K. K. Keterbatasan dn alam peneelililitititiana ini aaantntntararara la la lain :

aa. PePePenelititiaan ini hanya mem mbahas bagagaiimannaaa niat pt pt pememembebebelian dan

tidak memmbab has variaabbel yang bbeerpengaruh teerhrr addapapap kekk puasaaan

pelanggan settelelaha melaakkukan ppemembelian melalui si sititususs lazaaadadada.c.c.cooo.id.

b. Penenelilititiana ini hhananyya tterbbattaas ppada pepembmbeleliian onlinee berdddasarkakakannn

berbagai variriababelel yyang meempmpennggaruhi situs jual beli onlineee, dannan

titiddak mk mmememembababahahah s pengaruh ph ph pememembebebelilil aan onliline bebeberdasarkakakannn

produknya secara lllaana gsgsgsuuung, item pertanyaan pada kuesisisionononeeer

penelitian pada variabel resiko pembelian dan niat beli ulululaaanggg.

c.c PPePenenenelililitititiananan iiini hhhanananyayaya mememengngnggagaga bmbarkakakan pn pn perererilil kakak ku ku konsumememen dn dn dalam

mememelalalakkukan pembeelililian secararara online melalulului si si sititus lllazaaadadada.co.id saja,

sedangkan situs jjual beli oono line di Indonesia yang lain tidak

diikutsertakan dalaaam pm enelitttiiian ini.

d. Kepuasan konsumenn pep ntttiiing untuk dibahas, mengingat hal tersebut

merupakan tujuan yang igg ngin dicapai oleh para pemilik situs jual

76

5.3. Impikasi Manajerial

Kepercayaan online signifikan memediasi niat beli ulang pada situs

lazada.co.id. Kepercayaan online merupakan modal utama untuk

melakukan proses pembelian ulang, jika tidak ada kepercayaan online

seseorang tidak akan melakukan pembelian ulang pada situs tersebut. Ada

beberapa faktor yang dapat digunakan untuk meningkatkan kepercayaan

online. Kemudahan dalam menggunakan teknologi akan berpengaruh

langsung pada niat beli ulang pada situs lazada.co.id melalui kepercayaan

online, seperti menu yang mudah digunakan, adanya menu bantuan ketika

ada konsumen baru yang akan melakukan pembelian disitus tersebut.

Reputasi perusahaan berperan dalam meningkatkan kepercayaan

konsumen untuk melakukan niat beli ulang dengan adanya promosi iklan,

diskon, penjual yang jujur. Semakin sedikit resiko pembelian dalam situs

lazada.co.id juga akan meningkatkan kepercayaan konsumen terhadap

situs tersebut.

lazada.co.id. Kepercayayayaaaan online me mmerupakan modal utama untuk

melakukan ppprrroses pembelian ulang, jika tidakakak ada kepercayaan online

seseorororaang tidak akan meeelakukukukakk n pn pn pememembeeelill an ulang pada sa sa situs tersebut. Ada

bbbeberapa faktktktororor yayayangn dapat digunakkkan unununtututuk mk mk meningkatkan kn kn kepercayaan

onlililinenene.. KeKK mudadadahahahan dn alam mem nggunakakan tn tn teke nolooogigigi akakakanaa berpppenee garuh

lalalangngngsungngng ppada nin at beli ulang ppada situs lazadada.co.ididid melall luuui ki ki kepepepercaayyyaan

onliiinnen , seperti meenunu yang muudadah digunakkanan, adanya menennu bananantututuanaa ketikkka

adadada ka konsumen baru yu yang akakan melakakuukan pembelian dn disisisitusss tetetersrsrsebee ut.

Reputasi peperurusahaan beberpperaan dadalam mmeneniningkatkan kekk percayaaaaaannn

konsumen untuk melelakakukukaan niat bebelili ululanang dengan adanya prooomosi ikikiklann,n

diskon, penjujualalal yayayangngng jujujujujuj r. Semakin sssedededikikikititit rereresisis koko pe bmbelian dn dn daalam siiitututusss

lazada.co.id juga akan meeenininingggkakakatkan kepercayaan konsumen terhahahadadadappp

situs tersebut.

77

DAFTAR PUSTAKA

Aberg, J dan Shahmehri, N (2000). “The Role Of Human Web Assistants

In E- Commerce: An Analysis And A Usability Study.”

Internet search, Vol. 10

Baek, C.H., Seo C.S., Hong, J.W. and Suh, W.J. (2006), “Empirical

Research about the Effects on Customer Trust and Purchase

Intention In China Internet Shopping Mall,” Spring Semi

Annual Conferences of KMIS

Chaffey, Dave (2007). Business & E-Commerce Management 3rd edition.

Harlow: Pearson Education, Inc.

Constantinides, Efthymios (2004). Influencing The Online Consumer’s

Behavior: The Web Experience. Journal of Internet Research,

Vol. 14.

Deitel, Harvey. Et.al (2001). E-Business & E-Commerce for Managers.

New Jersey: Prentice-Hall, Inc.

Dowling, G.R., and Staelin, R. (1994). “A model of Perceived Risk and

Intended Risk-Handling Activity,” Journal of Consumer

Research, Vol. 21.

Engel, J.F., R.D., Blackwell., and P.W, Miniard, 1994. Perilaku

Konsumen. Jakarta : Binarupa Aksara

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in

Online Shopping: An Integrated Model.

Hair, Joseph F., C.Black, William,. Babin,Barry J. 1998. Multivariate

Data Analysis. New Jersey: Prentice-Hall, Inc.

Internet World Stats (Mei 2014). www.internetworldstat.com

Aberg, J dan Shahmehrhrhriii, NNN (20000))). “ThThT e Role Of Human Web Assistants

In EEE- Commerce: An Analysiis As As And A Usability Study.”

Internet searrchcc , Vol. 10

BaBaaeek, C.H., Seeeo Coo .S., HoHoHongngng, JJJ.W.W.W. a. a. andndnd SuSS h,h,, W.J. (20000006)6 , “Empirical

Resesesearaa chh about the Effffeff cts on CuCuCustss omerrr Trust andndnd Purchase

Intentntntioioion In Chinaa Internet ShShShopopoppip ng MaMaMallllll,”,, Springngng Semi

AAnAnnnual Conferenceess of KMIS

Chafffffefey, Dave (2200007). Businessss & E-Commmeerce Managegegementntnt 3r3r3rddd editiononon.

Harlow: P: Peae rson EEduucation, InIncc.

CCoC nstantntininidides, Efthymimioso (220004). InInffluencing TThehe Onlinee Consnsnsumumumer’s’ss

Behahavivioror: Thehe WeWeb EExpxpeerienccee. J. Jouournal of Internneeet Researrrchchch,

Vol. 14.

DeD itel, Hararrveveveyyy. EEtEt.al (l (l (20202001). E-Busisisinneness & E& E& E-CoCoCommm erce fofor Mr Mr Managggerererss.

New Jersey: Prenenentitit cee---HaHaHall, Inc.

Dowling, G.R., and Staelin, R. (1994). “A model of Percei“ veeed Rdd Risisisk ak aand

InInIntetetendndnd deded RiRiRisksksk-kk HaHH nddlililingngng AcAcActiti ivivitytyty,”,”, JoJoourururnananal ol ol of Cf Cf Conononsusuumer

ReReReseeearararchchch V, V, Voloo . 21.11

Engel, J.F., R.D., Blaccckkkwell., aaand P.W, Miniard, 1994. Perilaku

Konsumen. Jaakarta : Binaaarupa Aksara

Gefen, D., Karahanna, E.,,, & Straauaub, D. W. (2003). Trust and TAM in

Online Shoppingg: A:: n IIIntegrated Model.

Hair Joseph F C Black WiWiWilliam Babin Barry J 1998 Multivariate

78

Istijanto, 2009, Aplikasi Praktis Riset Pemasaran, PT. Gramedia Pustaka

Utama, Jakarta.

Jarvenpaa, S.L. and Tracktinsky, N. (2000). “Consumer trust in an

Internet store”, JCMC, pp. 45-71

Kaur, K. (2005). Consumer Protection In E-Commerce In Malaysia: An

Overview.

Koufaris, M. dan Sosa, H. (2004). “Applying The Technology Acceptance

Model and Flow Theory to Online Consumer Behavior,”

Information Systems Research, Vol. 13, No. 2, pp. 205-223.

Kotler, Philip dan Kevin Keller. (2009). Marketing Management 13th

edition. New Jersey: Pearson Education, Inc.

Laudon, Kenneth C. And Jane P. Laudon. (2010) 11th edition.

“Management Information System: Prentice Hall.

Lee, T. M. and Lee, E. Y. (2005). “A Study on the Determinants of

Purchase Intention in Mobile Commerce: Focused on the

Mediating Role of Perceived Risks and Perceived Benefits,”

Journal of MIS Research, Vol. 15, No. 2.

Li, Rong,. Kim, Jaejon,. Park, Jaesung (2007). The Effects Of Internet

Shoppers’ Trust On Their Purchasing Intention In China.

JISTEM J. Inf. Syst. Technol. Management (Online).

Malhotra, N.K. (2010). Marketing Research: An Applied Orientation 5th

Edition. New Jersey : Pearson Education, Inc.

Nielsen Global Consumer Report (2012).

Pavlou, P.A. (2004). Building Effective Online Marketplaces with

Institution-Based Trust.

R. Solomon, Michael. (2007). Consumer Behavior: Buying, Having, and

Being 8th edition. New Jersey: Pearson Education, Inc.

Jarvenpaa, S.L. and TTrararackckcktititinsskykyky, N, N, N. (2000). “Consumer trust in an

Internrnneetet store”, JCMC, pp. 45-717171

Kaur, KKK. (2005). Consumuu er Protection In E-Commmerererce In Malaysia: An

Ovvveeerview.

Koufarrrisisis, M, . dddaana Sosa, H. (. (. (202020040404).).). “AApppp lying Tg Tg The Teeechcc nology AcAA ceptance

Mooodededell al nd Flow Theory to OnOnOnlill ne CoCoConsnsnsumumumer Behahh vior,”

IInfoformr ation Systemsms Research, Volol. 13, NoNN . 2, pp.p.p. 202020555-2233.3.

Kotllleeer, Philip danan Kevin Keelller. (2009)). Marketing MMManagggeeememement 133thtt

edition. NNeew Jerseyy: Pearsonn EEducation, Inc.

LLaudon, KKenenneth C. AnA d JJane Pe P. Laudon.n. (2(2010) 1111 th ededediiition.nn

“MaMananagegemem ntt Innfoformmatatioonn Sysystetemm: P: Prentice Hall.

Lee,, T.. M.M. anand Ld Leeee E, E. Y. (2005).) “AA StStududy oy on tn the Deteerrminnnaaants ofofof

PuPuPurchase Intetetentnn ion in MMMobile Commmmememercrcr e:e FoFocucused on ttthehehe

Mediating Role ooof Pf Pf Peeerceived Risks and Perceived Benefefefititits,s,s,”

Journal of MIS Research, Vol. 15, No. 2.

LiLiLi, R, R, Ronono g,g,g,. KK. Kimimim, J, J, Jaeaeaejojoon,n,n,. P. P. Pararark,,, JaJaJaesesesununungg (2(2(20000007)7)7). T. T. Thehehe EfEfEffefefectctcts Os Os Of If If Intntnteeernet

ShShShopopopppers’ TTTrurr st OnOnOn Thhheieieir Purchchchasaa inining Ig Ig Intntntenenentititiononon InInIn China.

JISTEM J. Inf. SSyS st. Teecechnol. Management (t (t (OnOnOnlililine).

Malhotra, N.K. (2010). MMaM rketing RRResearch: An Applied Orientation 5th

Edition. New JJJeere sey : Peeearson Education, Inc.

Nielsen Global Consumer ReReepopp rtrtrt (2012).

Pa lo P A (2004) B ildi Eff ti O li M k t l ith

79

Schiffman & Lazar Kanuk, 2004. Customer Behavior. International

Edition. Prentice Hall Inc.

Sekaran, U., dari Bougie, R., 2010, Research Methods for Business, Fifth

Edition, Wiley, United Kingdom.

Situs lazada.co.id, www.lazada.co.id

Turban, Efraim; Rainer, R. Kelly, Jr, Potter, Richard E. (2003).

Introduction to Information Technology, 2nd Edition. John

Wiley & Son, New York.

Sekaran, U., dari Bougie,e,e, RRR. 2, 2010101000, R, R, Research Methods for Business, Fifth

Editioioionnn, Wiley, United Kingdom.

Situs lazazazaadada.co.id, www.w.w.lazada.co.id

TuTuTurban, Efrrraiaa m;m;m; Rainererer, RRR. KKKelelllylyly, J, J, Jrr, PoPoPotttt er, Richaaardrdrd E. (2003).

Intrtrtrodododuction tototo InInInfofoformrmrmation Technhnhnologgy,yy 2nd Edddititition. John

Wiileleleyy &y & Son, New YYork.

80

Lampiran 1

Validitas dan Reliabilitas Pengetahuan E-commerce

Scale: ALL VARIABLES

Case Processing Summary

N %

Cases

Valid 200 100.0

Excludeda 0 .0

Total 200 100.0

a. Listwise deletion based on all variables in the

procedure.


N %

Cases

Valid 200 100.0

Excludeda 0 .0

Total 200 100.0

Item-Total Statistics

Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if Item

Deleted

PE1 11.47 1.798 .311 .102 .686

PE2 11.59 1.651 .487 .271 .587

PE3 11.67 1.500 .537 .336 .548

PE4 11.81 1.250 .506 .270 .573

Scale Statistics

Mean Variance Std. Deviation N of Items

15.51 2.482 1.576 4

cale: ALL VARIABLES

Case Processing Summmmmmary

N %

ases

Valid 20000 100.0

Excludedededa 0 .0

Totaaalll 200 10101000.0.000

Listwise dddeletion bababasesesed od od onnn all vaariririaabables in the

ocedurrre.e

Casese Processing Summary

N %%

aaaseeesss

VaVaValililidd 200 1000000.000

Excludeddda 00 00.0

Total 200 100.0

Item-Total Sl Sl Stat tistics

Scale Mean if

ItItItemee Deleted

Scale Variance

if Item Deleted

Correcctetetedd Id temmm--

Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if Item

Deleted

E1 111111 44.4777 111.79797988 .311111 11.1020202 6.6.6868686

E2 11.59 11.651 .44.4878787 22.2717171 .58777

E3 111111 6.6.6777 1.500 .537 .333363636 55.54448

E4 111 88.8111 1.250 .50666 .270 .573

Scale Statistics

Mean Variance Std. Deviation N of Itemsmsms

15.51 2.482 1.576 444

81

Lampiran 2

Validitas dan Reliabilitas Reputasi Perusahaan



N %

Cases

Valid 200 100.0

Excludeda 0 .0

Total 200 100.0


procedure.

Reliability Statistics

Cronbach's

Alpha

Cronbach's

Alpha Based on

Standardized

Items

N of Items

.746 .752 3


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if Item

Deleted

RP1 7.32 1.152 .575 .360 .660

RP2 7.41 1.096 .630 .408 .599

RP3 7.48 1.015 .525 .281 .731

Scale Statistics


11.10 2.171 1.473 3

cale: ALL VARIABLES


N %

ases

Valid 20000 100.0

Excludedededa 0 .0

Totaaalll 200 10101000.0.000


ocedurrre.e

ReReRelililiaabilityy Sy tatistics

CCCronbachchch's's's

Alphhhaa

CCrC onbach's

AAlAlpha BaBases d on

Standardizzeded

Items

N of IItetemsm

.746 .752 33

Item-Total Statistticics

ScScScalaa e MMMeaeaean in f

ItItIteeme DeDeDeleleleteteteddd

Scale Variance

ififif ItItItemmem DeDeDelleletttedd

Corrected Item-

ToToTotatat l

Correlelelatatatioioionnn

Squared

MuMuMultl ipi lele

CoCoorrrrrrelelelata ion

Cronbachchh'sss

AlAlAlphphpha iaa if If If Itetetemmm

Deleted

P1 77.7 323232 1.152 .575 .36000 .660600

P2 7.41 1.096 .63000 .408 .599

P3 7.48 1.015 .525 .281 .731

Scale Statistics


11 10 2 171 1 473 3

82

Lampiran 3

Hasil Uji Validitas dan Reabilitas Resiko Pembelian



N %

Cases

Valid 200 100.0

Excludeda 0 .0

Total 200 100.0


procedure.


Cronbach's

Alpha

Cronbach's

Alpha Based on

Standardized

Items

N of Items

.663 .662 4


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if Item

Deleted

RS1 6.95 2.912 .437 .271 .601

RS2 6.66 3.180 .313 .102 .679

RS3 6.73 2.442 .623 .419 .464

RS4 6.68 2.822 .420 .237 .613

Scale Statistics


9.00 4.538 2.130 4

cale: ALL VARIABLES


N %

ases

Valid 20000

Excludedededa 0

Totaaalll 200


ocedurrre.e

ReReRelililiaabilityy Sy tatistics

CCCronbachchch's's's

Alphhhaa

CCrC onbach's

AAlAlpha Ba Basased on

Standardizzeded

Items

N of IItetemsm

.663 .662 44

Item-Total SSStatatatititistss icsss

ScScScale Mean if

ItItIteme DeDeDeleted

Scale Variance

if Item Deleted

Corrected IItetem-

Total

CoCoCorrrrrrelee ation

Squared

Multiple

CoCoCorrrrrrelelatatatioioionnn

Cronbach's

Alpha if Item

Deleteteeddd

S1 6.95 22.912 .44.4373737 22.2717171 .60111

S2 66.6.666666 3.180 .313 .11020202 66.67779

S3 66.737373 2.442 .62333 .419 .464

S4 6.68 2.822 .420 .237 .613

Scale Statistics


9.00 4.538 2.130 4

83

Lampiran 4

Hasil Uji Validitas dan Reabilitas Teknologi yang dirasakan



N %

Cases

Valid 200 100.0

Excludeda 0 .0

Total 200 100.0


procedure.


Cronbach's

Alpha

Cronbach's

Alpha Based on

Standardized

Items

N of Items

.706 .717 4


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if Item

Deleted

TE1 11.64 1.921 .452 .215 .685

TE2 11.79 1.376 .538 .304 .614

TE3 11.64 1.399 .531 .285 .618

TE4 11.67 1.398 .503 .257 .639

Scale Statistics


15.58 2.467 1.571 4

as Uj Va d tas da eab ttas e o og ya g d asa a

cale: ALL VARIABLESSS

Case Processisiinnng Summary

N %%%

ases

Valid 200 100.0

Exxxcccludeda 0 .0

TTotal 20202000 100.0

Listwwiwise deletitiiooon bababased onn an ll variablees is n the

ocedededure.

ReRR liabbbiililitity Sy Statistics

CrCrCronbaaachchch's's's

AAAlpha

Cronbachch's's

Alpha Based onn

Standardized

Items

N of Itemsms

.77060606 7.71717 4


ScScScalalale Mee ean in in ifff

ItIttememem Deleletteteddd

ScScScalalale Ve Ve Vararariaiiancncee

ififif ItIItemmem DeDeDeleleleteteteddd

CoCoCorrr ected Item-

ToToTotatatalll

CoCC rrelatatatioioionnn

SqSqSquauuareredd

MuM ltltltipipplelele

CoCoCorrellatatatioioionn

CrCrCronononbababa hchch's

AlAlAlphphpha iaa if If If Ittetem

DeDD leteddd

E1 111111.64 1.921 .45222 .215 66.688585

E2 11.79 1.376 .538 .304 .614

E3 11.64 1.399 .531 .285 .618

E4 11.67 1.398 .503 .257 .639

Scale Statistics

84

Lampiran 5

Hasil Uji Validitas dan Reabilitas Kepercayaan Online



N %

Cases

Valid 200 100.0

Excludeda 0 .0

Total 200 100.0


procedure.


Cronbach's

Alpha

Cronbach's

Alpha Based on

Standardized

Items

N of Items

.671 .666 4


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if Item

Deleted

KB1 11.60 1.658 .336 .159 .673

KB2 11.66 1.363 .501 .280 .570

KB3 11.77 1.233 .557 .349 .527

KB4 11.72 1.451 .422 .253 .624

Scale Statistics


15.58 2.285 1.512 4

cale: ALL VARIABLES


N %

ases

Valid 20000 100.0

Excludedededa 0 .0

Totaaalll 200 10101000.0.000


ocedurrre.e

ReRR liabililility Statistics

Cronbaaachchch'sss

AlAA pha

Cronbabachch's

AAlpha Based onon

Standardized

Items

N of Itemms

.6.6.671 6.66666 4

Item-Total Statistticics

ScScScalaa e MeMeMeananan if

ItItIteeme DeDeDeleleleteteteddd

Scale Variance

ififif ItItItemmem DeDeDelleletttedd

Corrected Item-

ToToTotatat l

Correlelelatatatioioionnn

Squared

MuMuMultl ipi lele

CoCoorrrrrrelelelata ion

Cronbachchh'sss

AlAlAlphphpha iaa if If If Itetetemmm

Deleted

B1 11111 6.66000 1.658 .336 .15999 .673733

B2 11.66 1.363 .50111 .280 .570

B3 11.77 1.233 .557 .349 .527

B4 11.72 1.451 .422 .253 .624

Scale Statistics

Mean Variance Std Deviation N of Items

85

Lampiran 6

Hasil Uji Validitas dan Reabilitas Niat Beli Ulang

Scale: ALL VARIABLESCase Processing Summary

N %

Cases

Valid 200 100.0

Excludeda 0 .0

Total 200 100.0


procedure.


Cronbach's

Alpha

Cronbach's

Alpha Based on

Standardized

Items

N of Items

.679 .685 3


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if Item

Deleted

NP1 7.87 .964 .487 .251 .593

NP2 7.78 .933 .537 .293 .532

NP3 7.85 .832 .464 .219 .635

Scale Statistics


11.75 1.764 1.328 3

cale: ALL VARIABLESCase Processing Suuummmmary

N %

ases

Valid 20000 1101 0.0

Excludddeeda 0 .0

Tooottatal 200 10100.0

Listwiseee deletion bababassesed odd n all vavavaririabables in the

oceduurure.

ReRR liabbbiililitity Sy Statistics

CrCrCronbaaachchch's's's

AAAlpha

Cronbachch's's

Alpha Based onn

Standardized

Items

N of Itemsms

.66797979 6.68585 3


ScScScalalale Mee ean in in ifff

ItIttememem Delel tteteddd

ScScScalalale Ve Ve Vararariaiiancncee

ififif ItIItemmem DeDeDeleleleteteteddd

CoCoCorrr ected Item-

ToToTotatatalll

CoCC rrelatatatioioionnn

SqSqSquauuareredd

MuM ltltltipipplelele

CCoCorrellatatatioioionnn

CrCrCronononbababa hchch's

AlAlAlphphpha iaa if If If Itttem

DeDeDeleteeddd

P1 7.7.7.87878 .964 .48777 .251 55.59993

P2 7.78 .933 .537 .293 .532

P3 7.85 .832 .464 .219 .635

Scale Statistics


86

Lampiran 7

Hasil Regresi Linier Berganda Pengetahuan E-commerce, Reputasi Perusahaan, ResikoPembelian, Teknologi yang dirasakan Terhadap Kepercayaan Online

Variables Entered/Removeda

Model Variables

Entered

Variables

Removed

Method

1

RATA_TE,

RATA_RS,

RATA_RP,

RATA_PEb

. Enter

a. Dependent Variable: RATA_KB

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .520a .270 .255 .32608

a. Predictors: (Constant), RATA_TE, RATA_RS, RATA_RP, RATA_PE

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 7.686 4 1.922 18.073 .000b

Residual 20.734 195 .106

Total 28.420 199


b. Predictors: (Constant), RATA_TE, RATA_RS, RATA_RP, RATA_PE

Variables Entered/Remmmovovoveda

odel Variables

Entered

VVVariables

Removed

Method

RATA_TE,

RATA_R_RRSSS,

RATATAA_RP,

RARARATA_PEb

. Enteerrr

Dependndndent Vararariaiaiabblble:e:e: RARR TA_K_K_KBB

All reeequested vavavariababables eeenntntered.

MoModel Summary

ooodedeel R R Squaree Adjusted RR

SqSquauare

Sttd. Errorr of tf the

EEstitimamatte

.52202 a .270 .225555 3.3260808

PPrPreededictors: (: (: (Connsnstatantnt),), RARATATA T_TEE, RARATATA_RS, RATA_RP, RATA_PE

ANOVAa

odel Sum of Squares df Mean Square F Sig.

ReReegrgrgressssisisiononon 77.7.686868666 444 1.1.1.929292222 18118 0.00737373 00.0000000bbb

Reeesiss dududualalal 202020.734344 1955 .1.11060606

Total 28.420 1919199

Dependent Variable: RATA_KB

Predictors: (Constant), RATA_TE, RATA_RS, RAATA_RP, RATAA_PE

87

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) 2.045 .339 6.037 .000

RATA_PE .097 .064 .101 1.507 .134

RATA_RP .094 .050 .122 1.863 .064

RATA_RS -.103 .045 -.145 -2.281 .024

RATA_TE .349 .066 .362 5.304 .000


B Std. Errorr Beta

(Constant) 2.045 .339 6.037 .000

RATA_PE .00099797 .064 .101 1.507 .134

RATA_RP .094 .050 .122 1.8666333 .064

RATA_RS -.103 .0004445 ---.114544 -2.281 .024

RATA_TE .3334494 .0666 .3.336222 5.304 .0.0.0000

Dependent VVVaaariable: RATATATA__K_KB

88

Lampiran 8

Hasil Regresi Linier Sederhana Kepercayaan Online Terhadap Niat Beli Ulang


Model Variables

Entered

Variables

Removed

Method

1 RATA_KBb . Enter

a. Dependent Variable: RATA_NP


Model Summary


Square

Std. Error of the

Estimate

1 .459a .211 .207 .39427

a. Predictors: (Constant), RATA_KB

ANOVAa

Model Sum of Squares Df Mean Square F Sig.

1

Regression 8.221 1 8.221 52.884 .000b

Residual 30.779 198 .155

Total 38.999 199


b. Predictors: (Constant), RATA_KB

Coefficientsa


Coefficients

t Sig.

B Std. Error Beta

1(Constant) 1.824 .289 6.301 .000

RATA_KB .538 .074 .459 7.272 .000



odel Variables

Entered

Variababableleles

RRRemoved

Method

RATA_KBb . Enntteer

Dependent Variablleee: RATA_NP

All requested vavavariables enttere ed.

Moodededel Summmam ry

odeelll RRR R SSSqquare AdAdjujusted R

SqSquauare

Stdd. Error of the

EEstimate

.4.. 59a .211 .207 .39442727

PrPredictototorsrsrs: (:: Consssttatant), RARATATA_K_ B

ANANOVO AAaa

odededel SuSum om of Sf Sququara es Df Mean Square F SiSigg.

Regression 888.2222211 111 8.8..222222111 525252 88.8848484 0.000b

RRResidual 30.779 1988 .155

ToToTotaalll 38.999 199

Depeeendndndenenent Vt Vt Varaa iablle:: RARARATATATA NN_NPPP

Predictototorsss: (: (: (CoCC nstttantntnt)), RARARATATATA KK_KBBB

Coefficieeentsa

odel Unstandardized Coefficiennts Standaardized

Coeffffficients

t Sig.

B Std. Error Beta

(Constant) 1.824 .28999 6.301 .000

RATA_KB .538 .074 .459 7.272 .000

89

Lampiran 9

Hasil Regresi Linier Sederhana Teknologi yang dirasakan Terhadap Niat Beli Ulang


Model Variables

Entered

Variables

Removed

Method

1 RATA_TEb . Enter



Model Summary


Square

Std. Error of the

Estimate

1 .297a .088 .083 .42382

a. Predictors: (Constant), RATA_TE

ANOVAa


1

Regression 3.434 1 3.434 19.119 .000b

Residual 35.565 198 .180

Total 38.999 199


b. Predictors: (Constant), RATA_TE

Coefficientsa


Coefficients

t Sig.

B Std. Error Beta

1(Constant) 2.616 .299 8.735 .000

RATA_TE .335 .077 .297 4.373 .000



odel Variables

Entered

Variababableleles

RRRemoved

Method

RATA_TEb . Enntteer

Dependent Variablleee: RATA_NP

All requested vavavariables enttere ed.

Moodededel Summmam ry

odeelll RRR R SSqSquare AdAdjujusted R

SqSquauare

Stdd. Error of the

EEstimate

.2.. 97a .088 .083 .42338282

PrPredictototorsrsrs: (:: Consssttatant), RARATATA_T_ E

ANANOVO AAaa

odededel SuSum om of Sf Sququara es Df Mean Square F SiSigg.

Regression 333.4344344 111 3.3..434343444 191919 11.1191919 0.000b

RRResidual 35.565 1988 .180

ToToTotaalll 38.999 199

Depeeendndndenenent Vt Vt Varaa iablle:: RARARATATATA NN_NPPP

Predictototorsss: (: (: (CoCC nstttantntnt)), RARARATATATA TT_TEEE

Coefficieeentsa

odel Unstandardized Coefficiennts Standaardized

Coeffffficients

t Sig.

B Std. Error Beta

(Constant) 2.616 .29999 8.735 .000

RATA_TE .335 .077 .297 4.373 .000

90

Lampiran 10

Hasil Regresi Linier Berganda Teknologi yang dirasakan dan Kepercayaan OnlineTerhadap Niat Beli Ulang


Model Variables

Entered

Variables

Removed

Method

1RATA_KB,

RATA_TEb . Enter



Model Summary


Square

Std. Error of the

Estimate

1 .469a .220 .212 .39308

a. Predictors: (Constant), RATA_KB, RATA_TE

ANOVAa


1

Regression 8.561 2 4.281 27.705 .000b

Residual 30.438 197 .155

Total 38.999 199


b. Predictors: (Constant), RATA_KB, RATA_TE

Coefficientsa


Coefficients

t Sig.

B Std. Error Beta

1

(Constant) 1.585 .330 4.796 .000

RATA_TE .119 .080 .106 1.485 .139

RATA_KB .480 .083 .410 5.760 .000


Variables Entered/Remmmovovoveda

odel Variables

Entered

VVVariables

Removed

Method

RATA_KB,

RATA_T_TTEEEb . Enteter

Dependent VVVariable: RARARATATATA_N_N_NP

All requeeesssted variababableleles es es entntntered.

Model Summaaryry

oodddel RRR R Square Adjuststeded R

Squarere

Sttdd. Error of thehe

EEstimatee

.46999a .22020 .2.2122 .3939308

PrPrPredicicictors: (Connsn tant), RATA_KB, RAATAT _TTEE

ANANOVOVAa

odededel Sum om oof Sf Sf Sqquares DfDff Mean SqSqSquare FFF Sig.g.

gReRR gression 8.561 222 4.281 27.705 .000b

ReReResisis dual 30.438 197 .155

ToToTotatatall 38.999 199

Dependndndenenent Vtt arriaiaiablblble:e:e: RARARATATATA_NNNPPP

Predictorsrsrs: (: ((CoCoConsnn tant), RATATAA_K_K_KBBB, RAATATATA TT_TEEE

Coefficiiientsa

odel Unstandardized Coefficiennnts Standdad rdized

Coeeefficients

t Sig.

B Std. Error Beta

(Constant) 1.585 .330 4.796 .000

RATA TE 119 080 106 1 485 139

91

Kuesioner Penelitian Survey Pengaruh Kepercayaan Konsumen Terhadap Niat Pembelian Barang di Situs Lazada.co.idSaya Nicolaus Krisna Palmawan Bakti, Mahasiswa Magister Manajemen Universitas Atma Jaya

Yogyakarta yang sedang melakukan penelitian untuk tugas akhir mengenai Peran Kepercayaan

Online Sebagai Variabel Pemediasi Niat Beli Ulang Pada Situs Lazada.Co.Id. Mohon kesediaan

saudara/i untuk bersedia secara suka rela mengisi kuesioner ini secara lengkap dan jelas, sesuai

dengan yang saudara/i ketahui dan alami. Hasil dari kuesioner ini akan dipergunakan untuk

tujuan penelitian. Terimakasih atas bantuan dan partisipasi saudara/i sebagai responden dan

secara sukarela mengisi kuesioner ini.

* Wajib

Bagian A Petunjuk : Pilihlah jawaban di bawah ini yang sesuai dengan diri anda 1. Nama

2. Jenis Kelamin :

o Laki-laki

o Perempuan

3. Usia

o < 18 tahun

o 19 - 24 tahun

o 25 - 30 tahun

o 31 - 36 tahun

o > 37 tahun

embelian Barang ddii SSiittuuss Lazada.co.idya Nicolaus Krisna Palmawawann Bakti, Mahasiswa Magister Maananajej men Universitas Atma J

gyakarta yang sedangng melakukan penele itiaian n ununtutuk k tugag s akhir mengenenai Peran Kepercay

ine Sebagai VVaariabel Pemeeddiasasi NNiat BBeli i Ulang PaP dad SSititusus LLaazada.Co.Id. MMohon kesedi

dara/i untutuk bersedediaia ssece ara suukaka rela mengisi kuesioner r inini secaraa llenengkgkap dann jjelas, ses

gan yyang saaududarara/a/i keetatahui dan alami. Hasil dari kuesioner inii akann dipiperergugunakakan un

uann penelititiaiann. Tereriimakasih atas bantuan dan partisipasi saudara/i seebab gai i reresspondeen n

arra sukararelela a menngisi kuesioner ini.

WWajjibb

Baaggiaan A PePetunjuk : Pilihlah h jajawawabban didi bbawawahah iinin yanng g sesesusuaiai ddengan didi iri anda 1.1. NNama

2.2. JJenenisi KKelelamamin :

oo Laki-l-lakakii

o PePererempmpuan

3. Usia

o < 18 tahun

o 19 - 24 tahun

o 25 - 30 tahun

92

4. Lokasi tempat tinggal Anda saat ini :

o Yogyakarta

o Jawa Tengah

o Jawa Barat

o Jakarta

o Yang lain:

5.Pendidikan terakhir Anda

o SMP

o SMA

o D3

o S1

o S2

o S3

6. Pekerjaan

o Pelajar/Mahasiswa

o Guru/Dosen

o Pegawai Negeri Sipil

o Pegawai Swasta

o Wiraswasta

o Yang lain:

7. Rata-rata pendapatan per bulan Anda

o < Rp. 1.500.000,00

o Rp. 1.500.100,00 – Rp. 3.000.000,00

o Rp. 3.000.100,00 – Rp. 4.500.000,00

o Rp. 4.500.100,00 – Rp. 6.000.000,00

o > Rp. 6.000.100,00

o Jawa Barat

o Jakarta

o Yangg llaain:

5.Pendidikanan terakhir AnAndada

o SMMPP

o SMSMAA

o D3D

oo S11

oo SS2

oo S3

6.6. Pekerjaaan

o Pelajar/Mahasisiswswaa

o Guru/Dosen

oo Pegawai Negeri Sipil

oo PeP gag wai Swasta

oo WiWiraraswswasastata

o YaYangng llaiain:

7. Rata-rata pendapatan per bulan AnAnda

o < Rp. 1.500.000,00

o Rp. 1.500.100,00 – Rp. 3.0000.00000,00

o Rp. 3.000.100,00 – Rp. 4.5000.0 0000,00

Rp 4 500 100 00 Rp 6 000 000 00

93

8. Rata-rata untuk pengeluaran kebutuhan sehari-hari Anda dalam satu bulan :

o < Rp. 500.000,00

o Rp. 500.100,00 – Rp. 1.500.000,00

o Rp. 1.500.100,00 – Rp. 3.000.000,00

o Rp. 3.000.100,00 - Rp. 4.500.000,00

o > Rp. 4.500.100,00

Bagian B Petunjuk : Berdasarkan pengalaman Anda melakukan transaksi online pada situs jual beli lazada.co.id selama ini,pilihlah jawaban yang Anda anggap sesuai dengan kondisi Anda. 1. Anda pernah mengunjungi situs jual beli lazada.co.id dalam 3 bulan terakhir? *

o Ya

o Tidak

2. Apakah Anda pernah bertansaksi online di lazada.co.id dalam 3 bulan terakhir? *

o Ya

o Tidak (Jika menjawab tidak, maka Anda dapat berhenti mengisi kuesioner ini)

3. Berapa sering Anda mengunjungi situs jual beli lazada.co.id dalam 3 bulan terakhir?

o < 5 kali

o 6 kali - 10 kali

o 11 kali - 15 kali

o > 16 kali

4. Dari mana Anda mengetahui tentang situs jual beli lazada.co.id?

o Keluarga, teman

o Iklan TV

o Internet

o Yang lain:

o Rp. 1.500.100,00 – RpRp. 3.000.000,00

o Rp. 3.000.101000,00 - Rp. 4.500.000,00

o > Rp. 44.500.100,00

Bagiann BB Petunjnjuk : Bererdadasasarkrkan penngagallaman Anda melakukkann ttraransaksii oonlnlininee pada ssititus jual blazaaaddad .co.iddd seselalamam inii p,piilihlah jawaban yang Anda anggapp ssesuaii dedengngan kondidisi And1.. Anda a pepernrnahah menengunjungi situs jual beli lazada.co.id dalam 33 bulaan teterarakhkhir? **

oo YaYa

oo TTidak

2. Apakahh Anda pernah bertansaksi online di lalazazada.co.idd dalam 3 bulan tet rakhkhir? **

o Ya

o Tidak (Jika memenjnjawawabab tidak, maka Andndaa dadapapat berhenti mengigisisi kkuesiononerer ini)

3.3. BBere apa sering Anda mengunjungi situs s juj al beli lazada.co.id dalam 3 bulan teterarakhkhir?

oo << 55 kakalili

oo 6 kalii -- 1100 kkalii

o 1111 kkalalii - 15 kali

o > 16 kali

4. Dari mana Anda mengetahui tentaang situs juual beli lazada.co.id?

o Keluarga, teman

o Iklan TV

94

5. Dimanakah Anda melakukan transaksi online selain pada situs jual beli lazada.co.id?

o olx.com

o bhinneka.com

o zalora.co.id

o elevenia.co.id

o Yang lain:

6. Kategori produk apa yang pernah Anda beli pada situs jual beli lazada.co.id?

o Komputer & Laptop

o Handphone & Tablet

o Kamera

o Peralatan Elektronik

o Fashion

o Otomotif

o Lifestyle

o Tas & Koper

o Jam & perhiasan

o Yang lain:

7. Berapa pengeluaran Anda pada saat melakukan transaksi online pada situs jual beli lazada.co.id dalam 3 bulan terakhir?

o < Rp. 1.000.000,00

o Rp. 1.000.100,00 – Rp. 5.000.000,00

o Rp. 5.000.100,00 – Rp. 10.000.000,00

o >Rp. 10.000.100,00

o zalora.co.id

o elevenia.co.o..ididid

o Yangg llaain:

6. Kategori i pproduk apa yyanangg ppernahh AAndnda a bebelii ppadadaa situtuss jujual beli lazaadadd .co.id?

o KoKompmpututer & Lapaptotop

o HaHandn phonone & Tablet

o KaK memera

oo Peeralatan Elektronik

oo FFashion

oo Otomotif

o Lifestyle

o Tas & Koper

o Jam & perhiasasann

o Yang ll iain:

77. BBererapa a pengeluaran Anda pada saat melakukan transaksi online pada situss jjuaall bebeli lalalazazazadadada.co.oo ididid dalam 3 bulan terakhir?

oo < Rpp.. 1.1.000000. 0000,0,0000

o RpRp.. 1.1.000000.100,00 – Rp. 5.5 000 0.000,0,00

o Rp. 5.000.100,00 – Rp. 110.000.0000,00

o >Rp. 10.000.100,00

95

BAGIAN C Petunjuk : Berdasarkan pengalaman Anda melakukan transaksi online pada situs jual beli lazada.co.id selama ini,pilihlah jawaban yang Anda anggap sesuai dengan kondisi Anda.

Pengetahuan e-commerce dalam lazada.co.id 1. lazada.co.id adalah situs jual beli online

o Sangat Tidak Setuju

o Tidak Setuju

o Netral

o Setuju

o Sangat Setuju

2. Saya mengetahui bagaimana mencari produk yang saya perlukan melalui situs jual beli lazada.co.id.


o Tidak Setuju

o Netral

o Setuju

o Sangat Setuju

3. Saya memahami cara untuk melakukan pembelian online di situs jual beli lazada.co.id.


o Tidak Setuju

o Netral

o Setuju

o Sangat Setuju

4. Saya memahami syarat dan ketentuan yang berlaku dalam melakukan transaksi jual beli online di lazada.co.id.


Pengetahuan e-coommmerce dalamm lllaaazada.co.id 1. lazada.co.id adallahah situs jual belil online

o SSangat Tidakak SSetetuuju

o Tidak k SeS tuju

oo NeNetrt alal N

o SeSettuju

o Sangngat Setuju

2. SSayaya a mengngetahui bagaimana mencari produk yang saya perlukan mele aluii ssitituus jual l blazazazadadada.co.iddd.


o Tidak Setuju

o Netral N

o Setuju

oo Sangat Setuju

33. SSayaya a mem mahahamimi ccararaa unttukk memelalakuk kan pembmbeleliaian n onlilinene ddii sisitutus jujual bbeleli i lalalazazazaddad .co

o SaSangngatat TTiidakak SSetuju

o Tidak Setuju

o Netral N

o Setuju

o Sangat Setuju

4. Saya memahami syarat dan ketentuan yyang berlaku dalam melakukan transaksi jualb li li di l d id

96

o Tidak Setuju

o Netral

o Setuju

o Sangat Setuju

Reputasi perusahaan dalam lazada.co.id1. Saya mendengar komentar / testimonial positif tentang lazada.co.id.


o Tidak Setuju

o Netral

o Setuju

o Sangat Setuju

2. Menurut saya, situs jual beli lazada.co.id mempunyai reputasi yang baik.


o Tidak Setuju

o Netral

o Setuju

o Sangat Setuju

3. Menurut saya, situs jual beli lazada.co.id mempunyai kemampuan sumber daya manusia yang baik dalam mengelola situs tersebut.


o Tidak Setuju

o Netral

o Setuju

o Sangat Setuju

o Sangat Setuju

Reputasi pperusahaan ddaallaamm lllaaazzzaaaddda.co.id1. Saya mmendengar komementntarar / testimonial positif tentatangng lalalazadaaa.co.id.

o SaSangngata Tiddakak Setuju

o TiTidak k SSetuju

o Neetrtral N

oo SSetuju

oo Sangat Setuju

2.2 Menurruut saya, situs jual beli lazaadada.c.coo.idd mempunyai reputasi yang baikik.


o Tidak k SeSetutujuju

o Netral N

oo Setuju

oo SaSangngatat SSetetujujuu

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-TERRIIMA KKASSIIHH-

Revista de Gestão da Tecnologia e Sistemas de Informação Journal of Information Systems and Technology Management Vol. 4, No. 3, 2007, p. 269-286 ISSN online: 1807-1775

_____________________________________________________________________________________

Recebido em/Manuscript first received: 05/10/2007 Aprovado em/Manuscript accepted: 30/11/2007

Endereço para correspondência/ Address for correspondence

Rong Li is graduated from Fudan University (China) in 2005 and got his master's degree in Electronic Commerce from Chonnam National University (Korea) in 2007. He is currently a staff of Amazingsoft, Inc. providing Web Analytics service for global customers. His research concentrates on e-Commerce in East Asia, Online Marketing and Search Engine Optimization. E-mail: [email protected]

JaeJon Kim is Professor of Information Systems at the School of Business Administration, Chonnam National University, Korea. He received his Ph.D. degree in Computer Information Systems from Arizona State University in the U.S.A. His current research interests are strategic use of information technology including E-business and IT-enabled innovation. E-mail: [email protected]

JaeSung Park received his M.A. degree in Management Information System from Chonnam National University in Korea. He is currently a Senior Research Associate at the Business Incubator, Chonnam National University, Korea. His research interests include Management Information Systems, E-Business Start-up, and Web Analytics. E-mail: [email protected] Chonnam National University. Gwangju Campus 300 Yongbong dong, Buk-gu, Gwangju 500-757, Korea ISSN online: 1807-1775 Publicado por/Published by: TECSI FEA USP – 2007

THE EFFECTS OF INTERNET SHOPPERS’ TRUST ON THEIR PURCHASING INTENTION IN CHINA Rong Li Amazingsoft, Inc. JaeJon Kim BK21 Biz Convergence Team, Chonnam National University, Korea JaeSung Park Chonnam National University, Korea _____________________________________________________________________________________

ABSTRACT Owing to the rapid development of the Internet and information technology in China, the growth of consumers’ purchasing activities in Internet shopping malls has been truly phenomenal in recent years. Taobao.com, Ebay.com.cn, and Paipai.com have 67,360,000 customer to customer (C2C) users and 99% of the market share in China’s C2C market (www.163.com). Dangdang.com and Joyo.com have occupied 87% of the business to customer (B2C) market with 58,360,000 users (www.sohu.com). Because of these significant numbers of users, it is important to understand what affects Chinese consumers’ decisions to purchase in Internet shopping malls. Based on past studies, trust is considered a key factor affecting a Chinese consumer’s purchasing intention. The purpose of this study is to investigate the effects of Chinese shoppers’ trust on their purchasing intention in Internet shopping malls. In order to accomplish the purpose of this study, we developed a research model. This model suggests that there exists a significant relationship between trust and purchasing intention. According to this model, on purchasing intention, trust also mediates effects of other independent variables such as e-commerce knowledge, perceived reputation, perceived risk, and perceived ease of use. The results of this study show that the relationships between these variables are all significant except that between

_______________ ____________________________________________________________________________________ _________

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Endereçoo ppparraaa cococorrrrrresesespopopondndndênênêncicicia/ AdAdAddrdrdress fof r rr cocc rrespopoponndndencee

Rong Li is gradadduauauateteteddd frfrfromomom Fudan University (C(CChina) inn 222005 and got his mastststererer'sss dddegegegrereree in Electronic Commerce from Chonnam National Universityy y (Korea) ininn 2007. He is currently a staff of Amazingsoft, Inc. providing Web Analytics service for globbbal customers. His research concentrates on e-Commerce inEast Asia, Online Marketing and Search Enginnne Optimizatiooon. E-mail: [email protected]

JaeJon Kim is Professor of Information Systeeems at the SSSchool of Business Administration, Chonnam National University, Korea. He received his PhP .D. deeegree in Computer Information Systems from Arizona State University in the U.S.A. His currrrrent resesesearch interests are strategic use of information technology including E-business and IT-enabled innnnnnovvaaation. E-mail: [email protected]

JaeSung Park received his M.A. degree in Managemee entk Information System from Chonnam National t

Rong Li Amazingsoft, Inc. JaeJon Kim BK21 Biz Convergence TTTeam, Chonnammm National University, Koreeeaaa JaeSung Park kChonnam Nationononal Universrsrsittty,y,y, KKKorea _________________________________________________________________________ _________ _____________ _________

ABSTTTRACTCC Owinining g tototo the rrraapapid developpmem nt of the Inteernnet and informatitioon technollogogogy in CCChihihinanana, , the growowwth offf ccconononsumersrsrs’’ purchasing aactctivities in Intterrnet shoppingg mmalls has been n truly y phphphenenenomoo enal inrererecececent yeaarrsrs. Taobao.com, Ebbayay.com.cn, andnd Paipai.comm hhave 67,360,000 cusuustomememerr r ttoto ccususu tomeeer (C(C(C2C2C2C) usususers and 99% of tthehe market sshare in CChhina’s C2C market t (wwwww.1116663.com).). DaDaDanngdaanngn .ccomom and Joyo.com haaveve occupieed 87% of tthehe business to customemerr (BBB2C) mamamarkrkrkettt with h585858,3600,,0,000 usersrs ((www w.sohu.com)m). BeB causse oof thesese ssiignificant numbmbererss of usersrss, it is s s immmpopoportannnt tott undddere stand what aaffffecectst Chinesee consn ummers’ deecissiions to puurcrchahasese in Internet ssshoppiiing mallllllsss. Basedd d on past studies, trurustst iiss considdere eded aa keyey fafacttoor affecctitingng aa Chinese consummeeer’s purchasssiningg intentttiion. The purpose of ththisis stutudydy iis too invvestigigattee ththe e efeffects of Chinese shoppppers’ truuussst onn their ppup rchasing intention in Inteernrnet sshhopping mamalls.. IIn order to accomplish the pppurpose e e of thiiis stststudy,y,, wwee dedevevelolopepedd aa rereseseararchch model. This moddelel ssuguggegeststss ththatat ttheherere eexix stsss a sisisignificcannnt t t relalaatititioononship between ttrururuststst aaandndnd pppurururchasing intention.n.n. AAAccccccororordididingngng tto hthiis model, , ononon purchasssinnnggg intention, ttruruststt aaalslslsooo mememedidiatateseses eeeffectststs of otheeerrr indedepepependnndenentt t vavavariririababableleless s susuchch as e-commmmererercecee knowledge, perceived reputation, perceceeivivivedee rrisisisk,k,k, and perceived ease of use. The results ofofof ttthihihis study show that the relationships between thththesee e variables are all significant except that bebebetwtwtweeeeen

Li, Rong. Kim, JaeJon, Park, JaeSung 270

trust and perceived reputation. This research confirms the significant effects of Chinese shoppers’ trust on purchasing intention. Implications of these findings are discussed for researchers and practitioners.

Keywords: Internet shopping mall, purchasing intention, trust, e-commerce knowledge, reputation, risk, PEOU

1. INTRODUCTION

Since 1997, China’s information industry has been developing very rapidly. According to the 19th Statistical Survey Report on Internet Development in China released in January 2007 by the China Internet Network Information Center (CNNIC, 2007), the number of Internet users has reached 137 million. Of these users, 79.1% said that they would surely or possibly go Internet shopping in the first half of the year. 23.6% of the users replied that they were frequently using an Internet shopping service when they went on the Internet. In 2006, there were over 30 million Internet users who had an Internet shopping experience.

Despite the fact that Internet shopping malls are now winning more and more customers by providing these customers with a discounted price for products, favorable price alone may not be sufficient. Trust is still regarded as an order qualifier for consumers’ purchase decisions (Doney & Cannon, 1997). Consumers are unlikely to patronize Internet stores that fail to create a sense of trust (Jarvenpaa & Tractinsky, 1999). Trust can only exist if the consumer believes that the seller has the ability to provide and deliver goods of the quality expected by the consumer. At this point in time, there is another important question: How can consumers gain trust in an Internet shopping mall? In traditional contexts, a consumer’s trust has been found to be affected by the seller’s investment in physical buildings, facilities, and personnel (Doney & Cannon, 1997). We note, however, that since there is no physical contact between consumers and sellers in an Internet shopping mall, it is reasonable to suggest that a consumer’s trust is affected by the consumer’s characteristics, instead of some actions of the seller’s .

The purpose of this study has been to identify factors which may affect the purchasing intention in B2C market and examine the relationships among the identified factors. For this purpose, we conducted an empirical study on Chinese users using Internet shopping malls. 2. THEORETICAL BACKGROUND 2.1 Trust

Trust has been conceptualized by many prior researchers. In this paper, we only discuss the concept of trust in an Internet shopping mall (or e-commerce context) because the concept of “trust” is so broad. We, now, briefly discuss several prior

R. Gest. Tecn. Sist. Inf. RGTSI/JISTEM Journal of Information Systems and Technology Management, Brazil

reputation, risk, PEOU

1. INTRODUCTION N N

Since 1191997, Chinaa’a s s inininffof rmatattioioionnn ininindududustststryryry hahahasss bebebeenenen developing gg very rapidly.According ttoto the 19t99 h Statatatissstitt cal Surveyy Repport on III tntntererernenn t DeDeevelopmenennt t in China released iiinn Januararry y y 20202007 by theee ChChChiinina Inteernet NNN tetetwowoworkr Informaamatititiononon Center (C(( NNIC,2007), ttthhhe nummbebeber rr ofofof IInterrnenenett t users has reacchhed 137 milliiononn.. OfO thehh sesese uuuseseersr , 79.111% %% said that ttthhhey woww ulululd dd sus relyyy oor ppossibly go Inteerrnet shopping inn ththhe e e firsttt hahahalflflf ooof the yeyy ar.23.666% of ttthehehe uuusers rrreepeplied thahat they were frfreequently usingg an Inteernrnrnet shohohoppppppinining servvvice whwhhen theey y y wewewent oonn n the Internetet. In 2006, thheere were oveer r 30 million IIIntn ernenenett t usususers whwhwhohahahad an IIIntntnteree net shshshopping experieiennce.

DeDeDespitee e ththe e fact that Internrnetet shopppinng malllss aare now winninng g mmmore aandndnd mmmoreecucustomomomererers byyy pproviididingng these custoomemers wiithh a disscoounted price fforor produuuctcc s, fffavavavorororablele prprricii e alllone mam y not bebe ssufufficient. TTrur stt iss sttiill regardeded aass an order qqqualiliifififier ffforoo ccoconnsnsumers’ ppurchase decisiionons (D(Doneyey && CaCannnoon, 19199797).). Consumers areee unlikelyy y tooo ppaatronononize Innntternet stores that faiaill to ccrereatte a sesensnse ofof trust (Jarvenpaa && Tractiinnnskyyy,1999999 ). TTTrusttt can only y exist t ifif tthehe ccoonsumer believeveses tthahatt tht e seller has thhhe abibibillity ttto o rprprprovovo ide annnd d d deliliver gooodods s ofofof ttthehehe qqquau lity expecteddd bbbyyy thththee e coconsnsuumer. AAt ttthihihisss pointt t ininin tititimememe,, there is anonoththererer iiimpmpmpororortattantnt qqqueueueststs ioioion:nn Howww ccann ccconononsusumememersrsrs gggaiaiain nn trtrusustt in an Inteeernrnrneetet shhhopopopppping mall? In traditional contexts, a cococonsssumumumer’s trust has been found to be affefefectctctededed byyy tthehehe sseller’s investment in physical buiuiildlddings, facilities, and personnel (Doononeyeyey &&& Caaannnnnnononn,,, 19191997). We note, however, that since there is no physical contaaactctct bbbeetetweweween consssumumumererersss ananand d sellllllererersss ininin aann IIntteernrnrnetetet ssshoh pping mmmalalall,l,l, iiittt iis rreaeasosonananablblblee ttto suguguggegegeststst ttthahahat a consumumumererer’s truststst iiisss afafaffeffectctctededed bbbyyy thththe cocoonsnsnsumumumererer’s’s’s ccchahaharacttterererisisistititicscscs, , ininnstststeaeaeaddd fofof sommmeee acacactions of the sselee leleler’r’r’sss ...

The pupupurprprposososeee ofofof this study has bebb en tooo identify factors wwwhihihichchch mmmaaay affect thepurchasing intention in B2C market andndnd examinee e the relationships among the identified factors. For this purpose, we conduccted an empppirical study on Chinese users using Internet shopping malls.

2. THEORETICAL BACKGROUND

2 1 Tr st

The effects of internet shoppers’ trust on their purchasing intention in china 271

research studies dealing with the conceptualization of trust in an e-commerce context.

Jarvenpaa & Tractinsky (1999) define trust in the Internet shopping mall context as a consumer’s willingness to rely on the seller and take action in circumstances where such action makes the consumer vulnerable to the seller. According to Mayer et al. (1995) trust is a willingness of a party to be vulnerable to the actions of another party based on the expectation that the other party will perform a particular action important to the vulnerable party, irrespective of the vulnerable party’s agility to monitor or control that other party. Kim et al. (2004) define trust as the belief that the other party will behave in a dependable manner in an exchange relationship. Trust, in this study, is broken down into two constructs according to different trust subjects: (1) potential consumer trust is referred to as the initial trust that a potential customer has in an unfamiliar trustee, and (2) repeat consumer trust is referred to as the trust that a repeat customer has in a familiar trustee after having some transaction experience with the trustee. McKnight et al. (1998) also divide trust into two constructs: (1) trusting intention, meaning that one person is willing to depend on the other person in a given situation, and (2) trusting beliefs, meaning that one person believes the other person is benevolent, competent, honest, or predictable in a situation.

David Gefen and his colleagues’ (2003) construct a table which provides a summary of the prior conceptualizations of trust along with the trust object and measures used to operationalize the construct. As a result, the table shows that researchers’ view of trust can be separated into 4 divisions:

(1) A set of specific beliefs dealing primarily with the integrity, benevolence, and ability of another party.

(2) A general belief that another party can be trusted.

(3) Affect reflected in “feelings” of confidence and security in the caring response of the other party

(4) A combination of these elements.

In e-commerce contexts, when neither the buyer nor seller can be face-to-face with the other party directly, uncertainty exists all the time because of both parties’ unpredictable action and opportunistic behavior (Jang et al., 2005). Hence, the key to removing this kind of uncertainty and making successful economic transactions is avoiding opportunistic behavior (Hosmer, 1995). In this case, any general belief or feeling without evidence cannot be regarded as equivalent to “trust”. Real trust, therefore, can only exist if the consumer believes that the seller has the ability to provide and deliver goods of expected or better quality as a consequence of the consumer’s Internet shopping behavior. With such definitions and distinctions in mind, trust or consumer trust is operationally defined for our study as “a set of specific beliefs dealing primarily with the integrity, benevolence, and ability of another party.”

The trust object in Internet shopping malls is totally different from that in an offline store. That is to say, due to no seller-related management apparent during buying or selling in the Internet shopping mall, the subject of consumer trust becomes the Internet shopping mall itself (Jong & Lee, 2000). Thus trust plays an important role during the transaction in this uncertain and risky circumstance.

Vol.4, No. 3, 2007, p. 269-286

(1995) trust is a willingness of a party to bbee vulnerable to the actions of another party based on the expectation that the otototheheher party willl ppperererfof rm a particular action important to the vulnerable party, irresesespppective of the vulnerablee ppparty’s agility to monitor or control that other party. KiKiKim et al. (2004) define trust as tht e bebebelief that the other partywill behave in a deppenenendddable manner iiin an exchange relationship. TTTrust, in this study, is broken down intotoo two constructs aacccc ororordididinggg tttooo didid fffffferent trust subjbjbjecece ts: (1) potential rrconsumer truststt iis referreddd tttooo aaas the iinininitititiaala trururuststst ttthahahat t t aa a popopotetetential custtomomomer has in an unfamiliar ttrururustee, and (2) ) rrer pepp ataa consumer trust is reffferererreeed d d ttto as the trust t ththt at a repeat customer hhhas in a fafafamimm liar trusteeee aaaftftfteerer hhhavaviiningg g sososomemm transactionnn eeexpxx eriencee e with the trustee. MMcKnighghght tt etetet al. (((19191999898) also dividi e trust intto o twtwtwo cooonsnsstrtrtrucucucts: (1) trtt usting intentttiioion, meaaanininingngng thhat ononone e person is willingng to depend on ththhe ee otherrr pepepersrsrsonnn in a gigg ven situaaation, aandndnd (((2)2)2) truustststining beelil efs, meaning tthat one person n believvvesee the ooothththererer persooon n is beennnevoleentntn , , cococommpetetetene t, honest,t, or predictablee in a situatioon.n

DaDaDavivv d GeGeGefen and his cocolll eagues’ (22003) connststruct a table wwwhihh chhh ppprooovivividedd s aa asssummararary y y of ttthhehe prior conceptuualilizationss oof trustt aalong with the trtruuust obobobjejejectctct anddd meassururureseses useeed d too oopep rationalize thhe connsttruct. AsAs a result, tthehe tablelee shohohowswsws that reeeses archchcheeers’ vvview of truustst ccana be separa atated intto 4 ddivvisions:

(11) A AA set of ssspecific beliefs dedealalining g prp imimarariily wwith h the e inintetegrgrity, benevolenceee, and abilililityyy yoff anonoother para ty.

(2(2(2)) A geneneneraaall bebeliliefef tthahatt anothher party can be trusted.

(3(3(3))) AAAffect reflectteded iiinn n “f“f“feeeeeelililingnngs”s oooff f cococ nfnfnfidii ence aaanndnd sssecececuruuritityy ininin ttthehehe cccarara iningg response off f ththheeeotttheheher r r papp rty

(4))) A A A cococombmm ination of these elements.

InInIn eee-commmmememercrcrceee cococontntntexeextsts, whwhwhenenen neither theee bbbuyuyuyereer nnororor ssselelellelelerrr cacacannn bbbe fffacacace-e-e-tototo-f-f-face with ttthehehe othththererer pppararartytyty dddirrirececectltltly,y,y, uuunncncererrtatataininintytyty exxxisisistststs aaallllll ttthehehe tttimimme e e bebebecacacausususeee ofofof bbboth h h papaparrrties’ unpredddicicictatatablblblee e actionn aaandndnd oooppppppororortututunininistststic behehehavavavioioior (JaJaangngng eeettt ala ., 2220000005)5)5). Henccce,e,e, ttthehehe kkey to removing ttthihih s kikikindndnd of f f unununcertainty annnd d makingngg successful ecececonoo ommmicicic tttraransnsnsaaactions is avoiding opportu inistic behavior (Hosmmmer, 199995)55 . In this case, any general belief orfeeling without evidence cannot be regarded asa equivalent to “trust”. Real trust, therefore, can only exist if the consuumer believvves that the seller has the ability toprovide and deliver goods of expectteted or betttter quality as a consequence of theconsumer’s Internet shopping behavior. With suuuch definitions and distinctions in mind, trust or consumer trust is operationally deefefineddd for our study as “a set of specific beliefs dealing primarily with the integrity, benevooolennnce, and ability of another party.”

The trust object in Internet shopping malls is totally dit fferent from that in an


2.2 E-commerce Knowledge E-commerce knowledge means a customer’s knowledge of e-commerce related

technology and basic knowledge necessary for actually using e-commerce. This kind of knowledge includes the know-how and skill of accomplishing an e-transaction activity and using the various kinds of technologies and methods required to perform an e-transaction (Baek et al., 2006). E-commerce knowledge can be broken into two divisions: “skill” and “control or familiarity”. While “skill” refers to the Web consumer’s capacity for action during the online navigation process, “control or familiarity” comes from both the Web user’s perception of their ability to successfully navigate the Web environment and their perception of how the Web responds to their inputs (Novak et al., 2000). “Control” is considered as necessary as an external condition for a customer’s Internet shopping. “Control”, in this context, is similar to a kind of external equipment such as a computer used when the customer is connected to the Internet. While engaged in an online pursuit, the skills and familiarity of consumers must be above a critical threshold (Novak et al., 2000).

In this paper, e-commerce knowledge is defined as knowledge about product searching through the Internet shopping mall, the purchasing method, the payment process and the mall’s personal information protection policy, etc.

2.3 Perceived Reputation A customer may have perception of an Internet shopping mall’s reputation (Jong

& Lee, 2000). The perceived reputation of an Internet shopping mall provides assurances about the seller’s ability, integrity, and goodwill (Jarvenpaa & Tractinsky, 1999). It is the extent to which buyers believe that the selling organization is honest and concerned about its customers (Doney & Cannon, 1997). It also can be referred to as a customer’s recognition of the seller based on indirectly collected information from friends, relatives, colleagues, etc. (Lee & Lee, 2003). Doney & Cannon (1997) suggest that the costs to an Internet shopping mall acting in an untrustworthy manner are quite high for an e-vendor with a good reputation. In the China C2C market, high-reputation Internet shopping malls have occupied almost all of the entire market. Taobao.com has already taken 67.3% of the online auction market share in China (www.chinatechnews.com). Hence, it is considered necessary to study more deeply the relationship between perceived reputation and costumer trust.

2.4 Perceived Risk Perceived risk was first introduced in the field of consumer activity (Yang &

Baeg, 2003). Nowadays, even though there are a number of transactions that include safety mechanisms being developed, consumers still feel uneasy (risky) in participating in transactions in virtual space (Park, 2005). The past studies show two different views on the perceived risk: (1) the uncertainty on the result after online selection of a product or service, and (2) the expected loss that could result by the online selection. Perceived risk can be defined as consumer’s perceptions of the uncertainty and adverse


technology and basic knowledge necessasasaryryry fffororor aaacctually using e-commerce. This kind of knowledge includes the know-hohoowww and skill of accomommplplp ishing an e-transaction activity fand using the various kindddsss of technologies and methodsdsds required to perform an e-transaction (Baek et aaall.l., 2006). E-commerce knowledge ccananan be broken into two divisions: “skill” aaandndnd “control or familiarity”. While “skill”” rrrefers to the Web consumer’s cappacacaciiity for action durrrinini ggg tththe e e onononlill nenene nnnavigation prococcesee s, “control or familiarity” comomomes from bobobothhh ttthheh Webbb uuuseseser’r’r sss peppercrcrcepepeptititiononon ooof f f thththeir ability tototo successfully navigate thehee Web envnvnvironmemem nnnt aaand their perception of ff hohohowww the WeWW b responononds to their inputs (NoNoNovak ettt aaal.ll ,,, 2000). “CoCoCo tntntrorol”l” is s consnsidididerereredeed as neceeessssssarara yyy as an nn external conditioioion for a cucucustttoomomer’s IIInntnternet shoppingng. “Control”, ininin ttthis contntntexexext,t,, is simiiilalal r to a kind ooof exteteternnnalalal eeequipmemementt ssuch as a computu er used when tthehe cccusuu tomememer r r isisis ccconoo nectctctedee to hthe IIInternetetet. WhWhWhiile enenengaged inin an online pururssuit, the skillss aand fammmilii iaritytyty ooof ff ccoconsummmersmuuust be abababovovove a crrriititical threshholold (Novak ett aal., 2000).

InInIn ttthis ppapaper, e-commercrcee knowledgdge is deffinineed as knowledggee e abbbooout t t prprprodo ucct ttssearchhinininggg throoougugh h the Internet sshohoppp ing mmall, thee ppurchasing methohod,d,d theee pppayayaymemm nttt prprp ocesesessss anaa d thhhe mallll’s’s ppersonal infoformmationn pprotecctitioon policy, etcc..

22.3 PPPerceiveeed Reputation A AA cuuuststomomerer mmayay hhavavee pepercrcepeptition of an Internrnetet sshohoppppiningg mamallll’ss rrepeputuutatioioonn n (Jononng gg

& & & LeL e, 2220000000)0). The pepepercrcrceieieiveveveddd rererepupuputation of aaannn IInIntteternrnrnetetet ssshhopping mamamallllll proviiidededesssfaasassususurrar nces about thehe s llelllle ’’r’s babbilililititityyy, ininintetetegrgg ity,y,y, aaandndnd gggooddwililillll (J(J(Jarvenpaa & Tractinnnskskskyyy, 1999999999).).). It is the extent to which buyers belililieeveve e e tththat the selling organization is honesesest tt ananandd coooncncnceerernenened about its customers (Doney & CCaannon, 1997). It also can be referreed d d tooo aaasss acustststomomomererer’s rrrecececoggnition of the seller based on indirectly collected infoormrmrmaatiooon n n frfrfrooom friendndnds,s,s, rrrelelelatatativiviveseses,, cocolllleaeeagugugueseses, etetetcc. (((LeLeLeeee &&& Lee,, 202020030303).).). DDDonononeyeyey &&& CCCanannonononnn (1(1(19999997)7)7) sssuguguggest that thehehe cccososo ts to an IInterrnnnetetet ssshohohopping mamamallllll aaactctctinininggg in an unnntrtrtrususustwtwtwor hthy mannererer aaareee quite high forr aaan n e-e-e-vevevendndndororor wwwititithhh a a a gggood reppputuu ation.nn In thhheee CChina C2C2C2C CC mamamarkrkrketetet,, hihihighghgh-repepeputation Internet shohooppppppinininggg mamamallllllsss hhave occupied alaa most aaallllll of the entire mmmarararkekeket. TTTaoaoaobababao.com hasalready taken 67.3% of the ooonline auaa ction market share in China (www.chinatechnews.com). Hence, it iis considereede necessary to study more deeply therelationship between perceived reputatitition and cossstumer trust.

2.4 Perceived Risk Perceived risk was first introduced dd iinin the field of consumer activity (Yang &

B 2003) N d h h h b f i h i l d


consequences of engaging in an activity (Dowling & Staelin, 1994). It is a kind of subjective risk completely different from the objective risk that can be expressed as a probability. In other words, only if a consumer perceives the problem by himself/herself (subjectively) will the problem be evident to the consumer, even though the problem has been a real problem in reality for a long time (Yang & Baeg, 2003).

Several distinct types of perceived risk have been defined in the previous research. As Jarvenpaa & Tractingsky (1999) point out, a probable difference in physical goods with respect to quality as perceived by a consumer is a main type of perceived risk.

There are other types of perceived risk such as a consumer’s perceived wariness about possible leakage of credit card related information, a consumer’s perceived lack of feeling secure about protection of personal information (Ratnasingham, 1998; Jeong et al., 2004), a consumer’s perceived wariness about the possibility of no-refund or no-return after ordering (Jeong et al., 2004).

In addition, some other studies like one by Kim & Park (1999) classify perceived risk into a functional trust risk, a payment method risk, a contract related risk, and a delivery related. In their paper, Kim & Park define perceived risk as a consumer’s perceptions of the uncertainty and adverse consequences of engaging in an activity. The comprehensive definition by Dowling & Staelin (1994) includes the notions of a consumer’s perception of the probable difference of the quality of the physical goods, the possibility of no-refund and no-return on the order, danger from the payment method, lack of personal information protection, and the probable long delivery time.

2.5 Perceived Ease of Use (PEOU) In essence, a Web site is actually a kind of information technology. The

technology acceptance model (TAM) has been used in the study of IT acceptance for recent decades. Perceived ease of use (PEOU), a major construct of TAM, is an indicator of the cognitive effort needed to learn and to utilize the new IT (Gefen et al., 2003). Internet shopping enables customers to save their time and effort when purchasing desired products (Park & Yoon, 2001).

It is worth pointing out, in this context, that if a speedy connection to (Li et al., 2005), a quick search of (Shin et al., 2001), a convenient purchasing process at (Shin et al., 2001; Chi & Yeom, 1999), and a concise and quick payment at (Li et al., 2005) the Web site can be provided and available, customer’s PEOU in the Internet shopping malls occurs. Especially, fast access to Internet or a Web site may have a key influence on a consumer’s purchasing intention. We have extended discussion on this issue in the discussion and conclusion part of this paper.

3. RESEARCH MODEL AND HYPOTHESES

3.1 Research Model Based on the relevant concepts discussed above, a research model is suggested

to examine the direct and mediating effects of Internet shoppers’ trust on purchasing intention in China C2C market. It is shown in Figure 1.

Vol.4, No. 3, 2007, p. 269-286

Several distinct types of pepeperceived riskk hhhavavave been defined in the previous kkresearch. As Jarvenpaa & TTTrrractingsky (1999) point ououout,t a probable difference in physical goods with respspspeeect to quality as perceived by a cononnsuss mer is a main type of perceived risk.

There areee oother typepp s of perceeeiiivedede rrrisssk kk sususucchch aaas aaa cocc nsumer’s perererceived warinessabout possiblelee leakage of f crcrc edededititit card rrerelalalatett d dd innnfooformmmatatatioioion,n,n, aaa ccconsumer’s pepeperceived lack of feeling ssseeecure abououout protototececectttion of personal informatiiionn (((RaRR tnasssingham, 19191998; Jeonget al., 200000404), a conononsususummmer’s perccceieieivvevedd d warineess abob utut ttthehehe pppossibiliiitytyty oooff f non -refununnd or no-return aaaffter orddderererinining g g (J( eongngng eet t al., 2004).

In aaadddddditititioioion, sssoomo e otother studies liikke one by KKimim && PPPark kk (1(1(19999999)9)9) classss ify peerrcrceived riririsksksk iiinto aa a functionaall trust risk, a payment methhodo risk, a cococontractctct relelelata ed risisisk, annnd a deeelililiveeryrry relellaaated. In their ppapaper, Kim && Park definne e perceived riskskk as a aa cococonsnn umerr’s’’ ppep rceppptititionononsss of ttthheh uncertainty andnd adverse ccoonsequennceces of engaging in aaan actitiivivivitytyty. Theee ccomppreeehehehennnsivee e dedefinition by Doowlwling && SStaelin (1(1994) includes ththe e notititiononons ss of aa cococonsumumumeeer’s pppere ceptioion n ofo the probaablblee difffferrencee off the quality y ofof the phyhyhysicaaal gogogoods,ss thhheee possibiliitity of no-refufundnd and noo-rrete urrn onn thehe orderr, dadannger from ttthe paymenenent mmmethththod, lackkk of personal infforormamatitiono ppror tetectionon, aand ththee prproobable long delivvvery time.e.e.

2.2.2.555 Perceieieivevevedd EEase off UsUse e (P(P(PEOEOEOU)U)U) In essence, a WWebb site iiis acacactututualaa ly aaa kkkinininddd of infforma ition technology. TTThehehe

teeechchchnononology acceptance model (TAM) has bebebeeeen used in the study of IT acceptannncecece fffooor recececentntnt dddecee ades. Perceived ease of use (PPEOU), a major construct of TAMAMM, isisis aaan indididicacacatototorr r offf ttthehehe ccogggnitive effort needed to learn and to utilize the new ITITIT (((GGGefefefen n n eetet al., 200333).).). IIIntntntererernenenettt shshshopopoppippingngng eeennanablblblesees cccususustototomersrss tototo sssavaave thththeieeirr r titiimememe aaandndnd eeeffffffororrttt wwwhen purchahahasisiingngng desired produduduccctststs (((PPPark & Yooooon,n,n, 22200000011)1).

It iiisss wowowortrtrthhh ppo nintititingngng out, in this cocc ntext, ttthhahat if a speedddyyy cococ nnececectititioon tttooo (((Li et al., 2005), a quick search of (Shin et al., 20000001), a cooonvn enient purchasing process at (Shin et al., 2001; Chi & Yeom, 1999), and a ccconcise andd d quick payment at (Li et al., 2005) the Web site can be provided and availaaba le, custommmer’s PEOU in the Internet shopping malls occurs. Especially, fast access too Internet ooorr a Web site may have a key influence on a consumer’s purchasing intention. WWWe havee extended discussion on this issue in the discussion and conclusion part of this papppere .


Figure 1. Research Model

In this model, the theoretical relationships of all the variables have been incorporated in the Internet shopping context. A consumer’s intention to purchase products from Internet shopping malls is contingent on a consumer’s trust. Consumers are less likely to patronize stores that fail to create a sense of trustworthiness and an easily usable context. In the meantime, trust would also be influenced by e-commerce knowledge, perceived reputation, perceived risk, and perceived ease of use, all of which are set as independent variables in the model. Hence trust serves as a mediating variable while purchasing intention is a dependent variable. Based on this model, relationships among the variables are hypothesized below.

3.1.1 E-commerce Knowledge and trust As has been pointed out, e-commerce knowledge is the knowledge about

product searching through an Internet shopping mall, purchasing methods, payment processes, individual information protection policies, etc. To express the concept of e-commerce knowledge in another way, e-commerce knowledge can be equivalent to a familiarity which corresponds to how well a consumer comprehends a Web site’s procedures including when and how to enter credit and other required information in the e-commerce Web site (Gefen, 2003). More familiarity implies an increasing amount of accumulated knowledge derived from experience of previous successful interactions through the Web site (Gefen, 2003). Then the accumulated trust-relevant knowledge lead to higher levels of trust in turn.

In parallel, as we mentioned before, e-commerce knowledge is regarded as a kind of skill as well (Novak et al., 2000). This skill is similar to computer self-efficacy, which can be defined as an individual judgment of his/her capability in using an Internet shopping mall (Koufaris, 2002). Only when consumers perceive that their own skills are able to make them capable of performing Internet shopping can consumer trust occur. Otherwise, consumers may become bored or anxious (Ellis et al., 1994).

Therefore, with respect to e-commerce knowledge, we can reasonably hypothesize:

H: E-commerce knowledge will positively affect consumer trust in an Internet


FiFiFigure 1. Reses arch Mododdelelel

In ttthihihis s s momm del,l,l, the ttheoretical relatationships of alalll thhee e variiababableleesss hahahave bbbeen incococorporatededed iiin nn theee Internett sshopping contntext. A consusumtt er’s intntntenee tionono tttooo purchahahase prrrooducts fffrororommm Inteteternet shoppinng g malls is conontingent onn aa consumer’s s trust.t.t. CCConoo sumeerrsr aara e lesss lllikikikely tototo patronize storees s that fail too create aa ssense of trustworororthinininessss s ananand annn eeasily uuusasasabblb e ccocontntext. In the meanntitime, truustt would d alalso be influencedd bbby yy e-cocc mmmmmmerceee rrknk owwwleleledgdgdge, pppeere ceivveded rreputation, peercceivedd riisk, anand d perceived eaasese of use,ee, alll ooof f f whww ich hararre ee set as indddeependent vvarariaiablb es in thehe mmoddel. Heencncee trust seervrveses as a mediataating vavariabbblelele whwhwhilili e purchaaasing intention iiss a a dedependndennt vaaririabablele. BaBasesed d oon this model, rrrelationshihihippss ammmonnng g the vvavariables are hypothessizizeded bbele ow.

33.3.1.1.1.111 E-commmerercece KKKnononowlwlededdgege aaandn truruustss As has been pointed out, e-commmmememerrcr e knowledge is the knowledge aaabobobouutu

prrodododuucuct tt searching through an Internet shooppp ing mall, purchasing methods, papapaymymymeenent procococesesesseseses,s iiindndndividual information protection policies, etc. To express the cococonncepepeptt t ofofof e-commmmmmererercecece kkknononowlwwledede gegeg iin n annanototothehherrr wawaway,y,y, eee-commmmererercecece kkknononowlwlwledededgegege cccana bbbee eqeqequiuiuivavavalelelentntnt ttto afamiliiiarararititity y y whici hh h corressspopopondndndss to howww wwwelelell ll a a a cconsumererr cccomomomprp hhehenddds a WWWebebeb site’sprocedururureses iiincncnclululudididingngng wwwhehehen anananddd how tototo entererer credittt aaand othththererer reqqquiuiuirerereddd inininfofoformrmrmaaatioioionn in the e-commerceee WWWeb ssitititeee (G(G(Gefen, 2003). MMMore fammmiililiarity implies aaannn ininincrc easisisingngng amount of accumulated knowledge derived frommm experiennncecc of previous successful interactions through the Web site (Gefen, 2003). Then the aaaccumulated trust-relevant knowledge lead to higher levels of trust in turn.

In parallel, as we mentioned beeefore, e-cccommerce knowledge is regarded as a kind of skill as well (Novak et al., 2000))).. This sskill is similar to computer self-efficacy, which can be defined as an individual judgggmemeennnt of his/her capability in using an Internet nshopping mall (Koufaris, 2002). Only when nn ccconsumers perceive that their own skills are


shopping mall.

3.1.2 Perceived Reputation and Trust As reputation is a valuable asset, sellers usually try to avoid getting a bad

reputation. A vendor can signal its trustworthiness by building a good reputation. Reputation is a main characteristic of a vendor’s trustworthiness (Doney & Cannon, 1997). Reputation requires a long-term investment of resources, effort, and attention to the customer relationship. Since reputation represents a third-party or public opinion of the vendor, it must be transferred from the third party or the public to customers, influencing them to build trust in the Internet shopping mall owned by the vendor.

Not only a potential customer but also a repeat customer can build trust through the perception of the good reputation that an Internet shopping mall has. When a potential customer has little or no direct experience with the vendor, the third-party opinion about the trustworthiness of the Internet shopping malls can be a major source of information for trust building. Moreover, repeat customers estimate the trustworthiness of the Internet shopping mall by its reputation (Kim et al., 2004). The better the seller’s reputation, the more the mall has presumably committed resources to build that reputation and the higher the penalty when violating a consumer’s trust. Hence, the more trustworthy the seller is perceived to be (Jarvenpaa & Tractinsky, 1999). Doney & Cannon (1997) also find that a supplier firm’s reputation invokes the trust building-process and it serves as a significant factor on the calculative step and transference step of the trust-building process. Retailers are probably a big part of e-customers in B2B market. Their favorable perception of an e-vendor’s reputation leads to its increased credibility which is one of the two dimensions of trust (Ganesan, 1994).

At the same time, perceived reputation is regarded as a kind of indirect information. Indirect information cannot provide consumers with as much belief as direct information that is collected during an actual Internet shopping experience. However, for those consumers who are short of purchasing experience in using an Internet shopping mall, indirect information works as a sort of piece of key information to establish their trust (Lee & Lee, 2003).

In general, e-vendors with a good reputation recognize that there should be a great loss of reputation if they act unreliably. The recognition becomes much stronger especially in the case that the consumer network remains small-scaled and the opportunities for communication or interaction between consumers are increasing (Jong & Lee, 2000). Thus, we set up the following hypothesis:

H : The perceived reputation of an Internet shopping mall is positively associated with consumer trust.

3.1.3 Perceived Risk and Trust The Internet is an open, global, and constantly changing marketing channel. The

Internet shopping malls in the virtual channel makes it hard to inspect physical goods (Jarvenpaa & Tractinsky, 1999). Compared to the traditional offline marketing channel, an e-customer can get less assurance of transaction safety and product quality.

Vol.4, No. 3, 2007, p. 269-286

p , y y g greputation. A vendor can signal itititsss ttrustworthhhinininesesesss by building a good reputation. Reputation is a main characttterereriiistic of a vendor’s trustttwowoworthiness (Doney & Cannon,1997). Reputation requireseses a long-term investment of resourcececes,s effort, and attention to tthe customer relationsnsshihihip. Since reputututation represents a third-parrtytyty or public opinion of the vendor, it musususttt be transferred fffrror m mm thththeee thththirirird ppap rty or the pububublic to customers, influencing thememm to build trtrtrususst t t ininin the InInInteteernrnrnetetet shohoh ppppppinining g g mamamall oowned by thhhe ee vendor.

Not t ooonly a ppotoo entialall ccuuustott mer but also a repeat cussstototomemm r cacaan build trtrtrusuu t through the percepepeption of f f thththeee good repupuputatatatitition t ahatt an IInttntererernenenet shoppingngng mmmall has. When a potentiaiaiall customommeerer hhhas littltltlee e or no direct eexperience wiiththh tttheh vennndododor,r,r ttthe thirdrdrd-party opinioioion abbouoo ttt thththe ee ttrt ustwtwtwoorththiness of the Inteternet shopping mam llllllss s can bebebe aaa mmmaja or ssouoo rce of iinformamamatititiononon fororor trust bbuilding. Mororeover, repeaeatt custtomomomers esesestititimmmate the truuustworththhininineese s offf tthhe Internet t shs opping malll by its repuputtation (Kimm et alll.,.,, 2220000004). ThThThe bebebetter thehehe sseller’sss reputation, thehe mmore the mam ll has preresusumably commititittet d rereresososourururcecc s tototo bbbuild thththatatt repuututation and the hiighgher the ppeenalty whwheen violating a cononnsumememer’r’r’sss trt ust.. HHenccce,e,e, ttthhhe mmmoore trtrusu tworthy the seelll er iss pperceivvedd to be (Jarveenpnpaa &&& Trararactctctinininsky, 199999))). DDDoneyyy & Cannononn (1(1997) alsso o fif nd thahat a suuppplier firm’m s s rreputationnn invvvokokokesese thehehe trrususust buildingngng-process andd iit t seserves aas aa ssignniffiicanant factctoror oon the calculattiivi e step aaandndnd trrransfss erencee step of the trust-bubuilldidingng pproocessss. ReRetataililererss are probably a bigig part ofofof eee-ucuustommmers innn B2B market. Their ffavavoro abable perceeptiionon of an e-vendor’s repuuutationnn leaddsd

tototo iiits incccrerereassseded ccreredidibibililityty whihi hch is one of the two dimensiions off trtrusust t (G(Gananeeesannn, 199444)...

At the samamee e tititimememe, pepercrcceieieiveveved dd rerr putatiiiooon isisis rrregegararrdedededdd asasas aa kkiind of indiririrececectt innnfofoformrmrmation. Indirect information cannototot ppprovovoviidide consumers with as much belieieiefff aaas diiirererectctct iiinfnn ormation that is collected duringng an actual Internet shopping expppeererieieiencncnce. Hooowwewevevever, ffforo those consumers who are short of purchasing experience iiinnn usssinininggg an rrInteernrnrnetetet ssshohohoppppppini g g mamaallllll, iinindididirre tct iiinfnfnfororormamamation workrkksss asasas aa sortt t fofof ppieieiececece oofff kkkey y y inininfofoformrmrmatatation to estttababablililishss thhheiririr tttrururuststst (((LeeLee e e &&& LeLeLee, 2220000003)3)3).

InInIn ggenenenerereralalal,,, e-e-e-vevevendndndorororss with a gggood reputaaatttioioion recogngngnizizi e thththatatat ttthehehererere ssshohohouuuld be a great loss offf rererepupuputatatatititioonon if they act unreleleliiably. TThThe recognition bebebecococomememesss mmumuch stronger especially in the case that the consnsnsumer netetetwork remains small-scaled and theropportunities for communication or intteraction beeetween consumers are increasing (Jong & Lee, 2000). Thus, we set up the follooowing hypoootthesis:

H : The perceived reputation of an Intererrnet shopopopping mall is positively associated with consumer trust.


Moreover, when problems arise, sellers will probably attribute them to technical problems or systems problems. All these increase the uncertainty and may result in adverse consequences for those engaged in transaction activities. When perceived risk is present and it is increasing, consumers’ trust will suffer.

Ratnasinghams (1998) argues that if e-commerce is to become the dominant business platform of the next millennium, systems should be integrated and the transaction process should be standardized to ensure that any business transaction is operated in a secure way. By addressing every aspect of a consumer’s concern, the consumer can place complete reliance on the confidentiality, security, integrity, and availability of information processed and stored.

H : Perceived risk will reduce consumer trust.

3.1.4 Perceived Ease of Use and Trust (PEOU) PEOU is one of the major constructs of the technology acceptance model

(TAM). Improving PEOU is an issue that has received attention of researchers for over a decade. Many researchers cited in this paper have agreed that TAM can be used to study the purchasing intention by treating an Internet shopping mall as a technical system and the consumers as technology users. When consumers believe that a Web site is easy to use and thus have perceived ease of use, they are more likely to go shopping to that Web site (Koufaris, 2002).

A considerable amount of restrictions that used to exist in traditional transactions have disappeared in virtual space. Thus, Internet shopping becomes a new powerful kind of transaction (Kim & Park, 2002). The easier executing this kind of transaction is made for consumers, the more transactions will be likely to occur (Gefen et al., 2003). In the meantime, Venkatesh & Viswanath (2000) suggest that individuals attempt to minimize effort in their behaviors, which supports a relationship between consumer’s PEOU and their usage behavior. PEOU in Internet shopping malls is defined as a catalyst for the revitalization of e-transaction (Han & Noh, 1998). Ko & Mok (1999) also suggest that the PEOU is a key factor of consumer satisfaction.

H : Perceived ease of use will positively affect consumer trust.

3.1.5 Trust and Purchasing Intention In Jarvenpaa & Tractinsky (1999)’s research, trust is described as having an

indirect influence on purchasing intention. As the researchers suggest, a consumer’s willingness to buy from an Internet seller is contingent on the consumer’s attitude towards the store. This attitude, in turn, is affected by the seller’s ability to evoke consumer’s trust. However, most other prior researchers refer to trust as a key factor that directly has a great influence on purchasing intent. Trust helps reduce the social complexity that a consumer faces in e-commerce by allowing the consumer to subjectively rule out undesirable yet possible behaviors of the e-vendor including inappropriate use of purchase information. In this way trust encourages business activities of online customers (Gefen et al., 2003). According to reasoned action theory,


Ratnasinghams (1998) argggueueuesss thththat if e-cooommmmm erce is to become the dominant business platform of the nexexexttt millennium, systems ssshohh uld be integrated and the transaction process shouuldldld bbe standardized to ensure that ananany yy business transaction is operated in a secure wwway. By addreeessing every aspect of a coonsnsnsumer’s concern, the consumer can plaaaccece complete reliannnccce oon nn thththeee cococonfffidii entiality, securururiti y, integrity, and availability of ininnffformationnn prororocececessed aaandndnd ssstottorerered.d.d

H : Perceiveeeddd risk wililill reduuuccece ccconoo sumer trust.

3.1.4 PPPerceiee veeeddd EaEaEase of f f UUsUse e and Trust (PEOE U) PEOUOUOU is ononnee of thee major construructs of the ttece hnologgy y y accececeptptptananance modododel

(TTTAAAM). IIImpmpmprror vingngng PEOU is an n issue that hhaas received aattttention of reesses archchcherererss s for ovvveree aa a decadedede. MaMM nyyy researchers citeded in this ppapper have agagreed that TAM M M can nn bbbe uuusess d tooo ssstudy thththe e e purccchahasing intention bby y treatingg an Interernnet shopping malall l asaa aa teccchnhnhnicalll syystememm aaannnd thehehe consusumem rs as technonolologyg usserrs. Whehenn consumers bebelilieve ththhat a WWWebebeb siteisss easy tototo useee and thus hahaveve perceiveded eeasee oof uusse,, they are mmorore likely too go shohohoppinnnggg tooo ttthahh t Web ssis te (Koufaris, 20200202).).

A ccoconsiderable amount ofo rreestrictionnss thhatat used to exist inn traditititiiionaaal trtrtranansactttioioions hhavavee didisasappppeaeareredd iinn vivirtrtual space. Thuh ss, IInndd teternrnetet sshohoppppining g bebecocomeeesss a neew w wpopopoweww rful kkkininind d of transacacctititiononon (K(K(Kimimim &&& Park, 2002)2)2). ThThThe e eaeaeasisisierere executingng ttthhhiis kindd d ofofof trrrananansssaction is madde fofof r consumerss, ttthehehe mmmoro e trrrananansasasactctctiioions wililillll bbbe lllikikikelly to occur (GeGeGefefefennn ettt aaall.l., 2003). In the meantime, Venkatesh hh & & & ViVV swanath (2000) suggest that indivivividududualalalsattetetempmpmpt tt tott minimize effort in their behavioro s, which supports a relationship bbbetetetwewew eeen consnsnsumumumerere ’s’s’s PPPEOU and their usage behavior. PEOU in Internet shoppppppininingg mamamalllllls s s is definenened d d asasas aaa cccatatatalalalysysy tt fofoforrr thththeee rererevivivitatatalililizazazatititionono of e-e-e-trtrtrananansasasactcctioioionnn (H(H(Hananan && NNNohohoh, 191919989898).).). KKKoo & Mok (1(1(19999999)9)9) also suggest t thththatatat ttthhhe PEOUUU isisis aaa kkkeyeyey fffactor offf ccconononsususumem r satisfactiononon..

H : Perceieieivevev ddd eaeaeasesese ooofff use e e wiwiwilll positivelelely yy affect conononsumer trusttt.

3.1.5 Trust and Purchasing Intentionn In Jarvenpaa & Tractinsky (199999)’s resseeearch, trust is described as having an

indirect influence on purchasing intentitition. Asss the researchers suggest, a consumer’swillingness to buy from an Internet selllll er isiss contingent on the consumer’s attitude towards the store. This attitude, in turn, is s affected by the seller’s ability to evokeconsumer’s trust. However, most other priooor researchers refer to trust as a key factor


Internet shopping activity can be referred to as a kind of intentional activity phenomenon affected strongly by consumer trust as well (Jong & Lee, 2000). Some other studies also suggest that there exists a direct and significant relationship between trust and purchasing intention (Jang et al., 2005; Yu & Choe, 2003; Yoon, 2000).

Based on most of the prior studies that have demonstrated the direct influence of trust on purchasing intention, the following hypothesis is suggested in this study:

H : Higher consumer trust on an Internet shopping mall will generate more purchasing intention in that Internet shopping mall.

3.2 Operationalization of the Research Variables Operational definitions and measurements of the research variables are provided

in Table 1. We developed outcome variables based on relevant literature. The variables were measured by a seven-point Likert scale (1=strongly disagree, 7=strongly agree).

[Table 1] Operational Definitions and Measures of the Variables

Variable Operational Definition Measurement Reference

Trust

A set of specific beliefs dealing primarily with the integrity, benevolence, and ability of another party’

1. Effort to meet customer satisfaction 2. Customer care 3. Trust worthiness 4. Effort to provide high quality service

Gefen et al., 2003

E-commerce Knowledge

A kind of knowledge about product searching through the Internet shopping mall, purchasing method, payment process and individual information protection policy, etc.

1. Familiarity with the online shopping process 2. Familiarity with the online shopping method 3. Familiarity with the online payment process 4. Familiarity with the online returning process

Baek et al., 2006

Perceived Reputation

A customer’s perception of the Internet shopping mall’s reputation

1. Good reputation 2. Effort to make itself regarded favorably 3. Good image

Jong & Lee, 2000

Perceived Risk

A consumer’s perceptions of the uncertainty and adverse consequences of engaging in an activity

1. More risk compared with other ways of shopping 2. Low security 3. Difference between the purchased product and the product actually delivered 4. Low product quality 5. Harm to human body 6. Uncertainty associated with Internet shopping

Dowling & Staelin, 1994

Vol.4, No. 3, 2007, p. 269-286

Based on most of the prior studies thahat have demonstrated the direct influence of trust on purchasing intention, the fofofolllllloowing hypothhhesesesisisis is suggested in this study:

H : Higher consumer trust ononon an Internet shopping mall wwililillll geg nerate more purchasing intention in that Internetetet shopping mall.

3.2 Operationananalization of f f ththheee RRResearrrchchch VVVarrriaaablblbleseses Opppeeerational dededefinitionono s and mememeasasasurururemememenenentststs oof the reseseearch vvvarriai bles arerere provided

in Tableee 11. We dddevevevelelelopopoped outututcococomme variableses based on rrelelelevee ant liiteteteraraatututurerr . The vvvariables were mmmeasureeed dd bybyby aaa sevvenenen--point Likert scala e (1=strongly dididisasasagrg ee, 7=7=7=stststrrongly aaagrgg ee).

[T[ able 1] Operattioionan l Definitioonss and Measurreses of the Variableesss

VVaVariabbblelele OOpO erattioionanal Definition Measuuremement ReReRefefeferererence e

TrTrTruusu t

A set of specific beliliefe s dedealinng gpprimarily with the integrirityty, bbenevolence,, and abilityty ooff ananother ppaartrty’y’

rr 1. EEfffort toto mmeet customersasatisffactionon 2. CCususttomemerr care 3. Truustst wwororththiness

y y 4. Effort toto ppprovividede hhigighh ququalalitityyservvicicice

Gefen n n eet al..., 2202003

E-cooommmmmmererercecc Knowwwleeedgdgdgee e

t tA kind of knowledge about producttsearching through t the Internet

shshshopopoppipipingngng mmmalalalll,l, pppuruurchchasassinining gg method, ppppapapaymymymenenenttt prpprocococessess s and d d ininindididivivividududual

information prprprotototececectitition policy, etctctc..

1.1.1. FFFamiliarity with the onlinesshopping process

2. Familiarity with the onlineshopping method

3. FFFamamamilililiaiaiarirritytyty wiitithhh hththe onlineneeppppapapaymymymenenentt prococcesesessss

4.4.4. Fammmilililiaiaiaririritytyty wititithh h thththe onlinereturnnniining process

aBaaekekek eeettt aaal., 202020060606

Perceived Reputation

A customer’s perception of theee Internet shopping mall’s reputtation

1. GGGood reputation 2. Effffffort to make itself regarded favoraaably 3. Gooood image

Jong & Lee,2000

Perceived Risk

A consumer’s perceptions of theuncertainty and adverse

f i i

1. MMMore risk compared with other waayyys of shopping 2... Low security 333. Difference between the ppurchased product and the product

t ll d li dDowling &

St li 1994


PEOU An indicator of the cognitive effort needed to learn and to utilize the new IT

1. Time and effort saving 2. Convenience of doing Internet shopping 3. Brief and easily- remembered URL 4. Effort to provide concise and quick payment methods

Gefen et al., 2003

4. RESEARCH METHOD 4.1 Data Collection

To test our research model, a survey was conducted. In this survey, all the participants answering the questionnaire are Chinese who live in China, regardless of whether they had had an Internet shopping experience before or not. Respondents were not informed about the objective of this survey. This survey lasted for 4 months, from Dec. 2006 to Mar. 2007. Among the 187 respondents, 72.1% said that they frequently had Internet shopping experience with Taobao.com; 36.4% with Ebay.com.cn; 28.9% with Paipai.com; 49.2% with Dangdang.com; 11.2% with Joyo.com; 36.4% with others; and 3.2% had no Internet shopping experience at all. These are the result of multiple selection. The demographics profiles of the 187 participants, taking part in the survey, are summarized in Table 2.

[Table 2] Sample Characteristics (n=187) Measure Items Frequency Percent Measure Items Frequency Percent

female 101 54% <1year 0 N/A

Gender male 86 46%

1-3 years(3

included) 36 19.30%

<20 10 5.30% 3-5 years 50 26.70% 20-30(30 not

included) 108 57.80%

Years of Internet

experience

>5 years 101 54%

30-40(40 not included) 40 21.40% never 0 N/A

40-50 18 9.60% <5 times 61 32.60%

Age

>50 11 5.90% 5-20 times 100 53.50%

employee 75 40.10%

Internet shopping

experience

>20 times 26 13.90%

self-employed 3 1.60% once a month 52 27.80%

student 78 41.70% 1-10

times per month

89 47.60%

professional 12 6.40%

Frequency of access to an Internet Shopping

site >10 times per month 46 24.60%

housewife 7 3.70% home 83 44.40%

Profession

other 12 6.40% office 57 30.50% dial-up 30 16% school 39 20.90%

broadband or leased line 148 79.10% PC cafe 6 3.20%

Internet access method

others 9 4.90%

Internet access place

others 2 1%


d 4. EEEffffffororort to provide concise andquick paymmenenent t methods

4. RESEARCH MEEETTTHOD 4.1 Data Collectctctiion

To ttteest our rerr searchchch mmmooodel, a survey was condndnducucuctett d. In nn this surrvvev y, all the participannntts answeeeririringngng the questttioioionnnnnaiaire arere ChChininesessee e whww o live in nn ChChChina, regarara dless of whetheeerrr they hadadd hhhadadad an Innnteteterrnet shopping eexperience befofoforerere or not.t.t. RRResesespopp ndennntstt were not innnfformmedee aaaboboboutu theee ooobjbjeective of this survrvey. This surveey y lalaastststed fffororor 444 mmmonoo ths, ffrom Deccc. 2006 tototo MMMar. 202020007. Ammono g the 187 rerespondents, 72.2.11% saiid dd that tttheheheyyy frfrfrequeentnn ly haaaddd Internrnrnetetet shoppppppiini g experienence with Taobobao.com; 366.4.4% with EEbababay.cooom.m.m.cncncn; 28.999%%%wwiw th Paiaiaipapapai.com;m;m; 449.2% with DaDangngdang.comm;; 11.2% wiwithth Joyo.com; 36.6.6.4% wwwititith h h ototo hersss;aaand 3..2%2%2% hhhad nnno Internet shoppining g experiiennce at aallll.. These are the resususult oof f f mumum ltl ipleee selectttioioion.n.n. Theee demmogographics profililess of thee 187 paparttiicipants, takingng ppart iiin thhhe e e susurvey, ararre suuummmmmmarizzzeed in Tablble e 2.2

[T[Tabablel 2] Sammplele CCharractcteerisistics (n=18187)7) MeMM asssuruu e Items Frequeuencn y y PePercceent MMeasasuure Items Frequeeency Peeercenenent

female 101 5454% <1year 0 N/AA A

GeGGendnn er male 868686 46%

11-33yeyeyearara s(s(3

inininclclcludududededed) ))36366 199.3.3.30%0%0%

<20 10 5.30300%% % 3-5 years 50 262626.777000%20-30(30 not

included) 108 57.80%

YeYeYeararars of Innnteteternrnrnetetet

experience

>5 years 101 545454%

30303 -40(0(0(404040 nnnototot inininclclcludududededed))) 404040 212121.4.4.40% neneevevverr r 000 NN/N A

40-50 188 9.606060% % % <5<5<5 tttimimimesee 611 32.60%

Agggee e

5>5>5000 11 5.55 90% 5-5-5-20times 10000 53.50%

employee 75 404040.10%

IIInternet shopopopppiping

exexxperience

>20 times 26 13.90%

self-employed 3 1.60% once a month 52 27.80%

student 78 4111.7.. 0% 1-10

times per month

89 47.60%

professional 12 6.40%%%

Frreequency offf access to aaan Internet Shopping

site >10 timesper month 46 24.60%

housewife 7 3 70% home 83 44 40%

Profession


4.2 ANALYSIS AND TESTING 4.2.1 Reliability and Validity

The internal consistency reliability of the variables was assessed by computing Cronbach's alphas. The Cronbach's alpha values of all the variables, ranging from 0.768 to 0.876, were well over 0.700, which was considered satisfactory for measures (Nunally, 1978).

Each variable was measured with multiple items. A factor analysis was conducted to check their unintentionality. To test for the construct validity of a variable, a principal-component factor analysis was conducted with a VARIMAX rotation. The results of factor analysis are shown in Table 3. Most of the factor loadings for the items appear above 0.6. The items were collected corresponding to each singular factor demonstrating a high convergent validity. As the factor loadings for a variable (factor) are bigger than the factor loadings for the other variables, it supports the instrument's discriminant validity (Chin, 1998).

[Table 3] Factor Analysis Results for Variables

Component 1 2 3 4 5

Cronbach’s Alpha( )

RISK6 RISK7 RISK8

RISK12 RISK13 RISK4

.779

.747

.745

.710

.691

.536

-.148 .020 .106 -.286 -.143 -.175

-.175 .033 -.163 -.113 -.149 -.325

.016 -.075 -.068 .023 -.327 -.043

-.006 -.279 -.078 .017 .176 -.299

0.829

ECK1 ECK2 ECK3 ECK4

-.052 -.082 -.128 -.175

.816

.799

.794

.719

.286

.273

.282

.016

.261

.250

.210 -.039

.084

.154

.027

.250

0.873

TRU5 TRU7 TRU6 TRU4

-.180 -.150 -.175 -.175

.145

.213

.299

.158

.797

.774

.731

.711

.140

.213

.320 -.039

.224 -.030 .142 .196

0.854

PEOU2 PEOU1 PEOU5 PEOU3

-.069 -.201 -.045 -.005

.021

.118

.136

.322

.260

.330 -.065 .084

.786

.714

.645

.627

.160

.233

.236

.092

0.768

REP1 REP2 REP3

-.005 -.132 -.124

.239

.116

.084

.072

.176

.268

.155

.276

.283

.836

.820

.777 0.876

Eigenvalue 7.382 2.434 1.855 1.403 1.293 Percentage of

Variance Explained 35.153 11.59 8.834 6.679 6.158

Cumulative Percentage 35.153 46.743 55.577 62.256 68.414

-

Vol.4, No. 3, 2007, p. 269-286

The internal consistency reliabibibilililitytyty oofff ttthehehe variables was assessed by computing fffCronbach's alphas. The Cronbacch'h'h sss alpha values of allllll ttthehh variables, ranging from 0.768to 0.876, were well overr 000.700, which was considerededed satisfactory for measures (Nunally, 1978).

Each variaaabblble was measurededed wwwititith mumumultltl ippplelel items. A fafafactcc or analysis wasconducted to cheheheck their uuuninn ntntntenenentionaalililityyy. ToToTo tesesest foffor r r thhheee cocc nstruct validididitytyty of a variable,rra principal-cococomponent factctctororor aanann lysis was conductett d dd wiwiwiththh aaa VARIMAX rrroto ation. The rrrresults of fffaactor analalalysysysis areee shown iinn n TaTaTablblblee e 333. MMMososost of the fffactor llloadada ings fororr the items appear aaabbbove 000.66.6. ThThThe itemmmsss wwere collectcted corresppononondidid ng to oo eaaachchch singulalaar rr factor ddemonnnstrating g g aa a hihihighhgh connnvvevergent validity. AsA the factor loadididingngngs for rr a aa vavavarir able (((fafaf ctor)are bbbigger ttthahahann n thththe fafaacctctor loaoadings for the oother variables,s, it suupppppports thehehe iiinsnsnstrummmenee t'srrdiscscscriminantntnt vvvalaa iditttyy y (Chin, 199998).

[Table 3] Faactc oror Analyysis Resultlts fofor VariablesCComponenent t

1 22 3 4 55CrCronbabaach’s Alpha(a(( )

RISK6 RISK7 RISK8

RIRISKSK1212RISK13RIISKSKK4 44

.7.7797 7.74747

.745 7.71010 66.6919191 5.5363636

--.1448 .002020 .11006 -.286 -...143 -.-..17775 55

--.1775 .0.0333 -.16633-.113 -.149 -.32323255

.0166 -.070755-.068 0.02323

-.323232777-.04040433

-.006 -.279 -.078 0.01717

.1.1.17676 -.292929999

0.8299

ECK1 ECK2 ECK3 ECECECK4K4K4

-.052-.082-.128-.17177555

.81666

.799

.794

.7.7. 19

.2.2.28886

.273

.282

.016

.261

.250

.210 -.0300399

.084

.154

.027 22.2505050

0.873

TRTRTRU5U5U5 TRU7 TRTRTRU6U6U6 TRTRTRU4U4U4

-...181818000 -...151515000 -.175-.175

1.1.1454545 .21333 .2.2.299 .155858

.7.7.7979797 7.7.7774

.7311

.71111

1.11404040 22.2131313

.320 -.039

22.224224 -.030303000 1.1.14244

.196

00.0.858585444

PEOU2 PEOU1 PEOU5 PEOU3

-.069-.201-.045-.005

.000221

.1118

.11136

.332222

.26000

.330 -.06555.08444

.786

.714

.645

.627

.160

.233

.236

.092

0.768

REP1 REP2 REP3

-.005-.132-.124

.2393939

.11666

.084

.07772

.11176

.268

.155

.276

.283

.836

.820

.777 0.876

Eigenvalue 7 382 2 434 1 855 1 403 1 293


· RISK : Perceived Risk · ECK : Electronic Commerce Knowledge · TRU : Trust · PEOU: Perceived Ease of Use · REP : Perceived Reputation

4.2.2 Hypothesis Testing The simple correlation among all of the research variables is shown in Table 4.

A Pearson correlation was calculated for the variables measured by interval or ratio scales. Before conducting the multiple regression analysis, we checked potential multicollinearity among the antecedents since some variables were significantly correlated to others. Though several variables showed significant correlations, their tolerance values, given by the SPSS 12.0 statistical application, running on a Windows PC, ranged from 0.387 to 0.956, indicating that multicollinearity was not a likely threat to the parameter estimates (Hair et al., 1995).

[Table 4] Correlation Analysis between the Research Variables (n=187)

1 2 3 4 5

1. E-commerce Knowledge -

2. Trust .523*** -

3. Purchasing Intention .432*** .608*** -

4. Perceived Ease Of Use .448*** .473*** .520*** -

5. Perceived Risk -.329*** -.440*** -.399*** -.289*** -

6. Perceived Reputation .393*** .414*** .362*** .506*** -.276***

***p<0.01

Table 5 shows the results of the multiple regression analysis testing the five hypotheses. The results indicate that the two regression models were significant at the p<0.01 level (F-value=32.222 and 108.708). Also, the predictors of each model explained 40% and 37% of the total variance, respectively. In Hypotheses H1- H4, e-commerce knowledge, perceived risk, and perceived ease of use were significantly related to trust ( =0.301, p<0.01; =-0.093, p<0.01; =0.203, p<0.01).

However, perceived reputation was not significantly related to trust. Hence, Hypothesis H2 was not one of the supported hypotheses; while the other hypotheses were supported. In Hypothesis H5, trust was significantly related to purchasing intention ( =0.608, p<0.01). Consequently, Hypothesis H1, H3, H4, and H5 were


· REP : Perceived Reputation

4.2.2 Hypothesis Tesstititinng The simppplelele correlation amonggg allllll ooof thththe e e rerereseaeaearccch h variables is shshshown in Table 4.

A Pearson corrrrrrelation waaass s cacacalclclculateddd fffororor thehh vvvaraariaaablblleseses mmmeaeaeasured by iintntnterval or ratio scales. Befofofore conducting g g thththe ee multiple regression anananalalalysysysis, weww checkkkedede potential multicolliiinnnearity ammamononong the annntetetecceceddede tnts isince e sososomemm variableseses wwwere signgngnificantly correlatatteeed to ottthehehersrss... TTThouuughghgh several variaabbles showed d sisisigngngnificannt t t cococorrrrelationsnsns, their toleraaannnce vavavaluuueseses,,, gigg ven bybyby ttheh SPSS 12.0 statatistical applicattioion,n,n rrunnininingngng ooon n n a aa WindndndowsPC,, , rangeddd fffrororomm 0.383838777 to 0.99565 , indicating tthat multicollinenearity wawawas nooot t t a lililikekek ly thrhrhreat to ttthe parrramamametee er estststimi ates (Haairir et al., 1995)..

[T[Tabablele 44] ] Correlation AnAnallysysis betwween the ReReseearch Variables ((n=n=18187)7))

11 2 33 4 5

1. E-cccommerce Knowledgee --

2. TTTrururusts .5.5.523*** -

3. Purchasing Intention .43222******* .6660008*** -

4.44 PPPerererceived Ease Of Use .448*** .473*** .520*** -

5.5.5. PPPerceceivivivededed RRRisisiskk -.3232329*9*9***** .-.-.4444440*0*0******* -.3939399*9*9******* -.2828289*9*9 ***** -

6. PPPerere ceeeivivivededed RRRepepputututatatatiion .3393939 *** .4141414*** .362******** .5. 060606********* -.2727276*6*6***

***p<0.01

Table 5 shows the results of tttheh multiplplple regression analysis testing the five hypotheses. The results indicate that thhhee e two reeeggression models were significant at the p<0.01 level (F-value=32.222 and 1088.8 708))). Also, the predictors of each model explained 40% and 37% of the total variaaanncecee, respectively. In Hypotheses H1- H4, e-commerce knowledge, perceived risk, and dd perceived ease of use were significantly


supported.

[Table 5] Results of the Hypotheses Tests (From H1 to H5)

Model R2 adj. R2 F

Standardized coefficient

( ) Tolerance Results

(1) Trust (TRU) TRU = ECK+REP+RISK+PEOU+errors ECK (E-commerce Knowledge) REP (Perceived Reputation) RISK (Perceived Risk) PEOU (Perceived Ease of Use)

0.415

0.402

32.222***

0.301***

0.125 -0.093*** 0.203***

0.730 0.701 0.855 0.663

H-1 was supportedH-2 was not supportedH-3 was supportedH-4 was supported

(2) PI (Purchasing Intention) PI = TRU+errors TRU (Trust)

0.370

0.367

108.708***

0.608***

1.000

H-5 was supported

** p < 0.05, ***p < 0.01

5. DISCUSSION AND IMPLICATIONS 5.1 Discussion

This study has found that trust has a strong significant relationship with purchasing intention. In other words, greater consumer trust motivates customers generate more intention to purchase in the Internet shopping mall. We also confirmed that trust should be built first if a Chinese e-vendor wants to improve its Web site’s competence.

While a few previous studies have proposed e-commerce knowledge as an important trust-building factor for Internet shopping malls, this study found it as a key antecedent of trust. This finding makes sense because of the fact that in China, unlike in Korea or in other advanced economies, only a fraction of the Internet users have a clear idea about the e-shopping method and processes involved to make them have strong trust on an Internet shopping mall. Other Chinese Internet users probably doubt an e-shopping Web site due to lack of e-commerce knowledge. In other words, the amount of e-commerce knowledge directly influences Chinese consumers’ judgments on an Internet shopping mall’s trustworthiness. On the other hand, in some developed e-markets such as Korea, U.S.A, and Japan, almost every Internet user has some amount of e-commerce knowledge, so few studies treat e-commerce knowledge as a research variable in trust context.

In addition, we found that perceived reputation does not have a statistically significant relationship with trust while e-commerce knowledge, perceived risk, and PEOU do. It is probably because several largest Internet shopping malls have occupied

Vol.4, No. 3, 2007, p. 269-286

** ppp << 000.0.0.05,5 ******p < 0.0 0101

Model RR2 adadadjjj. R2 FFF coefficient

( ) Tolerance Results

(1) Trust (TRU) TRU = ECK+REP+RISKSKSK+PEOU+++eererroroorsrsrs ECK (E-commmmerce Knowledgdgdge)e)e) REP (Perceeeiivived Reppputututatatatioii n) RISK (Peeerceived RiRiRisksksk) ) ) PEOU (((PPerceiveveveddd EaEaEase of f f UUsUse)e

0.0 415

0.0.0.404040222

323232.2.2.2222222*****

0.0.0.3030301***

0.00 1211 5 -0.093*****

0.0 202020333***

0.7373730 0.701110.00 855 0.0.0.6666 3

H-1 was supportedH-2 wasnot supportedH-3 wassususupportedH-4 44 wassupppporo ted

(2))) PPI (Pururrchchchaasa ing InInIntention) PI I I = TRU+U+U+errorss TTTRU (TrTrTrususust)tt)

0.0.370

0.3677

1108.708***

0.608***

1.1.000 0

HHH-5 waaas suuupppppporteeed

5.5.. DISSSCUCC SSSSION AND IMPLICATTIOI NSN 5.5.5.11 Discussisisioonon

This study has found that trtt ususustt t haasss aaa strong significant relationship wiwiwiththth puuurcrcrchahahasiss ng intention. In other words, grrreaeaeater consumer trust motivates cussstototomememerrrs geneneneraaatete mmmore intention to purchase in the Internet shopping mall. We also ccconononfififirmmrmeed thattt tttrururuststst sshoh ulululddd bebebe bbbuiuiuiltltlt fffiiirsrrstt ififf aaa Chinese e-vendoorr r wawwantnttsss tototo iiimpmpmprororoveveve iiittts WWWebebeb sssititite’scompppetetetenenenceee.

WhWhWhililile ee aa a fefefewww prprpreeevioooususus studididiesee havvveee propppoosos ded ee-c-c-comoo mememercrcrceee knknowowowleleledgdgdgeee as an important trtrtrususust-bbbuiiildidingngng fffactor for Internrnrnet shopppppping malls, thihiis ss stststuduu y y fffoununnddd iitit as a keyantecedent of trust. This finding makes sssense beeccac use of the fact that in China, unlike in Korea or in other advanced economiesss, only a fraacaction of the Internet users have a clear idea about the e-shopping method andndnd processesss involved to make them have strongtrust on an Internet shopping mall. Otthheh r Chineesese Internet users probably doubt an e-shopping Web site due to lack of e-commmmerce kknnnowledge. In other words, the amount of e-commerce knowledge directly influennnces Chinese consumers’ judgments on anInternet shopping mall’s trustworthiness. OOOnn the other hand, in some developed e-markets such as Korea, U.S.A, and Japan, aallmost every Internet user has some amount


most of China’s C2C and B2C markets (Tabao.com, Ebay.com.cn, Paipai.com, Dangdang.com, and Joyo.com) that a majority of e-consumers purchase products only through these big Web sites. As a consequence, it makes it difficult for consumers to identify whether there are any distinct effects on trust caused by reputation or not. Furthermore, we came to understand by the demographic statistics above that most respondents were customers with prior e-shopping experience (repeat customers). According to Kim et al. (2004), reputation usually has a weaker effect on repeat customers’ trust than on potential new customers.

Regarding perceived risk, like many other prior research efforts, we also found it to be significant to trust. Uncertainty of the purchasing process, perception of little guarantee on quality and after sales services, wariness about the privacy issue, etc. still remain as obstacles in the relationships between consumers and Internet shopping malls in China.

Finally, PEOU is found to play another key role on the generation of trust. When consumers view an Internet shopping mall as a Web site providing quick search, a convenient purchasing process (Shin et al., 2001), speedy access and concise payment (Li et al., 2005), they will probably trust this Internet shopping mall and intend to purchase in that Web site.

5.2 Implications Trust is found to be a key factor in this e-commerce context according to many

previous studies (Jarvenpaa and Tractinsky, 1999; Gefen, 2003; Jong et al., 2006; Doney & Cannon, 1997; Park, 2005; Jang et al., 2005). This study also confirms that trust can strongly affect e-shoppers’ intention to purchase products at an Internet shopping mall. Trust can also mediate the influence of consumers’ characteristics and perceptions such as e-commerce knowledge, perceived risk, and perceived ease of use on their purchasing intention as well. Thus this trust factor is worthwhile to be included in further studies on e-commerce.

In addition, e-commerce knowledge was found to be a key trust building factor while perceived reputation was not. This result is totally different from a majority of previous studies. It is probably because of this study’s special research context, that is, e-commerce in China. We believe it to be highly desirable for researchers to conduct more e-commerce research based on a specific market or other contexts in their future work.

Since trust has proven to have a significant effect on purchasing intention, China’s e-vendors as well as the Chinese government should work out some measures to build strong consumer trust in order to accelerate the development of China’s e-shopping market. Below are some suggestions based on this study.

1. Education and training on the Internet and on e-shopping as well as related support should be provided by the Chinese government and Internet shopping mall providers. This will be a significant contributor toward building consumers’ trust.

2. According to consumers’ different perceptions of risk, different measures should be taken to relieve their wariness.


Furthermore, we came to understanddd bbbyyy thhthee e dededemom graphic statistics above that most respondents were customers wwwititithhh prior e-shoppinggg eeexperience (repeat customers). According to Kim et al. (((20202004), reputation usually has s a aa weaker effect on repeat customers’ trust than onnn pppotential new customers.

Regardinggg ppeperceived risk, likeee mmmananany yy otototheheher r r prprp ioioior research efforrrtststs, we also found it to be significanannttt to trust... UnUnUncececertaintttyy y ofofof thhhe pupupurcrcrchahahasisisingngng ppprocess, percrccepe tion of little guarantee onnn quality and aaafftftererer sssaala es services, warinessss abababououout tt the privacy isisissuss e, etc. still remain as ooobstacles ininin the reeelationshihipspsps bebebetwtwtweeeeeenn n cococonsn umers aand Innnttternrnrnet shopppppping malls in Chinaaa.

Finanaally,y,y, PPPEOEE U isisis ffouound to play anothher key role on thhe gegegenen ratititiononon ooof f f trtt ust. WWWhenconsnsnsumersss viviviewewew an IInInternett sshopping mallll as a Web sititee providididing quququicicickk k searchchh, a cooonnnvenieentntnt pppuru chasasasiing processs (S( hin et al.,, 22001), speedydy access annd dd concncncisisise ee papp ymeenent (LLLi et aaal.l.l.,,, 2005)),), they will proobab bly trustt tthis Internenett shopping malalall annnd d d inininteteendnn tttoo opppurchaaasesese iiin nn thatatat Web site.

55.222 Implicatttiions Trust t is found to be a kkeyey ffacactotor r inn thhiis ee-c-comommmerce context accorddiiing to mmmanyy y

prprprevioousuu stuuudies (Jarvenpaa and TrTraca tiinsky, 199999; ; Gefen, 2003; Jong etet al.,,, 200666;DoDoDonnen y & && CaCaCannnonon, 19199797;; PaParkrk,,, 20202005; Jang et al., 2000005)5)5).. ThThisis sstutudydy aalslsoo cocoonfnfnfiiirms ttthahahattt trtrtrususust tt can strongnglyly aaaffffffececect t e-ee sshohohoppppppers’’’ iiintentionnn to pupupurcrchahahassee prprprodododucuctsts at an Intererernennett tshhhopopopppip ng mall. Trust can also mediate thththeee innnflflfluuuence of consumers’ characteristiccsss anananddd peeercrccepepeptitit ons such as e-commerce knowledgege, perceived risk, and perceived easeee ooff f usususe on ttheheheiriri pppurururchasing intention as well. Thus this trust factor is worthwhile to bbbeee ininncclc udududed in fffurururthththererer ssstutut dididieseses ooonn n e-e-e-cococommmmmmerercece.

InInIn aaaddition, e-commmmememerrrce knowleleledgdgdge e e wawawasss fffound to bebebe aaa kkey trust buildddinining g g fffactor while peeercrr eieieivevevedd d rererepupuputatatatititionn wwwas nott. ThTT is rrreeesult isisis ttotally dddifii ferererentntnt fffrororommm a a a mamamajojojority of previous stutuudididieseses. Itt iiisss ppprobably because ee of this sststudy’s special reseseseaeaearcrr h cococontntntext, that is, e-commerce in China. We believe it ttooo be highhhlylyl desirable for researchers to conduct more e-commerce research based on aa specific mmmarket or other contexts in their future work.

Since trust has proven to haveee a signininificant effect on purchasing intention, China’s e-vendors as well as the Chineseee goveeernment should work out some measuresto build strong consumer trust in order ttto aaaccelerate the development of China’s e-shopping market. Below are some suggestiononons based on this study.


3. Systems used in implementing Internet shopping malls should be augmented

continuously in order to meet and exceed consumer’s requirements for easy-to-use Web sites.

4. Due to trust’s critical role in the e-commerce context, it is necessary for the Chinese government and Internet shopping mall providers to include in their goals consumer trust building when taking actions targeted at increasing customer purchasing on their sites. Actions for solving such problems as consumers’ lack of e-commerce knowledge, wariness about the various risks, and perceived inconvenience of use during Internet shopping should be targeted at contributing to building and increasing consumer’s trust. In fact, at ebay.com.cn, there are many trust-building education oriented contents provided for consumers for free in such areas as payment protection policy, assurance on refund, and other contents.

5.3 Study Limitations and Future Research Directions We acknowledge several limitations in this study. First, the survey should have

covered a more general population instead of mainly including college students, even though the majority of e-consumers are aged 18-30 (CNNIC, 2007). Second, unlike many previous studies that have dealt with e-consumers’ purchasing intention or e-consumer trust, the concept “consumer” has not been separated into potential and repeat consumers. If this separation is made clearly from the beginning, it could be possible that different kinds of consumers’ purchasing intention might relate to different variables (Kim et al., 2004). However, all the respondents answered that they had prior Internet shopping experience, that is, the subjects of this survey, fortunately enough, turned out to be repeat consumers. Third, similar to splitting consumers into two groups based on experience, China’s e-shopping market can also be broken into B2C and C2C markets for study. Thus more thoughtful market segmentation will result in better explanation in the study of trust and its effects on purchasing intention.

REFERENCES Baek, C.H., Seo C.S., Hong, J.W. and Suh, W.J. (2006). “Empirical Research about the Effects on Customer Trust and Purchase Intention in China Internet shopping mall,” Spring Semi Annual Conferences of KMIS, pp.548-555.

Chi, H.W. and Yeom, C.S. (1999). “Empirical Research about Effects on Consumer Satisfaction of Internet shopping mall,” Combination Conference of KAIS, pp. 621-626.

Chin, W.W. (1998). "Issues and Opinion on Structural Equation Modeling," MIS Quarterly, Vol.22, No. 1, pp. 7-16.

China Internet Network Information Center(CNNIC) (2007), “The 19th Survey Report of Internet Development in China,” Jan. 2007, http://www.cnnic.net.cn/uploadfiles/pdf/2007/2/14/200607.pdf.

Doney, P.M., and Cannon, J.P. (1997). “An Examination of the Nature of Trust in

Vol.4, No. 3, 2007, p. 269-286

Chinese government and Internet shopppipp ngngg mall providers to include in their goals consumer trust building when takinininggg actions targetttededed aat increasing customer purchasing on their sites. Actions for sososolvlvlviing such problems as conononsusus mers’ lack of e-commerce knowledge, wariness abouououttt the various risks, and perceit ved innncococonvenience of use during Internet shopping shshshoould be targeteteted at contributing to buililildidd ng and increasingconsumer’s trust.. IIn fact, at ebay.cccomomom.cccnn,n ttthehehererere aaarrre many trust-bububuilding education oriented contentntnts provideddd fffororor ccconsumememersrsrs fffororor fffrereree ee ininin sssucucuch h ararareas as paymmment protectionpolicy, assurararance on refund,d,d, aaandndnd other contents.

5.3 Stttuudu y Limimimitatatatititions ananandd d Future Researcch h Directions Weee aaackckcknowledededge sevvereral limitationss in this study. FFirst, thhheee survvveyeyey ssshohohould hahahave

cooovvvered a aa momomore gggeeneneral popululatation insteadd oof mainly innclc uding colllllegege e stststudududenenents, evvenee fthhhough ttthehehe majoorority of e-consuummers are aageed 18-30 ((CCNNIC, 2007)).. Secococondndnd,,, unuu likekeke mmmany ppprererevivv ousss studies that havee ded alt wiithh e-consusummers’ purchasingg iiintenntititionnn oor e--consumumumererer trusstst, thhee coconcept “consuumemer” has nnot beeeen n separated intoo ppoto enntitit al aaandndnd rrrepeat tcocoonsumumumeree s. IIfff this seppararatatioi n is madade cleaarlyy fromom the beginnniningng, it coulddd be popoposssss iblelele thhatatat differenntn kinds of coconsnsumumers’ puurcchaasinng intenttioion n might relate to differenenent vvvariiiabaa les (KKKiim et al., 2004). HoHowewevever,r, alll thee resspopondndenentsts answered that theheey had pppriooor InInnternnnetee shoooppping experience, thatt iis,s tthhe subjece ts ooff this survey, fortunatetely ennonoughh,h utututurnrnned oututut too o bebe rrepepeaeat t consumers. Third, similar to split iting consumumererss inintotoo twowowo groupupupssbababasesesed on expppererieience,e, CCChihihinanana’’’s e-shoppppinining g markettt cccan also beb bbbrororokekek n n intoo BBB222C and CCC2C2C2C mamamarkrkrkets for study. Thus more thouggghththtfufuful mamamarkrkrk tetet segmentation will result in bbbetete teteter exxxplplplanananataa ion in the study of trust and its effefefectctctsss on purchasing intention.

REFEERERERENCNCNCES

Baek, C.H., Seo C.S., Hong, J.W. and SSuh, W.J. (((2006). “Empirical Research about the Effects on Customer Trust and Purchhah se Intentiooon in China Internet shopping mall,” Spring Semi Annual Conferences of KMMMIS, pp.54448-555.

Chi, H.W. and Yeom, C.S. (1999). “EmEmEmpiricaaall l Research about Effects on Consumer Satisfaction of Internet shopping mall,” CoCoCombininination Conference of KAIS, pp. 621-626.

Chin, W.W. (1998). "Issues and Opinionnn on Structural Equation Modeling," MIS Q l V l 22 N 1 7 16


Buyer-Seller Relationships,”Journal of Marketing, Vol. 61, pp. 35-51.

Dowling, G.R., and Staelin, R. (1994). “A Model of Perceived Risk and Intended Risk-Handling Activity,” Journal of Consumer Research, Vol. 21, pp. 19-134.

Ellis, G.D., Judith, E.V. and Catherine M. (1994). “Measurement and Analysis Issues with Explanation of Variance in Daily Experience Using the Flow Model,” Leisure Res, Vol. 26, No. 4, pp. 337-356.

Ganesan, S. (1994). “Determinants of Long-Term Orientation in Buyer-Seller Relationships,” Journal of Marketing, Vol. 58, No. 2, pp. 1-19.

Gefen, D., and Silver, M. (1999). “Lessons Learned from the Successful Adoption of an ERP System,” Proceedings of the 5th International Conference of the Decision Science Institute, pp.1054-1057.

Gefen, D., Karahanna, E. and Straub D.W. (2003). “Trust and Tam in Online Shopping: an Integrated Model.” MIS Quarterly, Vol. 27, No. 1, pp. 51-99.

Hair. J.F., Aderson, R.E., Tatham, R.L., Black. W.C. (1998)." Multivariate Data Analysis. 5th edition, " Prentice - Hall.

Hosmer, L.T. (1995). “Trust: the Connecting Link between Organizational Theory and Philosophical Ethics,” Academy of Management Review, Vol. 20, No. 2, pp. 379-403.

Jang, H.Y., Jeong, K.H., Jeong, D.Y. (2005). “The Consequences of Customer Trust and the Determinants of Purchasing Intention in Internet shopping mall,” Journal of MIS Research, Vol. 15, No.2, pp. 23-49.

Jarvenpaa, S.L. and Tractinsky, N. (1999). “Consumer Trust in an Internet Store: A Cross-Cultural Validation,” JCMC, Vol. 5, No. 2, pp. 1-35.

Jeong, C.H., Jeong, Y.S. and Lee, K. (2006). “Research about the Effects of Internet shopping mall User Trust on Royalty: Comparison Analysis between Korea and China Trust Determinate Factors,” Spring Semi Annual Conferences of KMIS, pp. 659-666.

Jong, K.E. and Lee, D.M. (2000). “Research about Consumer Trust on Internet shopping mall,” Fall Semi Annual Conferences of KMIS, pp. 561-573.

Kim, H.W., Xu, Y., Koh, J. (2004). “A Comparison of Trust Building Factors between Potential Customers and Repeat Customers,” Journal of the Association for Information Systems, Vol. 5, No. 10, pp. 392-420.

Kim, S.W. and Park, B.J. (2002). “An Empirical Study on Consumer Purchase Behavior by the Benefit in Internet shopping mall,” Journal of Consumer Studies, Vol. 13, No. 4, pp. 201-219.

Kim, S.Y. and Park, S.Y. (1999). “A Study of Decision Factors for Purchase Intention at the Electronic Commerce,” Journal of Consumer Studies, Vol. 10, No. 3, pp. 45-66.

Ko, E.J. and Mok, B.K. (1999). “Analysis of Internet Shopping Process to Develop the Web Design Strategy for Apparel Consumers,” Journal of Korean Academy of marketing Science, Vol. 4, pp. 111-131.

Koufaris, M. (2002). “Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior," Information Systems Research, Vol. 13, No. 2, pp. 205-


Ellis, G.D., Judith, E.V. and Catherine e MMM. (((191919949494).)) Measurement and Analysis Issueswith Explanation of Variance in DaDaDaiilily Experience UUUsisisingngng the Flow Model,” Leisure Res, Vol. 26, No. 4, pp. 337-356...

Ganesan, S. (1994))).. . ““Determinantntnts of Long-Term Orientatatatitt on in Buyer-Seller Relationships,” Jourururnal of Marketing,,, Voool... 58,8,8, NNNo.o 2, ,, ppp . 1-19.

Gefen, D., anddd SSSilver, M. (1(( 9999999)9)9). “Lessssosoonsnsns LLLeaeaearnrnrnededed fffrororomm m thththe e e Successfull AAAdoption of an ERP Systemmm,” Proceedingsss ooof ff thhhe 5th International Confnfnferrrenene ce of the Deciiisisis on Science Institute, pppp.1054-10505057.77

Gefen,n,, DD., Karararahahahananannann , E. aaandndnd Straub D.W. (2(2003). “Trust anndd d TaT m ininin OOOnlnlnline Shopopopping: an Intntntegrateteed dd MoMoModedd l.” MMIMIS QQuarterly, Vol. 272 , No. 1, pp. 51-1-99.

Haaaiiir. J.F.F.F., ,, AdAdAdersooon,n R.E., TTata ham, R.L.,, Black. W.CC.. (1998)."" MMultiiivavavariririataa e DaDaata AAnAnalysisisis.. 5t5 hhh editittiioion, " Prenticee -- Hall.

HHHosmererer, L.LL T. (((191995). “Trust: thee CConnectinngg Link bbetetwween Organizationonnal TTheheheororory yy andd dPhilosososopopophhhical EEthiicscs,”, Academy of f MManageemment RReveviiew, Vol. 20, , NoNo. 2, pppp.pp 3797979-4-44030 .

Jaaanngn , H.Y., JJeJ ong, K.HH., JJeoeong, D.YY. ((20005). ““Thhe Conseseququeences of Cususstomer Trususust aannd dd the Detteterminants of Purcrchahasising IInttenentioonn inn Intnterernenett shopping mall,””” Journaalll oof MMMIS ReRR searcchc , Vol. 15, No.2, pp. 223-4949..

JaJaJarvrvvenpaaaa,a,a, SSS.LL. anand d TrTractitinskky, N. (1999). “Consumer Trustt inin aann InInteternrnnett SSStore: A A ACrCrCrosososs-Culturrralal VValiddatattioioionnn,””” JCMC, VoVoVol.l.l 5, No. 2,2,2, pppp. 1-35.

Jeeeononong,g,g, C.H., Jeong, Y.S. and Lee, K. (((2020200606).).). “““Research about the Effects of Innnteteternrnrneete shhhopopoppipipingngng mall User Trust on Royalty: Commpap rison Analysis between Korea andd d ChChChininina Trususust tt DDeD tetetermrmrminate Factors,” Spring Semi Annual Conferences of KMIS, pp.p 666595959-6-66666666..

Jonggg,, , K.K.K.EEE. aaandndnd LLLeeeeee, DDD.MMM. (((202020000000).).). “““ReRR seararrchchch aaabobobo tuut CCConononsususumememerrr TrTrTrususustt t ononon IIIntntnteere net shoppipipingngng mmmall,” Fall Semmmiii AnAnAnnual Confefeferererencncnceseses ooof KMIS,, pppp. 5656561-1 573.

Kim, H.WWW.,,, XXXuuu, YYY., KKohohoh, JJJ. (2004). “AA A Comparrisisison of Trust t BuBuBuili dididingngng FFFaactotoorsrsrs between Potential Customers and Repeat Customememers,” Jooouruu nal of the Associatiion ffor Information Systems, Vol. 5, No. 10, pp. 392-420.

Kim, S.W. and Park, B.J. (2002). “An EEEmpirical SSStudy on Consumer Purchase Behavior by the Benefit in Internet shopping malllll,” Journaaal of Consumer Studies, Vol. 13, No. 4, rrpp. 201-219.

Kim, S.Y. and Park, S.Y. (1999). “A Studddy yy ooof Decision Factors for f Purchase Intention at the Electronic Commerce,” Journal of Cooonnnsumer Studies, Vol. 10, No. 3, pp. 45-66.


223.

Lee, T.M. and Lee, E.Y. (2005). “A Study on the Determinants of Purchase Intention in Mobile Commerce: Focused on the Mediating Role of Perceived Risks and Perceived Benefits,” Journal of MIS Research, Vol. 15, No. 2, pp. 1-21.

Lee, Y.K. and Lee, K.Y. (2003). “The Effect of Perceived Trustworthiness on Consumer Trust in Internet Store,” Korean Industrial Economic Association, Vol. 16, No. 3, pp. 127-140.

Li, M.Z., Han, K.S. and Kim, G.Y. (2005). “An Empirical Study on the Utilizing Conditions of Online Shopping Mall of Chinese Consumers - focused on Beijing and Shanghai,” Internet business Research, Vol. 6, No. 2, pp.55-78

Mayer, R.C., Davis, J.H. and Schoorman, F.D. (1995). “An Integrative Model of Organizational Trust,” Academy of Management Review, Vol. 20, No. 3, pp. 709-734.

McKnight, D.H., Cummings, L.L. and Chervany, N.L. (1998). “Initial Trust Formation in New Organizational Relationships (Special Topic Forum on Trust in and Between Organizations)”, Academy of Management Review, Vol. 23, No. 3, pp. 472-490.

Novak, T.P., Hoffman, D.L. and Yung, Y.F. (2000). “Measuring the Customer Experience in online Environments: A Structural Modeling Approach,” Marketing Science, Vol. 19, No. 1, pp. 22-42.

Nunnally, J. (1978). Psychometric Theory, 2nd ed., McGraw-Hill, New York.

Park, H.R. (2005). “Effects of Consumer`s Characteristics on Trust and Purchase Intention of On-Line Shopping Mall - Focusing on Chinese Users of Internet shopping mall,” Journal of The Korean Society for Clothing Industry, Vol. 7, No. 5, pp. 501-508.

Park, J.C. and Yoon, M.H. (2002). “ Factors Affecting on Internet shopping mall Members` Relationship Quality,” Journal of MIS Research, Vol. 12, No. 3, pp. 21-43.

Ratnasingham, P. (1998). “The Importance of Trust in Electronic Commerce.” Internet Research, Vol.8, No. 4, pp. 313-321.

Shin, K.Y., Choo, G.W. and Park, T.J. (2001). ”Determinants of Using Internet shopping malls,” Journal of MIS Research, Vol. 10, No. 1, pp. 279-301.

Shin, M.K., Joung, S.H. and Yuh, Y.K. (2004). “Consumer`s Perceived Risk and Information Search in Internet Shopping,” The Journal of Korean Home Economics Association, Vol. 42, No. 9, pp. 195-212.

Viswanath, V. (2000). “Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance model.” Information Systems Research, Vol. 11, No. 4, pp. 342-365.

Yang, Y. and Baeg, W.Su. (2003). “Perceived Risks and Consumer Characteristics on Internet Shopping,” Korean Journal of Consumer and Advertising Psychology, Vol.4, No. 2, pp. 73-103.

Yoon, S.J. (2000). “A Study on the Antecedents of Trust toward Shopping Mall Web Sites and Its Effects on Purchase Intention,” Journal of Business Administration, Vol. 29, No.3, pp. 353-376.

Yu, I. and Choe, H.L. (2003). “Factors Influencing the Consumer Trust and Mediating

Vol.4, No. 3, 2007, p. 269-286

Lee, Y.K. and Lee, K.Y. (2003). “TTThehehe EEEffffffececect of Perceived Trustworthiness on Consumer Trust in Internet Stoorerere ””,” Korean Industriai lll EcEE onomic Association, Vol. 16, nNo. 3, pp. 127-140.

Li, M.Z., Han, K.S. aaand Kim, G.YYY. (2005). “An Empirical StStStuduu y on the Utilizing Conditions of Onnlililinne Shopping Mallll of f f ChCC ininineseseseee Cooonnsn umers - focuusesesed on Beijing and Shanghai,” Intererernnet busineeess RRResesesearch,h,h, VVVoolol.. 6,66 NNNo.o.o. 222,, pppppp.5.5.55-787

Mayer, R.CCC., Davisss,, J.H. aaandnn Schoorman, F.D. (19999 )5)5). “An InInIntegrativeee Model of Organizaaatititional Trururuststst,”,”” Academy y y ofofof MMManagemment ReReR viviviewewew, Vol. 20,0,0, NNNoo.o. 3, pp. 7070709-734.

McKnnniigight, D...H.H.H , CuCuCummininingsg , L.L. and Chervrvany, N.L. (19998)8)8). .. “Initititialala TTTrrurust Formmmation in NNNeewe Orgrgrganananizizizataa ionanaall l Relalatit onships (Specicial Topic Forumum on n TrTT ust tt innn aaandndnd Betwwween Orrggganizatiiononons)s)s)”,”” Acacacademy of f MManagement RReview, Vol. 2323, No. 3, pppp.pp 444727272-4-4-490990.

NNNovak, TTT.P., HHHofo fman, D.L. aand Yung, Y.F. (2000000). “Measuringngg thehehe CCCususustomeeer EEExperiiienenencecece in online Environmementn s: A SStrructural MModeling Approaachchc ,” MMMarararketinggg Sciencncnce,e,e, VVVol. 1191 , NoNo. 1, pp. 22-42.

NuNuNunnallylyly, J. (((1978). Pssycychohometric TThheoeory,, 2nnd eded.,, McGraw-w-HiHillll, New Yooorkrr .

PPParkrkk, H.R. ((2005). “Effects ofof CConssumemer`ss CChharaactcterrisistics on Trust annnd Purchhahasee InInntentntntioii n offf On-Line Shopping MaMalll - FFocusingg oon ChChinese Users of Internnnet shopopoppingngng mamam ll,” JJJououo rnnnalal ooff ThThee KoKorereanan SSococieietyty for Clothing IIndndusustrtryy, VVolol. 77, NNoo. 55, , ppppp. 50500111-5008..8.

PaPaParkrkrk, J.C. andnd YYooooon,n,n, MMM.H. (2( 002)). “ Factorsss AAAffecting g on IIIntntnterere nenett shshhopping mmmaaallllMeMeMembmbm ers` Relationship Quality,” Journaaall l ofoo MMMISISIS RRResearch, Vol. 12, No. 3, pp. 21-434343.

Raaatntntnaaasinininghgg am, P. (1998). “The Importance oof Trust in Electronic Commerce.” InInIntteternrnrnet fResesesearararchchch, VoVoVol.8, No. 4, pp. 313-321.

Shinnn,, K.K.K.Y.YY , ChChChoooooo,,, G.GG.W.W.W. aaandndnd PPPararrk,k,k, TTT.J.J.J. (2(2(20000001)1)1). ”D”D”Detetererermimiminnanantntntsss ofoof UUUsisisinnng IIIntntnteeernet shoppipiingngng mmmalaa ls,” Journnnalalal ooofff MMIMISSS ReReResearchchch,,, VoVoVolll. 10, NNNooo. 11, , pppp. 2727279-99 301.

Shin, M.K.KK , , JoJ ung,, SSS HH.H. and Yuh, YYY.K. (20000444). “Consumeer`rr sss PeP rc iieiveveeddd Risk and Information Search in Internet Shoppinining,” Thehehe Journal of Korean Home EconomicsAssociation, Vol. 42, No. 9, pp. 195-21112.

Viswanath, V. (2000). “Determinants of Perceivvev d Ease of Use: Integrating Control,Intrinsic Motivation, and Emotion into tttheh Technnnology Acceptance model.” Information Systems Research, Vol. 11, No. 4, pp. 34442-22 365...

Yang, Y. and Baeg, W.Su. (2003). “Perceieieiveeedd Risks and Consumer Characteristics on dInternet Shopping,” Korean Journal of Connnsumer and Advertising Psychology, Vol.4,

Li, Rong. Kim, JaeJon, Park, JaeSung


286

Roles of Trust on Purchasing Intention in B2C Electronic Commerce,” Journal of MIS Research, Vol. 13, No.4, pp. 49-72.

bab v penutup 5.1. kesimpulan - e-journal.uajy.ac.ide-journal.uajy.ac.id/6604/6/mm501950.pdf ·...

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