International Journal of Electronic Commerce / Fall 2007, Vol. 12, No. 1, pp. 127–158.Copyright © 2007 M.E. Sharpe, Inc. All rights reserved.

1086-4415/2007 $9.50 + 0.00.DOI 10.2753/JEC1086-4415120105

The Moderating Effect of Transaction Experience on the Decision Calculus in On-Line Repurchase

Sumeet Gupta and Hee-Woong Kim

ABSTRACT: Repeat customers are fi ve times more profi table than new customers. Inter-net vendors have to retain customers to reap the benefi ts of repeat sales, but more than 50 percent of repeat customers seldom complete a third purchase. One reason is the inability of on-line vendors to manage customers’ changing expectations. Vendors need to understand the decision calculus of repeat customers at every stage of their transac-tion experience with the vendor. This study uses a customer repurchase decision-making model to examine the effect of transaction experience on customers’ decision calculus in on-line repurchases. The model shows that the effects of perceived convenience and perceived price change over the transaction experience, whereas the effects of perceived value and pleasure do not. These fi ndings have signifi cant theoretical and practical implications.

KEY WORDS AND PHRASES: Internet shopping, mental accounting theory, repurchase decision calculus.

Repeat customers are a source of profi t for any venture because they are less sensitive to price, have greater spending capacity, can be served at a lower cost, and pass on positive recommendations to others [69]. However, profi ts will remain elusive unless customers return to buy again. Although on-line sales are growing very fast, the expectations of on-line shoppers are also increasing [60, 74]. Consequently, on-line customers are spending less time on individual Web sites or pages [29]. DoubleClick reports that fewer than 5 percent of Web site visitors actually make a purchase during a visit, and most of those who put something in their shopping cart eventually abandon it [28, 29]. Moreover, only a very small minority of Web site visitors (about 1%) returns to make purchases [28, 29]. More than 50 percent of repeat customers seldom complete a third purchase with the Internet vendor [70].

Not properly understanding the expectations of on-line customers is one reason that on-line stores fail to maintain repeat sales [58]. On-line vendors tend to apply the same strategy to all repeat customers and do not cater to changing expectations. Barnes and Noble, for example, offers a 10 percent discount to all member customers regardless of their transaction experience (i.e., the total number of purchases they have made from Barnes and Noble). To properly understand on-line customer behavior, it is necessary to understand the factors that affect the decision to purchase [32]. From a decision-making perspective, Internet vendors should use transaction experience to differen-tiate repeat customers, because customers modify their repurchase decision

The authors thank Dr. Vladimir Zwass and three anonymous reviewers. The second author (kimhw@comp.nus.edu.sg) is the corresponding author.

05 kim.indd 12705 kim.indd 127 8/6/2007 12:22:55 PM8/6/2007 12:22:55 PM

128 SUMEET GUPTA AND HEE-WOONG KIM

calculus in successive transactions by adjusting their current beliefs to new information [45, 46].1 This means that Internet vendors have to understand the repurchase decision calculus of repeat customers over the span of the transaction experience.

Researchers have given little attention to the subject of on-line customer re-purchase decision-making based on transaction experience. The present study examines the moderating effect of transaction experience on the repurchase decision calculus of on-line customers. Since researchers in economics and marketing have found value to be a main determinant of customer choice and decision-making [e.g., 26, 48, 78, 85], the issue of on-line customers’ repurchase decision calculus is examined from the value perspective. Specifi cally, the study seeks to answer two research questions: (1) What factors infl uence an on-line customer’s repurchase decision calculus? (2) How does an on-line customer’s transaction experience moderate the repurchase decision calculus?

Literature and Theoretical Background

Previous Research

Table 1 provides a list of recent studies in electronic commerce compiled from a review of major information systems journals. The studies were presumably conducted in the context of on-line stores, such as bookstores, on-line banks, retailing sites, sports, and video stores, all of which have in common that the products they offer fall in the category of search products (i.e., vary little in quality) [40].

The studies in the table can be classifi ed on the basis of their subjects. The fi rst group of studies does not differentiate among various on-line customers [14, 15, 25, 71, 84]. The second group examines only inexperienced customers [73]. The third group differentiates experienced and inexperienced customers [8, 51]. The fourth group focuses solely on experienced customers. An underly-ing assumption when experienced customers are used as subjects is that their decision-making remains the same regardless of their experience. Most of the studies fall in this fourth category.

The studies can also be classifi ed by their background theories. The domi-nant theories in most of the studies are the expectation-disconfi rmation model and the technology acceptance model or its variants. While these two theoreti-cal concepts can explain on-line customer adoption and post-adoption, they cannot satisfactorily explain customer-purchase decision-making. Adoption cannot be studied the same way as purchase decisions because the process of making purchase decisions may differ across adopters.

Thus, the earlier studies have two main limitations—they do not differ-entiate repeat customers by transaction experience, and they lack a proper theoretical foundation for explaining on-line purchase decision-making. The present study overcomes these limitations by its focus on the changes in cus-tomer decision-making based on previous purchase experience at an on-line store. Because Internet shopping is characterized by risk and uncertainty on

05 kim.indd 12805 kim.indd 128 8/6/2007 12:22:55 PM8/6/2007 12:22:55 PM

INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 129

Au

tho

r(s)

D

epen

den

t va

ria

ble

(s)

Ind

epen

den

t va

ria

ble

(s)

Cont

ext

Res

po

nden

ts

Bhat

tach

erje

e [8

] C

ontin

uanc

e in

tent

ion

Use

fuln

ess,

con

fi rm

atio

n, s

atisf

actio

n, lo

yalty

ince

ntiv

es

On-

line

brok

erag

e Ex

perie

nced

use

rsBh

atta

cher

jee

[7]

Con

tinua

nce

inte

ntio

n Sa

tisfa

ctio

n, c

onfi r

mat

ion,

per

ceiv

ed p

erfo

rman

ce,

On-

line

bank

ing

Expe

rienc

ed u

sers

exp

ecta

tion

Bhat

tach

erje

e [9

] W

illin

gnes

s to

tran

sact

Tr

ust a

nd fa

mili

arity

A

maz

on a

nd b

ank

Expe

rienc

ed u

sers

/

c

usto

mer

sBh

atta

cher

jee

and

Po

st-u

sage

inte

ntio

n Sa

tisfa

ctio

n, d

iscon

fi rm

atio

n, p

re-u

sage

and

C

ompu

ter-b

ased

trai

ning

U

sers

(fro

m

Pre

mku

mar

[10]

pos

t-usa

ge b

elie

fs a

nd a

ttitu

des

i

nexp

erie

nced

to

exp

erie

nced

)C

azie

r [12

] e-

busin

ess

diffe

rent

iatio

n Tr

ust,

valu

e co

ngru

ence

, val

ue c

onfl i

ct

Hyp

othe

tical

on-

line

St

uden

ts

boo

ksto

re

Cha

ng e

t al.

[13]

O

n-lin

e sh

oppi

ng a

dopt

ion

Risk

, tru

st, b

enefi

ts, s

ervi

ce q

ualit

y,

Liter

atur

e de

rived

Lit

erat

ure

sho

ppin

g ex

perie

nce

Che

n an

d

Purc

hase

inte

ntio

n Pe

rcei

ved

valu

e, p

rodu

ct p

rice,

per

ceiv

ed ri

sk,

Gen

eral

e-c

omm

erce

site

s Ex

perie

nce/

Dub

insk

y [1

5]

p

erce

ived

pro

duct

qua

lity,

val

ence

of e

xper

ienc

e

ine

xper

ienc

ed u

sers

Che

n et

al.

[14]

A

ctua

l use

C

ogni

tive

abso

rptio

n, fa

shio

n in

volv

emen

t,

Gen

eral

e-c

omm

erce

site

s Ex

perie

nce/

per

ceiv

ed e

ase

of u

se, p

erce

ived

use

fuln

ess

i

nexp

erie

nced

use

rs

Cho

[17]

In

tent

ion

Atti

tude

, per

ceiv

ed e

ase

of u

se, p

erce

ived

use

fuln

ess,

O

n-lin

e le

gal s

ervi

ces

and

Pote

ntia

l use

rs

c

ompa

tibili

ty, r

isk, t

rust

, fac

ilita

ting

cond

ition

s v

irtua

l law

yers

Del

one

and

In

tent

ion

to u

se, u

ser

Info

rmat

ion

qual

ity, s

yste

m q

ualit

y, s

ervi

ce q

ualit

y Ba

rnes

and

Nob

le,

Cas

e st

udy

McL

ean

[23]

s

atisf

actio

n, n

et b

enefi

ts

M

E el

ectro

nics

De

Wul

f et a

l. [2

1]

Web

site

suc

cess

Pl

easu

re, e

valu

atio

n W

eb s

ites

Inte

rnet

mar

ketin

g

r

esea

rch

sour

ces

Din

ev a

nd

Inte

ntio

n to

pro

vide

Tr

ust,

priv

acy-

risk

belie

fs, c

onfi d

ence

and

G

ener

al e

-com

mer

ce s

ites

Expe

rienc

e/ H

art [

25]

per

sona

l inf

orm

atio

n e

ntic

emen

t bel

iefs

ine

xper

ienc

ed u

sers

D

inev

and

Har

t [24

] In

tent

ion

to tr

ansa

ct

Priv

acy

conc

erns

, soc

ial a

war

enes

s, In

tern

et li

tera

cy

Gen

eral

e-c

omm

erce

site

s St

uden

ts(c

ontin

ues)

05 kim.indd 12905 kim.indd 129 8/6/2007 12:22:55 PM8/6/2007 12:22:55 PM

130 SUMEET GUPTA AND HEE-WOONG KIM

Au

tho

r(s)

D

epen

den

t va

ria

ble

(s)

Ind

epen

den

t va

ria

ble

(s)

Cont

ext

Res

po

nden

ts

Fran

cis

and

Pe

rcei

ved

Inte

rnet

So

urce

s an

d in

hibi

tors

of u

tilita

rian

and

hedo

nic

valu

e G

ener

al e

-com

mer

ce s

ites

Expe

rienc

ed u

sers

Whi

te [3

5]

sho

ppin

g va

lue

rel

ativ

e to

eac

h fu

lfi llm

ent-p

rodu

ct c

ateg

ory

Gef

en [3

6]

Inte

nded

use

Pe

rcei

ved

usef

ulne

ss, p

erce

ived

eas

e of

use

, hab

it G

ener

al e

-com

mer

ce s

ites

Expe

rienc

ed u

sers

Gef

en e

t al.

[37]

Tr

ust,

purc

hase

inte

ntio

n Pe

rcei

ved

ease

of u

se, p

erce

ived

use

fuln

ess,

trus

t,

Gen

eral

e-c

omm

erce

site

s Ex

perie

nced

use

rs

f

amili

arity

, disp

ositi

onH

ampt

on-S

osa

and

In

tent

ion

to u

se

Trus

t, W

eb s

ite a

ppea

l, W

eb s

ite u

sabi

lity

On-

line

Web

site

s Po

tent

ial u

sers

Kou

faris

[44]

Kim

et a

l. [5

1]

Trus

t (in

itial

and

Re

puta

tion,

ass

uran

ce, q

ualit

y (in

form

atio

n an

d se

rvic

e).

Book

stor

e

Pote

ntia

l cus

tom

er a

nd

ong

oing

) F

or o

ngoi

ng tr

ust,

thes

e fa

ctor

s ar

e al

so m

edia

ted

rep

eate

d cu

stom

er

b

y sa

tisfa

ctio

n Ko

o [5

2]

E-C

omm

erce

Loy

alty

Be

nefi t

s an

d at

tribu

tes

of p

rodu

ct, s

ense

of b

elon

ging

ness

G

ener

al o

n-lin

e st

ores

Ex

perie

nced

use

rsKo

ufar

is et

al.

[53]

U

npla

nned

pur

chas

es,

Perc

eive

d co

ntro

l, sh

oppi

ng e

njoy

men

t O

n-lin

e vi

deo

rent

al

Expe

rienc

ed u

sers

i

nten

tion

to re

turn

Liu e

t al.

[55]

Be

havi

oral

inte

ntio

n Pr

ivac

y an

d tru

st

On-

line

book

stor

e Po

tent

ial u

sers

Lope

s an

d

Will

ingn

ess

to p

ay

Expe

cted

ben

efi ts

, tec

hnic

al q

ualit

y, s

ervi

ce p

rovi

der

Spor

ts W

eb s

ite

Expe

rienc

ed u

sers

Gal

letta

[56]

rep

utat

ion

Mah

moo

d et

al.

[57]

O

n-lin

e sh

oppi

ng b

ehav

ior

Trus

t, ec

onom

ic c

ondi

tions

, tec

hnol

ogy

savv

y,

On-

line

stor

es

Inte

rnet

mar

ketin

g

e

duca

tiona

l lev

el

r

esea

rch

sour

ces

Mas

sad

et a

l. [5

9]

Satis

fact

ion

Serv

ice

qual

ity, t

rans

actio

n sa

tisfa

ctio

n E-

reta

il sit

es

Expe

rienc

ed u

sers

Nik

olae

va [6

3]

Web

site

traf

fi c

E-ta

il ch

arac

teris

itcs,

site

visi

bilit

y en

hanc

ers

Gen

eral

e-c

omm

erce

In

tern

et m

arke

ting

s

ites

res

earc

h so

urce

sPa

vlou

[65]

A

ctua

l tra

nsac

tion,

Pe

rcei

ved

ease

of u

se, p

erce

ived

use

fuln

ess,

per

ceiv

ed

Gen

eral

Web

site

s

Expe

rienc

ed u

sers

i

nten

tion

to tr

ansa

ct

risk

, tru

st

05 kim.indd 13005 kim.indd 130 8/6/2007 12:22:56 PM8/6/2007 12:22:56 PM

INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 131

Pavl

ou a

nd

Inte

ntio

n to

tran

sact

and

act

ual

Risk

, tru

st

Am

azon

Ex

perie

nced

use

rs G

efen

[66]

t

rans

actio

n be

havi

orSh

ang

et a

l. [7

1]

On-

line

cons

umer

beh

avio

r En

terta

inm

ent,

fash

ion,

cog

nitiv

e ab

sorp

tion

expe

rienc

es

On-

line

shop

ping

Ex

perie

nced

and

I

nexp

erie

nced

use

rsSh

ih [7

3]

E-sh

oppi

ng a

ccep

tanc

e Pe

rcei

ved

ease

of u

se o

f tra

ding

on-

line,

per

ceiv

ed

Gen

eral

Web

site

s

Pote

ntia

l cus

tom

ers

use

fuln

ess,

atti

tude

s to

war

d e-

shop

ping

Suh

and

Han

[76]

Be

havi

oral

inte

ntio

n an

d

Atti

tude

and

trus

t Ba

nk

Expe

rienc

ed u

sers

a

ctua

l use

Ts

ai e

t al.

[80]

Re

purc

hase

inte

ntio

ns

Expe

cted

val

ue s

harin

g, p

erce

ived

sw

itchi

ng c

osts

, Sh

oppi

ng m

all

Expe

rienc

ed u

sers

com

mun

ity b

uild

ing,

per

ceiv

ed s

ervi

ce q

ualit

y,

per

ceiv

ed tr

ust

Wu

[83]

Be

havi

oral

inte

ntio

n an

d

Atti

tude

, sub

ject

ive

norm

, per

ceiv

ed b

ehav

iora

l con

trol

Inte

rnet

boo

ksto

res

Use

rs

act

ual b

ehav

ior

Yang

et a

l. [8

4]

Trus

t tow

ard

e-ta

iler

Ass

uran

ce p

erce

ptio

n, re

sult

dem

onst

rabi

lity,

pro

duct

G

ener

al W

eb s

ites

In

expe

rienc

ed a

nd

i

nfor

mat

ion

qual

ity, d

ispla

y of

third

-par

ty s

eals

e

xper

ienc

ed u

sers

Tab

le 1

. E-C

om

mer

ce S

tud

ies

Rel

ate

d t

o O

n-lin

e Cu

sto

mer

Beh

avi

or.

05 kim.indd 13105 kim.indd 131 8/6/2007 12:22:56 PM8/6/2007 12:22:56 PM

132 SUMEET GUPTA AND HEE-WOONG KIM

the part of customers, theories that explain customer choice and decision-mak-ing under conditions of risk and uncertainty should shed light on customer behavior. One such approach utilizes mental accounting theory, which is widely used for explaining customer choice and decision-making under risk and uncertainty [78].

Mental Accounting Theory

Mental accounting theory models actual customer behavior rather than rational/optimal customer behavior. The concepts of mental accounting, al-though new in marketing, have been widely used to study consumer behavior [see 49]. This is an important factor in Internet shopping, because it is risk and uncertainty that make customers deviate from rational/optimal behav-ior. According to mental accounting theory, customers maximize the value of their choices and decision-making under conditions of uncertainty. The value function is characterized as gains or losses from a reference point [48].

Mental accounting theory holds that customers analyze transactions in two stages. In considering a possible transaction, fi rst they evaluate it, and then they either approve or disapprove of it. Thaler proposed that two types of utility are involved in evaluations of potential transactions: acquisition utility and transaction utility [78].2 Acquisition utility is the value of the product or service received compared to the payment. It is a function of the equivalent value of the product and its objective price. Equivalent value is the amount of money that would leave the individual indifferent between receiving the cash or the product as a gift. Objective price is the total amount a customer has to pay to obtain or use the product.

Transaction utility refers to the perceived merits of a transaction. It is based on the difference between the product’s reference price and objective price. The reference price is the price a customer expects to pay for the product [78]. Customers derive a reference price from their previous experiences or the sales messages they receive [67]. Internet shop bots, which compare prices offered by different Internet vendors, help customers to derive reference prices. The total utility from a repurchase is simply the sum of its acquisition utility and transaction utility.

In making purchase decisions, customers maximize their total utility with reference to the mental account corresponding to the product being purchased [78]. The specifi c mental account is restricted by the budget allocated to it.

Thaler argues that when the ultimate consumption of the product is the same, the acquisition utility is the same regardless of where the product is purchased [78]. The price difference between on-line stores is captured by transaction utility if the product that can be purchased from different on-line stores is the same. Although it is theoretically possible to distinguish between acquisition utility and transaction utility, actually doing so is conceptually and empirically diffi cult [39]. For this reason, many previous studies conceptualize acquisition utility as similar to perceived value (total utility) [26, 78]. For the same reason of empirical and conceptual feasibility, the present study consid-ers and measures only transaction utility and total utility.

05 kim.indd 13205 kim.indd 132 8/6/2007 12:22:56 PM8/6/2007 12:22:56 PM

INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 133

Determinants of Value-Driven Internet Shopping

Since the study examines Internet shopping decision-making at a specifi c on-line store, transaction utility will be measured with reference to a specifi c on-line store. In conceptualizing transaction utility, mental accounting theory focuses on its monetary aspect. A number of previous studies also focus on the monetary aspects of transaction utility [26, 39, 82]. However, customers do not necessarily purchase from the lowest-priced on-line stores [70]. Nonmon-etary aspects, such as time and effort, are also considered in making purchase decisions [30, 85]. In particular, customers carefully consider time and effort savings in making on-line purchases [79].

Apart from these purely cognitive reasons, customers are infl uenced by their intrinsic (hedonic) motivation (a nonmonetary aspect) to shop on-line [22]. For example, customers derive psychological satisfaction or pleasure from the advantages of the fi nancial terms of the deal, and this increases their transaction utility [39]. Therefore, the study measures transaction utility from both monetary and nonmonetary perspectives. Perceived price is considered from the monetary perspective. Convenience (representing time and effort) and pleasure (representing intrinsic motivation) are also considered from the nonmonetary perspective. The European Interactive Advertising Association further found that 7 of the top 10 products sold on-line belong to a search-product category [81]. Because most hot products sold on-line do not vary in quality, product quality is not here treated as a determinant of value by focusing on the search-product context.

Mental Assessment (Coding) of Attributes of Internet Shopping

The evaluation and decision-making processes are affected by the way cus-tomers assess the attributes of a transaction, such as price and convenience [78]. This assessment of attributes is referred to as hedonic editing. According to the hedonic-editing hypothesis, customers assess attributes in a manner that allows them to be as happy as possible—or put differently, in a way that enables them to derive maximum utility [78].

According to mental accounting theory, multiple attributes can be assessed either jointly (integration), that is, v(x + y), or separately (segregation), that is, v(x) + v(y). Taking two attributes of a transaction under consideration, Thaler classifi ed customer choice into four types and proposed the preferred evaluation approach: (I) multiple gains (segregation), (II) multiple losses (integration), (III) larger gains and smaller losses (integration), and (IV) smaller gains and larger losses (segregation) [78]. As purchasing in the frame of loss is not expected, customers would make their purchases only when they have all gains or larger gains on some attributes and smaller losses on other attributes [80]. Therefore, Options II and IV are dropped. According to mental accounting theory, in the frame of gain (x > 0, y > 0), that is, when all the attributes of a purchase are favorable, segregated evaluation (gain frame, Option I) is preferred for decision-making because value is concave in the frame of gain: v(x) + v(y) > v(x + y). If a

05 kim.indd 13305 kim.indd 133 8/6/2007 12:22:56 PM8/6/2007 12:22:56 PM

134 SUMEET GUPTA AND HEE-WOONG KIM

situation is of mixed gain (x > |y| or |x| < y), it is possible that v(x) + v(–y) < 0 or v(–x) + v(y) < 0 because value is steeper in loss. However, it is always v(x – y) > 0 or v(–x + y) > 0 in the frame of gain. For this reason, integrated evalua-tion (mixed frame, Option III) is preferred for value maximization in the case of mixed gain: v(x) + v(–y) < v(x – y) or v(–x) + v(y) < v(–x + y).

Role of Transaction Experience in Repurchase Decision Calculus

Explanation of Moderating Effect

The belief-adjustment model and cognitive-dissonance theory are two comple-mentary theories that explain the moderating effect of a customer’s purchase experience on the decision calculus in on-line repurchase [33, 45].

Belief-Adjustment Model. Hogarth and Einhorn proposed the belief-adjust-ment model for studying how customers update their beliefs over time [45]. When decisions are made in a sequence, customers update their beliefs with the current information according to a sequential anchoring-and-adjustment process. During the fi rst purchase, a customer develops a general perception of the attributes pertaining to the purchase-decision. This perception is known as the anchor. The strength of the anchor is adjusted with the new information received in the next transaction. The degree of adjustment depends upon the strength of the anchor and the polarity of the new information. If the anchor is strong and the new information received is positive, there would only be a slight increase in the strength of the anchor. If the new information is negative, there could be a considerable decrease in the strength of the anchor. Over the number of transactions, these positive and negative adjustments decrease in magnitude and the strength of the anchor becomes constant. Thus the mag-nitude of adjustments in a customer’s beliefs will decrease as the customer’s transaction experience with the on-line vendor increases.

Cognitive Dissonance Theory. Cognitive dissonance theory suggests that as customers gain fi rst-hand experience with Internet shopping, they evaluate the extent to which their initial cognition (beliefs, affect, and value) is consonant or dissonant with actual experience, and they revise their cognition or behavior to achieve greater consonance. With transaction experience, repeat customers’ cognitions reach steady-state equilibrium in that the customers have become more realistic about the observed behavior [8, 33].

In essence, the belief-adjustment model and cognitive dissonance theory explain that adjustments in beliefs or cognition attain steady-state equilibrium as a customer’s transaction experience with the Internet vendor increases. Once steady-state equilibrium is attained, the customer does not need to go through the process of cognitive evaluation of purchases and decision-making becomes more or less automatic.

Polarity of Moderating Effect

Although transaction experience certainly has a moderating effect, its polarity is still unclear. Information- processing theory gives some general propositions

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about the polarity of the moderating effect [5]. According to information-pro-cessing theory, as transaction experience increases, customer decision-making becomes a function of the most important and decision-relevant information. The most important and decision-relevant information could either be an overall evaluation or a belief. Therefore, the polarity of moderating effect would be positive for most important and decision-relevant information and negative for unimportant information.

Research Model and Hypotheses

The research model shown in Figure 1 is based on the preceding theoretical discussion. It shows two stages of decision-making as proposed by Thaler [78]. The fi rst, or judgment, stage consists of three components of transaction utility: perceived price, convenience, and pleasure. The overall evaluation of these three components represents perceived value, which is the total utility of a transaction. Based on previous research [78, 85], perceived value is defi ned as the net benefi ts (perceived benefi ts vis-à-vis perceived sacrifi ces) of a transac-tion with an Internet vendor.

According to mental accounting theory, customers assess the value of alternatives as gains or losses relative to a reference point. Customers derive their reference points from their expectations, their buying objectives, the sales messages they receive, or their need to justify their choices [67]. In any case, customers compare the net benefi ts resulting from the comparison between benefi ts and sacrifi ces and their reference points to derive total utility or perceived value. For example, customers would compare the convenience of purchasing at the on-line store with their previous experiences.

The second, or decision-making, stage consists of making a repurchase decision based on the total utility. As discussed earlier, customers can make

Figure 1. Research Model

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136 SUMEET GUPTA AND HEE-WOONG KIM

purchase decisions based on either integrated or segregated evaluation of at-tributes depending upon whether all the attributes are in the gain frame or the mixed gain and loss frame. Because it is not clear whether customers would go for segregated evaluation or integrated evaluation at this stage, both direct and indirect effects of attributes of Internet shopping on purchase intention will be proposed below.

A number of studies on consumer decision-making share the assumption that customers seek value maximization [48, 78, 85]. In other words, customers prefer to conduct transactions with vendors whose products and services offer maximal value. According to mental accounting theory, customers evaluate prospects based on the value of the prospect relative to their reference point and the degree of risk involved in choosing the prospect. Customers make repurchase decisions based on maximum value at the decision-making stage. Previous research supports the view that perceived value leads to repurchase intention [26, 85]. Hence:

H1: Perceived value positively infl uences repurchase intention for repeat customers.

In the context of Internet shopping, the monetary aspect of transaction utility is the difference between objective prices at an on-line store (whether characterized as a premium or an economy store) and a customer’s reference price. This monetary aspect of transaction utility is referred to as the perceived price. In marketing, perceived price is the same as reference price [26, 42]. It is, however, empirically measured as a reference price discrepancy variable (e.g., reference price–observed price) [42]. Therefore, perceived price is here defi ned as the perceived level of (monetary) price at a vendor (i.e., objective price) in comparison with the customer’s reference price. In practice, customers do not usually remember the actual price of a product [85]. They perceive prices in ways that are meaningful to them, such as being higher or lower than their reference price [26]. Thus, perceived price represents the monetary aspect of the transaction utility of purchasing from the on-line store.

According to Zeithaml, price plays the dual role of monetary sacrifi ce for obtaining a product and signal of product quality [85]. Most of the products sold on-line are search products whose quality can be assessed prior to pur-chase (e.g., books and CDs) [40]. Thus repeat customers of on-line vendors more often consider price a monetary sacrifi ce than a signal of product quality [68]. According to mental accounting theory, perceived price affects the mon-etary dimension of transaction utility. An increase in perceived price implies lower transaction utility. As transaction utility is a component of overall value according to mental accounting theory, perceived price should negatively af-fect total value. Prior research also supports the point that perceived price is negatively related to the perceived value of a transaction [e.g., 26]. Hence:

H2: Perceived price negatively infl uences perceived value for repeat customers.

In addition to the indirect effect of perceived price on repurchase intention through perceived value, perceived price may also infl uence repurchase inten-

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tion directly when customers make their purchase decisions using segregated evaluation. According to mental accounting theory, customers make choices based on segregated evaluation of attributes in the frame of multiple gains. Perceived price in the frame of gain means that the prices in the on-line store are lower than the customer’s reference price [26]. In such a case, perceived price may have a direct effect on repurchase intention. Risk-averse customers also tend to minimize expenses or ”losses” that are inevitable [48]. In such a situation, customers discount the available information and opt for the low price to minimize immediate expenses or fi nancial loss [48, 77]. Previous research also supports that customer repurchase intentions are related to customer price perceptions [26, 62]. Hence:

H3: Perceived price negatively infl uences repurchase intention for repeat customers.

Convenience is here considered to be a nonmonetary aspect of making transactions on the Internet. Convenience is one of the most important factors infl uencing on-line customers’ purchase decisions [79]. Using the conceptu-alization of convenience proposed by Berry, Seiders, and Grewal, convenience is here defi ned as customers’ perceptions of the time and effort involved in shopping on the Internet [4]. Although shopping on-line tends to be conve-nient, especially for search products that vary little in quality, on-line stores may differ in respect to certain aspects of convenience in shopping-related activities, such as searching, product information, ordering, payment, and delivery [40, 50]. For the same product, customers would prefer whichever on-line store offers the greater convenience.

Because convenience is a nonmonetary aspect of transaction utility, an increase in convenience should increase transaction utility and therefore perceived value. According to mental accounting theory, greater convenience means that less mental and physical energy (and thus time and effort) are expended in obtaining a product, thereby increasing transaction utility [30]. As transaction utility is a component of overall perceived value, convenience in Internet shopping would infl uence customers’ perceived value of shopping on the Internet. Hence:

H4: Convenience positively infl uences perceived value for repeat customers.

Convenience also may have a direct infl uence on repurchase intention through segregated evaluation. When all attributes are in the frame of gain, customers would opt for segregated evaluation of attributes when making repurchase decisions. Convenience would be in the frame of gain when the current on-line store is perceived as more convenient than other on-line stores. In such a case, customers would be inclined to make repurchases from the current on-line store. In addition, customer shopping behavior is enhanced by effi ciency in consumption [30]. Particularly for low-cost standardized items, customers regard time as more important than money [30]. As convenience represents customer perceptions about the time and effort involved in shop-ping on the Internet, customers would be motivated to decide their repurchases

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138 SUMEET GUPTA AND HEE-WOONG KIM

based on time savings and reduced hassles, especially for routine repurchase items. Hence:

H5: Convenience positively infl uences repurchase intention for repeat customers.

Consumption emotion refers to a set of emotional responses elicited specifi -cally during product usage or consumption experiences. The pleasure–arous-al–dominance (PAD) confi guration is used here to study consumption emotion because it allows for a greater range of positive emotions, as compared to only joy, happiness, and interest in other emotion models [61, 64]. According to the PAD confi guration, all emotional states can be represented by some combination of two major dimensions, pleasure and arousal [61]. Because the empirical evidence is inconsistent for arousal regarding purchase [27], only pleasure is used here to represent a customer’s intrinsic motivation to shop on the Internet. Pleasure is a post-consumption phenomenon, but it has been studied from the acquisition perspective, whereby previous consumption experiences infl uence future decisions. Pleasure refers to the degree to which a person feels good, joyful, happy, or satisfi ed in the situation [61]. De Wulf et al. defi ne pleasure as the extent to which the visitor perceives the Web site visit as enjoyable [21]. Gupta and Kim use pleasure gained from interactions in a virtual community as an antecedent of a customer’s attitude toward inter-acting in the virtual community [41]. Chen, Gillenson, and Sherrell suggested that the level of entertainment offered by a Web site is a key predictor of user attitude toward the site [14]. Childers et al. argue that the extent to which a Web site evokes hedonic feelings signifi cantly infl uences the customer’s In-ternet shopping experience [16]. Following Mehrabian and Russell, pleasure is defi ned here as the degree to which a customer feels good or happy about previous transactions with the Internet vendor [61].

Research in customers’ affective processing mechanisms posits that the emotions elicited during consumption experiences leave strong affective traces or markers in episodic memory [19]. These memory elements are highly ac-cessible to current cognitive operations. When an evaluation of the relevant consumption experience is required, the affective traces are readily retrieved and their variances are integrated into the evaluative judgment (perceived value). Thus, pleasure as an emotional response to previous transactions with the Internet vendor would infl uence a customer’s perceived value of Internet shopping.

H6: Pleasure positively infl uences perceived value for repeat customers.

Pleasure also may have a direct infl uence on repurchase intention through segregated evaluation. When customers experience positive pleasure in con-ducting transactions with the on-line store, their pleasure is in the frame of gain. If multiple attributes are in the frame of gain, customers may go for seg-regated evaluation. In such cases, the pleasure of purchasing from the current Internet vendor will infl uence their repurchase intention. In addition, according to Lazarus, coping responses are important mechanisms for inferring action

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and goal attainment from feelings [54]. Depending on the feelings generated, behavioral intentions emerge to activate plans for avoiding undesirable out-comes or increasing/maintaining positive outcomes [3]. Coping with positive emotions often involves sharing one’s good fortune, savoring the experience, and working to continue to increase the rewards. Pleasure, as a positive affect, will result in actions to savor the experience longer and increase the rewards. Thus, consumers experiencing pleasure would be encouraged to repurchase from the same vendor. Hence:

H7: Pleasure positively infl uences repurchase intention for repeat customers.

According to cognitive dissonance theory, customers modify their evalu-ations to remove any dissonance between current and previous evaluations. After a few transactions this adjustment will reduce in magnitude until the customer reaches steady-state equilibrium. With transaction experience, the customer’s perceived value of purchasing on-line would change, which con-fi rms the moderating effect of transaction experience.

Attitude-behavior theories can contribute to an understanding of the polar-ity of this moderating effect. According to Ajzen, frequent performance of a behavior infl uences future purchase intention to such an extent that the be-havior becomes largely independent of attitudes and intentions [1]. Frequency of past behavior has been shown to infl uence additional variance in purchase intention. Ajzen argues that intentions may become largely irrelevant when a behavior has been performed many times. Behavioral intentions become independent of attitudes and conscious evaluation when the behavior has been performed very frequently [2]. In the present research, perceived value plays a role similar to that of attitude in predicting intentions. Perceived value is an individual’s evaluation of the attributes of purchase decision, while at-titude entails a summary evaluation of the psychological object based on its attributes [1]. Therefore, transaction experience would negatively moderate the infl uence of perceived value on purchase intention. Hence:

H8: Transaction experience negatively moderates the relationship between perceived value and purchase intention for repeat customers.

Customer perceptions of prices at an on-line store may vary with succes-sive transactions according to the belief-updating model. Since the total price consists of product price, search costs, and disappointment costs, customers may perceive in later purchases that even when the product price is low, other costs (e.g., delivery costs, which are not usually bundled with product costs in on-line stores) raise the overall costs, and vice-versa [31]. Thus, with every subsequent purchase, customers would adjust their price perceptions at the on-line store. The magnitude of such adjustments would, however, become smaller over the span of the transaction experience.

The polarity of moderating effect is discussed in several previous studies. Studies of customer repeat-purchase behavior suggest that repeat customers are less price-sensitive and spend more at Internet stores, which suggests that the infl uence of perceived price on purchase intention should weaken in

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140 SUMEET GUPTA AND HEE-WOONG KIM

strength with transaction experience [70]. Consistent with the proposition of customer choice in information-processing theory, one reason for the decrease in price-sensitivity could be that customers become less motivated to evalu-ate price information as their transaction experience with the Internet vendor increases. Another reason for the decrease in price-sensitivity among repeat customers is that customers who are not price-sensitive are the only ones who stay with the on-line vendor [68]. Therefore, the impact of perceived price on repurchase intention would decrease with transaction experience. Hence:

H9: Transaction experience negatively moderates the relationship between perceived price and repurchase intention for repeat customers.

According to the belief-updating model, customers adjust their beliefs about the convenience of purchasing from the Internet vendor with every successive transaction. Convenience is subject to great fl uctuation, because problems may creep in with search, payment, and delivery during any transaction. However, these adjustments in convenience would become of less magnitude with a customer’s transaction experience.

Regarding the polarity of moderating effect, Gefen, Karahanna, and Straub found that the relationship between perceived usefulness and behavioral intention becomes stronger as individuals gain direct experience with infor-mation technology [37]. Convenience is one of the most important benefi ts (perceived usefulness) of Internet shopping [79]. Thus the relationship between convenience and purchase intention should become stronger with transaction experience. In addition, according to the information-processing theory of customer choice, customer decision-making also focuses on the most impor-tant and decision-relevant information as transaction experience increases. Bhatnagar, Misra, and Rao assert that the customer’s risk perception of shop-ping on Internet is overshadowed by its relative convenience, implying that convenience is an important attribute for customer-purchase decision-making [6]. Therefore, as transaction experience increases, customers rely on simple cues like convenience rather than complete rational assessment, according to the information-processing theory of customer choice. Hence:

H10: Transaction experience positively moderates the relationship between convenience and repurchase intention for repeat customers.

There is always the chance of failure in a transaction with an Internet vendor. Problems may occur during ordering, processing, or delivery. Failures cause dissatisfaction, displeasure, and a negative perception of the Internet store. A customer’s cumulative pleasure with previous purchases is adjusted dynami-cally with new information, such as service failure/success. This may affect the customer’s purchase intention. However, the magnitude of the adjustment will depend upon the customer’s transaction experience with the Internet store. Customers who have more transaction experience with the store weigh prior cumulative pleasure more heavily than the new information as proposed by the belief-adjustment model [45].

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Regarding the polarity of moderating effect, Lazarus asserts that coping responses are important mechanisms for inferring action and goal attainment from feelings [54]. Depending on the feelings generated, behavioral intentions emerge to activate plans for the avoidance of undesirable outcomes or the increase/maintenance of positive outcomes [3]. Coping with positive emo-tions (e.g., pleasure) often involves sharing one’s good fortune, savoring the experience, and working to continue or increase the rewards. Since pleasure is a positive affect, it will result in actions to savor the experience longer and increase the rewards. Thus, consumers experiencing pleasure in shopping with an on-line vendor would be encouraged to repurchase. Many studies support the assertion that affect is an important predictor of customer repurchase inten-tion (e.g., [7]). When prior experience has been extensive, emotional factors may emerge as a dominant infl uence on behavior [2]. Hence:

H11: Transaction experience will positively moderate the relationship be-tween pleasure and repurchase intention.

Research Methodology

Data Collection

Products sold on-line can be categorized as search products or as experience products [20, 40]. Books, airline tickets, and CDs fall under the category of search products, because their quality can be assessed before purchase. Experi-ence products, such as wine and stereo systems, are products whose quality can be ascertained only by use [40]. The study controlled for quality variation, which might have confounded the results, by using search products—more specifi cally, an Internet bookstore. The empirical data for the study were col-lected from actual repeat customers of an on-line bookstore over a period of 10 days. The survey was publicized with a banner on the bookstore’s Web site, and respondents accessed the survey Web site from the store’s home page. To ensure that customers actually browsed the Web site, they were asked to note the title and price of a book they were interested in before going on to answer the questions. Participation was encouraged by accompanying the survey with a lottery that offered prizes of 10,000 Korean won (about $10) to 200 respondents.

Respondent Characteristics

The Internet survey collected 814 responses. The database was checked for duplicate responses. Four duplicates ( the result of pressing the ”submit” button twice) were dropped. This left 810 valid responses. Table 2 shows the demographic characteristics of the on-line customers. Nonresponse bias was assessed by comparing the on-line customer sample to the registered on-line customer database of the Internet bookstore. As shown by t-tests, the sample of on-line customers and the population of registered on-line customers did

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142 SUMEET GUPTA AND HEE-WOONG KIM

not differ signifi cantly in terms of age and repurchase experience with the bookstore. A Mann-Whitney test revealed no signifi cant difference in gender ratio between the sample of on-line customers and the population of registered on-line customers.

Instrument Development

The survey instrument was developed by adopting existing validated ques-tions wherever possible. Some items were self-developed for more accurate fi t between the instrument and the context of the study. Items for repurchase intention were adopted from Dodds, Monroe, and Grewel [26]. Items for convenience were adapted from Torkzadeh and Dhillon and from Childers et al. [16, 79]. Items for pleasure were adopted from Holbrook et al. [47]. Items for perceived value were adapted from Sirdeshmukh, Singh, and Sabol, with an additional item related to risk included for completeness in the measure [75]. Because customers form their perceptions of price by comparing actual prices with their reference prices [26], items for perceived price were devel-oped that allowed customers to make such comparisons using the prices of other bookstores as references. The variables were measured on a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree).

Three IS scholars and one marketing scholar reviewed the instrument for face validity. Next, a focus group of 10 people was formed to review the in-strument. Some of the members had Internet shopping experience, but others did not. Feedback was obtained pertaining to the length of the instrument, the clarity of the questions, and the completeness of coverage of the questions. The fi nal instrument is shown in Appendix A.

Data Analysis and Results

Instrument Validation

Validation began with principal component analyses using varimax rotation (Appendix B). The analysis reveals fi ve factors with eigenvalues greater than

Demographic variable Data

Age (years) M(SD) 30.2 (7.5)Internet usage experience (years) M(SD) 7.0 (2.3)Transaction experience with the bookstore (times) M(SD) 14.5 (14.3)Gender Female 62.9% Male 37.1%Number of responses 810

Table 2. Descriptive Statistics of Respondents’ Characteristics.

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1.0, the minimum being 1.10. All items were loaded on a distinct factor and explained a total variance of 78.73 percent. There was no evidence of any cross-loading.

Next came a check for unidimensionality. Following standard LISREL methodology, the measurement model was revised by discarding, one at a time, items that shared a high degree of residual variance with other items [38]. The test results indicated that the second item of perceived price (PRCE2) violated unidimensionality, and discarding it would reduce χ2 signifi cantly. Therefore PRCE2 was dropped. Other items were not dropped because the error covariance between a pair of items resulted in little change in χ2 (< 20), thus preventing over-fi tting. After PRCE2 was dropped, the CFA showed good fi t.

The convergent validity and discriminant validity of the constructs were assessed next. Convergent validity was assessed on the following criteria: (a) Individual item lambda coeffi cients should be greater than 0.70, and each path loading should be greater than twice its standard error. (b) A signifi cant t-statistic should be obtained for each path (standardized path loadings indi-cating the degree of association between the underlying latent factor, and each item should be signifi cant) [38]. (c) The composite reliabilities (CR) for each construct should be greater than 0.7. (d) The average variance extracted (AVE) for each factor must exceed 50 percent [34]. As shown in Table 3, all standard-ized path coeffi cients (except PRCE1) were greater than 0.7. The individual path loadings were all greater than twice their standard error. The t-statistic was signifi cant for all the items. The CR for each construct was greater than 0.7, and the AVE for each construct was greater than 0.5. Thus convergent validity was adequately established.

The discriminant validity of the measurement model was assessed by comparing the squared average variance extracted for each construct with the correlations between that construct and other constructs [34]. As shown in Table 4, the average variance extracted for each construct exceeded the squared correlations between the construct and other constructs, thus indicating dis-criminant validity. Hence, discriminant validity was established.

Hypothesis Testing

The structural model was examined using LISREL. First, the model fi t indices were checked. The fi t indices for the repeat customer structural model sug-gested an excellent fi t: The normed χ2 was 3.43, which was good. Usually, the recommended value of normed χ2 is below 3.0. However, as it is sensitive to sample size, a more liberal limit of 5.0 has been recommended [43]. The root mean square error of approximation (RMSEA) was 0.055, indicating a good fi t, being below the maximum desired cut-off of 0.06 [38]. The root mean-square residual (RMR) was 0.034, lower than the desired maximum cut-off of 0.05 [43]. The goodness-of-fi t index (GFI) was 0.94, and the adjusted goodness-of-fi t index (AGFI) was 0.92, both of which were above the recommended thresholds of 0.9 and 0.8 [38] respectively. The other fi t indices were all satisfactory: CFI = 0.99, NFI = 0.98, and the non-normed fi t index (NNFI) = 0.98. These results suggest that the structural model adequately fi tted the data.

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144 SUMEET GUPTA AND HEE-WOONG KIM

Std.I tem loading t-value AVE CR α

PINT1 0.88 31.50 0.76 0.93 0.92PINT2 0.92 33.83PINT3 0.90 32.81PINT4 0.76 25.21PVAL1 0.89 31.65 0.74 0.92 0.92PVAL2 0.87 30.92PVAL3 0.80 26.89PVAL4 0.89 31.85PRCE1 0.57 16.43 0.60 0.81 0.86PRCE3 0.88 27.32PRCE4 0.84 25.91CONV1 0.91 33.17 0.80 0.94 0.94CONV2 0.96 36.49CONV3 0.86 30.67CONV4 0.85 30.07PLEA1 0.86 30.70 0.85 0.96 0.96PLEA2 0.93 35.01PLEA3 0.95 35.94PLEA4 0.94 35.76

Table 3. Results of Convergent Validity Testing.

I tems M(SD) PINT PVAL PRCE CONV PLEA

PINT 5.99 (1.03) 0.87PVAL 5.58 (1.07) 0.57** 0.86PRCE 3.41 (1.21) –0.29** –0.34** 0.77CONV 5.60 (1.14) 0.49** 0.64** –0.25** 0.89PLEA 5.50 (1.15) 0.47** 0.57** –0.24** 0.55** 0.92

Table 4. Correlations Between Latent Variables.

Note: Bold number shows square roots of AVE for that construct (**p < 0.01)

Figure 2 shows the standardized LISREL path coeffi cients and the overall fi t indices. All paths were signifi cant. Perceived value (H1), perceived price (H3), convenience (H5), and pleasure (H7) had signifi cant effects on repur-chase intention, explaining 39 percent of the variance. Perceived price (H2), convenience (H4), and pleasure (H6) had signifi cant effects on perceive value, explaining 57 percent of the variance.

A moderated regression analysis (MRA) was conducted to test the mod-erating role of transaction experience, as suggested by Sharma, Durand, and Oded [72]. As the scale of the moderator variable was different from that of the predictor variables, there was a possibility of multicollinearity between the interaction terms and the constituent variables. Multicollinearity due to scale invariance was prevented by centering the predictor variables and the moderator variable [18]. Interaction terms were then obtained by multiply-ing the centered predictor variables with the centered moderator variable.

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The criterion variable was not centered. When it is in the original scale, the predicted scores will be in the units of the original scale and will have the same arithmetic mean as the observed criterion scores [72]. The correlation between the centered variables is shown in the Table 5. The results reveal that none of the interactions was highly correlated (> 0.06), thus preventing multicollinearity.

The testing results are shown in Table 6. They reveal that the transaction experience moderates the effect of perceived price on repurchase intention (β = 0.108, p = 0.001) and of convenience on repurchase intention (β = –0.125, p = 0.003). Also, Models I, II, and III are all signifi cantly different from each other. However, there is no moderating effect of transaction experience on the relationship of perceived value and pleasure with repurchase intention. Thus, H11 and H13 were supported, whereas H10, H12, and H14 were not supported.

To further analyze the exact nature of the moderating effect, a subgroup analysis test was adopted, as suggested by Sharma et al. [72]. The repurchase-customer data were split into two sets about the mean transaction experi-ence (14.5): a low transaction experience group (LTE) and a high transaction experience group (HTE). Figure 3 shows a graphical representation of the moderating effects.

Discussion and Implications

Discussion of Findings

The study made several interesting fi ndings. First, it identifi ed the factors that infl uence on-line customer purchase-decision calculus from the perspective of mental accounting theory. The empirical results confi rmed that all the identi-

Figure 2. Structural Model for Main Effects HypothesesNotes: Normed χ2 = 3.43, RMSEA = 0.055, RMR = 0.033, NFI = 0.98, NNFI = 0.98, CFI = 0.99, GFI = 0.94, AGFI =0.92. ns = not signifi cant; * p < 0.05; ** p <0.01; *** p <0.001.

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146 SUMEET GUPTA AND HEE-WOONG KIM

Moderator Predictors

Interaction TranExp PVAL PRCE CONV PLEA

TranExp*PVAL 0.161** –0.119** — — —TranExp*PRCE –0.135** — –0.067 — —TranExp*CONV 0.189** — — –0.043 —TranExp*PLEA –0.130** — — — 0.000

Table 5. Correlation of Centered Variables with Interaction Terms.

* p < 0.05; ** p <0.01; *** p <0.001.

Standardized Beta

Variables Model I Model II Model III

Criterion Repurchase intention

Predictors Perceived value (PVAL) 0.166*** 0.164*** 0.190*** Perceived price (PRCE) –0.298*** –0.301*** –0.288*** Convenience (CONV) 0.162*** 0.144*** 0.125** Pleasure (PLEA) 0.110** 0.115** 0.108**

Moderator Transaction experience 0.114*** 0.133*** (TranExp)

Interaction TranExp*PVAL 0.079terms TranExp*PRCE 0.105** TranExp*CONV –0.107** TranExp*PLEA –0.027

Results of R2 0.323 0.335 0.352analysis ∆R2/F-stat Models I and II 0.012/14.49 *** Models II and III 0.017/5.24 ** Models I and III 0.029/7.15***

Table 6. Moderated Regression Analysis for Transaction Experience.

* p < 0.05; ** p < 0.01; *** p < 0.001.

Figure 3. Graph of Moderating Effect of Transaction Experience on Potential and Repeat Customers

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fi ed monetary and nonmonetary factors signifi cantly infl uence the perceived value of purchasing from an on-line store. The results also show that all four factors (perceived value, perceived price, pleasure, and convenience) signifi -cantly infl uence customer-purchase intention.

Second, the empirical results of the study show that customer-transaction experience acts as a quasi-moderator rather than a pure moderator, because it has a signifi cant infl uence on customer-purchase intention (β = 0.114, p = 0.000). This fi nding is consistent with the many previous studies that found past experience to be a signifi cant predictor of repurchase behavior when the behavior is performed a number of times [1, 2, 33]. However, Ajzen also argues that past experience explains a substantial amount of variance in repurchase behavior when the behavior becomes habitual [1]. Because the amount of vari-ance explained in customer-purchase intention by the addition of transaction experience is quite low, one can infer that the customers in the study have not yet become habitual.

Regarding individual relationships, transaction experience had a signifi -cant moderating infl uence on the relationship between perceived price and purchase intention. With increasing transaction experience, the infl uence of perceived price on purchase intention loses strength. This is consistent with the belief-adjustment model in that the adjustments customers make in their beliefs lose strength over the span of purchase experiences. This also provides support for the studies holding that repeat customers are less price-sensitive [e.g., 70].

Another interesting fi nding is that the infl uence of convenience on purchase intention loses strength with increased transaction experience. It was argued above that convenience, as an important benefi t of Internet shopping, may be important and decision-relevant, and therefore may infl uence customer-purchase intention with increasing transaction experience. However, the importance of convenience decreases with transaction experience, perhaps because once customers develop perceptions about convenience, their ex-pectations about the convenience of the on-line store either become neutral or increase (so that they need not even recall it for purchase decision-making in later transactions).

However, the study found that transaction experience has no signifi cant moderating effect on the relationship between pleasure and purchase inten-tion. Since pleasure is an affective attribute, it may not undergo cognitive evaluation with every subsequent purchase. If so, there would be no change in its infl uence on purchase intention with transaction experience. The study also found that transaction experience had no signifi cant moderating effect on the relationship between perceived value and purchase intention. Previous research found that emotion is a better predictor of behavior than attitude (i.e., value in the present research) when the behavior has become habitual [2]. As was argued above, the relatively low infl uence of past behavior on intention implies that the customer-purchase behavior in the present study had not yet become habitual. Therefore, the infl uence of perceived value on purchase intention does not change with transaction experience.

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148 SUMEET GUPTA AND HEE-WOONG KIM

Further Analysis

The path estimation for the LTE and HTE groups is shown in Figure 4. For the LTE group, perceived value, perceived price, and convenience had a sig-nifi cant infl uence on purchase intention. The total variance explained was 45 percent. For the HTE group, only perceived value had a signifi cant infl uence on purchase intention, and the total variance explained was 23 percent.

A between-groups constrained test was used to compare the LTE and HTE groups [11]. For perceived value, the χ2 difference was signifi cant (∆χ2 = 6.08, ∆df = 1, p = 0.014). The path coeffi cients indicate that perceived value had a stronger infl uence on purchase intention for the HTE group than for the LTE group (see Figure 4). For perceived price, the χ2 difference was also signifi cant (∆χ2 = 7.17, ∆df = 1, p = 0.007). The path coeffi cients indicate that perceived price had a weaker infl uence on purchase intention for the HTE group than the LTE group (see Figure 4). For convenience, the χ2 difference was signifi cant (∆χ2 = 7.86, ∆df = 1, p = 0.005). The path coeffi cients indicate that convenience had a weaker infl uence on purchase intention for the HTE group than for the LTE group (see Figure 4). The chi-square difference was not signifi cant for pleasure (∆χ2 = 0.09, ∆df = 1, p = 0.764).

As for the moderating effect of the transaction experience on the relationship between perceived value and purchase intention, the results of the subgroup analysis show that the infl uence of perceived value on purchase intention increased with transaction experience. As is apparent from Figure 4, only perceived value infl uences purchase intention for HTE group customers. This contradicts the hypothesis that transaction experience should have a negative infl uence on the relationship between perceived value and purchase intention. One reason could be the mean split. Since it is not known exactly where to split the sample, the split itself could produce biased results. However, accepting the mean split, the results imply that perceived value is the most important purchase- decision criterion for more experienced repeat customers accord-ing to information-processing theory, and therefore, its infl uence on purchase intention increases with transaction experience.

Figure 4. Structural Models for LTE and HTE Groups Notes: LTE: Normed χ2 = 2.42, RMSEA = 0.054, RMR = 0.035, NFI = 0.97, NNFI = 0.98, CFI = 0.98, GFI = 0.92, AGFI =0.89. HTE: Normed χ2 = 1.93, RMSEA = 0.054, RMR = 0.044, NFI = 0.97, NNFI = 0.98, CFI = 0.98, GFI = 0.90, AGFI =0.87. ns = not signifi cant; * p < 0.05; ** p < 0.01; *** p < 0.001.

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Limitations and Future Research

The results of the study must be interpreted in the context of its limitations. First, the data for the study were collected from the customers of a single Internet bookstore. It would be useful to replicate the study over a variety of Internet vendors to establish the robustness of the results. Second, since the questions of all the constructs were collected at the same time, and via the same survey instrument, there was a possibility of common method bias. Last, books fall into the category of low-involvement products. Transaction experi-ence may have a different moderating effect for high-involvement products. Future studies should replicate the study with high-involvement products to establish the generalizability of the results.

Implications for Theory and Practice

The present research has several implications for theory. First, it explains decision calculus in on-line repurchase from the value perspective based on mental accounting theory [78]. By applying the transaction utility of mental accounting theory, the study identifi es the antecedents of perceived value (perceived price, convenience and pleasure) and explains how they infl uence on-line repurchase decision-making. The results of the study indicate that all the identifi ed factors, including perceived value, have a signifi cant impact on repurchase intention.

Next, the study explains the moderating effect of transaction experience on the decision calculus in on-line repurchase. Most previous e-commerce research neglected the change in the customer-repurchase decision calculus with transaction experience. This study explains how transaction experience affects the decision calculus in on-line repurchase based on the belief-adjust-ment model and cognitive dissonance theory [33, 45].

The study further examines the polarity of moderating effect. There is no theoretical support to the direction of moderating effect of transaction experi-ence. The empirical examination of the moderating effect of transaction experi-ence reveals that the moderating effect is signifi cant for beliefs (i.e., perceived price and convenience) but not for affect (i.e., pleasure) and evaluation (i.e., perceived value). In other words, affective attributes do not change over the span of transaction experience. Since the difference in decision-making arises due to beliefs, satisfaction-based models would not show differences in the customer-repurchase decision calculus over the transaction experience [7]. In addition, because both pleasure and perceived value signifi cantly infl uence customer repurchase intention over the transaction experience, the fi ndings of this study extend those of previous studies, which identify only satisfaction as a signifi cant predictor of customer continuance intention [7].

The present research has several practical implications for Internet vendors. First, the results indicate that Internet vendors should differentiate repeat customers based on their transaction experience. Repeat customers become less sensitive to convenience and perceived price over the span of transaction experience. Therefore, Internet vendors should provide greater convenience

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150 SUMEET GUPTA AND HEE-WOONG KIM

options, price discounts, or coupons for less experienced customers. They can increase purchase convenience by offering express delivery, convenient pay-ment options (e.g., prepaid cards, debit cards, cash-on delivery) and convenient search options (e.g., mylist for frequently purchased items). They can also make it easy to return items, perhaps by arranging for nearby off-line stores to accept returns. Internet vendors also can offer varied delivery options to suit the customer’s schedule and other requirements.

With repeat customers who are more experienced, Internet vendors should focus on providing greater value in shopping. This could include value-added options, such as same-day delivery, browsing excerpts from a book, and so on. Amazon.com, for example, provides customer reviews of books and products that help potential customers to evaluate them better, and it allows customers to view excerpts from the books so that they can make informed purchase decisions. Amazon.com also provides several value-oriented services, such as same-day delivery in selected cities and favorable payment options, all of which enhance customer perceptions of the value of shopping with Amazon. Similarly, an Internet vendor can provide free book covers or bundle the book with another title on a related topic to enhance the customer’s perceived value of shopping with the store. To add value to their offerings, Internet vendors can provide discussion platforms or chat areas where customers can post queries to the vendors as well as to other Web site members or shoppers. In this way, customers can share information and guidance among themselves for all to see. By providing a platform for discussion and criticism, the Internet vendor projects an image of openness and honesty.

Next, Internet vendors should take steps to accelerate the transaction ex-perience of repeat customers, because greater transaction experience has a direct and signifi cant effect on customer repurchase intention. For example, Internet vendors can develop a rebate program whereby a customer with a specifi c number of transactions can enjoy special services on repurchases. By adopting this strategy, Internet vendors would accelerate less-experienced repeat customers to the more-experienced stage.

Last, since repeat customers reduce the cognitive effort in decision-making by recalling prior experiences, Internet vendors should help them to recall past experiences. For example, an Internet vendor could enumerate the customer’s previous successful transactions or could aid recall by enumerating the suc-cess rate of other customers with the Internet vendor. Vendors could also give points for purchases and display the point tally when a past customer visits the vendor again, reminding the customers of the benefi ts the vendor will provide for different levels of accumulated points.

Conclusion

Studies of on-line repurchase behavior often neglect the changes in the cus-tomer’s repurchase decision calculus that come with transaction experience. In addition, hardly any research has examined the polarity of the moderating effect of transaction experience on decision calculus in on-line repurchase. Going beyond previous research, the present study has examined the moderat-

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INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 151

ing effect of transaction experience on on-line repurchase decision calculus in addition to identifying factors infl uencing decision calculus based on mental accounting theory. The results show that customers’ beliefs, such as perceived convenience and perceived price, have different effects on repurchase deci-sions over the span of transaction experience. The study also found that the effects of perceived value and pleasure on repurchase decisions do not change over the transaction experience. As customers tend to change their decision criteria with transaction experience, Internet vendors should adopt different sales strategies for different groups of repeat customers. The fi ndings in this study thus have signifi cant implications for both the theory and practice of Internet shopping.

NOTES

1. The term “decision calculus” was coined to designate the cognitive processes a customer undergoes when making purchase decisions.

2. Thaler uses the term “utility” instead of “value” [78]. Since the two terms have the same meaning, they will be used interchangeably in this study [49].

REFERENCES

1. Ajzen, I. Nature and operation of attitudes. Annual Review of Psychology, 52 (2001), 27–58.

2. Allen, C.T.; Machleit, K.A.; and Kleine, S.S. A comparison of attitudes and emotions as predictors of behavior at diverse levels of behavioral expe-rience. Journal of Consumer Research, 18, 4 (March 1992), 493–504.

3. Bagozzi, R.P. The self-regulation of attitudes, intentions, and behavior. Social Psychology Quarterly, 55, 2 (June 1992), 178–204.

4. Berry, L.L.; Seiders, K.; and Grewal, D. Understanding service conve-nience. Journal of Marketing, 66, 3 (July 2002), 1–17.

5. Bettman, J.R. An Information Processing Theory of Consumer Choice. Read-ing, MA: Addison-Wesley, 1979.

6. Bhatnagar, A.; Misra, S.; and Rao, H.R. On risk, convenience, and Inter-net shopping behavior. Communications of the ACM, 43, 11 (November 2000), 98–105.

7. Bhattacherjee, A. Understanding information systems continuance: An expectation-confi rmation model. MIS Quarterly, 25, 3 (September 2001), 351–370.

8. Bhattacherjee, A. An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32, 2 (December 2001), 201–214.

9. Bhattacherjee, A. Individual trust in online fi rms: Scale development and initial test. Journal of Management Information Systems, 19, 1 (summer 2002), 211–241.10. Bhattacherjee, A., and Premkumar, G. Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28, 2 (June 2004), 229–254.

05 kim.indd 15105 kim.indd 151 8/6/2007 12:22:59 PM8/6/2007 12:22:59 PM

152 SUMEET GUPTA AND HEE-WOONG KIM

11. Byrne, M.B. Structural Equation Modeling with LISREL, PRELIS, and SIM-PLIS: Basic Concepts, Applications, and Programming. Mahwah, NJ: Lawrence Erlbaum, 1998.12. Cazier, J.A.; Shao, B.B.M.; and St. Louis, R.D. E-business differentiation through value-based trust. Information & Management, 43, 6 (September 2006), 718–727.13. Chang, M.K.; Cheung, W.; and Lai, V.S. Literature derived reference models for the adoption of online shopping. Information and Management, 42, 4 (May 2005), 543–559.14. Chen, L.; Gillenson, M.L.; and Sherrell D.L. Consumer acceptance of vir-tual stores: A theoretical model and critical success factors for virtual stores. ACM SIGMIS Database, 35, 2 (June 2004), 8–31.15. Chen, Z., and Dubinsky, A.J. A conceptual model of perceived customer value in e-commerce: A preliminary investigation. Psychology and Marketing, 20, 4 (April 2003), 323–347.16. Childers, T.L.; Christopher, L.C.; Peck, J.; and Carson, S., Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retail-ing, 77, 4 (winter 2001), 511–535.17. Cho, V. A study of the roles of trusts and risks in information-oriented online legal services using an integrated model. Information & Management, 43, 4 (June 2006), 502–520.18. Cohen, J.; Cohen, P.; West, S.G.; and Aiken, L.S. Applied Multiple Re-gression/Correlation Analysis for the Behavioral Sciences, 3d ed. Hillsdale, NJ: Lawrence Erlbaum, 2003.19. Cohen, J.B., and Areni, C.S. Affect and consumer behavior. In S.R. Thomas and H.H. Kassarjian (eds.), Handbook of Consumer Behavior. Engle-wood Cliffs, NJ: Prentice Hall, 1991, pp. 188–240.20. Darby, M.R., and Karni, E. Free competition and the optimal amount of fraud. Journal of Law and Economics, 16, 1 (1973), 67–88.21. De Wulf, K.; Schillewaert, N.; Muylle, S.; and Rangarajan, D. The role of pleasure in Web site success. Information & Management, 43, 4 (June 2006), 434–446.22. Deci, E.L. Intrinsic Motivation. New York: Plenum, 1975.23. DeLone, W.H., and McLean, E.R. Measuring e-commerce success: Ap-plying the DeLone & McLean information systems success model. Interna-tional Journal of Electronic Commerce, 9, 1 (fall 2004), 31–47.24. Dinev, T., and Hart P. Internet privacy concerns and social awareness as determinants of intention to transact. International Journal of Electronic Com-merce, 10, 2 (winter 2005), 7–29.25. Dinev, T., and Hart, P. An extended privacy calculus model for e-com-merce transactions. Information Systems Research, 17, 1 (March 2006), 61–80.26. Dodds, W.B.; Monroe, K.B.; and Grewal, D. Effects of price, brand and store information on buyers’ product evaluations. Journal of Marketing Re-search, 28, 3 (August 1991), 307–319.27. Donovan, R.J.; Rossiter, J.R.; Marcoolyn, G.; and Nesdale, A. Store atmo-sphere and purchasing behavior. Journal of Retailing, 70, 3 (autumn 1994), 283–294.

05 kim.indd 15205 kim.indd 152 8/6/2007 12:22:59 PM8/6/2007 12:22:59 PM

INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 153

28. DoubleClick Q1 2004 E-commerce Site Trend Report. (June 2004), www.spunlogic.com/media/pdf/DoubleClickQ12004ECommerceTrendReport.pdf.29. DoubleClick Q3 2004 E-commerce Site Trend Report. (November 2004), http://emea.doubleclick.com/WEB_ADMIN/documents/dc_q304ecom-merce_04111.pdf.30. Downs, A. A theory of consumer effi ciency. Journal of Retailing, 37, 1 (spring 1961), 6–12.31. Ehrlich, I., and Fisher, L. The derived demand for advertising: A theo-retical and empirical investigation. American Economic Review, 72, 3 (June 1982), 366–388.32. Elliott, M.T., and Speck, P.S. Factors that affect attitude toward a retail Web site. Journal of Marketing Theory and Practice, 13, 1 (winter 2005), 40–51.33. Festinger, L. A Theory of Cognitive Dissonance. Evanston, IL: Row & Peter-son, 1957.34. Fornell, C., and Larcker, D. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Re-search, 18, 1 (February 1981), 39–50.35. Francis J.E., and White, L. Value across fulfi llment-product categories of internet shopping. Managing Service Quality, 14, 2/3 (2004), 226–234.36. Gefen, D. TAM or just plain habit? A look at experienced on-line shop-pers. Journal of End User Computing, 15, 3 (July 2003), 1–13.37. Gefen, D.; Karahanna, E.; and Straub, D.W. Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on Engineering Management, 50, 3 (August 2003), 307–321.38. Gefen, D.; Straub, D.W.; and Boudreau, M. Structural equation model-ing and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4, 7 (August 2000), 2–76.39. Grewal, D.; Monroe, K.B.; and Krishnan, R. The effects of price com-parison advertising on buyers’ perceptions of acquisition value, transaction value, and behavioral intentions. Journal of Marketing, 62, 2 (April 1998), 46–59.40. Gupta, A.; Su, B.C.; and Walter. Z. An empirical study of consumer switching from traditional to electronic channels: A purchase-decision process perspective. International Journal of Electronic Commerce, 8, 3 (spring 2004), 131–161.41. Gupta, S., and Kim, H.W. Developing the commitment to virtual com-munity: The balanced effects of cognition and affect. Information Resources Management Journal, 20, 1 (January/March 2007), 28–45.42. Gurumurthy, K., and Russell, S.W. Empirical generalizations from refer-ence price research. Marketing Science, 14, 3 (summer 1995), 161–169.43. Hair, J.F., Jr.; Anderson, R.E.; Tatham, R.L.; and Black, W.C. Multivariate Data Analysis, 5th ed. Upper Saddle River, NJ: Prentice Hall, 1998.44. Hampton-Sosa, W., and Koufaris, M. The effect of Web site perceptions on initial trust in the owner company. International Journal of Electronic Com-merce, 10, 1 (fall 2005), 55–81.45. Hogarth, R., and Einhorn, H. Order effects in belief updating: The belief-adjustment model. Cognitive Psychology, 24, 1 (January 1992), 1–55.

05 kim.indd 15305 kim.indd 153 8/6/2007 12:22:59 PM8/6/2007 12:22:59 PM

154 SUMEET GUPTA AND HEE-WOONG KIM

46. Hogarth, R.M. Beyond discrete biases: Functional and dysfunctional aspects of judgmental heuristics. Psychological Bulletin, 90, 2 (March 1981), 197–217.47. Holbrook, M.B.; Chestnut, R.W.; Olivia, T.A.; and Greenleaf, E.A. Play as consumption experience: The roles of emotions, performance, and personal-ity in the enjoyment of games. Journal of Consumer Research, 11, 2 (September 1984), 728–739.48. Kahneman, D., and Tversky, A. Prospect theory: An analysis of decision under risk. Econometrica, 47, 2 (March 1979), 263–291.49. Kahneman, D., and Tversky, A. Choices, Values, and Frames. Cambridge: Cambridge University Press, 2000.50. Kaufman-Scarborough, C., and Lindquist, J.D. E-shopping in a multiple channel environment. Journal of Consumer Marketing, 19, 4, 333–350.51. Kim, H.W.; Xu, Y.; and Koh, J. A comparison of online trust building factors between potential customers and repeat customers. Journal of the As-sociation of Information Systems, 5, 10 (October 2004), pp. 392–420.52. Koo, D.M. The fundamental reasons of e-consumers’ loyalty to an online store. Electronic Commerce Research and Applications, 5, 2 (summer 2006), 117–130.53. Koufaris, M.; Kambil, A.; and LaBarbera, P.A. Consumer behavior in Web-based commerce: An empirical study. International Journal of Electronic Commerce, 6, 2 (winter 2001), 115–138.54. Lazarus, R.S. Emotion and Adaptation. New York: Oxford University Press, 1991.55. Liu, C.; Marchewka, J.T.; Lu, J.; and Yu, C.S. Beyond concern: A privacy-trust-behavioral intention model of electronic commerce. Information & Management, 42, 1 (December 2004), 127–142.56. Lopes, A.B., and Galletta, D.F. Consumer perceptions and willingness to pay for intrinsically motivated online content. Journal of Management Infor-mation Systems, 23, 2 (fall 2006), 203–231.57. Mahmood, M.A.; Bagchi, K.; and Ford, T.C. On-line shopping behavior: Cross-country empirical research. International Journal of Electronic Com-merce, 9, 1 (fall 2004), 9–30.58. Manning H.; Bodine, K.; Temkin, B.D.; and Amato, M. Best and worst of site design. Forrester Research, Cambridge, MA (March 2005) (www.for-rester.com/Research/Document/Excerpt/0,7211,36074,00.html).59. Massad, N.; Heckman, R.; and Crowston, K. Customer satisfaction with electronic service encounters. International Journal of Electronic Commerce, 10, 4 (summer 2006), 73–104.60. Meeker, M.; Pitz, B.; and Fitzgerald, B. Internet Trends. Morgan Stanley (August, 2004), www.morganstanley.com/institutional/techresearch/pdfs/internet_themes082004.pdf.61. Mehrabian, A., and Russell, J.A. An Approach to Environmental Psychol-ogy. Cambridge, MA: MIT Press, 1974.62. Monroe, K.B., and Chapman, J.D. Framing effects on buyers’ subjective product evaluations. Advances in Consumer Research, 14 (1987), 193–197.63. Nikolaeva, R. Strategic determinants of Web site traffi c in on-line retail-ing. International Journal of Electronic Commerce, 9, 4 (summer 2005), 113–132.

05 kim.indd 15405 kim.indd 154 8/6/2007 12:22:59 PM8/6/2007 12:22:59 PM

INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 155

64. Oliver, R.L. Satisfaction: A Behavioral Perspective on the Consumer. New York: McGraw-Hill, 1997.65. Pavlou, P.A. Consumer acceptance of electronic commerce-integrating trust and risk with the technology acceptance model. International Journal of e-Commerce, 7, 3 (spring 2003), 101–134.66. Pavlou, P.A., and Gefen, D. Building effective online marketplaces with institution-based trust. Information Systems Research, 15, 1 (March 2004), 37–59.67. Puto, C.P. The framing of buying decisions. Journal of Consumer Research, 14, 3 (December 1987), 301–315.68. Reibstein, D.J. What attracts customers to online stores, and what keeps them coming back. Journal of the Academy of Marketing Science, 30, 4 (fall 2002), 465–473.69. Reichheld, F.F., and Sasser, W.E., Jr. Zero defections: Quality comes to services. Harvard Business Review, 68, 5 (September/October 1990), 105–111.70. Reichheld, F.F., and Schefter, P. E-loyalty: Your secret weapon on the Web. Harvard Business Review, 78, 4 (July/August 2000), 105–113.71. Shang, R.A.; Chen, Y.C.; and Lysander, S. Extrinsic versus intrinsic motivations for consumers to shop on-line. Information & Management, 42, 3 (March 2005), 401–413.72. Sharma, S.; Durand, R.M.; and Oded, G.A. Identifi cation and analysis of moderator variables. Journal of Marketing Research, 18, 3 (August 1981), 291–300.73. Shih, H.P. An empirical study on predicting user acceptance of e-shop-ping on the Web. Information & Management, 41, 3 (January 2004), 351–368.74. Shop.org. The State of Retailing Online 7.0. www.shop.org (May 2004), (www.shop.org/research/SRO7/SRO7ExecSumm.asp).75. Sirdeshmukh, D.; Singh, J.; and Sabol, B. Consumer trust, value and loy-alty in relational exchanges. Journal of Marketing, 66, 1 (January 2002), 15–37.76. Suh, B., and Han, I. The impact of customer trust and perception of secu-rity control on the acceptance of electronic commerce. International Journal of Electronic Commerce, 7, 3 (spring 2003), 135–161.77. Tellis, G.J., and Gaeth, G.J. Best value, price-seeking, and price aversion: The impact of information and learning on consumer choices. Journal of Marketing, 54, 4 (April 1990), 34–45.78. Thaler, R. Mental accounting and consumer choice. Marketing Science, 4, 3 (summer 1985), 199–214.79. Torkzadeh, G., and Dhillon, G. Measuring factors that infl uence the suc-cess of Internet commerce. Information Systems Research, 13, 2 (June 2002), 187–204.80. Tsai, H.T.; Huang, H.C.; Jaw, Y.L.; and Chen, W.K. Why on-line custom-ers remain with a particular e-retailer: An integrative model and empirical evidence. Psychology & Marketing, 23, 5 (May 2006), 447–464.81. UK leads the way in online shopping, European Interactive Advertis-ing Association Web Site (2006), www.eiaa.net/news/eiaa-articles-details.asp?lang=1&id=85.

05 kim.indd 15505 kim.indd 155 8/6/2007 12:22:59 PM8/6/2007 12:22:59 PM

156 SUMEET GUPTA AND HEE-WOONG KIM

82. Urbany, J.E.; Bearden, W.O.; Kaicker, A.; and Borrero, M.S. Transaction utility effects when quality is uncertain. Journal of the Academy of Marketing Science, 25, 1 (winter 1997), 45–55.83. Wu, S.I. A comparison of the behavior of different customer clusters towards Internet bookstores. Information & Management, 43, 8 (December 2006), 986–1001.84. Yang, S.C.; Hung, W.C.; Sung, K.; and Farn, C.K. Investigating initial trust toward e-tailers from the elaboration likelihood model perspective. Psychology & Marketing, 23, 5 (May 2006), 429–445.85. Zeithaml, V.A. Consumer perceptions of price, quality and value: A means-end model and synthesis of evidence. Journal of Marketing, 52, 3 (July 1988), 2–22.

05 kim.indd 15605 kim.indd 156 8/6/2007 12:22:59 PM8/6/2007 12:22:59 PM

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ount

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ano

ther

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line

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deve

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d

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E2

It m

ay b

e ch

eape

r to

buy

book

s at

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I will

pro

babl

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ve m

ore

mon

ey b

uyin

g bo

oks

at a

noth

er o

n-lin

e st

ore.

PR

CE4

I m

ay n

eed

to p

ay m

ore

mon

ey b

uyin

g bo

oks

at th

is st

ore

than

at a

noth

er o

n-lin

e st

ore.

Con

veni

ence

C

ON

V1

Inte

rnet

sho

ppin

g at

this

stor

e sa

ves

me

time.

[1

6], [

79]

C

ON

V2

Inte

rnet

sho

ppin

g at

this

stor

e m

inim

izes

my

effo

rt in

sho

ppin

g.

CO

NV3

In

tern

et s

hopp

ing

at th

is st

ore

is ea

sy fo

r me.

C

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Inte

rnet

sho

ppin

g at

this

stor

e m

inim

izes

per

sona

l has

sle in

sho

ppin

g.Pl

easu

re

H

ow d

o yo

u fe

el a

bout

you

r pre

viou

s tra

nsac

tion

with

this

stor

e?

[47]

PL

EA1

Uns

atisfi

ed/

Satis

fi ed.

PLEA

2 U

nhap

py/H

appy

.

PLEA

3 A

nnoy

ed/P

leas

ed.

PL

EA4

Disa

ppoi

nted

/Del

ight

ed.

05 kim.indd 15705 kim.indd 157 8/6/2007 12:22:59 PM8/6/2007 12:22:59 PM

158 SUMEET GUPTA AND HEE-WOONG KIM

Appendix B. Principal Component Analysis Using Varimax Rotation

1 2 3 4 5

PINT1 0.14 0.19 0.80 0.16 –0.22PINT2 0.08 0.16 0.86 0.15 –0.21PINT3 0.16 0.14 0.83 0.19 –0.21PINT4 0.13 0.08 0.78 0.10 –0.09PVAL1 0.23 0.33 0.15 0.77 –0.11PVAL2 0.19 0.28 0.15 0.78 –0.13PVAL3 0.16 0.16 0.17 0.78 –0.25PVAL4 0.17 0.29 0.21 0.77 –0.18PRCE1 –0.04 –0.05 –0.12 –0.03 0.81PRCE2 –0.12 –0.08 –0.14 –0.12 0.84PRCE3 –0.12 –0.15 –0.19 –0.21 0.76PRCE4 –0.14 –0.14 –0.23 –0.26 0.69CONV1 0.17 0.83 0.16 0.29 –0.12CONV2 0.21 0.87 0.16 0.26 –0.12CONV3 0.25 0.72 0.22 0.36 –0.12CONV4 0.22 0.84 0.13 0.18 –0.13PLEA1 0.84 0.15 0.14 0.20 –0.10PLEA2 0.91 0.19 0.12 0.16 –0.12PLEA3 0.90 0.20 0.13 0.15 –0.11PLEA4 0.89 0.20 0.13 0.17 –0.10Total eigenvalue 3.57 3.23 3.10 3.06 2.78% of variance 17.87 16.15 15.51 15.29 13.91Cumulative % 17.87 34.02 49.53 64.82 78.73

SUMEET GUPTA (sumeetgu@comp.nus.edu.sg) is a Ph.D. candidate in the Depart-ment of Information Systems at the National University of Singapore. His research work has been accepted or published in Decision Support Systems, Information Resource Management Journal, and Information & Management, and has been presented at ICIS, PACIS, AMCIS, and ECIS.

HEE-WOONG KIM (kimhw@comp.nus.edu.sg) is an assistant professor in the Depart-ment of Information Systems at the National University of Singapore. He received his Ph.D. from the Korean Advanced Institute of Science and Technology, was a post-doctoral fellow in the Sloan School of Management at Massachusetts Institute of Technology, and was an ICIS Doctoral Consortium fellow (1997). He has worked as a senior consultant at EDS and is on the editorial board of Journal of Database Management. His work has been accepted or published in Journal of the Association for Information Systems, Communications of the ACM, International Journal of Human-Computer Studies, Journal of the American Society for Information Science and Technology, IEEE Software, Data Base, Information and Management, Information Resource Management Journal, and Deci-sion Support Systems, and has been presented at ICIS, HICSS, ECIS, PACIS, AMCIS, IRMA, and DSI.

05 kim.indd 15805 kim.indd 158 8/6/2007 12:22:59 PM8/6/2007 12:22:59 PM