Rochester Institute of Technology
RIT Scholar Works
Articles
2003
An Exploratory study of operant conditioning
theory as a predictor of online product selection
Victor Perotti
Patricia Sorce
Stanley Widrick
Follow this and additional works at: http://scholarworks.rit.edu/article
Recommended Citation
Journal of Electronic Commerce in Organizations, vol. 1, no. 1, pp. 42-54, 2003
This Article is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Articles by an authorized
administrator of RIT Scholar Works. For more information, please contact [email protected].
4 2 Jour",,1 of Electron!< Com....."" In O<gbnluti.... , 1(1), 42-64 • .II n _M., 2003
An Exploratory Study of
Operant Conditioning Theory as
a Predictor of Online Product
Selection
Victor Perotti. Patrieia Sorce and Stanley Widriek
Rochester Institute of Technology, USA
AB$T RACf
The pres<:m re,eaN;h applie. opem~1 ctmdilioning Iheary ", Ihe quesrjon of"'hal p~I' OM
.<enl;e•• e",)$" me" will shop fo r wid buy on/j~e . Operon! condilion;ng theory aplains
diU'",e""e. be/Ween pl"Qdu(t!. thaI are IUW 10 all""jate """ omfim able t:;<perienc:e. (negoti""
re;nforceme"') Ot1d 'hose provld;ng enjoyable v:periences (posWve reinforcemenl). TI,e
preliminary res"lts de$cribed i~ this Stu<& confirmed Ihe Importance of operanl "" ndiNoning
a; a flUlor in the behav;or oj online shoppen. For example. ,,'hen asked ro provide on openended liJl ofp""""," that ' hry hod . hopped for, our respondc~ts mentioned prodltCU thm
produa po;ili"" rel~fi>rceme nt 476 time; ""rsus only 4 mentiMSfor those Ihtll crM I. negoliw:
reinforcement. Furthenn ore, fw a litt of s_nlee~ commOn prodUCI categories. Ih. resul,.
showed Ihol " ,sl"!mlenlS were nol only less likely 10shop for negaU"" re;nfON;emom p~,.
bill 01.0 e""n less likely to p urt:!uue negon"" products online Ihon posil i"" ~IJ. The
results ofthis exploratory . ru<& lay the graundworlcfo rfu ture researeh by I,"rod"dng negati""
and po.i'i"" reiujO!U?mem Wi a predjelor of Inlernel shopp ing behavior.
Keywo rds: electronif' commerce, OperOnl <:anditioulng. Nlinforcement. buyer behoo;or
INTROD UCTIO N
bavior of' online shoppers. Much of this
research focuses on understan ding the
Withthe failure of many onlinecom- shop pers themselves. However, several
mercial sites, business managers are re- important man agement q uestions about
eval ua t ing lhe i r appro a e h e ~ t o products ",,,,,,ill: Wh " t type ofp rodnct'l
cCommerce . In the last few years, som e w ill cons ume rs see k o r buy on the
res earch has been ab le to clarify th e be- Intcrnt1? Why do consumers choose these
C""yriJbt C 2003. ld<> !JrouI' In<. Copyi"ll or ~t1"l1 ;n poinl or 01"""""", _
p<nTIi.._ of ldoo Group toc. ;, ""","," 1«1.
wi1llo<l!....m.oo
Coo" ,,",""'" erg.a-. 1 ( 1 ~ 42-M. .II<>-M¥2003 43
.......... ol _
productsover other produc ts? Ate there
diffamt categories of productsC01SUlllers will shop f(X" but not purchase online?
Theauthcn propose using opmml COlIditiouing lhtoy llulIJS\\'Cl'"the$e q N '"• b
The present research begins wilb a deSCI iprioo of lheaisting Iilel1lturt: relevant
to Intemrt shopping behavior. Afterthe
literature review, opennt conditioning
theory and itsapplicatioo to understanding buyer behavior is discussed. Lastly,
three hypotheses are presented that te St
whether the use of operant conditioning
theory contributes to a more complete
unclel'Standing ofthc online shopping precess.
,o:,ocptioo ofshopping on the Internet. [n
addition. BbaWgar. Misra&. Rao(2000)
developed • two-pan de finition of perceived risk 10 aamine its Impact on
Internet $hopping behavior. Their 1'....0
types of risk were product category risk
and financialrisk. Higb productrisk repmiaItc:dc:ondiriom "'ben the product "'lIS
lechnologically co mplex satisfied ego
necdsofthe purchaser. was highpeiced,
or was sold based on its feel or ic ucb.
lligh financial risk was defined by fears
about the safely of consumers' finan cial
inflll1ll:'ltion online. Theirresults indicated
that increases in both types of risk genetally decreased the likelihood ofoolinc pur-
ehasebehavior.
Shopping Beh . ,-ior on the I nt~1'H1
Why do peopl e use the Internet to
shop? Evans and Wurster (1999) ce Who will buy online? Much of the scribe the lnlcmet as a rich S(lIlI"CIe of Inexisting research on Internet shopping formatioo for many products that is eonbc:ba\ubils b::uscdmjXcdictil~the lypC vaUmllO -=ccs.s. As the lnlcmetTI1RllftS,
of consumer who is likely 10 usc: the ils importanceas a source fa: produa inIntemetlOseardtfor(X"blypmduas. Fa: formation is also iua casiug. Fa: ~
example, Bellm an, Lohse &. Joh nson Ramaswami, Slndcr and Breit (2000)
( 1999) investigated liIC\"CI"&l pttdictors for reportedh t onlinesllOJlPCl'S of financial
wtlelher., inofuiOOal "inbuyoolinc. They produClS used both online channels and
found thatthe most imporw1t ddmninant personal channels [e.g., a broker) intheir
of buying on the web WIlS previous be- infonnation search activities. Further. ina
havior suchas using the Internet to search
forproduct information {i.c. pre-purchase
search). Bellmaner al. concluded that S(WCTlII demographic variables such as income. education. and age have a modest
impact on the decision of whether to buy
onlme. Otherresearch has pointed 10the
importance of cooswner risk perttption
in pm,iietinglnlemrt purcllase behavia:.
For ~, VcUido, Lisro.. &. Mcchao
(2000) fOUDd that online purctIase behavior was best predicted by consumer risk
eo".iaIi C
pa
_ I. Idoo
~
01" ... 0r<I0p lK
~
F q
L'"
...
study of new car buyers, Ratchford,
Telukdar andLee(2001) found111.11beavy
users ofIntemet sources were also heavy
users of printed sources of information
SlICh as ear ratings books and dealer br0chures. Though information-rich. the
Internet does not seem to be used as a
sub;tituIc forothasoun:esofinfa:maoon.
If the richoess oflhe Internet is not 1M
wk '<:lIWll foc using it, it seems reas0nable that the oonvenieoce of ooline sh0pping could be. primarymotivalion.
_z ... _...
_---
.. .....- ",E-.,.,;,;Cao IO_ca in ~
Tile perceived convenience of usc
oflbe Internet for searching and buying
products has been well docwnented. The
surveys ( 1994-1998) conducted by the
Graphics, Visual ization end Usability
group al GeorgiaTechconclude thai the
web delivus cceveeieece IIDd time sav-
ings for thc online shopper. Bbamagar;
Mism aDd Rao(2000) foundthat cust0mera' perceived convenience ofshopp ing
on the Internet had a positive impact 011
purchase beh avior. How e ver,
Ramaswami, Strader and Bren (2 000)
found Wt for those tlw useonline inform.1t>on sources forbuying financial pr0ducts, time l\llIilabililywasDOt associated
With the plOpensily 10 conduc:t an OIlline
search for lhese products or 10 purchase
these produces. That is, tbcse that were
pressured fortimedid no! use online sh0pping1TllR!ban!hose "w were less pressuredfortime. This~ltopenstbequt:s­
lion of the primarybenefit for consu~
of using the lntemel ; is it be<:ause of ilS
informationrichnessor is it because ofilS
time savings? As discussed above, boIh
I ll ~ ~2-601 ,''''''''''
2003
Peter & Nord , 1982; Rothschild &
Gaidis, 198 1). Positive rcinfOTCemeJU is
the situatioo where anoperant behavior
ina i in frequency with thesubsequem
presentatioo of positive stimuli. For example, the behavior of a child sayiDg
'thank you' increases with the parent's
presentat)oaofa snack or treat only subsequent to the child uttering the words.
Negative reinforcement is the situation
where the operanl behavior increases in
frequency wi1l1lhe sub6equent rm1O\"l1 of
an aversive: stimulus. For example, the
be!la'lior of taking aspirin at the onset ofa
headache incn:ases if it has removed the
headache in the past. With both types of
retofQl cemc:nl, the probabilityoflhe coerant behavior increases in futuresimilar
3
0 """"""",,
It is important to note that there is
still ooofliSion in the ]itCf3lUre as to the
defmltion of negative reinfOlccmel11. For
example, one researcher has written, ... ..
if a eustOll'ln bas a bad experience (receivesoegative: reinfotm 'k:iJI), the dlance
of her resuming lIS a repeat purchases deate operative. The pri mary det erminant creases. .... In fact, this is an example of
oflntemet shoppiog may depend on the PIlJl ish ment, IlOI TlCplive rrinfOlm1K:ill.
t)'pe o f product. A product typology Figure: I providesgraphical depiction of
based on Op a311l conditioning theay that the process for positive IIDd negative rebas been found 10 affecl U1Iditional sh0p- infttmtlCi4.
ping behavior ispresented below.
It is useful to recognize that with
positive rcinforccmcnt, the pmou isIt'ying to maximize or at least satisfy his or
Op era nt Conditioning T heor')' and
RU~'jn g Behavlcr
her utility. With negativereinfOlttll....u,
the p=on is lI)ing to minimize his or her
Operant conditioning research has disutility, A rapid and long-term removal
teen discussed by marketers and pub- of the avesive stimulus thai iscausing the
lished undertmnssuch asbehav..... modl- dislJtilily willlQlllt. instrongncgarive reinfit"31ion, reinforcement research, andc0n- r~ ,
d itioning theory (Nord & Pete r, 1980;
c..,...,.
ptnl - . _
I)
lODl, ... GoDop ....
of ldloo
~
~
100<. 10 ; :3 - II.
... ._ _
' _ , _,.. ...
_
Figurt) 1:
..
.......
..'--
1'iEGAll\IE R[JJ\'FORQJ\l£ll,T
, .. ot
---
"'0,...._
Research into negativereinforcement
has found several important behaviorpatterns (Blair & Shimp, 1992; Widrick &
Frnm, 1983). [n the presence ofan aver_
sivc stimulus, escape behaviors are normal fornegative reinforcement consumption. Forcxample, with the infestationof
household pests (an aversive situation),
there will be shorttime intervals bel\\"CCII
need awareness and consumption . In
contrast, for products that result in positive reinforcement, product search behavior may result in moreleisurelybrand decisions. Widrick and Hibbs (1985) provided empirical suppon for-this position
whereCOIlSIJmCrs reported a longcrscarch
process (more time spent shopping and
greater distance traveled) for products that
provided positive reinforcement(henceforth called positive products) than for
those thatprovided negative reiufoccement
(henceforthcalled negativeproducts). [n
their longer search, consumers also considered more brands of positive than
negativeproducts. Moreover, underconditions of negativereinforcement, avoid-
ance ofthe aversivestimulus may generalize to shopping for the product. In a
test ofthishypothesis, Widrick and Frnm
(1983) found thaipeople generally enjoy
the process ofshoppingforpositive productsand services(e.g., sportsequipment)
but do notlike 10 shop for products that
providenegativereinforcemem (e.g., auto
repairs).
Thc research reviewed above indlcared that for positive products, people
like to shop for the products,spend more
time inpre-purchasesearchactivities and
considermore brands. For negative products, people don't like to shop for them,
spend less time 10 pre-purchase searcharxI
consider fewer brands. Thc research
question addressed by this studyis ' docs
this partcru hold for shopping on the
Internet?'
Int ernet Sho pping for j'I,'ega t i~'c and
Positive Re inforeeme nI Products
Sorce, Pcrctu and Widrick (2002 )
tested the proposition that lntemel shop-
CDpyriglll 0 2003, Ideo Gn:>up I"". C<>pylna '" 0_ "",;, ,..;'" or el« ~
p<rmi<sio:>n or 1<100 Oooop IDe. i' proho,;t«!.
r""", . . -
"";0\<,
•
pingbdlaviod s differenl for-positive versusnegative ptXIucts. They ~ that
for positi\'Cproducts the Interoet ~
a rich information!iOlJltt; thatClIhanccs the
shJpping t:.'lpel iencc aDd people weuld be
~ likely IOshopforthemOfl the lntemet
rather than oegative products. However,
Ih isriclme:s:s benefitcould be offset by the
time-sa\mg'ibenefil fornegativeproduas.
[fthe Internet provides efficiency that reduces the tirnelOShop for a product, thm
the Internet may be used more for ocga~
live than positive products. They tested
the propensity 10shop for, bul no! necessarilypurchase, scvet1teen prt::IOOcts ooline.
1ky found thaI ~ts were more
likcly to shop for positive products (e.g.,
spon inggoods)ool ine in a 3:I ratio versus negativeproO.Jcts(e.g....-..cuum cleaners). Theyposwlated that this was due 10
tnc esca pe behavior for some siN alions
what negativercinforcemml pnxb;t\arc
sought. In tbe:se cases. some COIISUlnI::n
jeave shopping 1D the vay last lI'litu.e whm
dley canoot afford10 wait for-themail delivery of the product to solve the problem. This eltplanatioo was supported by
!he finding thaIooe Ill"gative product thaI
had rdati\'ely high reported shopping behavior - virus protection softw~ - "'-as
ablelo be delivettd irnmodiately viaooline
download. This product had the highest
reported online shopping behavior ofall
negative )X'Oducts tested,
The present research was designed
10 extend and test the understanding of
Internet sboppmg behavior for negative
and positive pru<lueu by cxmm;ning the
prope nsity of consumers 10 buy online .
which was confirmed in Sorce, et al.
(1002) will also be restated.
HI: Fe....er negative products ...iII be
shopped Jor online tlull1 pos itive
producrs.
The second hypothesi s will test
whethertbe results of jJI"t-JUtflasc search
will also extend 10 the actual buying of
goods andservices 0Dline. Thus, thetrClld
forplll'Chasing products should be similar
to the trend for-shopping for-products.
HZ: Ff!lt<'f!T negalil-eproducG ....ilI bt!pIiTchased online than positil-e prod-
ucu.
Thethirdh}poIhesi:s "'in teSt whether
the gap bet....eea searching for-a product
and lICIUaiIy pwcllasing that prodocI will
be ~ttfor Degalive proWcts than positiVI: products.
IB : Giwn tMt a penon did ShopOl1 fiM
for /l produ ct, a smaller percentage
will htiy negati~"C products than
positive prrx1lJcu online.
R ESEARCR METROD
Sa mple
A four-page questionnaire about
cetioe shopping habits....as disnibuled 10
the samplal population ofstaff membm
at a large privatenortheastern university.
Staff were r.elected using the StaffCounCll mailing list. whidl was grouped iruosix
Two newhypotheses will be tested (H2 blocks. One-half of the voting blocks
and 113). As a basis forcompaOsoo. H I ....en: selected (lF68 3) to receive the
~
Cl lOOJ, . . .
"'_ 01: .....
a.- 100<. e-..... --.... ill _
~Ia<-
... "
1
... _ _
wi_ ...-
qt!C'StitnPtt viairccrofIicemail The re- whetber they had ever used the [nlemc1
sponse rate for the staff was 29% (198 toshopfororb.rytbisiIem. The 17 prodqucstiOllDilirts wm: mumed)
ucts were selected 10 balance high price
The questionnaire included several and lowpriDe as \10'l::ll as positi\'l:: reinforcedemographic variables: gender, age aod ment and negative n:inf'ot OCllleiII p-oducts.
years on the Internet. Almost thrce-quar- Table I shows the 17 products as they
tets (73 .9%) of the respondents were were classified by the researchers before
women; 140/. were 29 years of age or thcstudy.
younger. 58"10 were age 30-49, and 28%
wen: age 50 oroldcT. The a~"ttagc num- Ruu lts
ber ofyears using the 1ntcnrl was Nighdy
(n'C\'" 5 )'caB.
Ofthe 198people surveyed. a1mosl
all (90.5%) indicated thaI they used lhe
Inlernet 10 shelp for oneormcee products
tt services. Consistent with previous find·
For the present study, three sectices ings, those who spent most lime on the
from the questionnaire were used. Re- web were ntOSI likely to shop on me web
spondents were firsl asked if they had (Pearsoneom:lation r- .5(9). Ajso conshopped online forany goods or services. sisu:nt with the findinss ofodas, ywngCl"
Shopping "'<IS explicitly defined as''using people bothspent more time on the web
the Intemetto research, browse for. or (Pearson r " -.294)and were more likely
compare the prices of products and/or to shop OIl-line ( Pe&r$OIl r = -.287) and
services. but DO( rtO\'( esCllnlypurcha$c!he buy on-line (PeaJSOfI r - -. 195).
When asked 10 identify what they
nem." They were then asked 10 lisl exhad purchased on the Internet nearly evamples ofitems they showed for.
To supportstatistical testing, respon- ery prodtlC! menlioned using unaided reo
dents were then given a Jist of I7 prod- callwas a product thaI provided primarily
ucts. and for each were asked 10 indicale positive reinforcement. On average. each
TlIbl.. J.- A-~ ~ 0/ /1 ~ byprW and typicDI
r osltln Rtt.-r.t ct _ al
H lt~
I'riu (> $.l5)
M" 'lIf srnita
p,-elfy
lDvt'1 K'J"icQ
sponi"l cood-
~,.,..c
_
....
..........
N _Un Rtt.-fM'ft.....1
securicy sysl.....
",",uum c1u ner
in,unna:
to..· PrI... « SJS)
cd/mWlk
.....den lOGI_
.~
virus dtk:Ction wllware
viumins
insecl ~1I"'1
r-in relic ' n>ediealioa
" ';ne
~
~
C> ZlIlJ. _
Gt-. """-
or _ 0...
100<. io p
~.
II
,
_? ."..._---
respoocknt reported 2.42 items. A content analysis of the op en-ended item re\'eaIOO tha t only 4 of the 480 products
menuceed wen: classified lIS ne ga tive
~1aIing IO the first b)'JlOlhcsis, Table:
3 shows that the useoflbe lnteme:t IOshop
fo r positive produclS ranged from 69%
for travel services to a low ofJ.S". fo r
prodUClS (one mention each of: vitamillS, massa ge services. In co ntrast the neg.ck:aning equipment or products, pepper rive products wen:much Iov>~r ranging
spray, security systems) . Tbc top prod- fro m a high of 17% for virus detection
UClS mentioned are presemed in Table 2 software to~.4 for insect repeltarn. A
similar pattern is seen when we: examine
"'low.
Hypo thesis I st ated that peopl e the pe rceruages o f those who purc ha se
\\-'OO1d have a lower probabilityof lntem:t on line. Positively rein forci ng product
shopping for products that provide nega- purchasing ranged from 44 % for- travel
tiven:inforcemcnt than they will for prod- service to 0-;' for massage services.
uets that provide pcsinve reinfo...x menL Negat ive ly reinforcin g product perchesA st:lli:srical test: of eese cliffet t'l as is ~ ing ranged from a high of 8.6% for virus
scored in Table a . Table 3 on the foll ow- detection so ftll..a re to W. Ior dishweshing page pro vides . summary of the per- era and vacuum clearers. The fmal colcentage of ~ 1 S who n:ported us- umn in Table: 3 contains the ratio oflhose
ing the trncrocnc shco fe r and purchase pe op le who shopped fOf each product
tbeo;eri~positivoeIIld nineoegative p-od- ....'ho wem 00 IOpurtbaseit ooline(% purehasedlpm:ent shopped fo r). Fo r exucts.
Na"'...... or
Moollo n<
."
(....198
BoobImu,iclmov;cs
C
,~~
~cr Retale<! Hardw...:
c~
FImlilun: .I: bousdIold
"
"c
33
"
Elccmlaics
rc
""
"
""•
...
c..,..,.
O:atlJ. Woo
. . . - - of _
CO<»4>
GrIq>
~.
...-004.
_ -•
•__ • .10<._ ..... _ _
- - - -
T<>bk J:
p~
oj.upo, tMJW .... JIoopp m<Vor ~ ftldopn1duc. 0trJ_
(.. -/ H)
_
Pcrcmtagc woo
eo.",.,
hlI,~
P
Pon;I;,", ProdIKtS
Tnovel Services
mD~U SK
S
'"
mo",-J
product
oaliDc
Garden Tool,
~ e Scrviecs
N al l,", Produ cts
V\fUS Detct'lioo scnwee
Vilamins
.........,
T'"
Di$llwasherVacuum CIeancn
Pain Rel ief Mcdicioc
Sc:Qui S "5talI
In$ecI
lan'
=t
~
(> :lOG).
*"
3<.'
.63
.66
16.2
1l.1
,
,.,
.67
.51
.39
.25
.lJ
3.'
0
0
17.3
15.7
8.'
IV
1.0
' .0
0
0
1.0
1.0
0.5
A'
A
'
os
7.1
,.,,.,8.'
' .1
' .0
' 0
.lJ
0
0
.19
.se
.,5
Positive purchased nwnber - # of positive productspurchased
• Negative purchased number " # of
negative productspurchased
Positive Ratio .. Positive purchased
number I Positive shopped fornumber
• Negali,·c Ratio .. Negalive purchased
number J Negali"e shopped for ownber
Tbesc variables an S1 lmmarizcd III
Tablc4and pennit formallCSl of our hypolheses. A nJlllehed paiB I-lest was
aJl!1MCd on d1c1\'aagCoflheIllCal runberof positiveandnegarive products each
100<. Coprioa .. - - . . • _
100<. ;, ""tit 100/.
~
n tio
30. 3
20.8
' .1
ample, 63%oflhoscwho used the InICmCl
to shop for travel services also purchased
travelservices online. The highest ratios
"'"eft for navelservices.cdmusic and toys,
all positiveproducts.
Table 3 provides considerable evidence thai customers are more likely 10
use the Internettoshop for IIIld purehase
positive products than for negativepr0ducts. In order10 swistically test dt.is, six
new variables were examined for each
It:5jXllD:OL The six ''aliables were:
Positive shopped for number - , of
positive ~ shopped for
• Negalive shopped for number - ' of
negative prod uctli shopped for
,,", . ... or I0I<o Gmop
"""'-1
,_10-
IK1 Ollline
43.9%
69.2%
"
Jewe l
Wine
,,'110 hlI,~
~Iwfti
45.5
41.1
23.2
Good,
!'=moll'
.. ...... _ _ ..._
..-
I
penon reponed shopping for. People
reponed sfloA:ling oiline signilk:artly ItXn:
for positive products (mean - 2.645) than
for eeganve products (mean :0.73 1)(1 16.56, dfa l96, p< 0.00( 1). f or purchasing theproduCionline. respondents
"-1JOI1ed buying online significantly more
for positive products (mean "'1.469) !han
for negative ptodu<:ts (meanpoportiIxP
.2(9)(1 - 13.9. ar- ise-. p< 0.00(1 ).
Iiypothcsis 3 stares that given thai a
person did shop for a prod uct online, a
negative producrs (mean - .286) (14.49, df ~ 75, p< 0.0001).
ln SUlJIITlary, ourresultsindicated that
forpositive products, about one·third of
,e:spoodaltS have seardlCd 1be 1nremeI for
infamatioo IIId abouI halfo flbose: went
00 to make an Internet purchase. In contrast, fornegative prodlletS, about ooeoot
of ten of our respoodents have ~hed
tbc IDICmCI for inf.. Il..... lDdaboot ooe
thirdofthese went 011 to make anInternet
purchase.
smallerpcm:nlage will buy DCglIIi\.-c prod.
lIClS than pos.itive products. As prt::SentCd
above, aboul ODe ou t of three (33%) of
DISC USSIO N
ourrespondents reported shopping online
for posi tive products while only one o ut
of ten (8. 1%) reported sbopping on line
for negative productS. The ntio ofrespoecems who reponed shopping for
positive products who also I"C'fIOrt pur chasing the positive prod uct online was
signiftcanlJy Iargeo-(mean "" .555) than for
Tabl~ f
Llmha tlens
The study was intended as a preliminaryexploratioooftbcopelBtt c0nditioning theory IS an explanation forooline
buying be havior. As such, it suffers from
scvaaI limiIations. Foroo; aconvmience
sample oflUliversityStlffwas used. lt is
"" 'e"'¥4' ~umbu <Jj Pwili "" und N~/w p~" IMI SIII'W}' Rapon<k~ u
~ SIt0PP"WP CJ,Ji,w"..
~'M
s.......,
Prod. c"
Prod~ (OYt
O" t of . i. hll
Ao'"al'
,.
SO.....
I""rlo<uiAg 0ttJ_
W,
(1) ,1%)
......
Hat
........
freed.....
1 ~.06
'"
""
4.~9
"
0.7)1
(1.. 1%)
[7.S<!
0.209
(2.3%)
"'"
,
S. ..bor
,%,
AI'eng.
P..... h _
,, ~
N~r
(11.4%,
hm_
,......
S"ppod
Foe
~~,a
~
0 lOOl. _
..... 10<.
~ .
_
.
..
..
SiKnlfkn «
of ala.
. . . prioo ... ,lo<
,--1
.......... d _ c . . i . _ .... ~e-;a
possiblethallhi511J'OUP is noI ~nta­
tive of the wul!population in general
A second and lIlOll: important limitation is tbc subjectivity in defining posilh'e and negative rrinf.... c..tiIeot proWcu.
wbereas some people considergardening 10 be a necessary, but tedious task,
others see gardening as an enjoyable
hobby. In this srudy, the authors pee-cbssified the products into the ncgatiw and
positive categories, but il is likely thai
some: subjc<:ts ....,ouid classify them differently. In retrospect. tw o of the products
(garden tools and massage services) that
.....e re classified by !he aulhors as proW:\ingposiriverrinf....OC"tlenl, lXlUId iDstead
provide negative reinf....cemcm to some
people, To address this issue. the analyses presented in Table 4 were repeated
omitting these rwoproducts; ncithcr the
means ncr tbc slalisliClll significance
clanged in any meaniogful ......y. A ItIl:R
eonuuIled expei
thatseeks to beacr
u,lUIl
OOIltrol the 5Ubjc:ctivity ofthe~t oon-
ditioning theory is planned for future research
Finally,addirit:Nal = c h isneeda:l
to clarify how operant aJDdiUouiog~­
acts with Olher kno.....n predictors of
Inlerne! shoppingbehavior. For example.
consumer involvement with the product
category and perceived risk aft' strong
predictoQ of consumer search behavior
(Bhatnap, e, al.. 20(0). ThescU!iCl"charac!cnstic variablesshould be includedin
furure research 10 determine their contributiooon the search and purchase ofboW
positive IIld negative reinfooOC't,..... pr0duct c::u.epits
Co,rriaIo 0 lOOl, _
~ ... ~ co dioo?
...
of .... 0I0l0f 100:. io po tl '
'
.. l tl ~Q-60._2lXI3 51
IMPLIC ATIO NS AND
CO NCLUSIONS
As mtraditional showing, tbcoolinc
s100pping bcbavior ofCOIlSUD"ICn is multifaceted and COlI1l1ex, Ccnainly. there ~
many fllCloo that impact both the process
and the frequency ofonline shopping. The
present R:SC'lITCh is consistent ..... ith earlier
research th.lit time spenl oolinc and age
.e IISSOCiMed withtbc likelihoodofusmg
the Internet to search fo r and buy products. Theullique contribution of'this explol'lltOf)' study expands our undersl:mding oflntemel shopping by illlrOducing
opnant conditioning tbtory as lIII expla·
nation of oo1inc buying. The results indicared that people were less likely to shop
online fer products and servicesthat provide primarily Dcgative re inforc ement.
Rdp •• Ioes Diicatrd sb:W*lgonlDc for
ptlSitiw products over negative products
in alnlO$l a four to oneratie(2.65 to .73).
Moreover, those thaidid shop online for
negative prod ucts actually completed a
DliIlSLtiooonline less fiaIucotlyltm ecse
shopping fOl" positi\'e products. Th us, if
!bey used the1ntauel: 1IIaI1, negatr.e pr0duct coosumers pimarilyused the 11lIemef
as a source for informa tion rather than as
a means 10 make !he transaction.
lbe T$llts presentedtee have implicarioos for pnIctllionm o f electronic
commerce. ForWeb retailers ....ito sell
ambiguous products (suchas the gardeningexample used in this study). they would
be welladvised to emphasize the positive
reinforcingaspcrts oftbcirproducts.l'eI"baps some shopJx::l s will be moved from
•
io'" <If .-
--•
the apathy of a negative product shopper
to the activity of a positiveproduct shop-
I'"
This studyoilersa secondbenefit for
practitioners: a clearer understanding of
the importance ofconvenience in Internet
shopping . One commonly-held belief is
that the Internet is primarily used for efficiency and time-savings in shopping. Opcram conditiOlling IDeoI)' furthersuggests
that this efficie ncy would be especially
usefulinavoiding the aversiveexperience
of'shcpping for products thai have negative reinforcement. However, ourresults
sug gest that this is not true. If shoppers
were primarily using the convenience of
the Internet to shorten the shopping experiencefor negative products, one would
expect to sec more reports ofonlineshopping for negative products than for pcsitive ones. However, the opposi te was
found. Sorce, Perotti & Widrick (2002)
speculate that people postpone shopping
forthe negativereinforcing productsuntil
the onset of the aversive stimulus, The
present study adds more support to this
posit ion hy showing that shoppers for
negative pmduct~ make use oflhc Internet
more as a source ofinfonnation than as a
storefront to perform transactions. f or
Web Sites that also have a real-world
presence (" Bricks and Mortar stores"],
providinge:<tensive infonnatioo about the
negative products might be a good way
10 drive new business tothe off-line shop.
However for pure e'Iailers, the cos t of
providing this information may well outweigh thcbenefit from presenting it.
For Internet shopping scholars, the
researchpresentedhere demonstrates the
usefulness of understanding online shop.-
Copynrh' C 200l. l<leo GtoI4> 10<. ~yi ..
I'</TO"'ioo of 16<> (lo,.,p ""'. ;, pn>h;t,;,od.
0<
ping behavior in the cont ext of the specifictype ofproduct the consumer is buying. One implication is that the construct
of shoppingconvenience may need to be
furtherrefined. Forexample, fornegative
products, the convenience of the Internet
as an information source may be significantly more importantto consumers than
itspurchasingconvenieoce. A second implication is that models that focus primarityoo the stxwers themselves shou1d also
take into account aspects of the product.
It seems likely thatacompound model that
combines both buyer characteristics and
product attributes would be most successful in predicting online buyer behavior,
Such a model presents 8 good opportunity for additional research.
R EF ERENCE S
Bellman, S., Lohse, G., & Johnson,
E. (1999). Predictors of online buying
behavior. CommlmicationsoftheACM,
42(12),32 -38.
Bhatnagar, A, Misra, S., & Rao, H.
R. (2000). On risk, conve nience and
Internet shoppingbehavior, CommuniCi1tim's ofthe A CM, 43( 11), 98-10 5.
Blair, M.E. & Shimp, T. A. (1992).
Consequences of an unpleasant experience with music: A second-order negative conditioning perspective. Journal of
Advertising, (March), 35.
Evans, P, & Wurster, T. (1999).
Blo wn 10 Bi ts: How the New Economics ofinf ormation Transf orms Strategy .
Harvard Business School Press,
GV U. ( 1998). ''OVU 's 10'"WWW
User SUlVcy;' http://www.gvu.gateeh.eduI
uscr_surveyslsurvey-I998-IOJ.
<Ji>oibuti' s ia
pm.
0< , ~
f"",,,
"i_
'OTi"'"
Nord. W. R. & Peter, J.P. (1980).
Sorce , P., Peroni, V., & Widrick, S.
A bdlaviof-modificationpmpttriveon (2002). Predicting Internet shopping bemarketing. Journal of Marbring , havior asa function ofoegalh"eand posi(Spins). 36-47.
rive reinf(lJ"Cemenl. Prot:~ 0/ I~
Peter, J. P. & Nord, W.R. (1982). America" Marketing Association :S
A clarification and ~Iensioo of operant ]001 Hointotr Educolor:S Conference.
OOIditit:Rmt;~
WI ' Iwtdii ~ bI~ F"","",.
flal of MQTuting, Vol. 46 (Summer),
Vellida , A., Lisboa , P.l .G . &
Mc:chan, K.. (2000). Quantilath'l: characRamaswami. 5., Su-adtt, T. & Brett, teriwion IIDd ptOOicIion of oolint; purchasK. (2000·0 1). Det erm inants of online ing behavior. A lalent variable ilpJrollCh.
102-107.
channel useforpurchasing financial prod- Internal/an al Jo urnal 0/ Electro nic
ucts.lnternational Journal ofElectrr:)nic
Commerce, 5(2), 95-118.
Commerce, 4(4) ,83- 104.
Widrick, S.M. & Frarn, Ell. (1983).
Ratchford. B..Telukdar, D. & Lee, kk:ntifying negati Ye~ : DocustomM. (2001). A model of consumer choice ers like to purchase your products? The
afme Intem et as an information source. Journal a/Consumer Morlceting. 1(2),
Internolional Jo"rnol of Electronic 59 - 66.
Commerce, 5(3), 7-22 .
Widrick, S. M & Hibbs. J. ( 1985).
Rothschild, M. L &.Gaidis, W.e. Negati\'l: mnfOl CZmenl lhel:wy: Speedof
(198 1). Bcha\ionllca 1m'S \heofy: Its rd- purchase ....hen buying a negative prodevaece to marketing and pOluocions. ucL SoutMast American lnstiru~ flw
Jour"ol 0/ Marketing , Vol. 45 D«uion Scienu, (February). 228-230.
(Spring), 7()"78.
Hctor Perotti is an A.ssistafll P7tJfrnor 0/ Management In/annation Systmt;r
(MIS) in the College 0/ Businus a/ the Rochesler/nslilUte a/Technology. He
teoches MiS and e· BusinUJ courses at lKith the gram-ate a"d undergraduate
levels. Dr. PerDui has recell/lybeen honored/ or both Iris teaching and research.
In March 2000, he was awarded the Richard a"d Virginio Ei.fenharl Provost s
Award/ or Excellence in Teaching. BeJorejoining Ihe College (}/Bu~iness, Victor
was a t'l'.Jearch us.tMtant at The VISion Labat The OMoState University. At Ohio
Stale. he ",role se\'f!ral pllblicotions in the area oJvisl.a1 perception. Since that
time, Victor has been doing research on a voriety ofMIS topics, centeredon the
theme 0/underslamling busint$s data through vuuoli:ation.
Pomcla Son:e u Co-Director O/tM RIT Printing Industry Center and A.ssociate
Pro/esJOF 0/ Mamting, RIT College 0/ BlUilldS, "'~re JM has sUVl'd/or 11
)~an. ~ l-e~ in t},earnu a/murk/ins n:R'UTCh, buyer behavior and dorabase "'lrieting. ~ eomed a Ph.D. in cognitiWi and expuimOital FYChoIogy
fir- t~ Unil'U$lty ofMcwochusetlS. Mo1l)' o/ ' - scho1QTf)' publicotlOllS build
c..,...
0 1WI. Idol
,",'
ofldoo Govop 10<. io .. "
0..., 10<.
~ .
'
oliN
$
_
..
,...
.
d
.... _ _ ....-
Oil a t~ afllndtmtDnding hlmlQn decision maliJtg ....lth a[ocus on ct1ftSllmU
belwvior. SM has PIIblishw in referT61 mD'*eling, lIflUIQgemf'nt andpS}''C1IDlogy
jaurno/s. T1tese ,mb/icatiof/S Jrao.-e spannm" wide range of /opics including bar
sic research in psychology (retrieval processes in long ter", ",emory), "'''rketing
segmentolion analysis (lifeslyles af older consumers). alld most recently. internet
bllying behavior: Before h er appoi ntment as co-director ofthe RlT Printing Industry e;,.,mer, s~ s('n/ed as Assoc iale Dean of the C4lftge of Buslneu f rom
1996 through ]00 / .
Stan/I!)' ltidrid: ser'l'Q as /)epQrtmem CIUllrmDtl of MonogemDIt, Marbling
and llllernQ/ional Busine.u in the C4f/ege ofBlUlneu at RocJ,ester hUlitule of
Technology. He is an o p en in r!eve/aping InlernDtiallQl buslne5s plalU based
sOllndly in Ihe theories afm arketing. in!efllatio nal business and fi nonciai performallce. He hIlS also worked IlS a consr/flalll for a wide variety ofinduuries
Including: Eas/mon Kodak Company, General MOlOrs, Bausch and Lomb. Roch.
esler Telephone Company. and numerous other corporalions and governmemal
agencies. His "'''itinp on marketing strategies, pricing decisions. and bu)'O" beha\ior Nn-e lINn publishedin a IIQriety ofjouroois andconf~ proc«dings
including Business and Society. T1te Journal afConsumer Marutlng. Ad\'(ltlCf:S
In CalUumer Research, TM Journal ofRl'taifmg. TheJoufllol ofConsumer AJfairs. The Joufllol a/Product and Brand Management und a/hers.
~C Z'OllJ. _""iIr<. ~ "'_?
....
0( _
. . . . 110<. • •
' n,
,10_", _
---