Available online at www.sciencedirect.com
Omega 31 (2003) 349 – 363
www.elsevier.com/locate/dsw
Assessing the consumer decision process in the digital
marketplace
Thompson S.H. Teo∗ , Yon Ding Yeong
Department of Decision Sciences, School of Business, National University of Singapore, 1 Business Link, 117591 Singapore
Received 21 November 2001; accepted 7 April 2003
Abstract
This research focuses on the consumer decision process in the context of the online shopping environment in Singapore. An
Internet survey was implemented and 1133 responses were received. Using structural equation modeling, our 3ndings show
that perceived risk has a negative relationship with consumers’ overall evaluation of the deal, and overall evaluation of the
deal has a positive relationship with consumers’ willingness to buy online. In addition, there is a positive relationship between
perceived bene3ts of search and overall deal evaluation. The implications of the above results are discussed and suggestions
for future research are also proposed.
? 2003 Elsevier Ltd. All rights reserved.
Keywords: Consumer decision process; Internet; Search; Deal evaluation; Risk; Electronic commerce
1. Introduction
The Internet o8ers many advantages to businesses, such
as the ability to reach new segments since products can be
sold globally rather than locally or regionally, and the potential to reduce cost through streamlining of supply chain.
In addition to the attractiveness of a global reach, the Internet also allows businesses to have virtually unlimited shelf
space compared to traditional stores where decisions regarding product assortment and shelf space allocation need to be
made.
The US online retail sales are expected to reach US$104
billion in 2005 and US$130 billion in 2006, up from US$34
billion in 2001 [1]. The projected value of worldwide
e-commerce is expected to surge to US$5 trillion in 2005, up
from US$354 billion in 2000 [2]. Similarly, the Asia-Paci3c
e-commerce market is growing rapidly. E-commerce in
the Asia-Paci3c is projected to grow from US$39.4 billion
in 2000 to US$338 billion by 2004. Asia is on the verge
∗ Corresponding author. Tel.: +65-874-3036; fax: +65-7792621.
E-mail address: [email protected] (T.S.H. Teo).
of an unprecedented e-commerce boom with the number
of active Internet users expected to increase to more than
27% of the world’s online population by 2004. Speci3cally,
the number of Internet users will increase from 49 million
in 2000 to 173 million in 2004, a 38% compound annual
growth rate [3]. Overall, the Asia-Paci3c region appears
ready to take the e-commerce plunge, with strong Internet
penetration in countries such as New Zealand, Australia as
well as Singapore.
This strong growth in e-commerce makes it necessary for
marketers to know their customers well. Marketers can perform their function better if they understand what decision
process potential customers go through when considering the
adoption of e-commerce [4]. With this in mind, this research
is aimed at understanding the consumer decision-making
process of online shoppers in order to better design marketing strategies and create more e8ective websites for achieving marketing objectives.
As research in marketing is increasingly being marginalized and focuses on speci3c areas such as strategy, quality,
satisfaction and product design, Lehmann [5] suggested the
need for a broader focus, building on more general theories that link multiple constructs. Narrowness of perspective may help the advancement of academic knowledge, but
0305-0483/03/$ - see front matter ? 2003 Elsevier Ltd. All rights reserved.
doi:10.1016/S0305-0483(03)00055-0
350
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
it can also be destructively limiting in a problem-oriented
3eld such as marketing [6]. Therefore, in this research, we
applied consumer behavior in a more generalized context.
This research attempts to 3ll some of the gaps in the research on consumer decision making by focusing on several di8erent stages of the consumer decision process in the
context of the virtual shopping environment in Singapore
where speed, cost, scope and quality might vary from that of
a retail store [5]. Further, an attempt is made to secure empirical data on the applicability of the Engel, Blackwell and
Miniard (EBM) model [7] in an online context. The intention is to examine the decision-making and choice behavior
of consumers prior to their purchase online.
2. Literature review
Business use of the Internet is still in the early stages of
development, and much needs to be learned about how consumers will respond to these new forms of electronic retailers. In recent years, online shoppers have been recognized
as a specialized segment of the market for a variety of products and services and their behavior as consumers have now
started to receive increasing attention among marketers and
public policy makers (e.g. [8–12]).
Research on consumer behavior in interactive settings is
relatively new. Peterson and Balasubramanian [13] compared the Internet to conventional retail and catalog distribution channels on dimensions of product features and kinds
of consumer decision process. Lynch [14] presented a study
in which price and quality search costs were manipulated in
an electronic store. It was found that lowering search costs
increased price sensitivity only when quality search costs
were high and comparison with stores was diKcult. Rangaswamy [15] presented 3ndings on how consumer choice in
electronic settings di8ered from traditional in-store choice.
Likewise, research on consumer decision making has
been far from systematic, focusing on a speci3c stage rather
than several stages of the process. For the past 20 years,
the dominant paradigm in consumer behavior has been
information processing. Empirical research on consumers’
information-search behavior has a long tradition in marketing. For example, Beatty and Smith [16] showed that
search e8ort is related positively to purchase involvement,
time availability and attitude towards shopping. Srinivasan
and Ratchford [17] attempted to develop and build a comprehensive search model that provides insights into the
determinants of search. Moorthy et al. [18] presented a
theoretical model that identi3es not only what factors affect consumers’ search behavior but also how these factors
interact with each other. They explored the e8ect of prior
brand perceptions on information search. Similarly, Bei
and Widdows [19] ascertained the extent to which consumers achieved highest value for money under di8erent
conditions and examined the inLuences of information on
consumers’ purchase decisions in an experimental setting,
with allowance for the e8ects of prior product knowledge
and product involvement. Also, Hoque and Lohse [20]
provided a theoretical basis for predicting how subtle differences in the user interface design inLuences information
search costs.
Practitioners and academics alike have speculated that as
consumer decision move online, the cognitive and social
context of decision-making will change in ways that are as
yet only partially understood [21–23]. For instance, at the
individual cognitive level, the proportion of consumers that
engage in a sub-optimal degree of search is likely to decrease with a lower information search cost for the typical
online consumer. This is due to the increasing availability of
extensive, easily retrievable and easily stored databases relevant to product or service purchases. Alba et al. [24] speculate that if online retailing reduces the information search
costs for price information, consumers will become more
price sensitive.
Others have explored consumer behavior at the evaluation
stage [25–30]. Some studies examined consumer behavior at
the purchase stage and one factor that research has identi3ed
as a critical determinant of consumers’ willingness to buy a
new product or brand is the perceived risk associated with
the purchase [31].
Previous research has also attempted to integrate these
various individual stages of the decision-making process.
For example, Hollander and Rassuli [32] attempt to integrate
these stages by examining consumer decisions of surrogate
shoppers who shopped on behalf of clients and had a
3duciary responsibility to them. They also provided a
framework for investigating the marketing management
implications of the shoppers.
3. Research framework
Consumer behavior involves a very wide variety of personal and situational variables. Many models that attempt
to explain or predict consumer decision-making and resulting actions have been proposed. They are an abstract representation of the consumer decision process and simplify the
description of complex consumer behavior. With this simpli3ed picture, the marketer can be much more e8ective in
understanding how consumers respond to his marketing effort. Knowing this, he can better design marketing plans to
encourage sales.
Some of the so-called grand models of consumer behavior which attempt to comprehensively explain all those aspects of the buying situation which their creators deem to
be signi3cant are: Nicosia model, Howard–Sheth model and
EBM model. They provide profound insights into the nature
of consumer buying and consuming which the human mind,
otherwise cannot grasp the immense complexities of this
phenomenon [33]. Here, we will examine the EBM model
[7], which is a development of the original Engel, Kollat
and Blackwell (EKB) model, 3rst introduced in 1968 [34].
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
351
The process begins with the stimulation of a need where
the consumer is faced with an imbalance between the actual
and desired states of a need, which may be suKciently large
to stimulate search. After identifying the need, the consumer
searches for information about the various alternatives available to satisfy the need. The consumer’s information search
will eventually generate a set of preferred alternatives. The
consumer will use the information stored in memory and
those obtained from outside sources to develop a set of criteria. These criteria will help the consumer evaluate and
compare alternatives. Purchase is made based on the chosen
alternative. Post-purchase evaluation is carried out with a
view to aid future decision-making. Good experience with a
brand will provide information that may lead the customer
to that brand when a similar product is to be purchased. Dissatisfaction may result in post-purchase dissonance.
In this study, we focus on the core decision-making process, i.e., the information search, alternative evaluation and
purchase. The hypothesized model in Fig. 2 incorporates the
e8ects of the perceived bene3ts of search, the 3nancial and
performance risk to the overall evaluation of the deal and,
ultimately on the willingness to purchase online. The arrows
indicate the proposed relationships and the plus and minus
signs indicate the nature of their relationships. The various
terms in the model are de3ned in Table 1.
Need Recognition
Information Search
Alternative Evaluation
Purchase
After Purchase Evaluation
Fig. 1. Consumer decision process model.
The EBM model is an attempt to theoretically stimulate
the decision-making process of consumers [35]. One of the
advantages of the EBM model is its generality and its applicability to a wide range of situations. It is also coherent
in its presentation of consumer behavior and it speci3cally
introduces memory, information processing and consideration of both positive and negative purchase outcomes, which
may not be explicitly considered in other models [33].
It is explanatory but can be used to predict and is suitable for comparison of unrelated alternatives, assumes rational consumer and considers post-purchase dissonance. As
such, its strength lies in its ability to help interpret a wide
range of research 3ndings in almost any situation [36]. The
model is plausible in that on the surface it seems to make
sense. This quality is essential for the theory to be easily accepted by other people. Although there are several variations
of the EBM model, we focus on the core decision process
(that captures the essence of the EBM model) as shown in
Fig. 1 [36].
4. Research hypotheses
4.1. Total amount of external search e4ort
External search e8ort is the degree of attention, perception and e8ort directed toward obtaining environmental data
or information related to the speci3c purchase under consideration [16]. In other words, it is a buyer’s willingness to
search for additional information. This e8ort is a8ected by
information that consumers obtain prior to considering the
purchase. According to this de3nition, information obtained
from memory and information obtained passively are not
part of the external search e8ort. Conceptually, these are
+H4
Perceived
Benefits of
Search
+H1
External
Search Effort
+H5
+H2
Overall Deal
Evaluation
+H6
Willingness
to Buy
-H3
Perceived Risk
Information Search Stage
Alternative Evaluation Stage
Purchase Stage
Fig. 2. Hypothesized model of core consumer decision process in the digital marketplace.
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T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
Table 1
De3nition of constructs
Construct
De3nition
Source
Bene3ts of search
Bene3ts of search refer to perceived bene3ts that are expected to result from the external search, including price
reductions, obtaining the most desired model and satisfaction with the decision-making process
External search e8ort is the degree of attention, perception and e8ort directed toward obtaining environmental
data or information related to the speci3c purchase under
consideration
Perceived risk is de3ned as the probability of any loss that
can occur
Overall deal evaluation is de3ned as the perceived
net gains associated with the products or services
acquired
Willingness to buy is de3ned as the likelihood that the
buyer intends to purchase the product. All things being
equal, buyers’ willingness to buy is positively linked to
their overall evaluation of the deal
Moorthy et al. [18].
External search e8ort
Risk
Deal evaluation
Willingness to buy
separate variables that may of course, a8ect external search
e8ort.
While it is conceptually useful to di8erentiate ongoing
search from pre-purchase search, the true concepts are diKcult to separate in practice. The problem lies with precisely
specifying when a purchase problem has been recognized
and when the decision process started. Here, no attempt is
made to distinguish between the two since the border between the two processes are further obscured by the possibility of impulse purchasing [40].
4.2. Perceived bene7ts of search
Buyers generally are uncertain which website to purchase
from because of variations in products o8ered online. To
reduce this uncertainty, buyers must seek information. Willingness to search for information is contingent on buyers’
trading o8 perceived bene3ts (e.g. money saved) relative to
costs of the search (e.g. time, money, e8ort spent in conducting the search) [41].
Bene3ts of search refer to perceived bene3ts that are expected to result from the external search, including price reductions, obtaining the most desired model and satisfaction
with the decision-making process. The bene3ts of search
are driven by how a consumer perceives the uncertainty in
his choice environment (problem framing), the importance
he gives to the product category (involvement) and his risk
aversion [18]. If an individual believes that greater bene3ts
will accrue from search, he will be more inclined to search
because the perceived bene3ts will outweigh the perceived
Beatty and Smith [16].
Grewal et al. [31]; Rice [36].
Dodds et al. [28]; Zeithaml [37].
Della Bitta et al . [38]; Grewal et al. [39].
costs. It is proposed that perceived bene3ts are the trigger
of external search behavior.
H1 : Perceived bene7ts of search is positively associated
with external search e4ort.
4.3. Perceived risk
One factor that research has identi3ed as a critical determinant of consumers’ willingness to buy online is the perceived risk associated with the purchase. Individuals, both
experts and non-experts di8er in their perceptions of risks
depending on the nature of the online product. Risk is personal and related to consumer’s perception of what they
consider to be risky [31,36].
Perceived risk is de3ned as the probability of any loss
that can occur. A possible type of risk related to adoption
of non-store retailers may be source credibility [42]. Internal search involves a scan of memory for knowledge or
experience of past events [43] while external information
search is an examination of sources outside the individual’s
personal knowledge. This may include the media, retailers,
and word-of-mouth communication with other consumers,
or neutral sources. The consumer is thus faced with a problem of requiring information to reduce perceived risk of
purchase.
Information search for tangible-dominant products may
include pre-purchase trial or observation of others when
they purchase products online. Search may continue when
products are unsatisfactory. Search may also continue with
the aim of providing reassurance that a good purchase has
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
been made particularly in ambiguous situations when the
consumer is highly involved but lacks con3dence. The increased consumer uncertainty leads to a consequent need for
greater reassurance [44] and the consumer is thus faced with
a problem of requiring information to reduce perceived risk
of purchase. It follows that:
H2 : External search e4ort is positively associated with
perceived risk.
4.4. Overall deal evaluation
Overall deal evaluation is de3ned as the perceived net
gains associated with the products or services acquired
[28,37]. Hence, deal evaluation refers to the assessment of
value of online purchase. In assessing the value of online
purchase, various potential bene3ts could be examined such
as perceived quality [25], product features [45] and desirability [46]. All of these bene3ts are part of the overall deal
evaluation the consumer makes. Here, an evaluation of the
alternatives found during the search is undertaken which
takes into account our attitudes and beliefs.
Perceived risks have been negatively related to overall
deal evaluation by previous research on catalog and online
shopping [47]. The work on perceived risk in marketing
[48,31] suggests that a consumer forms perceptions regarding the intangible costs such as “psychic costs” in the form
of anxiety, frustration, downtime as well as the performance
and 3nancial risk associated with purchasing online. Risk
represents an uncertain, probabilistic potential future 3nancial outlay. Here, it is therefore proposed that perceived risk
be negatively related to consumers’ evaluation of the deal.
H3 : Perceived risk is negatively associated with overall
evaluation of the deal.
Consumers exert varying degrees of energy to seek out
and process information as they learn about available
products. One of the key bene3ts of searching for more
information is to reduce uncertainty and facilitate consumers’ overall evaluation of the deal. However, the level
of information necessary to make an informed choice, will
obviously depend upon the monetary size of the purchase.
Other inLuences include the existence of customers’ previous experiences, and patterns of learned behavior involving
ways in which attitudes have been formed and other social
factors like reference group inLuence and personal contacts
[36].
Sellers can increase consumers’ overall evaluation of the
deal by enhancing consumers’ perceptions of the product’s
quality or bene3ts relative to the selling price. It is proposed that the perceived bene3ts of search and the amount
of external search e8ort are linked positively to their overall
evaluation of the deal.
H4 : The perceived bene7ts of search is positively associated with the consumers’ overall evaluation of the deal.
H5 : The amount of external search e4ort is positively
associated with the consumers’ overall evaluation of the
deal.
353
4.5. Willingness to buy
Since online shopping is still in the early stage, the purchase decision component of the model is adapted to examine willingness to purchase instead of actual purchase.
Furthermore, early preference patterns of innovators were
shown to be predictive of actual decision of innovators [49].
Willingness to buy is de3ned as the likelihood that the
buyer intends to purchase the product. All things being
equal, buyers’ willingness to buy is positively linked to their
overall evaluation of the deal [38,39].
Past acquisition value-based models (e.g. [28,37]) have
de3ned overall deal evaluation as the perceived net gains
associated with the products or services acquired. That is, the
perceived acquisition value of the product will be positively
inLuenced by the bene3ts buyers believe they are getting by
acquiring and using the product and negatively inLuenced
by the money given up to acquire the product i.e., the selling
price. The perception of value in turn directly inLuences
willingness to buy. It follows that
H6 : Consumers’ overall evaluation of the deal is positively associated with their willingness to buy online.
5. Method
5.1. Sample and procedures
The Internet is used as the data collection tool as our topic
of interest, consumer decision-making in e-commerce is of
interest to general Internet users. To stimulate response, 100
sets of S$2 (US$1.15) Singapore phone cards were given
to respondents selected at random. Recipients of the phone
cards were contacted by e-mails for their mailing addresses
and the phone cards were posted accordingly.
A preliminary survey was created using HyperText
Markup Language. The survey pages were tested thoroughly to ensure that they appear properly with di8erent
Web browsers. To ensure that respondents answer all
questions, or at least those that are absolutely necessary,
JavaScript programming was added to the electronic survey
to verify and perform all necessary checking of a user’s
input before the survey is submitted. For example, if the
respondent omits answering certain questions, a dialog box
would direct users to backtrack, correct the problem and
resubmit the data.
The 3rst round of pretesting was done on a working
adult (male) and an undergraduate (female). Feedback was
obtained regarding the wording of the questions asked and
the layout of the survey. Modi3cations were made and a
next round of pretest was conducted on six students and six
working adults. There were no adverse comments and the
survey was deemed ready for data collection after minor
modi3cations based on feedback. The survey site was
resided at a Web page within the Faculty of Business Administration server. Messages announcing the survey were
354
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
posted in various local discussion newsgroups to reach out to
Internet users. Repeated postings of the advertisement message were also subsequently made, from three times a week
initially and gradually to once a week to encourage more
responses.
Other than newsgroup advertisements, personalized
e-mails were also sent to Internet users. These e-mail
addresses were randomly solicited from Internet Service
Providers (Singnet, Paci3c Internet, Cyberway) and the
National University of Singapore Web pages. In total, 3606
e-mails were sent to invite participation in the online survey.
However, 148 e-mails were returned due to expired e-mail
addresses or fault in the mail delivery system, resulting in
e8ectively 3458 e-mails sent.
5.2. Instrument
The measures for various stages of the consumer
decision-making process (external information search, alternative evaluation and purchase) were adapted from existing
literature. The measures comprise Likert-type statements,
measured on seven-point scales ranging from (1)“strongly
disagree” to (7)“strongly agree” (see Table 2). Where appropriate, the wordings of the items were adapted to the
context of the Internet.
6. Results
6.1. Demographic pro7le of respondents
A total of 1133 responses were collected. For the personalized e-mail category, a total of 3458 e-mails were sent
but only 887 replied, thus yielding a survey response rate of
25.7%. The newgroups category comprises 246 responses.
Before the analysis, a source bias test using the chi-square
(2 ) statistic was performed to determine if there is a di8erence between respondents from e-mail and those from newsgroups. The results of the chi-square test on demographic
pro3le of respondents indicate that there is no signi3cant
source bias in the response sample (Table 3).
The demographic pro3le in Table 3 indicates that respondents were predominantly males (64.5%) and single
(90.5%). Females made up 35.5%, which was slightly higher
than the 26% reported in www.research [51]. Ethnic Chinese
made up the majority (93.0%) of the respondents. The respondents were also relatively young, with 89.8% of them in
the age group of 15 –29 years and the majority in their early
twenties. Most of the respondents are highly educated with
78.7% of them attaining at least a diploma or other higher
quali3cations. Further, 67.8% of respondents are currently
pursuing their education.
The demographic pro3le is consistent with the study on
e-commerce behavior of Singaporeans by Roy and See [52],
the study on cyberbuying by Wee and Ramesh [53] as well
as the www.research online survey [51] which reported that
the local Internet user community is predominately male,
Chinese, tertiary educated and aged 36 years and below.
Similarly, the demographic pro3le of our respondents is
also in line with previous research on Internet adoption by
Singapore Press Holdings’ (SPH) [54] and research by Teo,
Lim and Lai [55] who reported that the typical Singapore
Internet users are Chinese, male, young and highly educated.
As the survey was carried out online, all respondents were
Internet users. About 90% of respondents were Internet users
for more than a year while 21% had bought online.
6.2. Structural equation modeling
In this study, structural equation modeling (SEM) with
AMOS is used to test and analyze hypotheses. In building
structural equation models, the measurement model must
3rst be speci3ed. Test of the measurement model involves
specifying which observed variables de3ne a construct and
ascertaining the extent to which the indicator items are actually measuring the latent construct proposed in the research
model [56].
6.2.1. Validity and reliability analysis
Construct validity refers to the extent to which the indicators “accurately” measure what they are supposed to measure. In this analysis, we examine the individual indicators’
standardized loading (i.e. standardized estimates) and test it
for statistical signi3cance. From an analysis of the individual indicators, we conclude that the measure was valid as all
the indicators were statistically signi3cant. Those indicators
with R2 below 0.4 were dropped because of low loading and
explanatory power [57].
In addition, we examined the correlations among various
construct and noted that there is high correlation between
overall deal evaluation and willingness to buy (r = 0:68).
Subsequently, we carried out factor analyses to determine
whether they are distinct constructs. The results show that
d04 and d05 loaded on both factors. These two items were
dropped and the correlation between overall deal evaluation
and willingness to buy is reduced to 0.52.
Table 4 presents the means, standard deviations and
correlations among the model components. The results
show that perceived risk is negatively correlated with
bene3ts of search (r = −0:037; p ¡ 0:05), overall deal
evaluation (r = −0:429; p ¡ 0:05) and willingness to buy
(r = −0:350; p ¡ 0:05).
For the SEM analysis, we use the composite reliability
to assess the reliability of the construct indicators. Bagozzi
and Yi [58] suggested the composite reliability should be
greater than or equal to 0.60. The composite reliabilities for
various constructs were found to be satisfactory as shown
in Table 5.
6.2.2. Estimation and 7t criteria
For SEM, it is a common practice to evaluate the model
using a few goodness-of-3t measures to assess the model
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
355
Table 2
List of construct indicators
Construct
Source
Total amount of external search e4ort
e01
9. I spent a lot of time sur3ng before I decide upon online purchase
e02
10. I spent a lot of time sur3ng the Website for information about online products
e03
11. I made a lot of visits to sites before the purchase of products online
Perceived bene7ts of search
b01
1. It pays to surf around before purchasing online
b02
2. By searching for more information, I am certain of making the best buy
b03
3. I learned which products are suitable for me by sur3ng around
b04
4. Sur3ng around various sites helped me to 3nd the lowest price when I purchase
online
b05
5. I got exactly what I wanted by searching enough before I purchase online
b06
6. There is too much to lose by being ignorant about products when I have to
purchase online
b07
7. By rushing into an online purchase, one is bound to miss a good deal
Overall deal evaluation
d01
1. Purchasing online is de3nitely worth the money
d02
d03
d04
d05
d06
d07
d08
d09
2. Considering the price, products purchased online are of excellent quality for
the price
3. If I buy online, I will be saving a signi3cant amount of money
4. I am con3dent that buying online is a good decision
5. My attitude about purchasing online is favorable
6. The prices of products online are very acceptable
7. Considering everything, I think purchasing online is an excellent deal
8. Purchasing online is desirable
9. The Web’s product features are very attractive
Perceived risk
r01
1. Considering the amount I would have to pay for online purchase, purchasing
online would be risky
r02
2. I think that the purchase of product online would lead to 3nancial loss for
me because of the possibility of such things as uncertainty in the quality of item
purchased
r03
3. Given the potential 3nancial expenses associated with purchasing online, the
overall 3nancial risk associated with purchasing online is high
r04
4. As far as I am concerned, this 3nancial loss would be important
r05
5. I am not con3dent that the product purchased online will perform the functions
as described
r06
6. I have serious doubts that the product purchased online will work satisfactorily
r07
7. I am not certain whether the product purchased online will perform the functions
that were described in the Website
Willingness to buy
p01
1. If I were going to buy a product, the probability of buying the product online is
p02
p03
2. The probability that I would consider buying online is
3. The likelihood that I would purchase online is
in terms of model 3t and model parsimony. Chi-Square 3t
index is not used here because of our large sample size
(n=1133), which increases the likelihood of rejection of the
model as not being a good 3t with the data and the likelihood
of a Type II error.
Srinivasan and Ratchford [17]
Srinivasan and Ratchford [17]
Urbany et al. [27]; Dodds et al. [28]; Lichtenstein et al. [50]; Wood and Scheer [29]
Srinivasan and Ratchford [17]; Grewal et al.
[31]; Wood and Scheer [29]
Dodds, Monroe and Grewal [28]; Grewal,
Monroe and Krishnan [39]
To assess the overall 3t of the hypothesized model, the
research model is evaluated according to the goodness-of-3t
index (GFI) and the adjusted goodness-of-3t index (AGFI).
The comparative 3t index (CFI) and the root-meansquare error of approximation (RMSEA) which attempt to
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T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
Table 3
Demographic pro3le of respondents
Demographic pro3le
E-mail
Newsgroups
Gender
Male
Female
568
319
163
83
Marital status
Single
Married
805
82
Ethnic group
Chinese
Malay
Indian
Eurasian
Others
Age
Under 15
15 –19
20 –24
25 –29
30 –34
35 –39
40 – 44
Missing (99)
Education
Primary
ITE certi3cate
Secondary/ GCE ‘O’ level
Pre-university/ GCE ‘A’ level
Polytechnic diploma
University degree
Postgraduate diploma
Masters degree
Employment category
Full-time
Part-time
Self-employed
Student
Not in the labor force
Unemployed
National serviceman
Missing (99)
Percent
Chi-square
731
402
64.5
35.5
df = 1
chi-sq = 0:416
p = 0:519
220
26
1025
108
90.5
9.5
df = 1
chi-sq = 0:392
p = 0:531
820
31
21
13
2
234
5
5
—
2
1054
36
26
13
4
93.0
3.2
2.3
1.1
0.4
df = 4
chi-sq = 7:027
p = 0:134
1
245
387
160
56
24
7
7
—
70
110
45
14
4
2
1
1
315
497
205
70
28
9
8
0.1
27.8
43.9
18.1
6.2
2.5
0.8
0.7
df = 6
chi-sq = 1:409
p = 0:965
8
5
28
147
294
309
32
64
2
1
9
41
79
91
10
13
10
6
37
188
373
400
42
77
0.9
0.5
3.3
16.6
32.9
35.3
3.7
6.8
df = 7
chi-sq = 1:737
p = 0:973
218
16
26
598
7
4
10
8
59
2
9
170
2
1
3
—
277
18
35
768
9
5
13
8
24.4
1.6
3.1
67.8
0.8
0.4
1.1
0.7
df = 6
chi-sq = 1:629
p = 0:950
minimize the impact of sample size, and shift the research focus from exact 3t to approximate 3t are used
as well [59].
The GFI is based on a ratio of the sum of the squared
di8erences between the observed and reproduced matrices
to the observed variances, thus allowing for scale. To have a
good model 3t therefore, GFI of close to 0.90 reLects a good
3t. In addition, the AGFI, which is adjusted for the degrees
of freedom of a model relative to the number of variables
Total
should be over 0.80, the CFI over 0.9 and RMSEA less than
0.10 [56].
6.2.3. Test of structural equation model
The total sample (N = 1133) in this study was split into
two sub-groups (odd and even respondents) for cross validation purposes [60]. The calibration sample consisted of
the odd respondents (n1 = 567) and the even respondents
(n2 = 566) served as the validation sample. The results for
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
Table 4
Composite reliability analysis
Constructs
Reliability
Items eliminated
E8ort (1 )
Risk (2 )
Deal (3 )
Purchase (4 )
Bene3ts (1 )
0.735
0.841
0.854
0.900
0.796
—
—
d04, d05, d06, d08, d09
—
b06, b07
the calibration sample show that the model exhibit reasonable 3t (GFI = 0:862; AGFI = 0:823 and CFI = 0:914 and
RMSEA = 0:088).
To avoid post-hoc 3tting of mis-speci3ed models, the validation sample (n2 = 566) was used to cross validate the
causal model before its acceptance. In this study, a “tight”
cross validation approach was used where all the parameters obtained from the calibration sample were 3xed for the
validation sample. The model yields high values for GFI
(0.876), AGFI (0.844), CFI (0.926) and RMSEA (0.082)
and provides favorable evidence in support of replication
and generalizability. Fig. 3b depicts a graphical summary
of the model based on the validation sample. Further, both
Figs. 3a and b indicate that the model for both the calibration
and validation samples were very similar to each other. This
provides further empirical evidence supporting the generalizability of the model.
6.2.4. Structural model (based on total sample)
Subsequently, the model was tested on the total sample (N = 1133) because a larger sample size enhances the
accuracy and stability of coeKcient estimates [56]. A review of the overall measures of 3t reveals a consistent pattern of good support for the model (GFI = 0:879; AGFI =
0:846; CFI = 0:919 and RMSEA = 0:086). The diagrammatic summary of the model is presented in Fig. 4. Since
the overall 3t of the model is acceptable, we now move on
and focus on the speci3c elements of 3t.
As a measure of the entire structural equation, an overall
coeKcient of determination (R2 ) is calculated. Structural
equation 3t of the endogenous constructs is desirable for the
model. In terms of the individual constructs:
• R2 of external search e4ort construct (1 = 0:372) shows
that 37.2% of the variance in E4ort was accounted for by
perceived bene3ts of search.
• R2 of perceived risk construct (2 ) remains low at 0.003.
Only 0.3% of the variance in perceived risk was accounted for by external search e8ort. The relatively low
coeKcient of determination for risk suggests that there
may be other variables that determine risk. For example,
other variables that may determine risk might be the
number of alternative channels from which products can
be purchased and the perceived di8erences between these
channels.
357
• R2 of deal construct (3 ) indicates that 38.4% of the
variance in the overall deal evaluation was accounted
for by external search e8ort and perceived risk.
• R2 of purchase (4 ) has a value of 0.343 which implies
that 34.3% of the variance in willingness to purchase was
accounted for by overall deal evaluation and perceived
risk.
6.3. Hypotheses testing
The standardized coeKcients for each path closely approximate the e8ect sizes usually shown by beta weights in
regression. Thus, low coeKcients have limited substantive
e8ect, whereas increases in values signify their increasing
importance in the causal relationships [61].
In the statistical analysis, supportive 3ndings for H1 ( =
0:60; p ¡ 0:05) suggest an association of perceived bene3ts
of search with external search e8ort. Hence, the greater the
perceived bene3ts of search, the more likely will a user
conduct more external search.
In addition, external search e8ort is found to have no relationship with perceived risk (H2 ) ( = −0:05; p ¿ 0:05).
Supportive 3ndings for H3 ( = −0:49; p ¡ 0:05) suggest
that there is a negative relationship between consumers’ perceived risk and their overall evaluation of the deal. In addition, the relationship between perceived bene3ts of search
and consumers’ overall evaluation of the deal (H4 ) ( =
0:60; p ¡ 0:05) was statistically signi3cant. However, the
relationship between the amount of external search e8ort
and consumers’ overall evaluation of the deal (H5 ) ( =
−0:00; p ¿ 0:05) was statistically insigni3cant. For the 3nal hypothesized relationship between consumers’ overall
evaluation of the deal and their willingness to purchase online (H6 ), signi3cant support was also found for the hypothesized relationship ( = 0:81; p ¡ 0:05).
7. Discussion
7.1. External search e4ort
It was hypothesized in our research model that the total
amount of external search e8ort is inLuenced by perceived
bene3ts of search (H1 ). Results from SEM showed a significant positive relationship between the two variables. This
result is consistent with Srinivasan and Ratchford [17] who
found a strong positive relationship between bene3ts and the
amount of external search.
The vast information on the Web equips people with the
latest and best o8ers. Store navigation features include product search functions, site maps, product indices and the overall site design and organization. Potentially, the Web makes
the information search portion of the decision-making process much easier. In contrast to the time and e8ort required
to go to many physically located stores or phone around,
visiting multiple sites on the Web requires minimal e8ort.
358
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
Table 5
Means, standard deviations (SD) and correlations
Constructs
Mean
SD
1
2
3
4
1.
2.
3.
4.
5.
5.201
4.970
4.525
3.824
3.706
1.435
1.670
1.471
1.210
1.503
0.610∗
−0:037∗
0.363∗
0.077∗
−0:061
0.193∗
0.127∗
−0:429∗
−0:350∗
0.521∗
Bene3ts
E8ort
Risk
Deal
Purchase
∗ Correlation
is signi3cant at p ¡ 0:05.
Information Search
Purchase
Alternative Evaluation
.35*
.53*
Benefits
-.01
.83*
Deal
Effort
-.06
-.47*
Risk
(a)
Purchase
GFI = 0.862
AGFI = 0.823
CFI = 0.914
RMSEA = 0.088
n1= 567
chi-square = 1075.1
df = 198
Alternative Evaluation
Information Search
Purchase
.31*
.68*
-.00
Benefits
Effort
Deal
-.51*
-.03
Risk
(b)
.78*
Purchase
GFI = 0.876
AGFI = 0.844
CFI = 0.926
RMSEA = 0.082
n2 = 566
chi-square = 945.8
df = 198
Fig. 3. (a) Structural model (based on calibration sample); (b) structural model (based on validation sample). Note—Bene7ts = perceived
bene3ts of search; Risk = perceived risk; Deal = overall deal evaluation; E4ort = external search e8ort; Purchase = willingness to buy
∗ p ¡ 0:05.
Directories and search engines allow users to browse through
comprehensive lists of vendors arranged by product and service, or to search for a vendor by name or page content all
from the convenience of a home computer [62].
When the online retail website made quality information easier to search and compare, consumers may become
less price sensitive and purchase higher quality and more
expensive products. Clearly, di8erent ways of presenting
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
359
Purchase
Alternative Evaluation
Information Search
.60*
eb01
eb02
eb03
eb04
eb05
ed01
ed02
ed03
ed07
d01
d02
d03
d07
b01
b02
b03
Benefits
ee04
ee01
ee02
ee03
e01
e02
e03
ed10
b04
b05
-.00
.60*
Deal
.81*
Effort
-49*
.05
Purchase
Risk
r01
r02
er01
er02
r03
-.49*
er03
r04
r05
r06
r07
er04
er05
er06
er07
p01
p02
p03
ep01
ep02
ep03
ep04
er08
GFI = 0.879
AGFI = 0.846
CFI = 0.919
RMSEA = 0.086
N = 1133
Chi-square = 1852.7
Df =198
Fig. 4. Structural model (based on total sample). Note—Bene3ts: perceived bene3ts of search; E8ort: external search e8ort; Deal: overall
deal evaluation; Risk: perceived risk; Purchase: willingness to buy ∗ p ¡ 0:05.
information online alters the information search costs. For
example, if the intent is to provide customers with the ability
to search out detailed technical information, then a website
with deep structured information and search facilities must
be designed. Also, general “help” functions can assist users
in error recovery or include information about the store’s
navigation on the use of ordering features like a shopping
cart function.
The basic idea is that decision-makers trade o8 some accuracy in representing their preferences for a saving in a
cognitive e8ort. More generally, the observed importance
of attributes seems to be a8ected by the cost of processing those attributes. Researchers had shown that generally
consumers are not more price sensitive when shopping online. In fact, consumers conduct less price comparison online
when the store provides information in relevant non-price
attributes and when quality information is made easier to
process [63].
Consumers may also learn how to search e8ectively
through experience and hence, there is a negative relationship between amount of experience and search e8ort [17].
This experience increases the likelihood that a consumer
will perceive lower bene3ts of search and consequently
to search less. Therefore, those who had the least need
conducted the least amount of search.
7.2. Perceived risk
In our hypothesized model, the total amount of external
search e8ort is positively associated with perceived risk
(H2 ). This relationship was not supported. One possible
explanation is that not all websites are equally credible.
Consumers infer information about quantity, quality and a
variety of products from the brand names or reputation of the
store. A website’s credibility is determined by the source’s
trustworthiness. Attribution theory [64] suggests that
consumers who are exposed to a website often attempt to
assess whether the website provides an accurate representation or whether the website lacks credibility. When source
credibility is low, attribution theory suggests that consumers
will discount the information in the website. As a result, the
product attribute claims made by a low credibility website
source are perceived as less useful in judging risk.
Price however, is a particularly unambiguous cue that may
be useful for making judgements about a product’s performance or quality when other cues are unavailable or unreliable [28]. The price cue is expected to have a greater e8ect
on perceived risk when source credibility is low. In this case,
consumers are more likely to use attribution information to
judge perceived risk.
Another possible explanation is that online consumers
may be more likely than their non-wired counterparts to
spend more to get the best and to make an e8ort to use new
devices and methods [65]. Therefore, they may value the
time saving provided by the Web rather than its cost savings.
Online shopping saves time and hassle. Hence, they may
view convenience, rather than cost savings as a key bene3t
o8ered by good online stores [66] which is something not
captured in our construct measurements. This may account
for the insigni3cant relationship between total amount of
external search e8ort and perceived risk.
360
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
7.3. Overall deal evaluation
The negative relationship between perceived risk and consumers’ overall evaluation of the deal (H3 ) is supported. This
is consistent with previous research by Wood and Scheer
[29] which showed that perceived risk has a powerful e8ect
on the overall evaluation of the deal. This study o8ers insights into the importance of communicating information to
consumer that allays their concerns about performance risk
and 3nancial risk such as information about product quality,
warranties and money-back guarantees.
Customers are likely to try the online product when they
come across a purchase trigger like coupon or free trial. Trial
is an important part of most evaluation processes and reduces
the consumer’s risk [62]. For instance, bookstores can o8er
samples of speci3c o8erings, music stores can provide audio
clips of their discs and software companies can let users
download versions of their software for a limited trial period.
The responsiveness of sta8 to the customer service link
is critical. Types of service include sales clerk service for
merchandise selection, answers to frequently asked question
(FAQ) and credit, return and payment policies. Spiller and
Lohse [67] surveyed 137 Internet retail stores and found
that most stores provided a pittance of service information.
Almost one-third did not provide any information on the
company’s history policies or background and 80% had less
than 10 lines of information. This is a surprising number
since customers want to know who they are dealing with
when making online purchase. This is especially important
for new “virtual companies” solely operating on the Internet.
The positive relationship between the perceived bene3ts
of search and consumers’ overall deal evaluation (H4 ) was
supported. This result is expected as one of the key bene3ts of search is to facilitate deal evaluation through providing more information. However, the relationship between
the amount of external search e8ort and consumers’ overall
evaluation of the deal (H5 ) was not supported. One possible explanation for this 3nding is that consumers’ search for
information can be divided into two general categories: internal search and external search [68]. Information search
initially covers internally stored information and experience.
Personal selling will inLuence their choice as well. This level
of search is quick and largely unconscious and if there are
fairly strong beliefs and attitudes, automatic or routine problem solving follows. Where enough information is available
in memory to make a decision, then an internal search is
all that is required. If such information is scarce, external
search for information is then undertaken [36].
Perhaps, the information environment for e-commerce is
so rich that even unknowledgeable consumers can acquire
enough relevant information, to feel they can make a purchase decision without seeking large amounts of externally
obtained information from the Web prior to beginning the
buying decision process. Consumers are able to acquire
relevant product and purchase knowledge without having
detailed website information. This might well explain the
relatively weak relationship between total amount of external search e8ort and consumers’ overall evaluation of the
deal.
Another possible explanation is that users invest their
own time and energy in the interaction with a store’s digital
marketing application, therefore creating an important disincentive for them to repeat that investment with another application. For instance, they might have spent time revealing
their preferences in music or movies and the store is now
able to o8er them suggestions on other things they might enjoy based on the information gained in previous purchases.
This may account for the minimal amount of external search
e8ort among shoppers.
7.4. Willingness to buy
The results indicate a signi3cant positive relationship between consumers’ overall evaluation of the deal and their
willingness to purchase (H6 is supported). This is similar to
past research by Wood and Scheer [29], which found that
purchase is apparently driven by the overall cost or bene3t
tradeo8 represented by overall deal evaluation. In addition,
Dodds et al. [28] also found a signi3cant positive relationship between overall evaluation of the deal and willingness
to buy.
It must be noted that consumers balance the bene3ts of
purchase against the costs. Our research supports the idea
that overall evaluation of the deal has a positive relationship
with consumers’ willingness to buy online. Bene3ts can be
functional, operational in terms of durability and reliability
or personal. Costs include both 3nancial and non-3nancial
aspects such as time and e8ort [37].
Today’s information technology enables consumers to
compare bene3ts and prices with unprecedented ease and
accuracy. Managers must understand the variables a8ecting
consumers’ evaluation of the deal. Some consumers are
sensitive towards price, whereas others are more bene3t
oriented. Hence, the consumers’ overall evaluation of the
deal will vary across segments. Managers should therefore
determine which value strategy is appropriate for their
target segments and develop their positioning strategies
appropriately.
8. Limitations
Although this study provides several interesting insights
into consumers’ decision process in the Singapore context,
there are however, some limitations that restrict the extent to which 3ndings can be generalized. First, the Internet and online shopping patterns used in this study were
largely self-reported as opposed to objectively measured.
Self-reported data are subject to the fallibility of people’s
memories or even deliberate alteration through social desirability biases [66].
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
Second, as the data was collected using Internet survey, the problems associated with the medium would also
a8ect the quality of our data collected. Although precautions had been made to minimize these problems, such as
using JavaScript programming to ensure necessary data
are submitted, there might still be other inherent problems
such as self-selection that could a8ect the quality and representativeness of our data collected. Further, as the online
population tends to comprise people with certain characteristics (e.g., age, Internet experience), generalizability to the
general population may be limited. In addition, although
responses were screened for duplicated responses from the
same email address, it is possible that some respondents
might use another email address to answer the survey. However, given the length of the survey, this appears unlikely
and is not a serious limitation.
Third, the measures used in this study were obtained at
one point in time. Hence, one has to be cautious about interpreting our results as indicative of casual relationships.
Strictly speaking, they indicate only correlational relationships between the various constructs. Fourth, as the sample
was collected in Singapore, generalizability to other countries might be limited due to di8erences in buying behavior
across di8erent cultures. However, as the results appear consistent with previous research done in other countries, this
limitation is not serious.
Fifth, we only examined a sub-set of variables a8ecting
consumer’s decision process to buy online. Future research
can include other variables such as nature of products (physical or digital), delivery mechanisms, prices, brand name
and speci3c attributes of the web sites through which selling
is done.
9. Implications
Market researchers have been trying for decades to understand consumer behavior. Our study provides further
evidence on the consumer decision model for the understanding of consumer behavior in the digital marketplace.
The results con3rm the argument by O’Keefee and McEachern [69], who proposed a framework called consumer decision support systems (CDSS) that the consumer decision
process is applicable in the context of the virtual shopping
environment. According to their framework, both CDSS facilities and Generic Internet and Web facilities can support
each of the stages of the consumer decision process model.
Our study has important implications for the understanding of the consumer decision process in e-commerce. In this
research, attempt is made to develop and empirically test
the consumer decision process model with speci3c attention
to the applicability of online consumer behavior. The results provide more substantive understanding of the factors
a8ecting consumers’ willingness to purchase online. Given
the relatively good overall goodness of 3t of the model, we
believe that this study could be a valuable addition to re-
361
searchers in their e8orts to understand consumer behavior
in the digital marketplace.
However, further development of the consumer decision
process model could be done so as to improve the ability of
the model in predicting online sales. In particular, it is noted
here that the R2 for the perceived risk structural equation
is relatively weak. At 0.3%, the ability of the model in explaining risk is on the low end. This could be because external search e8ort, which is hypothesized to a8ect perceived
risk only explained 3% of the variance. Hence, this may indicate that there could be other factors a8ecting perceived
risk, which were not examined here.
It is also possible that the performance risk and 3nancial
risk constructs impact this framework at di8erent points.
For example, perceived performance risk may be a determinant of product desirability as well as potential costs, while
perceived 3nancial risk may play a more important role
in whether or not positive evaluation of the deal is translated into willingness to purchase. Future research could
perhaps separate perceived risk into these two di8erent
constructs.
In addition, the results indicate positive relationships between perceived bene3ts of search and consumers’ overall deal evaluation. This suggests that perceived bene3ts
of search could have a signi3cant inLuence on consumers’
overall deal evaluation and subsequently on their willingness to purchase online.
Further, it was found from our results that external search
e8ort is not statistically signi3cant with consumers’ overall
deal evaluation. This may signify that other factors, such as
internal search may inLuence consumers’ overall deal evaluation. Thus, this 3nding could be a new research topic area
for researchers to explore. Further examinations of search
cost models can be conducted to determine the distribution
of prior beliefs among consumers and their e8ect on the total amount of external search.
10. Future research directions
There are several avenues where future research can be
conducted. First, the study involved asking general Internet
users about their perceptions of purchasing online. With the
rapid growth of e-commerce, future research can replicate
our study solely on e-commerce users, measuring actual purchase behavior instead of intentions. This is to determine if
there are any signi3cant di8erences in the core consumer decision process of general Internet users versus e-commerce
users.
Second, the investigation of the consumer decision process model can also be extended to cover the same issue
from the point of view of business consumers, in their procurement of goods so as to have a more holistic perspective
on this phenomenon. Third, a comprehensive comparative
analysis can be carried out between the di8erent consumer
behavior models in the investigation of consumer behavior
362
T.S.H. Teo, Y.D. Yeong / Omega 31 (2003) 349 – 363
in the digital marketplace. This will increase our understanding of the relative strengths and weaknesses of applying the
various consumer behavior models in the context of the virtual shopping environment.
Fourth, detailed studies may be conducted to reveal key
push and pull factors surrounding the use of the Internet for
search activities. For example, research can investigate the
e8ect of di8erent website attributes on consumer’s search
e8ort and their ultimate decision to purchase online. Fifth,
future research may also use di8erent methodologies such as
longitudinal studies, focus groups and interviews to examine
consumer decision processes in online buying. Such studies
would enhance the richness of the 3ndings. Sixth, the study
can be replicated in a di8erent culture for cross-cultural comparisons. As Singapore has a strong pro-IT culture, it would
be interesting to compare the results with studies in other
cultures where IT culture is less dominant or developed.
Last but not least, e-commerce is still currently in its
infancy stage here. It would be interesting to replicate this
study in the near future, where e-commerce would have
possibly become part of the lifestyle of local residents and
compare whether there are any signi3cant di8erences in the
results obtained.
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