1. Why do shoppers abandon shopping cart?
Perceived waiting time, risk, and transaction
inconvenience
Rajasree K. Rajamma
Department of Marketing, Charles F. Dolan School of Business, Fairfield University, Fairfield, Connecticut, USA
Audhesh K. Paswan
Department of Marketing and Logistics, College of Business Administration, University of North Texas, Denton, Texas, USA, and
Muhammad M. Hossain
Department of ITDS, College of Business Administration, University of North Texas, Denton, Texas, USA
Abstract
Purpose – The purpose of this study is to explore the factors leading to the consumer’s propensity to abandon the shopping cart at the transaction
completion stage.
Design/methodology/approach – Data were collected using a self-administered survey distributed through the internet. The sample consisted of
consumers who shopped online at least once during the preceding one-year period.
Findings – The results indicate that perceived transaction inconvenience is the major predictor of shopping cart abandonment. The other predictors are
perceived risk and perceived waiting time. Positive relationship was found between perceived transaction inconvenience, perceived risk and propensity
to abandon the shopping cart. It was also found that propensity to abandon the shopping cart is negatively associated with the perception of waiting
time.
Practical implications – The paper provides transaction completion stage specific guidance to the managers operating in an online environment to
prevent shopping cart abandonment at the transaction completion stage. Specifically, the findings suggest that marketers must pay attention to the
perception of risk and transaction inconvenience; otherwise they risk losing consumers during the final stage of transaction.
Originality/value – The paper examines the unexplored area of consumer behavior at the final stages of transaction culmination and, hence, is an
initial step toward filling that gap.
Keywords Internet shopping, Electronic commerce, Consumer behaviour
Paper type Research paper
An executive summary for managers and executive
readers can be found at the end of this article.
Marketers spend enormous amount of time, effort, energy,
and resources to market and sell their products and services to
their consumers. They engage in activities such as
segmentation, targeting, positioning, and use the four Ps to
ensure that the consumers select their product and service
from a shelf full of competing products. Once the consumer
selects a product and puts it in his/her shopping cart, he/she
takes it to the checkout point. However, in some cases, for
various reasons (e.g. long lines, cumbersome and tedious
checkout process, etc.) consumers may abandon the cart. All
the time, effort, energy, and resources spent till then goes to
waste. While marketing literature is replete with investigations
focusing on virtually every aspect of consumer and shopping
behavior, little academic research focus has been directed
toward understanding why consumers abandon a shopping
cart towards the end, after they have selected the product.
This is the impetus for our investigation and we hope that our
findings would help fill this crucial knowledge gap.
This phenomenon is especially pertinent in the context of
e-commerce. Studies estimate that approximately 60-75
percent of the shopping carts are abandoned before
purchase is made (Goldwyn, 2002; Eisenberg, 2003; Oliver
and Shor, 2003; Gold, 2007). In addition, trade data suggests
that each incidence of shopping cart abandonment represents
approximately $175 in lost sales to the online retailer
(Mullins, 2000). The total online retailing industry loss,
thus, would amount to more than $6.5 billion per year
(McGlaughlin, 2001). Thus, online shopping behavior
provides an ideal context for this investigation. It is
important for managers (e.g. product, marketing, and
retailing) operating in an online environment as well as
researchers to understand the factors leading to shopping cart
abandonment by consumers, so that they can avoid future
financial losses as well as customer erosion.
Shopping cart abandonment as examined in this paper
comes right after the consumer has decided to purchase the
products, but before the purchase is completed. A lack of
understanding regarding this stage in the existing literature
points to the need for this research. Several studies have
focused on the antecedents to the decision whether to shop
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1061-0421.htm
Journal of Product & Brand Management
18/3 (2009) 188–197
q Emerald Group Publishing Limited [ISSN 1061-0421]
[DOI 10.1108/10610420910957816]
188

2. online or not (Goldsmith, 2002; Koufaris et al., 2001; Shim
et al., 2000; Wolfinbarger and Gilly, 2001), and the
demographic and psychographic profile of the internet
shopper. A few studies with a focus on satisfaction with
online retail environment (Szymanski and Hise, 2000) have
either tried to examine satisfaction in general (i.e. with no
specific focus on any particular stage) or have tried to explain
consumer satisfaction/dissatisfaction from the web developers’
point-of-view (Chen and Wells, 2001). Moreover, most of the
studies have focused on the initial stages of the buying
process, i.e. from problem recognition to the evaluation of
alternatives stages (“five stage model of consumer buying
process” – Kotler, 1999), or the factors influencing the
consumers’ propensity to shop online.
Although these studies have made undeniable contributions
toward explaining consumer satisfaction with the retailing
environment, there is paucity of research aimed at
understanding why a customer who may be otherwise
satisfied with all aspects of the shopping environment would
quit without completing the transaction. This paper is
expected to fill that lacuna, by focusing on three situational
factors such as perceived waiting time, perceived risk and
transaction inconvenience as potential determinants of the
decision to abandon shopping carts. Expectancy-disconfirmation
model is used as the overarching theory for
the current research aimed at examining the above-mentioned
knowledge gap in the context of online shopping.
The organization of the paper is as follows: a brief
discussion on expectancy-disconfirmation model is presented
followed by literature review, methodology for the study,
findings and analysis, and discussion on the major findings
and their implications. The paper ends with a section on
limitations of the study.
The expectancy disconfirmation model
According to the expectancy disconfirmation model,
satisfaction/dissatisfaction is a function of expectations and
disconfirmations of the consumer (Oliver, 1980; Oliver and
DeSabro, 1988). That is, “consumer’s expectations serve as
the base line for satisfaction assessment” (Szymanski and
Henard, 2001). Thus, expectations are either positively
disconfirmed when the experience exceeds expectations,
confirmed when experience equals expectations, or are
negatively disconfirmed when the experience falls below
expectations (Swan and Trawick, 1981). One of the possible
outcomes of negative expectancy disconfirmation is
dissatisfaction and that of positive disconfirmation of
expectations is satisfaction (Oliver, 1980).
Previous studies in the expectancy disconfirmation area
have focused on the post-purchase satisfaction (LaBarbera
and Mazursky, 1983; Swan and Trawick, 1981; Bearden and
Teel, 1983; Woodruff et al., 1983; Oliver and DeSabro, 1988;
Szymanski and Henard, 2001). However, researchers like
Simintiras et al. (1997) and Simonson (1992) have found pre-purchase
satisfaction as an important logical antecedent to
purchase. In this study, we take the stance that the
expectation-disconfirmation model may operate in the
checkout process as well. We argue that consumers may
have some pre-conceived expectations about the checkout
process based on their expectations of online retailing.
Existing literature has identified several characteristics that
have become stereotypical expectations from an online retail
environment, e.g. quick and easy checkout, shorter or no
queue, convenient process, more control over the process, risk
associated with giving the credit card to someone else, and
more control over payment device such as credit card (e.g.
Prince, 2004; Photo Trade News, 2006; Schelmetic, 2006).
While convenience (e.g. Girard et al., 2003; Donthu and
Garcia, 1999; Wolfinbarger and Gilly, 2001) has been
suggested as one of the important factors associated with
the decision to shop online, once the consumer decides to
shop online other factors such as privacy and safety of
transactions and enjoyment may also come into the picture
(e.g. Wang et al., 2006). Moreover, the convenience concept
discussed in the extant literature (e.g. Girard et al., 2003;
Donthu and Garcia, 1999; Wolfinbarger and Gilly, 2001)
encompasses convenience of saving time, of not having to go
to the store and ability to shop at any time.
Based on the expectancy disconfirmation literature, it is
reasonable to assume that if the consumers’ experiences in the
online store fall short of their expectations, they are likely to
experience dissatisfaction, and vice versa. This would in turn
affect their purchase intention (Cronin and Taylor, 1992;
Oliver, 1980), ultimately resulting in shopping cart
abandonment. It is important to note that satisfaction and
dissatisfaction are not necessarily bipolars of a continuum
with respect to online shopping (Chen and Wells, 2001).
Hence, consumers may be satisfied with some aspects of a
shopping environment and dissatisfied with others. Therefore,
it is not unreasonable to assume that despite the presence of
satisfiers pertaining to the e-commerce environment, e.g.
convenience of online shopping, merchandise, site design and
financial security (Szymanski and Hise, 2000), shopping cart
abandonment may still take place purely based on whether
the checkout process fulfills the consumer’s expectations
formed by their experiences up to that point. In fact, one
would argue that if a retailer (online or offline) does a good
job of making consumers extremely satisfied till they reach the
checkout point, they in fact build higher consumer
expectations. Now, if the checkout process does not meet
their expectations, then consumers are likely to be
disappointed, even if the infraction is minor. On the other
hand, one could argue that, because consumers have been
very satisfied up until they reach the checkout point, they may
be willing to overlook minor infractions. In this study, we
focus on three key factors:
1 Perceived waiting time (Davis and Heineke, 1998).
2 Perceived risk (Belanger et al., 2002).
3 Transaction inconvenience (Childers et al., 2001; Donthu
and Garcia, 1999; Srinivasan et al., 2002).
We take the stand that if consumers have to wait longer in the
checkout line, the transaction suddenly starts to look risky, or
the final transaction process seems inconvenient, which may
lead to shopping cart abandonment, even if, the consumer
was satisfied with his/her experience with the e-commerce site
until then.
Perceived waiting time
Waiting time is the amount of time a customer has to wait for
service. According to Davis and Heineke (1998), “customers’
reaction to waiting in line can color his/her perception of the
service delivery process”. Other researchers have also alluded
to the inverse relationship between perceived waiting time and
Why do shoppers abandon shopping cart?
Rajasree K. Rajamma, Audhesh K. Paswan and Muhammad M. Hossain
Journal of Product & Brand Management
Volume 18 · Number 3 · 2009 · 188–197
189

3. satisfaction (Maister, 1985; Davis and Vollmann, 1990; Davis
and Heineke, 1998). Davis and Vollmann (1990) argue that
when the amount of time available to customers is limited,
they tend to be more and more impatient. Others (e.g. Katz
et al., 1991; Pruyn and Smidts, 1998) have found that
customers usually overestimate the amount of time they had
to wait for service, and this makes the perception of waiting
time to be much more than the actual wait.
Perception of waiting time assumes more importance in the
case of online shoppers, profiled as convenience seekers who
wish to economize on time (Childers et al., 2001;
Wolfinbarger and Gilly, 2001; Balabanis and Vassileiou,
1999). Thus, any delay and increase in actual waiting time, or
the perception of it, is likely to disconfirm the consumer’s
expectation of a quick shopping episode. This results in their
dissatisfaction leading to abandonment of the shopping cart.
Some of the factors that contribute to the delay in
completing online transactions include slow page downloads
(waiting for pages to open up), uploads (waiting for page
submissions to be uploaded to the site), lengthy forms and
unique formats of forms for clearances. Nielson (1996) found
that consumers are likely to lose interest in a website if the
response time is greater than 10 seconds, a finding also
supported by Selvidge et al. (2002) and Kuhnmann (1989).
Further, Selvidge et al. (2002) noticed that a longer waiting
time (delay) leads to increased frustration, which eventually
results in the participant’s failing to complete tasks. A similar
conclusion was reached by Taylor (1994) in her research on
factors affecting service evaluations. Thus, we propose that a
longer perceived waiting time during the final stages of
transaction can lead to greater propensity to terminate the
purchase activity and thus abandonment of the shopping cart:
H1. Perceived waiting time to complete a transaction is
positively associated with the propensity to abandon
the shopping cart.
Perceived risk
Several researchers and practitioners have identified perceived
risk as a key factor deterring consumers from shopping online
(Ranganathan and Ganapathy, 2002; Belanger et al., 2002;
Liao and Cheung, 2001; Landrock, 2002; Olivero and Lunt,
2003; Butler and Peppard, 1998; Wingfield, 2002; Odom
et al., 2002; Harrison-Walker, 2002). To counter this
perceived risk associated with online shopping, e-commerce
firms are making use of web assurance seals (Odom et al.,
2002; Belanger et al., 2002), privacy seals, privacy statements
(Belanger et al., 2002), consumer feedbacks and expert
reviews (Wingfield, 2002). However, the fact remains that
even after an online retailer succeeds in winning the trust of
its customers by employing all the trust evoking techniques
mentioned above, a large number of customers still leave
without completing their purchase. A possible explanation for
this anomaly could stem from the privacy and security risks
that consumers might perceive during the process of
checkout. Online shoppers often find that many websites
require them to reveal great deal of personal and financial
information (like credit card number) before their orders are
accepted and the checkout process is completed. It is possible
that this might act as a red flag to shoppers in spite of their
initial trust in the online retailer. Research has found that
“consumers are unwilling to reveal personal information over
the web, despite assurances given by the online retailer”
(Ranganathan and Ganapathy, 2002). A possible explanation
for this could come from the finding that there is a gap
between the assurances that consumers expect from the so
called trust enhancing tools (like web seals, privacy statements
etc.) used by e-tailers and their perception of what is currently
provided by these tools (Odom et al., 2002). Supporting this
argument is the finding by Pandya and Dholakia (2005) that
one of the reasons behind the dot.com bust was the mismatch
between consumer-seller perceptions and expectations.
Based on the above, it is argued that when consumers’
expectations about the risk (e.g. security and privacy of the
information asked) during the checkout process are negatively
disconfirmed, they may get de-motivated from completing the
transaction, thus leading to shopping cart abandonment.
Further, researchers have pointed out that contextual
factors affect an individual’s risk evaluation (see Dowling,
1986; Bromiley and Curley, 1992; Lopes, 1987; Conchar
et al., 2004). In the case of online shopping, this risk
evaluation can take place on all web pages that online
shoppers visit within a website, as each page presents them
with a different set of information and a different context. Of
particular importance is the entry page where consumers
decide whether to shop at that website, and the transaction
conclusion/checkout page where they input personal
information such as credit card information and address.
Since perception of loss (in terms of personal information,
credit card information, etc.) could be heightened at the
transaction conclusion/checkout stage, we argue that the
perception of risk is also at the highest level at this stage.
Based on the above arguments, it is hypothesized that:
H2. Perception of checkout process specific risk will be
positively associated with the propensity to abandon
the shopping cart.
Perceived transaction inconvenience
While convenience is a critical factor determining consumer
behavior in general, it is considered as one of the most
important predictors for the choice of online shopping
(Childers et al., 2001; Donthu and Garcia, 1999). Online
shoppers have been found to expect fast and efficient
processing of their transactions online (Srinivasan et al.,
2002). However, complex shopping procedures, long
registration forms to be filled up, shipping and handling
charges that are not revealed until late in the purchase
process, out of stock product information revealed at the
checkout, technical glitches that bounce back orders and non-availability
of alternative methods of payment (other than
credit cards) are all considered to be major transaction
inconveniences that make transactions complex and cause
disconfirmation of consumers’ expectations leading to
dissatisfaction (Seiders et al., 2000; Harrison-Walker, 2002;
Anon., 2002). This leads to the hypothesis that perceived
transaction inconvenience associated with the transaction
completion process would result in a higher propensity to
abandon the shopping cart:
H3. Perceived inconvenience associated with transaction
completion process will be positively associated with
the propensity to abandon the shopping cart.
Why do shoppers abandon shopping cart?
Rajasree K. Rajamma, Audhesh K. Paswan and Muhammad M. Hossain
Journal of Product & Brand Management
Volume 18 · Number 3 · 2009 · 188–197
190

4. Method
The initial sample for the study was selected from among the
business undergraduate students at two universities; one in
the northeast and the other a major university in the
southwest. Convenient sampling techniques were used to
select the initial sampling units. Students who volunteered to
complete the survey were then requested to recruit at least
two other individuals who shop online. Through this snowball
sampling technique, we obtained a final sample of 720
respondents. After initial scrutiny of the data, 13 respondents
were eliminated as they were not online shoppers. Self-administered
electronic questionnaires were used to collect
the data for the study. The online methodology is appropriate
for this research as online customers provide the sampling
frame for this study.
Owing to the non-availability of existing scales in the
context of shopping cart abandonment, scale items had to be
developed to measure all constructs. The scales were
developed using information collected from trade journals
and discussions with several online shoppers during the
exploratory stage. Perceived risk scale items were derived
from the interview summaries given by Szymanski and Hise
(2000). Responses to the scale items were measured on a five
point Likert scale anchored between “strongly disagree” (1)
and “strongly agree’ (5). The dependent variable, “shopping
cart abandonment”, was measured as a continuous variable
which asked the respondents how many times they had
abandoned the shopping cart in the last year. The responses
were then grouped (using cluster analysis) into two clusters of
respondents: those with a high incidence of shopping cart
abandonment, and those with low incidence of shopping cart
abandonment. To respond to the scale items measuring the
rest of the constructs in the questionnaire, the subjects were
asked to think of a particular episode when they abandoned
the shopping cart. In addition to the main constructs, several
demographic variables were also collected from the
respondents.
Analyses and results
The sample consisted of 42 percent male and 58 percent
female. A total of 64 percent of the respondents were between
the ages of 18 and 25. On an average the respondents had
abandoned the shopping cart 4.58 times in the past year. The
respondent groups from both the regions (northeast and
southwest) were comparable on their age distribution, gender
distribution as well as the number of times they had
abandoned the shopping cart.
The data were first factor analyzed to identify the factors
influencing shopping cart abandonment. Three factors
pertaining to constructs such as perceived risk involved in
online shopping, transaction inconvenience and perceived
waiting time were extracted (Table I). After factor
purification, the scale for measuring perceived risk,
perceived transaction inconvenience and perceived waiting
time consisted of six, five and five items, respectively. The
factors were tested for internal consistency (Cronbach’s
alpha) revealing that all the constructs possess acceptable
levels of internal consistency (Cronbach’s alpha . 0.69)
(Table I; Nunnally, 1978; Robinson et al., 1991). Inter-item
correlations (within factor correlations were greater than
across factor correlations) indicate that the scales have
adequate levels of convergent as well as discriminant validity
(Table II). We also relied on Gaski and Nevin (1985) to check
the discriminant validity of the factors. As the inter-factor
correlations (using composite factor scores) were less than the
reliability of each scale, the factors can be considered to have
acceptable levels of discriminant validity (Table III).
Using split samples, the data was tested to ensure that the
results are not affected by non-response error. Analysis of the
demographic variables showed that there are no significant
differences between genders, income categories or age
categories with respect to their propensity to abandon
shopping cart. For further analyses, composite score for
each factor was computed. Logistic regression was carried out
with the composite scores of factors as independent variables
and the two clusters (high vs low) based on shopping cart
abandonment score as the dependent variable. The results of
this analysis are presented in Table IV. The regression output
showed that the model under consideration has a good fit
(Hosmer and Lemeshow test: p-value ¼ 0.650). As can be
seen from Table IV, all three factors: perceived waiting time
(p-value ¼ 0.044), perceived risk (p-value ¼ 0.010), and
perceived transaction inconvenience (p-value ¼ 0.010) were
found to significantly influence the shopping cart
abandonment. As hypothesized, perceived risk and
perceived transaction inconvenience were positively related
to the dependent variable (shopping cart abandonment), thus
offering support for H2 and H3. In comparison, the
relationship between perceived waiting time and the
propensity to abandon shopping cart was significant, but
inverse (H1 was not supported). The results were found to
have reasonable predictive validity as indicated by a hit ratio
of 58.6 percent.
Discussion and implications
The results of this study provide interesting insights into the
factors influencing shopping cart abandonment in an online
environment. Some of the findings challenge the commonly
held perceptions about the reasons for shopping cart
abandonment. However, we urge the readers to keep in
mind that this study is focusing on just one stage of online
shopping namely, the transaction completion stage, and not
the entire shopping process. The premise of this study is that
even after being very satisfied with their experience leading up
to the transaction completion stage, there may be some
factors that are specific to the transaction completion stage
that could make a consumer abandon his/her shopping cart.
In our study, perceived transaction inconvenience was seen
to have the greatest influence on shopping cart abandonment
by consumers. During online shopping, perception of
transaction inconveniences are created mainly by lengthy
order forms to be filled up, lack of flexibility of the website in
accepting commonly required information, and the technical
glitches which delay transaction at the time of checkout.
Online retailers should be more empathetic to the consumers’
needs by requiring them to fill only the bare minimum
information needed for fulfilling the order; offering ability to
store commonly requested information in secure servers and
offering real-time storage of information. The real-time
storage of information will enable consumers (especially the
ones with dial-up connection which get disconnected
frequently) to continue from where they left off even in the
event of technical glitches kicking them off the website.
Why do shoppers abandon shopping cart?
Rajasree K. Rajamma, Audhesh K. Paswan and Muhammad M. Hossain
Journal of Product & Brand Management
Volume 18 · Number 3 · 2009 · 188–197
191

5. Why do shoppers abandon shopping cart?
Rajasree K. Rajamma, Audhesh K. Paswan and Muhammad M. Hossain
Table I Scale items: perceived risk, waiting time and transaction inconvenience
Journal of Product & Brand Management
Volume 18 · Number 3 · 2009 · 188–197
Perceived risk (PR)
PR3: I was afraid that someone might steal my personal information 0.873
PR1: I was worried that someone might steal my credit card number 0.809
PR6: I was worried that the company might misuse my information 0.799
PR2: I was worried about dealing with a company unknown to me 0.798
PR4: I suddenly got suspicious of the site 0.752
PR5: The online shop did not promise secure transaction 0.732
Perceived waiting time (PWT)
PWT3: The graphics on the web site delayed my order processing 0.780
PWT1: I had to wait for some time (e.g. for more than 10 sec) for a page to upload 0.779
PWT4: It took a while to get online confirmation for my purchases 0.749
PWT2: It took more than ten seconds for the site to process my order 0.725
Perceived transaction inconvenience (PTINC)
TINC4: The online shop required me to register before making a purchase 0.793
TINC3: The order forms were very lengthy 0.401 0.679
TINC6: I got logged off in the middle (for some reason) and had to go through the entire process of
completing information again 0.616
TINC1: Technical glitches in the site made the transaction difficult 0.529
Percentage of variance (total 5 63.34%) 28.780 19.890 14.669
Mean 3.035 2.615 3.285
Std deviation 1.006 0.874 0.822
Skewness 20.164 0.091 20.582
Kurtosis 20.655 20.414 0.206
Cronbach’s alpha 0.905 0.813 0.692
Note: Scale anchor: 1 = strongly disagree; 5 2 strongly agree
Another potential area for improvement is with respect to the
format in which information is accepted. E-tailers should offer
consumers the flexibility of entering information in commonly
used formats (for example mm-dd-yy instead of mm-dd-yyyy).
PR PWT PTINC
Simple, modifications to the website will go a long way
in enhancing consumer’s satisfaction with the shopping
process. In a brick and mortar context, transaction
inconvenience could translate into making the checkout
Table II Inter-item correlations
PR3 PR1 PR6 PR2 PR4 PR5 PWT3 PWT1 PWT4 PWT2 TINC4 TINC3 TINC6 TINC1 Mean SD
PR3 1.00 1.34 0.93 1.16 0.87 0.81 0.38 0.36 0.41 0.34 0.27 0.28 0.31 0.44 3.06 1.27
PR1 0.82 1.00 0.84 0.99 0.79 0.75 0.41 0.39 0.46 0.35 0.25 0.25 0.27 0.44 2.94 1.29
PR6 0.64 0.57 1.00 0.85 0.89 0.96 0.36 0.42 0.35 0.38 0.32 0.35 0.30 0.48 3.05 1.14
PR2 0.73 0.62 0.60 1.00 0.84 0.80 0.35 0.38 0.34 0.36 0.33 0.34 0.33 0.42 3.12 1.25
PR4 0.58 0.52 0.67 0.58 1.00 0.88 0.38 0.36 0.39 0.32 0.36 0.37 0.30 0.47 2.92 1.17
PR5 0.53 0.48 0.69 0.53 0.62 1.00 0.32 0.30 0.37 0.33 0.31 0.33 0.31 0.50 3.11 1.21
PWT3 0.29 0.31 0.31 0.27 0.32 0.26 1.00 0.64 0.67 0.51 0.19 0.41 0.30 0.42 2.50 1.02
PWT1 0.23 0.25 0.30 0.25 0.25 0.21 0.52 1.00 0.62 0.73 0.22 0.50 0.32 0.46 2.78 1.22
PWT4 0.30 0.33 0.29 0.25 0.30 0.28 0.61 0.47 1.00 0.54 0.22 0.48 0.33 0.42 2.53 1.08
PWT2 0.26 0.26 0.32 0.28 0.26 0.26 0.48 0.58 0.48 1.00 0.28 0.56 0.28 0.53 2.65 1.04
TINC4 0.19 0.17 0.25 0.23 0.27 0.23 0.16 0.16 0.18 0.24 1.00 0.58 0.39 0.42 3.60 1.14
TINC3 0.19 0.17 0.27 0.24 0.28 0.24 0.36 0.36 0.39 0.48 0.44 1.00 0.46 0.53 3.02 1.14
TINC6 0.21 0.18 0.23 0.23 0.23 0.22 0.25 0.23 0.27 0.23 0.30 0.35 1.00 0.44 3.38 1.15
TINC1 0.31 0.30 0.37 0.30 0.36 0.37 0.37 0.33 0.34 0.46 0.33 0.41 0.34 1.00 3.14 1.12
Note: Numbers below the italicized diagonal are correlations and those above the diagonal are covariance estimates
192

6. Why do shoppers abandon shopping cart?
Rajasree K. Rajamma, Audhesh K. Paswan and Muhammad M. Hossain
Table III Discriminant validity: inter-factor correlations using
composite scores
PR PWT PTINC Mean SD
PR 0.905 0.37 0.35 3.03 1.01
PWT 0.42 0.813 0.37 2.62 0.87
PTINC 0.42 0.52 0.692 3.28 0.82
Note: Diagonal elements are alpha scores; lower diagonal elements are
correlations; and upper diagonal elements are covariance estimates
process more complex, especially the self-checkout process.
The results of this study clearly indicate that marketers should
try to make the checkout process as convenient as possible in
order to reduce shopping cart abandonment incidents.
Perceived risk was another important factor that was seen
to influence shopping cart abandonment. Thus, although it is
most important to create positive perceptions about a website
at the stage when a consumer starts information search about
the safety of the site, detailed unambiguous information
regarding the security and privacy offered by the site should
be displayed in the page where the customer starts his/her
shopping conclusion activities. It would be advantageous to
the retailers if they can provide consumer education (vignettes
such as “look for an s after http” or providing links to trusted
third party sites such as BBB online which authenticates
claims of security of the site). It might also be worthwhile for
the e-tailers to reassure their consumers about the key aspects
of risk captured in our study (e.g. security of the credit card
information, personal information and the company misusing
the information provided on the checkout pages) to alley their
concerns about risk, especially during the transaction
completion stage of shopping.
Finally, we hypothesized that if consumers are made to wait
for the completion of the transaction process they may
abandon the shopping cart. However, the result does not
provide support for this line of thinking. In fact, the finding
suggests that as perceived waiting time increases, incidence of
shopping cart abandonment reduces (a negative relationship).
We believe that this negative relationship could probably be
due to the retrospective evaluation of the time and effort that
has already gone into the shopping episode similar to the
psychology of refusal to renege from a queue proposed by
Zhou and Soman (2003). So, if the website can hold on to the
consumer till the last stages of shopping, minor delays may
not influence the completion of transactions. However,
merely holding on the consumers and hoping for the best
may not be good enough. Marketers in an online environment
should actively try to reduce the perceived waiting time
because it may increase the perception of inconvenience and
thus affect shopping cart abandonment indirectly. Online
shoppers are found to be time conscious consumers. During
online shopping, perception of long waiting times are created
mainly by delays in page uploading due to graphics,
inordinate amount of text or by the amount of information
asked for at the time of checkout. These delays not only
negatively disconfirm the consumer’s expectation of quick
shopping, but also increase their frustration with the process
and leads to subsequent abandonment of shopping cart
(Selvidge et al., 2002). Online retailers can manage these
delays by designing their web pages (especially the checkout
pages) with the least amount of graphics and limited amount
of text. Since, consumers from all over the world access online
shops, web designers should keep in mind the vastly varying
data transfer speeds available in different parts of the world.
Thus, what is ideal for consumers in the US may be far from
optimal for consumer in another part of the world. Hence,
online retailers should strive to make transactions as quick as
possible to prevent customer erosion.
Managerial implications
Further to the contributions made to the hitherto unexplored
area of shopping cart abandonment by consumers in an
online shopping context, the results hold several implications
for managers operating in online environment as well as brick
and mortar environment. The findings suggest that not paying
attention to consumers’ expectations during the checkout
phase might be as detrimental as ignoring consumer needs in
the earlier stages of consumer purchasing process. The
characteristics of the checkout process must match the
experiences delivered during the pre-checkout stage.
Convenience, time savings, and risks are some of the crucial
characteristics of an online shopping environment, and these
Table IV Logistic regression: determinants of shopping cart abandonment
B SE Wald df Sig. Exp(B)
Perceived risk (PR) 0.225 0.088 6.582 1 0.010 1.252
Perceived waiting time (PWT) 20.215 0.107 4.059 1 0.044 0.807
Perceived transaction inconvenience (PTINC) 0.296 0.115 6.648 1 0.010 1.344
Constant 21.363 0.354 14.836 1 0.000 0.256
22 Log likelihood 949.791
Cox and Snell R square 0.025
Nagelkerke R square 0.034
Hit ratio (%) 58.56
Hosmer and Lemeshow test:
Chi-square 5.977
df 8
Sig. 0.650
Note: Shopping cart abandonment: low abandonment ¼ 1 and high abandonment ¼ 2
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7. become part of a consumer’s expectation from an online retail
store, including the checkout stage. If the checkout stage does
not fulfill these expectations, marketers risk shopping cart
abandonment and the loss of a customer. Managers of online
stores must ensure that these characteristics are embedded
into the checkout process as well.
While the research context for this study is online shopping
environment, and hence the findings have direct implications
for the managers of online retail shops, the findings could also
be relevant to brick and mortar stores. The topics of
convenience and waiting time may be equally critical to the
shoppers at brick and mortar stores and online stores. The
notion of risk may remain same in the two shopping contexts,
but the manifestation may be different and managers must
understand the contextual nature of risk to reduce the
negative perceptions. The findings could also be useful for the
managers of non-retailing firms. Most firms, whether retailing
or manufacturing, in today’s day and age have an online
presence and allow their consumers to access their goods and
services either through online medium or through traditional
off-line medium. In order to provide a consistent message and
maintain a uniform image, marketing managers of these firms
must be cognizant of the phenomenon invested in this study.
Marketing and brand managers cannot focus exclusively on
brand building and leave the transaction completion to the
retailers. Incorporating the transaction completion stage and
the factors relevant to this stage (e.g. transaction stage specific
convenience, risk, and the process) into the marketing plan
will not only ensure reduced shopping cart abandonment, but
also enhance the effectiveness of the resources spent in getting
consumers to the transaction point.
Limitations and future directions
Since our goal was to investigate the effect of key
determinants of success during the checkout process, we did
not include constructs that may have had an influence on
consumer’s shopping behavior prior to reaching the checkout
point. Although this narrow focus, i.e. including only three
constructs in this study, was based on an exploratory research
of the extant trade and academic publications as well as
qualitative research of online shoppers, we acknowledge that
online shopping is a complex process (probably not unlike
shopping process at a brick and mortar store) and future
studies should look at more related constructs to form a more
comprehensive picture of shopping cart abandonment
behavior. Another limitation of this paper could be related
to the sampling procedure and sampling frame used.
However, given the fact that the abandonment behavior
does not differ across gender, age and income indicates that it
probably has no influence on the generalizability of the
findings.
This paper opens up several avenues for future researchers.
Future research should investigate the shopping cart
abandonment phenomenon by incorporating more
determining factors. Another possible avenue for future
research could be to investigate some of the mediating factors
such as consumer anxiety associated with the perception of
risk involved in the checkout process. Further, researchers
could use an experimental design to compare websites with
different levels of checkout specific convenience; risk and,
waiting time, and examine their effect on shopping cart
abandonment. Finally, since, online shops open themselves to
consumers from all over the world with vastly varying data
transfer speeds, and very different notions about time, future
research should try to investigate the notion of perceived
waiting time and its effect on shopping cart abandonment
from a cross cultural perspective. We hope that the present
research provides an impetus for further investigation in this
area.
References
Anon. (2002), “Reasons for abandonment”, available at:
www.globalmilleniamarketing.com (accessed 23 August
2007).
Balabanis, G. and Vassileiou, S. (1999), “Some attitudinal
predictors of home shopping through the internet”, Journal
of Marketing Management, Vol. 15, pp. 361-85.
Bearden, W.O. and Teel, J.E. (1983), “Selected determinants
of consumer satisfaction and complaint reports”, Journal of
Marketing Research, Vol. 20, February, pp. 21-8.
Belanger, F., Hiller, J.S. and Smith, W.J. (2002),
“Trustworthiness in electronic commerce: the role of
privacy, security and site attributes”, Journal of Strategic
Information Systems, Vol. 11, pp. 245-70.
Bromiley, P. and Curley, S.P. (1992), “Individual differences
in risk taking”, in Yates, J.F. (Ed.), Risk Taking Behavior,
Wiley, Chichester, pp. 87-132.
Butler, P. and Peppard, J. (1998), “Consumer purchasing on
the internet: processes and prospects”, European
Management journal, Vol. 16 No. 5, pp. 600-10.
Chen, Q. and Wells, W.D. (2001), “Com satisfaction and.com
dissatisfaction: one or two constructs?”, Advances in
Consumer Research, Vol. 28, pp. 34-9.
Childers, T.L., Carr, C.L., Peck, J. and Carson, S. (2001),
“Hedonic and utilitarian motivations for online shopping
behavior”, Journal of Retailing, Vol. 77, pp. 511-35.
Conchar, M.P., Zinkhan, G.M., Peters, C. and Olavarrieta, S.
(2004), “An integrated framework for the conceptualization
of consumers’ perceived-risk processing”, Journal of the
Academy of Marketing Science, Vol. 32 No. 4, pp. 418-36.
Cronin, J.J. and Taylor, S.A. (1992), “Measuring service
quality: a reexamination and extension”, Journal of
Marketing, Vol. 56 No. 3, pp. 55-68.
Davis, M.M. and Heineke, J. (1998), “How disconfirmation,
perception and actual waiting times impact customer
satisfaction”, International Journal of Service Industry
Management, Vol. 9 No. 1, pp. 64-73.
Davis, M.M. and Vollmann, T.E. (1990), “A framework for
relating waiting time and customer satisfaction in a service
operation”, Journal of Services Marketing, Vol. 4 No. 1,
pp. 61-9.
Donthu, N. and Garcia, A. (1999), “The internet shopper”,
Journal of Advertising Research, Vol. 39 No. 2, pp. 52-8.
Dowling, G.R. (1986), “Perceived risk: the concept and its
measurement”, Psychology and Marketing, Vol. 3 No. 3,
pp. 193-210.
Eisenberg, B. (2003), “20 tips to minimize shopping cart
abandonment, Part 1”, available at: www.clickz.com/sales/
traffic/article.php/2245891I (accessed 8 August 2008).
Gaski, J.F. and Nevin, J.R. (1985), “The differential effects of
exercised and unexercised power sources in a marketing
channel”, Journal of Marketing Research, Vol. 22, pp. 130-42.
Girard, T., Korgaonkar, P. and Silverblatt, R. (2003),
“Relationship of type of product, shopping orientations,
Why do shoppers abandon shopping cart?
Rajasree K. Rajamma, Audhesh K. Paswan and Muhammad M. Hossain
Journal of Product & Brand Management
Volume 18 · Number 3 · 2009 · 188–197
194

8. and demographics with preference for shopping on the
internet”, Journal of Business and Psychology, Vol. 18 No. 1,
pp. 101-20.
Gold, K. (2007), “Tackling the shopping cart abandonment
rate”, available at: www.searchmarketingstandards.com
(accessed 7 October 2008).
Goldsmith, R.E. (2002), “Explaining and predicting
consumer intention to purchase over the internet:
an exploratory study”, Journal of Marketing, Vol. 10 No. 2,
pp. 22-8.
Goldwyn, C. (2002), “The art of the cart; survey results from
a study by Vividence Corporation”, available at: http://
visibility.tv/tips/shopping_cart_abandonment (accessed
27 September 2008).
Harrison-Walker, J.L. (2002), “If you build it, will they come?
Barriers to international e-marketing”, Journal of Marketing,
Vol. 10 No. 2, pp. 12-21.
Katz, K., Larson, B. and Larson, R. (1991), “Prescription for
the waiting in line blues: entertain, enlighten and engage”,
Sloan Management Review, Vol. 32, pp. 44-53.
Kotler, P. (1999), Marketing Management, Prentice-Hall of
India Private Limited, New Delhi.
Koufaris, M., Kambil, A. and LaBarbera, P.A. (2001),
“Consumer behavior in web-based commerce: an empirical
study”, International Journal of Electronic Commerce, Vol. 6
No. 2, pp. 115-38.
Kuhnmann, W. (1989), “Experimental investigation of stress-inducing
properties of system response times”, Ergonomics,
Vol. 32 No. 3, pp. 271-80.
LaBarbera, P.A. and Mazursky, D. (1983), “A longitudinal
assessment of consumer satisfaction/dissatisfaction:
the dynamic aspect of the cognitive process”, Journal of
Marketing Research, Vol. 20, pp. 393-404.
Landrock, P. (2002), “Security: the building block for
e-commerce growth”, Computer Fraud and Security, Vol. 9,
pp. 7-8.
Liao, Z. and Cheung, M.T. (2001), “Internet-based
e-shopping and consumer attitudes: an empirical study”,
Information and Management, Vol. 38, pp. 299-306.
Lopes, L.L. (1987), “Between hopes and fear: the psychology
of risk”, Advances in Experimental Social Psychology, Vol. 20,
pp. 255-95.
McGlaughlin, F. (2001), “The order process: how you can
increase your sales by improving your completed sales
ration”, The Marketing Experiments Journal, available at:
http://retailindustry.about.com (accessed 27 September,
2007).
Maister, D. (1985), “The psychology of waiting lines”,
in Czepiel, J., Solomon, M. and Surprenant, C. (Eds),
The Service Encounter, Lexington Books, DC Heath and
Co., Lexington, MA.
Mullins, R. (2000), “Do or die for e-tail”, Silicon Valley/San
Jose Business Journal, 24 November.
Nielson, J. (1996), “Response times: the three important
limits”, available at: www.useit.com (accessed 29 September
2007).
Nunnally, J.C. (1978), Psychometric Theory, 2nd ed.,
McGraw-Hill Book Company, New York, NY.
Odom, M.D., Kumar, A. and Saunders, L. (2002), “Web
assurance seals: how and why they influence consumers’
decisions”, Journal of Information Systems, Vol. 16 No. 2,
pp. 231-50.
Oliver, R.L. (1980), “A cognitive model of the antecedents
and consequences of satisfaction decisions”, Journal of
Marketing Research, Vol. 17, pp. 460-9.
Oliver, R.L. and DeSabro, W. (1988), “Response
determinants in satisfaction judgments”, Journal of
Consumer Research, Vol. 15, pp. 495-507.
Oliver, R.L. and Shor, M. (2003), “Digital redemption of
coupons: satisfying and dissatisfying effects of promotion
codes”, Journal of Product & Brand Management, Vol. 12
Nos 2/3, pp. 121-34.
Olivero, N. and Lunt, P. (2003), “Privacy versus willingness
to disclose in e-commerce exchanges: the effect of risk
awareness on the relative role of trust and control”, Journal
of Economic Psychology, Vol. 25 No. 2, pp. 1-20.
Pandya, A.M. and Dholakia, N. (2005), “B2C failures:
toward an innovation theory framework”, Journal of
Electronic Commerce in Organizations, Vol. 3 No. 2,
pp. 68-82.
Photo Trade News (2006), “Google checkout opens”, Photo
Trade News, Vol. 70 No. 7, p. 19.
Prince, M. (2004), “Online retailers try to streamline
checkout process”, The Wall Street Journal, p. D2,
November 11.
Pruyn, A. and Smidts, A. (1998), “Effects of waiting on the
satisfaction with the service: beyond objective time
measures”, International Journal of Research in Marketing,
Vol. 15 No. 4, pp. 321-34.
Ranganathan, C. and Ganapathy, S. (2002), “Key dimensions
of business-to-business web sites”, Information and
Management, Vol. 39, pp. 457-65.
Robinson, J.P., Shaver, P.R. and Wrightsman, L.S. (1991),
“Criteria for scale selection and evaluation”,
in Robinson, J.P., Shaver, P.R. and Wrightsman, L.S.
(Eds), Measure of Personality and Social Psychological
Attitudes, Academic Press, San Diego, CA.
Schelmetic, T.E. (2006), “Your mother was a hamster and
your father smelt of elderberries”, Customer Inter@ction
Solutions, Vol. 24 No. 8, p. 72.
Seiders, K., Berry, L.L. and Gresham, L.G. (2000),
“Attention, retailers! How convenient is your convenience
strategy?”, Sloan Management Review, Vol. 41 No. 3,
pp. 79-89.
Selvidge, P.R., Chaparro, B.S. and Bender, G.T. (2002),
“The world-wide wait: effects of delays on user
performance”, International Journal of Industrial
Ergonomics, Vol. 29, pp. 15-20.
Shim, S., Eastlick, M.A. and Lotz, S. (2000), “Assessing the
impact of internet shopping among mall shoppers and
internet users”, Journal of Shopping Center Research, Vol. 7
No. 2, pp. 7-43.
Simintiras, A., Diamantopoulos, A. and Ferriday, J. (1997),
“Pre-purchase satisfaction and first time buyer behavior:
some preliminary evidence”, European Journal of Marketing,
Vol. 31 Nos 11/12, pp. 857-72.
Simonson, I. (1992), “The influence of anticipating regret
and responsibility on purchase decisions”, Journal of
Consumer Research, Vol. 2 No. 19, pp. 105-18.
Srinivasan, S.S., Anderson, R. and Ponnavolu, K. (2002),
“Customer loyalty in e-commerce: an exploration of its
antecedents and consequences”, Journal of Retailing,
Vol. 78, pp. 41-50.
Why do shoppers abandon shopping cart?
Rajasree K. Rajamma, Audhesh K. Paswan and Muhammad M. Hossain
Journal of Product & Brand Management
Volume 18 · Number 3 · 2009 · 188–197
195

9. Swan, J.E. and Trawick, F.I. (1981), “Disconfirmation of
expectations and satisfaction with a retail service”, Journal
of Retailing, Vol. 57 No. 3, pp. 49-67.
Szymanski, D.M. and Henard, D.H. (2001), “Customer
satisfaction: a meta-analysis of the empirical evidence”,
Journal of the Academy of Marketing Science, Vol. 29 No. 1,
pp. 16-35.
Szymanski, D.M. and Hise, R.T. (2000), “E-satisfaction:
an initial examination”, Journal of Retailing, Vol. 76 No. 3,
pp. 309-22.
Taylor, S. (1994), “Waiting for service: the relationship
between delays and evaluations of service”, Journal of
Marketing, Vol. 58, pp. 56-69.
Wang, E.T.G., Yeh, H. and Jiang, J.J. (2006), “The relative
weights of internet shopping fundamental objectives”,
Psychology and Marketing, Vol. 23 No. 5, pp. 353-7.
Wingfield, N. (2002), “A question of trust”, The Wall Street
Journal, 16 September, p. R6.
Wolfinbarger, M. and Gilly, M.C. (2001), “Shopping online
for freedom, control and fun”, California Management
Review, Vol. 43 No. 2, pp. 34-55.
Woodruff, R.B., Cadotte, E.R. and Jenkins, R.L. (1983),
“Modeling consumer satisfaction processes using
experience-based norms”, Journal of Marketing Research,
Vol. 20, pp. 296-304.
Zhou, R. and Soman, D. (2003), “Looking back: exploring
the psychology of queuing and the effect of number of
people behind”, Journal of Consumer Research, Vol. 29 No. 4,
pp. 517-30.
Corresponding author
Audhesh K. Paswan can be contacted at: paswana@unt.edu
Executive summary and implications for
managers and executives
This summary has been provided to allow managers and executives
a rapid appreciation of the content of the article. Those with a
particular interest in the topic covered may then read the article in
toto to take advantage of the more comprehensive description of the
research undertaken and its results to get the full benefit of the
material present.
Customers abandoning their shopping cart before purchase
completion are a huge source of frustration for retail outlets
and marketers. Cumbersome checkout processes and lengthy
queues are assumed to be among the main reasons for this
behavior.
Online retailers in particular are affected by the problem. It
is estimated that up to 75 percent of shopping carts are
abandoned before the transaction is complete. This can cost
the US online retail industry around $6.5 billion in lost sales
every year.
Factors that might lead to aborted transactions
Many previous studies into e-commerce have aimed to
identify the factors that persuade consumers to shop online.
However, the majority of these investigations have
concentrated on the early stage of the buying process. On
the contrary limited research exists into why customers take
the decision to abandon their shopping cart towards the end
of the activity after investing effort into selecting their
purchases.
In the present study, Rajamma et al. aim to address this void
by identifying reasons why consumer abort their shopping
activities during the checkout phase after evidently being
satisfied with the process up to that point. Perceived waiting
time, perceived risk and transaction inconvenience are
investigated as possible determinants of this action.
The authors use the expectancy-disconfirmation model to
frame the current research. A key premise of this model is that
consumer satisfaction or dissatisfaction will be determined by
whether or not their prior expectations are confirmed. It is
suggested that consumers who make purchases online have
certain expectations based on their previous experience of
internet shopping. For instance, it is usual to anticipate that
making a process will involve a quick, simple and convenient
process in which the consumer has a significant amount of
control. Privacy is also commonly associated with online
shopping, although most consumers will acknowledge that
submitting payment details via the Internet involves a degree
of risk.
Based on the model employed, the overall assumption is
that consumer purchase intention may be influenced by
whether these prior expectations are met. Academics have
previously pointed out that satisfaction and dissatisfaction are
not absolute to explain why consumers may be content with
some aspects of their shopping experience but not others. To
Rajamma et al. this might explain why shopping cart
abandonment takes place even when the experience has met
prior expectations earlier on. They also put forward the
notion that higher levels of satisfaction during the initial stages
of the shopping activity may even result in consumers raising
their expectations about what should happen at the checkout.
Several studies have discovered that correlation exists
between perceived waiting time and satisfaction. This can be
of particular significance to shoppers with other demands on
their time. In view of the anticipation of convenience, many
researchers believe that perceived waiting time is also highly
relevant to those who buy online. Factors that may impact on
waiting time include sluggish uploading or downloading of
pages and lengthy or complex forms to complete.
Perceived risk has also been widely identified as significant
in respect of online shopping. Organizations have attempted
to allay fears about security and confidentiality by including
the likes of web assurance seals, guarantees of privacy and
feedback and reviews from both other consumers and experts.
Despite such measures, many transactions are still curtailed
leading to the assumption that the risks associated with
handing over confidential information are considered too
great. According to some sources, consumers may feel that e-commerce
firms are not doing enough. They also note that
the risk level may be perceived as higher during certain stages
of the shopping process. The entry page to a website and
when the transaction is nearing completion are suggested as
stages where consumers will feel most anxious.
For internet shoppers, convenience is regarded as one of the
most influential factors. They expect transactions to be speedy
and efficient but the experience is often made more complex
by technical problems, items being out of stock, hidden
charges, limited payment options and being forced to
complete detailed registration forms. The dissatisfaction that
can result from these inconveniences heightens the possibility
Why do shoppers abandon shopping cart?
Rajasree K. Rajamma, Audhesh K. Paswan and Muhammad M. Hossain
Journal of Product & Brand Management
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196

10. of consumers abandoning their transaction before it is
completed.
Study and implications
To explore the significance of the above factors, Rajamma et al.
invited undergraduates at two universities from different
regions in the US to complete online questionnaires relating
to shopping cart abandonment. Of the 720 who participated
in the main study, 58 percent were female and 42 percent
males. The age and gender distribution and the number of
aborted online shopping transactions were similar in both
respondent groups.
Results showed perceived transaction inconvenience to be
the most influential factor on aborted transactions. The
authors suggest that online retailers could help by requesting
only essential information and providing the facility to store
details in secure servers to save time during subsequent visits.
Real-time storage is also recommended as this would
minimize inconvenience to consumers who lose their
connection due to website malfunctions. Simplifying what
formats shoppers can use to enter data may likewise improve
satisfaction.
Perceived risk was identified as another significant factor.
To counter this, companies should display “detailed
unambiguous information” relating to the site’s security and
privacy and this information should appear on the page
signaling that the transaction is nearing conclusion. Retailers
might also consider better informing consumers about how to
check for indicators that the site is secure and provide
reassurance about other key risk factors.
The relationship between perceived waiting time and
aborting the transaction was opposite to that hypothesized.
Findings revealed that consumers became less likely to
abandon their shopping cart as waiting time increased. This
might suggest that consumers may not be deterred by slight
delays towards the end of the online shopping process.
However, Rajamma et al. warn against complacency here
because longer waiting time may increase perceptions of
inconvenience and indirectly result in an aborted transaction.
They urge web retailers to consider redesigning their web
pages to utilize fewer graphics and lower amounts of text to
hasten loading times.
Managers need to appreciate the significance of context
with regard to risk in order to counter negative consumer
perceptions. It is, however, maintained that the implications
of these findings may be relevant to conventional retail outlets
as well as online stores. The authors likewise put forward the
idea that marketing and brand managers have a role to play in
improving the transaction completion process.
Future investigations could consider other factors that
might result in shopping cart abandonment, such as
consumer anxiety. Researchers might also examine earlier
stages of the online shopping process. Levels of perceived
convenience, risk and waiting time may prove significant as
might cultural factors and the wide variety in data transfer
speeds around the world.
(A pre´cis of the article “Why do shoppers abandon shopping cart?
Perceived waiting time, risk, and transaction inconvenience”.
Supplied by Marketing Consultants for Emerald.)
Why do shoppers abandon shopping cart?
Rajasree K. Rajamma, Audhesh K. Paswan and Muhammad M. Hossain
Journal of Product & Brand Management
Volume 18 · Number 3 · 2009 · 188–197
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