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Confusion in internet retailing:
causes and consequences
Confusion in
internet
retailing
Marion Garaus
Department of Marketing, University of Vienna, Vienna, Austria
Abstract
Purpose – The purpose of this paper is to introduce the new construct online shopper confusion and to
identify online confusion causes and consequences.
Design/methodology/approach – Data obtained from a projective technique and a quantitative study
were analyzed to identify online shopper confusion causes. Two experiments employing different stimulus
materials tested the conceptualized consequences of online shopper confusion.
Findings – Confusing online store elements are classified into three online confusion causes. Data yielded
from two experiments using fictitious and real shopping scenarios as stimulus material show that a confusing
internet retail process leads to negative consumer reactions.
Research limitations/implications – The resulting taxonomy of confusing online store elements offers
guidance on the creation of non-confusing online shopping trips, and highlights the relevance of a
non-confusing internet retail process. Online shopper confusion is linked to negative behavioral reactions.
Consequently, this research offers an explanation for undesirable consumer reactions in internet retailing.
Practical implications – The findings provide practitioners with concrete insights into how the internet
retail process confuses shoppers which help to assess the confusion potential of their existing online stores
and consider confusion issues in the development of new online stores.
Originality/value – This research is the first to explore confusion during the internet retail process.
The multi-method approach offers highly valid insights into the causes and consequences of online
shopper confusion.
Keywords Internet retail process, Online shopper confusion, Online store design, Shopping cart abandonment
Paper type Research paper
Introduction
Current figures affirm the importance of online shopping for every industry. In 2013, 191.1
million US citizens shopped or browsed products, compared prices, and purchased goods
online at least once. Business-to-consumer customers spent US$ 322 billion online in 2013,
with the majority of this figure accounting for retail e-commerce sales (US$ 210.6 billion)
(Statista.com, 2014). In general, companies strive to generate additional profit through online
stores; however, many e-shops fail in this objective (Hausman and Siekpe, 2009). According
to the website analytic provider Fireclick.com (2014), conversion rates are 3.8 percent, with a
cart abandonment rate of 70 percent.
One possible way to improve order conversion rates is a pleasant online store design
(Mummalaneni, 2005). The design of an appealing website that encourages users to stay
and surf represents one of the main challenges for internet retailers. Indeed, website
appeal has been identified as a reliable predictor of purchase intention (Liu et al., 2017).
Nevertheless, research revealing insights into the website design of online stores is scarce.
Hausman and Siekpe (2009, p. 11) state that “the absence of theoretically-based guidance
for internet design has resulted in over-blown technologies that may irritate or frustrate
the online user rather than aiding the user in undertaking shopping tasks.” Not only
do advanced technologies overstrain and confuse shoppers, but the increasing amount
of information provided by websites and complex website structures do as well
(Chen et al., 2009; Cho et al., 2006; Mai et al., 2014).
In line with these theoretical considerations, confusion represents a state that likely occurs
in online shopping situations. Thus, studies have investigated the phenomenon of confusion
only from a product-related perspective, arguing that too much product information on a
477
Received 24 November 2016
Revised 18 March 2017
21 June 2017
28 July 2017
Accepted 30 July 2017
Internet Research
Vol. 28 No. 2, 2018
pp. 477-499
© Emerald Publishing Limited
1066-2243
DOI 10.1108/IntR-11-2016-0356
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478
website overloads and confuses shoppers (Lee and Lee, 2004). However, recent research
in stationary retailing reveals that not only products but also physical surroundings confuse
shoppers (Garaus et al., 2015). Although studies have not examined confusion in an online
setting, they reveal that online store elements trigger emotions that result in either positive or
negative reactions (Hsieh et al., 2014; Shen and Khalifa, 2012). With confusion representing a
negative state of mind, the central premise of the current research is that shoppers react to
confusing online store elements with negative behavioral intentions. Under this assumption,
the confusion triggered by online store elements is of serious concern for e-retailers.
The relevance of understanding drivers of confusion during internet retailing is even more
pronounced by evidence suggesting that negative information outweighs positive information
( for a review, see Mizerski, 1982) and that negative experiences influence behavior more than
positive experiences (Baumeister et al., 2001).
Despite the growing amount of research on negative incidents in internet retailing, these
studies have largely focused on negative online shopping incidents in general, without
examining what consumers actually feel during negative online shopping trips
(e.g. Holloway and Beatty, 2003). However, because different emotions cause different
consequences (Gelbrich, 2010; Laros and Steenkamp, 2005), the knowledge of particular
mental states experienced during the internet retail process would provide a deeper
understanding of online shoppers’ behavior. Moreover, the extant literature points to the
potential of the online store environment to evoke emotions (Lim, 2015; Manganari et al.,
2011; Jeong et al., 2009).
In addition, this stream of research does not link internet retail failures to outcomes of
high interest for e-retailers, such as shopping cart abandonment or word of mouth (WOM).
Scholars have criticized that reasons for this undesirable behavior are scarcely researched
(Kukar-Kinney and Close, 2010). Against this background, the current research addresses
the following two research questions:
RQ1. What are the causes of online shopper confusion in internet retailing?
RQ2. How do shoppers react to a confusing internet retailing process?
Three objectives guide the research. First, this study aims to transfer the confusion
construct to an internet retailing context; in doing so, it conceptualizes online
shopper confusion. In addition, the study presents a research framework by tying
online shopper confusion to its causes and consequences while taking the particularities of
online retailing into consideration. Second, drawing from the extant literature in the
field of online store design, this research theoretically identifies various online store
elements that confuse shoppers during the internet retail process. Both qualitative and
quantitative data collection (Study 1) verify the classification of online confusion causes.
Third, this research aims to tie online shopper confusion to consumer reactions.
Every construct gains theoretical importance, and relevance from a practical standpoint,
when it is related to specific consequences. Accordingly, two experiments theoretically
identify and validate consumer responses that are likely occurring during a confusing
internet retail process.
The findings of this research contribute to the literature in several important ways.
The findings highlight the risk of online shopper confusion during the internet
retail process. The results offer detailed insights into the confusion potential of various
online store elements. The knowledge of consumers’ negative responses to a confusing
internet retailing process contributes to explain their online shopper behavior. From a
practical perspective, the taxonomy of online confusion causes helps retailers avoid online
shopper confusion during the internet retailing process. In addition, the findings reveal
which online store elements bear the highest confusion potential. From a more
general perspective, the research finds support for the notion that not only products but
also the (online) store environment possesses confusion potential. However, stationary and
online confusion causes differ in some important respects due to the particularities of
internet retailing.
Literature review and model development
Ample studies in internet retailing have explored the role of attitude in predicting online
shopping usage. Most of these studies draw on the technological acceptance model (TAM)
(Davis, 1986), which represents an advancement of the theory of reasoned action (Fishbein
and Ajzen, 1975). The two main constructs of the TAM are perceived ease of use and
perceived usefulness, both of which predict attitude toward a new technology and, in turn,
usage intentions and actual use (Davis, 1986). For example, Childers et al. (2001) report
that online store characteristics affect ease of use, perceived usefulness, and enjoyment.
The importance of these constructs for determining attitude toward interactive online
shopping varies depending on the shopping context (hedonic vs utilitarian). Another
study (Ha and Stoel, 2009) confirms the importance of these three constructs in
determining attitude toward online shopping and, in turn, online shopping intention.
In addition, the authors emphasize the relevance of website design as a predictor of
e-shopping quality, which reflects an antecedent of trust, ease of use, and enjoyment.
In confirmation of this result, Al-Debei et al. (2015) reveal that website quality (defined as
“the extent to which the web site design and processes are simple, smooth, reliable and
effective,” p. 710) determines the attitude toward online shopping. Accordingly, research
has recognized the importance of website design in determining online shopping
acceptance. More recently, studies exploring consumers’ online shopping behavior
have successfully employed Mehrabian and Russell’s (1974) stimulus-organism-response
(S-O-R) model as a theoretical framework (Koo and Ju, 2010; Li et al., 2012). In contrast with
the TAM, the S-O-R model does not explore consumers’ acceptance of new technologies in
early adoption stages (Al-Debei et al., 2015), but rather explains why shoppers react with
approach or avoidance behavior to certain internet environments. Thus, Mehrabian and
Russell’s environment psychological model offers an appropriate theoretical foundation to
explore the construct online shopper confusion.
Following the S-O-R paradigm, Mehrabian and Russell (1974) postulate that various
environmental stimuli induce approach or avoidance behavior and that emotional states
mediate this relationship. Donovan and Rossiter (1982) were the first researchers to
validate the model in a consumer behavior setting. They demonstrate that store
environmental stimuli induce the three emotional states of arousal, pleasure, and
dominance, which, in turn, result in either approach or avoidance behavior. Since then, the
model has served as a theoretical basis in studies exploring shoppers’ responses to store
environmental stimuli (e.g. Baker et al., 1992; Bitner, 1992). In line with these extant
studies, the current research treats confusing online store elements as stimulus (S).
Online shopper confusion represents the organism variable (O) that mediates the
relationship between confusing online store elements and responses (R). As online shopper
confusion reflects a negative mental state, consumers react with avoidance behavior.
In particular, shoppers experiencing online shopper confusion are likely to have lower
revisit intentions, higher shopping cart abandonment rates, lower spending time,
lower unplanned expenditures, and spread less positive WOM than non-confused
shoppers (see Figure 1).
While confusion in internet retailing has not attracted research attention so far,
studies on physical retailing offer profound conceptualizations of the confusion construct.
Extant definitions conceptualize confusion either through its causes (e.g. Walsh et al., 2007)
or as a mental state (e.g. Garaus and Wagner, 2016). The current research implements
Confusion in
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Stimulus (S)
Figure 1.
Conceptual research
framework: online
shopper confusion
Consequences
Online confusion
causes
Design and
navigation
480
Response (R)
Organism (O)
Informational
content
Website
functionalities
Study 1
Online
shopper
confusion
Cognitive
Affective
Conative
–
Revisit intention
+
Shopping cart
abandonment
–
Spending time
–
Low revisit
Unplanned
Intentions
expenditures
–
WOM
Study 2 and Study 3
both perspectives by identifying the causes (i.e. confusing online store elements) that
evoke the mental state online shopper confusion. For the sake of clarity, the terminology
used throughout the paper is specified as follows: online confusion causes (e.g. design and
navigation) comprise various confusing online store elements (e.g. unstructured
layout, unclear layout) that result in the mental state of mind online shopper confusion
during the internet retail process. Accordingly, confusion causes/elements represent
objective confusion characteristics of online stores, while online shopper confusion reflects
a subjectively experienced state of mind, which constitutes a part of the shopping
experience. The following subsections elaborate on each variable of the theoretical model
in detail.
The mental state online shopper confusion
Garaus et al. (2015) define (stationary) retail shopper confusion as a mental state that likely
occurs during shopping situations. In line with this definition then, online shopper
confusion reflects a mental state that occurs during the internet retail process and
therefore constitutes a part of the shopping experience. In line with Blackwell et al.’s (2006)
consumer decision model, the internet retail process consists of five stages: need
recognition, information phase, evaluation of alternatives, purchase decision, and
post-evaluation. Studies agree that shoppers generally perceive confusion as negatively
and respond with negative consumer responses. While other cognitive challenging states
of mind, such as distraction, might result in positive consumer reactions (e.g. decrease of
counter arguments) (Petty et al., 1976), confusion reflects a mental state that is perceived
as unpleasant (Garaus and Wagner, 2016; Mitchell et al., 2005).
However, a much-debated question is whether confusion reflects a cognitive or an
emotional construct, or both. For many years, the cognitive perspective represented the
prevailing view in the consumer behavior literature (e.g. Mitchell and Papavassiliou, 1999;
Mitchell et al., 2005). During the state of confusion, information processing is negatively
affected (Turnbull et al., 2000) because the cognitive resources required exceed the cognitive
resources available (Anand and Sternthal, 1989). Consumers therefore feel overloaded and
inefficient ( Jacoby, 1984; Jacoby et al., 1974).
More recently, literature has emerged that considers not only a cognitive but also an
emotional and a conative component of the confusion construct (Garaus and Wagner, 2016).
This three-dimensional approach also appears in the psychological literature, which
proposes that confusion is “a feeling state characterized by bewilderment and uncertainty
[cognition], associated with a general failure to control attention [conation] and emotions
[affect]” (Terry et al., 2003, p. 127). Neglecting one dimension when exploring a construct can
lead to inconsistent results among studies and failure to capture the whole construct under
investigation (see Bagozzi et al., 1979; Mayer, 2001). In line with this perspective,
well-established constructs have recognized the importance of considering the cognitive,
affective, and conative dimensions of the human mind for construct development
(e.g. attitude; Fishbein and Ajzen, 1975). Informed by this evidence and in line with extant
confusion conceptualizations (Garaus and Wagner, 2016), the current study defines online
shopper confusion as a mental state evoked during the internet retail process, capturing a
cognitive dimension, an affective dimension, and a conative dimension. Similar to stationary
retail shopper confusion (Garaus and Wagner, 2016), online shopper confusion is a
reflective, second-order construct (Netemeyer et al., 2003). Accordingly, during the mental
state of confusion, changes in these three dimensions, which are operationalized by feelings
(Garaus and Wagner, 2016), occur simultaneously.
In particular, the cognitive dimension captures negative feelings caused by the
exceedance of consumers’ cognitive processing abilities during the mental state of confusion
(Schweizer et al., 2006). Confused consumers experience emotions such as anger and
frustration (Walsh et al., 2007), representing the affective confusion dimension.
Finally, negative feelings (e.g. feeling helpless and lost) associated with an inhibition of
shopping task fulfillment reflect the conative dimension (Dogu and Erkip, 2000).
Transferring the confusion construct to an online context requires differentiating similar
constructs. The construct information overload and online shopper confusion share several
characteristics. Information overload occurs when “consumers are provided with too much
information at a given time, such that it exceeds their processing limits” (Malhotra, 1982,
p. 419). Chen et al. (2009, p. 50) offer a more profound definition of perceived information
overload in an online shopping context: “a perception of having too much product
information to deal with while making a buying decision.” Accordingly, similar to the
mental state of mind online shopper confusion, information overload occurs when cognitive
processing abilities are exhausted. However, both constructs differ in three fundamental
respects. First, these constructs differ in terms of the number of subsystems involved.
While online shopper confusion captures a cognitive, an affective, and a conative
component, information overload represents an entirely cognitive phenomenon. Second,
online shopper confusion reflects a negative state of mind and therefore lacks positive
consumer reactions, while information overload can also lead to positive consumer reactions
(Huang, 2003; Jacoby et al., 1974). Third, the extant literature ties information overload to
product-related factors, such as the number of alternatives available or the amount of
attributes (Lee and Lee, 2004). In contrast, online shopper confusion is not limited in scope to
product-related information but captures information provided during the whole internet
retail process (e.g. payment and delivery terms).
E-satisfaction reflects another state of mind that has attracted research attention in internet
retailing research. In contrast with online shopper confusion, satisfaction judgment requires a
comparison referent. Thus, studies tie satisfaction with a given online shopping experience to
prior online shopping experiences (Evanschitzky et al., 2004) or to brick-and-mortar shopping
experiences (Anderson and Srinivasan, 2003). Accordingly, e-satisfaction represents a
deliberate evaluation process of the whole shopping experience, which includes both favorable
and unfavorable incidents. Thus, in accordance with evidence in stationary retailing
(see Foxman et al., 1990; Mitchell and Papavassiliou, 1999; Wang and Shukla, 2013; Walsh and
Mitchell, 2010), confusion likely contributes to e-satisfaction, in addition to all other
positive and negative experiences during a particular online shopping trip. Compared with
e-satisfaction, the exploration of online shopper confusion offers a more focused picture of and
detailed insights into consumers’ feelings during negative shopping experiences.
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Confusing online store elements
Recent studies in internet retailing have highlighted the importance of understanding
consumers’ interactions with and perceptions of a website and the influence on their
behavior for the internet retailing success (Cho, 2014; Montoya-Weiss et al., 2003). However,
no study so far has explored confusion during the internet retail process, though some
studies have theoretically postulated the potential of specific online store elements to evoke
negative and confusing shopping experiences. From this initial evidence, three major
confusion causes (design and navigation, informational content, and website functionalities)
emerged, each comprising various confusing online store elements.
Design and navigation. Specific website features might stimulate need recognition
(Alhudaithy and Kitchen, 2009); however, they can also create confusion (Hoque and
Lohse, 1999; Lohse, 1993). Animations, various color schemes, and innovative elements
constitute website design and might entertain shoppers, but they also might distract them
from their ultimate shopping goal. If these elements delay completion of the shopping task,
shoppers easily become annoyed (Eroglu et al., 2001).
A navigational structure describes the organization and hierarchical layout of the
website’s content. It reflects consumers’ movement through the website, as characterized by
the number of clicks it takes to go through the content (Dailey, 2004). Prior research has
found that complex websites negatively affect pleasure and inhibit consumers’ shopping
tasks (Mai et al., 2014; Wang et al., 2011). An unclear and disorganized store structure
evokes confusion (Hausman and Siekpe, 2009; Koo, 2006). In addition, a poor structure or
non-working navigation cues (e.g. buttons, search function) can result in irritation and
frustration (Dailey, 2004), both of which are associated with confusion (Garaus and
Wagner, 2016); as such, confusion is likely to arise from poorly structured online stores
with non-working navigation cues.
Informational content. Website informational content comprises the sum of
communicated content on the website and consists of information utility (the extent to
which consumers perceive the information as useful), information accuracy (the extent
of perceived correctness of information), information timeliness (consumers’ up-to-date
perceptions) (Montoya-Weiss et al., 2003), and information format (Hong et al., 2004).
Several researchers have stressed the confusion potential of information on websites
(Chen and Dibb, 2010; Everad and Galletta, 2006; Konradt et al., 2003). Cho et al. (2006)
argue that internet retailing has more confusion potential than brick-and-mortar stores
because of the unlimited space of websites to provide information. The wealth of
information likely overloads shoppers (Lee and Lee, 2004). Because human beings’
cognitive processing abilities are limited, individuals process information efficiently to a
certain threshold (Sicilia and Ruiz 2010); however, when the amount of information
exceeds this threshold, consumers engage in dysfunctional behavior and make poorer
decisions ( Jacoby et al., 1974).
If consumers deem information as useless, inaccurate, or outdated, they need to acquire
further information. This search task requires additional cognitive abilities and, in turn,
raises the risk of confusion. Moreover, inappropriate terminology causes confusion
(Everad and Galletta, 2006). Similarly, hard-to-find information or information that is not
available entails enhanced cognitive processing abilities and inhibits shopping goal
achievement (Griffith, 2005). As a consequence, useless, inaccurate, or outdated
information, as well as poorly organized information, are assumed to confuse shoppers
during the internet retail process.
Website functionalities. Website functionalities comprise all elements related to
operational procedures during the internet retail process, including website links, order
forms, and customer support. Consumers use website links and might contact the internet
retailer in different stages of the shopping process (e.g. when requiring additional
information in the information phase or support for product use in the post-purchase stage).
Prior research has identified a confusing transaction process as retailer failure
(Holloway and Beatty, 2003) and as a driver of online shopping hesitation (Cho et al., 2006).
In addition, some specific features that differentiate the online shopping process from
the physical retail process possess confusion potential. For example, the lack of physical
presence of products and personal advice largely distinguishes online shopping from
purchases made in brick-and-mortar stores (e.g. Holloway and Beatty, 2003). Informed by
this theoretical evidence, poor website functionalities are likely to confuse shoppers
during the internet retail process.
Consequences of online shopper confusion
In contrast with an interesting or a complex website design (which might result in positive
responses at moderate levels; Mai et al., 2014), confusion implies nothing positive
about a website, causing negative consumer reactions (see Figure 1). Consumers tend to
dislike confusing physical service or retail encounters (Bitner, 1992), resulting in low
revisit intentions (Garaus and Wagner, 2016). Revisit intentions are related to actual
purchase behavior, and hence reflect an important determinant of internet retailing
(Zheng et al., 2017). Negative experiences influence behavior more strongly than positive
experiences (Baumeister et al., 2001), so shoppers will likely not return to a confusing
online store because they neither enjoy the online shopping experience nor fulfill their
shopping trip efficiently.
A major consequence of consumer confusion in physical retail environments is shopping
abandonment (Mitchell and Papavassiliou, 1999). Shopping cart abandonment is also an
undesirable behavioral reaction in online retailing. It describes “consumers’ placement of
item(s) in their online shopping cart without making a purchase of any item(s) during that
online shopping session” (Kukar-Kinney and Close, 2010, p. 240). Confused shoppers feel
angry and helpless to pursue their shopping goals, so they give up and do not complete their
shopping trip. Indeed, prior research provides empirical evidence that confusion leads to
online shopping hesitation (Cho et al., 2006).
In a confusing online store environment, shoppers intend to escape this confusing
situation as soon as possible. Research demonstrates that online store perception affects the
desire to stay in an online store and patronage intention (Kim et al., 2007). In stationary
retailing, confusion negatively affects spending time (Garaus and Wagner, 2016).
Shen and Khalifa (2012) point to the relevance of impulse buying in an online retail
context. The overall online shopping experience correlates with the number of purchases
(Anderson and Srinivasan, 2003). Shoppers experience confusion as a negative state
and thus do not enjoy their shopping drip and do not spend more money than intended.
While a favorable online store design leads to higher purchase intention (e.g. Shen and
Khalifa, 2012; Richard, 2005), a confusing store design is assumed to negatively influence
unplanned purchases.
Finally, López-López et al. (2014) show that consumers tend to share negative emotions
caused by unsatisfactory service experiences with other people. While positive online
shopping experiences evoke positive WOM (Anderson and Srinivasan, 2003), a confusing
shopping experience is assumed to reduce positive WOM.
Methodology
From an epistemological perspective, this research follows a positivist research approach.
In line with this paradigm, the overall objective of the empirical studies is to generate
reliable and valid results. Particular effort is devoted to the minimization of the subjective
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influence of the researcher (Becker and Niehaves, 2007). To achieve this aim and avoid
inherent method biases (Davis et al., 2011), the methodological part of the research employs
a sequential mixed-method design using within- (Study 1) and across-method triangulation
(Study 2 and Study 3) (see Jick, 1979). Such a research design benefits from strong and
robust results with a high degree of external validity (Davis et al., 2011). In addition, the
mixed-method design offers a holistic understanding of the phenomenon of online shopper
confusion ( Jick, 1979).
Four data collections constitute three studies, each of which builds on the other.
Thus, the data of each study are analyzed and interpreted before implementation of another
method. This procedure allows for informing subsequent studies and expanding insights
into the research problem (Davis et al., 2011) and is particularly suitable for the exploration
of novel constructs (Creswell, 2009). The objective of Study 1, which consists of an
exploratory and a descriptive data collection, is to identify confusing online store elements.
Study 2 and Study 3 test the proposed consequences of online shopper confusion with
causal research designs.
Study 1: identification of online shopper confusion causes
Study design. Premised on the notions and statements of the extant literature, the theoretical
part of this research identifies three online confusion causes, each consisting of confusing
online store elements. Nevertheless, as no study has specifically explored confusion in
internet retailing, the three theoretically derived online confusion causes may not fully
capture the nature of online shopper confusion. These causes represent an explicit
theoretical perspective that guides the empirical identification of online confusion causes
and confusing online store elements (Creswell, 2009).
The exploratory nature of Study 1 qualifies for a sequential exploratory design
(Creswell, 2009) that employs across-methods triangulation ( Jick, 1979). In the first phase,
qualitative data are collected, followed by a second phase in which quantitative descriptive
data are collected; the data collection of the second phase builds on the results of the
first phase (Davis et al., 2011). Such an approach is particularly useful for the initial
exploration of a phenomenon or an emergent theory (Creswell, 2009) and thus is suitable for
the exploration of the new construct online shopper confusion.
Considering the scope of the research, an online survey was considered an appropriate
data collection method. Moreover, in line with the positivism research paradigm applied
herein, an online survey benefits from the elimination of interviewer bias and therefore
fulfills the requirement of minimizing the subjective influence of the researcher.
Respondents might feel uncomfortable admitting confusion in shopping situations;
therefore, a projective technique (see Chang et al., 2013) served as the data collection
instrument. Research indicates that projective questions provide valid data that are less
biased by socially desirable responses (Fisher, 1993). The stimulus was drawn by means
of an open source software with effort to create a realistic talking situation. A cartoon
showed two people chatting on a street. The first person said, “I have recently ordered
something at an online shop.” The second person answered, “I experience online shops
confusing, because […].” The subsequent open-ended question asked respondents to
complete the sentence; they were allowed to provide as many statements as they wanted.
Respondents were recruited by students in marketing classes, who received course credit
for participant acquisition. Students forwarded the link to family members, other
students, and work colleagues, resulting in a convenience sample of 100 respondents
(47 women, mean age: 37 years).
The sampling procedure of the qualitative data collection limits the generalizability of
the findings. To overcome this drawback, a large-scale descriptive data collection based on a
pre-defined quota representing the population under investigation (in age and gender) aimed
to validate online shopper confusion consequences.
The 23 confusing online store elements obtained through the projective technique
constituted the items for the questionnaire. Respondents were asked to indicate on a
five-point Likert scale (1 ¼ do not agree at all, 5 ¼ totally agree) the extent to which they
agreed that the respective elements evoke confusion. The questionnaire proceeded with
items assessing demographic data, internet and online shopping experience, and online
shopping motivation.
A personal survey was chosen as data collection method. In total, 26 business
administration students received course credit for interviewing 30 individuals each based on
an individual quota. This procedure resulted in a final sample of 733 respondents.
The sample represents online shoppers in the country under investigation in terms of
age and gender; however, the 25-34 age group was slightly over-presented, while the
oldest age group (55-75) was underrepresented, which is likely due to the ease/difficulty in
reaching these target groups (see Table I).
Results. The data of the qualitative data collection were evaluated with content analysis
in line with the procedure and quality criteria of Kassarjian (1977) and Keaveney (1995).
Each statement (i.e. sentence completion) reflected a unit of analysis. After elimination of
meaningless entries, 100 respondents provided 114 statements. In a first step, one
coder reduced the statements into basic units (unitizing). For example, statements such
“There is not sufficient information regarding shipping costs” were unitized into “limited
Study 1 (n ¼ 733)
38
Study 2 (n ¼ 185)
36
Gender (%)
Men
Women
50.7
49.3
41.2
58.8
50
50
Education (%)
University
High school
Vocational school
Apprenticeship
Compulsory schooling
38.5
32.1
11.3
11.6
6.4
38.4
46.3
7.3
6.2
1.7
37
61.1
0
0.9
0.9
1.8
4.2
4.5
5.3
12.7
71.4
Mean (SD)
3.78 (1.12)
3.32 (1.19)
–
4.9
3.8
5.4
15.2
70.7
Mean (SD)
3.82 (1.10)
3.25 (1.22)
–
0
0.9
0
2.8
96.3
Mean (SD)
4.21 (0.84)
3.90 (1.00)
Mean age
Frequency of internet usage (%)
Nevera
Every few weeks
Once or twice a week
Three for five times a week
Once a day
Several times a day
Internet experience
Online shopping experience
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Study 3 (n ¼ 107)
24
Online shopping motivation
Hedonic beliefs
3.40 (0.95)
3.30 (1.06)
3.89 (1.00)
Utilitarian beliefs
3.48 (0.89)
3.45 (0.96)
3.73 (0.90)
Notes: aAnswer category “Never” was only available in Study 1; Study 2 use an online survey and Study 3
a student sample, making this answer category irrelevant; internet experience, online shopping experience,
Table I.
and online shopping motivation were measured on five-point Likert scales (1 ¼ do not agree at all, Sample characteristics
5 ¼ totally agree)
(study 1-study 3)
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Table II.
Classification
system for online
confusion causes
information regarding shipping costs,” and statements such as “They all have a different
structure” were unitized into “different structure.” The purpose of this step was to
eliminate unnecessary words in each statement to facilitate further analysis.
In the second step, three coders independently coded the content material.
The theoretically derived online confusion causes (design and navigation, informational
content, and website functionalities) served as a preliminary category system. Thus, a
deductive approach guided the content analysis (see Table II).
Individual variables that cause confusion during the online shopping process
(e.g. experience with online shopping) did not receive consideration in the categorization
process, as individual traits do not fulfill the requirement of objective confusion characteristics.
The judges were instructed to add additional confusion categories if necessary;
however, no new categories emerged. Cohen’s κ served as an indicator of intercoder
reliability (or interjudge reliability), which measures “whether different judges classify
the same phenomena into the same categories” (Keaveney, 1995, p. 73). Values above
80 percent constitute a good result (Kassarjian, 1977). High agreement among the three
judges resulted in an average intercoder reliability of 0.82 (Cohen’s κ), representing an
acceptable result.
Statements related to the first category design and navigation included “The offers are
unclearly arranged,” “There is no clear structure,” “One cannot conduct a search,” and
“I can’t find items quickly.” The second confusion category reflects misleading
informational content. As proposed in the theoretical part, respondents indicated that a
missing cost overview, unclear product information, and too little or too much information
confused them. These statements seem to reflect the utility dimension of information, such
that information is provided in a way that diminishes its usefulness and confuses
shoppers. Information accuracy was represented by statements such as “Ambiguous
information as regards to delivery costs” and “Sometimes, items are not clearly marked.”
Several respondents mentioned information formatting issues as a confusion driver:
“There is [too] much information printed in small font” and “Contract terms are often
hidden.” Finally, the third online confusion cause comprises website functionalities.
Answers included payment issues, such as “Payment is always very complicated,”
“Shops accept only debit cards,” and “Terms of payment are explained at the very end.”
The lack of personal advice was identified as another confusion driver within this
category: “There is no qualified personnel available for advice” and “There is no contact
person.” Statements indicating problems with reclamation were also allocated to the
website functionalities category (see Table II).
A descriptive analysis evaluated the confusion potential of each online store element as
obtained through the second data collection. The average internet experience was 3.78, and the
average online shopping experience was 3.32. Respondents shopped for both hedonic and
utilitarian reasons ( for sample characteristics, see Table I). Table III presents the means and
standard deviations of each item arranged according to the three online confusion causes.
On an item level, all means exceeded the scale mid-point of 3.00, with the exception of the
item “too much information.” Problems with reclamation exhibited the most confusion
potential, followed by hidden information and complicated payment.
Online confusion causes
Descriptions
Design and navigation
Informational content
Website functionalities
Organization of content and navigation through the website
Format, content, and timeliness of information
Payment, order procurement, and reclamation issues
Online confusion causes
Confusing online store elements
Design and navigation
Unstructured layout
Unclear layout
Overloaded design
Poor orientation
Long search time
Overall mean
3.31
3.30
3.20
3.19
3.11
3.22
Informational content
Hidden information
Unspecific information
Unclear product information
Too little information
Information not up-to-date
No overview of costs
Too much information
Overall mean
3.57
3.45
3.43
3.26
3.21
3.15
2.84
3.27
Website functionalities
Problems with reclamation
Complicated payment process
Order process interrupted
Payment not working
Payment options differ among various stores
No contact person
Payment options not clear
Limited payment options
No advice
Unwieldy order process
Product out of stock
Overall mean
Note: For each online confusion cause, confusing online store elements are sorted according to
confusion potential (measured on a five-point Likert scale (1 ¼ do not agree at all, 5 ¼ totally agree))
M
3.59
3.48
3.47
3.46
3.29
3.29
3.28
3.26
3.24
3.22
3.13
3.34
their
The results of this study provide first empirical evidence of the confusion potential of the
internet retail process. The findings confirm the category system developed from extant
literature. The study employed a projective technique to avoid any social desirability bias
at this early stage of research. The answers obtained are relatively consistent among
respondents and thus affirm the stability of the conceptual online confusion causes
framework. Data from the second data collection validated the results with a representative
sample and offer insights into the confusion potential of each specific online store element.
Study 2: online shopper confusion consequences: fictitious shopping scenarios
Study design. From both a practical and a scientific point of view, it is of interest to test
whether confusion is linked to specific outcome variables. Prior research on stationary
retailing has demonstrated that shoppers react with negative responses to confusing
shopping situations. Drawing on these notions, the online shopper confusion framework
suggests that online shopper confusion negatively affects revisit intentions, shopping
cart abandonment, spending time, unplanned expenditures, and WOM (see Figure 1).
Study 2 aims to test these proposed consequences of online shopper confusion with a
causal research design.
A one-factor, between-subjects design was employed, with confusion as the
manipulated variable. An online survey employed fictitious written online shopping
scenarios, with one scenario describing a confusing internet retail process and the other a
non-confusing internet retail process. Respondents were randomly allocated to one of the
Confusion in
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Table III.
Categorization of
confusing online store
elements into online
confusion causes
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experimental conditions. The first page of the questionnaire instructed respondents to
read the scenario carefully and to put themselves into the described shopping situation.
Then, a short text of four paragraphs described a confusing/non-confusing
internet retailing process. The results of Study 1 served as basis for creating fictitious
shopping scenarios. In the confusing shopping scenario, the online store had an
unusual and unclear structure, making the orientation difficult. Many products and
information were presented, the search function was underdeveloped, and illustrations of
the products had poor quality. In addition, the preferred payment option was not
available, and the shopping cart was missing. In the non-confusing shopping scenario,
these elements were presented in a favorable way (i.e. the online store had a usual
and clear structure, a comfortable amount of information was presented, the search
function worked, the preferred payment option was available, and the ordering process
went smoothly).
After stimulus exposure, respondents answered questions about what they would feel
and how they would react in such a shopping situation. The 13-item retail shopper
confusion scale developed by Garaus and Wagner (2016) was used to measure confusion.
The consequences shopping cart abandonment and unplanned expenditures reflect
concrete and singular constructs (Donovan and Rossiter, 1982; Rajamma et al., 2009),
justifying the assessment by a single item (Bergkvist and Rossiter, 2007; Rossiter, 2002).
Items assessing revisit intentions (Hausman and Siekpe, 2009), spending time
(Kim et al., 2007), and reduced WOM ( Janda et al., 2002) measured the proposed
consequences (see Appendix). All measures consisted of five-point Likert scales (1 ¼ low
agreement, 5 ¼ high agreement). The questionnaire concluded with questions about age,
gender, educational background, internet and online shopping experience, and online
shopping motivation. The online survey produced 185 completely answered
questionnaires (41 percent men, mean age: 36 years, mean internet experience:
3.82, mean online shopping experience: 3.25; for sample characteristics, see Table I).
The scales exhibited satisfactory psychometric properties (Cronbach’s α above 0.7,
significant correlations among items), justifying the calculation of composite scores
( for item reliabilities, see Appendix).
Results. An analysis of variance with the average confusion score as the dependent
variable and the experimental setting as the independent variable revealed that respondents
perceived the confusing scenario as significantly more confusing (M ¼ 3.72, SD ¼ 0.69) than
the non-confusing scenario (M ¼ 1.89, SD ¼ 0.75, F(1,183) ¼ 296.68, po0.01).
A multivariate analysis of variance (MANOVA) tested the influence of online shopper
confusion on the theoretically derived consequences. The analysis yielded a significant
model (Pillai’s trace V ¼ 0.75, F(5, 162) ¼ 95.42, p o 0.01). The results confirmed all
proposed consequences (see Table IV ).
The results confirmed the assumption that confusion drives low revisit intentions
(3.87 vs 1.43, F(1, 183) ¼ 322.89, p o0.01, η2 ¼ 0.64). In the confusing retail process condition,
respondents indicated that they would abandon their shopping trip (1.61 vs 4.37,
F(1, 183) ¼ 262.30, p o0.01, η2 ¼ 0.59). Furthermore, confused shoppers tried to escape a
confusing online shopping situation as soon as possible, leading to reduced spending time
(2.64 vs 1.25, F(1, 183) ¼ 126.15, p o0.01, η2 ¼ 0.41). In the confusing internet retail process,
respondents indicated that they would have less unplanned expenditures than in the
non-confusing online shopping situation (2.62 vs 1.33, F(1, 183) ¼ 74.10, p o0.01, η2 ¼ 0.29).
Finally, confusion hindered shoppers from spreading positive WOM (3.40 vs 1.28,
F(1, 185) ¼ 255.96, p o0.01, η2 ¼ 0.58).
Study 2 aimed to test the negative consequences of online shopper confusion.
The analysis revealed that consumers respond negatively to confusion during the internet
Consequence
Non-confusing
M
SD
Confusing
M
SD
2
Difference
F
p
Effect size (η )
Study 2: fictitious online shopping scenarios
Revisit intention
3.87
(1.14)
Shopping cart abandonment 1.61
(1.13)
Spending time
2.64
(1.15)
Unplanned expenditures
2.62
(1.27)
WOM
3.40
(1.15)
1.43
4.37
1.25
1.33
1.28
(0.69)
(1.15)
(0.49)
(0.75)
(0.63)
2.44
2.76
1.39
1.29
2.12
322.89
262.30
126.15
74.10
255.96
o0.01
o0.01
o0.01
o0.01
o0.01
0.64
0.59
0.41
0.29
0.58
Study 3: real online shopping experiences
Revisit intention
4.37
(0.82)
Shopping cart abandonment 1.73
(1.17)
Spending time
3.50
(0.92)
Unplanned expenditures
3.65
(1.44)
WOM
3.98
(0.74)
3.11
2.64
2.63
2.52
2.56
(1.15)
(1.71)
(1.29)
(1.59)
(1.16)
1.26
0.91
0.87
1.13
1.42
44.05
10.61
16.92
14.59
60.06
o0.01
o0.01
o0.01
o0.01
o0.01
0.30
0.09
0.14
0.12
0.36
retail process. Medium-to-large effect sizes provide empirical evidence of the relevance of the
confusion construct in explaining consumers’ negative responses to online shopping
situations. All identified negative consumer reactions are of high relevance for e-retailers.
Study 3: online shopper confusion consequences: real shopping experiences
Study design. While the results of Study 2 offer important insights into online shopper
confusion consequences, the findings should be interpreted with caution in terms of their
generalizability. The employment of fictitious shopping scenarios benefits from high
internal validity but suffers from limited external validity. To account for this limitation,
Study 3 uses the recall of real shopping experiences as a stimulus for testing the postulated
consequences (see Figure 1) and employs a one-factor (confusing vs non-confusing real
online shopping experiences) between-subjects design. This approach is based on the
critical incident technique, which aims to collect data on extremely positive or negative
incidents (Flanagan, 1954). Internet research studies have successfully applied this
technique (e.g. Holloway and Beatty, 2003, 2008). Moreover, past shopping experiences have
served as a stimulus when exploring emotions in consumption situations (e.g. Machleit and
Eroglu, 2000; Richins, 1997).
Business administration students were randomly allocated to one of the two groups.
Students are more likely to experience online shopping trips negatively (see Cho et al., 2006)
and therefore represent a reasonable target group for examining online shopper confusion.
The first page of a paper-and-pencil questionnaire informed respondents about the subject
of the study (i.e. exploring what consumers feel during confusing online shopping
experiences). In the confusing group, few examples of a confusing online shopping
experience were provided (e.g. unstructured layout, hidden information, problems with
reclamation complicated payment process); in the control group, these examples were
provided in a non-confusing way. The provision of the examples helped respondents
understand the confusion construct. Afterward, respondents were asked to recall an online
shopping situation they experienced as confusing (non-confusing). To ensure that they
recalled and elaborated on a confusing shopping situation, they were asked why they
considered the shopping situation confusing (non-confusing) and to describe the situation in
as much detail as possible. The questionnaire proceeded with an assessment of online
shopper confusion. Subsequently, respondents were asked how they behaved in the
shopping situation. For all constructs, the same items as in Study 1 were used (the Study 1
items were slightly adapted to reflect real and not anticipated behavior). The questionnaire
Confusion in
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Table IV.
Comparison of mean
scores for online
shopper confusion
consequences
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ended with items assessing demographic data, internet and online shopping experience, and
online shopping motivation.
The data collection resulted in 107 questionnaires (50 percent men, mean age: 24 years,
mean internet experience: 4.21, mean online shopping experience: 3.90; for sample
characteristics, see Table I). The reliability analysis exhibited satisfying results for all
constructs ( for item reliabilities, see Appendix). Composite scores were calculated for the
subsequent analysis.
Results. Descriptions of confusing online shopping experiences are mainly consistent
with the confusing online store elements identified in Studies 1 and 2, and therefore require
no further elaboration. Detailed descriptions of confusing/non-confusing shopping
experiences among all respondents confirm their ability to recall past shopping experiences.
The majority of respondents (60 percent) experienced confusion when shopping online
for clothes, followed by shopping for general merchandise (25 percent, e.g. Amazon.com).
The remaining respondents recalled confusing shopping experiences in food
stores, jewelers, opticians, ticket services, and travel operators. In the non-confusing
shopping condition, 43.1 percent of respondents recalled an online shopping trip in a
clothing store, and 34.5 percent described online shopping in a store selling general
merchandise (e.g. Amazon.com). The remaining respondents recalled shopping
experiences in stores selling cosmetics, food, games, jewelry, travel, leisure activities,
music, and sports equipment.
An analysis of variance (F(1,105) ¼ 142.04, p o 0.01, η2 ¼ 0.58) tested whether the
confusion manipulation worked as intended. Respondents in the confusing condition
experienced the shopping situation as more confusing (3.44) than respondents in the
non-confusing condition (1.79). A MANOVA involving the condition (confusing vs
non-confusing) as a between-subjects factor and the proposed consequences (see Figure 1)
as the dependent variables yielded a main effect of online shopper confusion on all
consequences (Pillai’s trace V ¼ 0.42, F(5,101) ¼ 14.61, p o 0.01) (see Table IV ).
As predicted, respondents in the confusing condition reported lower intentions to
revisit the store (4.37 vs 3.11, F(1,105) ¼ 44.05, p o 0.01, η2 ¼ 0.30). Respondents also
had a higher shopping cart abandonment rate in the confusing internet retail process
(1.73 vs 2.64, F(1,105) ¼ 10.61, p o 0.01, η2 ¼ 0.09). Moreover, a comparison of time spent
between the two conditions showed that confused shoppers spent less time in the store
(3.50 vs 2.63, F(1,105) ¼ 16.92, p o 0.01, η2 ¼ 0.14). In the confusing condition, respondents
also had less unplanned expenditures (3.65 vs 2.52, F(1,105) ¼ 14.59, p o 0.01, η2 ¼ 0.12).
Finally, confused shoppers were less likely to spread positive WOM than non-confused
shoppers (3.98 vs 2.56; F(1,105) ¼ 60.06, p o 0.01, η2 ¼ 0.36). Overall, the results validate
the findings of Study 2 and confirm that shoppers react negatively to confusing online
shopping experiences.
Discussion
While various studies have demonstrated the effect of physical surroundings in brick-andmortar stores, the research on the influence of online store design on consumer behavior is
still at the beginning stage. Considering that online retailers have operated only for 20 years
(e.g. Amazon.com), compared with traditional retailing, which has existed for more than
100 years, the lack of knowledge on online shoppers’ responses to specific online store
elements is not surprising. The current study attempts to contribute to a better
understanding of consumers’ mental states and behavioral responses during the internet
retail process.
Recent research has examined popular consumer behavior constructs under an online
retailing perspective (e.g. information overload (Chen et al., 2009); e-satisfaction
(Evanschitzky et al., 2004)). Although constructs developed under the premise of traditional
shopping are applicable to online services to some extent, the internet retail process is
characterized by particular characteristics (e.g. lack of possibility to evaluate products or
services before the transaction; Zheng et al., 2017) that justify the amendment of constructs
to an online context. In line with such an approach, the current research adapts the
confusion construct (Garaus et al., 2015; Walsh and Mitchell, 2010) to the internet retail
process. Confusion is experienced as a negative mental state that consumers tend to avoid.
Drawing from theoretically derived conceptualizations of confusion, this research identified
23 confusing online store elements that constitute three online confusion causes.
Online shopper confusion results in undesirable consumer behavior.
Theoretical implications
This research explores confusion in an internet retailing context. In doing so, it adds a new
construct of high relevance to the internet retailing literature. This study not only
develops a theoretically well-founded conceptualization of the mental state online shopper
confusion but also integrates online shopper confusion into a comprehensive research
framework. On the one hand, the development of this research framework benefits by
considering context-specific causes and consequences of online shopper confusion; on the
other hand, it affords researchers a holistic perspective on the online shopper confusion
construct. Furthermore, by exploring online shopper confusion, this research answers the
call to explore a specific mental state occurring during negative online shopping incidents
(e.g. Holloway and Beatty, 2003).
The findings provide theoretical and empirical evidence of the risk of confusion during
the internet retail process. The identification of confusing online store elements
offers insight into the confusion potential of specific elements of the internet retail process.
As expected, online shopper confusion causes vary considerably from stationary
consumer confusion causes. The inclusion of various elements particular to internet
retailing justifies the necessity of differentiating online shopper confusion from extant
confusion constructs (e.g. Garaus and Wagner, 2016; Walsh and Mitchell, 2010).
In stationary retailing, confusing elements include ambient, design, and social factors
(Garaus et al., 2015), whereas the three major confusion factors design and navigation,
information content, and website functionalities emerge in online shopping situations.
These differences are mainly due to the lack (or limitation) of ambient and social factors in
retailing. Nevertheless, for both online shopper confusion and stationary retail shopper
confusion, the design and navigation category exhibits some similarities. A poor
orientation and an unclear customer flow lead to confusion in brick-and-mortar retailing
(Garaus et al., 2015); similarly, poor orientation and an unstructured or unclear online store
layout result in confusion in online retailing. Aggressive visual merchandising evokes
confusion in stationary retailing, which is comparable to an overloaded design in online
retailing. In addition, in both retail formats, different payment methods confuse shoppers.
Given these similarities and reasonable differences among stationary and online shopper
confusion, the findings are face valid.
Overall, a comparison of stationary and online shopper confusion causes leads to the
conclusion that confusion in internet retailing is stronger with regard to the shopping
process itself (e.g. problems with reclamation, complicated payment process, order process
interrupted). The online shopper confusion cause website functionalities has the highest
number of confusing online store elements, and the average confusion potential in this
category was the highest among the three online confusion causes.
As expected, online shopper confusion exerts a strong negative influence on consumer
responses. In particular, the results indicate that confusion negatively affects revisit
intentions. As with many other consumer behavior constructs, consumer loyalty has
Confusion in
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attracted recent research attention in the context of online shopping. Researchers have
acknowledged the importance of retaining customers for success because online customer
acquisition costs are often higher than the average lifetime value of customers (Hoffman and
Novak, 2000). In addition, reduced spending time reflects another negative behavioral
reaction to confusion. Moreover, confusion evoked by the internet retail process results in
shopping cart abandonment. Still, the reasons for shopping cart abandonment are largely
unexplored (e.g. Cho et al., 2006). By identifying a confusing store environment as a driver of
shopping cart abandonment, the current research contributes to the understanding of this
undesirable online shopper behavior.
Online retailers need to expend more effort in prompting impulse purchases without any
physical evidence. Brick-and-mortar stores rely on environmental stimulation and personal
advice to increase unplanned expenditures; both stimuli are not available in virtual stores.
Therefore, e-retailers have a comparative disadvantage in this context and should invest
effort to trigger impulse purchases. Study 2 and Study 3 demonstrate that a confusing
internet retail process explains low impulse purchase rates; thus, retailers should try to
counteract these low rates by providing a clear and non-confusing retail process.
Finally, with confusion identified as a factor that reduces WOM, retailers should pay special
attention to avoid confusion so as to create favorable WOM.
Although Study 3 validates the findings of Study 2, the effect sizes of the tested consequences
vary considerably between the two studies. The smaller effect sizes of Study 3 were expected to
some extent because real confusing online shopping experiences might not reach the same
confusion potential as confusion manipulated in fictitious online shopping scenarios. In Study 2,
revisit intentions, shopping cart abandonment, and WOM exhibit the largest effect sizes,
indicating the high relevance of both consumer reactions. While revisit intentions and WOM
exhibit the largest effect sizes in Study 3 as well, anticipated shopping cart abandonment differs
from real shopping cart abandonment. This finding helps guide further research on shopping
cart abandonment by underscoring the importance of assessing real behavior.
Managerial implications
Holloway and Beatty (2003) report that online retailers are not adequately recovering from
failures and stress the importance of avoiding negative consumer shopping experiences.
Some research effort has already been devoted to internet retail failures. However, little is
known about the reasons for negative online shopping experiences. By exploring the
specific mental state online shopper confusion, the current research offers important
insights into shoppers’ online behavior from a practical perspective.
First, the conceptualization of the online shopper confusion construct helps managers
understand why shoppers might experience online shopping negatively and react with
undesirable responses. This knowledge might help marketers create recovery strategies
tailored to a confusing online store environment by giving consumers some indication of
what went wrong and why.
Second, the negative consumer responses associated with confusing online store
elements highlight the importance of a non-confusing store design. Confusion negatively
affects not only short-term consequences (i.e. shopping cart abandonment, spending time,
and unplanned expenditures) but also long-term consequences (i.e. revisit intention and
WOM). Despite the relevance of shopping cart abandonment from both a theoretical
and practical perspective, research on the mental states that trigger such behavior is scarce.
Retailers confronted with the undesirable consumer responses explored in this research
should examine the confusion potential of the online store.
Third, the findings provide practitioners with concrete insights into how the internet
retail process confuses shoppers. The taxonomy of confusion causes should help
managers assess the confusion potential of their existing online stores and consider
confusion issues in the development of new online stores. Especially, clothes stores and
stores selling general merchandise bear the risk of confusing shoppers. From a more
general perspective, the results reveal the importance of focusing not only on the creation
of positive experiences but also on the avoidance of negative experiences. This implication
is even more pronounced when considering that negative information outweighs positive
information (Mizerski, 1982) and that recovery strategies do not always turn out to be
successful (Holloway and Beatty, 2003).
Finally, the taxonomy of confusing online store elements can be used by policy makers
who review consumer complaints about a confusing and misleading online purchase
process. For example, they could use the knowledge of confusion drivers in the online retail
environment to set a standard among different industries.
Limitations and further research
The findings reveal several areas that further research might extend. The study set out to
determine confusion causes and consequences among various industries. Consequently,
data collection was not limited to a specific internet retail sector. Related research indicates
that not all facets of website appeal can be generalized to all sectors. In contrast, differences
exist among various service and product domains (Blake et al., 2017). Further research could
test whether specific sectors have a higher risk of confusion. In a similar vein, personality
characteristics might influence the extent of online shopper confusion experienced in online
retailing. Yan et al. (2017) report that individuals with high levels of self-efficacy seem to be
better able to cope with the plenty of information available in internet services. In line with
this finding, it could be argued that self-efficacy in online shopping might reduce online
shopper confusion.
Online shopper confusion might be of relevance not only in a retailing environment but
also in e-service industries (e.g. online banking), though this assumption requires further
empirical evidence.
The identified confusion causes might serve as an initial step in the development of an
index to assess online shopper confusion. Although this study shows severe consequences
of online shopper confusion, not all possible consequences have been examined.
For example, online shopper confusion might have a negative impact not only on
unplanned purchases but on planned purchases as well. It would be worthwhile to test
consumers’ reactions to different levels of confusion in real online shopping situations with
additional outcome variables of interest, such as sales data. In addition, linking confusion at
the different stages of the internet retail process to particular outcome variables would
be useful.
Finally, the segmentation of shoppers prone to online shopper confusion is another rich
field for study. Central to such research would be the question whether some consumer
groups bear a higher risk of experiencing online shopper confusion than other groups. Such
a classification of online shoppers prone to confusion would help retailers target their web
store design to their main customers. In addition, the knowledge of consumer characteristics
that foster confusion would help shed light on the personality of online shoppers.
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Appendix. Constructs, items, and Cronbach’s α values of studies 1-3
Online shopper confusion (Garaus and Wagner, 2016), αstudy3 ¼ 0.94
Helplessness
Helpless
Lost
Awkward
Baffled
Weak
Overstrained
Irritation
Annoyed
Irritated
Nerved
Inefficiency
Efficient*
Careful*
Productive*
High-performing*
Revisit intention (Hausman and Siekpe, 2009; Kim et al., 2007), αStudy2 ¼ 0.96, αStudy3 ¼ 0.89:
I am encouraged to revisit this site in the near future.
I would visit this online retailer again.
In the future, I would very probably shop at this online retailer.
Spending time (Kim et al., 2007), αStudy2 ¼ 0.80, αStudy3 ¼ 0.58:
I would like to stay at this online store for as long as possible.
I enjoyed spending time at this online store.
Shopping cart abandonment (Rajamma et al., 2009):
In such a shopping situation, I would abandon the shopping process.
Unplanned expenditures (Donovan and Rossiter, 1982):
I would spend more money than initially planned in this store.
WOM ( Janda et al., 2002), αStudy2 ¼ 0.94, αStudy3 ¼ 0.82:
I would say positive things about this online retailer to other people.
I would recommend this online retailer to someone who seeks my advice.
I would encourage friends and relatives to do business with this online retailer.
Hedonic consumer beliefs (Punj, 2011), αStudy1 ¼ 0.71, αStudy2 ¼ 0.78, αStudy3 ¼ 0.78:
I enjoy online shopping.
I like to have many choices while shopping.
I like to research products in online shops.
Utilitarian consumer beliefs (Punj, 2011), αStudy1 ¼ 0.66, αStudy2 ¼ 0.65, αStudy3 ¼ 0.50:
Shopping online saves me time.
Shopping online saves me money.
Shopping online helps to find the right product.
Internet experience (Scarpi, 2012):
How expert would you rate yourself with the internet?
Frequency of internet usage (Punj, 2011):
How often do you use the internet?
Online shopping experience (Scarpi, 2012):
How expert would you rate yourself with online shopping?
“*” indicates reversed item.
About the author
Marion Garaus is an Assistant Professor in Marketing at the Institute of Business Administration,
University of Vienna, Austria. Her research interests include consumer behavior, environmental
aesthetics, and store design. Her research appears in Journal of Business Research, Journal of Consumer
Behaviour, Psychology & Marketing, and Technological Forecasting and Social Change. She is the
co-author of the book Store Design and Visual Merchandising: Creating Store Space That Encourages
Buying. Marion Garaus can be contacted at: marion.garaus@univie.ac.at
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