2. decision-making and channel choice? How does media richness affect
perceived media richness-task fit? And, how does media richness impact
post purchase evaluation? With increasing technological advances that
enable channels to incorporate new capabilities (e.g., embed audio/
video, pay through mobile device) and with firms interested in engaging
with the customer through multiple channels, it is imperative to conduct
a formal study to investigate the effects of media richness on consumer
decision-making.
This investigation focuses on three levels of media richness
(i.e., high, medium, low) and undertakes three experimental studies
that examine the effects of media richness on information search, and
post purchase evaluation (e.g., satisfaction). The research examines per-
ceived media richness-task fit, and evaluates the appropriate levels of
media richness for different types of decision-making tasks. Product
type and task complexity are potential moderators. Findings provide
evidence that consumers prefer channels with medium media richness
level over high richness for carrying out complex decision-making
tasks. Findings reveal that consumers are likely to undertake simple
decision-making tasks on channels that incorporate low levels of
media richness. Findings also demonstrate that product type moderates
the effect of media richness on perceived channel-task fit, post-
purchase evaluation, and channel choice.
The contributions of this paper are manifold. First, this paper in-
vestigates the effect of media richness on consumer decision-
making and channel choice, a crucial under-researched area, and
provides evidence that media richness is a driver of channel choice.
Second, from theoretical and managerial perspectives, this paper
provides insights on media richness continuum and perceived
media richness-task fit, which are essential for retail channel design
and content management. Finally, this research opens up many new
avenues for future research. The paper proceeds as follows: the next
section discusses media richness theory, delineates the effect of
media richness on decision-making tasks in terms of cognitive cost
and behavioral decision theory, and presents the hypotheses. The
section that follows, presents three studies and the findings. The
paper ends with theoretical and managerial implications of the find-
ings, and proposals for future research.
2. Literature review and hypotheses
2.1. Media richness
McGrath and Hollingshead [35] classify media along a continuum
of “increasing potential richness of information” ([50], p. 297). The
four types of media that the researchers identify along this richness
continuum are: text, audio, video and face-to-face communications.
MRT is usually used in the context of media choice. Face-to-face is
the richest medium as it allows mutual feedback and simultaneously
conveys a variety of cues (e.g., tonal, facial, emotional). A text-based
interaction (e.g. texting through mobile devices or browsing infor-
mation through text-only cell phone browsers) is less “rich” than
audio, video or face-to-face interaction (e.g. in-store interactions or
communications using Apple iPhone's Facetime feature). Media
that are highest and lowest in media richness anchor the two ends
of the continuum. Researchers extensively use MRT in the context
of dyadic intra-organizational communication situations in information
systems literature [50].
Extending MRT to decision making in marketing channels, media
richness is a characteristic that imparts to a marketing channel the
ability to communicate information to consumers, and help them un-
dertake decision-making on that channel. The degree of media richness
may not only vary across channels, but also within a specific channel.
For example, a mobile channel with audio/video capabilities is richer
than a mobile channel with text-only capabilities. Also, an in-store set-
ting suggests a face-to-face interaction with a sales associate, as well as
the possibility of physical inspection of goods. Similarly, an e-commerce
setting suggests that consumers access a website through a computer
terminal, whereas an m-commerce setting suggests that a mobile de-
vice is used as a channel. This study examines the effects of channel
characteristics on consumer decision making across three channels:
in-store, e-commerce and m-commerce that represent three levels of
media richness.
Decision-making inside a store involves face-to-face interactions,
and has feedback and communication capabilities due to the avail-
ability of a channel representative (in-store salespeople). These fea-
tures afford a personal focus and a wide variety of language support.
Hence, the in-store channel is high in media richness. Decision-
making through e-commerce involves accessing websites through
computer terminals [13]. Most websites use text and images (in
exclusion of audio and multi-media) to provide consumers with
the required information [48], and is the scope of e-commerce in
this study. These websites do not have the spontaneity in communi-
cation, feedback, and personal focus capabilities of the in-store chan-
nel. Such websites may have wider language support than that
available in-store, but are typically limited to a database of non-
customized vocabulary. Hence, the media richness of e-commerce
is medium. Finally, m-commerce refers to the pairing of mobile
devices with commercial transactions, providing consumers with
the ability to carry out transactions through wireless Internet-
enabled devices [13]. In addition to all the features that are available on
e-commerce, m-commerce also offers portability [1], and ubiquity [1].
The other major difference between e-commerce and m-commerce is
the interface (large versus small screen) of the mobile device. The setting
of m-commerce imposes limitations, notably those of attention
constraints and small-screen access devices [9]. These differences limit
the extent of communication, feedback and personal focus capabilities
that are possible on m-commerce. For example, in e-commerce, a con-
sumer may simultaneously browse a product listing on a webpage, and
watch a video about the product on the same computer screen. However,
in m-commerce, such simultaneity is difficult to attain due to channel-
specific limitations. Moreover, this investigation includes internet-
enabled mobile devices that support text and images. Therefore, com-
pared to in-store and e-commerce, m-commerce has the lowest media
richness.
It is important to note that the goal of this research is to examine
how channel characteristics defined by media richness impact
consumer decision making. The selection of the three channels (i.e.,
m-commerce, e-commerce and in-store) and their settings are
used to manipulate three levels of media richness (i.e. low, medium
and high). Different settings of these channels may yield different
richness levels.
2.2. Task-media fit
McGrath and Hollingshead [35] suggest task-media fit hypothe-
ses as an extension to media richness theory, and present a matrix
(Fig. 1a), which classifies patterns of differential fit along the two di-
mensions of communication media and task type (see [50], for expla-
nation). Communication media is aligned along the “increasing
potential richness of information” continuum ([50], p. 297). Task
type includes choice (e.g., choosing) and negotiating (e.g., generat-
ing ideas) tasks. The best fits for the choice tasks between media
and task type appear to be in media that offer medium richness.
Task-media fit hypothesizes that media at the two ends of the con-
tinuum (i.e., media with the highest and lowest media richness)
are ineffective for carrying out the communication tasks, as they
cause distraction (too rich) or are incapable of transmitting the nec-
essary information (too lean) [50]. In our study, the choice task that
consumers are required to undertake is similar to “choosing”. Our
study advances this literature and investigates task-media fit in the
context of choice tasks.
35M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
3. 2.3. Channel choice
Gupta et al. point out that it is “critically important” ([26] pp. 31) for
both practitioners and academic researchers to understand the fac-
tors that drive consumers to choose one channel over another. Previ-
ous research shows that a multiple-channel retail strategy enhances
the performance of the portfolio of services offered to the customer,
thus leading to high customer satisfaction and ultimately customer
loyalty [55]. Therefore, a greater understanding of consumer deci-
sion making and channel choice is necessary for making multichan-
nel strategies more efficient [39]. Extant literature has investigated
the effects of various drivers of channel choice and preferences, in-
cluding consumer characteristics [52], consumer perception of
price, quality, value and risk of channels [49], interactivity of the
channel [22], different purchase tasks [54], and customer satisfaction
[37].
While these studies focus on how consumer and product character-
istics impact channel choice, limited research exists on how channel
characteristics impact consumer decision-making and channel choice.
A notable exception is Michaelidou, Arnott and Dibb [36] where the au-
thors present a framework for examining the effects of channel involve-
ment, channel perceived risk, channel loyalty, channel similarity and
channel hedonism on channel choice, albeit without empirically testing
it. Our study addresses this gap in the literature by examining the effect
of media richness (a pertinent and important channel characteristic) on
channel choice and consumer decision-making.
2.4. Behavioral decision theory and cognitive cost model
Behavioral decision theory [51] and the cognitive cost model [44]
play important roles in understanding consumer decision-making. The
cognitive cost perspective in behavioral decision theory indicates that
consumers undertake cost-benefit tradeoffs when going through a
decision-making task [51]. The cognitive cost model theorizes that con-
sumers display limited cognitive abilities and thus, their actions during
decision-making tasks depend on the associated cognitive costs [41].
Payne [41] proposes a cost-benefit framework of cognition, which is
subsequently supported by other researchers [4]. As per this framework,
consumers continue to search for additional information only until the
benefit from additional information is equal to or greater than the
cognitive cost of search.
Theories explaining consumer decision-making, such as behavioral
decision theory, cognitive cost model, and cognitive cost-benefit frame-
work suggest that consumers might not be equally at ease when under-
taking the same decision-making task across channels that incorporate
different degrees of media richness. A decrease in media richness leads
to an increase in cognitive costs [50]. For example, technological
advances in e-commerce and m-commerce may reduce the physical
efforts of going to a store to make a choice, but these two channels
can incorporate different capabilities that can result in different degrees
of media richness, and may impose different levels of cognitive cost
on the consumer when undertaking decision-making. Hence,
consumer decision-making is more cognitively “costly” in m-commerce
(e.g., wifi-enabled phones with browsing capabilities that support only
text and images) than in an in-store setting (e.g. store setting where
the consumer can interact with a sales associate and physically inspect
the product).
2.5. Effect of media richness on information search
The cognitive cost-benefit framework and the cognitive cost model
predict a positive linear relationship between media richness and infor-
mation search. The amount of information search that a consumer un-
dertakes decreases as cognitive cost increases over benefit. In other
words, consumers search less as media richness decreases (from high
to medium to low) because cognitive cost increases concomitantly
Source: McGrath and Hollingshead 1994
Fig. 1. Media richness-task fit.
36 M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
4. (while associated benefits remain constant). Therefore, according to the
cost-benefit framework, information search is the highest in the high
media richness condition and the lowest in the low media richness
condition.
On the other hand, task-media fit hypotheses predict that the
richness of the channel can adversely affect the outcome of a choice
task, especially when media richness is too high or too low. The task-
fit hypothesis predicts that as media richness increases too much, it
distracts the consumer and leads to ineffective task completion. As
media richness decreases too much, channels become less capable of
transmitting sufficient information for effective completion of the task.
In other words, the amount of information search that a consumer un-
dertakes increases as media richness decreases from high to medium,
but as media richness further decreases from medium to low, the
amount of information search decreases as well. Therefore, according
to task-media fit, information search is the highest in the medium
media richness condition and low in the high media richness and low
media richness conditions.
The cognitive cost-benefit framework (the cognitive cost model)
and the task-media fit hypotheses make competing predictions about
the direction of information search for the high and medium media
richness conditions. Therefore, we remain agnostic in formulating our
hypotheses about information search in the high and medium richness
conditions. However, the two sets of theories predict that information
search will be low in the low media richness condition. Therefore, we
hypothesize:
H1. Amount of information search is different across the three levels of
media richness conditions, where the amount of search is the lowest in
the low media richness condition.
2.6. Effect of media richness on perceived fit, post purchase evaluation,
channel choice
This study next investigates whether consumers perceive a fit
between a decision-making task and specific levels of media richness.
More specifically, this study examines two questions: a) do consumers
perceive all levels of media richness as fit for undertaking all types of
decision-making tasks? or, b) do consumers perceive only specific levels
of media richness as fit for certain types of decision-making tasks
(specifically, whether product type and task complexity act as
moderators)?
The concept of fit has been widely used in extant literature. Using
task-technology fit theory, Goodhue [24] advances the perspective of
fit as the interaction of technology, task, and individual characteristics.
This research defines media richness-task fit as the match that
consumers perceive between a type of decision-making task and the
level of media richness, and extends the concept of fit into the context
of channel choice and consumer decision-making in a multichannel
environment. Following Goodhue's [24] suggestion, this study employs
user evaluation to measure the construct of perceived fit.
Media richness theory postulates that rich media allow people to
interpret and understand difficult and complex communication issues,
while less rich media enable the handling of simple communication
situations [35]. Media richness theory predicts a positive relationship
between media richness and the complexity of the choice task undertak-
en (originally, “message equivocality” communicated [16], pp. 359).
However, task-media fit tempers this postulation (i.e., media that are
too high or too low in richness are judged as ineffective for carrying
out choice tasks), and predicts an inverted-U shaped curve. Therefore,
applying task-media fit hypotheses to consumer decision-making
tasks (in relation to the first question), media richness is expected to
affect perceived fit, post purchase experiences and channel choice in
the future such that consumers find specific levels of media richness
more suitable for carrying out certain decision-making tasks. As in the
case of H1, no predictions can be made about the direction of the
dependent variables for the high and medium media richness condi-
tions as the competing theories contradict each other and there is no
reason to believe that one theory will overshadow the other. Therefore,
the following hypothesis is presented:
H2. Perceived media richness-task fit, satisfaction and channel choice
intention (for information search and future purchase) are different
across the three levels of media richness conditions, where the fit, satis-
faction and channel choice intention are the lowest in the low media
richness condition.
2.7. Moderators: product type, task complexity
Next, this study investigates whether effects of media richness on
channel choice and consumer decision-making vary across product
type and task complexity (i.e. moderating effect). Extant literature
classifies products in diverse ways, including business vs. consumer
products, tangible vs. intangible goods, credence vs. experience vs.
search goods, high cost vs. low cost products, and convenience goods
vs. shopping goods vs. specialty goods [28], among others. In particular,
researchers frequently use search and experience to describe a product
or service [30]. As per Euromonitor [20], the top product categories that
consumers buy on the Internet are books and airline tickets, both of
which are search products. Food is an experience product [38].
Most people are comfortable buying travel and food delivery via
e-commerce and m-commerce [19]. In this investigation, product
type is either search or experience (airline tickets or food menu from
restaurants), across the three levels of media richness, resulting in six
media richness-product type conditions. The distinction between
search and experience products is whether consumers can assess the
quality of the product before or after experiencing it [38]. Since con-
sumers assess an experience product by consuming the product, the
risk (i.e., the uncertainty) associated with choosing an experience prod-
uct is higher than that associated with choosing a search product. Ac-
cording to risk theory [18], consumers are expected to undertake
more information search in the context of increased perceived risk.
We argue that consumers will prefer to undertake decision-making
for experience products in a medium that offers high richness, as it al-
lows the consumer to collect different types of information (i.e., text-
based as well as those made possible by the objective characteristics
of the media). Similarly, consumers are expected to prefer medium
richness condition for undertaking a decision-making task for search
products due to the lower associated risk with the decision. Based on
this literature and the ones discussed while presenting the previous hy-
potheses, it is expected that for both product types consumers will indi-
cate low fit, satisfaction and channel choice intention in the low media
richness condition. Therefore:
H3. Product type moderates the effect of media richness on information
search, media richness-task fit, satisfaction, and channel choice inten-
tion such that the effects are different across the six levels of media
richness-product type conditions: a high media richness condition is
preferred for undertaking a decision-making task on an experience
product, a medium media richness condition is preferred for undertak-
ing a decision-making task on a search product, a low media richness
condition is not preferred for undertaking a decision-making task on
either search or experience products.
Similarly, extant literature often defines task [60] in terms of simple
vs. complex categorization. Simple tasks require processing fewer cues,
i.e., pieces of data, while complex tasks require processing of higher
number of cues [41]. Information search literature also suggests that
as task difficulty increases, consumers' cognitive effort increases [43],
and consumers search less for complex tasks and more for simple
tasks, indicating a possible moderating effect for task complexity.
Therefore, this study defines task complexity in terms of number of
37M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
5. alternatives/brands, and this factor three levels are: simple (few alterna-
tives), moderate (more than a few alternatives) and complex (many
alternatives), thus generating nine media richness-task complexity
conditions.
It is expected that consumers perceive a higher risk in completing a
complex task and will prefer the high media richness condition for carry-
ing out this task. A possible reason is that a high media richness condition
helps in collecting more information (as mentioned in the preceding sec-
tion), and reduces risk in the choice process. Similarly, a simple task has a
lower associated risk, and it is expected that consumers will prefer to use
a channel with medium media richness for completing a simple
decision-making task. Therefore, we hypothesize that:
H4. Task complexity moderates the effect of media richness on infor-
mation search, media richness-task fit, satisfaction and channel choice
intention, such that the effects are different across the nine levels of
media richness-task complexity conditions: a high media richness con-
dition is preferred for undertaking a complex decision-making task, a
medium media richness condition is preferred for undertaking a moder-
ate decision-making task, a low media richness condition is preferred
for undertaking a simple decision-making task.
Three experiments test the hypotheses presented in this
section. Study 1 is a between subjects experiment. Studies 2 and 3
(mixed design experiments) are designed to provide more convincing
evidence for decision-making at individual levels, and also repli-
cate the findings of Study 1. In particular, Study 2 revisits the com-
parison between channels that incorporate medium and low
richness (e-commerce and m-commerce) and, Study 3 compares
medium and high richness (e-commerce and in-store) channels.
3. Study 1
Study 1 manipulates media richness (high, medium, low)
through three channels: in-store, e-commerce and m-commerce.
Product type is operationalized by search and experience products.
Task complexity (few, moderate, many) is operationalized by the
number of brands that consumers can access for making a choice.
Participants with prior experience of using hand-held mobile
devices and familiar with e-commerce for at least two years partici-
pate in the experiment. Stimuli, including information presentation
format, layout, fonts, colors and images, are the same for all tasks
across all levels of media richness.
3.1. Study 1: Method
3.1.1. Design and participants
Study 1 was a 3 (media richness: high, medium, low) × 2 (product
type: search, experience) × 3 (task complexity: few, moderate, many)
between subjects design. To test the hypotheses, participants were
randomly assigned to one of the eighteen cells where each participant
carried out a decision-making task. Responses were solicited for volun-
tary participation from undergraduate students registered as business
majors at a major south-eastern university in the United States. Partici-
pants earned extra credit for taking part in the study. Further, partici-
pants were entered in five raffle drawings of US$ 30.00 each. In all,
162 participants completed the study. There were 54 participants in
each channel. All participants were under 30 years of age (18–27).
The mean age was 20.7 years. Females constituted 53.1% of the sample,
35.2% had a family income of over a hundred thousand US$, and 85.2%
were Caucasian Americans.
3.1.2. Stimuli
Participants undertook decision-making tasks specifically designed
for this study. The output generated by the website www.orbitz.com
(for specific dates) provided the format for the stimuli for the airline
ticket task. Information was on round-trip flight options. The brand
names of airlines were changed to names of airlines that operated
mainly in parts of the world other than the United States. Similarly,
the output generated by the website www.delivery.com for specific
restaurants, provided the format for the stimuli for the food menu
task. The names of the restaurants were changed for the final stimuli.
For the in-store environment, brochures presented each brand on a sep-
arate page. Separate screens presented information on separate brands
on websites for the e-commerce and m-commerce channels.
3.1.3. Pretest
Thirty-nine participants took part in a pretest that tested for per-
ceived media richness, product type and task complexity. Each par-
ticipant undertook a decision-making task across one of the three
levels of media richness (high, medium, low), one of the two levels
of product type (search, experience), and one of the three levels of
task complexity (high, moderate, low). Upon completion of the
task, participants answered questions on perceived media richness,
perceived product type, perceived task complexity, and additional
questions on perceived cost. A three-way ANOVA of media richness,
product type and task complexity on perceived media richness
revealed that participants rated richness across the three media as
sufficiently different (MHigh = 5.4; MMedium = 4.7; MLow = 3.1;
F(2,37) = 14.1, p b 0.05; where, 1 = strongly disagree; 7 = strongly
agree). A separate three-way ANOVA on the amount of information
search showed a significant effect of all three factors (Media richness:
MHigh = 3.7; MMedium = 6.6; MLow = 4.4; F(2,37) = 5.9, p b 0.05)
(product type: MAirline Ticket = 5.7; MFood = 4.0; F(1,38) = 5.5,
p b 0.05) (task complexity: MFew = 2.4; MModerate = 5.8; MMany =
6.3; F(2,37) = 19.7, p b 0.05), demonstrating the successful manipula-
tion of all the factors.
3.1.4. Independent variables
Each participant began the assigned task either by meeting a re-
search associate, or by logging onto the Internet (through an internet-
enabled laptop computer or through a web-enabled mobile device).
All subjects were asked to imagine themselves in a situation where
they had to choose either an airline ticket or a food menu for a friend.
Existing validated scales measured the independent variables (see
Table 1): perceived media richness [50] and perceived task complexity
[25]. A two-item measure asking whether the participants can assess
the product before using it measures perceived product type. The two
statements were: “I can collect information about the product before
purchasing it” and “I can determine if the product is ‘good’ or ‘bad’ im-
mediately before using the product” (as per the definition of search and
experience attributes [23]). An averaged index was formed for each of
the constructs of perceived media richness (Cronbach's α = 0.85), per-
ceived product type (Cronbach's α = 0.88) and task complexity
(Cronbach's α = 0.89). All statements were measured on a seven-
point scale (1 = strongly disagree; 7 = strongly agree).
3.1.5. Manipulation checks
As intended, a 3 × 2 × 3 ANOVA of media richness × product type ×
task complexity on perceived media richness showed a main effect of
media richness: participants considered in-store (i.e., high media rich-
ness condition) the most rich, and m-commerce (i.e., low media rich-
ness condition) the least rich (MHigh = 5.1; MMedium = 4.8; MLow =
3.5; F(2,144) = 28.9, p b 0.05; where, 1 = strongly disagree; 7 =
strongly agree). A post-hoc Tukey's HSD test reveals that participants
do not perceive significant difference between high and medium levels
of media richness. A reason for this finding may be that participants
assigned to each channel responded to the scale items (see Table 1)
with particular reference to that channel only. We also measure partic-
ipants' responses of their perception of the comparative differences in
decision-making on the three channels, separately. This measure
provides an additional method for measuring perceived (relative)
38 M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
6. media richness. The participant responses (MM–E = 4.1; MM–I = 2.9;
MI–E = 3.0; where, 1 = strongly disagree; 7 = strongly agree) reveal
that participants perceive decision-making across in-store and
e-commerce as different (also see Studies 2 and 3). A separate
three-way ANOVA on perceived product type was found to have a
main effect of product type (MAirlineTickets = 6.3; MFood = 5.7;
F(1,144) = 3.9, p b 0.05). Task complexity (i.e., perceived complexity)
was not significant (MFew = 2.9; MModerate = 4.7; MMany = 4.7;
F(2,144) = 1.2, n.s.).
3.1.6. Procedure
The research associate posed as a travel agent or a concierge for the
high media richness condition. Participants walked into the office and
interacted with the live representative in order to complete a task. Par-
ticipants, in this condition, were free to examine a brochure (which was
identical in terms of the content that participants in the e-commerce
and m-commerce environments examined) and interact with the asso-
ciate who posed as the travel agent. In the medium media richness con-
dition, participants logged on to specific websites via a wifi-enabled
laptop computer, while participants logged on to the same websites
through wifi-enabled mobile devices for the low media richness condi-
tion. Participants accessed the same wireless network for undertaking
the decision-making tasks on e-commerce and m-commerce. In an ex-
perimental set-up, it was ensured that all participants had to reach the
same venue for the experiment. For each of the tasks, the price informa-
tion was included. For the airlines task, participants chose an airline
ticket for a friend. For the restaurant task, participants chose the food
that they would order for a group of friends. After completing the
tasks, all participants completed questionnaires that were administered
in the paper-and-pen format. In the end, all participants responded to
the following question: “Now tell your friend about you decision-
making experiences. What will you say?”.
3.1.7. Dependent Variables
The dependent variable, i.e. amount of information searched, was
operationalized by counting the number of brands examined by the
participants for each task [5]. Perceived fit [25], satisfaction [57],
channel choice intention [42] and the covariate perceived cost [47]
were measured with items adapted from validated scales. The
Cronbach's alpha for each of the scales were (Table 1): 0.97, 0.80,
0.94 and 0.91.
3.2. Study 1: qualitative results
Critical Incident Technique (CIT) is selected for identifying underly-
ing critical factors that affect consumer decision-making across the
three channels (by analyzing the qualitative responses from the partic-
ipants). CIT has been used in several studies, and is effective when the
incidents reported are fairly recent and the observers are motivated to
make detailed observations and evaluations at the time of the incident
[10]. Data analysis using CIT has been executed in two different ways
in extant literature: a) to identify distinct ‘critical incidents,’ where
each respondent's experience is treated as a critical incident [21]; and
b) to identify distinct ‘critical factors,’ where each respondent's experi-
ence is considered as an accumulation of a number of critical factors
[2005]. This study adopts the latter approach and identifies the critical
Table 1
Scales used for the independent variables, dependent variables and covariate (studies 1, 2, 3).b
Construct Measure Item Reliability (Cronbach's α) studies 1, 2, 3 Scales based
on/adapted
S1 S2
E-com
S2
M-com
S3
E-com
S3
In-store
Perceived media
richnessa
MR1 While shopping at online stores, the e-commerce environment helps
me in making a good decision.
0.85 0.86 0.84 0.85 0.89 [50]
MR2 While shopping at online stores, when I do not understand a
piece of information, the e-commerce environment makes it more
difficult for me to come to a decision. (RC)
MR3 While shopping at online stores, the conditions provided by an
e-commerce environment slow down the decision-making process. (RC)
MR4 While shopping at online stores, the e-commerce environment makes
it easy for me to come to a decision.
MR5 While shopping in an e-commerce environment, I can easily
understand things.
MR6 An e-commerce environment helps me reach a decision quickly.
Pcvd diff across m-com
and e-com
PD1 “The decision-making experience on a mobile device is very similar to
the experience in the e-commerce environment.”
N/A N/A N/A N/A N/A
Pcvd diff across m-com
and in-store
PD2 “The decision-making experience on a mobile device is very similar to
the experience in the in-store environment.”
N/A N/A N/A N/A N/A
Pcvd diff across in-store
and e-com
PD3 “The decision-making experience in the in-store setting is very similar
to the experience in the e-commerce environment.”
N/A N/A N/A N/A N/A
Perceived product PT1 “I can collect information about the product before purchasing it.” 0.88 0.91 0.87 0.88 0.86
Type PT2 “I can determine if the product is ‘good’ or ‘bad’ immediately before using
the product.”
Perceived media PF1 “This task is best carried out in an e-commerce environment.” 0.97 0.97 0.98 0.94 0.96 [25]
Richness-task fita
PF2 “An e-commerce environment is the most conducive for carrying out this task.”
Satisfactiona
S1 “If I could do it over again, I would not have made the decision using an
e-commerce environment.”
0.8 0.82 0.8 0.82 0.84 [57]
S2 “I feel bad about using an e-commerce environment.”
S3 “I'm sure it was the right thing for me to use an e-commerce environment.”
Channel choicea
C1 “I intend searching for information and making a purchase decision in an
e-commerce environment.”
0.94 0.94 0.95 0.84 0.92 [42]
C2 “I shall recommend others to search for information and make a
purchase decision in an e-commerce environment.”
Perceived cost PC1 “I felt it took a lot of effort to search.” 0.91 0.92 0.84 0.93 0.84 [47]
PC2 “I felt it took a lot of time to search.”
a
In the items included in the questionnaires, e-commerce appears in questionnaires administered to participants assigned to the e-commerce channel, while m-commerce and in-store
appear in the questionnaires administered to participants in the m-commerce and in-store channels respectively.
b
All scales are measured on a 7-point Likert scale, where 1 = strongly disagree; 7 = strongly agree.
39M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
7. factors that shape positive and negative consumer experiences in each
of the three channels.
The 162 responses written by the participations after completing the
tasks were content analyzed, as CIT proposes [6], with the purpose of
identifying categories of critical factors that lead to outcomes of interest.
For the purposes of this study, critical factors were defined as those fac-
tors that contribute toward consumer experiences after some decision-
making tasks (information search and choice) were undertaken in each
of the three channels (in-store, e-commerce, and m-commerce). An
analysis of the responses yields critical factors. These critical factors
were either positive or negative in nature.
The first phase of analysis of the responses required the inductive
identification of major categories of critical factors that lead to positive
and negative consumer experiences. Two judges with considerable ex-
pertise in consumer behavior grouped the responses into separate cate-
gories, identifying factors that led to positive and negative experience in
each of the three channels. None of the judges had any knowledge of the
objectives of the study. Three categories of factors emerged: channel-
related (convenience, ease), user-related and product-related (along
with various sub-categories). Inter-rater agreement was 82.7%. In the
next phase, the judges met to compare the classifications, and the dis-
agreements were resolved (see Table 2a).
As per the classification provided by the independent judges, analy-
sis is undertaken in order to identify the level of “importance” that par-
ticipants associate with the identified critical factors. To that end, the
number of times a critical factor is mentioned by a participant is tabulat-
ed and then converted to an associated percentage (see Table 2a). These
percentages are then taken as the “weights” that consumers attach to
their experiences (i.e., positive or negative) to each of the sub-
categories that are identified. Those factors that are “common” across
the three channels and those that are “unique” to a specific channel
are also identified (Table 2a). The associated weights for the “common”
and “unique” channel factors are calculated (as reported in Table 2b),
and then the cumulative weights are assigned to each channel. These
weights are used in a mediation analysis that is reported in the next
section.
3.3. Study 1: quantitative results
3.3.1. Effect of media richness on information search
Three-way ANCOVAs with cost of search as covariate and the three
independent factors, on the amount of information search as dependent
variable, suggest that cost of search is significant (F(1,143) = 15.0,
p b 0.05). Information search across the levels of media richness is
such that it is the highest for the medium richness condition
(MHigh = 4.9; MMedium = 5.9; MLow = 3.6; F(2, 143) = 13.8, p b 0.05)
(Table 3a), which demonstrates the main effect of media richness,
providing support for H1, and for task-media fit hypothesis. Hence,
participants in the high (i.e., in-store) and low (i.e., m-commerce)
media richness conditions consider less number of brands than par-
ticipants in the medium (i.e., e-commerce) media richness condi-
tion. Participants consider more number of brands in the high
media richness condition compared to that in the low media richness
condition. (Post-hoc tests (Bonferroni) confirm the differences
across channels. Results can be obtained from the authors upon
request.)
3.3.2. Effect of media richness on perceived fit, satisfaction, channel choice
Separate three-way ANCOVAs with cost of search as covariate and
perceived media richness-task fit, and satisfaction as dependent vari-
ables, suggest that cost of search is significant for media richness-task
fit (F(1,143) = 30.3, p b 0.05), and satisfaction (F(1,143) = 16.9,
p b 0.05).
A main effect of media richness is noted on perceived media richness-
task fit (MHigh = 4.0; MMedium = 4.9; MLow = 2.3; F(2, 143) = 51.9,
p b 0.05), satisfaction (MHigh = 4.7; MMedium = 5.4; MLow = 3.7;
F(2, 143) = 16.3, p b 0.05), and channel choice (MHigh = 4.9;
MMedium = 5.3; MLow = 2.4; F(2, 143) = 58.9, p b 0.05) (Table 3a).
These findings reveal that perceived fit, satisfaction, and channel
choice are the highest in the medium richness condition. All the indi-
cators are the lowest in the low richness condition. These findings
provide support for H2, and demonstrate that consumer behavior
follows the predictions made by task-media fit hypotheses.
3.3.3. Moderating effect of product type and task complexity
The interaction effect between media richness and product type is sig-
nificant for information search in the choice task (MHigh*AirlineTickets = 5.2;
MHigh*FoodMenu = 4.8; MMedium*AirlineTickets = 6.4; MMedium*FoodMenu = 5.5;
MLow*AirlineTickets = 4.8; MLow*FoodMenu = 2.3; F(2, 143) = 3.6, p b 0.05).
The interaction effect between media richness and product type is also
significant for perceived media richness-task fit (MHigh*AirlineTickets = 3.4;
MHigh*FoodMenu = 4.7; MMedium*AirlineTickets = 5.7; MMedium*FoodMenu =
4.2; MLow*AirlineTickets = 2.4; MLow*FoodMenu = 2.2; F(2, 143) = 15.2,
p b 0.05), satisfaction (MHigh*AirlineTickets = 4.4; MHigh*FoodMenu = 5.0;
MMedium*AirlineTickets = 5.8; MMedium*FoodMenu = 4.9; MMedium*AirlineTickets =
3.8; MMedium*FoodMenu = 3.6; F(2, 143) = 3.3, p b 0.05), and channel choice
(MHigh*AirlineTickets = 4.7; MHigh*FoodMenu = 5.1; MMedium*AirlineTickets = 5.5;
MMedium*FoodMenu = 5.0; MLow*AirlineTickets = 2.8; MLow*FoodMenu = 2.0;
F(2, 143) = 3.2, p b 0.05) (Table 3b, Figs. 2a and b).
Though Information search for both product types is high in the me-
dium richness condition, and medium in high richness condition, in the
low richness condition, consumers display a sharp decrease in informa-
tion search for the experience product compared to the search product.
A possible reason for this finding is that consumers may prefer under-
taking information search in the medium richness condition. Therefore,
irrespective of what the product type is, consumers prefer searching for
information in this level of media richness.
However, perceived media richness-task fit, satisfaction, and chan-
nel choice intention for experience products, are high in high richness
condition and medium in medium richness condition, while for search
products the outcomes are high in the medium richness condition and
medium in the high richness condition. These results reveal a moderat-
ing effect of product type and sharpen the findings presented in
Table 3a.
Findings demonstrate that participants prefer undertaking tasks
on search products in the medium richness condition. For tasks on
experience products, participants indicate a higher fit, satisfaction,
and channel choice intention in the high media richness condition,
thus demonstrating their preference for undertaking this type of
task in the high richness condition. As per our predictions, participants
in the low media richness condition do not perceive media richness-
task fit for either task (Table 3b). These findings support the task-
media fit hypotheses, which predict that certain levels of media rich-
ness are fit for undertaking certain types of tasks. These findings provide
support for H3 for perceived fit, post purchase evaluation, and channel
choice. Interaction effect between media richness and task complexity
is not significant for any of the variables. Hence, no support is obtained
for H4 (see Table 3c). A possible reason for the non-significant finding
for task complexity is that perceived cost plays a reduced role in infor-
mation search in the online environment [45].
3.3.4. Mediation analysis
This study does not explicitly manipulate media richness, but uses
three channels that differ, a priori, in terms of media richness, among
other things. This observation raises the issue of whether some aspect
of channel type, other than media richness, might be responsible for
the estimated differences in the dependent variables across treatments.
Note, as already mentioned, Table 2a identifies all the characteristics
that participants associate with each of the three channels, along with
the “weights” associated with the “unique” and “common” channel
characteristics (Table 2b).
40 M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
8. In order to establish that media richness is an essential characteristic
that contributes to the estimated differences between channel types,
two different mediation analyses [2] are undertaken: a) mediation anal-
ysis without controlling for any other channel characteristic, and b) me-
diation analysis by controlling for the “unique” channel characteristics.
In the first analysis, the weights are not considered (since the mediation
analysis does not control for any factors). In the second analysis, the
cumulative weights associated with the “unique” channel characteristics
are assigned to each of the corresponding level of media richness, and
this variable is included in each of the three regression equations that
test mediation analysis. The results of the two analyses demonstrate
that media richness mediate the influence of channel type on all depen-
dent variables, except information search (Figs. 3a and b). Specifically, it
should be noted that even after controlling for the unique characteristics
for each channel, media richness mediates the relationship between the
channel type and media richness-task fit, satisfaction and channel choice.
3.4. Study 1: discussion
Study 1 reveals that media richness does impact consumer decision-
making and channel choice, where consumers at different levels of
media richness respond differently to the same tasks. Information
search is medium in high media richness condition, high in medium
media richness condition, and low in low media richness condition.
This study also demonstrates the moderating effect of product type on
perceived task-media fit, satisfaction, and channel choice. Consumers
indicate that they will choose the channel with medium richness for un-
dertaking decision-making tasks on search products, and the channel
with high richness for undertaking decision-making tasks on experi-
ence products. Overall, we find support for task-media fit hypotheses.
Finally, a mediation analysis confirmed that the results are not con-
founded by other channel characteristics, thus enhancing the reliability
of our findings.
4. Studies 2 and 3
Studies 2 and 3 examine whether the findings of the hypotheses
hold at an individual level (i.e., e-commerce and m-commerce; e-
commerce and in-store). Stimuli, procedures, participation and reward
norms for these two studies are the same as in Study 1. These two stud-
ies do not test H3 and H4, since product type is a nested factor, and sam-
ple size is low for testing these hypotheses.
4.1. Studies 2 and 3: method
4.1.1. Design and participants
Study 2 was a 2 (media richness: medium, low) × 3 (task complexity:
few, moderate, many) mixed design experiment, where media richness
was the within subjects factor, and task complexity was the between
groups factor. In Study 3 media richness was medium and high, while
Table 2a
Critical incident analysis.
Critical factors Low richness
positive
Low richness
negative
Medium richness
positive
Medium richness
negative
High richness
positive
High richness
negative
Common/unique
factors
Channel-related
Layout 1.21 0.80 6.35 1.59 α
Navigability 3.22 5.55 α
Convenience 5.62 3.97 α
Save time 0.40 1.61 3.17 1.31 5.88 γ
Usability/device features 2.41 12.05 Unique (m-com)
Mobility 2.41 0.40 Unique (m-com)
Atmosphere 1.95 0.65 Unique (in-store)
User-related
Decision making 10.04 24.10 25.39 7.93 20.27 15.69 γ
Amount of information
(overwhelming)
12.85 5.56 3.16 1.31 9.15 γ
Physical comfort (exhausting) 3.61 0.66 β
Emotional comfort (irritated) 3.21 Unique (m-com)
Compare with e-commerce 10.04 6.54 β
Privacy 0.79 Unique (e-com)
Product-related
Price 3.61 13.5 11.76 γ
Other product attributes 1.61 15.1 15.03 γ
Product selection 0.80 7.94 9.80 γ
Total 31.33 68.67 87.32 12.68 61.43 38.57
The figures in the table are the percentages (of each critical factor) for that channel. Therefore, the percentage of positive and negative critical factors for each channel adds up to a 100
(87.32 + 12.68; 31.33 + 68.67; 61.43 + 38.57). The blank cells indicate no responses were recorded for these cells. Interesting results are in bold.
E-commerce participants: (54 respondents: 110 positive critical factors; 16 negative critical factors).
M-commerce participants: (54 respondents: 78 positive critical factors; 171 negative critical factors).
In-store participants: (54 respondents: 104 positive critical factors; 49 negative critical factors).
α = Factors common to m-commerce and e-commerce.
β = Factors common to m-commerce and in-store.
γ = Factors common across all three channels.
Table 2b
Weights used in mediation analysis.
Critical factors Low rich
positive
Low rich
negative
Medium rich
positive
Medium rich
negative
High rich
positive
High rich
negative
Low rich
cumula
Medium rich
cumula
High rich
cumula
Common features 16.46 38.56 70.66 11.09 59.48 30.72 −22.1 59.57 28.76
Unique features 14.87 30.11 16.66 1.59 1.95 7.85 −15.24 15.07 −5.9
Total 31.33 68.67 87.32 12.68 61.43 38.57 −37.34 74.64 22.86
a
Cumulative weights used in the mediation analysis for each of the three media richness conditions
41M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
9. the other factor and the study design were similar to Study 2. To test the
hypotheses in the two studies, participants were randomly assigned to
one of the three cells of the between groups factor. Participants under-
took a task in the medium media richness condition (either search or
experience product). They returned after a month, and upon returning
undertook a task in the low/high media richness condition (Study 2 or
Study 3) on the other product type. Thirty-one participants completed
Study 2 (few = 9; moderate = 10; many = 12). Thirty-one participants
completed Study 3 (few = 11; moderate = 12; many = 8). Participant
demographics in these two studies were similar to that of participants
in Study 1.
4.1.2. Manipulation checks
In Study 2, a 2 × 3 repeated measures ANOVA of media richness × task
complexity on perceived media richness (αMedium =0.86; αLow = 0.84)
showed a direct effect of media richness: participants perceived the me-
dium media richness condition as more rich than the low media richness
condition (MMedium = 5.3; MLow = 3.8; F(1,28) = 45.5, p b 0.05) (1 =
strongly disagree; 7 = strongly agree). The repeated measures ANOVA
on task complexity (αMedium = 0.86; αLow = 0.94) was not significant.
In Study 3, a 2 × 3 repeated measures ANOVA of media richness x task
complexity on perceived media richness (αMedium = 0.85; αHigh =
0.89) indicated a direct effect of media richness: participants considered
the medium richness condition less rich than the high richness condition
(MMedium = 4.5; MHigh = 4.9; F(1,28) = 8.3, p b 0.05). The repeated
measures ANOVA on task complexity (αMedium = 0.91; αHigh = 0.82)
was not significant.
4.1.3. Dependent variables
The two studies measured perceived fit, satisfaction, channel choice
intention and perceived cost with the same scales as in Study 1.
Cronbach's alpha for each of the scales in the two studies are above
0.8, and are reported in Table 1.
4.2. Studies 2 and 3: results
4.2.1. Effect of media richness on information search
In Study 2, separate 2 × 3 repeated measures ANOVAs of media rich-
ness × task complexity on the amount of information search reveals a
main effect of media richness (MMedium = 6.5; MLow = 4.5; F(1,28)
= 18.8, p b 0.05). Participants in the medium richness condition con-
sider more number of brands than those in the low richness condition.
In Study 3, separate 2 × 3 repeated measures ANOVAs of media rich-
ness × task complexity on the amount of information search, sug-
gests a marginally significant main effect of media richness
(MMedium = 6.7; MHigh = 5.4; F(1,28) = 3.3, p b 0.10). Participants in
the medium richness condition search for a greater number of brands
than those in the high richness condition. Results from Study 2 agree
with that of Study 1, while the findings from Study 3 partially agree
with the findings of Study 1, providing partial support for H1, and to
task-media fit hypotheses.
4.2.2. Effect of media richness on perceived fit, satisfaction, and channel
choice
In Study 2, separate 2 × 3 repeated measures ANOVAs of media
richness × task complexity on perceived media richness-task fit, sat-
isfaction, and channel choice intention reveal significant effect of
media richness. Media richness-task fit (MMedium = 5.5; MLow = 2.3;
F(1,28) = 43.1, p b 0.05), satisfaction (MMedium = 5.9; MLow = 3.6;
F(1,28) = 51.2, p b 0.05), and channel choice intention (MMedium =
5.4; MLow = 2.6; F(1,28) = 101.5, p b 0.05) demonstrate that partici-
pants indicate a higher score in the medium richness condition
than in the low richness condition. This study provides support for
H2. In Study 3, media richness-task fit (MMedium = 5.2; MHigh =
3.2; F(1,28) = 66.2, p b 0.05) indicates that participants perceive a
higher fit in the medium media richness condition than in the high
media richness condition. Satisfaction (MMedium = 5.5; MHigh =
4.3; F(1,28) = 28.6, p b 0.05) concurs with the findings of media
richness-task fit. The direct effect of media richness on channel
choice is not significant. Study 3 provides partial support for H2,
and to task-media fit hypotheses.
4.3. Studies 2 and 3: discussion
Studies 2 and 3 broadly agree with the findings of Study 1. Con-
sumers undertaking the same decision-making task at medium and
low levels of media richness search for a greater amount of information
in the medium richness condition; those in the medium and high rich-
ness conditions also undertake the greatest amount of search at the me-
dium level of richness. Perceived media richness-task fit, satisfaction,
and channel choice are the lowest in the low media richness condition,
and highest in the medium richness condition.
5. General discussion
A retailer's channel mix often includes traditional (e.g., the in-store
environment) and new and evolving channels. Prior research on retail
channels does little to address the impact of channel characteristics on
channel choice and decision making. The media richness construct
(a pertinent channel characteristic), lends itself particularly well to
situations where users interact with retail channels that include tradi-
tional and new channels. This study focuses on three retail channels
with three levels of media richness (low, medium and high) and
shows that media richness is a key channel characteristic that affects
consumer behavior. In particular, H1, and H2 find support across all
three studies. In Study 1, product type moderates the effect of media
richness on perceived fit, satisfaction, and channel choice, which
provide broad support for H3. These findings resonate with the findings
obtained by Huang et al. [30], where the authors find that there are
important differences in the browsing and purchase behavior of con-
sumers between search and experience goods. However, the study
does not find support for the moderating effect of product type on infor-
mation search, indicating that consumers prefer to undertake informa-
tion search in the medium richness condition irrespective of the
product type. No support is obtained for the moderating effect of task
complexity (H4). This research tests competing hypotheses made by
task-media fit hypotheses and cognitive cost theory. The results of the
research find support for task-media fit hypotheses, which explain the
differences in consumer decision-making across the three levels of
media richness.
The results of the research bear out our suppositions that cognitive
cost contributes to the differences in consumer decision-making across
the three levels of media richness. It explains why results from Study 1
show that in the context of channel users' decision-making and channel
choice, channels with high media richness (e.g., in-store) are preferred
to channels with low media richness (e.g., m-commerce), and that users
prefer channels with medium richness (e-commerce) over channels
that have higher/lower media richness. Study 2 provides validation
to Study 1 and shows that e-commerce dominates m-commerce, as
expected. Study 3 replicates Study 1 and finds similar support, i.e.
e-commerce dominates in-store.
A few points of concern, often raised in the context of research that
requires consumer interaction with channels through devices, are ad-
dressed. First, it should be noted that, specifically for m-commerce
and e-commerce, the devices are integral to the channels as per the
definitions adopted in this study [13]. Hence, the devices through
which consumers undertake decision-making, contribute to user expe-
rience at that particular level of media richness. Consumer attitudes are
uncovered through their engagement with these levels of media
richness. Second, there is little possibility of “novelty” of the channels
impacting the study outcomes. Only participants who have at least
two years of prior experience with mobile devices are included in the
42 M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
10. study. Hence, consumer responses reflect their attitude toward the level
of media richness rather than toward the “novelty” of any device. Third,
as already mentioned, this research finds support for task-media fit hy-
pothesis, which is well-established in information systems literature.
Fourth, our research seems to bear out what is observed in actual con-
sumer behavior. We find that consumers prefer to undertake information
search on medium richness conditions, as is commonly observed — most
consumers prefer undertaking product information search online, even if
the purchase is through another channel.
5.1. Theoretical contributions
Overall, this research contributes to our understanding of user-
channel characteristic interaction across retail channels in the following
ways. From a theoretical perspective, first, building on media-richness
theory, this research contributes to the decision support systems litera-
ture on channels and consumer behavior and examines how media
richness of retail channels may influence channel users' decision-
making, as well as channel choice. Second, we conduct our study in a
multichannel environment where we compare decision making in retail
channels that incorporate advanced technology as well as those that do
not incorporate such technologies, thus extending the multi-channel lit-
erature in decision support systems [7]. Third, evidence for the media
richness-task fit hypotheses implies that specific decision-making
tasks are suited to specific levels of media richness. In particular, it
implies that consumers will choose to undertake complex decision-
making tasks in channels that have medium or high levels of media rich-
ness, and not on channels that offer low media richness. This finding
highlights that consumers prefer undertaking simple tasks on m-
commerce. Fourth, product type moderates the effect of media richness
on fit, satisfaction, and channel choice. Fifth, this study also establishes
that retail channels can be arrayed along a media richness continuum.
5.2. Managerial contributions
This study offers several implications for managers. First, from a
managerial perspective, support for the hypotheses suggests that
media richness is a fundamental channel characteristic and should be
considered while planning multi-channel strategies. A manager needs
to decide on how to manage channels that offer different levels of
media richness so as to provide the best possible offering to the cus-
tomers for particular decision-making tasks. For example, if a manager
wants a consumer to engage with a channel for more information, the
best channel would most likely be a channel that incorporates medium
level of richness (e.g. e-commerce), and not a channel that is low in rich-
ness (e.g. text and image based m-commerce). Likewise, for experience
products, managers may want to engage with consumers through a
channel that is high in richness (e.g., face-to-face). Similarly, managers
will want consumers to undertake simple tasks on mobile devices,
which echo Chiu et al. [11], who find that content providers should pro-
vide targeted content to users on their mobile devices.
Second, the findings demonstrate that managers need to note that
m-commerce is not the same as e-commerce. In the participants' final
evaluation, m-commerce is unable to help consumers achieve the
same things as e-commerce does, with the same ease. Managers need
to keep this finding in perspective, especially as mobile phone compa-
nies and service providers strive to make more features available on
their phones that compare with the features and capabilities available
on laptops and personal computers. Managers need to evaluate the fol-
lowing questions: How comfortable are consumers carrying out the
same tasks on their personal computers and mobile phones? How
often do consumers use the personal computer and the mobile phone
for carrying out similar tasks?
Third, managers need to “manage” the contents of services made
available to consumers on channels that are low in media richness.
This research suggests that it is desirable to limit the amount of informa-
tion made available to consumers on such channels. Findings suggest
that consumers may undergo negative experiences when there is an in-
crease in cognitive cost in low media richness environments. For exam-
ple, advertisers and content providers need to tailor not only the
content but also the task that they expect consumers to complete in
channels that have low levels of media richness, thus providing custom-
er satisfaction with the channel. Managers, therefore, need to have an
understanding of the level of media richness that is offered by each
channel, and match the channel's capabilities with what they expect
consumers to achieve at that level of media richness. This exercise will
result in high perceived media richness-task fit and post purchase
evaluations.
Fourth, firms interested in sending advertising materials to mobile
devices need to limit the amount of information transmitted, and
Table 3a
Effect of media richness on information search, perceived media richness-task fit, post-purchase evaluation and channel choice.
Decision-making task Amount of information searche
(brands) Perceived media richness-task fitf
(fit) Post-purchase evaluationf
(satisfaction) Channel choice intentionf
Media richness
High (in-store) Medium 4.9a
Medium 4.0b
Medium 4.7c
Medium 4.9d
Medium (e-commerce) High 5.9 High 4.9 High 5.4 High 5.3
Low (m-commerce) Low 3.6 Low 2.3 Low 3.7 Low 2.4
a, b, c, d
Results in the table are from Study 1. Results from Studies 2 and 3 agree with the findings of Study 1.
a
Amount of information search: F(2, 143) = 13.8, p b 0.05;
b
Perceived media richness-task fit: F(2, 143) = 51.9, p b 0.05;
c
Satisfaction: F(2, 143) = 16.3, p b 0.05;
d
Channel choice: F(2, 143) = 58.9, p b 0.05.
e
Actual number of brands is measured in the choice task.
f
Fit, Satisfaction and channel choice: scales are measured on a 7-point Likert scale, where 1 = strongly disagree; 7 = strongly agree.
Table 3b
Moderating effect of product type on information search, perceived media richness-task
fit, post-purchase evaluation and channel choice.
Product type media richness Search product
(airline ticket)
Experience
product (food menu)
High (in-store) 5.2a, e
4.8
3.4b, f
4.7
4.4c, f
5.0
4.7d, f
5.1
Medium (e-commerce) 6.4 5.5
5.7 4.2
5.8 4.9
5.5 5.0
Low (m-commerce) 4.8 2.3
2.4 2.2
3.8 3.6
2.8 2.0
a
Amount of information search: F(2, 143) = 3.6, p b 0.05.
b
Perceived media richness-task fit: F(2, 143) = 15.2, p b 0.05.
c
Satisfaction: F(2, 143) = 3.3, p b 0.05.
d
Channel Choice: F(2, 143) = 3.2, p b 0.05.
e
Actual number of brands is measured in the choice task.
f
All scales are measured on a 7-point Likert scale, where 1 = strongly disagree;
7 = strongly agree.
43M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
11. make the information relevant [58]. Information disseminated via mo-
bile devices needs to be relatively simple as consumers find scrolling
(on m-commerce) extremely “stressful,” as one participant put it. Not
only the layout of the advertising material, but also the content needs
to be simple so as to keep cognitive load low. Finally, managers need
to ensure that consumers do not experience negative experiences due
to the unique factors of the m-commerce channel (usability, mobility,
emotional comfort).
5.3. Limitations, suggestions for future research and conclusion
There are several limitations in the study that need to be acknowl-
edged. First, the study participants are guided to visit sites and make de-
cisions. Although efforts are made to replicate actual shopping
environments, the findings might be different when consumers shop
in more natural settings. For example, since this study consists of a se-
ries of experiments, it keeps the information made available to partici-
pants the same, across all channels for specific levels of task
complexity. In the future, it is worthwhile to examine actual user behav-
ior through a field experiment setting to validate our findings. Second,
the sample consists of college students based in the United States who
are savvy with respect to Internet use. Future research can potentially
test the media richness theory across other settings including additional
product categories and other consumer groups. Third, this study in-
cludes only search and experience products in the investigation,
which might not be generalizable to other product categories. Fourth,
this research measures media richness with items that gauge the
consumer's ability to undertake decision-making on a particular medi-
um, and not with items that measure the objective characteristics of
the medium (e.g., cue multiplicity, and feedback [56]). Future research
may use the latter group of items to measure media richness.
Finally, this research opens up avenues for studies that delve into
user-channel characteristic interactions in the context of retail channel
choice. Future research can explicitly manipulate media richness within
a channel type to study the effects of media richness on different aspects
of consumer behavior. Further research needs to investigate audio,
video and face-to-face elements that can be incorporated to define spe-
cific levels of media richness that might affect channel choice. In addi-
tion, user interactions with other channels need to be examined in
order to find their position along the media richness continuum for re-
tail channels. Researchers might also be interested in testing other types
of choice tasks that vary the amount of information across the different
channels, as well as measure the choice accuracy of tasks. Research also
needs to study the moderating effect of other types of products (includ-
ing credence, durable and frequently purchased products, among
others), on media richness.
Recent studies focusing on media choice have identified the impor-
tance of task situations such as urgency, confidentiality, accountability,
social interaction, and information integrity in the decision making pro-
cess [40]. Therefore, future studies should investigate the interplay
Search Product Experience Product
a b
Figs. 2. a and b: Interaction effect.
Table 3c
Summarizing the hypotheses.
Hypotheses Study 1 Study 2 Study 3
H1 Amount of information search is different across the three levels of media
richness conditions, where the amount of search is the lowest in the low
media richness condition.
Supported Supported Partially s
upported
H2 Perceived media richness-task fit, satisfaction and channel choice intention
(for information search and future purchase) are different across the three
levels of media richness conditions, where the fit, satisfaction and channel
choice intention are the lowest in the low media richness condition.
Supported Supported Supported (for all DVs,
except channel choice)
H3 Product type moderates the effect of media richness on information search,
media richness-task fit, satisfaction, and channel choice intention such that the
effects are different across the six levels of media richness-product type
conditions: a high media richness condition is preferred for undertaking a
decision-making task on an experience product, a medium media richness
condition is preferred for undertaking a decision-making task on a search
product, a low media richness condition is not preferred for undertaking a
decision-making task on either search or experience products.
Supported (for all DVs,
except information search)
Not tested Not tested
H4 Task complexity moderates the effect of media richness on information search,
media richness-task fit, satisfaction and channel choice intention, such that the
effects are different across the nine levels of media richness-task complexity
conditions: a high media richness condition is preferred for undertaking a
complex decision-making task, a medium media richness condition is
preferred for undertaking a moderate decision-making task, a low media
richness condition is preferred for undertaking a simple decision-making task.
Not supported Not tested Not tested
44 M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
12. between media richness of channels and these task situations on con-
sumers' media-task fit. As new features are incorporated into
established channels, it is becoming imperative to understand how
channel characteristics impact consumer decision making. The types
of information that can be effectively provided to consumers at different
levels of media richness need to be examined. We hope that this paper
will encourage future studies to investigate the importance of media
richness in decision making across retail channel mix.
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Loyalty
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Moutusy Maity (mmaity@iiml.ac.in) is an associate profes-
sor of marketing at the Indian Institute of Management,
Lucknow, India. She holds a Ph.D. in Business Administration
from the University of Georgia. Her research focuses on is-
sues regarding adoption of technology.
Mayukh Dass (mayukh.dass@ttu.edu) is an associate profes-
sor of marketing at the Rawls College of Business, Texas Tech
University. He holds a Ph.D. in Business Administration, an
M.S. in Statistics and an M.S. in Artificial Intelligence from
the University of Georgia. His research focuses on statistical
and analytical methods with applications to valuation issues
in dynamic economies and brand management. He is a mem-
ber of the INFORMS.
46 M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46
![Consumer decision-making across modern and traditional channels:
E-commerce, m-commerce, in-store☆,☆☆
Moutusy Maity a,
⁎, Mayukh Dass b,1
a
Marketing Area, Indian Institute of Management Lucknow, 1309 Faculty Block, Plot B1, Sector 62, Noida, UP 201307, India
b
Marketing, Rawls College of Business, Texas Tech University, MS 2101, Lubbock, TX, USA
a b s t r a c ta r t i c l e i n f o
Article history:
Received 7 April 2013
Received in revised form 20 December 2013
Accepted 15 January 2014
Available online 24 January 2014
Keywords:
E-commerce
M-commerce
Channel choice
Consumer decision-making
Media richness
Cognitive cost
Product type
This study investigates the effect of media richness on consumer decision-making and channel choice, and
grounds the investigation in media richness theory, task-media fit hypotheses and cognitive cost (behavioral de-
cision theory). Findings from three experiments provide evidence that consumers prefer channels with medium
(e.g., e-commerce) and high (e.g., in-store) media richness for carrying out complex decision-making tasks. Find-
ings reveal that consumers are likely to undertake simple decision-making tasks on channels that incorporate
low (e.g., m-commerce) levels of media richness. Findings also demonstrate that product type moderates the ef-
fect of media richness on perceived channel-task fit, post-purchase evaluation, and channel choice. These insights
should prove helpful to managers in managing content across different channels.
© 2014 Elsevier B.V. All rights reserved.
1. Introduction
With technological advancements, firms are increasingly reaching
out to their customers through a variety of channels such as electronic
commerce, mobile commerce, and brick and mortar establishments
[34]. The decision support systems literature has a strong tradition in
studying the relationship between channels and consumer decision
making [11,17,27,29,59]. These studies have mostly focused on how
customers evaluate, utilize [29], and adopt channels [59] and how the
purchase environment (e.g., factors such as risk [27], trust and uncer-
tainty [7,53]) plays a fundamental role during the adoption process.
Although these studies advance our understanding of how consumers
often undertake decision-making tasks on separate channels [39], limited
research exists on investigating how different channel characteristics
affect decision-making and channel choice [46], across a variety of
channels.
In this paper, we address this limitation in the literature, and
examine the role of media richness of channels on channel choice
and decision-making tasks [12]. Media richness, as originally de-
scribed by media richness theory (MRT) [15,16], is a set of objective
characteristics such as feedback and communication capability,
language variety, and personal focus, which determine a channel's
ability to communicate richness of information [35]. The more a me-
dium incorporates these elements, the richer it is [3,8,12,31,33].
Media richness of channels has been represented in two ways in ex-
tant literature: a) different forms of technology usage in the same
channel impart different degrees of media richness to the channel
[32]; and b) different channels represent different degrees of media
richness [12]. The latter method of representing media richness is
widely accepted [32], and thus is used in this paper to investigate
the effects of media richness of retail channels on consumer
decision-making and channel choice.
Originally developed in the context of organization communica-
tion channels, media richness has subsequently been applied to
retail channels. Prior literature considering media richness as a channel
characteristic has mostly focused on examining its effect on message
processing in interactive media [14], creation of telepresence [32],
understanding consumers' behavioral intentions [31], and efficacy of in-
formation acquisition [12], among other things. Although these studies
advanced our understanding of media richness in retail channels, we
still have limited knowledge on how media richness affects channel
choice and purchase tasks. In particular, this study raises and investigates
the following questions: How does media richness affect consumer
Decision Support Systems 61 (2014) 34–46
☆ This work was undertaken as part of the first author's dissertation at the University of
Georgia, Athens, GA, USA.
☆☆ The authors wish to thank the New Media Institute at the University of Georgia,
Athens, GA, for providing infrastructure-related support for conducting the research
reported in this paper. The study design, data collection, analyses and interpretation
of data, the writing of the report and the decision to submit the article for publication
are undertaken entirely by the submitting authors.
⁎ Corresponding author.
E-mail addresses: mmaity@iiml.ac.in (M. Maity), mayukh.dass@ttu.edu (M. Dass).
1
Fax: +1 806 742 2199.
0167-9236/$ – see front matter © 2014 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.dss.2014.01.008
Contents lists available at ScienceDirect
Decision Support Systems
journal homepage: www.elsevier.com/locate/dss](https://image.slidesharecdn.com/paper4-150511005920-lva1-app6892/85/Paper4-1-320.jpg)
![decision-making and channel choice? How does media richness affect
perceived media richness-task fit? And, how does media richness impact
post purchase evaluation? With increasing technological advances that
enable channels to incorporate new capabilities (e.g., embed audio/
video, pay through mobile device) and with firms interested in engaging
with the customer through multiple channels, it is imperative to conduct
a formal study to investigate the effects of media richness on consumer
decision-making.
This investigation focuses on three levels of media richness
(i.e., high, medium, low) and undertakes three experimental studies
that examine the effects of media richness on information search, and
post purchase evaluation (e.g., satisfaction). The research examines per-
ceived media richness-task fit, and evaluates the appropriate levels of
media richness for different types of decision-making tasks. Product
type and task complexity are potential moderators. Findings provide
evidence that consumers prefer channels with medium media richness
level over high richness for carrying out complex decision-making
tasks. Findings reveal that consumers are likely to undertake simple
decision-making tasks on channels that incorporate low levels of
media richness. Findings also demonstrate that product type moderates
the effect of media richness on perceived channel-task fit, post-
purchase evaluation, and channel choice.
The contributions of this paper are manifold. First, this paper in-
vestigates the effect of media richness on consumer decision-
making and channel choice, a crucial under-researched area, and
provides evidence that media richness is a driver of channel choice.
Second, from theoretical and managerial perspectives, this paper
provides insights on media richness continuum and perceived
media richness-task fit, which are essential for retail channel design
and content management. Finally, this research opens up many new
avenues for future research. The paper proceeds as follows: the next
section discusses media richness theory, delineates the effect of
media richness on decision-making tasks in terms of cognitive cost
and behavioral decision theory, and presents the hypotheses. The
section that follows, presents three studies and the findings. The
paper ends with theoretical and managerial implications of the find-
ings, and proposals for future research.
2. Literature review and hypotheses
2.1. Media richness
McGrath and Hollingshead [35] classify media along a continuum
of “increasing potential richness of information” ([50], p. 297). The
four types of media that the researchers identify along this richness
continuum are: text, audio, video and face-to-face communications.
MRT is usually used in the context of media choice. Face-to-face is
the richest medium as it allows mutual feedback and simultaneously
conveys a variety of cues (e.g., tonal, facial, emotional). A text-based
interaction (e.g. texting through mobile devices or browsing infor-
mation through text-only cell phone browsers) is less “rich” than
audio, video or face-to-face interaction (e.g. in-store interactions or
communications using Apple iPhone's Facetime feature). Media
that are highest and lowest in media richness anchor the two ends
of the continuum. Researchers extensively use MRT in the context
of dyadic intra-organizational communication situations in information
systems literature [50].
Extending MRT to decision making in marketing channels, media
richness is a characteristic that imparts to a marketing channel the
ability to communicate information to consumers, and help them un-
dertake decision-making on that channel. The degree of media richness
may not only vary across channels, but also within a specific channel.
For example, a mobile channel with audio/video capabilities is richer
than a mobile channel with text-only capabilities. Also, an in-store set-
ting suggests a face-to-face interaction with a sales associate, as well as
the possibility of physical inspection of goods. Similarly, an e-commerce
setting suggests that consumers access a website through a computer
terminal, whereas an m-commerce setting suggests that a mobile de-
vice is used as a channel. This study examines the effects of channel
characteristics on consumer decision making across three channels:
in-store, e-commerce and m-commerce that represent three levels of
media richness.
Decision-making inside a store involves face-to-face interactions,
and has feedback and communication capabilities due to the avail-
ability of a channel representative (in-store salespeople). These fea-
tures afford a personal focus and a wide variety of language support.
Hence, the in-store channel is high in media richness. Decision-
making through e-commerce involves accessing websites through
computer terminals [13]. Most websites use text and images (in
exclusion of audio and multi-media) to provide consumers with
the required information [48], and is the scope of e-commerce in
this study. These websites do not have the spontaneity in communi-
cation, feedback, and personal focus capabilities of the in-store chan-
nel. Such websites may have wider language support than that
available in-store, but are typically limited to a database of non-
customized vocabulary. Hence, the media richness of e-commerce
is medium. Finally, m-commerce refers to the pairing of mobile
devices with commercial transactions, providing consumers with
the ability to carry out transactions through wireless Internet-
enabled devices [13]. In addition to all the features that are available on
e-commerce, m-commerce also offers portability [1], and ubiquity [1].
The other major difference between e-commerce and m-commerce is
the interface (large versus small screen) of the mobile device. The setting
of m-commerce imposes limitations, notably those of attention
constraints and small-screen access devices [9]. These differences limit
the extent of communication, feedback and personal focus capabilities
that are possible on m-commerce. For example, in e-commerce, a con-
sumer may simultaneously browse a product listing on a webpage, and
watch a video about the product on the same computer screen. However,
in m-commerce, such simultaneity is difficult to attain due to channel-
specific limitations. Moreover, this investigation includes internet-
enabled mobile devices that support text and images. Therefore, com-
pared to in-store and e-commerce, m-commerce has the lowest media
richness.
It is important to note that the goal of this research is to examine
how channel characteristics defined by media richness impact
consumer decision making. The selection of the three channels (i.e.,
m-commerce, e-commerce and in-store) and their settings are
used to manipulate three levels of media richness (i.e. low, medium
and high). Different settings of these channels may yield different
richness levels.
2.2. Task-media fit
McGrath and Hollingshead [35] suggest task-media fit hypothe-
ses as an extension to media richness theory, and present a matrix
(Fig. 1a), which classifies patterns of differential fit along the two di-
mensions of communication media and task type (see [50], for expla-
nation). Communication media is aligned along the “increasing
potential richness of information” continuum ([50], p. 297). Task
type includes choice (e.g., choosing) and negotiating (e.g., generat-
ing ideas) tasks. The best fits for the choice tasks between media
and task type appear to be in media that offer medium richness.
Task-media fit hypothesizes that media at the two ends of the con-
tinuum (i.e., media with the highest and lowest media richness)
are ineffective for carrying out the communication tasks, as they
cause distraction (too rich) or are incapable of transmitting the nec-
essary information (too lean) [50]. In our study, the choice task that
consumers are required to undertake is similar to “choosing”. Our
study advances this literature and investigates task-media fit in the
context of choice tasks.
35M. Maity, M. Dass / Decision Support Systems 61 (2014) 34–46](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)